Activating Kinase Mutations in
Melanoma
Ricardo Enrique Vilain
BSc (Hons) MBBS (Hons)
Doctor of Philosophy (Medical Genetics)
University of Newcastle
Newcastle, September 2012 ii
Declaration
The thesis contains no material which has been accepted for the award of any other degree or diploma in any university or other tertiary institution and, to the best of my knowledge and belief, contains no material previously published or written by another person, except where due reference has been made in the text. I give consent to this copy of my thesis, when deposited in the University Library, being made available for loan and photocopying subject to the provisions of the Copyright Act 1968.
I hereby certify that the work embodied in this thesis has been done in collaboration with other researchers. I have included as part of the thesis a statement clearly outlining the extent of collaboration, with whom and under what auspices.
I hereby certify that the work embodied in this thesis contains a published paper work of which I am a joint author. I have included as part of the thesis a written statement, endorsed by my supervisor, attesting to my contribution to the joint publication work.
______
Ricardo Vilain
BSc (Hons) MBBS (Hons)
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Dedication
To my four beautiful girls: Anne-Marie, Vivian, Connie and Ingrid for a lifetime of love and support.
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Publications from Thesis
Papers
1. Vilain RE, Dudding T, Braye SG, Groombridge C, Meldrum C, Spigelman AC, Ackland S, Ashman L, Scott RJ. Can a familial gastrointestinal tumour syndrome be allelic with Waardenburg syndrome? Clin Genet 2011;79:554-60.
2. Vilain RE, Braye SG, Ashman L, Scott RJ. (2011) Characterisation of KIT and other common oncogene mutations in malignant melanomas. In preparation.
3. Vilain RE, Braye SG, Ashman L, Scott RJ. (2011) BRAF and NRAS mutational status are prognostically important in thick and locally advanced cutaneous melanoma. In preparation.
4. Vilain RE, Braye SG, Ashman L, Scott RJ. (2011) MET juxtamembranous SNPs R988C and T1010I are over-represented in patients with Melanoma. In preparation.
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Conferences Proceedings
1. Vilain RE, Braye SG, Ashman L, Scott RJ. The Importance of NRAS and BRAF mutations in thick and advanced melanomas (Poster). 8th Internal Melanoma Congress, Tampa FL, USA 2011.
2. Vilain RE, Ashton KA, Bowden NA, Braye SG, Ashman L, Scott RJ. Clinicopathological and gene expression profiling of advanced melanomas harboring KIT and other common kinase mutations (Poster). 7th Internal Melanoma Congress. Sydney NSW 2010.
3. Vilain RE, Braye SG, Ashman L, Scott RJ. Characterisation of KIT and other common kinase mutations in melanomas: A step in the development of patient-tailored treatment for melanoma (Speaker). Hunter Medical Research Conference on Translational Research, Newcastle NSW 2010.
4. Vilain R, Braye S, Ashman L, et al. Characterisation of KIT mutations in thick/metastatic melanomas: An Australian Perspective (Poster). 6th Internal Melanoma Congress, Boston MA, USA 2009.
5. Vilain R, Dudding T, Braye S, et al. Familial Multifocal Gastrointestinal Stromal Tumours and an associated with a White Spotting disorder (Poster). Hunter Medical Research Conference on Translational Research, Newcastle NSW 2008.
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Acknowledgments
It takes a whole village to raise a child, and it has taken three supervisors, four laboratories and countless friends to make this PhD come to fruition. The lion’s share of my gratitude goes to my principal supervisor Prof. Rodney Scott, for his encouragement of my ideas and belief in my ability to see them through. I hope this is start of a long and productive research partnership. Those important early days of this research were conducted under the tutelage of Prof. Leonie Ashman, whose encouragement was instrumental in my decision to take on this work. To my mentor Dr. Stephen Braye, my eternal thanks for his unwavering support and enthusiasm for this project. I’m fortunate to have Dr. Braye as source of wise counsel in matters of research, pathology and life. The benefits of postgraduate studies are only realized when the students design, implement and, inevitably, troubleshoot their research. The best supervisors ensure their students feel this independence, while always making it clear they have their backs if things ever truly become dire.
Amongst all colleagues in the department of Genetics at the Hunter Area Pathology Service, I’d like to particularly thank Ms. Gordana Pecenpetelovska, Dr Cliff Meldrum and Mr Michael Hipwell for bringing me up to speed with the techniques required to make this work possible.
To all my friends in the Information Based Medicine laboratory at the Hunter Medical Research, thank you for welcoming a complete stranger with such warmth and kindness. A big thanks to Dr Katie Ashton for her advice, support and amazing technical abilities. Dr Ashton provided the gene expression data on the formalin fixed paraffin embedded tissues in this thesis. To Dr Nikola Bowden, my adoptive supervisor, whose passion for science, keen intellect and masterful use of social media were instrumental not only in the nature and direction of my work, but also in ensuring it was completed in a timely fashion. I hope to remain Dr Bowden’s pathologist-of-choice for years to come.
Despite all these collaborations, my home always remained the department of Anatomical Pathology at Hunter Area Pathology Service. I’m in the debt of all the consultants, the secretarial and technical staff but I need to highlight key individuals: Mr Robert Borwell made his considerable technical expertise and all the anatomical pathology department’s resources available to me; Ms Zenobia Haffajee and Ms Megan Clarke provided technical assistance with the automated immunohistochemical staining runs; Ms Tina Hope assisted in the construction of the melanoma tissue microarrays; Mr Michael Hadlow, whose knowledge of the NSW Human Tissue Act (1983) underpinned this entire project, I could not have got this PhD up off the ground without his advice; To my departmental director A/Prof. Barbara Young, I thank her for taking a risk in employing a half-baked PhD as one of the senior registrars.
I also would like to acknowledge Pfizer Australia, the National Health and Medical Research Council and the Royal College of Pathologists of Australasia for their financial support. Dr Darryl Irwin is thanked for his technical assistance with the generation of SequenomTM MassARRAY multiplex genotype assays. Dr Irwin also provided an independent assessment of the genotyping results.
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Despite the most supportive supervision and closest of collaborative partnerships, a PhD by its very nature can feel like a very lonely route to take. I believe the support only a family can provide gives the best chance of remaining sufficiently optimistic to see this massive undertaking through. I thank my mother for doing everything in her power to give her children the opportunity to achieve their full potential. She is the only role model I have ever needed. To my wife, who has borne the burden of my PhD, I thank her for the love and support which made it possible to see the successful completion of this project, my specialty exams and the birth of our daughter.
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Table of Contents
Declaration ...... i!
Dedication ...... iii!
Publications from Thesis ...... v!
Conferences Proceedings ...... vii!
Acknowledgments ...... ix!
Table of Contents ...... xi!
Figures and Tables ...... xvii!
Abstract ...... xxvii!
Chapter 1 Introduction ...... 1!
1.1! Epidemiology of Melanoma in Australia ...... 3!
1.2! Management of Melanoma ...... 6!
1.2.1! Clinical Detection ...... 6!
1.2.2! Melanoma Diagnosis ...... 6!
1.2.3! Melanoma Subtype ...... 7!
1.2.3.1! Superficial Spreading Melanoma ...... 7!
1.2.3.2! Nodular Melanoma ...... 7!
1.2.3.3! Lentigo Maligna Melanoma ...... 7!
1.2.3.4! Acral Lentiginous Melanoma ...... 7!
1.2.3.5! Desmoplastic Melanoma ...... 8!
1.2.4! Histopathological Prognostic Factors ...... 11!
1.2.4.1! Breslow’s Thickness ...... 11!
1.2.4.2! Melanoma Ulceration ...... 11!
1.2.4.3! Mitotic Rate ...... 12!
1.2.5! Melanoma Staging ...... 12!
1.2.6! Current Melanoma Treatment Guidelines ...... 16!
1.3! Melanoma Pathogenesis ...... 16!
1.3.1! Risk Factors ...... 16! xi
1.3.1.1! Inherited ...... 16!
1.3.1.2! Environmental ...... 17!
1.3.3! Melanoma Oncogenes ...... 18!
1.3.3.1! KIT ...... 18!
1.3.3.3! NRAS ...... 22!
1.3.3.4! BRAF ...... 24!
1.3.3.5! Constitutive Activation Of MAPK Signaling Cascade ...... 26!
1.4! Oncogenic Kinase Inhibition as a Novel Therapeutic Target ...... 27!
1.4.1! KIT Inhibition ...... 27!
1.4.2! BRAF Inhibition ...... 27!
1.4.3! NRAS Inhibition ...... 28!
1.4.4! MET Inhibition ...... 28!
Chapter 2 Can a Familial Gastrointestinal Tumour Syndrome Be Allelic With Waardenburg Syndrome? ...... 29!
Statement ...... 31!
2.1! Introduction ...... 33!
2.2! Materials and Methods ...... 35!
2.2.1! DNA extraction and sequencing ...... 35!
2.2.2! SNAI2 Copy Number Analysis ...... 35!
2.3! Results ...... 35!
2.3.1! Study Cohort And Histopathological Examination ...... 35!
2.4! Discussion ...... 37!
References ...... 38!
Chapter 3 Characterisation of KIT and other Kinase Mutations in Malignant Melanomas 39!
3.1! Introduction ...... 41!
3.2! Materials and Methods ...... 42!
3.2.1! Case Selection ...... 42!
3.2.2! Sun Exposure and Solar Elastosis Scoring ...... 42!
3.2.3! DNA extraction ...... 43! xii
3.2.4! KIT Sequencing ...... 43!
3.2.5! Mass Spectrometry Genotyping ...... 45!
3.2.6! MET Real Time PCR Genotyping ...... 46!
3.2.7! Confirmatory Sequencing ...... 47!
3.2.7.1! Sanger Sequencing ...... 47!
3.2.7.2! Pyrosequencing ...... 47!
3.2.8! Statistical Analysis ...... 48!
3.3! Results ...... 48!
3.3.1! Clinicopathological features ...... 48!
3.3.2! Sequencing Results ...... 49!
3.3.2.1! KIT Mutations ...... 49!
3.3.2.2! BRAF Mutations ...... 53!
3.3.2.3! NRAS Mutations ...... 56!
3.3.2.4! KRAS Mutations...... 56!
3.3.2.5! MET Mutations...... 56!
3.3.2.6! CTNNB1 Mutations...... 58!
3.3.2.7! EGFR Mutations...... 58!
3.3.2.8! RET Mutations...... 58!
3.4! Discussion ...... 59!
3.4.1! Patient Characteristics ...... 59!
3.4.2! KIT Mutations ...... 59!
3.4.3! BRAF Mutations ...... 61!
3.4.4! NRAS Mutations ...... 61!
3.4.5! MET Mutations...... 62!
3.5! Conclusion ...... 63!
Chapter 4 The Prognostic Importance of NRAS and BRAF Mutations in Melanoma ...... 65!
4.1! Introduction ...... 67!
4.2 ! Materials and Methods ...... 68!
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4.2.1! Study Cohort ...... 68!
4.2.2! Clinicopathological features ...... 68!
4.2.3! DNA Extraction ...... 68!
4.2.4! Mass Spectrometry Genotyping and Confirmatory Sequencing ...... 68!
4.2.5! Statistical Analysis ...... 68!
4.3! Results ...... 69!
4.3.1! Clinical Features ...... 69!
4.3.2! Mass Spectrometry Genotyping ...... 69!
4.3.3! Clinicopathological Features Of NRAS And BRAF Mutants ...... 69!
4.3.4! Survival Analysis ...... 72!
4.4! Conclusion ...... 79!
Chapter 5 Immunohistochemical and Morphological Profiling of KIT, BRAF and NRAS Mutant Melanomas ...... 83!
5.1! Introduction ...... 85!
5.2! Methods ...... 86!
5.2.1! Patient Samples ...... 86!
5.2.2! RNA Extraction and Quality Control ...... 86!
5.2.3! DASL Expression Arrays ...... 86!
5.2.4! Significance Analysis For Microarrays ...... 87!
5.2.5! Immunohistochemistry ...... 87!
5.2.6! Tissue Microarray Construction ...... 88!
5.2.7! TMA Image Analysis ...... 89!
5.2.7.1! Image Acquisition ...... 89!
5.2.7.2! Quantitative Immunohistochemical Analysis ...... 89!
5.2.7.3! Quantitative Cytomorphology Analysis ...... 90!
5.2.7.4! Quantification of Pleomorphism ...... 91!
5.2.7.5! Assessment of Nucleoli Prominence ...... 91!
5.2.8! Statistical Analysis ...... 92!
5.3! Results ...... 93! xiv
5.3.1! Gene Expression Analysis ...... 93!
5.3.1.1! SAM Analysis Of DASL Results ...... 93!
5.3.1.2! Candidate Proteins ...... 97!
5.3.2 ! Immunohistochemical Analysis ...... 97!
5.3.2.1! Immunohistochemistry Optimization ...... 97!
5.3.2.2! Immunohistochemical Staining Scores ...... 102!
5.3.2.3! Association between Kinase Genotype and Immunohistochemical Result .. 110!
5.3.2.4! Immunohistochemistry As A Predictor Of Kinase Mutational Genotype ...... 110!
5.3.3! Cytomorphology Results ...... 113!
5.3.3.1! Nucleoli Analysis ...... 113!
5.3.3.2! Quantitative Cytomorphological Analysis ...... 113!
5.3.3.3! Association between Kinase Genotype and Cytomorphology ...... 123!
5.3.4! Analysis of Immunohistochemistry and Cytomorphology to Predict Kinase Genotype ...... 127!
5.4! Conclusion ...... 129!
Chapter 6 General Discussion ...... 133!
6.1! An Activating KIT Mutation Is Associated With A Loss-Of-Function Pigmentation Phenotype...... 135!
6.2! KIT Mutations Are Infrequent In Cutaneous Melanoma ...... 137!
6.3! MET Juxtamembranous Mutations Are Over-Represented In Advanced Melanoma .. 138!
6.4! Knowledge Of BRAF And NRAS Mutational Status Confers Prognostic Information .. 140!
6.5! BRAF Melanomas Display A Specific Morphological Phenotype ...... 141!
References ...... 144!
Appendices ...... 173!
A.1! Calculation of Immunohistochemical Scoring...... 200!
A.2! Calculation of Missing Sun Damage Values by Neural Network Analysis ...... 204!
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Figures and Tables
Figure Page 1.1 Worldwide Heat Map Of The Incidence Of Melanoma. 3 Incidence And Mortality Rates (ASR) Of The Top 20 Malignancies In Australia Vs. 1.2 4 The Rest Of The World. 1.3 Projected Incidence Of Cutaneous Melanoma In Australia 2030. 5 Photomicrographs Of Melanoma Subtypes; Superficial Spreading and Nodular 1.4 9 Melanoma Photomicrographs Of Melanoma Subtypes; Lentigo Maligna and Acral Lentiginous 1.5 10 Melanoma 1.6 Photomicrographs Of Melanoma Subtypes; Desmoplastic Melanoma 11 Survival Curves From The American Joint Committee On Cancer Melanoma 1.7 15 Staging Database Comparing Staging Groupings 1.8 Diagram of the KIT protein 20 1.9 Diagram of the MET protein 22 1.10 Diagram of the NRAS protein 24 1.11 Diagram of the BRAF protein 25 (A) Familial GIST Syndrome Pedigree. (B) Characteristic Congenital Poliosis In 2.1 35 Patient II-2 Whole-Mount Image Of Formalin Fixed Paraffin Embedded Tissue From 2.2 Oesophagectomy (A) KIT Immunohistochemistry, (B) Haematoxylin And Eosin. 36 Macroscopic Photographs Of Multifocal Distribution Of GIST Nodules Microphotographs: (A) ICC Hyperplasia H&E; (B) ICC Hyperplasia KIT IHC; (C) 2.3 37 GIST H&E; (D) GIST KIT IHC 3.1 Histological Solar Elastosis Scoring Criteria 44 3.2 Melanoma Genotyping Results 53 3.3 Frequency Of Mutations In BRAF, NRAS, KIT, KRAS, CTNNB1 And MET. 54 3.4 Mass Spectrometry And Sanger Genotyping Results Of BRAF V600E And V600K 55 3.5 Coexistence Of MET Mutations With Other BRAF, NRAS And KRAS Mutations 58 Melanoma Specific Kaplan Meier Survival Curves According To BRAF And NRAS 4.1 73 Mutational Status Kaplan Meier Curves For Time To Locoregional Metastasis, Time To Distant 4.2 Metastasis, And To Melanoma Specific Survival From Time Of Distant Metastasis 74 According To BRAF And NRAS Mutational Status 5.1 SAM Plot Of KIT Mutant Vs. NRAS/BRAF Wildtype Mutants 94 5.2 SAM Plot Of BRAF Mutant Vs. BRAF Wildtype Mutants 95 5.3 SAM Plot Of NRAS Mutant Vs. NRAS Wildtype Mutants 96
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5.4 Distribution Of MYL9 IHC Scores 103 5.5 Distribution Of CEACAM3 IHC Scores 104 5.6 Distribution Of KIT IHC Scores 105 5.7 Distribution Of TMX4 IHC Scores 106 5.8 Distribution Of TBRG4 IHC Scores 107 5.9 Distribution Of S100 IHC Scores 108 5.10 Distribution Of Melanin Scores 109 5.11 KIT Mutant Vs. KIT Wildtype IHC Classifier Tree 104 KIT Mutant Vs. KIT Wildtype IHC Classifier Tree (Excluding NRAS And BRAF 5.12 104 Mutant Melanomas) 5.13 TMX4 IHC Score As A Predictor BRAF Mutational Status 112 5.14 TBRG4 IHC Score As A Predictor NRAS Mutational Status 112 Normal Quantile Plot Of Cell Length, Cell Size, Cell Size Coefficient Of Variance, 5.15 115 Cell Shape, Cell Shape Coefficient Of Variance And Cell Cross Sectional Area Normal Quantile Plot Of Cell Regularity Factor, Cell Roundness Factor, Nuclear 5.16 Size, Nuclear Size Coefficient Of Variance, Nuclear Roundness Factor, Nuclear To 116 Cytoplasm Ratio Distribution Of Morphology Score For Cell Size And Coefficient Of Variance Of 5.17 117 Cell Size 5.18 Distribution Of Morphology Score For Cell Size And Width (Cross Sectional Area) 118 Distribution Of Morphology Score For Cell Shape And Coefficient Of Variance For 5.19 119 Cell Shape 5.20 Distribution Of Morphology Score For Cell Regularity And Roundness 120 Distribution Of Morphology Score For Nuclear Size And Coefficient Of Variation 5.21 121 For Nuclear Size 5.22 Distribution Of Nuclear Roundness Factor And Nucleus To Cytoplasm Ratio 122 5.23 KIT Mutant Melanoma Specific Cytomorphology Decision Tree Analysis 126 5.24 BRAF Mutant Melanoma Specific Cytomorphology Decision Tree Analysis 126 5.25 NRAS Mutant Melanoma Specific Cytomorphology Decision Tree Analysis 127 BRAF Mutant Melanoma Specific Cytomorphology And IHC Decision Tree 5.26 128 Analysis NRAS Mutant Melanoma Specific Cytomorphology And IHC Decision Tree 5.27 128 Analysis KIT Mutant Melanoma Specific Clinical, Cytomorphological And 6.1 140 Immunohistochemical Decision Tree Analysis BRAF Mutant Melanoma Specific Clinical, Cytomorphological And 6.2 142 Immunohistochemical Decision Tree Analysis A.1 Graphical Representation Of Association Between The Presence Of Chronic Sun 197
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Damaged Skin And Primary Melanoma Site Full KIT Mutant Cytomorphology And IHC Classifier Tree. IHC Staining Does Not A.2 203 Contribute To The Six Highest Splits A.3 Calculation Of Missing Sun Damage Score By Neural Network Analysis 204 A.4 Fisher’s exact test for association between sun damaged skin and gender 205 A.5 ROC Curve For Neural Network Predicted Solar Elastosis 206 Kaplan Meier curves of Melanoma Specific Survival (weeks) for patients with AJCC A.6 209 stage I or II disease according to mutational genotype
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Table Page 1.1 7th Edition Of The AJCC Melanoma TNM Subcategories 13 1.2 7th Edition Of The AJCC Pathological Staging Grouping For Melanoma 14 3.1 Sun Exposure Scoring Criteria 43 3.2 PCR Primers And Amplification Conditions 45 Single/Double Nucleotide Substitution Mutations Selected For High Throughput 3.3 47 Screening 3.4 KRAS Pyrosequencing Primers 48 3.5 Clinicopathological Characteristics According To Mutant Kinase Genotype 50 3.6 KIT Sequencing Results 52 3.7 MET Sequencing Results In Tumour And Germline DNA 57 3.8 Odds Ratio Of Melanoma Risk And MET R988C 57 3.9 Odds Ratio Of Melanoma Risk And MET T1010I 57 3.10 Odds Ratio Of Melanoma Risk And MET Juxtamembranous Domain SNPs 58 Clinicopathological Characteristics Of BRAF Mutant, NRAS Mutant And 4.1 71 BRAF/NRAS Wildtype Melanomas Univariate Analysis Of Association Between Clinicopathological Features And Time 4.2 77 To Metastatic Events 4.3 Multivariate Analysis Of Statistically Significant Variables From Survival Analysis 78 5.1 Quantitative Cellular Cytomorphology Formulae 90 5.2 Quantitative Nuclear Cytomorphology Formulae 91 5.3 Nucleoli Scoring Criteria 92 5.4 Top 20 Gene Up Regulated In KIT Mutants Vs. BRAF/NRAF Mutant Melanoma 94 5.5 Top 20 Genes Down Regulated In KIT Mutants Vs. BRAF/NRAS Mutants 94 5.6 Top 4 Genes Up Regulated In BRAF Mutants Vs. BRAF Wildtype Melanoma 95 5.7 Top 20 Genes Down Regulated In BRAF Mutants Vs. BRAF Wildtype Melanoma 95 5.8 Top 19 Genes Up Regulated In NRAS Mutants Vs. NRAS Wildtype Melanoma 96 5.9 Top 13 Genes Down Regulated In NRAS Vs. NRAS Wildtype Melanomas 96 SAM Analysis Derived, Differentially Expressed Gene Products For 5.10 98 Immunohistochemical Validation. 5.11 Optimised Antigen Retrieval Conditions For All Antibodies 99 Optimised Antibody Concentration For Discrimination Between Low And High 5.12 100 Expressing Melanoma Samples For MYL9 And CEACAM3 Antibodies Optimised Antibody Concentration For Discrimination Between Low And High 5.13 101 Expressing Melanoma Samples For TMX4 And TBRG4 Antibodies 5.14 Intraobserver Reproducibility Of Nucleoli Scoring 113 5.15 Nucleoli Scoring Proportion Of Agreement (Observed Vs. Expected) 113 5.16 Summary Of Cellular Cytomorphology Measurements 114
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5.17 Summary Of Nuclear Cytomorphology Measurements 114 5.18 Cellular Cytomorphological Measurements According To Kinase Genotype 124 5.19 Nuclear Cytomorphological Measurements According To Kinase Genotype 125 A1.1 SNAI2 Sequencing And Real Time Primers 175 A1.2 Mutation Map Used For Design Of Melanoma Specific Massarray Panel. 176 Melanoma Specific Massarray PCR Amplification/Extension Primers And Product A1.3 178 Masses A1.4 Melanoma Specific Massarray Extension Product Masses. 181 A1.5 MET Real Time PCR Genotyping Primers And Probes 185 A1.6 BRAF Mutant Melanoma Patient’s Clinicopathological Characteristics 186 A1.7 NRAS Mutant Melanoma Patient’s Clinicopathological Characteristics 189 A1.8 MET Mutant Melanoma Patient’s Clinicopathological Characteristics 193 A1.9 KRAS Mutant Melanoma Patient’s Clinicopathological Characteristics 194 A1.10 CTNNB1 Mutant Melanoma Patient’s Clinicopathological Characteristics 195 A1.11 Association Between Solar Elastosis And Exposure Scores 196 A1.12 Association Between CSD Skin And Primary Site. 197 Proportional Hazards Fit Of Time To Locoregional Metastasis Including Ulceration A1.13 198 As A Variable Association Of Ratio Of Head And Neck Melanomas To Trunk Melanomas And A1.14 199 Incidence Of BRAF Mutations In Comparable Studies. Significant Cellular And Nuclear Cytomorphology Associations Using Spearman’s A1.15 202 Non-Parametric Correlation A1.16 Neural Network Prediction Of Sun Damage By Sun Damaged Skin 206 Comparison of demographic and clinicopathological characteristics of KIT mutation A1.17 208 status assessing studies
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Abbreviations
Abbreviation Description AKT v-akt murine thymoma viral oncogene homolog APC adenomatous polyposis coli ATP adenosine triphosphate BRAF v-Raf murine sarcoma viral oncogene homolog B1 CBL casitas B-lineage lymphoma COSMIC catalogue of somatic mutations in cancer CRC colorectal cancer CSD chronic sun damaged CT computer tomographic dH20 sterile distilled water DNA deoxyribonucleic acid EGFR epidermal growth factor receptor ERK extracellular signal regulated kinase FAK focal adhesion kinase FFPE formalin fixed paraffin embedded FTI farnesyltransferase inhibitors GAB-1 GRB2 associated binding protein 1 GDP guanosine diphosphate GIST gastrointestinal stromal tumour GRB2 growth factor receptor bound protein 2 GTP guanosine triphosphate HGF hepatocyte growth factor HNEHREC Hunter New England human research ethics committee HNPCC hereditary non-polyposis colon cancer HRAS v-Ha-ras Harvey rat sarcoma viral oncogene homolog ICC interstitial cells of Cajal JAK Janus kinase JMD juxtamembrane domain KIT v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog LOS low oesophageal pressure MAPK mitogen activated protein kinase MET hepatocyte growth factor receptor MITF micropthalmia transcription factor NCBI national center for biotechnology information NF1 neurofibromatosis type 1 NRAS neuroblastoma RAS viral oncogene homolog OS overall survival P1P3 phosphatidylinositol (3,4,5)-triphosphate PCR polymerase chain reaction PFS progression free survival PI3K phosphainositide 3-kinase PIP2 phosphatidylinositol (3,4,5)-triphosphate PLC-gamma phospholipase C-gamma PLX4032 vemurafenib PTEN phosphatase and tensin homolog PTKD protein tyrosine kinase domain qPCR quantitative polymerase chain reaction RAS rat sarcoma RASA1 RAS GTPase activating protein RET ret proto-oncogene ROS reactive oxygen species RTK receptor tyrosine kinase
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SCF stem cell factor SCLC small cell lung cancer SH2 Src homology 2 SHC SRC homology 2 domain-containing SNP single nucleotide polymorphism SOS Son of Sevenless STAT signal transducer and activator of transcription TKI tyrosine kinase inhibitor UVA ultraviolet A UVB ultraviolet B WS Waardenburg syndrome
UNITS °C degrees Celsius cm centimeters(s) g gram(s) hr hour(s) J joule(s) L liter(s) m meter(s) M molar min minute(s) s second(s) V volt(s) kbp kilobase pairs kDa kilodaltons bp base pairs
PREFIXES K kilo 103 m milli 103 µ micro 10-6 n nano 10-9
NUCLEIC ACIDS A adenine T thymine C cytosine G guanine
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Although it is the lot of man but once to die, the victim of malignant melanoma need not die by inches from the
Black Death. The measures Science gives us in abundance are to be used not timidly but bravely to combat this pigmented foe, which claims the fairest, the youngest, and the old, with equal impartiality.
- Sir Stanford Cade, Malignant Melanoma. The Bradshaw Lecture, UK 1960.
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Abstract
The treatment of patients with metastatic melanoma has been revolutionized by the discovery of crucial activating kinase mutations, making melanoma susceptible to targeted kinase inhibition. The result is that melanoma, once a notoriously treatment resistant malignancy, has become the poster child for the promise of personalized cancer therapy. We have sought to identify what the incidence of various kinase mutations are in a group of 192 melanoma patients, sampled from a population that harbors one of the highest rates of melanoma in the world, and what this may mean for their prognosis and treatment. In our cohort of patients with advanced melanoma, mutations in BRAF, NRAS, KIT and KRAS occurred with frequencies of 24.5%, 20.8%, 4.7% and 2.6%, respectively. We show that BRAF-mutant melanomas have a specific clinical profile. Furthermore, we reveal that BRAF and NRAS mutant melanomas show significant differences in their respective rates of metastatic spread and overall survival. Our mutation screen also revealed two relatively common juxtamembranous MET single nucleotide polymorphisms to be significantly over-represented in our melanoma patient cohort.
As the identification of some of these clinically relevant mutations will become routine in the workup of patients with advanced melanoma, we turned our attention to the search of a phenotype/genotype association that may be employed by anatomical pathologists to triage cases for molecular analysis. Using m-RNA extracted from formalin-fixed paraffin embedded melanoma tissue, we identified a number of genes differentially expressed in BRAF, NRAS and KIT-mutant melanomas. The levels of these differentially expressed genes were then assessed using immunohistochemical staining and quantitative scoring on a matching melanoma tissue microarray. We show a strong correlation in the expression data and immunohistochemical staining for some of the genes identified. Some of these differences in protein staining were also shown to be capable of having a good predictive capacity for identifying cases likely to harbor KIT mutations. Quantitative cytomorphological analysis identified a BRAF-mutant melanoma specific morphological phenotype, characterized by larger cellular volumes, more rounded shapes and lower degrees of cellular pleomorphism.
In conclusion, we show that incorporating discriminatory clinical; cytomorphological and immunohistochemical data can generate a decision tree algorithm with fair to good predictive power for the detection of KIT and BRAF-mutant melanomas.
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CHAPTER 1
INTRODUCTION
Chapter 1 – Introduction
2 Chapter 1 – Introduction
1.1 Epidemiology of Melanoma in Australia
Melanoma is the fourth most commonly diagnosed cancer in Australia with an age-standardized incidence of 47.9 cases per 100,000 population (AIHW, 2010). Within the New South Wales Hunter New England Health District, from which this dissertation’s patient population emanates, the rate is a staggering 62.5 per 100,000 (males 75.8, females 49.2) (2010). This stands in contrast to the rest of the world, where melanoma only ranks as the 18th most common cancer, with most industrialised countries showing half the incidence rates observed in Australia (US 15.6, Denmark 19.9, UK 11.6 per 100,000 population) (Baade et al., 2011) (figure 1.1).
Figure 0-1
Figure 1.1 Age standardised incidence of melanoma per 100,000 population. Sourced from Globocan 2008, International Agency for Research on Cancer (GLOBOCAN, 2011).
3 Chapter 1 – Introduction
This high incidence, and the poor prognosis in patients with metastatic disease combine to make melanoma the 9th most common cancer causing death in Australia. Globally, melanoma is only the 18th most frequent and 22nd most frequent cause of cancer death (Aitken et al., 2008) (figure 1.2).
Figure 0-2
Figure 1.2 Incidence and mortality (age standardise rate per 100,000 population) of the top 20 malignancies in Australia vs. rest of the world (GLOBOCAN, 2011).
4 Chapter 1 – Introduction
For Australian males, melanoma is the second most prevalent cancer, ranking behind prostate cancer, with a 5- year prevalence of 23,214 cases. In females, it ranks third behind breast and colorectal cancer, with a 5 year prevalence of 18,697 (Aitken et al., 2008).
Melanoma is the most rapidly increasing cancer in Caucasians world wide, a trend predicted to continue for the next two decades (Garbe and Leiter, 2009). In Australia the current incidence rate for both females and males is higher than it was in the early 1980s (26.2 per 100, 000) or 1990s (37.6 per 100,000) (AIHW, 2010). The ageing Australian demographic will have an additive effect upon the incidence of melanoma (figure 1.3).
Figure 0-3
Figure 1.3 Projected incidence of cutaneous melanoma in Australia, 2030(GLOBOCAN, 2011).
5 Chapter 1 – Introduction
1.2 Management of Melanoma
1.2.1 Clinical Detection
Most patients have their melanomas detected at an early stage by their primary care providers (Baade et al., 2011). In approximately half of these patients, they feature the malignant transformation of previously existing naevi (Weatherhead et al., 2007). The in situ or radial growth phase of a melanoma allows its early detection because it manifests changes in a naevi, which are best remembered by the acronym ABCDE (Asymmetry, Border irregularity, Colour variation, large Diameter and Evolution). However, once melanoma cells acquired an invasive phenotype, these lesions will become nodular and may ulcerate, bleed or show superficial crusting (Aitken et al., 2008).
The most sensitive clinical tool for the detection of invasive melanoma is a dermatoscope. The features of malignant melanomas on dermatoscopy have been summarized into the following seven points (Higgins et al., 1992): (1) atypical pigment network (irregular thick lines); (2) blue-white veil; (3) atypical vascular pattern; (4) irregular streaks (confluent streaks not combined with pigment network lines); (5) irregular pigmentation; (6) irregular dots or globules; (7) regression structure (white scar-like areas).
Although current evidence dos not support the implementation of population wide skin screening for melanoma (Aitken et al., 2008) in reality a significant proportion of the Australian public practice some form of skin check either at home or by a health care professional (Aitken et al., 2008).
1.2.2 Melanoma Diagnosis
The definitive diagnosis of a melanoma can only be established after a considered histopathological assessment of the entire skin lesion. Because no single diagnostic feature has a perfect sensitivity or specificity, a melanoma diagnosis is rendered on the balance of the histopathological features in combination with the patient’s clinical presentation. The most important histological features are dependent on the recognition of asymmetry within the lesion. This feature applies to: (1) asymmetry in the external silhouette of the entire lesion; (2) asymmetry of the lateral junctional component, where melanocytes display different cytological features at contralateral ends of the lesion; (3) asymmetry in the distribution of melanocyte nests at the junction of the epidermis and dermis; (5) asymmetry in the distribution of melanin pigmentation; (6) asymmetry in the distribution of the inflammatory response; (7) asymmetry in the architecture of the affected epidermis; (8) asymmetry in the cytological features i.e. melanocyte pleomorphism (Massi et al., 2004).
Three other architectural features characterize a melanoma’s disorganized growth pattern. These are; (1) poor delimitation of the lesion, with the malignant melanocytes trailing off the edge of the tumour; (2) large, irregular, expansive and confluent nests of melanocytes; (3) single cell (pagetoid) spread of melanoma cells within the upper epidermal layers.
Finally, a careful inspection of the fine cytological features of melanoma cells reveals cells with pleomorphic nuclei, often with variable chromatin content and prominent, active nucleoli. The appearance and volume of cytoplasm is often be variable, with large epithelioid cells admixed with cells 6 Chapter 1 – Introduction
only showing scant cytoplasm. Mitotic figures, especially within the deeper aspects of the lesion, are worrisome and should lower the cytological and architectural threshold for the diagnosis of a melanoma (Massi et al., 2004).
1.2.3 Melanoma Subtype
The current edition of the World Health Organization Classification of Tumours – Pathology and Genetics of Tumours of the Skin (6th edition) (LeBoit, 2006), recognizes five major classification types, which although some argue are of limited clinical value, nonetheless dominate both clinical practice and the research literature (LeBoit, 2006, Weedon, 2009).
1.2.3.1 Superficial Spreading Melanoma
This is the most common type of melanoma in Caucasians and can commonly be seen arising in the background of a compound or intradermal naevus in intermittingly sun-exposed sites. Its principal characteristics are the presence of a prominent intraepidermal pagetoid spread by atypical, at times mitotically active melanocytes. The melanocytes proliferation is typically highly cellular, focally growing as large irregularly dispersed junctional nests. The lesion will often show an irregular melanin distribution and the overlying epidermis is also irregularly altered (Massi et al., 2004, LeBoit, 2006) (figure 1.4).
1.2.3.2 Nodular Melanoma
This is an aggressive melanoma subtype with minimal in situ or radial growth phase. It is also commonly occurs in intermittent sun-exposed sites (LeBoit, 2006). Macroscopically, these tumours have an exophytic, polypoid growth pattern, presenting as a rapidly growing, variably pigmented nodule. Histologically, large confluent sheets of highly atypical, mitotically active melanocytes characterize it. An important diagnostic feature is the absence of intraepidermal melanoma cells beyond two rete ridges of the invasive component (figure 1.4). This has led some to dispute this group, as rather than being a distinct biological entity, instead represents a melanoma with an exaggerated vertical growth phase (Massi et al., 2004).
1.2.3.3 Lentigo Maligna Melanoma
This type of melanoma arises with a slowly growing in situ precursor termed a Hutchinson’s melanocytic freckle. It is a melanoma of elderly patients, arising in chronically sun-damaged skin, typically of the head and neck region. The principal histological feature of the in situ component is a proliferation of atypical melanocytes, singly and in small nests, along the base of the epidermis without pagetoid extension into the upper layer (Weedon, 2009) (figure 1.5).
1.2.3.4 Acral Lentiginous Melanoma
Melanomas which arise with the glabrous skin of the palms and soles of the feet commonly feature a lentiginous spread of malignant melanocytes, akin to lentigo melanoma, but in addition show upward
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scatter of melanoma cells reminiscent of superficial spreading melanoma (Weedon, 2009). These melanomas are notorious for bearing a deceptively benign appearing in-situ component. Some authors have suggested all melanoma arising in non-hair bearing skin, regardless of architectural features should be termed acral lentiginous melanomas (Ahmed, 2000) (figure 1.5).
1.2.3.5 Desmoplastic Melanoma
An uncommon melanoma subtype accounting for between 1% and 4% of melanomas (Weedon, 2009), desmoplastic melanoma are characterized by bland spindled, fibroblast-like, melanoma cells admixed with abundant collagen (Massi et al., 2004). The melanomas most commonly arise within sun-damaged skin or, less often, on mucous membranes or volar skin. This can be a diagnostically difficult entity due to not only the benign appearing cytology of the invasive cells, which can be mistakenly diagnosed as hyperplastic scar or neurofibroma, but also because the junctional component is characteristically absent or inconspicuous (LeBoit, 2006). The correct diagnosis requires a high-degree of suspicious and the judicious use of ancillary studies (i.e. a panel of immunohistochemical markers including S100, HMB-45 and MITF) to exclude a broad differential diagnosis that include dermatofibromas, malignant peripheral nerve sheath tumours, leiomyosarcoma and spindle cell carcinomas.
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Figure 1.4 Histomorphology of melanoma subtypes. Superficial spreading and nodular melanoma. Melanoma Histology (x100) Subtype Superficial Spreading Melanoma
Nodular Melanoma
Figure 0-4
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Figure 1.5 Histomorphology of melanoma subtypes. Lentigo maligna and acral lentiginous. Melanoma Histology (x100) Subtype Lentigo Maligna Melanoma
Acral Lentiginous Melanoma
Figure 0-5
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Figure 1.6 Histomorphology of melanoma subtypes. Desmoplastic melanoma. Melanoma Histology (x100) Subtype Desmoplastic Melanoma
Figure 0-6
1.2.4 Histopathological Prognostic Factors
The American Joint Committee on Cancer (AJCC) is the leading authority in the determination of the most powerful clinical and histopathological prognostic factors in many cancers, including melanoma. The latest AJCC guidelines (Edge et al., 2010) published in 2010 have been incorporated into the current Royal Australian College of Pathologists’ guidelines for the structured reporting of cutaneous melanomas (Scolyer et al., 2010). The three strongest predictive histopathological features are at the centre of the AJCC tumour classification: Breslow’s thickness, ulceration and mitotic rate.
1.2.4.1 Breslow’s Thickness
The single most important histopathological prognostic feature is the measurement of Breslow’s thickness. This is a measurement, recorded to the nearest 0.1mm, taken from the top of the granular layer to the deepest portion of the invasive tumour (Edge et al., 2010). Tumour measuring less than 0.5mm have a 10 year survival rate of 96%, 89% for those between 0.5-1.00mm, 80% for 1.01-2.00 mm, 65% for 2.01-3.00mm, 57% for 3.01-4.00mm, 54% for 4.01-6.00 and 42% for those with a thickness greater than 6.01mm (Edge et al., 2010).
1.2.4.2 Melanoma Ulceration
Melanoma ulceration is defined as, “the absence of a completely intact epidermis above the primary melanoma” (Edge et al., 2010). For any given Breslow’s thickness, the presence of ulceration confers a greater risk of mortality.
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1.2.4.3 Mitotic Rate
Mitotic rate is measured per mm2 within the region of greatest mitotic activity. The prognostic power of this feature, within thin and localized melanomas, is second only to Breslow’s thickness (Edge et al., 2010).
There are a number of other important histopathological features that are not factored in determining a melanoma’s stage, but are nonetheless recommended by the Royal College of Pathologists of Australasia and National Health and Medical Research Council as conveying important information to the treating clinician. These include vascular invasion, the presence of tumour infiltrating lymphocytes, evidence of regression (loss of invasive tumour associated with fibrosis, lymphocytes and macrophages), neurotropism, desmoplasia (spindled melanoma cells in associated with and separated by new stromal collagen), perineural invasion and the presence of adjacent but separate melanoma deposits i.e. satellite metastasis (Edge et al., 2010).
1.2.5 Melanoma Staging
The current AJCC staging guidelines (Edge et al., 2010) for cutaneous melanoma provides the definitive criteria for the identification of prognostic groups, which in turn determine the surgical and medical oncology treatment protocols. These guidelines are summarized in table 1.1, 1.2 and figure 1.4
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Table 1.1 7th Edition of the AJCC melanoma TNM subcategories Tumour (T) Classification Tx Primary tumour cannot be assessed T0 No evidence of primary tumour Tis Melanoma in situ T1 Melanoma 1.0mm in thickness T1a Without ulceration and mitosis 1/mm2 T1b With ulceration or mitosis ≥1/mm2 T2 Melanomas 1.01-2.0mm in thickness T2a Without ulceration T2b With ulceration T3 Melanomas 2.01-4.0mm in thickness T3a Without ulceration T3b With ulceration T4 Melanomas 4.0mm in thickness T4a Without ulceration T4b With ulceration
Lymph node (N) classification NX Regional lymph nodes cannot be assessed N0 No regional lymph node metastasis N1 1 node with metastasis N1a Micrometastasis* N1b Macrometastasis N2 2-3 nodes with metastasis N2a Micrometastasis N2b Macrometastasis N2c In transit metastasis without metastatic lymph nodes N3 Clinical: ≥ 1 node with in transit met(s)/satellite(s); pathologic: 4 or more metastatic nodes, or matted nodes, or in transit met(s)/satellite(s) with metastatic node(s)
Metastasis (M) classification M0 No distant metastasis M1 Presence of metastasis M1a Metastases to skin, distant lymph nodes M1b Metastases to lung M1c Metastases to all other viscera or any site with a combined raised serum lactate dehydrogenase *Micrometastasis are dependent on detection during a sentinel lymph node protocol assessment and a subsequent negative completion lymphadenectomy (adapted from Edge et al. 2010). Table 1
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Table 1.2 7th edition of the AJCC pathological staging grouping for melanoma (Edge et al., 2010). Stage T N M 0 Tis N0 M0 IA T1a N0 M0 IB T1b N0 M0 T2a N0 M0 IIA T2b N0 M0 T3a N0 M0 IIB T3b N0 M0 T4a N0 M0 IIC T4b N0 M0 IIIA T1-4a N1a M0 T1-4a N2a M0 IIIB T1-4b N1a M0 T1-4b N2a M0 T1-4a N1b M0 T1-4a N2b M0 T1-4a N2c M0 IIIC T1-4b N1b M0 T1-4b N2b M0 T1-4b N2c M0 Any T N3 M0 IV Any T Any N M1 Table 2
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A
B
C
IV M1a (n = 1,474) IV M1b (n = 1,895) IV M1c (n = 4,603)
Survival (years) Figure 1.7 Survival curves from the American Joint Committee on Cancer Melanoma Staging Database comparing staging groupings: Stage I and II (A), Stage I (B) and Stage IV (C). Adapted from (Balch et al., 2009). Figure 0-7
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1.2.6 Current Melanoma Treatment Guidelines
Over one hundred and sixty years ago, Dr. Samuel Cooper wrote the first English treatise on the pathology of melanoma. In it, he recognized that the aggressiveness of the disease meant that, ‘the only chance of benefit depends on the early removal disease by operation’ (Cooper, 1844). To this day, the early and wide excision of an early stage melanoma remains the best opportunity a patient has for undergoing a curative treatment.
The current NHMRC clinical practice guidelines (Aitken et al., 2008) for the management of melanoma in Australia and New Zealand prescribe a minimum margin of 1cm for invasive melanoma of no more than 4.00mm in thickness (preferably 2cm for melanomas 1.00 to 4.00mm) and 2cm minimum for melanomas greater than 4.00mm in thickness. This is combined with sentinel lymph node sampling for patients with melanomas greater than 1mm in thickness that do not harbor any clinical evidence of metastatic disease. For patients with lymph node metastasis or with a high risk of metastasis (AJCC IIB, IIC), adjuvant interferon-alpha therapy has been shown to improve the 5-year relapse free-survival by 10% (Aitken et al., 2008). The toxicity of this immunotherapy agent is considerable, necessitating prophylactic anti-depressants and careful monitoring for hepatotoxicity (Aitken et al., 2008).
Patients with in-transit or locally recurrent limb disease, isolated limb infusion (ILI) with alkylating agents, such as Melaphalan, may yield a sustained response in 50% of cases (Aitken et al., 2008). However, it needs to be remembered the aim of ILI is only to enhance local control, as it does not affect overall survival. In patients with widely disseminated disease, current chemotherapy agents are primarily used in a palliative setting (Aitken et al., 2008). Complete response rates for the best alkylating agents (temazolomide, dacarbazine) are in the order of 5%, generating a median survival of 6-7 months(Aitken et al., 2008). Surgical intervention is generally used in a palliative setting, but can have modest impact on survival if the metastatic disease is solitary (Aitken et al., 2008). Radiotherapy treatments can generate good palliative response in widely disseminated disease (Aitken et al., 2008).
1.3 Melanoma Pathogenesis
1.3.1 Risk Factors
1.3.1.1 Inherited
The definition of melanoma as being familial requires the diagnosis in two first-degree relatives or a kindred with at least 3 affected individuals, irrespective of the nature of the relationship (de Snoo and Gruis). In comparison to the general population, the individuals in these families are at 2-3-fold increased risk of acquiring melanoma. A mutation in the tumour suppressor CDKN2A has been identified in 25- 50% of such families. The CDKN2A changes are commonly inactivating mutations targeting an important negative regulator of the cell cycle and a promoter of cellular senescence (LeBoit, 2006). Inactivating mutations within CDKN2A are also associated with multiple tumour syndromes including Melanoma and Neural System tumour syndrome [OMIM 155755], Orolaryngeal Cancer syndrome [OMIM 155601] and Pancreatic Cancer/Melanoma syndrome [OMIM 606719](McKusick and Hamosh,
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2011). However, with a germline mutation incidence of 0.01%, this locus only contributes to a small fraction of the total cases (LeBoit, 2006).
A more important modifier of melanoma risk is the Melanocortin 1 Receptor (MC1R). MC1R is a g- coupled membrane receptor, found on melanocytes, that responds to alpha-MSH secreted by keratinocytes. MC1R functions in regulating skin pigmentation by influencing the ratio of light-coloured phaeomelanin to the dark-coloured, UV protective eumelain (Pruitt et al., 2009, McKusick and O'Neill, 2010). Thirty of the sixty known variants of MC1R are known to influence skin pigmentation (Landi et al., 2005, Pruitt et al., 2009), few classically generating a red head phenotype. The frequency of MC1R variant alleles has been shown to be as high as 50% in the Australian population (Sturm et al., 2003). Collectively, the presence of these alleles can confer a 2-3 fold increase in the risk of melanoma, although those alleles most strongly associated with the red head phenotype (D84E, R151C, P160N and D294H) are associated with increased disease odds ratios of between 60-120 (Sturm et al., 2003). However, the reason for MC1R’s link to melanoma goes beyond the absolute levels of eumelanin, as non-red headed variants have been shown to still be associated with a 2-3 fold increase in the risk of developing sporadic melanoma (Landi et al., 2005).
1.3.1.2 Environmental
UV radiation is the recognized environmental insult behind the development of most melanomas (El Ghissassi et al., 2009, Aitken et al., 2008, Garland et al., 2003). The relationship between the pattern of UV exposure and the subsequent risk of melanoma is complex, but a consensus has begun to emerge that intermittent episodes of high UV exposure, particularly in childhood, confers the main risk of melanoma. Melanomas developing as a result of intermittent UV-light exposure usually occur in relatively sun- protected sites such as the trunk and lower legs. Conversely, high levels of continuous sun exposure confers a high risk of melanomas arising in the elderly age groups and within sites susceptible to chronic sun-exposure, such as the head and neck (Aitken et al., 2008, LeBoit, 2006).
The UV spectra has of three major components: UVA (320-400nm); UVB (280-320nm; UVC (100-280 nm), the latter being blocked by the earth’s ozone layer. It has increasingly become apparent that both UVA and UVB are mutagenic through similar mechanisms. Both radiations can generate reactive oxygen species and subsequent oxidation of guanine nucleotides (8-oxo-7,8-dihydroguanine) as well as precipitating single stranded DNA breaks (Cadet et al., 2009). UVA is less readily absorbed by DNA than UVB, but is still capable of generating cyclobutane pyrimidine dimers (CPDs) with cells at the basal epidermis (including melanocytes), being particularly targeted by this type of UVA induced DNA damage (Tewari et al., 2011). UVB in addition can form pyrimidine (6-4) pyrimidine photoproducts (6- 4PPs) that, if unrepaired by the nucleotide excision pathway, can generate C!T or CC!TT mutations (Ziegler et al., 1996).
The National Health and Medical Research Council currently acknowledges the carcinogenic properties of UVA emitting solaria and recommend people of fair-skin, in particular those under 35 yrs. of age,
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should be warned of the small but significant increased risk of developing melanoma (Aitken et al., 2008).
1.3.3 Melanoma Oncogenes
The MAP kinase pathway has been a key topic of research for laboratories seeking to identify the mechanism behind the signalling deregulation responsible for driving the development of melanomas. The fruit of this labour has been an improved understanding of the mechanisms of oncogenic activation in the kinases KIT, MET, NRAS and BRAF. These genes represent the most fertile ground of research into new treatments for melanoma.
1.3.3.1 KIT
1.3.3.1.1 KIT Structure And Function
Originally discovered as a cellular homologue of the feline sarcoma viral oncogene v-kit, the KIT gene resides in chromosome 4q11-12. It codes for a member of the class 3 tyrosine kinase receptor family. This group of proteins is structurally defined by five external immunoglobulin-like domains, a single-pass transmembrane domain and an internal protein tyrosine kinase domain (PTK) with a large kinase insert domain region (Schlessinger, 2000). The mature protein contains 976 residues and is 145 kDa when fully glycosylated.
During embryogenesis, KIT signaling is required for successful hematopoiesis, melanogenesis, gametogenesis and the development of an intact gastrointestinal pacemaker cell network (reviewed by (Besmer et al., 1993). Within both melanoblast and mature melanocytes, KIT drives melanogenesis its MAPK-dependent activation of the so-called master regulator of melanocyte development, the microphthalmia-associated transcription factor (MITF). MITF is necessary for the successful migration and survival of melanoblasts out of the neural crest during embryogenesis and for the adoption of differentiated phenotype in mature melanocytes (reviewed by (Tachibana, 2000). After birth, KIT expression is detectable in neural ectodermal derived tissues, mast cells, haematopoetic precursors and some epithelia.
Non-activated KIT resides as a monomer in the plasma membrane. Bivalent binding of stem cell factor (SCF) initiates receptor homodimerization, triggering auto-phosphorylation of tyrosine residues within KIT, consequently generating docking sites for secondary signalling molecules containing Src homology 2 domains (Marquette et al., 2007). Relaying this extracellular signal to the intracellular domain requires the binding of adaptors proteins (Grb2 and Grb7) via their SH2 domains to KIT phosphotyrosine residues (residues 703 and 936) (Marquette et al., 2007). The active adaptor proteins recruit the guanine nucleotide exchange factor SOS to the underside of the plasma membrane, bringing it into proximity to the inner membrane bound RAS. As the name implies, the guanine nucleotide exchange factor (Sos) is responsible for switching the inactive guanine diphosphate bound RAS, for the active guanine triphosphate bound RAS (Lennartsson et al., 2005).
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Once switched on, RAS activates a number of signalling proteins and cascades, including RAF, a serine/threonine protein kinase and member of the MAP kinase-signalling cascade. In turn, RAF phosphorylates extracellular signal regulated kinase (ERK) 1 and 2. Active ERK phosphorylates numerous cytoplasmic and nuclear proteins including transcription factors and other protein kinases. The consequences of MAPK signalling are complex, but its overall effects in a wide spectrum of cell types, including melanocytes, are considered to be mitogenic and/or anti-apoptotic (Smalley, 2003).
KIT activation also triggers signaling via the PI3K pathway. Activated KIT binds to the p85alpha subunit of phosphoinositol-3-kinase, recruiting the enzyme to the underside of the plasma membrane where it converts phosphatidylinositol-4,5 bisphosphate (PIP2) into phosphatidylinositol-3,4,5 bisphosphate (PIP3) (Serve et al., 1994). PIP3 activates the Protein Kinase B family (AKT1, AKT2 and AKT3) acting upon cell-survival related proteins (BAD, BIM, BAX) and cell cycle inhibitors (p27Kip, p21Cip, MDM2) to affect a pro-survival, pro-cell cycle progression function. Recent findings suggest the transforming effects of mutant KIT in melanomas are more dependent on the strength of PI3K cascade activation than the constitutive activation of the MAPK cascade (Liang et al., Monsel et al., 2010). In GISTs, the recent identification of ETV1 as an obligatory lineage specific factor which cooperates with mutant KIT signaling, also serves to highlight the multifactorial nature of KIT driven tumorigenesis (Chi et al., 2010).
1.3.3.1.2 KIT Oncogenic Signaling
The Sanger Cosmic database catalogues 97 KIT mutations in 1157 melanoma samples (mutation frequency of 8%). The three most common types of mutations are missense substitutions (84.5%), followed by in-frame deletions (7.2%) and insertions (4.1%). The missense mutation preferentially target exon 11 (48%), followed by exon 13 (24%), exons 17-18 (23%) and exon 9 (4%). The two most common substitutions L576P and K642E occur at frequencies of 31% and 20% of KIT mutant cases, respectively (figure 1.8).
Despite the clear oncogenic action of these mutations, a series of case reports documenting the presence of the germline KIT mutation K642E show a complex phenotype, which included dysphagia and multifocal GISTs but surprisingly did not include the presence of melanoma tumours (Graham et al., 2007, Isozaki et al., 2000, Vilain et al., 2011). Paradoxically, the K642E KIT mutation, which seemingly is selected for in sporadic melanoma, also generates a congenital hypopigmentation phenotype characteristically associated with defects in the migration and survival of melanoblasts out of the embryological neural tube (Vilain et al., 2011, see chapter 2). These families serve to highlight that these KIT mutations are necessary but not sufficient for the development of some sporadic melanomas, a phenomenon also reflected in the presence of oncogenic BRAF and NRAS mutations in benign melanocytic nevi (Wu et al., 2007, Poynter et al., 2006). A possible unifying mechanism explaining why this KIT mutation has such contrasting outcomes when occurring congenitally vs. sporadically (i.e. melanoblasts vs. mature melanocytes) may be through the incomplete suppression of the master regulator of melanocyte development MITF via the constitutive phosphorylation of ERK 1/2. This possible mechanism is explored in sections 1.3.3.5 and 6.1.
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The character and distribution of the two most common melanoma KIT mutations stand in sharp contrast with the pattern of mutations occurring in GISTs, where missense substitutions make up 26% of mutations and L576P and K642E make only 3% of all mutations. Mutations that target the extracellular domain enhance the binding affinity between the extracellular domains, facilitating dimerization and receptor activation. Mutations targeting the juxtamembrane domain disrupt its capacity to insert into the kinase active site, rendering the PTK domain constitutively switched on (Mol et al., 2004).
Figure 1.8 Diagram of the KIT protein in scale. Numbers inside the boxes indicate the exon that translates each part of the protein. The extracellular region is coloured in red (ECR), the transmembrane domain in blue (TMD) and the intracellular region in purple (ICR). The green bars represent the Immunoglobulin-like domain (IGD), and the Tyrosine Kinase domains 1 and 2 (TKD1, TKD2). The location of the five most common melanoma KIT mutations identified in the Sanger Center COSMIC database is indicated by the arrow. C: Carboxyl-terminal; N: Amino- terminal. Adapted from Larizza et al., 2000. (Larizza and Beghini, 2000)
1.3.3.2 MET
1.3.3.2.1 MET Structure and Function
MET is a member of the tyrosine kinase receptor class VI, initially identified as an oncogene in a human osteosarcoma cell line (Cooper et al., 1984). MET is located on chromosome 7q21-q31(Angeloni et al., 2001). The gene is 125 kbp in length and is composed of 21 exons. Post-transcriptional splicing generates a 6.6 kbp mRNA composed alternatively spliced as three isoforms, with one transcripts lacking 18 amino acids in exon 10, and another isoform lacking 47 amino acids in exon 14 (Angeloni et al., 2001). The MET receptor is a 170 kDa, 1408 amino acid single chain protein, which after post-translation glycosylation, is cleaved into α and β chains (50 kDa and 140 kDa, respectively). In its final form, the protein is anchored at the cell membrane as a heterodimer where the α and β chains are linked by disulphide bridges (Angeloni et al., 2001). The α chain forms the extracellular hepatocyte growth factor (HGF) binding site (Sema domain). The β chain is composed of multiple domains which span the extracellular (Sema, cysteine-rich, glycine-rich and immunoglobulin-like domains) and intracellular spaces (juxtamembrane and tyrosine kinase domains plus the SH2-containing domain within the c- terminal tail region) (Angeloni et al., 2001, Kim and Salgia, 2009).
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MET expression is near ubiquitous, with the strongest expression pattern being present within the keratinized skin layers and neuronal cells (Uhlen et al., 2010). Up regulation of MET expression is observed in response to tissue injury across a number of organs, best exemplified by its regenerative capacity in the liver (Nakamura et al., 1989).
Classic activation of MET signaling is through the binding of the hepatocyte growth factor (HGF) to the extracellular Sema domain of the MET receptor, inducing homodimerization and transphosphorylation of the tyrosine kinase and c-terminal domains (Kim and Salgia, 2009), although HGF-independent activation of MET has also been recently recognized (Brevet et al., 2011). The phosphorylation of the SH2-domain within the c-terminal region allows the docking of a number of adaptor proteins including CBL (Fixman et al., 1997), GAB-1 (Weidner et al., 1996), GRB2 (Ponzetto et al., 1994) and SHC (Pelicci et al., 1995). These are responsible for the signaling cascade activation of ERK1/2 (Besser et al., 1997), focal adhesion kinase (FAK) (Beviglia and Kramer, 1999), PI3K (Ponzetto et al., 1994), PLC-gamma (Beviglia and Kramer, 1999) and signal transducer and activator of transcription (STAT) (Boccaccio et al., 1998).
Through the MAPK kinase pathway, MET promotes cell division (Day et al., 1999), the PI3K cascade enhances cell survival (Xiao et al., 2001) and both act synergistically to promote cell detachment, invasion and migration (Gual et al., 2000). MET signaling also promotes angiogenesis via the regulation of VEGF (Gille et al., 1998) and thrombospondin-1 secretion (Zhang et al., 2003). The negative regulation of MET signaling is dependent on the binding of a phosphorylated juxtamembranous domain by CBL, inducing receptor internalization and ubiquitin-mediated receptor turnover (Peschard et al., 2001, Fixman et al., 1997, Hammond et al., 2001).
Under normal conditions, melanocyte MET expression is regulated by the master regulator of melanocyte development and differentiation, the micropthalmia transcription factor MITF (McGill et al., 2006). Physiological MET activation is in turn dependent on fibroblast derived HGF (Nordlund, 2006). Knock out experiments reveal MET signaling to be necessary for the survival of melanoblasts during embryogenesis (Kos et al., 1997), and the ectopic expression of its receptor HGF, resulted in the aberrant localization of melanocyte precursors and in subsequent body-wide hyperpigmentation (Takayama et al., 1996). Melanocytes in vitro respond to HGF stimulation with enhanced proliferative, migratory and melanin synthesis rates (Halaban et al., 1992).
1.3.3.2.2 MET oncogenic signaling
Oncogenic MET signaling can arise from activating mutations, overexpression, gene amplification and autocrine/paracrine activation via HGF secretion. Such dysregulation has been identified in a large variety of carcinomas that include bladder (Cheng et al., 2002), breast (Yamashita et al., 1994), cervical (Baykal et al., 2003), colorectal (Kammula et al., 2007), oesophageal (Anderson et al., 2006), head and neck (Marshall and Kornberg, 1998), kidney (Tanimoto et al., 2008), liver (Daveau et al., 2003), lung (Okuda et al., 2008), ovarian (Ayhan et al., 2005), pancreas (Hill et al., 2010) and thyroid (Di Renzo et al., 1992), as well as osteosarcoma (Patane et al., 2006), rhabdomyosarcoma (Chen et al., 2007), synovial sarcoma (Oda et al., 2000), non Hodgkin lymphomas (Etto et al., 2008), leukaemias (Onimaru et al., 2008),
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glioblastomas (Kong et al., 2009), mesotheliomas (Jagadeeswaran et al., 2006) and melanomas (Beuret et al., 2007, Cruz et al., 2003, Elia et al., 2001, Kenessey et al., 2006, Lin et al., 1998, Mascarenhas et al., Otsuka et al., 1998, Puri et al., 2007, Rusciano et al., 1999, Stevens et al.).
The aberrant expression of HGF by melanoma cells, and the subsequent autocrine/paracrine activation of MET, is thought to play a central role in melanoma progression (Li et al., 2001). In particular, the presence of a hypoxic micro-environment is thought to promote pro-metastatic tumour adaptations via angiogenesis, an effect driven by the mitochondrial dependent stabilization of hypoxia-inducible factor 1 alpha, which in turn activates a metastatic program via MET signaling (Comito et al., 2011, Pennacchietti et al., 2003). Disruption of MET signaling by MET specific tyrosine kinase inhibitors (TKIs) has the capacity to impair melanoma cell migration and induce cell cycle arrest and apoptosis (Puri et al., 2007, Kenessey et al., 2006). This effect is independent of the presence of activating MET mutations or melanoma derived HGF production (Kenessey et al., 2006).
1.3.3.2.3 MET Mutations
Most activating MET mutations arise from missense substitutions (82%) targeting the tyrosine kinase domain, followed by in-frame deletions, which account for a further 5% (Forbes et al., 2011). These mutations target the Sema domain (codons 57 – 500), juxtamembranous domain (974-1078) and tyrosine kinase domains (1078-1337) of the MET protein (figure 1.9).
Figure 0-8
Figure 1.9 Diagram of the MET protein in scale. Numbers inside the boxes indicate the exon which translates each part of the protein. The extracellular region is coloured in red (ECR), the transmembrane domain in blue (TMD) and the intracellular region in purple (ICR). The green bars represent the Sema domain (SMD), the Plexin-Semaphorin-Intergrin domain (PSI), the immunoglobulin-like domain (IGD), the juxtamembranous domain (JMD) and the tyrosine kinase domain (KD). The location of the three melanoma MET mutations identified in the Sanger Center COSMIC database are indicated by the arrow. C: Carboxyl-terminal; N: Amino-terminal. Adapted from Angeloni et al., 2001. (Angeloni et al., 2001).
1.3.3.3 NRAS
The RAS gene products (HRAS, KRAS and NRAS) are the prototypical members of the RAS superfamily, a group of GTPase switch proteins that function as effectors of tyrosine kinase receptor signaling. This family of proteins were the first human oncogenes recognized when they were identified
22 Chapter 1 – Introduction
as the human homologues of the viral sarcoma oncogenes v-Harvey murine sarcoma (v-Ha-ras) and v- Kirsten murine sarcoma (v-Ki-ras) (Der et al., 1982, Marshall et al., 1982).
1.3.3.3.1 NRAS Structure And Function
NRAS is a 7 exon, 12.4kb gene located in chromosome 1p13 (Hall et al., 1983, Hall and Brown, 1985). The NRAS mRNA is a is translated into a 89 amino acids long, 21 kDa protein (Watzinger and Lion, 1999).
The RAS family of proteins shares a conserved G-domain, which spans the first 165 residues. The G domain is responsible for signaling function and contains highly conserved elements regulating GTP/GDP binding and GTP hydrolysis (Tartaglia and Gelb, 2010). The remainder of the protein is hypervariable and is responsible for post-translational processing and plasma membrane anchoring (reviewed by (Tartaglia and Gelb, 2010).
Under physiological conditions, NRAS is an inner membrane protein that is rapidly shuttled to and from the Golgi according to its palmitoylation status (Hancock et al., 1989). NRAS activation normally follows the stimulation of a receptor tyrosine kinase (RTK), in turn recruiting SOS (a guanine exchange factor, GEF) to the inner membrane. The interaction of SOS with a NRAS bound GDP, triggers the release of GDP in exchange for GTP (Lennartsson et al., 2005). The NRAS.GTP complex represents the active form of the protein, which is functional until the GTP is hydrolysed by the intrinsic GTPase-activating protein, a reaction that is accelerated by the RAS GTPase activating protein RASA1 (Alberts et al., 2002). The GTP bound RAS recruits cytoplasmic serine/threonine-specific protein kinases termed RAF. This action releases RAF from its negative regulator 14-3-3, in turn activating RAF kinase activity and triggering the remainder of the MAPK cascade (Alberts et al., 2002).
1.3.3.3.2 NRAS Oncogenic Signaling
Activating mutations almost exclusively target codons 12, 13 or 61, resulting in changes at its guanine nucleotide binding sites, locking in the GTP-bound form of NRAS and generating a constitutionally active NRAS. RAS mutations in melanomas are dominated by NRAS (~21%), followed by KRAS (2%) and HRAS (1%) (Dhomen and Marais, 2009). The most common mutation in NRAS, involving G61 are known to disrupt the protein’s GTPase function (Tartaglia and Gelb, 2010). In melanoma, NRAS mutations have the effect of switching the BRAF dependent-MAPK signaling to a CRAF driven one (Dumaz et al., 2006). Signaling through CRAF is normally impaired by cytoplasmic cAMP, it has been hypothesized that cAMP signaling needs to be disrupted to permit signaling through CRAF (Dumaz et al., 2006). Once active, CRAF can activate MEK-ERK independent targets such as NF-kappaB (Wellbrock et al., 2004a) that result in cellular proliferation.
23 Chapter 1 – Introduction
Figure 1.10 Diagram of the NRAS protein in scale. Numbers inside the boxes indicate the exon that translates each part of the protein. The untranslated regions are represented as white boxes. The G-domain is coloured in red (GD) and the hypervariable domain is in blue (HVR). The locations of the five most common NRAS mutations in melanoma, including their relative incidence, are indicated by the arrows. C: Carboxyl-terminal; N: Amino-terminal. Adapted from Watzinger and Lion, 1999. Figure 0-9
1.3.3.4 BRAF
1.3.3.4.1 BRAF Structure And Function
The RAF family of proteins (ARAF, BRAF and CRAF) was first identified as cellular homologues of the v-raf oncogene within the murine retrovirus 3611-MSV (Rapp et al., 1983). This group of proteins is characterized by 3 conserved regions (CR1, CR2 and CR3). CR1 is a GTP-RAS binding domain found within the N-terminal, along with a cysteine rich region involved with membrane binding. CR2 contains the regulatory domains for protein translocation and kinase activation. CR3 contains the kinase domain (Wellbrock et al., 2004a). BRAF is ubiquitously expressed, but the highest levels are found within neuronal tissues (Wellbrock et al., 2004a) and a number of different protein isoforms of uncertain biological potential, have also been recognized (Wellbrock et al., 2004a),
RAF activation is mediated by the GTP.RAS complex, to which it binds via the CR1 domain (Wellbrock et al., 2004a), switching on RAF proteins kinase activity, leading to the phosphorylation of dual specificity kinases, MEK1/2(Tartaglia and Gelb, 2010).
All 3 RAF proteins can stimulate the MAPK cascade but they differ in their signaling strengths, expression profiles and developmental functions (Tartaglia and Gelb, 2010, Dumaz et al., 2006, Wellbrock et al., 2004a). In its basal state, BRAF shows higher levels of kinase activity, and complete activation requires fewer phosphorylation events than ARAF or CRAF. As a consequence, it has been proposed that BRAF is more primed for activation and only requires RAS-mediated recruitment for activation, generating a protein which is more susceptible to oncogenic mutation (Wellbrock et al., 2004a). This may explain the scarcity of ARAF and CRAF mutations in comparison to the high incidence of BRAF mutations detected in thyroid, colorectal and ovarian cancers (Tartaglia and Gelb, 2010, Forbes et al., 2011).
24 Chapter 1 – Introduction
The effectors of RAF proteins are the two dual specificity mitogen-activated protein kinase kinase MEK1/2. These proteins are activated by serine phosphorylation on the activation domains. MEK in turn phosphorylates threonine and tyrosine residues located within ERK1/2 at its activation domains.
Active BRAF may also recruit CRAF to the inner plasma membrane and activate MEK in a CRAF dependent fashion (Wellbrock et al., 2004a). In melanocytes, G-coupled proteins such as alpha-MSH receptor, play an important role in inhibiting CRAF signaling via AKT-dependent phosphorylation of serine residues (Cook and McCormick, 1993). In melanocytes, 14-3-3 also plays a role in negatively regulating CRAF via binding to the NH2 terminus and preventing membrane recruitment (Dumaz and Marais, 2003). The consequence of this is that under normal circumstances BRAF alone is capable of stimulating MEK in melanocytes (Dumaz et al., 2006).
1.3.3.4.2 Distribution Of BRAF Mutations In Melanoma
Most of the 65 BRAF mutations identified in melanoma samples occur within the kinase domain of the protein (Dhomen and Marais, 2009). The mutations target the glycine-rich loop and the activation segment, resulting in destabilization of the inactive confirmation of BRAF (Wan et al., 2004).
The most common BRAF mutation (V600E) activates BRAF 500 fold, resulting in constitutive ERK signaling and melanocyte transformation in both in vitro (Wellbrock et al., 2004b) and in vivo models (Dhomen et al., 2009). Paradoxically, rare BRAF mutations occur which although prevent the active state of the protein, they can still generate aberrant ERK activation though binding and activation of CRAF, which can in turn activate MEK. Rarely, can these mutations inactivate BRAF, however signaling through the MAPK cascade still takes place due shunting through CRAF signaling (Wan et al., 2004, Garnett et al., 2005).
BRAF mutations are thought to disrupt an interaction between the glycine rich loop and the activation segment that otherwise functions to place BRAF in an inactive conformation. The introduction of a bulky residue allows the activation segment to adopt an active state (Wellbrock et al., 2004a).
Figure 0-10
Figure 1.11. Diagram of the BRAF protein in scale. Numbers inside the boxes indicate the exon which translates each part of the protein. The conserved regions are coloured in red (conserved region 1, CR1), blue (conserved region 2, CR2) and purple (conserved region 3, CR3). The green bars represent the RAS binding domain (RBD) and the kinase domain (KD). The arrow indicates the locations of the three most common BRAF mutations in melanoma, including their frequency in BRAF mutant melanoma according to the Sanger Center COSMIC database. C: Carboxyl- 25 Chapter 1 – Introduction
terminal; N: Amino-terminal. Adapted from Domingo and Schwartz, 2004. (Domingo and Schwartz, 2004).
1.3.3.5 Constitutive Activation Of MAPK Signaling Cascade
The consequences of MAPK signalling are complex, but its overall effects in a wide spectrum of cell types, including melanocytes, are considered to be mitogenic and/or anti-apoptotic (Smalley, 2003). In contrast to benign melanocytes, melanoma cell lines show constitutive ERK1/2 phosphorylation (Smalley, 2003, Zhuang et al., 2005). This phenomenon is likely explained by the high frequency of melanoma-associated mutations within this signalling cascade (KIT 1-20% (Davies et al., 2002, Curtin et al., 2006), NRAS 1-24%(Curtin et al., 2005, Smalley, 2003) and BRAF 26%-66% (Beadling et al., 2008, Davies et al., 2002). The mitogenic effects of aberrant and constitutive MAP kinase cascade activation can be understood by looking at the influence of this signalling pathway on the regulators of cell cycle progression. The transition from G1 to S is regulated by the inhibition of cyclins and cyclin dependent kinases (Halaban, 2005). ERK1/2 is thought to exert its mitogenic effects by promoting the expression of cyclin D1 (Welsh et al., 2001). This is tempered by the ERK dependent expression of p16 and p21 that serve to inhibit the action of cyclin dependent kinases (Serrano, 1997). Such mechanism serves to tightly regulate G1/S transition, inducing senesce in the presence of otherwise oncogenic ERK signalling (Serrano, 1997, Sewing et al., 1997, Woods et al., 1997). The balance between mitogenic and senescence signalling by ERK1/2 is commonly uncoupled in melanomas by frequent point mutations and deletions of p16 (Benjamin et al., 2007).
In contrast to the normal requirement for MAP kinase signalling in the generation of differentiated melanocytes, the effect of constitutive MAP kinase signalling in melanoma is the promotion of a dedifferentiated state (Englaro et al., 1998). Agents that increase intracellular cAMP (whereby melanocytes display high levels of melanin synthesis and dendritic cell morphology) generate a differentiated phenotype. One such mechanism is the activation of the melanocortin 1 receptor (MC1R) by the alpha melanocyte-stimulating hormone (αMSH), promoting the expression of MITF via the activation of cAMP responsive transcription factor (CREB) elements and MITF phosphorylation via p90Rsk activation (Wu et al., 2000). This mechanism remains in place even in melanoma cells where cAMP can induce tyrosinase activity, most probably via CREB mediated MITF up-regulation (Smalley, 2003). However this effect can be blocked by the generation of a constitutively active MAP kinase signalling cascade (Englaro et al., 1998) and consistent with this observation, agents that impair MAP kinase signalling in melanoma cells promote melanogenesis via up regulation of tyrosinase (Smalley and Eisen, 2000).
There is a growing body of evidence implicating MITF in melanoma biology. Minimum levels of MITF are required for abrogation of melanoma apoptosis via promotion of Bcl-2 action (McGill et al., 2002) and MITF disruption confers sensitivity to chemotherapy agents (Garraway et al., 2005). Conversely, high levels of MITF are thought to induce a differentiated state via the expression of melanocyte antigens and inhibition of cell cycle progression, in part by promoting the expression of p21 (Carreira et al., 2005) and p16 (Loercher et al., 2005). This has led to the proposition that incomplete suppression of MITF
26 Chapter 1 – Introduction
function generates a state whereby melanoma cells can escape cell cycle arrest and an associated differentiated state, while still ensuring protection against apoptosis (Gray-Schopfer et al., 2007, Levy et al., 2006).
1.4 Oncogenic Kinase Inhibition as a Novel Therapeutic Target
1.4.1 KIT Inhibition
Despite its constitutive expression in mature melanocytes, KIT inhibition in unselected melanomas is not clinically efficacious (Hofmann et al., 2009, Ugurel et al., 2005)[ref]. Indeed, it appears that despite KIT’s mitogenic and anti-apoptotic properties, most melanomas have a selective pressure to lose KIT expression (Oba et al., 2011, Carvajal et al., 2011, Natali et al., 1992) and the re-introduction of KIT expression to melanoma cell lines is capable of triggering apoptosis (Huang et al., 1996, Willmore-Payne et al., 2005). Not surprisingly, the use of KIT inhibitors in unselected patients has been shown not to be clinically useful.
However, there are melanoma cell lines dependent on KIT signaling, either via activating mutations or KIT/CDK4 amplification (Ashida et al., 2009, Jiang et al., 2008, Smalley et al., 2008). In such cell lines, there is a high susceptibility to KIT inhibition. This was first identified in vivo in a series of cases reports which documented significant clinical response to KIT inhibitors in patients with metastatic KIT mutant melanoma (Lutzky et al., 2008, Hodi et al., 2008) and in-vitro studies (Jiang et al., 2008).
The first clinical trials employing small molecule KIT inhibitors in patients with activating KIT mutations have recently confirmed the clinical efficacy of blocking this protein’s activity. Carvajal et al (NCT00470470) treated 28 metastatic melanoma patients and reported their progression free survival and overall survival being 12 and 46 weeks, respectively (Carvajal et al., 2011). However, the complete and most durable responses (53-95 weeks) amongst patients with classical activating KIT mutations (23% of KIT mutant cases), suggested some KIT variants represented ‘passenger’ mutations. Another phase II trial (Guo et al., 2011), exclusively treating patients with KIT activating mutations or gene amplification, demonstrated a median PFS of 3.5 months and OS of 14 months with partial response, stable disease and progressive disease in 23%, 30% and 46% of patients, respectively. No association between mutation type and clinical response was observed.
Currently, the second generation KIT inhibitor nilotinib is under investigation in multiple international Phase II/III clinical trials for metastatic KIT-mutant melanomas (NCT 01028222, NCT01395121, NCT01099514, NCT00788775), as is a new KIT and LYN inhibitor masitinib (NCT01280565).
1.4.2 BRAF Inhibition
The first selective BRAF inhibitor with proven clinical efficacy was PLX4032 (vemurafenib). When tested in patients with unresectable metastatic BRAF-mutant melanoma, against the standard treatment agent (dacarbazine), it showed a greater median progression free survival (5.3 months vs. 1.6 months), high response rates (48% vs. 5%) and a high 6-month overall survival 84% vs. 64%, hazards ratio 0.37, p<0.001) (Chapman et al., 2011). As impressive as these response rates are, primary resistance precludes 27 Chapter 1 – Introduction
a universal response, and secondary resistance (due to the acquisition of secondary mutations in NRAS, MEK or PDGFRb (Nazarian et al., 2010, Wagle et al., 2011), enhanced insulin-like growth factor 1 signalling (Villanueva et al., 2010) or BRAF amplification (Corcoran et al., 2010), results in high rates of relapse in most patients. Vemurafenib has the paradoxical affect of stimulating the MAPK cascade in NRAS mutant melanomas. This effect is thought to be driven due to the vemurafenib-enhanced recruitment of wildtype BRAF to the inner membrane signaling complex which brings together mutant NRAS with CRAF, promoting the signal transduction in the face of BRAF inhibition (Ji et al., 2011, Heidorn et al., 2010).
1.4.3 NRAS Inhibition
In vitro experiments have shown that farnesyltransferase inhibitors (FTIs) (tibifarnib) are capable of disrupting the post-translation modification and targeting of NRAS to the plasma membrane (Ji et al., 2011). Despite early promising results (Baum and Kirschmeier, 2003), phase II trials have shown the current FTI drugs are non-RAS specific and other post translational modifications can allow the NRAS proteins to escape inhibition (Sousa et al., 2008). Salirasib represents a new class of drugs which inhibit activated RAS recruitment to the inner membrane that has shown modest results in phase II lung (Riely et al., 2011) and pancreatic (Bustinza-Linares et al., 2010) cancer trials. Its role in melanoma is yet to be explored but maybe effective in a sub-group of patients.
1.4.4 MET Inhibition
The widespread expression of MET and HGF in normal tissues, in addition to their capacity to positively regulate mitogenic, migratory and anti-apoptotic signaling events (Birchmeier et al., 2003) has not surprisingly resulted in the identification of HGF and MET mutations or over expression, as an adverse prognostic factors in a wide range of carcinomas, gliomas, sarcomas, leukemias and lymphomas (Liu et al., 2011). This had made MET pathway inhibition the subject of many therapeutic trials, which are currently in the early stages of clinical development. Amongst the promising clinical opportunities is the treatment of hereditary and sporadic papillary renal cancer (which harbor MET mutations in 100% and 13% of cases, respectively (Schmidt et al., 1999)), where disease control is seen in ~80% of patients (Srinivasan et al., 2009). Despite these encouraging results, at the time of writing, there was only a single clinical trial (clinicaltrials.gov identifier NCT00827177) employing a selective MET inhibitor (ARQ 197) (Munshi et al., 2010) in patients with melanoma amongst other solid tumours.
28
CHAPTER 2
CAN A FAMILIAL GASTROINTESTINAL TUMOUR SYNDROME BE ALLELIC WITH WAARDENBURG SYNDROME?
The work embodied in this chapter has been published in Clinical Genetics 2011, 79(6), 199- 209
Chapter 2 – Can a Familial Waardenburg Syndrome be Allelic with Waardenburg Syndrome?
30 Chapter 2 – Can a Familial Waardenburg Syndrome be Allelic with Waardenburg Syndrome?
Statement
This statement presents the contribution of all authors in the published paper:
Can a familial gastrointestinal tumour syndrome be allelic with Waardenburg syndrome?
Ricardo E. Vilain, Tracy Dudding, Stephen G. Braye, Claire Groombridge, Cliff Meldrum, Allan D Spigelman, Steve Ackland, Leonie Ashman, Rodney J. Scott. Clinical Genetics 2011, 79(6): 554–560
Author Contribution Description of Contribution Signature Ricardo E. 30% Performed most of the molecular analysis Vilain and histopathological examination of
specimen material. Carried out literature search, formulated the structure and organization and wrote the manuscript. Tracy 30% Identification of Waardenburg syndrome. Dudding Provision of clinical data. Critical review of the manuscript. Stephen G. 6% Identification of multifocal, familial GIST Braye syndrome. Supervision of histopathological assessment.
Claire 6% Provision of clinical data. Groombridge
Cliff 6% Supervision of molecular analysis. Meldrum
Allan D 6% Provision of clinical data. Critical review Spigelman of manuscript.
Steve 4% Provision of clinical data, manuscript Ackland review.
Leonie 2% Concept and study design. Ashman
Rodney J. 10% Concept and study design, preparation Scott and critical review of the manuscript.
31 Chapter 2 – Can a Familial Waardenburg Syndrome be Allelic with Waardenburg Syndrome?
32 Chapter 2 – Can a Familial Waardenburg Syndrome be Allelic with Waardenburg Syndrome?
Clin Genet2.1 2011: 79:Introduction 554–560 2010 John Wiley & Sons A/S Printed in Singapore. All rights reserved CLINICAL GENETICS doi: 10.1111/j.1399-0004.2010.01489.x Short Report Can a familial gastrointestinal tumour syndrome be allelic with Waardenburg syndrome?
a b Vilain RE, Dudding T, Braye SG, Groombridge C, Meldrum C, RE Vilain ∗,TDudding ∗, Spigelman AD, Ackland S, Ashman L, Scott RJ. Can a familial SG Brayea,CGroombridgeb, gastrointestinal tumour syndrome be allelic with Waardenburg syndrome? CMeldrumc,ADSpigelmanb, Clin Genet 2011: 79: 554–560. John Wiley & Sons A/S, 2010 SAcklandd,LAshmane and RJ Scotta Familial gastrointestinal stromal tumours (GISTs) are rare but otherwise well-characterized tumour syndromes, most commonly occurring on a aHunter Area Pathology Service, Hunter New England Health Service, Newcastle, background of germline-activating mutations in the tyrosine kinase b receptor c-KIT. The associated clinical spectrum reflects the constitutive NSW, Australia, Hunter Genetics Unit, Hunter New England Health Service, activation of this gene product across a number of cell lines, generating Waratah, NSW, Australia, cDepartment of gain-of-function phenotypes in interstitial cells of Cajal (GIST and Pathology, Peter MacCallum Cancer dysphagia), mast cells (mastocytosis) and melanocytes Centre, Melbourne, VIC, Australia, (hyperpigmentation). We report a three-generation kindred harbouring a dDepartment of Medical Oncology, c-KIT germline-activating mutation resulting in multifocal GISTs, Newcastle Mater Misericordiae Hospital, e dysphagia and a complex melanocyte hyperpigmentation and Waratah, NSW, Australia, and School of Biomedical Sciences, University of hypopigmentation disorder, the latter with features typical of those Newcastle, Callaghan, NSW, Australia observed in Waardenburg type 2 syndrome (WS2F). Sequencing of genes known to be causative for WS [microphthalmia transcription factor ∗Both authors contributed equally to (MITF), Pax3, Sox10, SNAI2 ]failedtoshowanycandidatemutationsto this work. explain this complex cutaneous depigmentation phenotype. Our case report Key words: deglutition disorders – conclusively expands the clinical spectrum of familial GISTs and shows a gastrointestinal stromal tumours – proto-oncogene proteins hitherto unrecognized link to WS. Possible mechanisms responsible for c-KIT – Waardenburg syndrome this novel cause of WS2F will be discussed. Corresponding author: Dr Ricardo Conflict of interest Vilain, Hunter Area Pathology Service, Hunter New England Health Service, No conflict of interest declared. Newcastle, NSW 2305, Australia. Tel.: 61 2 4921 4046; + fax: 61 2 4921 4794; + e-mail: [email protected]. gov.au
Received 5 April 2010, revised and accepted for publication 22 June 2010
Gastrointestinal stromal tumours (GISTs) are the (ICC), specialized cells of the gastrointestinal most common mesenchymal tumour of the gas- tract believed to serve a pacemaker role in gut trointestinal tract, representing between 1% and motility (1). 3% of gastrointestinal malignancies (1). These Somatic-activating mutations of the tyrosine tumours arise throughout the gastrointestinal tract, kinase receptors c-KIT (2) or PDGFRα (3) are but are predominantly found within the stomach thought to be early molecular events in the genesis and small intestine (1). The putative cells of ori- of most sporadic GISTs. An inherited predisposi- gin for GISTs are the interstitial cells of Cajal tion to GISTs is recognized in tumour syndromes 33 554 Chapter 2 – Can a Familial Waardenburg Syndrome be Allelic with Waardenburg Syndrome?
Familial GIST/Waardenburg syndrome such as neurofibromatosis type 1 (NF1; MIM tumour histomorphology and immunohistochem- 162200) and Carney triad (MIM 604287). The ical detection of c-KIT and/or CD34 expression. best understood form of familial GIST is due to a More recently, the novel marker Discovered on dominantly inherited germline-activating mutation GIST-1 (DOG1) (4) has been shown to be a more of c-KIT or less frequently, activating mutations sensitive and specific marker for these tumours (5). in PDGFRα,anotherproto-oncogeneinthesame We report members of a three-generation fam- tyrosine kinase receptor subclass as c-KIT. ily (Fig. 1) with an activating mutation within The clinical syndrome of familial GISTs (MIM exon 13 of c-KIT.Theintriguingobservation 606764) is characterized by the hyperproliferation within this family is that while some mem- of cell lineages sensitive to c-KIT signalling. These bers show the typical features associated with a features include multifocal GISTs and dysphagia, germline-activating c-KIT mutation (i.e. multifocal mast cell disorders and cutaneous hyperpigmen- GISTs and cutaneous hyperpigmentation), others tation, reflecting a type of gain-of-function phe- present with clinical dysphagia on a background notype in the ICC, mast cells and melanocytes, of ICC hyperplasia (gain of function) and a para- respectively. doxical cutaneous depigmentation (loss of func- The diagnosis of GIST is dependent on the tion, Fig. 1b). This latter phenotype is clinically correlation of a patient’s clinical presentation, most consistent with Waardenburg syndrome (WS)
(a)
(b)
Fig. 1.(a)Mutationanalysisofc-KITinthefamilypedigreeinrelationtogastrointestinalstromaltumours(GISTs)(shaded lower right-hand quadrant), dysphagia (shaded lower left-hand corner), hyperpigmentation (shaded upper left-hand corner) and hypopigmentation (shaded upper-right hand corner). Square and circle symbols represent males and females, respectively. Deceased family members are indicated by symbols with a line through them. Patient II-2 shows radiological evidence of gastric and small- bowel GISTs, but a confirmatory tissue diagnosis has not been made.(b)Characteristiccongenitalpoliosis(white-forelock), patient III-7. 34 555 Chapter 2 – Can a Familial Waardenburg Syndrome be Allelic with Waardenburg Syndrome?
2.2 Materials and Methods Vilain et al. type2.2.1 2. WS DNA is a geneticallyextraction and and sequencing clinically hetero- Germline testing of the proband case for geneous disease typified by a variable penetrance WS-associated genes was carried out by Prof of auditory-pigment2.2.2 SNAI2 Copy disorders, Number Analysis mild craniofacial Andrew Reid, Department of Medical Genetics, abnormalities and rarely neurocristopathy (6). WS St Mary’s Hospital Manchester, UK (MITF and type2.3 2 is commonlyResults diagnosed on the basis of a Pax3 )andProfPaulTam,DepartmentofSurgery, combination of at least two of the following; polio- University of Hong Kong Medical Centre, Queen sis (white hair forelock), iridium heterochromia Mary Hospital, Hong Kong, China (SOX10 ). and2.3.1 sensoneural Study deafness Cohort And in theHistopathological absence of cranio- Examination facial anomalies (7). In terms of causative genes, WS2 remains a largely unexplained syndrome. SNAI2 copy number analysis Where a candidate gene is identified, it is most The proband case underwent quantification of commonly inactivating mutations of microph- SNAI2 copy number relative to c-KIT copy num- thalmia transcription factor (MITF)( 15%); how- ber. Using pooled male human genomic DNA ever, mutations in SNAI2 (8) and SOX10∼ (7) are (Promega, USA; catalogue # G1471) as con- also rarely implicated. We propose the activating trol sample, we employed comparative !!Ct mutation of c-KIT described herein (c-KITK642E) method (Sequence Detection System, Bulletin 2, should be considered as a novel mechanism for Applied Biosystems, USA), which utilizes SYBR WS type 2 (i.e. WS2F). Green chemistry real-time relative quantitation PCR (qPCR). The SNAI2 qPCR primer pair was designed to the 5#-untranslated region (UTR) of the Materials and methods SNAI2 gene. The KIT qPCR primer was designed DNA extraction and sequencing to exon 21. Primer sequences are available upon request. The reaction mixtures consisted of 75 ng In addition to Institutional Bioethics Approval of each forward and reverse primer, 25 ng of (Hunter New England Human Research Ethics genomic DNA, 12.5 µlofSYBRGreenPCRMas- Committee, reference number 08/08/20/5.17), pre- ter Mix in a total volume of 25 µl. Thermal cycling test counselling and an informed consent were conditions were as follows: denaturation step at obtained from all the individuals from whom surgi- 95 Cfor10min,followedby40cyclesof95C cal specimens were available for study. Germline ◦ ◦ for 15 s and 60◦Cfor1min.Thepresenceofnon- testing was carried out by extracting DNA from specific products was checked for by assessing the blood sampled. Formalin-fixed paraffin-embedded dissociation curves at the end of each run. All reac- (FFPE) tumour samples were biopsied using 2- tions were performed in quintuplicate, run on an mm core needles. FFPE cores were deparaffinized ABI PRISM 7000 instrument and analysed with (two xylene washes, each for 30 min at 55◦C) SDS software, followed by ddCt determination and rehydrated (two 100% ethanol washes at in Excel (Sequence Detection System, Bulletin 2, 55◦Cfor10min,followedbytwo70%ethanol Applied Biosystems). washes at 55◦Candincubationin55◦Cdis- tilled water for 10 min) before undergoing 37◦C overnight digestion in lysis buffer (Levy lysis Results buffer plus 0.5% Tween-20 and 0.45 mg/ml pro- teinase K). The supernatant was extracted and Study cohort and histopathological examination the DNA concentrated using ethanol precipitation. The proband (patient II-3), aged 57, presented for c-KIT exons 9, 11, 13, 17, as well as the entire investigation of his personal and family history of SNAI2 gene, were amplified using PlatinumTaq dysphagia (Fig. 1). He had dysphagia for solids DNA Polymerase (primers available upon request). since childhood and pureed his food from the The polymerase chain reaction (PCR) products age of 40 years. Oesophageal manometry stud- were treated with Agencourt AMPure prior to ies showed a weak and uncoordinated peristaltic sequencing with ABI PRISM BigDye reagents. activity and a weak low oesophageal sphincter Sequencing products were treated with Agencourt (LOS) pressure. Oesophageal endoscopy and bar- CleanSEQ prior to testing on ABI 3730 DNA ium studies identified a stricture proximal to the analyser (sequencing primer sequences available LOS and small oesophageal diverticula. No endo- upon request). Sequencing data was analysed for scopic evidence of a tumour was ever identified. mutations on Mutation Surveyor V3.24 (http:// He underwent a total oesophagectomy as defini- www.softgenetics.com/ms/index.htm) against the tive treatment for his longstanding and progressive reference NCBI Reference KIT sequence NC_0000 dysphagia. Haematoxylin and eosin (H&E)-stained 04.11 and SNAI2 sequence NC_0000008.10. tissue sections of this patient’s oesophagus showed 35 556 Chapter 2 – Can a Familial Waardenburg Syndrome be Allelic with Waardenburg Syndrome?
Familial GIST/Waardenburg syndrome
(a) (b)
(c) (d)
Fig. 2.OesophagealinterstitialcellsofCajal (ICC) hyperplasia and multifocal gastrointestinal stromal tumours (GISTs). Whole- mount image of formalin-fixed, paraffin-embedded tissue from oesophagectomy specimens showing diffuse ICC hyperplasia within the myenteric plexus of the proband’s oesophagus (patient II-3). Panel (a)highlightsabnormallyabundant CD117 (c-KIT) staining cells within the muscularis propria. Panel (b)isaserialsectionofthesametissuestainedwithhaematoxylinandeosin.The abnormal ICC content is evident as pale eosinophilic regions within the muscularis. Original magnification 200. Panels (c)and (d)showthemultifocaldistributionofGISTnoduleswiththe small-bowel resection specimen of patient III-2.× aproliferationofspindlecells,arrangedininter- Patients III-2 and III-3 have stable disease and are lacing fascicles along the boundary of the circular on imatinib mesylate therapy. One patient (III-1) and longitudinal oesophageal muscle fibres, focally has since died from his illness, despite the use of showing a nodular and infiltrative architecture second-line tyrosine kinase inhibitor drugs. Exam- (Fig. 2b). The cellular cytomorphology and strong ination of tissue sampled from macroscopically expression of the antigen CD117 (c-KIT, Fig. 2a) normal bowel (and the appendix of patient III-1) identified these cells to be hyperplastic ICC. of the three siblings with GISTs uniformly showed Dysphagia was a prominent feature in this ICC hyperplasia (Fig. 2a,b). H&E and CD117 patient’s family with his mother (patient I-2), sister immunohistochemistry microphotographs of high- (II-2) and son (III-7) all suffering from significant grade GIST nodule (patient III-1) are shown in dysphagia since early adulthood; all are currently Fig. 3c,d. Screening computed tomographic (CT) managed with conservative treatment. scans have identified clinically silent duodenal, The family members with dysphagia also shared jejunal and gastric tumours within the mother of features of a pigmentary disturbance. This mani- these three individuals (patient II-2). Although fested as a white forelock (patients I-2, II-2, II-3, clinically these tumours are highly likely to rep- III-7, III-9 and IV-1) (Fig. 1b), premature gray- resent GISTs, they have not been biopsied and ing (patient II-3), iridium heterochromia and/or thus a confirmatory tissue diagnosis has not been brilliant blue irides (patients I-2, II-2, II-3) and made. congenital leucoderma (patient II-2). Audiometry None of the siblings with multifocal GISTs testing in patients II-3 and III-7 was normal. Eye report difficulty swallowing, and furthermore, in measurements excluded dystopia canthorum, and contrast to the pigmentary defects in the dys- none of these individuals had a sign of cutaneous phagic relatives, these three patients with GISTs hyperpigmentation or a history of urticaria. show some cutaneous hyperpigmentation (multiple The third and most prominent clinical fea- nevi within the axillae and trunk and sponta- ture of this kindred is the presence of multifocal neously resolving childhood facial hyperpigmen- GISTs in the two nephews (patients III-1 and III-2, tation) reminiscent of that previously reported in Fig. 2c,d) and niece (patient III-3) of the proband. familial GISTs (9). 36 557 Chapter 2 – Can a Familial Waardenburg Syndrome be Allelic with Waardenburg Syndrome?
Vilain et al.
(a) (b) 2.4 Discussion
(c) (d)
Fig. 3.IntestinalinterstitialcellsofCajal (ICC) hyperplasia and high-grade gastrointestinal stromal tumour (GIST). The appendix of case III-1 shows striking ICC hyperplasia [haematoxylin/eosin staining panel (a), CD117 (c-KIT) immunohistochemistry staining panel (b)]. A high-grade GIST nodule from patient III-1 is pictured in panels (c)and(d)(haematoxylin&eosinandCD117 immunohistochemistry staining, respectively). All images with original magnification 200. ×
No mast cell disorders have been identified in that classic piebaldism is a loss-of-function phe- any of these patients and no familial history of notype arising from a loss-of-function mutation, melanomas is known. and not activating mutations, of c-KIT (11), the Analysis of DNA from blood and tumour authors concluded that the areas of hypopigmen- samples confirmed the presence of an adenine to tation were unrelated to the c-KIT mutation under guanine substitution at nucleotide 1924 (c.1924 investigation. A>G), translating to a missense substitution of ICC hyperplasia is the histopathological Sine lysine to glutamate at codon 642, exon 13 of c-KIT qua non of familial GIST syndromes and, in in the germline of patients II-2, III-1, III-2 and III- this case report, provides a common pathological 3, oesophagectomy specimen of the proband case process linking dysphagia to the multifocal GISTs. (II-3) and within tumour tissue of patients III-1, When dysphagia was reported, the onset of III-2 and III-3. No mutations were found within symptoms was in young adulthood, progressing SNAI2, MITF, Pax3 or Sox10.Real-timerelative insidiously to severely impair swallowing function. qPCR found no deletion or duplication of SNAI2 Previously published manometry results of a with respect to c-KIT copy number. familial GIST syndrome with patients suffering from dysphagia also showed a similar failure in the propagation of oesophageal peristalsis, a lower Discussion oesophageal resting pressure than that of typical This is the third reported instance of the germline- achalasia and an impaired swallow-induced LOS activating mutation, c-KITK642E.Thismutation relaxation (12). occurs within the tyrosine kinase I domain of However, other mechanisms for the dysphagia c-KIT and has previously being described in a reported in the various GIST familial syndromes French mother and son who presented with mul- are likely to exist. Hartmann et al. (13) describe tiple, low-risk, duodenal and jejunal GISTs (10). afamilialGISTkindredwherebyhypercontracted Isozaki et al. also showed the in vitro and in vivo lower oesophageal segments are associated with a transforming potential of the c-KITK642E mutation. dilated proximal oesophagus. Interestingly, recent A de novo germline c-KITK642E mutation has also reports on murine multifocal GIST models suggest been reported in a 57-year-old male with multi- normal electrical activity is still readily detectable focal GISTs and a history of vitiligo. Considering within hyperplastic ICC tissue (14), indicating that 37 558 Chapter 2 – Can a Familial Waardenburg Syndrome be Allelic with Waardenburg Syndrome?
π Vilain et al. aggressive GISTs also serves to stand them 8. Sanchez-Martin M, Rodriguez-Garcia A, Perez-Losada J out further from the rest of their family and et al. SLUG (SNAI2) deletions in patients with Waardenburg References disease. Hum Mol Genet 2002: 11: 3231–3236. further hints at the possibility of currently 9. Robson ME, Glogowski E, Sommer G et al. Pleomorphic unknown, paternally inherited, disease-modifying characteristics of a germ-line KIT mutation in a large kindred single nucleotide polymorphisms (SNPs). A com- with gastrointestinal stromal tumors, hyperpigmentation, and plex and multifaceted process, oncogenic c- dysphagia. Clin Cancer Res 2004: 10: 1250–1254. KIT signalling is dependent on more than just 10. Isozaki K, Terris B, Belghiti Jetal.Germline-activating mutation in the kinase domain of KIT gene in familial the presence of an activating mutation (23). It gastrointestinal stromal tumors. Am J Pathol 2000: 157: has been suggested that the c-KIT-dependent 1581–1585. phosphatidylinositol-3-kinase/AKT pathway, and 11. Graham J, Debiec-Rychter M, Corless CL et al. Imatinib in not the Mitogen-Activated Protein Kinase (MAPK) the management of multiple gastrointestinal stromal tumors or Janus Kinase/Signal Transducers and Activa- associated with a germline KIT K642E mutation. Arch Pathol Lab Med 2007: 131: 1393–1396. tor of Transcription (JAK/STAT) pathways, plays 12. Hirota S, Nishida T, Isozaki K et al. Familial gastrointestinal crucial roles in the survival and proliferation of stromal tumors associated with dysphagia and novel type GISTs (24) and certainly high levels of AKT phos- germline mutation of KIT gene. Gastroenterology 2002: 122: phorylation have been shown to correlate well with 1493–1499. aGIST’sproliferativeindex(23).Itislikelythat 13. Hartmann K, Wardelmann E, Ma Y et al. Novel germline mutation of KIT associated with familial gastrointestinal uncharacterized SNPs are playing a role in modu- stromal tumors and mastocytosis. Gastroenterology 2005: 129: lating the relative contribution of these signalling 1042–1046. cascades to the biological behaviour of the hyper- 14. Kwon JG, Hwang SJ, Hennig GW et al. Changes in the plastic ICC and in the migration and/or survival of structure and function of ICC networks in ICC hyperplasia and melanocytes. gastrointestinal stromal tumors. Gastroenterology 2009: 136: 630–639. 15. Tay YK. Neurofibromatosis 1 and piebaldism: a case report. Dermatology 1998: 197: 401–402. Acknowledgements 16. Gatto M, Giannaula R, Micheli F. Piebaldism associated with We thank the family members for their time and cooperation. Mr cancer. Med Cutan Ibero Lat Am 1985: 13: 545–556. Owen Lawrence is thanked for technical assistance with real-time 17. Giebel LB, Spritz RA. Mutation of the KIT (mast/stem cell qPCR. R. E. V. is a recipient of an Australian National Health & growth factor receptor) protooncogene in human piebaldism. Medical Research Council Postgraduate Scholarship and a Pfizer Proc Natl Acad Sci U S A 1991: 88: 8696–8699. Australia Cancer Research grant. 18. Liu XZ, Newton VE, Read AP. Waardenburg syndrome type II: phenotypic findings and diagnostic criteria. Am J Med Genet 1995: 55: 95–100. 19. Rubin BP, Antonescu CR, Scott-Browne JP et al. A knock-in References mouse model of gastrointestinal stromal tumor harboring kit 1. Miettinen M, Lasota J. Gastrointestinal stromal tumors: review K641E. Cancer Res 2005: 65: 6631–6639. on morphology, molecular pathology, prognosis, and dif- 20. Kissel H, Timokhina I, Hardy MP et al. Point mutation in kit ferential diagnosis. Arch Pathol Lab Med 2006: 130: receptor tyrosine kinase reveals essential roles for kit signaling 1466–1478. in spermatogenesis and oogenesis without affecting other kit 2. Hirota S, Isozaki K, Moriyama Y et al. Gain-of-function responses. EMBO J 2000: 19: 1312–1326. mutations of c-kit in human gastrointestinal stromal tumors. 21. Piao X, Paulson R, van der Geer P et al. Oncogenic Science 1998: 279: 577–580. mutation in the Kit receptor tyrosine kinase alters substrate 3. Heinrich MC, Corless CL, Duensing A et al. PDGFRA acti- specificity and induces degradation of the protein tyrosine vating mutations in gastrointestinal stromal tumors. Science phosphatase SHP-1. Proc Natl Acad Sci U S A 1996: 93: 2003: 299: 708–710. 14665–14669. 4. West RB, Corless CL, Chen X et al. The novel marker, DOG1, 22. Vanderwinden JM, Wang D, Paternotte N et al. Differences is expressed ubiquitously in gastrointestinal stromal tumors in signaling pathways and expression level of the phospho- irrespective of KIT or PDGFRA mutation status. Am J Pathol inositide phosphatase SHIP1 between two oncogenic mutants 2004: 165: 107–113. of the receptor tyrosine kinase KIT. Cell Signal 2006: 18: 5. Espinosa I, Lee CH, Kim MK et al. A novel monoclonal 661–669. antibody against DOG1 is a sensitive and specific marker for 23. Duensing A, Medeiros F, McConarty B et al. Mechanisms of gastrointestinal stromal tumors. Am J Surg Pathol 2008: 32: oncogenic KIT signal transduction in primary gastrointestinal 210–218. stromal tumors (GISTs). Oncogene 2004: 23: 3999–4006. 6. Dourmishev AL, Dourmishev LA, Schwartz RA et al. Waar- 24. Bauer S, Duensing A, Demetri GD et al. KIT oncogenic denburg syndrome. Int J Dermatol 1999: 38: 656–663. signaling mechanisms in imatinib-resistant gastrointestinal 7. Tagra S, Talwar AK, Walia RL et al. Waardenburg syndrome. stromal tumor: PI3-kinase/AKT is a crucial survival pathway. Indian J Dermatol Venereol Leprol 2006: 72: 326. Oncogene 2007: 26: 7560–7568.
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CHAPTER 3
CHARACTERISATION OF KIT AND OTHER KINASE MUTATIONS IN MALIGNANT MELANOMAS
Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
40 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
3.1 Introduction
In 2006, Curtin et al. discovered amplification and/or activating mutations in KIT, in a significant fraction of mucosal (39%), acral (36%) and chronic sun damaged (CSD) skin melanomas (28%), reinvigorating efforts at recruiting KIT as a therapeutic target in melanoma(Curtin et al., 2006). The remarkable success of tyrosine kinase inhibitors (TKIs) in treating KIT-driven gastrointestinal stromal tumours, served as a model of what could be achieved if the same oncogene-targeted approach could be used on selected melanoma patients.
The capacity of TKIs to trigger tumour cell death lies in their ability to inhibit the receptor’s role in the activation of the PI3K/AKT and MAP kinase pathways. The KIT receptor undergoes homodimerization upon binding to stem cell factor (SCF). The resulting auto-phosphorylation of KIT tyrosine residues generates docking sites for secondary signalling molecules containing phosphotyrosine binding or Src homology 2 domains (SH2) (Frolov et al., 2003).
The importance of this signaling cascade in melanomagenesis, is evident not only from studies revealing the common constitutive activation of AKT (Stahl et al., 2004), and cell lines (Boisvert-Adamo and Aplin, 2006), but also from the frequent loss of PTEN (Celebi et al., 2000), an important tumour suppressor gene responsible for the inactivation of PIP3.
Similarly, MAPK activation starts with the SH2-dependent recruitment of the Grb2-SOS complex to KIT to the underside of the plasma membrane, bringing it into proximity to the inner membrane bound GTPase RAS. The guanine nucleotide exchange factor (Sos) is responsible for switching the inactive guanine diphosphate bound RAS, for the active guanine triphosphate bound RAS (Lennartsson et al., 2005). Once switched on, RAS activates a number of signalling proteins and cascades, including Raf: A serine/threonine protein kinase and member of the MAP kinase-signalling cascade. In turn, Raf phosphorylates extracellular signal regulated kinase (ERK) 1 and 2. Active ERK phosphorylates numerous cytoplasmic and nuclear proteins including transcription factors and other protein kinases. The consequences of MAPK signalling are complex, but its overall effects in a wide spectrum of cell types, including melanocytes, are considered to be mitogenic and/or anti-apoptotic (Smalley, 2003).
In contrast to benign melanocytes, melanomas show constitutive ERK1/2 phosphorylation (Smalley, 2003, Zhuang et al., 2005). This phenomenon correlates with the high frequency of mutually exclusive, melanoma-associated mutations within this signalling cascade including KIT 1-20% (Woodman and Davies), NRAS 16-28% (Viros et al., 2008, Houben et al., 2004, Edlundh-Rose et al., 2006, Omholt et al., 2002) and BRAF 28%-66% (Maldonado et al., 2003, Viros et al., 2008, Edlundh-Rose et al., 2006, Omholt et al., 2003, Davies et al., 2002).
The recognition of distinct sets of genetic alterations in melanoma (Curtin et al., 2005), coupled with the realization of histomorphological correlates (Curtin et al., 2006, Viros et al., 2008) and the clinical application of kinase inhibitors (Hodi et al., 2008, Flaherty et al., 2009), provide important opportunities for the development of patient-tailored treatments for melanoma. Until recently, the incidence of KIT mutations in melanomas arising in CSD skin remained relatively unexplored. Recently, Handolias et al.
41 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
demonstrated KIT mutations in 2% of non-selected melanomas cases (257 patients screened) in a population with a 43% incidence of CSD (Handolias et al.).
We seek to determine the distribution of mutations in key genes of the MAP kinase pathways in a patient cohort with a high proportion of CSD melanomas, a combination of Sanger sequencing and mass spectrometric genotyping was used to analyze 192 cases of AJCC stage I-IV melanomas for mutations in BRAF, NRAS, KIT, MET, KRAS, EGFR, and RET.
3.2 Materials and Methods
3.2.1 Case Selection
Ethics approval was obtained from the Hunter New England Human Research Ethics Committee (HNEHREC) reference number 08/08/20/5.17) for the retrospective collection of 192 cases of melanoma, diagnosed at our institution between the years of 2005 -2008. Thick (Breslow thickness > 2.0mm) and/or metastatic melanomas were preferentially selected, as these were likely to represent cases where the knowledge of KIT mutation status might be of most clinical use.
Information regarding patient demographics, melanoma primary/metastatic site, Breslow thickness, recurrence free and overall survival, was obtained from pathology request forms and electronic discharge referrals. In a fraction of cases, the electronic patient records were ambiguous of the exact date of initial diagnosis, or of the initial extent of the patient’s melanoma. In these instances, these cases were excluded for the purposes of determining: (i) distribution of ages across mutational genotypes, (ii) AJCC staging (table 1.1 and 1.2) and (iii) time to locoregional disease or distant metastasis. However, the cases were included for the purposes of estimating all other relevant clinical and pathological characteristics.
A control population of 450 age matched individuals without a history of skin cancer was recruited from the Hunter Community Study. This study aimed at identifying the genetic and environmental factors influencing health in ageing Australians (McEvoy et al., 2010). The participants originated from the same geographical locale as the melanoma patients. The Hunter Community Study participants were recruited via invitation letters sent to randomly selected addresses on the Australian electoral roll. A total of 3253 individuals were successfully recruited (response rate 44.5%). These participants demographics were considered to reflect the state and national Australian profiles in terms of gender and marital status, but where of slightly younger in age (McEvoy et al., 2010).
3.2.2 Sun Exposure and Solar Elastosis Scoring
The degree of accumulated sun exposure associated with the primary melanoma site was estimated using a modified combination of previously published anatomical (Bulliard et al., 2007) and histopathological (Karagas et al., 2007) scoring criteria (table 3.1 and Figure 3.1). For statistical analysis, intermittent (low exposure) vs. chronic sun exposed (high exposure) sites were defined as having exposure scores of ≤2 and ≥3 respectively. Evidence of chronic sun damaged (CSD) skin was defined as having an elastosis score ≥2.
42 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
Table 3.1 Sun Exposure scoring criteria.
Site Exposure.Score Head%and%neck% 4 Back%of%hand 4 Wrist,%elbow,%forearm 3 Shoulder,%upper%arm 2.Male/3.Female Leg,%calf,%ankle,%dorsum%of%foot,%heel 2.Male./3.Female Back 2 Chest 2.Male./1.Female Hip,%thigh,%knee,%popliteal%space 1.Male./2.Female Abdomen,%flank,%axilla 1 Palm%of%hand 1 Buttocks 1 Sole%of%foot 0 Perineum,%groin 0 Table 3
3.2.3 DNA extraction
Formalin fixed paraffin embedded (FFPE) tumour samples were biopsied within tumour-enriched regions, using 2mm core needles. FFPE cores were deparaffinized, rehydrated, and digested overnight in Levi lysis buffer (Levi lysis buffer plus 0.5% tween 20 and 0.45mg/mL proteinase K). The lysate was extracted and ethanol precipitated to obtain a high DNA yield.
3.2.4 KIT Sequencing
To examine the mutation hotspots of KIT, exons 9, 11, 13, 17, were amplified by PCR. The products were purified using AMPure (Agencourt, Danvers, MA, United States) prior sequencing with ABI PRISM BigDye reagents (Applied Biosystems, CA, USA). Sequencing products were purified using CleanSEQ (Agencourt, Country) prior to sequencing on an ABI 3730 DNA analyzer (Applied Biosystems, CA, USA). The PCR conditions and primers are outlined in table 2.2. The sequencing data was analyzed for mutations using Mutation Surveyor V3.24 (Softgenetics, PA, USA) by comparing the NCBI Reference KIT sequence NC_000004.11.
43 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
Appearance Histology (x100) (elastosis score) Mostly intact papillary and reticular dermis (0)
Scattered blue gray elastotic fibers distributed through the superficial reticular dermis (1)
Clusters of elastotic fibers with intervening normal reticular dermis (2)
Replacement of reticular dermis by clumped elastotic fibers or amorphous masses of elastotic material (3)
Figure 3.1. Solar elastosis scoring criteria. Figure 0-11
44 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
To confirm the KIT somatic mutations, a second PCR was performed and the products were sequenced and analyzed as outlined above. Germline mutations were excluded by confirming wild-type sequences on non-tumour extracted DNA.
Table 3.2 PCR primers and amplification conditions. Gene/Exon Sequence Covered region PCR condition KIT Exon 9 Forward 5’-cagggcttttgttttcttcc-3’ 67903-68170 56°C 35 cycles Reverse 5’-tagacagagcctaaacatcc-3’ (268 bp) KIT Exon 11 Forward 5’-tctaatgactgagac-3’ 69424-69656 60°C 35 cycles Reverse 5’-tacccaaaaaggtgacatgg-3’ (228 bp) KIT Exon 13 Forward 5’-gcttgacatcagtttgccag-3’ 70026-70283 58°C 35 cycles Reverse 5’-gtttataatctagcattgcc-3’ (258 bp) KIT Exon 17 Forward 5’-gttttcttttctcctccaacc-3’ 75115-75292 56°C 35 cycles Reverse 5’-gactgtcaagcagagaatgg-3’ (178 bp) BRAF Exon 15 Forward 5’-cctttacttactacacctcag-3’ 171351-171505 54°C 40 cycles Reverse 5’-gtggatggtaagaattgaggc-3’ (154 bp) NRAS Exon 1 Forward 5’-aacaggttcttgctggtgtg-3’ 713-892 60°C 40 cycles Reverse 5’-caagtgagagacaggatcag-3’ (179 bp) NRAS Exon 2 Forward 5’-atggtgaaacctgtttgttgg-3’ 2945-3128 54°C 40 cycles Reverse 5’-catcctttcagagaaaataatgc-3’ (183 bp) MET Exon 14 Forward 5’-caagctctttctttctctctg-3’ 99417-99636 56°C 35 cycles Reverse 5’- ccactgaggtatatgtatagg-3’ (220 bp) MET Exon 19 Forward 5’- ttgtcctttctgtaggctgg-3’ 110885-111053 56°C 40 cycles Reverse 5’-ggtggtaaacttttgagtttgc-3’ (169 bp) CTNNB1 Exon 3 Forward 5-atagctgatttgatggagttgg-3’ 25071-25245 56°C 40 cycles Reverse 5’-ttgggaggtatccacatcc-3’ (174 bp) Table 4
3.2.5 Mass Spectrometry Genotyping
In addition to the search for commonly occurring KIT mutations, common mutations in kinase genes, previously associated with melanoma, as documented by the Sanger centre COSMIC database (Bamford et al., 2004), were also selected for high-throughput mutational screening (table 3.3). In addition, mutations in other treatable gene products (MacConaill et al., 2009) were selected for screening. The mutations chosen were single and double nucleotide substitutions and deletions within the mutational hotspots of KIT, BRAF, NRAS, KRAS, EGFR and RET. PCR primers for the amplification and extension steps were designed using Sequenom MassARRAY Assay Design software, using reference NCBI sequences. A total of 46 genotyping assays were incorporated into 6 multiplex reactions (appendix table A1.1-2).
The multiplex genotype assays were carried out using the Sequenom TypePLEX protocol, which employs single primer extension and matrix-assisted laser desorption ionization time-of- flight mass spectrometry as the screening platform. The PCRs were performed in 5µl volumes in 384-well plates using 50ng of genomic DNA, 1U Taq DNA polymerase, dNTP mix (500µM) and a forward and reverse amplification primer mix (100nM each). Thermocycling parameters were as follows: Denaturation at 94°C for two minutes, followed by 45 cycles of 30 seconds at 94°C, 30 seconds at 56°C and 1 minute at 72°C and a final extension phase of 5 minutes at 72°C. Unincorporated nucleotides were neutralised by adding a 2µL volume of 0.5U of shrimp alkaline phosphatase to the PCR reaction, incubating for 40 minutes at 37°C,
45 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
followed by 85°C for 5 minutes. The final extension reaction was carried out by adding 2µL termination mixture consisting of TypePLEX buffer (1x), TypePLEX termination mix (0.1 µL), extension primer mix (conc. 0.73-1.46 µM) and TypePLEX enzyme (0.5x). The subsequent 9µL reactions were thermocycled as follows: Denaturation at 94°C for 30 seconds, 40 cycles of 94°C for 5 seconds, 52°C for 5 seconds and 80°C for 5 seconds. The 52°C and 80°C steps also had a secondary, 5-cycle repetition for each of the 40 cycles. The TypePLEX products were diluted with 16µL of water and excess salts were removed by adding 6mg of Clean Resin (Sequenom, San Diego, CA, USA).
Approximately 15 nL of TypePLEX extension products were dispensed on a 384-spot SpectroCHIP array and analysed using matrix assisted laser desorption-ionization time of flight mass spectrometer (Bruker, Madison, WI, USA). Results were inspected manually using Typer 4.0 software version 4.0.3.18 (Sequenom, San Diego, CA, USA) by two independent observers.
3.2.6 MET Real Time PCR Genotyping
Genotyping of the MET polymorphisms rs34589476 (R988C) and rs56391007 (T1010C) was carried out using the TaqMan® SNP genotyping assays (assay identification numbers c 59054157 10 and c 88877997 10). Subsequent allelic discrimination was determined on the ABI PRISM 7500 Real Time PCR System (PE Applied Biosystems, Foster City, CA USA). Primers and probes used are outlined in appendix table A1.4.
The assay was carried out under standard conditions: 50 ng DNA, 0.125µL 40× Assay Mix, 2.5µL
TaqMan® Universal PCR master mix made up to 5µL with dH20. The standard thermal cycling conditions were; 50 °C for 2 min, 95 °C for 10 min, and 50 amplification cycles of 92°C for 15s and 60°C for 1min. Post amplification the plate was scanned to allow discrimination between the different genotypes. Reaction failures were re-genotyped using the same standard reaction and amplification conditions, with the exception of carrying out the reactions in 10µL volumes. A second failure resulted in the exclusion of the sample. The overall call rate in the control population was 98.6%.
46 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
Table 3.3 Single and double nucleotide substitution mutations selected for high throughput screening. Gene Mutant Alleles BRAF P.V600E C.1799T>A | P.V600G C.1799T>G | P.V600A C.1799T>C | P.V600K C.1798_1799GT>AA | P.V600R C.1798_1799GT>AG | P.V600E C.1799_1800TG>AA | P.V600D C.1799_1800TG>AT NRAS P.G12S C.34G>A | P.G12R C.34G>C | P.G12C C.34G>T | P.G12D C.35G>A | P.G12A C.35G>C | P.G12V C.35G>/T | P.G13R C.37G>C | P.G13C C.37G>T | P.G13D C.38G>A | P.G13V C.38G>T | P.G13D C.38G>C | P.Q61K C.181C>A | P.Q61E C.181C>G | P.Q61P C.182A>C | P.Q61R C.182A> G | P.Q61L C.182A> T | P.Q61H C.183A>C/T | P.Q61Q C.183A>G) KIT P.W557R C.1669T>C | P.V559A/D/G C.1676T>C/A/G | P.L576P C.1727T>C | P.K642E C.1924A>G | P.R634W C.1902G>A | P.D816H C.2446G>C | P.D816Y C.2446G>T | P.D816V C.2447A>T | P.D816A C.2447A>C KRAS P.G12S C.34G>A | P.G12R C.34G>C | P.G12C C.34G> T | P.G12D C.35G>A | P.G12A C.35G>C | P.G12V C.35G>T | P.G13D C.38G>A | P.G13A C.38G>C | P.G13V C.38G>T | P.Q61K/E C.181C>A/G | P.Q61P/R/L C.182A>C/G/T | P.Q61H C.183A>C/T MET P.R988C C.2962C>T | P.T1010I C.3029C>T | P.Y1248C C.3743A>G | P.Y1253D C.3757T>G | P.M1268T C.3803T>C CTNNB1 P.D32G C.95A>G | P.D32A C.95A>C | P.D32V C.95A>T | P.D32E C.96C>A | P.D32E C.96C>G | P.S33C C.98C>G | P.S33Y C.98C>A | P.S33F C.98C>T | P.S37P C.109T>C | P.S37C C.109T>G | P.S37F C.110C>T | P.S37Y C.110C>A | P.S37C C.110C>G | P.S45P C.133T>C | P.S45A C.133T>G | P.S45Y C.134C>A | P.S45F C.134C>T | P.S45C C.134C>G | P.S45DEL C.133_135DELTCT EGFR P.P753S C.2257C>T | P.L858R C.2573T>G RET P.C634R C.1900T>C | P.C634Y C.1901G>A | P.C634W>C C.1902C>G/T | P.M918T C.2753T>C Table 5
3.2.7 Confirmatory Sequencing
3.2.7.1 Sanger Sequencing
Cases positive for mutations by mass spectrometry genotyping were confirmed by using Sanger sequencing for BRAF, NRAS and MET (table 3.2). The Sanger sequencing protocol has been described in section 3.2.5. Where possible germline MET mutations were excluded by confirming wild-type sequences on non-tumour extracted DNA.
3.2.7.2 Pyrosequencing
Confirmation of KRAS (mutations was carried out using the pyrosequencing technique (table 3.3). Pyrosequencing was performed according to previously published protocols (Ogino et al., 2005). Briefly, 50 nM of PCR amplification primers (table 2.4) were mixed with 20 nmol of each dNTP, 1.5 mmol/L MgCl2, 1µL 10X PCR buffer, 0.4 U of AmpliTaq Gold, and 1.25 µL of DNA (50ng/µL) product in a total volume of 10 µL. The PCR conditions were: Initial denaturing at 94°C for 1 minute; 50 cycles of 95°C for 20 seconds, 58°C for 20 seconds, and 72°C for 40 seconds; final extension at 72°C for 60 seconds. The PCR products were sequenced by Pyrosequencing PSQ 96 HS System (Biotage AB,
47 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
Uppsala, Sweden) following the manufacturer’s instructions, using two Pyrosequencing primers (see table 3.4). A cyclic nucleotide dispensation (5’-CTAG-3’) was employed and subsequent results were read directly from the Pyrosequencing software. Each run contained one negative control (no template) and two positive controls with DNA extracted from cell lines SW480 and LoVo containing previously characterized KRAS G12V and KRAS G13D mutations, respectively.
Table 3.4 KRAS Pyrosequencing primers. Primer Sequencing PCR Forward 5’-ggcctgctgaaaatgactgaa-3’ PCR Reverse 5’-ttagctgtatcgtcaaggcactct-3’ (biotinylated) Pyrosequencing primer PKM 5’-cttgtggtagyyggagc-3’ Pyrosequencing primer PF2 5’-tgtggtagttggagct-3’ Table 6
3.2.8 Statistical Analysis
Two-tailed Welch’s t test was used to analyze differences in patient age at diagnosis across the various genotype groups. To determine differences in genotype frequencies and clinical characteristics of melanoma, chi-squared (χ2) statistics were used. The association between solar elastosis score and exposure score was also analysed using chi-squared (χ2) statistic. The relative risk ratio of melanoma patient and controls with respect to the MET SNPs R988C and T1010I was calculated using 2x2 contingency tables. The significance levels of all tests were set at p<0.05 and were two-sided. All statistical analysis was performed with JMP® 8 (SAS Institute Inc., Cary, NC, USA).
3.3 Results
3.3.1 Clinicopathological features
The cohort demographics and clinicopathologic characteristics are summarised in table 3.4. The patient group was predominantly composed of males (67.7%) and patients at an advanced age (median age males: 67.9yrs, females: 67.9yrs).
When the primary site was available for assessment (n = 112), CSD (i.e. elastosis score 2-3) was present in 74 cases (66.1%). 48 cases of metastatic melanoma corresponded to tumours with unknown primaries. When the primary site was known, melanomas arose in sites of chronic sun exposure (exposure scores 3- 4) in 73 cases (50.6%). There were 87 cases informative with regards to the Breslow thickness (median 3.5mm, mean 5.5mm ± 9.54) and metastatic disease was present in 171 cases (89.1%).
The histomorphological classification of the melanoma at the primary site was available in 66 cases. The most common melanoma subtype was nodular (n=37), followed by superficial spreading (n=15) and lentigo maligna (n=7).
48 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
3.3.2 Sequencing Results
3.3.2.1 KIT Mutations
Bidirectional sequencing of KIT exons 9, 11, 13 and 17, was successful in 159 cases (82%) and eight missense single nucleotide substitutions were identified (table 3.5). These were V559I, L576P, D572G in exon 11, K642E (2 patients) and V654M in exon 13, G498S in exon 9 and N822I in exon 17 (table 2). Comparing the clinical characteristics of the patients with and without KIT mutations, there were higher proportions of older patients (median age 73.4yrs vs. 67.5yrs), primary sites with moderate-severe solar elastosis (75% vs. 65.4%) and ≥3 site exposure scores (62.5% vs. 50.0%) in those with KIT mutations, although these differences were not statistically significant (table 3.5).
49 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
Table 3.5 Clinicopathological characteristics according to mutant kinase type Clinical and All BRAF NRAS KIT KRAS MET CTNNB p-value Histopathological Cases 1 Factors (n) Sex Male 130 28 27 4 3 12 1 a: 0.6798* Female 62 19 13 4 2 3 0 b: 0.4975** c: 0.2351** d: 1.000f** e: 0.2739** f: 0.6586** g: 0.3937** Age at diagnosis (yrs.) (174) Mean ± SD 66.2 61.5 65.1 72.9 78.3 63.6 76.5 a: 0.0178 (17.4) (15.8) (15.1) (3.4) (19.4) b: 0.2727 c: 0.0273 d: 0.6925 e: 0.1816 f: 0.0859 g: 0.8023 Solar elastosis 0 9 3 0 1 0 1 0 a: 0.5565* 1 29 11 6 1 0 2 0 b: 0.2076 2 24 7 3 2 0 4 0 c: 0.0350** 3 50 6 9 4 4 3 1 d: 0.5452** e: 0.6986** f: 0.2736** g: 0.4355** Sun damage No 38 14 6 2 0 3 0 a: 0.3018* Yes 74 13 12 6 4 7 1 b: 0.3587 c: 0.0240** d: 1.000** e: 0.5799** f: 0.2977** g: 1.000** Sun Exposure 0 3 2 0 1 0 1 0 a: 0.4056* 1 7 2 2 0 1 1 0 b: 0.4765 2 63 17 8 2 1 3 0 c: 0.3349** 3 43 9 11 3 1 3 1 d: 0.3755** 4 31 5 5 2 1 3 0 e: 0.2126** f: 0.3247** g: 0.2163** Exposure Low 73 21 10 3 2 5 0 a: 0.4305* High 73 14 16 5 2 6 1 b: 0.1902** c: 0.2546** d: 0.2800** e: 0.7189** f: 1.000** g: 1.000** a: Testing across all 3 genotypes; b: BRAF vs. NRAS; c: BRAF vs. BRAF wildtype; d: NRAS vs. NRAS wildtype; e: KIT vs. KIT wildtype; f: KRAS vs. KRAS wildtype; g: MET vs. MET wildtype. Abbreviations: N.A. Not available; H&N, Head and Neck; U. Limb, Upper Limb; L. Limb, Lower Limb; Glab. Skin, Glabrous Skin; ALM, Acral Lentiginous Melanoma; DM, Desmoplastic Melanoma; LMM, Lentigo Maligna Melanoma; NM, Nodular Melanoma; SSM, Superficial Spreading Melanoma; AJCC, American Joint Committee on Cancer; wt, Wildtype; * Pearson’s chi-squared test; ** Fisher’s exact test. Table 7
50 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
Table 3.5 Clinicopathological characteristics according to mutant kinase type (cont.) Clinical and All BRAF NRAS KIT KRAS MET CTNNB1 p-value Histopathological Cases Factors (n) Tumour Site Head and Neck 35 8 7 2 1 3 0 a: 0.4771* Trunk 42 12 5 1 0 3 0 b: 0.4741** Upper Limb 26 4 5 1 1 2 1 c: 0.8030** Lower Limb 38 10 9 3 2 2 0 d: 0.2910** Glabrous Skin 5 2 0 1 0 1 0 e: 0.1229** ** Unknown 1° 46 11 14 0 1 4 0 f: 0.6837 g: 0.8177** Histological Type ALM 3 1 0 0 0 0 0 a: 0.1080* DM 4 0 0 0 0 1 0 b: 0.6095** LMM 7 0 2 1 1 0 0 c: 0.4071** NM 37 8 6 4 1 4 0 d: 0.8183** SSM 15 3 3 1 0 1 1 e: 0.0594** Unknown/Other 126 34 29 2 3 9 0 f: 0.4413** g: 0.6246** Breslow Thickness (mm) (n=118) Mean ± SD 4.8 5.5 (6.4) 4.25 3.8 2.5 4.23 4 a: 0.8335 (5.0) (3.6) (3.3) (1.36) (3.3) b: 0.9811 c: 0.8597 d: 0.8330 e: 0.6299 f: 0.2700 g:0.8505 Ulceration (n=117) Present 51 11 8 3 1 4 1 a: 0.8069* Absent 24 3 5 3 1 2 0 b: 0.4197** c: 0.3471** d: 0.7446** e: 0.3775** f: 0.5405** g: 1.000** AJCC Staging at First Diagnosis I 21 2 4 2 1 1 0 a: 0.1657* II 64 15 10 1 1 8 1 b: 0.2073** III 53 17 11 2 1 4 0 c: 0.3772** IV 25 6 3 3 1 1 0 d: 0.0824** Unknown 29 6 11 0 1 1 0 e: 0.0783** f: 0.6601** g: 0.6315** a: Testing across all 3 genotypes; b: BRAF vs. NRAS; c: BRAF vs. BRAF wildtype; d: NRAS vs. NRAS wildtype; e: KIT vs. KIT wildtype; f: KRAS vs. KRAS wildtype; g: MET vs. MET wildtype Abbreviations: N.A. Not available; H&N, Head and Neck; U. Limb, Upper Limb; L. Limb, Lower Limb; Glab. Skin, Glabrous Skin; ALM, Acral Lentiginous Melanoma; DM, Desmoplastic Melanoma; LMM, Lentigo Maligna Melanoma; NM, Nodular Melanoma; SSM, Superficial Spreading Melanoma; AJCC, American Joint Committee on Cancer; wt, Wildtype; * Pearson’s chi- squared test; ** Fisher’s exact test. Table 8
51 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
Table 3.6 KIT sequencing results. Case No. Age, Sex Primary Solar Mutation Metastatic AJCCa Site Elastosis (Exon) Site Survival Status (Weeks) 13 75, M Head & 3 L576P (11) Widely IV Neck disseminated Dead (7.4) 23 89, F Lower leg 2 N822I (17) Inguinal IIIb Lymph Nodes Aliveb (28.3) 43 69, F Vulva 0 K642E (13) Pelvic Lymph IIIc Nodes Alive (148.1) 50 88, F Forearm 3 K642E (13) Axilla, Liver, IIIc Bone Dead (64.9) 80 78, F Lower leg 3 V559I (11) Local Ib cutaneous Dead (86.4) recurrence 157 72, M Foot 1 D572G (11) Inguinal IV (dorsum) Lymph Nodes, Dead (113.4) Liver, Bone, Spleen 181 72, M Head & 3 G498S (9) Parotid IV Neck Dead (37.9) 190 40, M Back 2 V654M Lung Ib (13) Alive (397.4) a: AJCC, American Joint Committee Cancer (2010) melanoma staging at time of initial diagnosis. b: Lost to follow up. Table 9
52 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
3.3.3 High-Throughput Mass Spectrometry Screening Hotspot mutations in six of the most commonly mutated kinase genes in melanoma were interrogated using a total of 46 SNP assays. The total cohort of 192 patient samples underwent analysis, resulting in 8,815 successful genotype calls (0.19% assay failure rate). To confirm the genotyping results, 1,766 (20%) of calls were re-genotyped, generating 100% concordance. Overall, 51.7% of cases harboured at least one single nucleotide substitutions (figure 3.1).
Figure 3.2 Melanoma genotyping results. High throughput and conventional Sanger sequencing results combined. Figure 0-12
3.3.2.2 BRAF Mutations
The majority of detected mutations (n=47, 24.5%) targeted BRAF codon 600 (figure 3.2). The MALDI- TOF assay was unable to differentiate between c. 1799T>A (V600E) and c. 1798_1799GT>AA (V600K) (figure 3.3).
53 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
Figure 3.3 Frequency of mutations in BRAF, NRAS, KIT, KRAS, CTNNB1 and MET. Figure 0-13
54 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
This was resolved by carrying out confirmatory Sanger sequencing on all flagged samples (figure 3.4). Sequencing differentiated 34 V600E from 11 V600K cases. The two cases with V600R were also confirmed. BRAF mutations occurred in younger patients (61.5 yrs. vs. 66.2 yrs., p=0.0178) and more commonly arose in sites with minimal solar elastosis than non-BRAF mutated cases (51.9% vs. 28.2%, p=0.0240) (table 3.4).
No statistically significant differences were identified between BRAF mutants and BRAF wildtype melanomas for any of the other clinical and pathological parameters.
Correlation of each patient’s clinicopathological features with the BRAF mutation type is outlined in appendix table A1.15.
A! B!
C! D!
Figure 0-14
Figure 3.4 Mass spectrometry genotyping and Sanger sequencing results of BRAF V600E (A and B, respectively) and V600K (C and D, respectively). These spectra are indistinguishable and require Sanger sequencing for the distinction between the mutations.
55 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
3.3.2.3 NRAS Mutations
NRAS was the second most commonly mutated oncogene (n=40, 19%, figure 3.1). NRAS mutations were unevenly distributed (figure 3.2), with codon 61 predominantly affected, followed by codons 12 and 13 (n = 36; n = 2; n = 2, respectively).
With the exception of two cases, for which insufficient DNA was available, all NRAS mutations were confirmed on Sanger sequencing. A sole case of a BRAF mutation coexisting with an NRAS mutation was identified. NRAS mutated melanomas failed to display any unique clinical or pathological properties in comparison to non-NRAS mutant melanomas (table 3.5).
Detailed correlations of the clinical and pathological characteristics of each NRAS mutant melanoma are outlined in appendix table A1.6.
3.3.2.4 KRAS Mutations.
Five KRAS mutations were identified all restricted to codon 12 (figure 3.2). The small numbers identified makes a sufficiently powered statistical analysis unachievable but a trend towards older patients was identified (78.3 yrs., p=0.0859, table 3.4).
3.3.2.5 MET Mutations.
Fifteen mutations were identified within the juxtamembranous domain of MET. Five cases harboured the R988C substitution, and ten showed the T1010I change. At the time of the design of the melanoma mutation screening panel (section 3.2.6), these single nucleotide substitutions were part of the COSMIC catalogue of cancer mutations, with multiple studies showing these changes were reported in a variety of cancer cell lines (Kenessey et al., 2006, Ma et al., 2003, Ma et al., 2005), tumour samples (Wasenius et al., 2005) mutations (Tengs et al., 2006) but also germline single nucleotide substitutions (Wasenius et al., 2005). Subsequently, these two MET mutations have since been removed from COSMIC and added to the NCBI dbSNP database (Sherry et al., 2001) as entries rs34589476 and rs56391007. Nonetheless, to assess the precise nature of these mutations in our melanoma samples we analysed the corresponding patient germline DNA for the presence of SNPs rs34589476 (MET R988C) and rs56391007 (MET T1010I).
56 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
Table 3.7 MET sequencing results in tumour and germline DNA Case Age Gender Primary site MET Metastatic MET No. Mutation Sites mutation Tumour DNA Germline DNA
14 45 F Unknown T1010I Adrenal, liver T1010I*† 16 86 M Head and Neck T1010I Lung T1010I*† 20 - M Forearm R988C Brain R988C*† 42 - M Unknown R988C R988C† 43 69 F Mucosa T101I T1010I* 71 23 M Head and Neck R988C Wildtype*† 90 43 M Chest T1010I Head and T1010I† neck, brain 93 77 M Abdomen T1010I T1010I* 106 83 M Head and Neck T1010I T1010I*† 158 78 M Leg R988C Neck, Lung, N.A chest wall 164 57 M Unknown R988C Wildtype*† 170 82 F Upper Arm T1010I N.A. 178 48 M Unknown T1010I T1010*† 183 58 M Thigh T1010I N.A. 186 78 M Back T1010I N.A. N.A: Germline DNA not available; *: Confirmed on Sanger sequencing; †: Confirmed on qPCR Table 10
Four of the five cases with the MET R988C mutation that had germline DNA available for testing two of which showed the same change within the matching germline DNA. In contrast, seven of the ten cases with the MET T1010I mutation that had germline DNA available for testing showed the same change within the matching germline DNA.
To assess the possibility that these mutations may confer an increased risk for melanoma development, 450 DNA samples were genotyped from control subjects who did not have a personal history of any type of skin cancer (tables 3.8, 3.9 and 3.10).
Table 3.8 Odds ratios of melanoma risk and MET R988C R988C Wildtype R988 Total Controls 2 448 450 Melanoma patients 5 187 192 7 635 642 Odds ratio: 5.86 (95% confidence interval: 1.15 – 29.9) Fisher’s exact test 0.0277 Table 3.9 Odds ratio of melanoma risk and MET T1010I T1010I Wildtype T1010 Total Controls 9 441 450 Melanoma patients 10 182 192 19 623 642 Odds ratio: 2.06 (95% confidence interval: 1.08 – 6.31) Fisher’s exact test 0.0397
57 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
Table 3.10 Odds ratio of melanoma risk for combined MET R988C and T1010I Met Met Total Juxtamembranous Juxtamembranous SNP wildtype Controls 11 439 450 Melanoma patients 15 177 192 26 616 642 Odds ratio: 3.20 (95% confidence interval: 1.50 – 6.83) Fisher’s exact test 0.0034 Table 11
These results show that in comparison to controls, both MET R988C and T1010I are over-represented in our cohort of advanced melanomas.
The MET changes were also frequently found in samples coexisting with other mutations (figure 3.4), unlike BRAF, NRAS, KIT and KRAS, which occurred predominately in a mutually exclusive fashion.
3
6 BRAF + MET KRAS + MET MET NRAS + MET
5 1
Figure 3.5 Coexistence of MET mutations with other kinase mutations. Figure 0-15
3.3.2.6 CTNNB1 Mutations.
A sole CTNNB1 mutation (S45F) was identified within a 4mm thick, superficial spreading melanoma arising in the sun-damage skin of the forearm of a 76yr old male.
3.3.2.7 EGFR Mutations.
No EGFR mutations were identified.
3.3.2.8 RET Mutations.
No RET mutations were identified. 58 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
3.4 Discussion
3.4.1 Patient Characteristics
We have established the incidence of KIT mutations, and other common kinase mutations, in advanced melanomas arising within a population of patients with a high prevalence of sun-damaged skin. The study cohort displayed an age (males: 67.9 yrs., females: 67.9 yrs.) and sex distribution (males: 67.7%) within the range reported for the New South Wales population (Tracey et al., 2008).
Our detection of moderate to severe elastosis in 66.1% of cases, was comparably high compared to international (20-35%) frequencies (Berwick et al., 2005, Purdue et al., 2005, Maldonado et al., 2003, Viros et al., 2008) and other Australian studies (43%) (Handolias et al.)(demographic and clinicopathological characteristics of these studies are outlined in appendix table A1.17). This is consistent with the prominent role chronic UV-light exposure has upon the extraordinarily high incidence rates of melanoma in Australia, 47.9 per 100,000 (AIHW, 2010), and, in particular, New South Wales, 50.7 per 100,000 (Tracey et al., 2008).
The largest proportion of melanomas was found on the limbs (43.8%), followed by trunk (28.8%) and head and neck (24.0%) (Table 3.4). A high association was observed between the sun exposure score and degree of solar elastosis (see appendix table A1.11, p<0.0001). More specifically, tumours from high exposure sites were highly correlated with CSD skin (head and neck, 84.0%; upper limb, 77.3%). In contrast, only 45.2% of trunk melanomas arose in CSD skin (p-value 0.037, data not shown).
It is important to underscore that this group of patients do not reflect the true spectrum of people in the community with a diagnosis of melanoma. Most cases of melanoma represent tumours with thin Breslow’s thickness, for which excision proves to be a curative treatment. The cohort contained herein reflects a portion of cases with a more typically advanced disease. These cases are thus considered to better represent the characteristics of patients for which a specific mutational genotype will be either informative with respective to the patients prognosis, or will direct the choice of individualised kinase inhibitor therapy. The main limitation of this approach lies in the selection of thin cases that ultimately behave more aggressive, an outcome that is not foreseeable at the time of the initial melanoma excision.
3.4.2 KIT Mutations
All KIT mutations identified were single nucleotide substitutions, confirming the high prevalence of this type of mutation in KIT associated melanoma. This stands in sharp contrast to the complex deletions and insertions in KIT mutated gastrointestinal stromal tumours (GISTs).
A rare mutation, previously identified in GIST (Kwon et al., 2009), KIT G498S is the first example of an exon 9 KIT mutation occurring in melanoma (CSD skin: head and neck of a 72yr.old male).
The KIT exon 11 mutation, L576P is the most commonly occurring KIT change in melanoma (Woodman and Davies). This mutation was detected in the CSD skin of the head and neck in a 75 yr. old male. A KIT V559I mutation was identified in the CSD lower leg of a 78 yr. old female, and represents a commonly affected codon in KIT mutated melanoma (Woodman and Davies). The D572G substitution, originating 59 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
in the dorsum of the left foot of a 72 yr. old male (primary not available for assessment), is a novel KIT mutation in melanoma. It should be noted that a mutation affecting this aspartate residue has been previously documented in a GIST case (Taniguchi et al., 1999).
Within exon 13, KIT K642E was identified in two cases; the CSD skin from the forearm of an 88 yr. old female, and in the vulva of a 69 yr. old female. This mutation is the second most frequent change in KIT mutated melanoma (Woodman and Davies). The third exon 13 mutation, V654M, was found in the CSD back of a 40 yr. old male. The substitution V654A has been reported to be a mutation representing an acquired resistance to the TKI, Imatinib Mesylate (Chen et al., 2004). The alanine substitution is reported to induce only modest structural changes resulting in decreased binding of Imatinib (Tamborini et al., 2006). The mutation has been shown to induce an increased sensitivity to low SCF concentrations, but is not considered to generate constitutive KIT phosphorylation (Roberts et al., 2007). The signalling effects of V654M substitution in KIT await in vitro functional studies.
The sole mutation identified in exon 17, N822I, has been identified in two previously reported melanoma samples, both co-occurring with other KIT mutations (K642E (Torres-Cabala et al., 2009) and G565V (Smalley et al., 2009)).
The incidence of KIT mutated melanomas have been best documented in mucosal (14%) and acral melanomas (18%) (Woodman and Davies). The 8 KIT mutated cases identified from this study’s 159 informative samples (34 specimens did not yield either PCR products or sequencing results of sufficient quality in all exons of interest) translate to an overall incidence rate of 5.0%. Focusing on cutaneous melanomas associated with either CSD skin or chronically sun exposed sites, there is a modest increase in the rate of mutations (8.1%, 6/74 cases). This trend for KIT mutations arising in sun-damaged skin is also weakly correlated with the older patient group (73.43 vs. 67.49 yrs., p-value 0.184).
A large number of cutaneous melanomas been studied in detail by (Handolias et al.). This group identified 5 KIT mutated melanomas (including 3 acral) in 257 cases screened with high-resolution melt analysis and confirmed by Sanger sequencing. The non-acral KIT mutated melanomas occurred in younger female patients (39, 54 and 55 yrs. of age), with CSD skin in 2 of the 3 cases. The cohort described therein, was composed of younger patients (Handolias median 56.0 vs. present cohort median 67.9 yrs. of age) with thinner melanomas (median 1.1mm) than our study group (median 3.5mm). There is also a marked difference in the incidence of nodular melanomas in the two study groups. The Handolias study consisted of 18.3% nodular melanomas and 64.1% superficial spreading melanomas (Liu et al., 2007); this breakdown was reversed in our group, where nodular melanomas comprised the largest melanoma histological subtype (56.1%), followed by superficial spreading melanomas (22.7%). This is consistent with the observation that thick melanomas are predominantly nodular melanomas, preferentially occurring in the limbs, head and neck of older males, with a higher association for solar keratosis (Chamberlain et al., 2002). The difference in the incidence of KIT mutations is likely to be due to the selection bias for advanced melanomas, and provides an insight into the biological mechanisms of the disease.
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3.4.3 BRAF Mutations
The BRAF mutation rate of 24.5 % is lower when compared to other large international (27.8% – 59.2%) (Maldonado et al., 2003, Viros et al., 2008, Edlundh-Rose et al., 2006, Omholt et al., 2003) and interstate studies (Queensland 31.5% (Hacker et al.), Victoria 45% (Liu et al., 2007)). In addition, 23.4% of mutations at V600 were double nucleotide substitutions (c.1798_1799GT>AA, V600K). This rate is considerably higher than other reports, which range between 2.5% and 9.7% (Omholt et al., 2003, Forbes et al., 2008, Houben et al., 2004, Edlundh-Rose et al., 2006).
The BRAF mutated melanomas occurred in younger patients (60.4 yrs. vs. 70.0 yrs., p=0.026) and in mildly or non-sun damaged skin (51.9% vs. 28.2%, p-value 0.024). Given the considerable range of disease presentation, these results are consistent with the known clinical profile of BRAF mutated melanomas. These include thinner melanomas, younger patients (< 50 yrs. of age), with the primary melanoma arising in intermittingly sun exposed skin, absence of solar keratoses and a superficial spreading histological subtype (Liu et al., 2007).
The existence of rare V600 mutations such as V600R makes it important to closely scrutinises currently widely used BRAF mutations assays, such as the Real-Time based PCR assay COBAS, in particular since it has been used as the mandatory companion test for Vemurafinib, despite COBAS having now been shown to display poor sensitivity to rare BRAF mutant alleles (Heinzerling et al., 2013). Moreover, our detection panel was not designed to identify the rare non V600 BRAF mutations such as BRAF K601E and BRAF L597V, mutations observed in up to 3% of BRAF mutant melanomas (Menzies et al., 2012). Preclinical studies have shown these rare mutations also appear to be capable of inhibition by vemurafenib (Dahlman et al., 2012). A mass spectrometry based approach, such as the one employed in this time, allows a sufficient testing flexibility to adapt to an increased the number of mutations types screened as more biological information comes to available.
3.4.4 NRAS Mutations
The percentage of cases harbouring NRAS mutations in our cohort (20.8%) is consistent with the published mutation frequencies of between 16.6% and 28.0% (Viros et al., 2008, Houben et al., 2004, Edlundh-Rose et al., 2006, Omholt et al., 2002)). The skewed distribution of mutations across exons 1 and 3 (10% vs. 90%) has also been observed previously.
NRAS mutated cases did not differ from non-NRAS mutants with respect to age (NRAS mutant: 67 yrs.), elastosis (NRAS mutant: elastosis score ≥2, 66.7%) or exposure score (NRAS mutant: exposure score ≥3, 60.0%).
The five KRAS cases occurred in older patients (median 80.7 vs. 67.8 yrs., p-value 0.086). This low rate in malignant melanoma contrasts with the high number of KRAS mutations in banal melanocyte aggregates (27.8% (Dadzie et al., 2009)). At the time of writing, this cohort represents the largest reported collection of KRAS mutated melanoma samples (Forbes et al., 2008).
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3.4.5 MET Mutations.
Mutations that target the MET juxta-membrane domain (JMD) are considered to disrupt an important modulator of tyrosine kinase activity. Ma et al. demonstrated that transfection of MET JMD mutations R988C and T1010I into small cell lung cancer (SCLC) cell lines enhanced constitutive protein tyrosine phosphorylation, altering cell morphology and adhesion with the effect of increasing in vitro tumourigenicity (Ma et al., 2003). The adoption of a more invasive phenotype was in part mediated by an enhanced activation of paxillin, which is a FAK protein involved in cytoskeletal reorganization, and a commonly mutated oncogene in lung adenocarcinomas (Jagadeeswaran et al., 2008). This enhanced tumourgenic effect by these JMD mutants has been seen in some (Lee et al., 2000) but not all cell lines (Tyner et al., 2010, Lee et al., 2000). The T1010I mutation is speculated to eliminate a “threshold for ligand-induced proliferation” in the mesothelioma cell line H513 (Jagadeeswaran et al., 2006) and also in removing the possibility of phosphorylation at residue 1010, thereby disrupting a known negative regulator of MET signaling (Ma et al., 2003). Jagadeeswaran et al. also demonstrated that in a panel of seven mesothelioma cell lines, the two cell lines most susceptible to the antiproliferative effects of the MET inhibitor SU 11274 harbored the T1010I mutation (Jagadeeswaran et al., 2006). However, the same effect was not seen with another small molecule MET inhibitor PHA-66572 (Brevet et al., 2011).
Evidence of an oncogenic effect in vivo comes from studies using worm and mice disease models. When C.elegans is engineered to express MET, the JMD mutants (R988C and T1010I) generate a greater number of abnormal phenotypes than wildtype MET. This is in keeping with their proposed carcinogenic properties (Siddiqui et al., 2008). Zaffaroni et al. showed the mouse strain SWR/J harbors the pulmonary adenoma resistance locus (Par4), which confers susceptibility to lung tumourigenesis and is linked to the Met R968C substitution, which is analogous to MET R988C (Zaffaroni et al., 2005).
In humans, the germline MET T1010I mutation has recently been identified as the SNP located on chromosome 7q31 that is linked to common familial colorectal cancer (Neklason et al., 2008). Neklason et al. have identified MET T1010I in two independent cohorts (4.1% and 5.2%) of sibling pairs affected with a non-APC/non-HNPCC familial colorectal cancer (CRC) syndrome. A similar rate of germline MET T1010I mutations has also been found in mesothelioma (4/70 cases, 4.3%) (Brevet et al., 2011) and thyroid carcinomas (6%). Wasenius et al. demonstrated 2 of the 6 thyroid carcinoma MET T1010I mutations were not detected in matching germline DNA (Wasenius et al., 2005) and similarly Ma et al. identified one MET T1010I as a somatic mutation on a SCLC patient, providing evidence that this missense substitution can also be somatically acquired (Ma et al., 2003).
Another possible oncogenic effect by these MET JMD mutations may be the generation of reactive oxygen species (ROS). When introduced in Baf3 cells, MET T1010I and MET R988C, but not wildtype MET, leads to an increase in ROS production (Jagadeeswaran et al., 2007). In SCLC and melanoma cell lines, HGF-induced ROS generation has been shown to enhance cell division, migration and phosphorylation of MET and it’s downstream effectors, including paxillin, an effect mitigated by antioxidants and the MET inhibitor SU 11274 (Jagadeeswaran et al., 2007, Puri et al., 2007). These finding are interesting in light of what is known about the role of oxidative stress in the development of
62 Chapter 3 – Characterisation of KIT and other Kinase Mutations in Melanoma
melanoma. Melanin within melanoma cells switches from being a ROS scavenger to a ROS producer (Meyskens Jr et al., 2001). This has the effect of generating melanoma cells with higher level of ROS than benign melanocytes, or even other types of skin cancers (Sander et al., 2003). Although high levels of oxidative stress are known to trigger melanocyte cell cycle arrest (Smit et al., 2008), it appears melanoma cells are not only capable of withstanding this stressor, but are in fact driven into higher levels of cell proliferation, apoptosis resistance, destruction of surrounding tissue, avoidance of immune surveillance and DNA mutagenesis (reviewed by (Joosse et al.).
The growing body of evidence, and our present findings, suggests that the JMD mutations (R988C and T1010I) do not represent classic ‘driver’ mutations, but rather confer a susceptibility to malignancy in which MET over-expression is an early feature of disease development. Disruption of post-activation down regulation of MET by mutations in the JMD may promote a more aggressive phenotype in melanomas driven by an autocrine/paracrine dysregulation of the HGF/MET signaling axis.
3.5 Conclusion
We and others (Handolias et al.) have shown mutations in KIT exons 9, 11, 13 and 17 in cutaneous melanomas, including those arising in CSD skin, to be relatively uncommon events. However, there remain further opportunities to study novel KIT melanoma subgroups. Of particular interest, the KIT amplified melanomas (Curtin et al., 2006, Smalley et al., 2008), which were not analysed in this study. Smalley et al. demonstrated KIT/CDK4 amplified melanoma cells lines to be dependent on KIT mediated MAPK signalling, and susceptible to TKIs, despite the absence of mutations in KIT exons 11, 13, 14, 17 and 18 (Smalley et al., 2008). Clinical trials exploring the susceptibility of KIT amplified melanomas to TKIs will be critical in determining their relevance to clinical practice (Hodi). Furthermore, oncogenic KIT mutations have been reported to occur outside exons 9, 11, 13 and 17 (Fontalba et al., 2006, Akin et al., 2004). A screening regime, combining clinical and histopathological parameters, aiming at detecting KIT amplifications, and mutations beyond the current conventional mutational hotspots, remains the best hope for recruiting KIT as a therapeutic target for a greater number of patients with advanced cutaneous melanoma.
The apparent clinical success of the BRAF inhibitor vemurafenib will have a significant impact on the assessment of metastatic melanoma. It is reasonable to expect assessment of the BRAF mutational status, will became part of the routine assessment of metastatic melanoma. If the indication of vemurafenib remains restricted to patients with unresectable stage III or stage IV, it appears at least in our patient population, BRAF inhibitors may be applicable to only approximately 25% of cases.
The juxtamembranous MET mutations identified in this report identify for the first time the possible role uncommon MET alleles may play in influencing the aggressiveness of melanoma. Further studies are required to replicate these findings in a wider cohort of patients, incorporating a greater proportion of cases with thin melanomas. It may then be possible to search for differences in the frequency of these mutations in thin vs. thick melanoma, providing further evidence for a role in the promotion of a more aggressive phenotype.
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64
CHAPTER 4
THE PROGNOSTIC IMPORTANCE OF NRAS AND BRAF MUTATIONS IN MELANOMA
Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
66 Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
4.1 Introduction
The study of oncogenic activation of the mitogen-activated protein kinase and phosphoinositide 3-kinase (MAPK) cascade in malignant melanoma has not only improved our understanding of the biology of melanoma, but has provided exciting therapeutic opportunities for this notoriously aggressive and treatment resistant disease. Amongst the best study drivers of this cascade are the regulatory GTPase NRAS, and the cytoplasmic serine/threonine kinase BRAF.
NRAS was the first identified melanoma oncogene (Albino et al., 1984). It is a member of the RAS subfamily of small GTPases, which function as signal transducers, coupling the activation of tyrosine kinase receptors to the pro-survival, pro-growth signaling MAPK cascade. BRAF is one of the immediate targets of non-oncogenic NRAS activation (Dumaz et al., 2006), and forms the first step in the MAPK signaling pathway. BRAF mutations in human cancers were first identified by Davies et al., who showed two thirds of melanoma cell lines harbored such mutations and they occurred in a mutually exclusive fashion with RAS mutations (Davies et al., 2002).
At first glance, the observation of mutually exclusive mutations in gene products involved in the activation of the MAPK cascade suggests these mutations represent alternate means to conferring an equivalent melanoma phenotype. However, there are well-characterised differences in the clinical, histological, genetic and chromosomal profiles between NRAS and BRAF driven melanomas. BRAF mutations are consistently reported to be over represented in young patients and in sites of non- chronically sun-damaged skin (Maldonado et al., 2003, Cohen et al., 2004, Liu et al., 2007). Conversely, NRAS associated melanomas are more common in older patient groups, and in sites of chronic sun damage (van Elsas et al., 1996). Furthermore, BRAF mutant melanomas more commonly show naevi remnants (Hacker et al., 2009, Edlundh-Rose et al., 2006, Poynter et al., 2006) and a positive mutational status influences the pattern of metastatic spread (Chang et al., 2004).
Recently, a review of melanoma histogenetic subclasses identified a significant association between the presence of a nodular melanoma phenotype and NRAS mutations, whereas BRAF mutant melanomas were more likely to be of a superficial spreading subtype (Platz et al., 2008). These phenotypic associations were stronger within chronically sun-exposed sites for NRAS mutations and intermittently sun-exposed sites for BRAF mutations. The presence of a BRAF mutation specific gene expression signature remains controversial, with studies finding evidence for (Johansson et al., 2007, Pavey et al., 2004, Bloethner et al., 2005, Kannengiesser et al., 2008) and against (Hoek et al., 2006) such an association.
Less controversial is the dissection of the complex but non-random chromosomal rearrangements in malignant melanoma revealing mutation-associated chromosomal copy number changes, with gains in large regions of chromosome 7 in a high proportion of BRAF mutant melanomas (84%), and gains of 3q13 in NRAS mutant melanoma (40%) (Jonsson et al., 2007). These findings are reported in a significant body of literature identifying and confirming the biological differences in melanomas associated with the presence of NRAS or BRAF mutations. It is therefore surprising that studies into the prognostic impact of BRAF/NRAS status have produced conflicting results. Impaired survival of patients with BRAF mutant
67 Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
melanomas, has been described by some (van Elsas et al., 1996, Ugurel et al., 2007) but not the majority of studies (Maldonado et al., 2003, Omholt et al., 2003, Chang et al., 2004, Shinozaki et al., 2004, Akslen et al., 2005, Edlundh-Rose et al., 2006).
The aim of this study was to assess the incidence of NRAS and BRAF mutant melanomas, and any prognostic impact this may have in a patient population living in an environment where there is high ambient UV-exposure and a significant risk of developing melanomas arising in chronically sun-damaged skin.
4.2 Materials and Methods
4.2.1 Study Cohort
192 cases of malignant melanoma were retrospectively recruited from the Anatomical Pathology Department of the Hunter Area Pathology Service’s formalin fixed paraffin embedded tissue archives. The selection criteria have been described in section 3.2.1.
4.2.2 Clinicopathological features
Detailed description of the histopathological parameters (Breslow thickness, ulceration, mitosis. histological subtype, exposure score and solar elastosis) and staging criteria are outlined in sections 3.2.1 and 3.2.2.
4.2.3 DNA Extraction
Formalin fixed paraffin embedded (FFPE) tumour samples were used for tumour DNA extraction as outlined in section 3.2.3.
4.2.4 Mass Spectrometry Genotyping and Confirmatory Sequencing
A selection of kinase mutations previously associated with melanoma, as identified in the Sanger Centre’s COSMIC database (Bamford et al., 2004), were selected for high-throughput mutational screening. The screening platform used was the Sequenom MassARRAY instrument, which employs single primer extension, and matrix-assisted laser desorption ionization time-of- flight mass spectrometry as the detection platform. The protocol is described in detailed within section 3.2.5. Cases positive for mutations by mass spectrometry genotyping were confirmed by Sanger sequencing using an ABI 3730 DNA analyzer (Applied Biosystems, Foster City, CA, USA) (see section 3.2.7).
4.2.5 Statistical Analysis
Two-tailed Wilcoxon t test was used to analyze differences in patient age at diagnosis, and tumour thickness, across the BRAF mutant, NRAS mutant and BRAF/NRAS wildtype (henceforth referred to as BRAF, NRAS and WT groups, respectively) genotype groups. To determine differences in genotype frequencies and clinical characteristics of melanoma, chi-squared (χ2) statistics were used. The significance levels of all tests were set at p<0.05 and were two-sided. Univariate Survival analysis for 68 Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
categorical data was analysed using the Kaplan-Meier curves and log-rank test of equality. Continuous data was converted to categorical variables before analysis. The time to end points studied included; time from initial diagnosis (AJCC I & II) to loco-regional metastasis (AJCC III) (distant metastases events were censored), time from loco-regional metastasis to distant metastasis (AJCC IV), time from initial diagnosis (AJCC I-III) to distant metastasis, time from distant metastasis to melanoma specific death and time from initial diagnosis (AJCC I-IV) to melanoma specific death. The variables statistically significant in univariate analysis were combined in a Cox proportional hazards multivariate analysis to confirm if these variables were independently prognostic. All statistical analysis was performed with JMP® 8 (SAS Institute Inc., Cary, NC, USA).
4.3 Results
4.3.1 Clinical Features
The demographics and clinicopathological features of the study cohort have been described in detailed within section 3.3.1. The clinicopathological characteristics of BRAF mutant, NRAS mutant and BRAF/NRAS wildtype groups are summarised in table 4.1.
4.3.2 Mass Spectrometry Genotyping
As outlined in section 3.3.3, 192 patient tumour samples underwent mass spectrometry genotyping, resulting in 8,815 successful genotype calls (0.19% assay failure rate). To confirm the genotyping results, 1,766 (20%) of the calls were re-genotyped, generating 100% concordance. Overall, 51.7% of cases harboured at least one single nucleotide substitutions (figure 3.1).
The following patient groups with respect to BRAF-mutant, NRAS-mutant and BRAF/NRAS wildtype status were analysed. The most commonly mutated codon was BRAF codon V600 (34 cases with the c. 1799T>A (V600E) substitution, 11 cases c. 1798_1799GT>AA (V600K) dinucleotide substitution, and 2 cases with c.1798_1799GT>AG (V600R).
The second most commonly mutated codon was NRAS Q61, with 36 NRAS mutations detected (19%). Most NRAS mutations were identified within exon 2 (Q61K c.181C>A, n=8; Q61R c.182A> G, n=16; Q61L c.182A> T, n=8; Q61H c.183A>C/T, n=4)). NRAS codon 12 (G12S c.34G>A, n=1; G12C c.34G>T, n=1) and codon 13 (G13R c.37G>C, n=1; G13D C.38G>A, n=1). With the exception of two cases, for which insufficient DNA was available (both NRAS mutants), all BRAF and NRAS mutations were confirmed on Sanger sequencing.
An isolated case of a BRAF mutation coexisting with an NRAS mutation was identified (BRAF c.1799T>A (V600E) and NRAS c.181C>A (Q61K)) in this patient cohort.
4.3.3 Clinicopathological Features Of NRAS And BRAF Mutants
No statistically significant differences with respect to gender, Breslow thickness, melanoma ulceration, sun exposure score, primary melanoma site, histological tumour subtype, or AJCC staging at diagnosis were identified across BRAF, NRAS and WT cases (table 4.1). However, patients with BRAF mutant
69 Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
melanoma were more likely to have primary sites associated with non-sun damaged skin (51.8% vs. 28.2%, p=0.0266) and be younger in age (60.4 vs. 70.0 yrs., p=0.0178), compared to their non-BRAF mutant counterparts.
70 Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
Table 12
Characteristic All Cases BRAF NRAS BRAF/NRAS p-Value Mutants Mutants wildtype Gender Male 130 (67.7 %) 28 27 75 0.4865a Female 62 (32.3 %) 19 13 31 0.4204b 0.1752c 0.9747d Age at diagnosis (yrs.) Median (range) 67.9 (23-94) 60.4 (23-94) 66.4 (28-85) 74.1 (31-91) 0.0617a 0.2727b 0.0178c 0.6925d Breslow Thickness (mm) (n=117) Median (range) 3.5 (0-33) 3.5 (0.6-28) 3.6 (0-15) 3.4 (0-33) 0.9812a 0.9811b 0.9926c 0.8330d Ulceration Present 51 (26.5 %) 11 8 32 0.5905a Absent 24 (12.5%) 3 5 16 0.3313b N.A. 117 (61.0 %) 23 27 0.3333c 0.5874d Solar Elastosis < 2 (non-sun damaged) 38 (19.8 %) 14 6 18 0.0742a ≥ 2 (sun damaged) 74 (38.5%) 13 12 49 0.2178b N.A. 80 (42.2%) 20 22 39 0.0266c 0.9535d Sun Exposure < 3 (Intermittent exposure) 71 (37.0%) 21 10 40 0.2840a ≥ 3 (Chronic exposure) 73 (38.0%) 15 16 43 0.1253b N.A. 48 (25.0%) 11 14 23 0.2102c 0.2199d Tumour Site Head and Neck 35 (18.2%) 8 7 20 0.3331a Trunk 42 (21.9%) 12 5 25 0.3680b Upper Limb 26 (13.5%) 4 5 18 0.7959c Lower Limb 38 (19.8%) 10 9 19 0.2222d Glabrous Skin 5 (2.6%) 2 0 3 Unknown 1° 46 (24.0% 11 14 19 Histological Type ALM 3 (1.6%) 1 0 2 0.2736a DM 4 (2.1%) 0 0 4 0.3845b LMM 7 (3.6%) 0 2 5 0.1956c NM 37 (19.3%) 8 6 23 0.4968d SSM 15 (7.8%) 3 3 9 Unknown/Other 126 (65.6%) 47 29 63 AJCC Staging at diagnosis I 21 (10.9%) 2 4 13 0.4470a II 64 (33.3%) 15 10 39 0.3823b III 53 (27.6%) 17 11 26 0.4098c IV 25 (13.0%) 6 3 16 0.5023d N.A. 29 (15.1%) 6 11 12 a: Testing across all 3 genotypes, b: BRAF vs. NRAS, c: BRAF vs. BRAF wildtype, d: NRAS vs. NRAS wildtype
Table 4.1 - Correlation between clinicopathological characteristics and mutational status.
71 Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
4.3.4 Survival Analysis
Median follow up was 160 weeks, during which time metastatic disease was detected in 171 cases (89.1%). A total of 108 patient deaths occurred within this time, 88 deaths were attributable to metastatic melanoma, generating a melanoma specific mortality rate of 45.8%. The outcome in 3 cases was unaccounted for in either the Hunter New England electronic patients records or within the New South Wales Registry of Birth’s Deaths and Marriages. These cases were considered as lost to follow-up and censored to the time of last review.
Analysis of all patients regardless of AJCC staging status reveals the survival rate from the time of initial diagnosis is considerably lower for patients with BRAF mutant melanomas (41 at risk, 25 deaths, median survival 164 weeks), than patients with NRAS mutant melanoma (33 at risk, 12 deaths, median survival 1194 weeks, p-value 0.0057) (figure 4.1A). The impaired survival of patients with BRAF mutations is also seen in comparison to WT melanoma but does not reach statistical significance (WT melanoma; 97 at risk, 41 deaths, median survival 300 weeks, p-value 0.1034) until BRAF wildtype cases are collectively assessed, in effect comparing BRAF mutants vs. NRAS mutant plus WT cases (BRAF wildtype; 130 cases at risk, 53 deaths, median survival 386 weeks, p-value 0.0180) (figure 4.1A).
Conversely, patients with NRAS mutant melanoma showed a strong trend towards improved survival than WT cases (p-value 0.0816), which became statistically significant when assessed against all NRAS wildtype cases (NRAS wildtype; 138 cases at risk, 66 deaths, p-value 0.0226). This pattern is still apparent when patients with unknown primaries are excluded and the analysis restricted to patients with locoregional disease (AJCC stages I, II and III) (figure 4.1B). A trend for this same difference in survival is seen when the analysis is restricted to AJCC stages I and II, but the result does not reach statistical significance (see appendix table A.6).
72 Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
Figure 4.1. Kaplan-Meier survival curves. (A) Survival curves for all patients regardless of AJCC status (melanoma specific death). (B) Survival curves for patients without distant metastasis (AJCC stages I, II and III, melanoma specific death).
BRAF vs. NRAS p:0.006 BRAF vs. BRAF wt p:0.029 NRAS vs. NRAS wt p:0.013
BRAF vs. NRAS p:0.029 BRAF vs. BRAF wt p:0.064 NRAS vs. NRAS wt p:0.082
Figure 0-16
73 Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
Figure 0-1774 74 Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
To further define the differences in disease progression across the three-genotype groups, we measured the time taken for primary disease to metastasise to locoregional sites (figure 4.2A). No differences were observed between BRAF mutant vs. BRAF wildtype cases (p-value 0.2614). However, NRAS mutant melanomas metastasise to locoregional sites at a slower rate than all other groups analysed: NRAS mutants 666 weeks vs. BRAF mutants 95 weeks (p-value 0.0203), 146 weeks WT group (p-value 0.0192), 146 weeks NRAS wildtype group (0.0115). Likewise, AJCC stage III NRAS mutant melanoma progresses to stage IV at a slower rate than BRAF mutants (BRAF median time 126 weeks, NRAS median time not reached, p-value 0.0320) (figure 4.2B), but the differences did not reach statistical significance when comparing NRAS mutant vs. WT (p= 0.1297), or NRAS mutant vs. NRAS wildtype groups (p= 0.0716). BRAF mutant melanoma does not show a more rapid stage III to stage IV progression in comparison to WT (p= 0.3971), although when compared to all BRAF wildtype cases a weak relationship is evident (BRAF mutant median time 126 weeks, BRAF wildtype 272 weeks) although it was not statistically significant (p= 0.1581) (figure 4.2B).
The survival curves for time to distant disease reflect the melanoma specific survival curves for each of the three genotype groups (figure 4.2C). NRAS mutant melanomas metastasise to distant sites more slowly than their BRAF mutant counterparts (1175 weeks vs. 221 weeks p=0.0074), WT (278 weeks, p= 0.1141) and NRAS wildtype groups (272 weeks, p= 0.0325). Whereas BRAF mutant melanoma metastasises to distant sites more rapidly than BRAF wildtype (221 weeks vs. 478 weeks, p= 0.0191) and WT cases (278 weeks, p=0.0928). Once the melanoma metastasises to a distant site, there was no survival advantage (figure 4.2D). Well defined melanoma clinicopathological features (Edge et al., 2010) also underwent univariate analysis for all the survival periods discussed above (table 4.1). Four of the five survival periods showed other variables that were also predictive of survival; Age of disease onset was associated with the time to locoregional metastasis (age: ≤60, 482 weeks vs. >61 yrs., 126 weeks. p=0.004); and the presence or absence of ulceration (absence, 67 weeks, present, median time not reached). p=0.043); gender (males, 281 weeks vs. females, 547 weeks. p=0.020); Breslow thickness (≤ 2mm, 821 weeks vs. >2.1mm, 200 weeks. p=0.054); and AJCC staging at diagnosis p<0.0001) (table 4.1).
Multivariate analysis using Cox proportional hazards (table 4.2) revealed that only ulceration is a favourable factor against time to locoregional metastasis. This unexpected result is likely to be influenced by the relatively small number of informative cases (n=52) and so this variable was excluded from further analysis (for complete proportional hazards fit analysis of time to locoregional metastasis see appendix table A1.12). When time to locoregional metastasis is reanalysed, young age and a positive NRAS mutational status are favourable variables in terms of the time to locoregional metastasis. Multivariate analysis of time to distant metastasis shows AJCC staging and the NRAS mutational status are independently significant predictors of time to distant metastasis (from AJCC stages I, II and III). Overall melanoma specific survival shows that between the significant factors identified from univariate analysis, only AJCC staging is significant. However, this analysis is also handicapped by the smaller number of informative cases of Breslow’s thickness (n=116). The removal of this result increases the number of eligible cases for analysis (n=172) and strengthens the statistical association of a positive BRAF 75 Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
mutational status with poorer prognosis (risk ratio 2.01, 95% confidence interval 1.17 – 3.41, p=0.012). The NRAS mutation hazard ratio for overall survival is 0.63, but does not reach statistical significance (95% confidence interval 0.29 – 1.213, p=0.182). Male gender also remains a strong negative influence on overall survival (risk ratio 1.84, p=value 0.018) (table 4.2).
Multivariate analysis also demonstrates that with regards to time to distant metastasis and melanoma specific survival, AJCC staging and tumour genotype were both independently associated with survival, whereas male gender was independently associated with a worse prognosis (table 4.2).
76 Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
Table 13
77 Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
Table 14
Multivariate Analysis Of Statistically Significant SurvivalSignificant Analysis Variables From Multivariate Analysis Of Statistically Table 4.3 4.3 Table
78 Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
4.4 Conclusion
We have identified that a positive NRAS or BRAF mutational status represent, respectively, a strong protective or adverse prognostic factors, for patients with thick, or locoregionally metastatic melanoma. Furthermore, the explanation for this effect appears to be associated with differences in the rate of melanoma spread to regional lymph nodes, and then onto distant sites. However, once patients develop stage IV disease, no difference in survival is apparent across NRAS, BRAF or WT melanomas.
The outcome of trials evaluating the clinical efficacy of BRAF inhibitors in metastatic BRAF mutant melanoma may soon result in treatment guidelines where tumour mutational genotype becomes a key determinant of therapeutic options. If the testing of tumour BRAF/NRAS genotype should then become routine, the differences in tumour progression and subsequently overall survival, between NRAS and BRAF mutant melanomas may also hold important prognostic implications for both the aggressiveness of treatment of locoregional disease, and the follow-up of patients at risk of developing distant metastasis.
An interesting finding was the identification of BRAF mutations at a lower incidence (24.5%) than is commonly reported by others, between 27.8% and 59.2% (Maldonado et al., 2003, Viros et al., 2008, Edlundh-Rose et al., 2006, Omholt et al., 2003) and other Australian studies (Queensland 31.5% (Hacker et al.), Victoria 45% (Liu et al., 2007)). We suspect the lower rate of mutations in our population might be the consequence of BRAF mutations being associated with younger patients and primary disease sites characterised by non-chronically sun-damaged skin. Our cohort characteristics include a median age of 67 yrs. and a high incidence of solar elastosis associated with 66% of primary sites (informative cases), suggesting that the clinicopathological profile of our patient population is less likely to be associated with a BRAF mutation.
Although the nature of a retrospective study leaves open the possibility of differences in interval assessment skewing the detection of disease progression (Chang et al., 2004), this cannot explain the difference in melanoma specific mortality. We are unaware of any other report identifying an impact on melanoma specific survival, from the time of initial diagnosis, for patients with BRAF or NRAS mutated melanoma. Ugerel et al. described a similar impact on patient survival for NRAS and BRAF mutant melanomas, but only when considering survival from time of metastatic disease (Ugerel et al., 2007). Indeed, the vast majority of reports have consistently failed to observe any difference in melanoma specific survival according to BRAF or NRAS mutational status (Maldonado et al., 2003, Omholt et al., 2003, Chang et al., 2004, Shinozaki et al., 2004, Akslen et al., 2005, Edlundh-Rose et al., 2006). A 2011 Victorian study of 249 prospectively recruited patients, identified NRAS mutations as adverse prognostic markers in multivariate analysis of melanoma specific survival (hazard ratio 2.46, p-value 0.06) (Devitt et al., 2011). NRAS mutant melanomas were also associated with a significantly higher mitosis score and tumour thickness.
The most obvious difference between our patient population and these preceding studies is likely to be the high proportion of people of Celtic descent (Higginbotham et al., 1999) in a geographical location characterised by high ambient UV levels (Gies et al., 2004). Alternatively, our selective recruitment of patient’s with advanced disease may generate a cohort of patients with a different melanoma biological 79 Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
profile. Our study cohort exhibits the consequences of chronic UV exposure, as reflected by the unusually high proportion of melanomas arising on chronic sun damaged skin. This has the effect of decreasing the proportion of BRAF mutations detected and increasing the ratio of head and neck melanomas to melanomas of the trunk in our study (table A1.13). In comparing different studies into novel melanoma prognostic markers, the influence of chronic vs. intermittent UV exposure, represented by the solar elastosis status, may be nontrivial.
The status of sun-damage skin associated with the primary melanoma site has been identified as a strong classifier for non-random chromosomal changes (Curtin et al., 2005). This has led to the proposal of the melanoma dual pathway hypothesis. The first pathway represents melanomagenic melanocytes on non- sun damaged skin with an intrinsic susceptibility to intermittent high exposures to UV stress, potential early in life, resulting in BRAF mutations or an intracellular milieu which confers a selective advantage for these mutations. The second pathway is represented by melanocytes resistant to high levels of intermittent sun exposure and/or also capable of adapting to chronic levels of UV exposure. These melanocytes require a much higher mutagenic UV load, resulting in disease that develops in chronically sun-exposed sites and is associated with solar elastosis (Curtin et al., 2005, Hacker et al., 2009). Potentially, within this very different cellular environment, the conditions for BRAF mutations, or BRAF oncogenic signalling, are unfavourable and signalling from mutant NRAS or KIT has a more melanomagenic effect.
The acute exposure of melanocytes to UV radiation results in the generation of DNA damage by photoproducts and reactive oxygen species (Kadekaro et al., 2005, Hauser et al., 2006). In response, melanocytes rely on immediate, melanin-independent adaptations (induction of the nucleotide excision repair pathway, and antioxidant defences), and latent responses (induction of melanin synthesis to protect against subsequent UV exposure), both reactions are mediated by signalling from the melanocortin receptor MC1R (Abdel-Malek et al., 2008).
Hypomorphic alleles in MC1R are well-recognised risk factors for melanoma (Valverde et al., 1996) and are over-represented in Anglo-Celtic populations (Gerstenblith et al., 2007), some reports documenting an increased association with a BRAF mutant genotype and an increased melanoma risk restricted to those melanomas arising in skin without histological evidence of chronic UV damage (Landi et al., 2006, Fargnoli et al., 2008). This has not, however, been replicated in other populations (Thomas et al., Hacker et al., 2009, Scherer et al.). Hypofunctional MC1R variants leave melanocytes more susceptible to UV photoproducts and cellular damage by reactive oxygen species (Hauser et al., 2006). This susceptibility may be more marked in the face of intermittent high UV exposure, where melanin levels are low, or more importantly, when the capacities for DNA repair and defence against reactive oxygen species have not been induced (Gilchrest et al., 1999). Paradoxically, the presence of solar elastosis (histological evidence of chronic sun damaged skin) has been identified with increased survival, independently of the known histological prognostic indicators (Berwick et al., 2005). We identified a similar, non-statistically significant trend in our data, for favourable outcomes in melanomas with chronic sun damage (Non sun
80 Chapter 4 – The Prognostic Importance of NRAS and BRAF Mutations in Melanoma
damaged melanomas: 36 at risk, 18 deaths, median survival 257.4 weeks; sun damaged melanomas: 70 at risk, 20 deaths, median survival not reached, p-value 0.1137).
Numerous mechanisms have been proposed as to why, despite the recognition of UV radiation as the leading environmental risk factor for melanoma, chronic sun damage is seemingly able to generate a less aggressive phenotype. These include an inhibitory effect by vitamin D (Berwick et al., 2005); chronic melanisation and increased DNA repair capacity (Gilchrest et al., 1999); and the existence of ‘genetic’ and ‘environmental’ melanoma variants. The explanation for this phenomenon remains to be understood but the trend for aggressive, non-chronically sun damaged melanomas, and less invasive chronically sun damaged melanomas is well established (Dellavalle and Johnson, 2005). Whatever the mechanism, it may be that chronic sun damage is associated with a NRAS melanoma subtype that has a less aggressive invasive/metastatic phenotype, or one that is more vulnerable to immunological surveillance. Few studies have looked into the interaction of the mutant NRAS epitopes and the immune system. Van Elsas showed patients bearing NRAS mutant melanomas exhibited extended times, when compared to patients with wild-type NRAS, between diagnosis of the primary tumour and the clinical appearance of metastases when receiving immunotherapy (interleukin-2 plus interferon-alpha). This study also illustrated that NRAS mutant melanomas demonstrated a trend (p=0.10) for an improved clinical response to immunotherapy when compared to the wild-type NRAS, with progressive disease being witnessed in 25% vs. 55% of the patients (van Elsas et al., 1997). Other studies have since then confirmed this observed capacity for mutant NRAS being capable of eliciting a cytotoxic medicated T-cell response (Linard et al., 2002, Hunger et al., 2001).
As to whether chronic sun damage per se generates a more indolent melanoma with the NRAS mutant cohort, our cases numbers were too small to answer this question (NRAS non-sun damaged cases, 5 at risk, 3 deaths; NRAS sun damaged cases, 11 at risk, 2 deaths, p-value 0.2416, appendix table A1.14).
In conclusion, we have identified significant differences in the prognosis, with respect to rates of disease recurrence, development of distant metastases, and melanoma specific mortality of patients with melanomas harbouring BRAF or NRAS mutations. As the molecular analysis of melanoma begins to influence therapeutic decisions, as is beginning to be seen with BRAF mutant inhibition (Flaherty et al., 2010), knowledge of BRAF and NRAS mutation status, could contribute to other aspects of this personalised treatment approach, such as prognosis-orientated patient follow-up regimens.
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CHAPTER 5
IMMUNOHISTOCHEMICAL AND MORPHOLOGICAL PROFILING OF KIT, BRAF AND NRAS MUTANT MELANOMAS
Chapter 5 – Immunohistochemical and Morphological Profiling of KIT, BRAF, NRAS Mutant Melanomas
84 Chapter 5 – Immunohistochemical and Morphological Profiling of KIT, BRAF, NRAS Mutant Melanomas
5.1 Introduction
The classification of melanoma began to take its modern form in the 1960’s by the work of Dr. Wallace Clark, who first described the three most common melanoma microscopic growth patterns: superficial spreading, lentigo maligna and nodular melanoma (Clark et al., 1969). In 1973, Dr. Vincent McGovern added the acral type to the Clark classification system (McGovern et al., 1973). Despite these different melanoma microscopic growth patterns, a patient’s prognosis was not predicted by any of these classification types. Instead, prognosis was consistently identified to be the product of the extent of skin invasion, first quantified in the Clark level classification (Clark et al., 1969) and later superseded by the Breslow thickness (Breslow, 1970).
Today, our evolving understanding of the genetic events driving melanomagenesis has provided an opportunity to reconsider the manner in which melanoma is classified.
The recognition of mutually exclusive activating mutations in BRAF, NRAS and less commonly KIT, which act as pivotal oncogenic events driving melanoma growth, provide an opportunity to search for a genotype/phenotype relationship that may underwrite a hitherto unrecognized set of histological features capable of predicting biological behavior. It has become apparent that melanomas harboring these mutations show different patterns of chromosomal rearrangements (Curtin et al., 2006, Curtin et al., 2005), altered gene expression signatures (Johansson et al., 2007, Kannengiesser et al., 2008), differences in anatomical distribution and age of onset (Bauer et al., 2011, Maldonado et al., 2003, Ellerhorst et al., 2011, Devitt et al., 2011). Evidence of the prognostic impact of these mutations is mixed, with some studies showing no difference in prognosis (Akslen et al., 2005, Edlundh-Rose et al., 2006), while others report differences in overall survival (Omholt et al., 2003, Ugurel et al., 2007, Devitt et al., 2011). Moreover, the advent of kinase inhibitors targeting mutant BRAF makes the identification of this melanoma subtype crucial when rendering a diagnosis of melanoma. BRAF mutant melanomas have been shown to exhibit distinct morphological features at the primary site. These melanomas are more likely to arise in non-sun damaged skin, where they also show a characteristic growth pattern with increased rates of epidermal scatter and nest formation (Viros et al., 2008, Broekaert et al.). Cytologically, BRAF mutant melanomas are larger, more round and more pigmented than their non-BRAF mutant counterparts (Broekaert et al., Viros et al., 2008). No such architectural or cytological features have been recognized in NRAS mutant melanomas, although some have described an association with increased Breslow thickness and higher mitotic counts (Devitt et al., 2011).
We have sought to use two complementary approaches with the aim of identifying a genotype/phenotype association. First, the use of whole genome expression profiling to identify differentially expressed proteins between the BRAF, NRAS and KIT mutant melanomas; and second, applying quantitative image analysis with the aim of identifying BRAF, NRAS and KIT mutant specific differences in cellular morphology. The capacity for these two approaches individually, and in combination, to predict the presence of a specific kinase mutation will then be explored. The aim of this approach is to identify possible immunohistochemical and or morphological hallmarks for the presence of KIT, NRAS or BRAF mutations, which could theoretically be used to select which of these genes, ought to be sequenced first.! 85 Chapter 5 – Immunohistochemical and Morphological Profiling of KIT, BRAF, NRAS Mutant Melanomas
5.2 Methods
5.2.1 Patient Samples
The selection of patient samples was outlined in section 3.2.1. Of the 192 samples enrolled in the study, 168 samples contained sufficient material for RNA extraction from formalin fixed paraffin embedded (FFPE) tissue sampled using a sterile disposable 2mm biopsy punch (Miltex, NY, USA). The core was extracted from a FFPE block with the aid of a matching haematoxylin and eosin slide, which was used to select a region that contained at least 90% melanoma material. Of paramount importance in determining case selection for TMA construction was the capacity to retain sufficient diagnostic material in the specimen blocks should there be a future requirement to return to the tumour for further diagnostic work.
5.2.2 RNA Extraction and Quality Control
Tissue cores were de-waxed using two 15 minute washes of xylene at 55°C, followed by rehydration in a series of 100%, 95%, 70% ethanol washes, 15 minutes each at room temperature. RNA was extracted using the High Pure Paraffin kit (Roche) according to manufacture’s protocol. The concentration of extracted RNA was calculated using Quant-iT RiboGreen RNA quantification kit (Invitrogen Life Technologies, CA, USA) as per the manufacturer’s protocol. cDNA was then generated from the first 96 samples using the High Capacity cDNA Reverse Transcription Kit (AB Applied Biosystems, CA, USA) as per manufacture’s protocol. The cDNA quality was assessed using the Taqman® gene expression assay and the Applied Biosystems® Human ACTB endogenous control probe (catalogue number 4326315E). This is a 171 bp amplicon targeting the house keeping gene beta-actin. Standard reaction conditions reactions, as outlined in manufacturer’s protocol, were used. The assessment of the PCR fluorescence threshold cycle (Ct) was used as a marker of quality of the cDNA, and indirectly RNA. Each sample was assessed in duplicates. Samples that failed to generate a Ct value were re-analysed using the Applied Biosystems® Human GADPH endogenous control probe (catalogue number 4356317E). The GAPDH probe generates a smaller and thus more sensitive amplicon 122bp in size. In total 96 out of 98 samples generated qPCR products, with mean Ct value of 38.1. The two failed samples were amongst the most heavily contaminated with melanin pigment.
5.2.3 DASL Expression Arrays
200ng of total RNA from 168 samples were amplified and biotinylated using the Illumina reagents as per manufacturer’s protocol. The resultant biotinylated cRNA was hybridised to Illumina Whole Genome Expression HumanRef-8 V3.0 expression Beadchips, which contain approximately 24,000 transcripts. Expression data underwent normalization (Beadstudio) and differences in relative expression were calculated with respect to a synthetic reference array (all samples averaged). The BeadChips were scanned using a Bead Array Reader (Illumina, CA, USA). A total of 141 out of 168 samples (87.5%) generated high quality signal fluorescence.
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The transcript expression results were cubic spline normalised using BeadStudio 2.0 software (Illumina, CA, USA) and the remaining analyses was performed using GeneSpring GX 11.0. To account for bias or skewing of expression results, all the gene expression profiles and each individual gene were normalized to the median resulting in two-way normalisation. For visualisation of the results, the data was log transformed.
5.2.4 Significance Analysis For Microarrays
The identification of differentially expressed genes between NRAS, BRAF and KIT mutants may provide proteins whose expression pattern theoretically could act as a surrogate biomarker of a particular mutation genotype. With this in mind, we aimed to identify significantly altered genes between three groups of melanoma samples; BRAF vs. BRAF wt; NRAS vs. NRAS wt; and KIT vs. BRAF/NRAS mutant. The bioinformatics tool Significance Analysis of Microarrays (Tusher et al., 2001) (SAM) was specifically designed to generate differential expression data with the use of a less stringent, but more biologically meaningful, multiple test correction. It calculates scores based on the change of gene expression between groups while taking into account the variation of gene expression within each group (i.e. relative difference group d(i)). It compares this result with the change of expression observed if there was a random assortment of the members in each group (i.e. expected relative difference de(i)). This last calculation is performed multiple times to obtain an average score. SAM derives a plot of the relative difference group vs. expected relative difference score and a distance from this plot d(i) = de(i) is measured as delta (Δ) and provides a cut-off at which the number of falsely significant genes are calculated and compared to the genes called significant between the two groups of interest (i.e. the false discovery rate, FDR). For any given Δ, increasing this value reduces the number of falsely identified genes at the expense of generating a smaller list of differentially expressed genes. The Significance Analysis of Microarrays Excel add-in Version 3.0 was used for the analysis. The SAM analysis computational settings used were: Two class unpaired data; minimum fold change of 2.0; Wilcoxon test statistic; log-transformed data and 600 permutations (highest number of permutations possible under available computing power). The Δ value was adjusted to produce the smallest list of genes possible (i.e. the lowest FDR).
5.2.5 Immunohistochemistry
The list of differentially expressed genes generated by SAM for each of the three groups (BRAF vs. BRAF wt, NRAS vs. BRAF wt and KIT vs. BRAF/NRAS wt) was searched for suitable candidate gene products to validate using immunohistochemistry. The selection criteria for choosing candidate proteins consisted of identifying the most prominent gene possible on the differentially expressed list for which an antibody was commercially available where there was published expression data of that antibody clone in melanoma FFPE samples. The last criterion was assessed with the aid of the Swedish human protein atlas database (HPA) (Uhlen et al., 2005). The search for a candidate protein was based on the premise that neither a uniformly highly expressed, nor a totally non-expressed protein would serve as a good discriminatory marker for a mutational genotype. Rather, highly variable protein expression across the
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referenced melanoma samples in the HPA would be more likely to represent a truly differentially expressed protein between the different genotype groups.
The antibody detection system employed was SuperPicturetm 3rd generation IHC detection kit (catalogue number 87-8973) (Invitrogen, CA, USA) as per manufacturer’s protocol. All antibodies required heat- induced epitope retrieval (HIER) and the corresponding antigen retrieval conditions were individually optimized by staining whole sections of various non-tumour tissues with a known high expression pattern for each protein. Three different antigen retrieval phosphate buffered saline (PBS) buffers at pH concentrations of 2.0, 6.0 and 10.0 were trialed during the IHC optimization protocol. These were used in combination with two alternate microwave HIER conditions (98oC for 25 minutes and 110oC for 20 minutes in a glass pressure reactor) both employed a cooling down step by submerging the boiler in cold running water for a further 15 minutes, resulting in a total processing time of approximately 40 minutes.
After determination of optimal antigen retrieval conditions for each antibody, whole sections of representative high-expressing and low-expressing melanoma samples for each targeted protein was used to titrate antibody concentration to identify the most-discriminatory staining possible between the high and low expressing cases. Cases of poor discriminatory resolution with initial attaining conditions (room temperature incubation for 1 hour) were then trialed at 4C with overnight incubation. Antibodies still producing similar staining intensities between high and low expressors were excluded from further analysis. The antibody clones and suppliers were as follows; Sigma Prestige Antibodies® (MO, USA): rabbit polyclonal anti-HYAL4 (HPA 029453), rabbit polyclonal anti-UCHL3 (HPA 019678), rabbit polyclonal anti-C22orf33 (HPA 005998), rabbit polyclonal anti-TBRG4 (HPA 020582), rabbit polyclonal anti-TMX4 (HPA 015752); Santa Cruz (CA, USA): mouse monoclonal anti-MYL9 (SC 28329).
Immunohistochemical staining for KIT and S100 was performed using commercially pre-diluted monocolonal antibodies (Ventana, AZ, USA) on a Ventana automated slide preparation system. KIT staining employed DAB detection, while S100 staining was used on an alkaline Phosphatase Red detection Kit (Ventana, AZ, USA).
All melanoma slides, including TMAs, were bleached of melanin by an overnight (16 hours) incubation with 10% H202 post immunohistochemical staining. This protocol, developed by Lawrence et al. results in removal of melanin with no disruption of tissue architecture or antigen detection (Lawrence Li et al., 1999).
5.2.6 Tissue Microarray Construction
Ten tissue microarray (TMA) blocks were constructed using three 1.5 mm cores of each of the 192 cases. The TMA architecture was designed using the TMALab microarray analysis software (AperioTm, CA, USA), using an 8 x 10 grid array. The TMA was constructed using a tissue arrayer from Beecher Instruments (MD, USA). The first spot in each alternate row contained a non-melanoma tissue sample that served both as a staining internal control and an orientation marker. In the month prior to the immunohistochemical staining, twenty consecutive 6 µM TMA sections were cut onto silanized slides, and stored wrapped in plastic film at 4C until required for staining.
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5.2.7 TMA Image Analysis
5.2.7.1 Image Acquisition
Immunohistochemically stained TMA slides were scanned digitally at 200x magnification using a ScanscopeTm GL digital scanner (Aperio®, CA, USA). The resulting digital images were imported into the image database TMALab (Aperio®, CA, USA).
The scanned images were visualized with Imagescope (Aperio®, CA, USA). Immunohistochemical staining of tumour regions were analyzed digitally using the Aperio Colour Deconvolution image analysis algorithms. The Colour Deconvolution algorithm (CDa) works by separating digital images into up to three colour channels, corresponding to the actual colours of stains used. This technique allows the accurate measurement of a particular stain’s intensity (Ruifrok and Johnston, 2001).
Briefly, the CDa works by controlling a set of input parameters that determine the data that defines the colour of interest. A stain’s colour is defined by its unique combination of red, green and blue components. The final value for the intensity of the stain of interest is a given a score out of 255, with 0 representing very dark staining (i.e. black) and 255 representing a very bright image field, which equates to very little tissue staining. The density of a stain is thus the opposite of its colour signal intensity.
For identification of DAB (3,3’-diaminobenzidine) staining, the software’s nascent image detection settings were employed (image analysis channel #3). Identification of the fast red chromagen (2-naphthol red) used in S100 staining, and melanin (analysed on haematoxylin-only counter stained slides) required algorithm calibration, which was carried out as per manufacture’s instructions.
5.2.7.2 Quantitative Immunohistochemical Analysis
The first step requires the annotation of the TMA spot containing the tumour area of greatest intensity. Most of the spots demonstrated a homogenous, highly dense tumour population. In these cases, a rectangular shape was used to outline the maximum possible area within the circular spot. In cases of great tissue heterogeneity, due to stromal cell contamination or extensive tumour necrosis, a free-hand outline tool was used to select the spot region with the highest tumour density. The annotation for each spot’s IHC stain was done sequentially in an effort to ensure as similar areas as possible were demarcated within all the sections corresponding to each TMA spot.
The immunohistochemical score is obtained by dividing the maximum possible intensity value (i.e. 255) by the average IHC intensity value and multiplying the resulting number by the fraction of the melanoma that was positively stained. This formula generates a hypothetical maximum score out of 255, with a 100% uniformly intensely stain spot scoring 255 (i.e. entirely black). However, because IHC intensity is often much closer to 255 than to 0, the resulting fraction is generally much smaller than 255, and after multiplying by the value of percentage of melanoma stained, the resulting number necessitates a conversion by a factor of 100 to generate results that are easier to compare.
This formula depends on an estimation of the average melanoma tissue content within the core, which is used to normalize the staining results of the experimental antibodies of interest. The protein S100 Ab-1 89 Chapter 5 – Immunohistochemical and Morphological Profiling of KIT, BRAF, NRAS Mutant Melanomas
belongs to a class of calcium binding proteins expressed in a wide variety of neural crest derived cells, as well as antigen presenting cells in the skin and lymph nodes (Thermo_Scientific, 2011). Polyclonal antibodies raised to S100 represent the most sensitive melanoma markers used in routine diagnostic practice. This stain was employed to quantify the density of melanoma tissue within the core sections. The percentage of positive S100 within the core yielded a theoretical maximum melanoma staining percentage within the spot. The strength of S100 staining is inconsequential to the determination of melanoma density. For the rare cases where the percentage of S100 staining was low, but other markers showed diffuse staining of one of the experimental antibodies, then the more diffusely staining antibody was used to estimate melanoma density. No case showed pan-loss of antigenicity, indicating adequate antigen preservation. A detailed description of the calculation of immunohistochemical scoring is outline in appendix A.1.
5.2.7.3 Quantitative Cytomorphology Analysis
To quantify melanoma cell morphology, haematoxylin and eosin (H&E) stained TMA sections were used to trace the cellular and nuclear contours of thirty melanoma cells per case (normally ten cells per core), with the free-hand annotation tools in Imagescope. The cells chosen for measurement were on the basis of being: (1) as representative as possible of the tumour histomorphology; (2) demonstrate defined cell outlines. The cores were scored randomly throughout each TMA block until the entire TMA block was completed.
From these measurements, Imagescope derives values for nuclear and cellular perimeter length plus nuclear and cellular area. In addition, the length of the long and short axis of each cell was also measured. These values, termed primary values, are used to derive a number of other cytomorphology measurements termed secondary values. These are outlined in table 5.1. Mean and median values were calculated for each of the measurements outlined in table 5.16 and 5.17.
Table 5.1 Quantitative cellular cytomorphology formulae Value Measurement Formula Reference Primary Cell Perimeter Automatically derived from cell tracing Cell Area Automatically derived from cell tracing Cell Length Longest linear measurement along the cellular axis Cell Width Longest linear measurement taken perpendicular to (maximum) Cell Length Secondary !"##!!"#$%ℎ (Viros et al., Cell Shape !"##!!"#$ℎ 2008) Cell Cross Sectional Area !"##!!"#$
(average cell !"##!!"#$%ℎ width) (Toyoda and Cell Regularity Morohashi, 1998) (Edgar and Cell Roundness Bennett, 1999) Table 15
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Table 5.2 Quantitative nuclear cytomorphology formulae Value Measurement Formula Reference Primary Automatically derived from cell Nuclear Perimeter tracing Automatically derived from cell Nuclear Area tracing Secondary (Edgar and Bennett, Nuclear Roundness 1999) Nuclei to Cytoplasm !"#$%&'!!"#$
ratio !"#$%&'()!!"#$ Table 16
Measurement of Regularity: This provides a measure to the degree which cells have smooth and irregular borders, where a smooth border has a value of 1 and trends towards 0 as the outline becomes less regular.
Measurement of Roundness: Defined perfectly round cells as having values of 1, while non-circular cells generates cells with values greater than 1.
5.2.7.4 Quantification of Pleomorphism
A hallmark of melanoma histomorphology is the extensive cellular and nuclear pleomorphism seen in most cases. An objective measure of size (cellular and nuclear) and shape (cellular) pleomorphism can be obtained by deriving the coefficient of variation of cell size (Cornelisse et al., 1981). The coefficient of variation is a dimensionless number that represents a normalized measure of the distribution of a given measurement (11).
!"#$%#&%!!"#$%&$'( !"#$$%&%#'(!!"!!"#$"%$&' = (11) !"#$
This normalized value is a better marker of pleomorphism than standard deviation alone as it corrects for the possible under or over calculation of pleomorphism in small and large cells, respectively.
5.2.7.5 Assessment of Nucleoli Prominence
The prominence of nucleoli size was determined using H&E stained TMA sections. Each case was assessed three times by scoring each of its spots in their corresponding TMA slide. The spots were assessed randomly across the entire TMA until all the spots were completely assessed. In this manner, each case was assessed independently on three separate occasions. The TMA spots were visualized within the TMALab database, using the Imagescope viewer.
A four point scale, based on a modification of the Fuhrman system for renal cell carcinoma nuclear grading (Fuhrman et al., 1982) was used to assess nucleoli size (table 5.3)
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Table 5.3 Nucleoli Scoring Criteria Nuclei Score Histological Features 400x H&E 0 Nucleoli not visible at 200x magnification
1 Nucleoli appear as small chromocenters-like structures, requiring 200x magnification for confirmation
2 Small but readily identifiable nucleoli at 100x magnification on most cells, or appearing as large eosinophilic macronucleoli in a minority of cells
3 Large eosinophilic, occasionally multiple macronucleoli readily identifiable at 100x magnification in a majority of cells
Table 17
The final nucleoli score was taken as the modal value for the three measurements. In cases were the modal value could not be calculated (i.e. there was a discrepancy between all three cores, or there was a discrepancy between the only two cores available), the scanned image of the full H&E section obtained at the time of the original tissue diagnosis was also reviewed using Imagescope, and a final value was then determined.
5.2.8 Statistical Analysis
For nuclei score intra-observer reproducibility, the first two scores recorded from each case were used for the calculation. A three score intra-observer reproducibility was not assessed for two principal reasons; (1) a small number of cases contained only two cores for assessment; (2) statistical software capable of handling a three observations was not available. Calculation of the reproducibility of the nuclei score classification was derived using a quadratic weighted intra-agreement statistic (Kappa) within the ordinal- scaled results (MedCalc Software, Belgium). The weighted kappa statistic is the appropriate analytical tool for nucleoli scoring because of the ordinal nature of this scoring system. In contrast, the more commonly employed non-weighted Cohen’s kappa measure of concordance is required for nominal classifications (Viera and Garrett, 2005). The kappa values were interpreted as per Altman et al.: <0.20, poor; 0.21-0.40, fair; 0.41-0.60, moderate; 0.61-0.80, good; 0.81-1.0, very good (Altman, 1991).
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Correlation of between the DASL gene expression values and corresponding immunohistochemical (IHC) score was calculated using the Spearman’s rank correlation coefficient as it represents a nonparametric measure of statistical dependence between two variables. The statistical interdependence between cytomorphological measurements was also tested under the Spearman’s rank correlation coefficient.
Two-tailed Wilcoxon signed-ranked T test was used to analyze differences in IHC scores across the mutant BRAF, NRAS and KIT genotype groups.
Prediction of mutation genotype on cytomorphology and IHC scoring was made using a single binary classification tree classifier with Kfold cross-validation. Kfold validation works by dividing the original data into K subsets. In turn, each of the K sets is used to validate the model fit for the rest of the data, with the model producing the best validation statistic becoming the final model. All classification trees were generated using 5-fold cross-validation. The classifier tree discrimination accuracy was obtained by plotting the receiver operating characteristic (ROC) curve and calculating the area under the ROC curve (AUC). The AUC value is a common method of comparing the discriminatory ability of different classifier models. This area is a measure of the probability the classifier has of correctly classifying two randomly drawn pairs into their respective groups. AUC values were ranked as excellent (AUC≥0.90), good (AUC≥0.80 and <0.90), fair (AUC≥0.70 and <0.80) and poor (<0.70) (Muller et al., 2010).
In determining whether parametric or nonparametric statistical analysis of the cytomorphological data was appropriate, the data distribution was assessed by normal quantile plot analysis. Data plots that do not follow the diagonal or fail to remain within the Lilliefors confidence bounds where considered to not be normally distributed (figures 5.15 and 5.16).
5.3 Results
5.3.1 Gene Expression Analysis
5.3.1.1 SAM Analysis Of DASL Results
The gene expression data produced by the Illumina Whole Genome Expression HumanRef-8 V3.0 was imported into SAM for analysis of differentially expressed genes between kinase mutant genotype groups BRAF vs. BRAF wildtype, NRAS vs. NRAS wildtype and KIT vs. NRAS/BRAF mutants.
BRAF and NRAS mutant cases were selected for comparison against KIT because these represent well- defined genotype groups, which would not be suspected to harbor unrecognized KIT mutant melanomas. In contrast, wildtype cases were not chosen because of the possibility of an unknown number of KIT mutant cases being missed through either failure of complete Sanger sequencing of KIT exon 9, 11, 13 and 17 (n=30) or through mutations outside KIT exons 9, 11, 13 and 17, which are known to occur (Akin et al., 2004, Ozer et al., 2008). The gene list for KIT, BRAF and NRAS are listed in table’s 5.4, 5.5, 5.6, 5.7, 5.8 and 5.9, in descending order of statistical significance. KIT analysis generated 1052 significant genes with a FDR 1.62 and a list of 92 down regulated genes with a FDR 11.66. The SAM analysis for BRAF mutants generated 66 genes down regulated at an FDR of 0.0% and four genes overexpressed at an
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FDR of 0.0%. NRAS mutant SAM analysis produced 19 over expressed genes with a FDR of 3.94% and 12 genes down regulated at FDR of 8.27%.
Figure 5.1 SAM plot of KIT mutant vs. NRAS/BRAF wildtype mutants. Average expression of 24,529 transcripts. The central line represents equal expression, the outer two lines indicate altered expression (Δ=0.57, FDR 1.6%). Genes in red are up regulated in KIT mutant melanomas compared to NRAS/BRAF mutant melanomas Figure 0-18 Table 18
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Figure 5.2 SAM plot of BRAF mutant vs. BRAF wildtype mutants. Average expression of 24,529 transcripts. The central line represents equal expression and the outer two lines indicate altered expression (Δ =0.20, FDR 0.0). Genes in red are up regulated in BRAF mutant melanomas compared to BRAF wildtype melanomas Figure 0-19 Table 19
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Figure 5.3 SAM plot of NRAS mutant vs. NRAS wildtype mutants. Average expression of 24,529 transcripts. The central line represents equal expression and the outer two lines indicate altered expression (Δ =0.37, FDR 3.95). Genes in red are up regulated in NRAS mutant melanomas compared to NRAS wildtype melanomas. Figure 0-20. Table 20
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5.3.1.2 Candidate Proteins
The genes selected for immunohistochemical confirmation were chosen primarily based on high fold change value and antibody availability. The presence of an annotated expression pattern in melanoma tissue documented in HPA was also taken in to consideration. The genes chosen for KIT, BRAF and NRAS mutant melanomas are outlined in table 5.10
5.3.2 Immunohistochemical Analysis
5.3.2.1 Immunohistochemistry Optimization
Each of the antibodies chosen was tested under different antigen retrieval methods (see 5.2.5) using the manufacturer’s recommended antibody concentration. The antibodies and their corresponding optimized antigen retrieval conditions are outlined in table 5.11. Once the optimal antigen retrieval was determined, high and low expressing melanoma samples for each of the antibodies were used to titrate the optimal antibody concentration to maximise discriminatory staining. The antibody against MYL9 yielded good discriminatory results at the antibody concentration and incubation times similar to the manufacturer’s recommended conditions. Anti-CEACAM3, anti-TMX4 and anti-TBRG4 also produced good discriminatory staining but at lower antibody concentrations. All antibodies generated better staining results with the use of lower than normal antibody concentration and longer, colder incubation conditions. The nature of this type of staining protocol allows sufficient time for antigen saturation with the antibody, while the lower titer minimizes the degree of non-specific staining. The antibodies against UCHL3, C22orf33 and HYAL4 did not generate a differential staining pattern between low and high expressing cases under a wide range of incubation and antibody conditions (data not shown). Having failed this optimization, none of these antibodies were used for the remainder of the study.
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Table 21
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Table 22
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Table 23
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Table 24
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5.3.2.2 Immunohistochemical Staining Scores
5.3.2.2.1 MYL9 Staining Results
There was a large range of MYL9 staining scores, with a median score of 77 (range 2.6 to 156.6). The staining pattern was diffusely cytoplasmic within the melanoma cells. The tumour vasculature as an internal positive control was noted to stain strongly (table 5.12). A Spearman rank correlation analysis between the DASL expression value and the MYL9 IHC score revealed no statistically significant correlation (Spearman rank ρ=0.0293, p-value 0. 7328).
5.3.2.2.2 CEACAM3 Staining Results
There was a marked positively skewed distribution in CEACAM IHC scores, with a median value of 21.8 (range 0.5 to 164) and a mean of 36.1(SD 36.2). Most of the melanoma specimens showed weak diffuse cytoplasmic staining (table 5.12). Their strong cytoplasmic staining highlighted the presence of neutrophils and macrophages. The Spearman correlation of DASL and IHC scores reveal no statistically significant correlation (Spearman rank ρ=0.0245, p-value 0.7751).
5.3.2.2.3 KIT Staining Results
Most melanomas did not express significant levels of KIT (median IHC score 5.78). There was blush of cytoplasmic staining evident at around the 75th quantile of scores and strong membranous staining in the 90th quantile and above (figure 5.6). There was a strong and statistically significant correlation between the DASL and IHC scores (Spearman rank ρ=0.4718, p-value <0.0001).
5.3.2.2.4 TMX4 Staining Results
TMX4 expression was detected in most melanoma samples. Staining was diffuse and cytoplasmic, becoming frequently accentuated in a paranuclear distribution at higher IHC scores (figure 5.7). This is consistent with TMX4 known ultrastructural location as a transmembrane protein within the endoplasmic reticulum. Correlation analysis confirms a statistically significant correlation between DASL and IHC scores (Spearman rank ρ=0.2332, p-value 0.0059).
5.3.2.2.5 TBRG4 Staining Results
Most melanoma samples showed a minimal cytoplasmic blush of TBRG4 staining. At higher IHC scores, there was a strong paranuclear staining accentuation, consistent with this antibodies known immunofluorescence staining pattern (figure 5.8) (Human_Protein_Atlas). No correlation was identified between the DASL and IHC scores (Spearman rank ρ=0.0444, p-value 0.6049).
5.3.2.2.6 S100 Staining Results
S100 staining was characteristically strong, uniform and diffuse (figure 5.9). A good correlation between DASL and IHC values was identified (Spearman rank ρ=0.4803, p-value <0.0001).
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5.3.2.2.7 Melanin Assessment
Significant melanin production was only apparent in a small minority of cases. A moderate proportion of cases (from around the 75th quantile) show fine cytoplasmic staining which is apparent with haematoxylin only counter staining (figure 5.10).
Figure 0-21
Figure 5.4 Distribution of MYL9 scores (A), with representative image of score around the 25th, 50th and 75th quantile (B) (corresponding IHC score within grey inset). Note the strong the strong internal control staining within the vessel in the bottom left hand panel of figure B.
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Figure 0-22
Figure 5.5 Distribution of CEACAM3 scores (A), with representative image of score around the 25th, 50th and 75th quantiles (B) (corresponding IHC score within grey inset). Strong staining of inflammatory cells can be seen within the low CEACAM3 expressing melanoma tissue (lower left hand panel IHC score 8.2)
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Figure 0-23
Figure 5.6 Distribution of KIT scores (A), with representative image of score around the 25th, 75h and 90th quantile (B) (corresponding IHC score within grey inset). Strong membranous staining is evident in melanomas with high staining intensities.
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Figure 0-24
Figure 5.7 Distribution of TMX4 scores (A), with representative image of score around the 25th, 50th and 75th quantile (B) (corresponding IHC score within grey inset). Staining is most commonly diffusely and cytoplasmic with some paranuclear accentuation.
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Figure 0-25
Figure 5.8 Distribution of TBRG4 scores (A), with representative image of score around the 25th, 50th and 75th quantile (B) (corresponding IHC score within grey inset). There is paranuclear accentuation of staining consistent with the protein’s function as an endoplasmic reticulum transmembrane protein.
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Figure 0-26
Figure 5.9 Distribution of S100 scores (A), with representative image of score around the 25th, 50th and 75th quantile (B) (corresponding IHC score within grey inset). Staining was commonly strong and diffuse.
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Figure 0-27
Figure 5.10 Distribution of melanin scores (A), with representative image of score around the 25th, 50th and 75th quantile (B) (corresponding IHC score within grey inset). Fine cytoplasmic staining is apparent in the two upper panels (melanin scores 4.2 and 4.6).
!
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5.3.2.3 Association between Kinase Genotype and Immunohistochemical Result
A comparison of the IHC scores between KIT mutant vs. BRAF/NRAS mutant groups shows, uniformly, trends for a higher level of expression in KIT mutants for MYL9 (92.1 vs. 65.3, p-value 0.100), CEACAM3 (51.7 vs. 39.1, p-value 0.1690) and KIT (81.42 vs. 25.0, p-value 0.1179). No such trend were seen for TBRG4 (46.0 vs. 49.5, p-value 0.8205), TMX4 (22.4 vs. 28.55, p-value 0.2750) or S100 (130.7 vs. 150.0, p-value 0.7742) (table 5.).The mean melanin score was also higher in KIT mutants (25.5 vs. 6.5) but there was a large overlap of scores (p-value 0.3365).
BRAF mutants showed a marginally higher expression of TMX4 than BRAF wildtype cases (53.2 vs. 43.5) but did not reach statistically significance (p-value 0.1193). No significant differences were identified in the expression of MYL9 (64.74 vs. 74.5, p-value 0.1435), KIT (27.7 vs. 27.8, p-value 0.8210), CEACAM3 (33.6 vs. 36.9, p-value 0.9618), TBGR4 30.2 vs. 24.0, p-value 0.2537) or S100 (149.1 vs. 150.2, p-value 0.7100). No differences were found within the level of melanin production (5.6 vs. 9.6, p-value 0.5783).
NRAS mutant melanomas showed a minimally increased expression of TBGR4 (28.2 vs. 24.7, p-value 0.1323). No significant differences were observed for any of the other proteins stained (MYL9 69.7 vs. 72.6. p-value 0.7558; CEACAM3 45.2 vs. 33.2, p-value 0.3657; KIT 20.5 vs. 29.8, p-value 0.1705; TXM4 47.2 vs. 45.6, p-value 0.8554; S100 152.4 vs. 149.2). NRAS melanoma showed minimal melanin production, but this difference did not reach statistically significance (3.9 vs. 10.9, p-value 0.2303).
5.3.2.4 Immunohistochemistry As A Predictor Of Kinase Mutational Genotype
Recursive partition analysis with 5 -fold cross validation analysis was used to identify the best combination of predictors of KIT mutation status using MYL9, CEACAM3 and KIT IHC scores. The results summarized in figure 5.11 shows the three split tree displays a combination of KIT, MYL9 and CECAM IHC scoring that increases the probability of identifying a KIT mutant melanoma from 4.17% to 27.7% (MYL9 IHC score >139.1) and 34% (MYL9 IHC score <139.1 + KIT IHC score >182.1). The resulting ROC AUC value is within the ‘good’ range. The structure of this classifier is different if all BRAF and NRAS mutant cases are excluded from the analysis, but the resulting ROC AUC value of 0.8638 remain comparable.
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Figure 5.11 KIT mutant prediction based on IHC scores. MYL9 staining is the most sensitive marker for the presence of a KIT mutant melanoma, with scores over 139 enriching the probability of identifying a KIT mutant over five fold. The discriminatory efficiency of this model is associated with a ROC AUC of 0.8775.
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Figure 5.12 KIT mutant prediction based on IHC scores, excluding BRAF and NRAS mutant cases. CEACAM becomes the most discriminatory marker followed by MYL9. The discriminatory efficiency of this model is associated with a ROC AUC of 0.8296. Figure 0-28
The presence of TMX4 IHC score over 79.5 was associated with a probability of identifying a BRAF mutant melanoma of 41.9%, representing a 1.7-fold enrichment. The ROC AUC values are within the poor discriminatory range (AUC 0.5818).
Figure 0-29
Figure 5.13 TMX4 IHC score as a predictor BRAF mutational status. The resulting classification tree has a ROC AUC 0.5818.
TBGR4 IHC scoring performed poorly as sole predictors for the presence of an NRAS mutational genotype, with a score above 13.2 enriching NRAS cases 1.3 fold, corresponding
Figure 5.14 TBRG4 IHC scoring NRAS classification tree. The ROC AUC is 0.6120.
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5.3.3 Cytomorphology Results
5.3.3.1 Nucleoli Analysis
An assessment of the intra-observer reproducibility nuclei scores for each of the 168 cases is summarized in table 5.14.
Table 5.14 Intraobserver Reproducibility Of Nucleoli Scoring
Nucleoli observation #2 0 1 2 3 Nucleoli 0 5 5 0 0 observation 1 4 33 6 0 #1 2 1 4 52 9 3 0 2 7 40 Table 25
It is apparent that the proportion of agreement (defined as the number of scoring agreements divided by total number of intervals) improves as the extremes of nucleoli scores (table 5.15).
Table 5.15 Nucleoli Scoring Proportion Of Agreement (Observed Vs. Expected). . Proportion of agreement Proportion of agreement Observed By Chance 0 0.3333 0.0307 1 0.6111 0.1487 2 0.6582 0.2421 3 0.6897 0.1707 Table 26
The scoring system is most reproducible the extremes of the scale with high proportion of agreement for nuclei scores 1 and 3 at rates four times the expected agreement. For this scoring system, the quadratic weighted kappa coefficient is measured at 0.821, which is within the range of very good intra-observer reproducibility (Altman, 1991).
5.3.3.2 Quantitative Cytomorphological Analysis
Normal Quantile plot analysis of the distribution in cytomorphology data shows that only nuclear to cytoplasm ratio and cross sectional area (average cellular width) follow a normal distribution (figures 5.15 and 5.16).
Visual inspection of representative images of 25th, 50th and 75th quantile values for all cytomorphology variables shows clear qualitative differences in the appearance of melanoma cells with respect to the corresponding variable (figures 5.17-5.22).
The measurements of cell size and length showed a broad range of positively skewed values, and as would be expected, both values are strongly correlated (Spearman rank ρ=0.6480, p-value <0.0001). Large cells were also more pleomorphic (Spearman rank ρ=0.4632, p-value <0.0001) and were associated
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with lower nuclear to cytoplasm ratios (Spearman rank ρ=-0.2410, p-value <0.0010) despite being associated with greater nuclear sizes (Spearman rank ρ=0.8950, p-value <0.0001). All statistically significant two variable correlations are listed in appendix table A1.15.
Cell shape values were also highly-positively skewed, with values above the 75th percentile essentially reflecting long cellular lengths. Large cell values (i.e. very elongate forms) were also positively correlated to cell roundness parameter (i.e. less round shapes; Spearman rank ρ=0.8953, p-value <0.0001) and negatively associated with cell regularity factor (Spearman rank ρ=-0.7411, p-value <0.0001).
Cell roundness and cell regularity values were inversely correlated, reflecting that less round cells represent less regular cellular shapes (Spearman rank ρ=-0.7363, p-value <0.0001). The cellular shape (pleomorphism) was also significantly correlated with lower cellular regularity scores (i.e. less regular cells; Spearman rank ρ=-0.4843, p-value <0.0001) and high cell roundness factors scores (i.e. less rounder cells; Spearman rank ρ=0.6707, p-value <0.0001).
Nuclear size was positively correlated with nuclear size pleomorphism (Spearman rank ρ=0.3173, p-value <0.0001) and large cellular size (Spearman rank ρ=0.8950, p-value <0.0001).
Table 5.16 Summary of cellular cytomorphology measurements Cellular Variable Median (range) Mean (SD Dev.) Cell Length ( M) 23.0 (9.9 - 70.4) 25.8 (10.1) Cell Size ( M2) 206.3 (57.0 - 230.8 (109.1) 677.3) Coefficient of Variance for Cell Size 33.6 (13.6 - 35.5 (12.6) 104.1) Cell Shape 1.75 (1.21-9.09) 2.45 (1.64) Coefficient of Variance of Cell Shape 34.3 (12.5 - 79.0) 35.3 (13.3) Cell Cross Sectional Area ( M) 9.12 (3.36 - 9.18 (2.90) 18.47) Cell Regularity Factor 0.93 (0.70 - 1.10) 0.92 (0.07) Cell Roundness factor 1.55 (1.17 - 6.30) 1.98 (1.01) Table 27
Table 5.17 Summary of nuclear cytomorphology measurements Nuclear Variable Median (range) Mean (SD Dev.) Nuclear Size ( M 2) 70.7 (15.5 - 192.5) 74.7 (32.0) Coefficient of Variance of Nuclear Volume 37.4 (19.9 - 85.7) 39.5 (11.6) Nuclear Roundness Factor 1.50 (1.14 - 2.76) 1.56 (0.28) Nucleus to Cytoplasm ratio 0.34 (0.14 - 0.52) 0.34 (0.054) Table 28
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Figure 0-30
. Lilliefors confidence bounds confidence Lilliefors cell crosssectional area.If the variable isnormally distributed,
Figure5.15 Normal quantileplot ofcell length, cellsize, cellsize coefficientof variance, cellshape, cell shapecoefficient of variance and Only line. straight diagonal approximates a plot quantile normal the cell crosssectional area data distributionapproximates a straightline the within
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Figure 0-31 If the
Figure5.16 Normal quantileplot ofcell regularityfactor, cell roundness factor, nuclearsize, nuclear coefficientsize ofvariance, roundnessnuclear factor,nuclear cytoplasmto ratio. line straight diagonal a approximates plot quantile normal the distributed, normally is variable to data only the is ratio cytoplasm to nuclear Thebounds. confidence Lilliefors the within clearly demonstratea normal distribution.
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Figure 5.17 Distribution of morphology score for median cell size and coefficient of variance of cell size (magnification x200). Figure 0-32 117 Chapter 5 – Immunohistochemical and Morphological Profiling of KIT, BRAF, NRAS Mutant Melanomas
Figure 5.18 Distribution of morphology score for median cell size and width (cross sectional area) (magnification x200). Figure 0-33
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Figure 5.19 Distribution of morphology score for median cell shape and coefficient of variance for cell shape (magnification x200). Figure 0-34
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Figure 5.20 Distribution of morphology score for median cell regularity and roundness (magnification x200). Figure 0-35 120 Chapter 5 – Immunohistochemical and Morphological Profiling of KIT, BRAF, NRAS Mutant Melanomas
Figure 5.21 Distribution of morphology score for median nuclear size and coefficient of variation for nuclear size (magnification x400). Figure 0-36
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Figure 5.22 Distribution of median nuclear roundness factor and median nucleus to cytoplasm ratio (magnification x400). Figure 0-37
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5.3.3.3 Association between Kinase Genotype and Cytomorphology
KIT mutant melanomas did not display any statistically significant unique cytomorphological pattern in comparison with KIT wildtype melanomas. A trend for a pattern of more spindled, longer cell lengths with thin cellular widths and smaller cellular volumes could be interpreted from the data, with the qualification that 8 mutant melanoma cases is an underpowered comparison. Interestingly, despite the slightly more elongate cellular shape the nuclei roundness factor (KIT mutant 1.54 vs. KIT wildtype 1.57, p-value 0.8579) does not suggest these cases show a more spindled nuclear shape. To be also noted, all the KIT mutant melanomas displayed high nucleoli scores (nucleoli scores 2 and 3).
The only statistically significant difference between the KIT mutant and wildtype groups was a lower degree of nuclear pleomorphism as measured by the nuclear size coefficient of variance (30.7 vs. 39.9, p- value 0.0218). The melanin content appeared to be higher in KIT melanomas (25.5 vs. 8.6) but it was not statistically significant (p-value 0.4493). Recursive partition analyses (classification trees) highlighted high nucleoli scores ( 2), in combination with high degrees of cellular pleomorphism (cellular shape coefficient of variance >53.9), results in a fifteen case group with a 25.3% incidence of KIT mutations (figure 5.) and a corresponding ROC AUC of 0.7943 (borderline fair/good discrimination).
BRAF mutant melanomas showed on average no difference in cellular size (254.9 µM2 vs. 222.5 µM2, p- value 0.1669), but were associated with a statistically significant difference in cell shape (i.e. less elongate; 2.15 vs. 2.55, p-value 0.0218), lower cell roundness factor scores (i.e. more round; 1.8 vs. 2.05, p-value 0.0241) and greater average cell width (10.1 vs. 8.9, p-value 0.0259). There was a trend for a lower degree of cellular pleomorphism (32.9 vs. 36.2, p-value 0.0852). These cellular features were associated with lower nuclear roundness factor scores (i.e. more round; 1.47 vs. 1.60, p-value 0.0105) and lower nuclear to cytoplasm ratios (0.33 vs. 0.34, p-value 0.0349), despite no significant differences in nuclear size (79.9 vs. 73.0 p-value 0.3632). No significant difference was seen in the distribution of nucleoli scores (p-value 0.2193) or in the melanin content (8.56 vs. 9.65, p-value 0.5783). The melanin score was skewed by a single outlier that if excluded results in half the original melanin score (4.01 vs. 9.65, p-value 0.4275). Classifier tree analysis confirms the association of cellular roundness with a BRAF mutation positive status, with cases scoring less than 1.4 (i.e. rounder shapes) showing a 40% incidence of BRAF mutations. Amongst cases with higher cell roundness factor scores, the presence of a high nuclear to cytoplasm ratio (>0.3) decreases the likelihood of identifying a BRAF mutation to 8.6%. The corresponding ROC AUC is 0.7044 (within the fair discriminatory range).
NRAS mutant melanoma failed to show any specific cytomorphological pattern across all cellular and nuclear cytomorphology variables. Melanin was infrequently detected in comparison with NRAS wildtype melanomas (3.8 vs. 10.9) but this difference did not reach statistical significance (p-value 0.2303). This result is reflected in a classification tree that despite four splits only maximally increases the likelihood of identifying a NRAS mutant melanoma 1.26 fold (ROC AUC 0.6231).
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Table 29
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Table 30
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Figure 5.23 KIT mutant melanoma specific cytomorphology decision tree analysis identifies a combination of high nucleoli scores and high cellular shape pleomorphism as the most sensitive combination for the detection of KIT mutant melanomas. ROC AUC: 0.7943.
Figure 5.24 BRAF mutant melanoma specific cytomorphology decision tree analysis identifies a combination of low cellular roundness factor and low cell regularity factor to increase the likely hood of identifying a BRAF mutant melanoma 2.7 fold. Cases with high cell roundness factor scores and high nucleus to cytoplasm ratios (>0.33) are associated the lowest likelihood of identifying a BRAF mutant melanoma (8.6%. The whole model ROC AUC is 0.7044. Figure 0-38
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Figure 0-39
Figure 5. NRAS mutant melanoma specific cytomorphology decision tree analysis fails to identify a combination of cytomorphological features that significantly enrich the fraction of NRAS mutant melanomas. Despite four consecutive splits, the ROC AUC remains low at 0.6231. !
5.3.4 Analysis of Immunohistochemistry and Cytomorphology to Predict Kinase Genotype
The inclusion of MYL9, CEACAM3 and KIT IHC scores into the partition analysis for KIT genotype did not improve the cytomorphology-based discrimination of KIT vs. KIT wildtype melanomas (complete KIT mutant cluster tree in appendix figure A1.2).
The combination of TMX4 IHC score and cytomorphology scores in BRAF partition analysis did improve the sensitivity and specificity of the decision tree but only after four splits, signifying the cell roundness, cell regularity, nucleus to cytoplasm ratio are all better discriminators than the TMX4 IHC score (corresponding ROC AUC 0.7583).
The inclusion of TBRG4 IHC scoring in the partition analysis of NRAS mutants improved the decision tree, resulting in an ROC score of 0.6630 after two splits based on melanin and TBG IHC scores, compared with a ROC score of 0.6231 after the three best cytomorphological discriminators (melanin score, cell length and pleomorphism of cell shape).
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Figure 0-40
Figure 5. 26 BRAF mutant melanoma specific cytomorphology and IHC decision tree analysis shows the cell roundness, cell regularity, nucleus to cytoplasm ratio are all better discriminators than the TMX4 IHC score (corresponding ROC AUC 0.7583).
Figure 5.27 The combination of melanin content and TBRG4 IHC scoring in the partition analysis of NRAS mutants results in a two-split subgroup 2 fold enrichment of NRAS mutants. ROC AUC value is higher than that seen in the 3 split cytomorphology only analysis (0.6231) but remains poor at 0.6630. 128 Chapter 5 – Immunohistochemical and Morphological Profiling of KIT, BRAF, NRAS Mutant Melanomas
5.4 Conclusion
There have been numerous research projects designed to identify novel microarray diagnostic markers for a range of tumours. Some highly successful examples include α-Methylacyl coenzyme A racemase (AMACR) in prostate cancer (Rubin et al., 2002) and DOG1 in gastrointestinal stromal tumours (West et al., 2004). The success of this approach is premised on the assumption that the brightness of a spot on a microarray is a reflection of whether a gene is differentially regulated and furthermore, that the difference between the groups will be directly reflected in protein levels. However, the correlation between microarray results and actual mRNA transcript levels (the latter when measured by qPCR represents the gold standard in gene expression quantification) is known to be imperfect. An inherent limitation of DNA microarray technology is that not all arrays can generate a linear signal with 100% specificity. A universal hybridization condition needs to be applied to thousands of different hybridization reactions, necessarily resulting in some suboptimal binding conditions, possibly with a degree of non-specificity (Pusztai and Hess, 2004).
The reported correlation rate (R) between microarray data for the FFPE derived whole genome DASL assay and qPCR under optimal conditions is between ~0.42 and ~0.63 (April et al., 2009). Others have reported qPCR validation of a microarray differential gene expression in up to 70-80% of candidate genes (Linton et al. average R2 0.46 (Linton et al., 2008), Lassman et al. average R2 0.49 (Lassmann et al., 2009)). This still leaves 20-30% of the candidate genes not meeting the predicted microarray expression pattern.
Our Spearman correlation scores between the DASL values and IHC results for KIT (0. 4170), TMX (0.2332) and S100 (0.4803) suggest the premise underling this experimental model has been partially validated. Nevertheless, MYL9, CEACAM3 and TBGR4 failed to show a statistically significant correlation between IHC scoring and DASL values. The association between gene expression and protein levels is far from being uniformly positive in the literature. Experiments employing the whole genome expression DASL platform report statistically significant correlations between mRNA and protein expression with success rates ranging from 0% to 96% (O'Connor et al., 2010, Fontaine et al., 2009, Belvedere et al., 2004, Vilmar et al., 2011). Even under ideal conditions, going as far as employing qPCR on mRNA extracted from frozen tissue, and taking great care at standardizing pre-analytical IHC conditions, the Spearman coefficient between protein and mRNA ranged from 0.509 to 0.664 (O'Connor et al., 2010).
The sources of this well recognized discrepancy between mRNA levels and IHC scoring are likely to be the product of differences in pre-analytical factors (tissue fixation time, temperature employed in tissue processing and the age of the paraffin block) as well as additional cellular variables such as differences in the complexity of protein regulation involving post-translation modifications and different rates of protein turn over (Badve, 2009). Interestingly, correlation between mRNA and protein levels have been shown in a variety of cell lines to show a bimodal distribution across some categories, with major and minor correlation peaks at values of ~0.3 and ~0.71 (Shankavaram et al., 2007, Gry et al., 2009), respectively.
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Genes with high correlation were mainly involved in cytoarchitectural functions (Shankavaram et al., 2007, Gry et al., 2009).
Nonetheless, all the candidate genes were expressed at higher levels in their respective kinase mutant categories, albeit at statistically non-statistically significant levels. The immunohistochemical staining was of a sufficient quality to show a staining pattern consistent with some of the proteins’ known subcellular location (cellular membrane for KIT, mitochondria for TMX4 and endoplasmic reticulum for TBRG4), as well as showing strong staining of internal controls (vascular spaces in the case of MYL9 and acute inflammatory cells for CEACAM3 staining).
The KIT mutant IHC 3 split classifier tree generated the highest AUC value at 0.8775. At a single split (MYL9 IHC 139.1), the KIT IHC classifier tree has an AUC value of 0.6581. In comparison, the BRAF and NRAS mutant IHC classifier trees have AUC values of 0.5818 and 0.6120, respectively.
The analysis of cytomorphological correlates revealed the average shape of a KIT mutant is relatively smaller and more spindled than KIT wildtype melanomas, although this was a non-statistically significant trend. The best two classifiers were high nucleoli scores and high cellular shape pleomorphism values (ROC AUC 0.7943). Interestingly, the only statistically significant variable under univariate analysis, nuclear size pleomorphism, was only the third most sensitive classifier. Its inclusion into a 3-split classifier tree only marginally improved the ROC AUC (0.8230).
To our knowledge, this represents the first attempt at defining the cytological properties of KIT mutant melanomas. The inherent limitation of classifier tree analysis is the heavy reliance on initial conditions and the susceptibility of incorporating too many branches, resulting in over fitting of the data (Viros et al., 2008). We have reduced the risk of over fitting by maintaining a small number of splits. However, confirmation of the observed trend for a specific histomorphological pattern will require validation in an independent melanoma cohort.
There have been prior studies into the histomorphological correlates of BRAF and NRAS mutant melanomas. Two studies (Broekaert et al., Viros et al., 2008) have identified, and confirmed on an independent data set, a morphological correlate for BRAF but not NRAS mutant melanomas. Primary cutaneous BRAF mutant melanomas show a specific growth pattern characterized by high degree of intra- epidermal scatter along with a nested growth pattern. Cytomorphologically, the melanoma cells were prominently associated with larger sizes and more rounded shapes than BRAF wildtype melanomas. Because of the inability to assess the primary cutaneous melanoma in the majority of our cohort, we focused on the cytomorphological features of metastatic disease.
We have confirmed previous observations that BRAF mutant melanomas are more rounded on at least two separate measures of shape. Interestingly, it appears that BRAF mutant melanoma cells trend towards being larger than BRAF wildtype (254.9 vs. 222.5, p-value 0.1669). These findings are made more remarkable by the observation that the average cell size in our cohort was over twice the size of that reported by Viros et al. where the overall median cell size in our study was 206 µM2 vs. only 99 µM2 and BRAF mutant cell size was similarly greater, 223 µM2 vs. 113 µM2 (Viros et al., 2008). These differences
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also extend to nuclear sizes (overall median nuclear size: present study 70 µM2 vs. Viros et al. study 35 µM2) and to more extreme cell shapes in our cohort (overall median cell shape: present study 1.75 vs. Viros et al. study 1.33). Both studies have used digital image analysis and the discrepancy between these results may indicate differences in measuring techniques. However, a more likely explanation may lie in differences in the patient groups. The Viros et al. study exclusively analysed primary melanomas. Their study was composed of cases with a smaller median Breslow thickness (1.2mm Viros et al. vs. 3.5mm current study) and the present study analysed metastatic samples in 73% of cases. In our cohort, the melanoma at the primary site displayed smaller median values for cellular size (195 µM2 vs. 210 µM2, p- value 0.2966), nuclear size (68.2 µM2 vs. 72.3 µM2, p-value 0.3345) and cell shape (1.70 vs. 1.92, p-value 0.0561) than metastatic samples, although the cells in the primary samples were still considerably bigger than those seen in the study by Viros et al., (2008).
We have identified that a combination of low cell roundness factor, low cell regularity factor and high nucleus to cytoplasm ratio can correctly distinguish BRAF mutant from BRAF wildtype cases 70.4% of the time (corresponding to a classifier tree with a ROC AUC of 0.7044). This classifier is only marginally improved with a high TMX4 IHC score, generating a 4-split decision tree with a ROC AUC of 0.7583.
In contrast, NRAS morphology does not show any specific morphological correlates and correspondingly, cytomorphology acts as a poor discriminator of NRAS mutational status in a 3-split decision tree (AUC ROC 0.6231).
In summary, an objective cytomorphological analytical approach to melanoma confirms previous reports of a BRAF specific melanoma phenotype. We also employed gene expression microarrays to identify differentially expressed proteins according to mutation type. All the proteins selected for immunohistochemical staining based on their gene expression analysis showed their largest difference in expression levels and their lowest p-value when compared within their original groups, strongly suggesting for the existence of a mutation specific expression pattern for these proteins. The inability to reach statistical significance may be the result of a sub-optimization of the IHC scoring algorithm.
Given the importance of accurately identifying all BRAF mutant melanomas, it is unlikely an immunohistochemical and cytomorphological triage system would replace blanket screening for this mutation. Furthermore, the recent development of a BRAF V600E specific antibody (VE1 (Capper et al., 2011)), which only became commercially available after the completion of this thesis’s experimental work, also provides a more simple and elegant tool with which to identify BRAF mutant melanomas. Nonetheless, a continued search for clues for the presence of the rare KIT mutant cases is worth pursuing. We have identified a possible trend for a small spindle shape phenotype and have derived 2-split classifier tree based on nucleoli size and cell shape pleomorphism can result in a borderline fair-to-good discrimination rate. Validation of these observations requires a replication study in an independent cohort.
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CHAPTER 6
GENERAL DISCUSSION
Chapter 6 – General Discussion
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6.1 An Activating KIT Mutation Is Associated With A Loss-Of-Function Pigmentation Phenotype.
There are a small numbers of reports identifying an association between piebaldism and neurofibromatosis 1 (Angelo et al., 2001, Chang et al., 1993, Spritz et al., 2004, Tay, 1998) as well as a single report of piebaldism associated with a non-specific tumour syndrome (Gatto et al., 1985). To the best of our knowledge, the association of a gain-of-function mutation within a proto-oncogene and a white spotting disorder (i.e. loss-of-function) is novel.
Studies into congenital failures in melanocyte development have resulted in descriptions of numerous interlinked genes involved in melanoblast and melanocyte proliferation, migration, differentiation and survival (Lin and Fisher, 2007). Well-characterized syndromes such as piebaldism are known to arise as a consequence of inactivating mutations of KIT (Giebel and Spritz, 1991, Fleischman, 1992). In contrast, Waardenberg syndrome a less well understood genetically and clinically heterogeneous disease typified by a variable penetrance of auditory-pigment disorders, mild craniofacial abnormalities and more rarely neurocristopathy (Dourmishev et al., 1999).
There are four recognized subtypes of Waardenburg syndrome associated with 7 different genes; WS1 (PAX3 (Tassabehji et al., 1992)), WS2A (Microphthalmia transcription factor, MITF (Hughes et al., 1994)), WS 2D (SLUG (Sanchez-Martin et al., 2002)), WS3 (PAX3 (Wu et al., 1993)), WS4 (endothelin- B receptor gene, EDNRB (Puffenberger et al., 1994); endothelin-3, EDN3 (Edery et al., 1996) and SOX10 (Herbarth et al., 1998)).
The presence within the kindred reported in Chapter 2 of a white forelock and heterochromia iridium and the absence of leukoderma favours the clinical diagnosis of Waardenburg syndrome over piebaldism. Within the clinical group of Waardenburg, the absence of dystopia canthorum, upper limb anomalies or neuropathies narrows the probable clinical diagnosis to Waardenburg syndrome type 2.
Approximately 20% of Waardenburg syndrome type 2 are associated with Microphthalmia-associated transcription factor (MITF) inactivating mutations (Tassabehji et al., 1995). Mice with disruptions of the microphthalmia locus demonstrate abnormalities in the maturation of neural crest derived melanoblasts. This leads to melanocyte loss, manifesting in variable pigment defects ranging from pale coat colors to white spotting and total pigment loss (Goding and Meyskens, 2006). They also show small eyes, abnormalities in bone metabolism, decreased numbers of mast cells and deafness (Takeda and Shibahara, 2006).
MITF is a member of the basic helix-loop-helix transcription factor family (Hodgkinson et al., 1993) and is considered to be the “master regulator of melanocyte development” (King et al., 1999). MITF is expressed in at least seven isoforms; MITF-A, MITF-B, MITF-C, MITF-H and MITF-M. MITF-M is a melanocyte specific isoform that with the aid of transcription factors co-factors CREB and p300 (Sato et al., 1997), co-ordinates the expression of melanocyte antigens and melanogenic enzymes through binding M-box motifs in their promoter regions (Aksan and Goding, 1998).
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Prior investigations by the treating medical geneticist failed to identify germline mutations of MITF, PAX3 or SOX10 in our kindred (T Dudding 2008, pers. comm., October 2007). In consideration of the Waardenburg clinical phenotype in this kindred, the absence of inactivating mutations in common candidate genes and the presence of an activating KIT mutation, the possibility of a downstream interference of Waardenburg candidate gene products had to be considered.
The modulation of MITF-M activity by KIT via MAPK signaling is well established (Hemesath et al., 1998). Activation of the Ras-MAPK pathways by KIT plays a dual role in the regulation of MITF; it couples a brief activation signal with rapid ubiquitin mediated degradation (Wu et al., 2000). Consistent with this observation, numerous studies utilizing melanocyte and melanoma cell lines have demonstrated net degradation of MITF as a consequence of prolonged phosphorylation and activation of ERK1/2 (Kim et al., 2003, Galy et al., 2002, Kim et al., 2002, Kim et al., 2004, Park et al., 2004, Kim et al., 2006, Kim et al., 2007). Conversely agents which impair ERK 1/2 phosphorylation have the effect of promoting melanin synthesis (Englaro et al., 1998).
Further evidence for this deleterious effect by prolonged ERK1/2 phosphorylation on melanogenesis, comes from the observation of a disrupted melanocyte development in a mouse knock-in model, designed for the study of familial GISTs. Rubin et al. created mice homozygous for the KIT K641E mutation (Rubin et al., 2005). These mice developed ICC hyperplasia and GISTs in a gene-dosage dependent manner. In addition, they also displayed a white coat, decreased mast cell numbers and sterility in the homozygous progeny but not in their heterozygous counterparts. Explanation for this loss of function, despite an activating mutation, could lie in the disruption in expression from the mutant allele containing the neomycin cassette utilised for the targeting construct (Rubin et al., 2005), as has been described in previous experiments (Kissel et al., 2000). This later explanation seems less likely as the heterozygous KIT K641E litter did not show a disturb pigment phenotype, such as those seen in the by Kissel et al., which resembled inactivating KIT mutations (W mutation) (Kissel et al., 2000).
An alternative explanation may lie within the mutant protein being unable to perform the same functions as the wild-type KIT (Rubin et al., 2005). Indeed, activating mutations in KIT can show shifts in substrate specificity (Piao et al., 1996, Casteran et al., 2003) and cells lines can react differently to mutations in different domains (Lennartsson et al., 2005, Vanderwinden et al., 2006, Kimura et al., 2004).
The KIT K642E mutation occurs within the tyrosine kinase I domain and targets a highly conserved lysine residue common to all members of the type 3 tyrosine kinase receptor class (Lux et al., 2000). The substitution of a positively charged residue for negatively charged one generates electrostatic repulsion between the closely apposed T574 and L576 (Tarn et al., 2005). The disruption of this stretch of residues has been hypothesized to disrupt part of the juxtamembrane domain that constitutes the auto-inhibitory loop (Tarn et al., 2005). Vanderwinden et al. demonstrated differences in the constitutive activation of MAP kinase pathway by comparing different KIT activating mutations in Ba/F3 cell lines (Vanderwinden et al., 2006). KITK641E (the mouse homologue for the human KIT K642E) showed constitutive phosphorylation of ERK1/2, a phenomenon not observed with KITΔV559 or wild-type KIT. Constitutive phosphorylation of ERK 1/2 is also observed in GIST cells lines harboring the KITK642E mutation (Sambol
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et al., 2006, Frolov et al., 2003), whereas most GISTs with other underling KIT mutations have shown low levels of ERK 1/2 phosphorylation in comparison to PDGFRa mutant GISTs (Kang et al., 2007).
If KIT K642E is shown to preferentially generate a similar constitutive activation of ERK 1/2 within melanoblast and melanocyte models, it could indicate a mechanism whereby certain KIT mutations are capable of generating a MITF insufficiency during critical stages of melanoblast development where there is an interaction between KIT and MITF.
Should differences in the activation of MAP kinase pathways be observed between KIT K642E and all other germline KIT mutations observed in familial GISTs, this would provide an example of activating proto- oncogene mutation resulting in a loss of function phenotype and the generation of a Waardenburg type 2 syndrome through a novel mechanism. The presence of a germline oncogenic KIT mutation generating a tumour syndrome which does not include melanoma as part of the clinical presentation, also serves to show that KIT K642E is not sufficient in of itself sufficient to generate a melanoma, but it is sufficient to disrupt normal melanocyte development.
6.2 KIT Mutations Are Infrequent In Cutaneous Melanoma
In the last five years, we have seen the recognition of molecularly distinct subtypes of melanomas. These are characterized by oncogene addiction to pivotal components of the MAPK and PIP3 cascades. This paradigm shift in our characterisation of melanomas has provided new therapeutic opportunities for patients with metastatic disease. Key amongst the molecular subtypes is the mutant KIT driven melanomas. Early work suggested the presence of this mutation in a fifth of melanomas arising from chronically sun damaged sites (Curtin et al., 2006). However, it has been shown by this work and others (Beadling et al., 2008, Handolias et al., 2010) that this early promise of uncovering a large fraction of Caucasian patients who may benefit from targeted KIT inhibition has not been met. However, a recent report has documented mutational rates as high as 10% of unselected Chinese melanoma patients, a population that bears a high prevalence (39%) of acral melanomas, a histological subtype with a strong association with KIT mutations (Kong et al., 2011).
We investigated 192 patients with advanced melanoma, successfully completing bidirectional sequencing of KIT’s mutational hotspots in exon 9, 11, 13 and 17 in 159 cases and have identified 8 cases harboring KIT mutations. We confirm prior observations that the most common KIT mutations in melanoma occur as single point substitutions in exons 11 and 13, particularly KITK642E and KITL576P. We also report the only second known instance of a KIT exon 9 mutation in melanoma, KITG498S (Langer et al., 2011).
All the nucleotides affected by these mutations have been previously reported as mutated in other KIT- dependent cancers, and thus are likely to represent driver and not passenger mutations. The small number of cases precludes confident assertions with regards to a specific clinicopathological and clinical outcome in patients with KIT-mutant melanoma, but we note that a trend for elderly patients (72.9 yrs. of age) and a high occurrence in chronically sun damaged skin (75%), a pattern which has been observed by others (Kong et al., 2011, Curtin et al., 2006).
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It is worthy to note that despite considerable optimization efforts, our bidirectional sequencing success rate of 82% (for all patients having the targeted 4 exons successfully bidirectionally sequenced), reflects the technical difficulty in successfully completing the relatively long requisite PCR reactions across severely formalin-fixed degraded DNA. This stands in contrast to a success rate of 99% for mass spectrometry based SNP analysis. Naturally, a Sanger sequence based approach offers a much higher sensitivity in the detection of complex nucleotide rearrangements and deletions. However, such mutations appear to be rare events in KIT, NRAS or BRAF mutant melanomas. Thus, in this particular clinicopathological setting, Sanger sequencing fails to outperform the more rapid and cheaper mass spectrometry based assay.
We also applied gene expression microarrays and a quantitative analysis of cell morphology in an attempt to identify immunohistochemical and histomorphological clues for the presence of a KIT mutation. Two genes were identified genes from microarray experiments that were overexpressed in KIT vs. BRAF or NRAS mutant melanomas. The protein products of these two genes (MYL9 and CEACAM3), and also KIT itself, were shown to have a higher, but not statistically significant, intensity of immunohistochemical staining in KIT mutant melanomas. The modeling of this data by recursive partition classifier trees results in a 3-split classifier with a good discriminatory rate (ROC AUC 0.8775), showing that in principle the immunohistochemical quantification of these three proteins can enhance the probability of identifying a KIT mutation.
Cytomorphological analysis also shows a non-statistically significant trend for a specific KIT mutant phenotype, with cells showing on average a smaller, more spindled shape with more prominent nucleoli and lower rates of nuclear size pleomorphism than KIT wildtype melanomas. A corresponding cytomorphology 3-split classifier tree results in a good discriminatory model (ROC AUC 0.8230) that does not perform as well as the immunohistochemically derived model.
The combination of clinical, cytomorphological and immunohistochemical data into one recursive classifier tree analysis model reveals that a 3-split classifier tree employing nucleoli prominence, cell shape pleomorphism and primary site, generates a predictive model with an excellent discriminatory score (ROC AUC 0.9195) (figure 6.1).
The small numbers of cases and the nature of the statistical modeling employed in this analysis leave open the possibility that this analysis represents a highly fitted result that may not be applicable to the population of KIT mutant melanomas as a whole. Confirmation of these results in an independent set of cases is vitally important before the definitive predictive powers of these features can be unequivocally demonstarted.
6.3 MET Juxtamembranous Mutations Are Over-Represented In Advanced Melanoma
We have identified the germline SNP METR988C and METT1010I are associated with acquiring an advanced melanoma with an odds ratio of 5.86 and 2.06, respectively. These changes affect the juxtamembranous domain of an important tyrosine kinase receptor involved in the oncogenesis of many cancers, including melanoma. These two mutations do not occur in a mutually exclusive manner to other kinase mutation
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known to be drivers of melanomagenesis and thus are not likely to represent classic driver mutations per se. The biochemical evidence in the literature suggests these changes may show cell lineage-dependent effects in constitutive MET receptor phosphorylation, MET receptor down-regulation, output in signaling cascades and the generation of reactive oxygen species. One of these changes (METT1010I) has recently been shown to be associated with a familial colorectal cancer syndrome (Neklason et al., 2011), pointing to the possibility that in cancers where MET over expression is an early feature of oncogenesis, a MET juxtamembranous SNP may promote a more aggressive tumour phenotype driven by an already existing dysregulation in the HGF/MET signaling axis. If this mechanism is correct, these MET SNPs should also be over represented in an advanced/thick melanoma cohort vs. a thin melanoma patient group. Confirmation of this hypothesis is the subject of an ongoing research project by our group.
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Figure 0-41
Figure 6.1 KIT mutant specific clinical and cytomorphological 3-split classifier tree (ROC AUC 0.9195). Cytomorphology and site of primary melanoma are the best predictors for the presence of a KIT mutant melanoma. Immunohistochemical detection of KIT, MYL9 or CEACAM3 does not improve the discriminatory score.
6.4 Knowledge Of BRAF And NRAS Mutational Status Confers Prognostic Information
The move towards a molecular classification of melanoma provides opportunities not only for new therapeutics but also for the identification of new prognostic groups. We report NRAS and BRAF mutant melanomas in our cohort show a different rate of metastatic spread, with NRAS mutant melanomas showing slower rates of progression to locoregional sites and also then onto distant sites. This translates to a significantly lower melanoma specific mortality in comparison to NRAS wildtype melanomas and, in particular, BRAF mutant melanomas (median survival 1194 weeks vs. 164 weeks, p-value 0.0057). The protective effect of an NRAS mutation on melanoma specific survival was independently significant when correcting for gender, age, Breslow thickness and AJCC staging (NRAS melanoma specific mortality hazards ratio 0.27, p-value 0.0056).
The body of evidence in the literature for the prognostic impact of BRAF and NRAS mutations in melanomas is highly contradictory and in dire need of a statistical meta-analysis. An undertaking that may not have been already embarked upon because of the difficulty in reconciling the wide disparity in patient demographics and clinicopathological characteristics. Because NRAS mutations in our cohort affect an elderly patient group (median age 66.4 yrs.), it is important to identify if patients with these mutant melanomas show a lower rate of metastatic disease, as these findings support a conservative 140 Chapter 6 – General Discussion
approach for a group of patients who are at risk of surgical complications. The answer to this question will require, at a minimum, an expansion of the number of cases analysed retrospectively including thinner melanomas to see if this effect is confirmed to an advanced/thick melanoma group only. However, an ideal approach requires prospective analysis and preferably a multi-institutional research effort.
6.5 BRAF Melanomas Display A Specific Morphological Phenotype
The recent American FDA approval of vemurafenib for the treatment of metastatic BRAF mutant melanoma (Oncology, 2011) is a watershed moment in the evolution of a modern personalized treatment for patients with melanoma. We, and others, have shown that BRAF mutant melanomas show a specific clinical profile; arising in younger patients and in primary sites less often chronically sun damaged. These melanomas have also been previously shown to exhibit a unique architectural and cytological pattern. We confirm the observation these melanomas are more often larger and more rounded than their non-BRAF mutant counter parts. In addition, we report these melanomas have lower rates of cellular shape pleomorphism, lower nuclear to cytoplasm ratios and exhibit more rounded nuclei, all with statistically significant differences. In addition to this, we also have identified, through gene expression microarrays, the over expression of TMX4 in BRAF mutant melanomas. This protein was also on average expressed at higher levels on immunohistochemical analysis in BRAF mutant melanomas but not to a statistically significant magnitude.
The combination of three cytomorphological features and immunohistochemical staining generates a 4- split classifier tree with fair discriminatory ability (ROC AUC 0.7583). A full clinical, cytomorphological and immunohistochemical recursive partition analysis generates a 4-split tree where the cell roundness remains the most effective predictor of the presence of a BRAF mutant melanoma, followed by low nucleus to cytoplasm ratio, high TMX4 IHC scoring and finally patient age (ROC AUC 0.7668) (figure 6.2).
As statistically significant as these results may be, the importance of correctly identifying a BRAF mutant melanomas means that neither clinical, histomorphological or immunohistochemical examination will ever replace molecular analysis of BRAF in determining eligibility for BRAF kinase inhibition. Nonetheless, these findings begin to show a way in which we can begin to integrate clinical, histomorphological and genetic information into a more clinically meaningful melanoma classification.
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Figure 0-42
Figure 6.2 Clinical, cytomorphological and immunohistochemical classifier tree of BRAF mutant melanomas (ROC AUC 0.7668). Cytomorphology and TXN IHC are the best predictors for the presence of a BRAF mutant melanoma. Clinical presentation with regards to patient age ranks as the fourth most discriminatory feature.
142 Chapter 6 – General Discussion
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APPENDICES
Appendices
174 Appendices
Table 31
Gene/Exon Sequence Covered region PCR condition SNAI2 Fwd: 5’-GATTCTTCCTCTGTTCACAGC-3’ -77bp 5’ upstream 55°C 35 cycles Amplicon 1 Rev: 5’-CTGTAGTTTGGCTTTTTGGAGG plus -201 to 57 SNAI2 Fwd: AAGATGCCGCGCTCCTTCC-3’ -3 to 449 55°C 35 cycles Amplicon 2 Rev: 5’-GTGAGTACAAAGATATGCCTGC SNAI2 Fwd: 5’-GTGAAAGATGAGCAACATAAGG-3’ 348 to 764 55°C 35 cycles Amplicon 3 Rev: 5’-CGCAAGTATACACACACTGG-3’ SNAI2 Fwd: 5’-GGGTCTATCTTCTTCCTAAG3’ 695 to 1170 55°C 35 cycles Amplicon 4 Rev: 5’-CCAGAAAAAGTTGAATAGGTC-3’ SNAI2 Fwd: 5’-CTTTCAGACCCCCATGCC-3’ 1094 to 1538 55°C 35 cycles Amplicon 5 Rev: 5’-GAAATGGAGCACTTTGTGCC3’ SNAI2 Fwd: 5’-GGCTGGCTGTTGGATTTGC-3’ 1424 to 1854 55°C 35 cycles Amplicon 6 Rev: 5’-GCAAAAAATCCCCAAACAAAGC-3’ SNAI2 Fwd: 5-TCACCCGTGGGTGACTTG-3’ 1781 to 2248 55°C 35 cycles Amplicon 7 Rev: 5’GACAGTCAGTGTTTTTAGAGG-3’ SNAI2 Fwd: 5’CTATTATGAGGGATTTTAATCAGG-3’ 2136 to 2562 55°C 35 cycles Amplicon 8 Rev: 5’-ATTTGGTTGGTCAGCACAGG-3’ SNAI2 Fwd: 5’-TGACGCAATCAATGTTTACTCG-3’ 2461 to 2899 55°C 35 cycles Amplicon 9 Rev: 5’-ACCTGAGTTCGCGTCTGG-3’ SNAI2 Fwd: 5’-GCAAAGATTTACATTGCTGCC-3’ 2785 to 3228 55°C 35 cycles Amplicon 10 Rev: 5’GAATCCTCTTTTGCACTTATTCC-3’ SNAI2 Fwd: 5’-ATTCCTGGCGCATGCTCC-3’ 3134 to 3229 55°C 35 cycles Amplicon 11 Rev: 5’-GGTTGTTTATAGACATAGCTATAG-3’ SNAI2 Fwd5’-CAGAGACTTTACTTGCTTCATG 3518 to 3953 plus 55°C 35 cycles Amplicon 12 Rev: 5’-GTGGTATGCTAACAAACCACC-3’ 137bp 3’ downstream) SNAI2 qPCR Fwd: 5’-GAACCTCTCAGCTGTGATTGG-3’ -237 to -219 55°C 35 cycles Amplicon 1 Rev: 5’-CCGGCTCCTTTACGAACTG-3’ SNAI2 qPCR Fwd: 5’-GCAGACCCATTCTGATGTAAAG-3’ 2347 to 2440 95°C 10 mins, Amplicon 2 Rev: 5’-CCAGATTCCTCATGTTTGTGC-3’ [95°C 15 secs, 60°C 1 min]x40 cycles KIT qPCR Fwd: 5’-CGCCTGGGATTTTCTCTGC-3’ 105 to 203 95°C 10 mins, Amplicon 1 Rev: 5’-GGCTTCGCCGAGTAGTCG-3’ [95°C 15 secs, 60°C 1 min]x40 cycles KIT qPCR Fwd: 5’-AGCCCCAACCGACAGAAGC-3’ 80525 to 80625 95°C 10 mins, Amplicon 2 Rev: 5’-ACATCGTCGTGCACAAGCAG-3’ [95°C 15 secs, 60°C 1 min]x40 cycles Table A1.1 SNAI2 and KIT sequencing and qPCR primers
175 Appendices
Table 32
SNP_ID Sequence BRAF!V600E tgctctgataggaaaatgagatctactgttttcctttacttactacacctcagATATATTTCTTCATGAAGACCTCA CAGTAAAAA(T/A)AGGTGATTTTGGTCTAGCTACAG[1_T/A]GAAATCTCGATGGAGTGGGTC CCATCAGTTTGAACAGTTGTCTGGATCCATTTTGTGGATGgtaagaattgaggctatttttccactgat(t/ A)aaatttttggccctgaga BRAF! TGCTCTGATAGGAAAATGAGATCTACTGTTTTCCTTTACTTACTACACCTCAGATATATTTCTTC V600EAD ATGAAGACCTCACAGTAAAAA(T/A)AGGTGATTTTGGTCTAGCTACAG[2_TG/AA/AT]AAATC TCGATGGAGTGGGTCCCATCAGTTTGAACAGTTGTCTGGATCCATTTTGTGGATGGTAAGAAT TGAGGCTATTTTTCCACTGAT(T/A)AAATTTTTGGCCCTGAGA BRAF! tgctctgataggaaaatgagatctactgttttcctttacttactacacctcagATATATTTCTTCATGAAGACCTCA V600MAR CAGTAAAAA(T/A)AGGTGATTTTGGTCTAGCTACA[3_GT/AA/AG]GAAATCTCGATGGAGTG GGTCCCATCAGTTTGAACAGTTGTCTGGATCCATTTTGTGGATGgtaagaattgaggctatttttccact gat(t/A)aaatttttggccctgaga KIT!SNP! ATTTACAGGTAACCATTTATTTGTTCTCTCTCCAGAGTGCTCTAATGACTGAGACAATAATTATT CLUSTER_1 AAAAGGTGATCTATTTTTCCCTTTCTCCCCACAGAAACCCATGTATGAAGTACAG[4_T/A/C/G] GGAAGG[5_T/A/C/G]TG[6+7_T/A/G/A ]TGAGGAGATAAATGGAAACAATTATGTTTACATAGACCCAACACAAC[8_T/C]TCCTTATGAT CACAAATGGGAG(T/A )TTCCCAGAAACAGGCTGAGTTTTGGTCAGTATGAAACAGGGGCTTTCCATGTCACCTTTTTGG GTACACATAACAGTGACTTTAAGGAACTCCAGTGGCTTCCTTTGTTTTGTTCCACCTGAAACAA TGAG(T/A )TTTCTGTGAAATTGCGCCCCTTTTGATAGGTTTGCCATAGA(G/C)AACATC(G/A)TAGGAAAA TGTCTCTGGACAACATTGTTTTTAATTCCTTTATTGATTTTGAAA(C/G)TGCACAAATGGTCCTT CAATTCCACCACCAGCACCATCACCACTTACCTTGTTGTCTTCCTTCCTACAGGGAAAACCCTG GGTGCTGG(A/T)GCTTTCGGGAAGGTTGTTGAGGCAACTGCTTATGGCTTAATTAAGTCAGAT GCGGCCATGACTGTCGCTGTAAAGATGCTCAAGCGTAAGTTCCTGTATGGTACTGCATGCGCT TGACATCAGTTTGCCAGTTGTGCTTTTTGCTAAAATGCATGTTTCCAATTTTAGCGAGTGCCCA TTTGACAGAACGGGAAGCCCTCATGTCTGAACTC[9_A/G]AAGTCCTGAGTTACCTTGGTAAT CACATGAATATTGTGAATCTACTTGGAGCCTGCACCATTGGAGGTAAAGCCGTGTCCAAGCTG CCTTTTATTGTCTGTCAGGTTATCAAAACATGACATTTTAATATGATTTTGGCAATGCTAGATTA TAAACTGCTTGG(A/G)AG KIT!SNP! TTTCTTTTCTCCTCCAACCTAATAGTGTATTCACAGAGACTTGGCAGCCAGAAATAT(C/T)CTCC CLUSTER_2 TTACTCATGGTCGGATCACAAAGATTTGTGATTTTGGTCTAGCCAGA[10_G/C/T][11_A/T]CA TCAAGAATGATTCTAATTATGTGGTTAAAGGAAACGTGAGTACCCATTCTCTGCTTGACAGTC CTGCAAAGGATTTTTAGTTTCAACTTTCGATAAAAATTGTTT(C/T)CTGTG MET!R988C! aaaacccatgagttctgggcactgggtcaaagtctcctggggcccatgatagccgtctttaacaagctctttctttctctctgt T1010I tttaagATCTGGGCAGTGAATTAGTT[12_C/t]GCTACGATGCAAGAGTACACACTCCTCATTTGG A(T/G)A(G/T)GCTTGTAAGTGCCCGAAGTGTAAGCCCAA[13_C/t]TACAGAAATGGTTTCAAA TGAATCTGTAGACTACCGAGCTAC(T/A )TTTCCAGAAGgtatatttcagtttattgttctgagaaatacctatacatatacctcagtgggttgtgacattg MET!Y1248C! GCATCATTGTAAATTATTCTATTTCAGCCACGGGTAATAATTTTTGTCCTTTCTGTAGGCTGGAT Y1253D! GAAAAATTCACAGTCAAGGTTGCTGATTTTGGTCTTGCCAGAGACATGT[14_A/G]TGATAAA M1268T GAATAC[15_T/G]ATAGTGTACACAACAAAACAGGTGCAAAGCTGCCAGTGAAGTGGA[16_T /C]GGCTTTGGAAA(G/A)T(C/T)TGCAAACTCA(A/G)AAGTTT(A/G)CCACCAAGTCA(G/T)ATG TGGTAATGTATTGGTTATCTCTGAGTTTCTCCTCTTTTACTTTCATATCCAACTTTTTTTGAAGTT T Table A1.2 Mutation map used for design of melanoma specific MassARRAY panel.
176 Appendices
Table 33
SNP_ID! Sequence! CTNNB1! TAAATTAAAGTAACATTTCCAATCTACTAATGCTAATACTGTTTCGTATTT(A/G)TAGCTGATTT SNP! GATGGAGTTGGACATGGCCATGGAACCAGACAGAAAAGCGGCTGTTAGTCACTGGCAGCAA CLUSTER_1 CAGTCTTACCTGG[17_A/G][18_C/A]T[19_C/G]TGGAATCCAT[20_T/C][21_C/T/A]TGGTG CCACTACCACAGCTCCT[22_T/C][23_C/A/T]TCTGAGTGGTAAAGGCAATCCTGAGGAAGAG GATGTGGATACCTCCCAAGTCCTGTATGAGTGGGAACAGGGATTTTCTCAGTCCTTCACTCAA GAACAAGTAGCTGGTAAGAGTATTATTTTTCATTGCCTTACTGAAAGTCAGAATGCAGTTTTG AGAACTAAAAAGTTAGTGTATAATAGT CTNNB1! TAAATTAAAGTAACATTTCCAATCTACTAATGCTAATACTGTTTCGTATTTATAGCTGATTTGAT SNP! GGAGTTGGACATGGCCATGGAACCAGACAGAAAAGCGGCTGTTAGTCACTGGCAGCAACAG CLUSTER_2 TCTTACCTGGACTCTGGAATCCATTCTGGTGCCACTACCACAGCTCCT[24_TCT/A ]CTGAGTGGTAAAGGCAATCCTGAGGAAGAGGATGTGGATACCTCCCAAGTCCTGTATGAGT GGGAACAGGGATTTTCTCAGTCCTTCACTCAAGAACAAGTAGCTGGTAAGAGTATTATTTTTC ATTGCCTTACTGAAAGTCAGAATGCAGTTTTGAGAACTAAAAAGTTAGTGTATAATAGT EGFR!L858R TCTGTTTCAGGGCATGAACTACTTGGA(G/C)GACCGTCGCTTGGTGCACCG(C/T)GACCTGGC AGCCAGGAACGTACTGGTGAAAACACCGCAGCATGTCAAGATCACAGATTTTGGGC[25_T/G ]GGCCAAACTGCTGGGTGCGGAAGAGAAAGAATACCATGCAGAAGGAGGCAAAGTAAGGAG GTGGCTTTAGGTCAGCCAGCA(T/A)TTTCCTGACACCAGGGACCAGGCTGCCTTCCCAC EGFR!P753S AATTGCCAGTTAACGTCTTCCTTCTCTCTCTGTCATAGGGACTCTGGATCCCAGAAGGTGAGA AAGTTAAAATTCCCGTCGCTATCAAGGAATTAAGAGAAGCAACATCT[26_C/T]CGAAAGCCA ACAAGGAAATCCTCGATGTGAGTTTCTGCTTTGCTGTGTGGGGGTCCATGGCTCTGAACCTCA GGCCCACCTTTTCTCATGTCTG(G/A)CAGCTGCTCTGCTC KRAS!G12SA atagtgtattaaccttatgtgtgacatgttctaatatagtcacattttcattatttttattataaggcctgctgaaaATGACT RACADAAAV! GAATATAAACTTGTGGTAGTTGGAGCT[27_G/a/c/t][28_G/a/c/t]TG[29_G/a]CGTAGGCA G13D AGAGTGCCTTGACGATACAGCTAATTCAGAATCATTTTGTGGACGAATATGATCCAACAATAG AGgtaaatcttgttttaatatgcatattactggtgcaggaccat KRAS!Q61KA ggtgcactgtaataatccagactgtgtttctcccttctcagGATTCCTACAGGAAGCAAGTAGTAATTGATGG KAEAPARALAH AGAAACCTGTCTCTTGGATATTCTCGACACAGCAGGT[30_C/a/g][31_A/c/g/t][32_A/c/t]G AGGAGTACAGTGCAATGAGGGACCAGTACATGAGGACTGGGGAGGGCTTTCTTTGTGTATTT GCCATAAATAATACTAAATCATTTGAAGATATTCACCATTATAGgtgggtttaa NRAS!G12SA ctttaaagtactgtagatgtggctcgccaattaaccctgattactggtttccaacaggttcttgctggtgtga(A RADAAAV! /A)aATGACTGAGTACAAACTGGTGGTGGTTGGAGCA[33_G/A/C/T][34_G/A/C/T]T[35_G/ G13RADAV C][36_G/A/T]TGTTGGGAAAAGCGCACTGACAATCCAGCTAATCCAGAACCACTTTGTAGATG AATATGATCCCAC(C/T)ATAGAGgtgaggcccagtggtagcccgctgacctgatcctg NRAS!Q61KA acttccc(A/C)tccctccctgcccccttacc(c/A PARALAHAQ )tccac(a/G)cccccagGATTCTTACAGAAAACAAGTGGTTATAGATGGTGAAACCTGTTTGTTGG ACATACTGGATACAGCTGGA[37_C/A][38_A/G/T][39_A/C/T/G]GAAGAGTACAGTGCCAT GAGAGACCAATACATGAGGACAGGCGAAGGCTTCCTCTGTGTATTTGCCATCAATAATAGCA AGTCATTTGCGGATATTAACCTCTACAGgtac RET!C634RA CACGGCAGGCTGGAGAGCCATGAGGCAGAGCATACGCAGCCTGTACCCAGTGGTGCCGAGC YAWAC CTCTGGCGGTGCCAAGCCTCACACCACCCCCACCCACAGATCCACTGTGCGA(C/T)GAGCTG[4 0_T/C][41_G/A][42_C/G/T]CGCACGGTGATCGCAGCCGCTGTCCTCTTCTCCTTCATCGTCTC GGTGCTGCTGTCTGCCTTCTGCATCCACTGCTACCACAAGTTTGCCCACAAGCCACCCATCTCC TCAGCT(G/A)AG RET!M918T cagggatagggcctggccttctcctttacccctccttcctagagagttagagtaacttcaatgtctttattccatcttctctttag GGTCGGATTCCAGTTAAATGGA[43_T/C]GGCAATTGAATCCCTTTTTG(A /T)ATCATATCTACACCACGCAAAGTGATGTgtaagtgtgggtgttgctctcttggggtggaggttacagaaac acccttatac(a/T)tgtagtg(A/G)gggccacgac(g/A)cc Table A1.2 Mutation map used for design of melanoma specific MassARRAY panel (cont.).
177 Appendices
Table 34
178 Appendices
179 Appendices
180 Appendices
Table 35
181 Appendices
182 Appendices
183 Appendices
184 Appendices
Table A1.5 MET Real Time PCR Genotyping Primers and Probes. Table 36
SNP MET TagSNP reference Taqman® Genotyping Comment sequence number Assay MET R988C rs34589476 C__59054157_10 Assay-on-demand MET T1010I rs56391007 C__88877997_10 Assay-on-demand
185 Appendices
Table 37
186 Appendices
187 Appendices
188 Appendices
Table 38
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190 Appendices
191 Appendices
192 Appendices
Table 39
193 Appendices
Table 40
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Table 41
195 Appendices
Table A1.11 Association between solar elastosis and exposure scores Solar Elastosis Score
0 1 2 3 0 2 0 0 0 2 1 0 3 1 2 6 2 7 19 15 9 50 3 0 4 6 20 30 4 9 3 2 19 24 Sun Exposure Score Exposure Sun 9 29 24 50 112 Fisher’s exact test <0.0001
Table 42
196 Appendices
Table A1.12 Association between CSD skin and primary site.
Primary Melanoma Site
Glabrous Skin Head and Neck Lower Limb Trunk Upper Limb No 2 4 10 17 5 38 Yes 2 21 19 14 17 74 Sun Damage Sun 4 25 29 31 22 112 Pearson chi-square test 0.0237
Table 43
Figure 0-43
Figure A.1 Graphical representation of association between the presence of chronic sun damaged skin and primary melanoma site
1.00
Upper Limb
0.75
Trunk
0.50
Lower Limb Primary Melanoma Site Melanoma Primary 0.25
Head and Neck
0.00 Glabrous Skin No Yes Sun damaged skin
197 Appendices
Whole Model
Number of Events 28 Number of Censorings 24 Total Number 52
Model -LogLikelihood ChiSquare DF Prob>Chisq Difference 4.58022 9.1604 4 0.0572 Full 92.29825 Reduced 96.87847
Parameter Estimates Term Estimate SD Error Lower 95% Upper 95% Age at Diagnosis 0.01101596 0.0167039 -0.020379 0.0458564 Ulceration[No] 0.43485589 0.2075376 0.0256047 0.8478568 BRAF group[BRAF] 0.17542757 0.2992638 -0.47881 0.7253513 NRAS group[NRAS] -0.5348474 0.3142229 -1.26447 0.0106172
Effect Likelihood Ratio Tests Source Nparm DF L-R ChiSquare Prob>ChiSq Age at Diagnosis 1 1 0.44968678 0.5025 Ulceration 1 1 4.32776718 0.0375* BRAF group 1 1 0.32558248 0.5683 NRAS group 1 1 3.67741843 0.0552
Risk Ratios - Unit Risk Ratios Per unit change in regressor Term Risk Ratio Lower 95% Upper 95% Reciprocal Age at Diagnosis 1.011077 0.979827 1.046924 0.9890445
Range Risk Ratios Per change in regressor over entire range Term Risk Ratio Lower 95% Upper 95% Reciprocal Age at Diagnosis 1.896359 0.306099 14.35184 0.5273263
Risk Ratios for Ulceration Level1 /Level2 Risk Ratio Prob>Chisq Lower 95% Upper 95% Yes No 0.4190723 0.0375* 0.1834683 0.9500797 No Yes 2.386223 0.0375* 1.0525433 5.4505342
Risk Ratios for BRAF group Level1 /Level2 Risk Ratio Prob>Chisq Lower 95% Upper 95% Wildtype BRAF 0.7040857 0.5683 0.2344056 2.6054905 BRAF Wildtype 1.4202816 0.5683 0.3838049 4.2661105
Risk Ratios for NRAS group Level1 /Level2 Risk Ratio Prob>Chisq Lower 95% Upper 95% Wildtype NRAS 2.9144899 0.0552 0.9789895 12.540195 NRAS Wildtype 0.3431132 0.0552 0.0797436 1.0214614
Table A1.13 Proportional Hazards Fit of Time to Locoregional Metastasis including Ulceration as a Variable Table 44 198 Appendices
Study Head and Neck BRAF Mutants (%) Melanoma:Trunk Melanoma Edlundh-Rose, Egyhazi et al. 2006 1:10.8 55 (Chang et al., 2004) 1:3 53 Viros, Fridlyand et al. 2008 1:2.4 47 (Liu et al., 2007) 1:1.5 45 Present study 1:1.2 25 Shinozaki, Fujimoto et al. 2004 1:0.8 31 Table 45
Table A1.14 - Association of ratio of Head and Neck Melanomas to Trunk Melanomas and incidence of BRAF mutations in comparable studies.
199 Appendices
A.1 Calculation of Immunohistochemical Scoring.
Terms used by the imaging analysis software defined:
1. Positive staining: Area of deconvoluted color that is below the image intensity defined as threshold for positive staining. This is derived from direct antibody staining observation, to avoid scoring background staining. This is a required step during the optimisation of the color deconvolution algorithm, as per software manufacturer’s protocol. 2. Area stained: Area of image identified by the image algorithm as containing section of tissue. This corrects for structures such as lumina, which do not have tissue to be stained, thus not counted towards the calculation of percentage of positive staining. This value is in part influenced by the positive staining threshold cut off (see Positive staining). Stain colours which are difficult to differentiated from unstained tissue, require high positive staining cut-off close values to distinguish positive staining from background. This was an issue only with the quantification of melanin content. 3. Percentage positive: The fraction of the area recognized by the software as ‘tissue’ staining above the threshold for positivity. This threshold for positivity is derived empirically and performed during the optimization of the image algorithm. 4. Area analysed: The total area enclosed by the software’s image annotation tools. Two tools were available, a free-hand draw function and a rectangular shape. 5. IHC: Refers to all the staining results from non-S100 antibodies. It also applies to the analysis of melanin staining which was assessed on haematoxylin-only counter stained sections
The first step in the deriving the immunohistochemical score is the calculation of the S100 melanoma tissue density (1), which is based on the percentage of S100 tissue staining within the TMA spot:
The calculation of the area stained by the antibody of interest is then calculated, IHC stained area (2):
The actual area of melanoma tissues to be stained with the IHC antibody (corrected IHC stained area (3)), is then worked out by multiplying the IHC analysed area by the S100 melanoma tissue density (1):
200 Appendices
The values obtained from formulae (1) and (2) are used to obtain a corrected IHC percentage positive (4):
!"#!!"#$"%&'("!!"#$%$&' !!!! 4 !"##$%&$'!!"#!!"#$%&'!!"#!
The value for the strength of IHC staining is derived by dividing the maximum possible intensity by the average IHC intensity; this is the fraction of average IHC staining intensity (5):
255 !!!! 5 !"#!!"#$!%#!!"#$%$&'!!"#$%$%&!!"#$"%!#&
Multiplying (4) and (5) produces the corrected IHC staining score (6):
Because each case is represented by 3 TMA cores, the above calculation generates 3 corrected IHC staining scores per case. These results then need to be collated in such a manner as to allow results obtained from larger areas of analysis to have a proportionally greater influence than results derived from small areas analysis; a collated IHC staining score (10):
!"#$!!"!#$%&'!!"#$!#! !"##$%!"#!!"#!!"#$%!!"#$!#1!× (7) !(!"#$!!"!#$%&'!!"#$!#!!!"#$!!"!#$%&'!!"#$!#!!!!"#$!!"!#$%&'!!"#$!#!)
!"#$!!"!#$%&'!!"#$!#! !"##$%&$'!!"#!!"#$%!!"#$!#2!× (8) !(!"#$!!"!#$%&'!!"#$!#!!!"#$!!"!#$%&'!!"#$!#!!!!"#$!!"!#$%&'!!"#$!#!)
!"#$!!"!#$%&'!!"#$!#! !"##$%&$'!!"#!!"#$%!!"#$!#3!× (9) !(!"#$!!"!#!"#$!!"#$!#!!!"#$!!"!#$%&'!!"#$!#!!!!"#$!!"!#$%&'!!"#$!#!)
7 + 8 + 9 = !!"##$%&'!!"#!!"#$%$%&!!"#$% (10)
201 Appendices
Table A1.15 Significant Cellular and Nuclear Cytomorphology associations using Spearman’s non- parametric correlation. Variable By Variable Spearman Prob>|ρ| ρ Mean Cell Shape Mean Cell Roundness Parameter 0.8953 <.0001* Mean Nuclear Size Mean Cell Size 0.8950 <.0001* Coefficient Of Variation Of Cell Shape Mean Cell Shape 0.7761 <.0001* Mean Cell Shape Mean Cell Regularity Factor -0.7411 <.0001* Mean Cell Roundness Parameter Mean Cell Regularity Factor -0.7363 <.0001* Mean Nuclear Roundness Parameter Mean Cell Roundness Parameter 0.7316 <.0001* Coefficient Of Variation Of Nuclear Coefficient Of Variation Of Cell Volume 0.7300 <.0001* Volume Mean Cell Length Mean Cell Regularity Factor -0.7134 <.0001* Mean Cross Sectional Area Mean Cell Roundness Parameter -0.7096 <.0001* Mean Cell Length Mean Cell Shape 0.6984 <.0001* Coefficient Of Variation Of Cell Shape Mean Cell Roundness Parameter 0.6707 <.0001* Mean Cross Sectional Area Mean Cell Size 0.6702 <.0001* Mean Cell Length Mean Nuclear Size 0.6502 <.0001* Mean Cell Length Mean Cell Size 0.6408 <.0001* Mean Cross Sectional Area Mean Cell Shape -0.6377 <.0001* Mean Cross Sectional Area Mean Nuclear Roundness Parameter -0.5743 <.0001* Mean Cell Length Mean Cell Roundness Parameter 0.5643 <.0001* Mean Cross Sectional Area Mean Nuclear Size 0.5498 <.0001* Mean Cell Shape Mean Nuclear Roundness Parameter 0.5485 <.0001* Coefficient Of Variation Of Cell Shape Mean Cell Regularity Factor -0.4843 <.0001* Coefficient Of Variation Of Cell Volume Mean Cell Size 0.4632 <.0001* Mean Cell Length Coefficient Of Variation Of Cell Shape 0.4523 <.0001* Mean Nuclear Roundness Parameter Mean Cell Regularity Factor -0.4385 <.0001* Mean Cross Sectional Area Mean Cell Regularity Factor 0.4311 <.0001* Mean Nuclear Size Coefficient Of Variation Of Cell Volume 0.4004 <.0001* Mean Cross Sectional Area Coefficient Of Variation Of Cell Shape -0.3674 <.0001* Coefficient Of Variation Of Cell Shape Mean Nuclear Roundness Parameter 0.3346 <.0001* Mean Cell Length Mean Nuclear Roundness Parameter 0.3326 <.0001* Coefficient Of Variation Of Nuclear Mean Nuclear Size 0.3173 <.0001* Volume Coefficient Of Variation Of Nuclear Mean Cell Size 0.3074 <.0001* Volume Mean Cell Length Coefficient Of Variation Of Cell Volume 0.2864 <.0001* Mean Cross Sectional Area Coefficient Of Variation Of Cell Volume 0.2783 0.0001* Mean Nuclear Size Mean Cell Regularity Factor -0.2487 0.0007* Mean Cell Size Mean Nuclear To Cytoplasm Ratio -0.2410 0.0010* Mean Cross Sectional Area Mean Nuclear To Cytoplasm Ratio -0.2294 0.0017* Mean Cell Length Coefficient Of Variation Of Nuclear 0.2015 0.0061* Volume Mean Cell Size Mean Nuclear Roundness Parameter -0.1867 0.0112* Coefficient Of Variation Of Cell Volume Mean Cell Regularity Factor -0.1813 0.0138* Mean Cell Size Mean Cell Regularity Factor -0.1804 0.0143* Mean Cross Sectional Area Coefficient Of Variation Of Nuclear 0.1714 0.0200* Volume Mean Nuclear To Cytoplasm Ratio Mean Nuclear Roundness Parameter 0.1639 0.0262* Mean Nuclear Size Mean Nuclear To Cytoplasm Ratio 0.1542 0.0367* Coefficient Of Variation Of Cell Volume Mean Nuclear To Cytoplasm Ratio -0.1490 0.0435* Table 46
202 Appendices
Figure 0-44
Prob 0.5806 0.4194 G^2 Rate 7.63817 0.6667 0.3333 6 LogWorth cient of Variation of 2.3956185 Prob f 0.2527 0.7473 Count Level KIT Wildtype Coe Volume<33.5068173 Nuclear G^2 Rate 0.2667 0.7333 17.397455 Prob cient of Variation of Cell 15 0.0065 0.9935 0 f Count G^2 Level KIT Wildtype Coe Shape>=53.9025263 Rate 0.0000 1.0000 9 cient of Variation of f Count Level KIT Wildtype Coe Volume>=33.5068173 Nuclear Prob IHCstaining doesnot 0.2270 0.7730 G^2 Rate 0.2500 0.7500 8.9973623 8 LogWorth 0.8309585 Prob Count 0.0609 0.9391 Level KIT Wildtype Coeficient of Variation of Cell Volume<21.5226938 G^2 Rate 0.0611 0.9389 60.233283 LogWorth 1.1926993
Prob 0.0417 0.9583 Prob 131 0.2924 0.7076 G^2 Count G^2 Level KIT Wildtype Nuclei Score(3, 2) Score(3, Nuclei Rate Rate 0.0417 0.9583 7.63817 66.510799 0.3333 0.6667 6 192 LogWorth LogWorth cient of Variation of Cell 0.5125225 0.8684865 Prob Prob Count f Level KIT Wildtype 0.0346 0.9654 0.1074 0.8926 All Rows Count Level KIT Wildtype Coe Shape<37.3859101 G^2 G^2 Rate Rate 0.0345 0.9655 0.1111 0.8889 34.798822 12.557955 0.767768 LogWorth Prob cient of Variation of Cell Prob 18 0.0036 0.9964 116 0 f 0.0187 0.9813 Count Count G^2 Level KIT Wildtype Level KIT Wildtype G^2 Coe Shape<53.9025263 Mean RegularityCell factor<0.86155223 Rate Rate 0.0000 1.0000 0.0185 0.9815 19.918668 cient of Variation of Cell 12 f 108 Count Count Level KIT Wildtype Coe Shape>=37.3859101 Level KIT Wildtype Coeficient of Variation of Cell Volume>=21.5226938 mutant cytomorphology and IHC classifier tree. tree. classifier IHC and cytomorphology mutant Prob KIT KIT 0.0004 0.9996 0 G^2 Rate 0.0000 1.0000 90 Count Level KIT Wildtype Mean RegularityCell factor>=0.86155223 Prob FigureA.2 Full contribute to the six highestsplits. ROC AUC 0.9695 0.0007 0.9993 0 G^2 Rate 0.0000 1.0000 61 Count Level KIT Wildtype Nuclei Score(0, 1) Score(0, Nuclei
203 Appendices
A.2 Calculation of Missing Sun Damage Values by Neural Network Analysis
A strong correlation exists between the presence of a sun-damaged skin (as assessed by the elastosis scores outlined in figure 3.1) at a melanoma’s primary site and both the patient’s age and the anatomical location of the involved site (appendix figure A1.3).
Figure A1.3. Calculation of missing sun damage score by neural network analysis. A Logistic regression of the presence of sun damaged skin as a product of patient age illustrate a highly statistically significant positive correlation. B Fisher’s exact test for association between sun damaged skin and primary melanoma site Figure 0-45
204 Appendices
In addition, there is an association between gender and exposure score, which in turn is also strongly associated with solar elastosis (appendix table A1.4). These strong correlations and association can be used to generate a statistical model for the calculation of the probability of solar elastosis being present at a melanoma’s primary site.
Figure A.4. Fisher’s exact test for association between sun damaged skin and gender post neural network analysis. Table 47
The technique of neural network with K-fold cross validation was used to explore the predictive capacities of age, gender and primary site in determining the presence of sun damage at the melanoma primary site. The three variables represented three hidden nodes and the standard setting in JMP8 were used to generate the neural network. The result was a model with an R square value of 0.57012 and a corresponding ROC value of 0.9355 (appendix figure A1.5). This equates to 11 misclassification events in a pool of 107 cases. The predictive formulas were used only for cases that had information regarding the location of the primary site (i.e. exposure scores) but for which the primary site was not available for elastosis assessment (n=30). This was done to maximize the number of cases eligible for partition modeling analysis in the assessment of clinical, histological and immunohistochemical predictors of kinase mutation genotype.
205 Appendices
Figure A.5 ROC curve for neural network predicted solar elastosis. ROC AUC value equals 0.9355
1.00 0.90 0.80 0.70
0.60 0.50
Sensitivity 0.40 0.30
0.20 0.10
0.00 0.00 0.20 0.40 0.60 0.80 1.00 1-Specificity
Figure 0-46
Table 48
Histologically Confirmation of Solar Elastosis Elastosis No Yes
of Solar Solar of No 28 3 31
Yes 8 68 76 36 71 107 Neural Network Network Neural Prediction Elastosis Table A1.16 Neural network prediction of sun damage by sun damaged skin. Fisher’s Exact Test Two-tailed p-value <0.0001.
The use of neural network to predict missing solar elastosis generates a larger cohort available for analysis. This expanded cohort still shows similar associations of sun-damaged skin to female gender (p- value 0.1182, Pearson Chi-square), advanced age at diagnosis (p-value <0.0001, Wilcoxon Chi-square) and primary melanoma sites (14 sites p-value 0.0004, Fisher’s exact test) (4 sites p-value 0.0038, Fisher’s exact test).
206 Appendices
Clinical and Histopathological Present Purdue Viros Handolias (2007) Factors (n) Study (2005) (2008) Sex Male 130 300 154 146 Female 62 293 128 105 Age at diagnosis Mean 66.2 59 (med) 55 <40 84 40-54 165 44-69 178 70+ 166 Solar elastosis 0 9 - 1 29 - 2 24 - 3 50 - None 228 - Mild 216 - Marked 115 - Sun damage No 38 NP 152 Yes 74 106
Tumour Site Head and Neck 35 98 48 58 Trunk 42 207 114 84 Upper Limb 26 127 37 - Lower Limb 38 142 52 - Extremities - - - 102 Glabrous Skin 5 - 35 7 Unknown 1° 46 19 - - Histological Type ALM 3 - 23 6 DM 4 - - 7 LMM 7 77 39 26 NM 37 69 16 46 SSM 15 388 189 161 Unknown /Other 126 59 - 5 Table A1.17 Comparison of demographic and clinicopathological characteristics of KIT mutation status assessing studies.
207 Appendices
Clinical and Histopathological Present Purdue Viros Handolias (2007) Factors (n) Study (2005) (2008) Breslow Thickness (mm) Mean 4.8 1.29(med) 2.1 (1.1med) ≤0.75 286 - <1.00 - 120 .076-1.50 175 - 1.0-4.0 104 - 1.51-4.00 - 86 ≥4.00 28 37
Table A1.17 (Con’t) Comparison of demographic and clinicopathological characteristics of KIT mutation status assessing studies.
208 Appendices
Table A.6 Kaplan Meier curves of Melanoma specific survival (weeks) for patients with AJCC stage I or II disease according to mutational genotype.
1.0 BRAF NRAS 0.9 Wildtype 0.8 0.7 0.6 0.5
Surviving 0.4 0.3 0.2 BRAF vs. NRAS p:0.1341 0.1 BRAF vs. BRAF wt p:0.2137 NRAS vs. NRAS wt p:0.2558 0.0 0 100 200 300 400 500 600 700 800 900 1100 Disease Specific Survival
209