Applications of Quantitative Proteomics and Phosphoproteomics to Study the Development of Resistance to Targeted Therapy in Cancer
Item Type dissertation
Authors Awasthi, Shivangi
Publication Date 2018
Abstract Targeted inhibition of protein kinases is a major approach to treat cancer. However, the effectiveness of kinase inhibitors is limited due to intrinsic and acquired resistance mechanisms that promote the progression and survival of cancer cells. The ...
Keywords biomarker discovery; biomarker validation; phosphoproteomics; Biomarkers; Mass Spectrometry; Molecular Targeted Therapy; Proteomics
Download date 06/10/2021 00:10:32
Link to Item http://hdl.handle.net/10713/7927 Shivangi Awasthi
Pre-doctoral fellow/Graduate Research Assistant
Center for Cancer Research National Cancer Institute(NCI) National Institutes of Health(NIH) 9000 Rockville Pike, Building 10 3B49 Bethesda, MD 20892 662-202-8213, [email protected]
Education:
University of Maryland, Baltimore, MD Ph.D. in Pharmaceutical Sciences, GPA: 3.83/4.0 Aug. 2013- May 2018 University of Mississippi, Oxford, MS M.S. in Medicinal Chemistry, GPA: 3.7/4.0 Aug. 2011 – July 2013 University of Delhi, New Delhi, India Bachelor of Pharmacy (B.Pharm.), GPA: 4.0/4.0 Aug. 2007 -May 2011
Professional Experience:
National Institutes of Health (National Cancer Institute), Bethesda, MD Pre-doctoral fellow July 2016-May 2018 • Utilized mass spectrometry based targeted multiple reaction monitoring approach on a triple quadrupole MS instrument to develop targeted protein assays for 12 biomarker candidates. • Validated these bioanalytical assays in drug sensitive and resistant cells for relative and absolute quantification of tyrosine phosphorylated markers in lung adenocarcinoma as potential biomarkers of drug sensitivity.
University of Maryland, Baltimore, MD Graduate Research Assistant Aug. 2013- May 2018 • Applied mass spectrometry based bottom-up proteomics and phosphoproteomics approach to identify cancer resistance markers in human melanoma cells. • Identified covalent binding of small molecule inhibitors of the ERK proteins as a potential cancer therapy. • Extensive experience in the development, optimization and validation of a wide variety of analytical methods, including: 1. LC MS peptide mapping,
2. bottom-up analyses for protein identification and quantification, 3. determination of post-translational modifications and protein interactions. • Contributed to several fee-for-service projects for the MS core facility at UMB, involving sample preparation from complex chemical matrices and LC MS method development.
Graduate Teaching Assistant, Baltimore, MD • PHAR 557 Pharmaceutical Abilities Lab, Fall 2013 • PHAR 566 Self-Care and Non-prescription Pharmacotherapy, Spring 2014
University of Mississippi, Oxford, MS Graduate Research Assistant Aug. 2011 – July 2013 • Successfully completed the thesis on the synthesis of dual-targeting ligands of sigma receptors and dopamine transporters as a potential pharmacotherapy for cocaine abuse. • Carried out synthesis of dual-targeting ligands of sigma receptors and dopamine transporters as a potential pharmacotherapy for cocaine abuse. • Synthesized six compounds using Friedal Craft’s, Suzuki coupling, Wittig, alkylation and anhydrous reactions. • Did organic/aqueous extractions and purification of compounds using flash column chromatography. • Characterization of compounds using High-resolution Mass spectrometry, HPLC and 1D and 2D NMR.
Publications: 1. Abstract 2203: A SRM/MRM based targeted proteomics strategy for absolute quantification of potential biomarkers of TKI sensitivity in EGFR mutated lung adenocarcinoma. Awasthi S, Zhang X, Maity T, Oyler BL, Goodlett DR, Guha U. Cancer Research, 2017 July; 77(13 Supplement): 2203-2203.
2. Quantitative Tyrosine Phosphoproteomics of Epidermal Growth Factor Receptor (EGFR) Tyrosine Kinase Inhibitor-treated Lung Adenocarcinoma Cells Reveals Potential Novel Biomarkers of Therapeutic Response. Zhang X, Maity T, Kashyap MK, Bansal M, Venugopalan A, Singh S, Awasthi S, Marimuthu A, Charles Jacob H. K Belkina N. Pitts, S, Cultraro CM, Gao S, Kirkali G, Biswas R, Chaerkady R, Califano A, Pandey A, Guha U. Molecular Cellular Proteomics, 2017 May; 16(5): 891-910.
3. Comprehensive glycosylation profiling of IgG and IgG-fusion proteins by top-down MS with multiple fragmentation techniques. Tran BQ, Barton C, Feng J, Sandjong A, Yoon SH, Awasthi S, Liang T, Khan MM, Kilgour DP, Goodlett DR, Goo YA. Journal of Proteomics, 2016 Feb 16; 134: 93-101.
4. Glycosylation characterization of therapeutic mAbs by top- and middle-down mass spectrometry. Tran BQ, Barton C, Feng J, Sandjong A, Yoon SH, Awasthi S, Liang T, Khan MM, Kilgour DP, Goodlett DR, Goo YA. Data in Brief, 2015 Nov 24; 6: 68- 76.
5. Quantitative targeted proteomic analysis of potential markers of TKI sensitivity in EGFR mutated lung adenocarcinoma. Awasthi S, Maity T, Oyler BL, Zhang X, Goodlett DR, Guha U. (Journal of Proteomics, https://doi.org/10.1016/j.jprot.2018.04.005)
6. Development, optimization and application of SRM/MRM based targeted proteomics strategy for quantification of potential biomarkers of TKI sensitivity. Awasthi S, Maity T, Oyler BL, Zhang X, Goodlett DR, Guha U. (Data in Brief, https://doi.org/10.1016/j.dib.2018.04.086)
Poster Presentations:
1. “Investigating the Pharmacology of Kratom (Mitragyna speciosa) Alkaloids and derivatives.” Awasthi S, Furr EB, Habib E, León F, Cutler SJ, McCurdy CR. Annual poster session of National Center for Natural Product research at University of Mississippi, 2013, University, MS.
2. “Effect of Chemical Matrices on The Charged Ion Peaks Produced by NSI And SAWN MS.” Awasthi S, Huang Y, Yoon SH, Kilgour DP, Goo YA, Goodlett DR. ASMS 2014, Baltimore MD.
3. “Analysis of High Frequency Surface Acoustic Wave Nebulizer for Improved Ion Sensitivity.” Heron S, Awasthi S, Yoon SH, Huang Y, Goodlett DR. ASMS 2014, Baltimore MD.
4. “Investigating Vaccine Cross-reactivity in Enteric fevers.” Awasthi S, Tran BQ, Young Ah Goo YA, Raphael Simon R, Goodlett DR. MSBM 2014, Dubrovnik, Croatia.
5. “Ultrasonic Acoustic Wave Nebulization-mass spectrometry (UltrAWN-MS) for Unconventional Explosives Characterization.” Oyler BL, MacKerell A, Hom K, Chipuk J, Lareau R, Awasthi S, Yoon SH, Goodlett DR, Kilgour DPA. ASMS 2015, St. Loius MO.
6. “Characterization of a Monoclonal Antibody (mAb) using Multiple Fragmentation techniques and novel FT data processing software.” Tran BQ, Awasthi S, Liang T, Khan MM, Kilgour DP, Goodlett DR, Goo YA. ASMS 2015, St. Loius MO.
7. “Phosphoproteomic Analysis of Differential Protein Expression in BRaf-Mutated Melanoma cells with Acquired Resistance to BRaf, MEK1/2, or ERK1/2 Inhibitors.”
Awasthi S, Sheenstra J, Tran BQ, Goo YA, Goodlett DR, Shapiro P. ASMS 2016, San Antonio TX.
8. “A SRM/MRM based Targeted Proteomics strategy for Quantification of Potential Biomarkers of TKI sensitivity in EGFR mutated Lung Adenocarcinoma.” Awasthi S, Maity T, Oyler BL, Zhang X, Goodlett DR, Guha U. AACR 2017, Washington D.C.
9. “Quantitative targeted proteomic analysis of potential markers of TKI sensitivity in EGFR mutated lung adenocarcinoma.” Awasthi S, Maity T, Oyler BL, Zhang X, Goodlett DR, Guha U. ASMS 2017, Indianapolis, IA
10. “Integrated proteogenomic analyses reveal extensive tumor heterogeneity and validate expression of somatic mutations in lung adenocarcinoma.” Zhang X, Rudnick P, Gao S, Awasthi S, Fenyo D, Guha U. US HUPO 2018, Minneapolis, MN.
Technical Experience:
• Sample preparation for quantitative proteomics in complex matrices: cell culture (SILAC, label-free, TMT), bottom-up, middle down, top-down proteomics sample preparation, solid phase extraction (SPE), immunoaffinity enrichment, BCA assay, immunoassays, gel-electrophoresis.
• Operation of instruments including Thermo Orbitrap Elite, Orbitrap QE HF, Agilent Triple Q 6495 and Agilent 1290 HPLC. Also, mass spectrometer maintenance including inserting/removing/cleaning of parts including ion funnel/tube lens/skimmer/pump and regular tuning and calibration of the instruments.
• Interpreting mass spectrometry data and statistical analysis using proteomics tools Xcalibur, Mass Hunter, Mascot, MaxQuant/Perseus, Trans-Proteomic Pipeline, Skyline, MSStats, Ingenuity Pathway Analysis, Cytoscape, R, GraphPad Prism, Excel.
Honors & Leadership:
• Awarded student travel award to attend ASMS 2014, 2016, 2017 and 2018.
• Initiated to the Rho Chi society in 2015.
• Senator for the Indian Student Association at the University Student Graduate Association (USGA) at UMB for the year 2014-2015.
Abstract
Dissertation Title: Applications of Quantitative Proteomics and Phosphoproteomics to
Study the Development of Resistance to Targeted Therapy in Cancer
Shivangi Awasthi, Doctor of Philosophy, 2018
Dissertation Directed By: Dr. David R. Goodlett, Dr. Paul Shapiro & Dr. Udayan Guha
Resistance to therapy is the major limitation to effective cancer treatment.
Although targeted inhibition of oncogenic kinases temporarily suppresses the disease, intrinsic and acquired cancer cell plasticity promotes progression and survival through biochemical or genetic alterations of the signaling pathways. The objective of this dissertation is to use liquid chromatography coupled to mass spectrometry (LC MS) based quantitative proteomics to identify potential biomarkers of resistance and response to molecularly targeted therapies in cutaneous melanoma and lung adenocarcinoma in vitro.
For the first part of this thesis, we conducted a proteomic analysis of the acquired drug resistance to extracellular signal-regulated kinase (ERK1/2) pathway inhibitors in a melanoma cell line model. A combination of simple western assays, global label-free bottom-up proteomics, phosphoproteomics and pathway analysis was used to characterize the differential protein expression in the drug resistant melanoma cells. Examination of the quantitative data pointed to an invasive and metastatic phenotypic signature in the resistant cells. We also identified and verified the overexpression of β-catenin, Signal transducer and activator of transcription 3 (STAT3) and Caveolin-1 (CAV-1) in MEK1/2 and ERK1/2 inhibitor resistant cells. These findings suggest that these proteins have a role in the development of resistance and may represent novel targets for co-therapy. For the second part of this thesis, we have utilized a multiple reaction monitoring (MRM) based targeted
proteomic technique to verify previously identified potential biomarkers of epidermal
growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) response in lung
adenocarcinoma. Previously published global phosphoproteomic data was used to select a list of phosphotyrsine peptides (pY) and MRM based relative and absolute quantitative methods were developed to measure their expression in TKI sensitive and resistant lung adenocarcinoma cells. Optimized scheduled MRM assays were developed using heavy
labelled synthetic peptide standards which identified the targets with good reproducibility
and repeatability. The results indicated that of the 11 chosen sites, EGFR-pY1197, Signal
transducer and activator of transcription 5A (STAT5A)-pY694 and CAV-1-pY14 can be used as potential biomarkers of EGFR TKI sensitivity, regardless of the EGFR TKI used.
Overall these data advance our understanding of the mechanisms of targeted therapy resistance and highlight candidate biomarkers of therapy resistance and sensitivity.
Applications of Quantitative Proteomics and Phosphoproteomics to Study the Development of Resistance to Targeted Therapy in Cancer
by Shivangi Awasthi
Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, Baltimore in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2018
ACKNOWLEDGEMENTS
First and foremost, I would like to thank my graduate mentor and co-adviser, Dr. David R.
Goodlett, for giving me the opportunity to join and work in his lab. Thanks Dave, for all your support and help. I am also grateful to my co-advisor Dr. Paul Shapiro for giving me the opportunity to work on interesting projects and helping me learn cancer biology and biochemistry. I also owe a great deal of gratitude to Dr. Udayan Guha, my supervisor at
NCI, for the pre-doctoral fellowship and an extremely rewarding tenure at NCI. A big thanks to the other members of my thesis committee Dr. Patrick Wintrode and Dr. Alex
MacKerell for their time and support.
I am fortunate to be a part of the Goodlett lab, which has been instrumental in shaping me as a scientist and a mass spectrometrist. I have really enjoyed my tenure working alongside some smart analytical chemists like Sung Hwan, Bao, DPAK and Thomas. I would also like to thank Young Ah for introducing me to proteomics projects and being extremely supportive and encouraging throughout my graduate training.
I would also like to acknowledge my fellow lab mates at UMB, Ben, Mohsin and Tao and my lab mates at Guha lab, Tapan, Abhilash, Conny, Khoa and Xu for being great colleagues and friends. A special thanks to Ben for long conversations about experimental design, instrument troubleshooting and data analysis.
On a personal note, I must thank my parents, brother and sister-in-law for their relentless support throughout the years, without which I would not have made it this far. Lastly, I want to thank my best friends Tanvi and Rahul for keeping me sane and being my constant support system.
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Table of Contents
ACKNOWLEDGEMENTS ...... iii
LIST OF TABLES ...... x
LIST OF FIGURES ...... xi
LIST OF ABBREVIATIONS ...... xvii
Chapter 1: Introduction and Background ...... 1
1.1. Development of resistance to cancer therapy: the biggest challenge facing cancer
drug discovery ...... 1
1.1.1. Drug efflux: ...... 2
1.1.2. Alteration in drug targets: ...... 2
1.1.3. DNA damage repair: ...... 3
1.1.4. Downstream aberrant regulation of apoptosis: ...... 4
1.1.5. Autophagy: ...... 4
1.1.6. Adaptive responses: ...... 5
1.1.7. Tumor microenvironment: ...... 5
1.1.8. Cancer stem cells: ...... 6
1.2. Mass spectrometry based techniques for biomarker discovery ...... 6
1.2.1 MS-based proteomics...... 7
1.2.3 Quantitative proteomics techniques: ...... 11
1.2.4 Quantitative proteomics and proteogenomics in systems biology ...... 11
iv
1.2.4.1. CPTAC studies from 2006 to 2011:...... 13
1.2.4.2. CPTAC studies from 2011-2016: ...... 15
1.3 Kinase signaling in cancer ...... 18
1.4 Dissertation objectives ...... 20
Chapter 2: Differential Protein Expression Patterns in BRaf-Mutant Melanoma Cells with
Acquired Resistance to BRaf, MEK1/2, or ERK1/2 inhibitors ...... 22
Abstract: ...... 22
2.2. Methods and materials: ...... 26
2.2.1. Cell lines and reagents ...... 26
2.2.2. Sample preparation and Trypsin digestion ...... 27
2.2.3. LC MS/MS analysis for global proteomic profiling ...... 29
2.2.4. Titanium dioxide (TiO2) enrichment ...... 30
2.2.5. LC MS/MS analysis for global phosphoproteomic profiling ...... 30
2.2.6. Protein and phosphopeptide quantification and bio-informatics ...... 31
Raw MS files for total protein data sets were uploaded and searched against the
Uniprot human proteome database (downloaded on 07/19/2013) in Maxquant
v.1.5.0.25 using the Andromeda search engine for label-free quantitation. For the
global proteomics analysis, raw files were searched with cysteine
carbamidomethylation as a fixed modification and N-term acetylation and
methionine oxidation as variable modifications. For the global phosphoproteomic
analysis, phosphorylation (STY) was added as a variable modification. The
v
digestion mode was set to specific with trypsin as the digestion enzyme and two
missed cleavages were allowed. Mass tolerances were set to 6 ppm for precursor
ions and 0.05 Da for-fragment ions. The search criteria further included false
discovery rates of 0.01 for both protein and peptide identifications. The minimum
peptide length was 7 amino acids. Decoy database search was activated and the
database searching was supplemented with the common contaminants often found in
cell culture and proteomics sample preparation experiments, these were later
identified and removed. All the other settings were set to default except the “match
between runs” feature was enabled with the default settings...... 31
Protein quantitation was carried out based on the LFQ intensities and
phosphopeptide quantitation was carried out peptide intensities for the modified
(STY) P-sites, which were then uploaded into the statistical package Perseus
(v.1.5.3.1) for further analysis. Intensities were pre-processed by log2 transformation
and filtered by a minimum three identifications per protein...... 32
2.2.7. Gene ontology annotations and pathway analysis ...... 33
2.2.8. Simple Western assays ...... 34
2.3. Results and discussion: ...... 34
2.3.1. Characterization of the drug resistant cells: ...... 35
2.3.2. Protein and phospho-peptide identifications in the drug sensitive and resistant
cell lines: ...... 38
2.3.3. Quantitative analysis of the drug resistant proteome and phosphoproteome: 39
2.3.2. Differential protein expression patterns common to all MAPK inhibitor
vi
resistant cells (Fig 2.15): ...... 45
2.3.3. Differential protein expression patterns common to BRaf and ERK1/2
inhibitor resistant cells: ...... 46
2.3.4. Differential protein expression patterns common to BRaf and MEK1/2
inhibitor resistant cells: ...... 47
2.3.5. Analysis of the protein-protein interaction networks and gene ontology
annotation clustering: ...... 47
...... 54
2.3.5. Validation of proteomic findings ...... 55
2.4. Conclusions ...... 58
Chapter 3: Quantitative targeted proteomic analysis of potential markers of Tyrosine
Kinase Inhibitor (TKI) sensitivity in EGFR mutated lung adenocarcinoma ...... 60
Abstract ...... 60
Significance: ...... 61
3.1. Introduction ...... 62
...... 66
3.2. Materials and methods ...... 66
3.2.1 Cell culture and lysate preparation ...... 66
3.2.2 Protease digestion, peptide clean-up and IP-enrichment of phosphotyrosine
peptides ...... 67
3.2.3 Synthetic peptide standards...... 68
vii
3.2.4 Nano-LC-MRM method development and analysis ...... 68
3.2.5 MRM assay refinement, generation of calibration curves and statistical data
analysis ...... 70
3.2.6 cBioPortal analysis: ...... 72
3.3. Results ...... 73
3.3.1 LC-MRM method development and optimization:...... 73
3.3.2 Relative quantitation of the phosphotyrosine sites upon TKI treatment in the
lung adenocarcinoma cells: ...... 83
3.3.3 Phosphosite abundance estimation in the lung adenocarcinoma cells from the
calibration curves: ...... 88
3.4. Discussion ...... 93
...... 99
The query against the same patient database for the targets EGFR, CAV1 and STAT5A identified alterations in 21% of the 230 sequenced patients (Fig. 3.20) and a significantly lower disease-free survival (Logrank test P-value:0.00583) (Fig. 3.20)...... 100
3.5. Conclusions: ...... 101
Chapter 4: Conclusions and Future directions ...... 102
4.1 Shotgun proteomics to investigate ERK1/2 therapy resistance in melanoma ...... 103
4.2. Targeted proteomics to investigate EGFR TKI therapy resistance in lung
adenocarcinoma ...... 104
Appendix I: Development, optimization and application of SRM/MRM based targeted
viii
proteomics strategy for quantification of potential biomarkers of EGFR TKI sensitivity
...... 107
Specifications Table ...... 108
Value of the data ...... 108
I.1 Data ...... 109
I.2 Experimental Design, Materials and Methods ...... 109
I.2.1 Spectral library generation and retention time approximation ...... 109
I.2.2 Immunoaffinity enrichment, LC MS/MS and data analysis ...... 110
I.2.3 Estimation of dwell times, quantitative data and analysis of the replicates ... 112
I.2.4 Quantitative assay characterization and calibration curve generation ...... 113
I.2.5 cBioPortal analysis of the target genes: ...... 113
References ...... 115
ix
LIST OF TABLES
Table 2. 1: IC50 values (µM) of the drug sensitive (A375) and drug resistant cell lines
(A375-PLX(R), A375-AZD(R) and A375-SCH(R))...... 35
Table 2. 2: IPA shows the top canonical pathways represented in the A375-AZD(R) and
A375-SCH(R) cells...... 48
Table 3. 1: The list of phosphotyrosine tryptic peptide targets used for the development of
the modified immuno-MRM assays...... 66
Table 3. 2: The list of precursor and product ions, their m/z values, charge and collision
energies for the optimized assays. Spectral library and individual heavy isotope labelled
peptide injections were carried out to choose optimally performing precursors and
product ions for each target...... 74
Table 3. 3: Phosphopeptide abundance determined from three biological replicates using
calibration curves. Mean abundance of target phospho-peptides expressed in fmol/mg
protein obtained from reverse calibration curves following treatment with DMSO/vehicle,
erlotinib and osimertinib in H3255 and H1975 cells. LOD, Limit of Detection, LOQ,
Limit of Quantification. *under LOD; ** under LOQ; *** above linear dynamic range. 90
x
LIST OF FIGURES
Fig. 1. 1: Downstream factors affecting the development of resistance...... 3
Fig. 1. 2: Overview of LC MS based studies. Reproduced with permission from (Gillet,
Leitner et al. 2016)...... 8
Fig. 1. 3: Overview of the acquisition sequence of the LC MS based proteomics. a. DDA
based shotgun proteomics b. MRM based targeted proteomics. Reproduced with
permission from (Gillet, Leitner et al. 2016)...... 9
Fig. 1. 4: Outline of the sample preparation and MS data analysis for proteomics studies.
...... 10
Fig. 1. 5: Overview of the EGFR signaling involved in tumorigenesis and targeted kinase
inhibitors...... 21
Fig. 2. 1: ERK1/2 signaling pathway and targeted MAPK inhibitors...... 24
Fig. 2. 2: Chemical structures of ERK1/2 pathway inhibitors: BRaf inhibitor (PLX4032),
MEK1/2 inhibitor (AZD6244) and ERK1/2 inhibitor (SCH772984)...... 28
Fig. 2. 3: Outline of the experimental design: three biological replicates were prepared for
each sample type for global proteomic and phosphoproteomic profiling...... 32
Fig. 2. 4: Overview of the sample preparation and LC MS/MS analysis...... 33
Fig. 2. 5: Cell morphology of the drug sensitive parent (A375) and drug resistant cell lines (A375-PLX(R), A375-AZD(R) and A375-SCH(R))...... 36
Fig. 2. 6: Simple western characterization of the cell lines for Braf, phosphorylated ERK2
and phosphorylation sites of MEK1 and MEK2. The phosphorylated data was normalized
to total protein data. The error bars show +/- standard deviations. * p-value < 0.05,
xi
** p-value < 0.01, *** p-value <0.005 ...... 37
Fig. 2. 7: Total number of LC MS protein and phosphosite identifications in the drug
sensitive and resistant cells. The error bars show +/- standard deviations...... 38
Fig. 2. 8: Experimental repeatability and overlap for the three biological replicates
(Experiments) for the LC MS based protein identifications...... 40
Fig. 2. 9: Distribution of the phosphosite multiplicity and percentages for the number of
pS, pT and pY sites identified...... 40
Fig. 2. 10: Experimental repeatability and overlap for the three biological replicates for
the LC MS based phosphosite identifications...... 41
Fig. 2. 11: Pearson correlation coefficients (blue numbers) for the overlap of the
biological replicates for the phosphosite normalized intensities...... 42
Fig. 2. 12: Label free proteomic quantification: heat maps showing the log 2 transformed
LFQ intensities for the differentially expressed proteins in the resistant cells. The proteins
have been clustered via unsupervised hierarchical clustering...... 43
Fig. 2. 13: Label free phosphoproteomic quantification: volcano plots showing log2 fold
change on the x-axis and the -log p value on the y-axis for A375-AZD(R) and A375-
SCH(R) cells...... 44
Fig. 2. 14: Venn diagram showing the overlap of the number of differentially expressed
proteins in the three resistant cell lines (BRAF(R): A375-PLX(R), MEK (R): A375-
AZD(R) and ERK(R): A375-SCH(R))...... 46
Fig. 2. 15: Protein-protein interaction maps showing the top functional annotations from
IPA in A375-PLX(R), A375-AZD(R) and A375-SCH(R) cells. The p-value is a measure of the likelihood that the association between the set of proteins with the given annotation
xii
is due to random chance. Red arrows signify activation and green arrows signify
inhibition. Black arrows signify that the manner of interaction is undetermined. The red
and green colored proteins mean upregulated and downregulated proteins respectively.
The color intensities correlate linearly to the extent of regulation (high or low log2 fold
change)...... 49
Fig. 2. 16: Gene ontology molecular function (GOMF) enrichment analysis via DAVID
for A375-AZD(R) and A375-SCH(R) cells...... 54
Fig. 2. 17: Simple western analysis of the three biological replicates for β-catenin. The β- catenin data has been normalized to the total protein data for each replicate...... 55
Fig. 2. 18: Maxquant output LFQ intensities for STAT3 for A375-control, A375-AZD(R) and A375-SCH(R) cells. *** p-value <0.005 ...... 56
Fig. 2. 19: Simple western analysis of the three biological replicates for phospho AKT.
The phospho AKT data has been normalized to the total protein data for each replicate. 57
Fig. 2. 20: Simple western analysis of the three biological replicates for CAV1. The
CAV1 data has been normalized to the total protein data for each replicate...... 58
Fig 3. 1: Graphical abstract ...... 61
Fig 3. 2: Spectral library built from previously performed Data Dependent Acquisition
(DDA) experiments to facilitate selection of optimal transitions ...... 71
Fig 3. 3: The linear regression obtained for retention time prediction using the SSRCalc
3.0 calculator...... 72
Fig 3. 4: Optimized chromatographic separation of the target phosphopeptides from
method development stage. A) The final scheduled method showing the elution of the
xiii targets with 2-minute retention time windows in DMSO/vehicle treated H1975 cells. ... 76
Fig 3. 5: The representative chromatograms for each target is marked by a black arrow.
The red peak represents the endogenous peptide chromatogram and the blue is the heavy labelled internal standard spiked-in at a constant amount of 2 fmol in all samples. The grey bars are the predicted retention times from SSRCalc 3.0. rdotp represents the correlation between the endogenous and internal standard chromatograms...... 77
Fig 3. 6: Graphs showing number of concurrent transitions being measured and estimated dwell time across the chromatographic elution of the targets for the final optimized scheduled MRM assays using a 2-minute retention time window...... 78
Fig 3. 7: Chromatographic profile for the endogenous (left) and the heavy labelled internal standards (right) for H3255 (DMSO/vehicle treated) cells...... 79
Fig 3. 8: Chromatographic profile for the endogenous (left) and the heavy labelled internal standards (right) for H1975 (DMSO/vehicle treated) cells...... 80
Fig 3. 9: Venn diagrams showing the number of unique and commonly identified phosphotyrosine sites from P-Tyr-100 and P-Tyr-1000 immunoprecipitations in control digested peptides from mouse liver extracts and digested protein extracts from H1975 cells...... 81
Fig 3. 10: Reproducibility of the optimized MRM assays from three independent proteolytic digestions and immunoprecipitations of DMSO/vehicle treated H1975 cells.
A) Retention time repeatability; the red and blue bars represent endogenous and internal standard peptides, respectively. The median CV was 0.28% for the endogenous peptides and 0.23% for the internal standards...... 82
Fig 3. 11: Peak area ratio CVs as determined by normalizing the signal intensities of the
xiv endogenous to the internal standards. The median CV was 27.47%...... 82
Fig 3. 12: Peak area ratio coefficient of variations obtained from three biological replicates for the relative quantification inH3255 and H1975 cells for DMSO/vehicle, erlotinib and osimertinib treatments...... 84
Fig 3. 13: A: Relative quantitation of phosphopeptides using peak area ratios. All the phosphotyrosine sites were significantly downregulated (p<0.005) in drug sensitive
H3255 cells upon treatments with erlotinib (Erl) and osimertinib (Osi)...... 85
Fig 3. 14: Relative quantitation of phosphopeptides using peak area ratios.
Phosphotyrosine sites EGFR-pY1197, STAT5A-pY694 and CAV1-pY14 in the H1975 cells show significant hypophosphorylation (p<0.05) with osimertinib, but not with erlotinib treatment (n.s.-not significant)...... 87
Fig 3. 15: Unsupervised hierarchical clustering of the peak area ratios from relative quantitation of phosphorylation of the targets. Heat map shows the average log2 transformed MRM ratios for erlotinib and osimertinib treatments from three biological replicates in H3255 and H1975 cells...... 88
Fig 3. 16: Response curves for the phosphotyrosine targets for quantitative analysis.
Linear regression was used to fit the data points using a 1/y weighting for each concentration...... 89
Fig 3. 17: Bar charts showing the quantitative data of phosphopeptide abundance obtained with and without TKI treatments from three biological replicates in H3255 and
H1975 cells. Error bars are +/- standard deviations...... 93
Fig 3. 18: cBioPortal query of the TCGA lung adenocarcinoma dataset for alterations in genes of the list of targets and correlation with disease-free survival. Combined
xv
alterations of 9 genes for our assay targets were seen in 292 cases (56%) of lung
adenocarcinoma in the TCGA dataset, and patients with alterations in our genes had a
statistically significant lower disease-free survival when compared to cases with no alterations...... 98
Fig 3. 19: cBioPortal query of the TCGA lung adenocarcinoma dataset for alterations in
the target list and correlation with disease-free survival...... 100
Fig 3. 20: cBioPortal query of the TCGA lung adenocarcinoma dataset for alterations in
targets EGFR, CAV1 and STAT5A and correlation with disease-free survival...... 101
Fig 4. 1: EGFR and ERK1/2 signaling ...... 102
xvi
LIST OF ABBREVIATIONS
ABC ATP-binding Cassette
ACN Acetonitrile
AGC Automatic Gain Control
ALDH Aldehyde dehydrogenase
ALK Anaplastic lymphoma kinase
ANOVA Analysis of Variance
ATP Adenosine triphosphate
ATTC American Type Culture Collection
BCA Bicinchoninic Acid Assay
BCL B-cell lymphoma 2
BCRP Breast Cancer Resistance Protein
CDK12 Cyclin-dependent kinase 12
CE Collision Energy
CETN3 Centrin-3
CID Collision Induced Dissociation
CIN Chromosomal Instability
CRL Clinical Research Laboratory
CV Coefficient of Variation
DDA Data Dependent Acquisition
DIA Data Independent Acquisition
DMSO Dimethyl sulfoxide
xvii
DNA Deoxyribonucleic acid
DTT Dithiothreitol
ECM Extracellular Matrix
EGFR Epidermal Growth Factor Receptor
EIF2 Eukaryotic Initiation Factor 2
ERBB3 Receptor tyrosine-protein kinase erbB-3
ERK Extracellular Signal–Regulated Kinases
FA Formic Acid
FASP Filter Aided Sample Preparation
FDR False Discovery Rate
FT Fourier Transform
FWHM Full width at Half Maximun
GTP Guanosine-5'-triphosphate
HCD Higher energy collisional dissociation
HDAC Histone deacetylases
HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid
HER2 Human epidermal growth factor receptor 2
HLA Human Leukocyte Antigen
IAM Iodoacetamide
IAP Immunoaffinity Purification iTRAQ Isobaric tags for relative and absolute quantitation
JAK Janus Kinase
LC-MS Liquid Chromatography–Mass Spectrometry
xviii
LFQ Label Free Quantitation
LTQ Linear Ion Trap m/z mass to charge ratio
MDR Multi-Drug Resistance
MHC Major Histocompatibility complex mM milliMolar
MRM Multiple Reaction Monitoring
MTOR Mechanistic target of rapamycin
MWCO Molecular weight cutoff
NCI National Cancer Institute
NF-κB Nuclear Factor kappa-light-chain-enhancer of activated B cells
NSCLC Non-Small Cell Lung Cancer
PAK1 Serine/threonine-protein kinase pak-1
PCA Principal Component Analysis
PD Pharmacodynamics
PDGFRβ Platelet-derived growth factor receptor beta
PD-L1 Programmed death-ligand 1
PI3K Phosphatidylinositol-4,5-bisphosphate 3-kinase
PIK3CA Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform
PK Pharmacokinetics
PRM Parallel Reaction Monitoring pSer Phospho Serine
PTEN Phosphatase and tensin homolog
xix pThr Phospho Threonine
PTM Post Translational Modification pTyr Phospho tyrosine
QTOF Quadrupole Time of Flight
RhoA Ras homolog gene family, member A
RIPK2 Receptor-interacting serine/threonine-protein kinase 2
RNA Ribonucleic acid
ROCK Rho-associated protein kinase
RPPA Reverse phase protein array
SDS Sodium dodecyl sulphate
SIL Stable Isotope labeling
SILAC stable isotope labeling by/with amino acids in cell culture
SKP1 S-phase kinase-associated protein 1
SRM Selected Reaction Monitoring
TCEP tris(2-carboxyethyl) phosphine
TKI Tyrosine Kinase Inhibitor
TLK2 Serine/threonine-protein kinase tousled-like 2
TP53 Cellular tumor antigen p53
UHPLC Ultra-High-Performance Liquid Chromatography
xx
Chapter 1: Introduction and Background
1.1. Development of resistance to cancer therapy: the biggest challenge facing
cancer drug discovery
Cancer is a leading public health concern and is the second major cause of mortality in the
Unites States. In 2017, the estimated number of new cancer diagnosis was 1,688,780 and
the estimated number of deaths were 600,920 for both the sexes (Rebecca L. Siegel,
Kimberly D. Miller, & Ahmedin Jemal, 2017).The development of resistance to cancer
therapeutics is one of the major challenges facing the ongoing cancer research. The study
of cancer drug resistance began as early as 1963, when the importance of PK alterations,
PD effects and modifications in the drug targets was highlighted (Brockman, 1963). As
the investigations began to study the so called multi-drug resistance (MDR) in the 1980s, the discovery of P-glycoprotein associated MDR(Blade et al., 1983; Kartner, Riordan, &
Ling, 1983; Shoemaker, Curt, & Carney, 1983) and ABC transporter class of proteins(Gottesman, Fojo, & Bates, 2002) further propelled the efforts for overcoming the therapy resistance in cancer. These remarkable discoveries led to clinical investigations into MDR inhibitors (Belpomme et al., 2000; Rowinsky et al., 1994; Warner et al., 1998), despite some improvement in the tumor responses and overall survival, the curiosity in these clinical trials had faded by the early 2000s (Garraway & Chabner, 2002). Although there were several decades of excellent basic science research providing conceptual insights into enzyme biochemistry, nutrient transport, cellular metabolism etc, the translation of the same to the clinical practice remained scarce.
The recent advances in the use of high-throughput genomic, proteomic and other analytical technologies have been successful in interrogating preclinical and clinical samples to
1 characterize genetic determinants, signaling networks involved in determining the tumor sensitivity to therapy. These screening approaches can also help identify new targets to overcome therapy resistance. Some of the factors (Fig. 1.1) contributing to resistance development are listed below:
1.1.1. Drug efflux:
Amongst the 49 members of the transmembrane protein family, ATP-binding cassette
(ABC) transporters (involved in regulating the flux of drugs across the membranes) three have been extensively investigated in relation to multi-drug resistance (MDR): MDR1,
MRP1 and BCRP. These proteins have overlapping specificity and lead to increased elimination of hydrophobic chemotherapeutics. MDR1 is over expressed in many tumors causing the intrinsic resistance or its expression can be induced by chemotherapy (Thomas
& Coley, 2003). The amplification of MRP1 has been co-related with resistance development in prostate, lung and breast cancer (Nooter et al., 1997; Triller, Korosec, Kern,
Kosnik, & Debeljak, 2006; Zalcberg et al., 2000). BCRP has been associated with drug resistance in breast cancer and leukemia (Doyle et al., 1998; Robey et al., 2007).
1.1.2. Alteration in drug targets:
Mutations or changes in the expression levels of the drug targets affects the drug response i.e. genomic amplification of androgen receptor is seen in 30% of prostate cancer cases that have acquired resistance to androgen deprivation therapy (Palmberg et al., 1997). Some cancers can be highly dependent on mutations in kinase oncogenes, however gatekeeper residue mutations in the oncogenic kinases often lead to acquired resistance. Examples include EGFR T790M in NSCLC (Bell et al., 2005; Kobayashi et al., 2005; Pao et al.,
2
2005) (discussed more in Chapter 3). Chromosomal rearrangements and alterations such
as those seen in ALK have been implicated in anaplastic large-cell lymphoma and pediatric
neuroblastoma and NSCLC (4% of patients) (Y. Chen et al., 2008; Coco et al., 2012;
Morris et al., 1995; Shin, Kim, Yoon, Kim, & Lee, 2012). Secondary mutations in ALK or
ALK fusion gene amplification have been linked to relapse (Camidge et al., 2012; Shaw et
al., 2011).
1.1.3. DNA damage repair:
The activating oncogenic mutations or inhibitory alterations to the tumor suppressor genes
Prosurvival signals
Chemotherapy Altered morphology e.g. Targeted therapy [DNA Damage] invasive (EMT) [Enzyme Inhibition]
Autophagy
Cell cycle arrest Pathway redundancy
Cell DNA damage Oncogenic bypass i Death
Deregulated apoptosis Deregulated apoptosis
Fig. 1. 1: Downstream factors affecting the development of resistance.
3
leads to dys-regulation of the cell cycle arrest in some cancers (Enoch & Norbury, 1995).
The mismatch repair deficiencies (MMR) like hypermethylation of MLH1 (MMR gene) has been associated with development of resistance to cisplatin and carboplatin (Fink,
Aebi, & Howell, 1998). Alterations to the chromosomal number and structure also knows as Chromosomal instability (CIN) has been investigated in the preclinical models for its role in both acquired and intrinsic resistance to chemotherapies such as taxanes (Duesberg,
Stindl, & Hehlmann, 2000; Swanton et al., 2009) and poor survival in myeloma and several other cancers (Faragher & Fry, 2003; W. Zhou et al., 2013).
1.1.4. Downstream aberrant regulation of apoptosis:
Usually the cancer inhibiting drug-induced damage is accompanied by apoptosis or cell- death, however, apoptotic pathways may become de-regulated as part of the process of transformation; this is one of the major hallmarks of cancer (Hanahan & Weinberg, 2000).
Mutations and amplifications of genes coding for anti-apoptotic proteins like BCL-2 family members(Kitada et al., 1998; Miyashita & Reed, 1992; Nita et al., 1998; Sentman, Shutter,
Hockenbery, Kanagawa, & Korsmeyer, 1991; G. Q. Wang et al., 2001), inhibitor of apoptotic proteins (IAPs) (Hunter, LaCasse, & Korneluk, 2007) and caspase 8 inhibitor
FLIP (Hurwitz et al., 2012; Kerr et al., 2012; Wilson et al., 2007; Wilson et al., 2009) have
been linked to several malignancies and resistance to both chemotherapy and targeted
anticancer drugs.
1.1.5. Autophagy:
Autophagy plays an ambiguous role in cancer as it can slow down tumor progression via
tumor suppressor pathways and help cancer cell survival during metabolic stresses caused
4 by cancer drugs (White, 2012). Several studies have shown that autophagy may promote cancer survival thereby promoting resistance to cytotoxic drugs (Amaravadi et al., 2007;
Sasaki et al., 2010).
1.1.6. Adaptive responses:
Activation of the EGFR signaling has been widely studied as a mechanism of cancer relapse in various chemotherapies(Kishida et al., 2005; Van Schaeybroeck et al., 2005).
Targeting these prosurvival signals may lead to increased drug sensitivity, but it has been shown that these molecularly targeted agents are prone to adaptive resistance mechanisms e.g. downstream signaling through ERBB3 and PI3K-AKT pathway was found to be an adaptive response to EGFR-targeted therapies in vitro and in vivo(Sergina et al., 2007;
Wheeler et al., 2008). This phenomenon is often described as “kinome reprogramming” as the main target continues to remain blocked while an alternate enzyme gets activated either via feedback loop or genetic alterations caused by the treatment (This will be discussed more in Chapter 3). Epithelial-mesenchymal transition (EMT) driven by various transcription factors has also been linked to targeted therapy resistance in the case of EGFR inhibitors in vitro(Fuchs et al., 2008) and in the clinic(Sequist et al., 2011; Uramoto,
Shimokawa, Hanagiri, Kuwano, & Ono, 2011).
1.1.7. Tumor microenvironment:
The microenvironment in the solid tumors consisting of ECM, fibroblasts, osteoclasts, osteoblasts, macrophages(Bhatia, McGlave, Dewald, Blazar, & Verfaillie, 1995;
Bhowmick, Neilson, & Moses, 2004; Caligaris-Cappio et al., 1991) provide protection to the cancer cells and help them escape apoptosis, leading to resistance to both chemotherapy
5 and targeted therapies. The higher rates of expression of integrins and autocrine, paracrine and endocrine factors like cytokines and growth factors, modulating signaling pathways like PI3K-AKT, ERK, NF- κB has been associated hyperactive oncogenic signaling, causing higher cancer cell proliferation and acquired therapy resistance (Damiano, 2002;
Danen, 2005; Gilbert & Hemann, 2010; Hoyt et al., 2008; Wilson et al., 2012).
1.1.8. Cancer stem cells:
In some instances, failure of the treatment is associated with the presence of cancer stem cells, that have intrinsic resistance to therapy(Valent et al., 2012). Under the Cancer Stem
Cell (CSC) model, only a small subpopulation of the cells give rise to a new tumor, therefore having serious impact on therapy selection and resistance development. Stem- cell-like cancer cells have been shown to be highly resistant due to features like high expression of ABC transported proteins(Allikmets, Schriml, Hutchinson, Romano-Spica,
& Dean, 1998; Begicevic & Falasca, 2017), ALDH activity(Resetkova et al., 2010), antiapoptotic proteins like BCL-2 and BCL-XL, activated DNA damage repair machinery and key prosurvival signaling molecules(Jiang et al., 2007).
1.2. Mass spectrometry based techniques for biomarker discovery
All the essential biological processes in a cell are controlled by proteins simultaneously and in a coordinated fashion. Proteins control the synthetic, catalytic and regulatory biochemical processes and often assume a higher structured order referred to as a proteome(Hartwell, Hopfield, Leibler, & Murray, 1999). Biophysical methods have been used to study individual protein characteristics and behavior for decades now. However, the study of the proteome as an integrated system has advanced at a slower pace. MS-based approaches have emerged as a technology of choice for reliable and exhaustive
6
investigation into the composition and function of the proteome.
The recent advances in the MS-based proteomics has led to successful identification and
accurate quantification of many proteins in complex biological matrices, thereby:
advancing the understanding of the cellular signaling pathways, characterizing the
dynamics of protein-protein interactions in varied cellular states and locations, shedding
light on the complex disease mechanisms and providing unique biological insights.
1.2.1 MS-based proteomics
LC-MS/MS based proteomics analysis may comprise of simply identifying the proteins,
looking into the nature and location of the post-translational modifications
(phosphorylations, glycosylations, etc), measuring dynamic quantitative changes between
conditions or studying protein conformations or interactions in the context of biological
signaling pathways. MS-based proteomics can be subdivided broadly in two categories:
top-down proteomics (intact protein measurement) and bottom-up proteomics (peptides
measured as proxy for proteins). In the context of biomarker studies aiming to identify diagnostic factors, the bottom-up MS acquisition strategy includes discovery and validation analyses (Fig. 1.2 and Fig. 1.3). The first discovery step involves identifying as many significantly differentially expressed candidates as possible. DDA acquisition coupled to spectrum-centric searching algorithms have been the traditional method of choice for this step.
7
it has several analytical disadvantages: stochastic selection of the precursor hampers the reliable measurement of proteins in complex samples, natural peptide variant identification, peptide intensities being used for protein quantification. The validation phase of the biomarker discovery projects aims to overcome some of these limitations of the DDA proteomics by allowing for accurate and consistent quantification of the candidate molecules across large sample cohorts. It is generally accomplished via targeted data acquisition schemes (SRM/MRM, PRM) or DIA methods.
Fig. 1. 2: Overview of LC MS based studies. Reproduced with permission from (Gillet, Leitner, & Aebersold, 2016).
The typical bottom-up (shotgun) proteomics pipeline is shown in the Fig. 1.4.
8
Fig. 1. 3: Overview of the acquisition sequence of the LC MS based proteomics. a. DDA based shotgun proteomics b. MRM based targeted proteomics. Reproduced with permission from (Gillet et al., 2016).
Trypsin digested peptides are separated through reverse-phase LC(Aebersold & Mann,
2003) and thereafter subjected to electrospray ionization(Fenn, Mann, Meng, Wong, &
Whitehouse, 1989; Yamashita & Fenn, 1984) followed by a full scan MS1 and
fragmentation followed by MS2(Aebersold & Mann, 2003; Y. Zhang, Fonslow, Shan,
Baek, & Yates, 2013). The bioinformatics analysis includes confidently assigning the
peptide identifications through untargeted spectrum-centric search to protein
9
identifications(Claassen, Reiter, Hengartner, Buhmann, & Aebersold, 2012; Nesvizhskii &
Aebersold, 2005). Standard instruments for DDA include Orbitraps(Eliuk & Makarov,
2015) or Q-TOFs(Andrews, Simons, Young, Hawkridge, & Muddiman, 2011; Beck et al.,
2015). SRM-based targeted proteomics acquisition on the other hand, do not involve full
MS2 scans, rather record pre-determined precursor and product ion pairs, knowns as
transitions. The predefined list of transitions for a targeted peptide/protein provides
reproducible qualitative and quantitative data for hypothesis testing and biomarker
validation studies(Picotti & Aebersold, 2012). Standard instruments for targeted
acquisitions include triple quadrupole instruments and quadrupole linear ion traps
(QTRAP) operated as triple quadrupoles.
Fig. 1. 4: Outline of the sample preparation and MS data analysis for proteomics studies.
10
1.2.3 Quantitative proteomics techniques:
Most biological studies involve the measurement of protein quantities in a sample beyond
generating long lists of protein identifications. Bottom-up studies suffer from technical
limitations e.g. equal amounts of multiple peptides do not generate equal MS signal, due
to differences in ionization efficiencies leading to ion suppression. To overcome these
challenges, various quantitative proteomic strategies have been developed that are based
on chemical isotopic labeling(Ong et al., 2002), metabolic isotopic labeling, isobaric
tagging(Thompson et al., 2003). The idea here is that isotopomers will have minimal
differences in ionization and will suffer from near-identical ion suppression, therefore the
labeling would provide good accuracy of quantification. The labelled spike-in
proteins/peptides have an important application in measuring the absolute abundances of
endogenous proteins in biological samples, assuming the labelled standard is present in the
same amount in all the samples(Gerber, Rush, Stemman, Kirschner, & Gygi, 2003).
Despite label-based approaches being the gold-standard for MS-based quantitative proteomics methods(D. L. Tabb et al., 2016), recently label-free quantitation (directly measuring the MS1 or MS2 responses) has gained momentum. Although this approach does require complicated normalization and chromatographic alignment to account for retention time shifts and false matching of peptides across samples, it has become more popular and versatile owing to developments in high-resolution and fast scanning Orbitraps
(Eliuk & Makarov, 2015) and advancements in software solutions for alignment of multiple
MS runs (Cox & Mann, 2008).
1.2.4 Quantitative proteomics and proteogenomics in systems biology
11
The multidimensional “-omics” technology has emerged as a powerful tool to integrate genomics, transcriptomics and proteomics to study complex cellular processes. An in- depth and antibody-independent proteomic analysis of the major human tissues generated a comprehensive map of the human proteome encoded by 17,294 genes that accounted for
84% of entire annotated human genome including tandem MS spectra for proteins encoded by 2,535 genes that were not observed previously. This study utilized a combination of proteogenomic approaches to identify many novel-protein coding regions of the human genome (Kim et al., 2014). Another study from the same issue in Nature, presented a mass spectrometry based draft of the human proteome through a publicly available database
ProteomicsDB which demonstrated protein evidence for 18,097 (92%) of the 19,629
Swiss-Prot annotated human genes(Wilhelm et al., 2014). These studies reported much higher numbers of the protein-coding genes than previously identified by the multinational
Human Proteome Project and quality tests showed that both the datasets use laxed false discovery rates and tend to overestimate the number of protein coding genes(Ezkurdia,
Vázquez, Valencia, & Tress, 2014; Omenn et al., 2015).
The Cancer Genome Atlas (TCGA) project established as a collaborative initiative between
NCI and National Human Genome Research Institute (NHGRI), has made unprecedented leaps to generate comprehensive genomic and transcriptomic profiles for various human tumors (J. N. Weinstein et al., 2013). This effort was directed to profile the alterations at the level of DNA and RNA caused by cancer-inducing mutations. TCGA has completed the genomic profiling of 33 cancer types coming from over 11,000 patients so far. To further advance this knowledge, the focus was shifted to fully realize the impact of the central dogma of biology which describes the framework of how the genetic information
12 is passed on from DNA to RNA through transcription and then to proteins via translation.
In collaboration with US Food and Drug Administration (FDA) and the National Institute of Standards and Technology (NIST), NCI launched the Clinical Proteomic Technology
Assessment for Cancer program in 2006, to assess the advances in the modern proteomic technologies for global proteome profiling and targeted quantitative analyses. The studies undertaken by the program from the year 2006 to 2011 evaluated the reproducibility, repeatability and interlaboratory comparison of the data generated from complex sample matrices via the proteomic platforms. The Clinical Proteomic Technology Assessment for
Cancer program was revived as the Clinical Proteomic Tumor Analysis Consortium
(CPTAC) to complement the TCGA generated cancer genomic profiles. The overarching goal of these integrated DNA, RNA, protein profiling of the individual tumors is to provide novel biological insights in the cancer biology with the aim to translate some of these discoveries to improve disease prognosis and treatment.
1.2.4.1. CPTAC studies from 2006 to 2011:
It is widely known in the proteomics field that the DDA-based discovery proteomics faces several analytical challenges e.g. variations caused due to sample preparation protocols including protein extraction and digestion, small differences in chromatography may alter the elution profile of the peptides, stochastic sampling of the peptide ions may lead to variability in MS/MS fragmentation causing variations in protein and peptide identifications. These issues can be especially hazardous in biomarker discovery studies, which operate on the underlying assumption that the relative differences observed reflect the actual differences between the biological sample states instead of the preanalytical or technical variability of the system. For this assumption to hold true, CPTAC investigators
13
described an objective criterion to evaluate the analytical performance of the LC MS
system to be used in the biomarker studies. A comprehensive list of 46 metrics was
described to assess the performance of the core LC MS system components like
chromatography, dynamic sampling, ion source, MS1 and MS2 signals and peptide
identifications (Rudnick et al., 2010). Another important consideration is selection of
appropriate quality control (QC) samples to monitor the instrument performance. Another
CPTAC study demonstrated the use of yeast S. cerevisiae reference sample as a QC
standard to benchmark the performance of the LC MS/MS platform for comparative
proteomics studies. In this study, the authors demonstrated a large-scale production of the yeast standard, characterized it through a series of interlaboratory studies and described optimal performance characteristics on ion-trap based LC MS systems(Paulovich et al.,
2010).
A CPTAC study systematically analyzed the LC MS interlaboratory datasets and assessed the repeatability and reproducibility of the proteomic identifications from different LC
MS/MS platforms. Using pre-defined protein mixtures and SOPs, variabilities arising from identifications generated by Thermo LTQ and orbitrap systems was evaluated. As expected, orbitrap systems performed better than LTQs, sample complexity and loading amounts had less bearing to repeatability and a standardized platform led to 70-80% repeatability and reproducibility in proteomic identifications (David L. Tabb et al., 2010).
An accompanying study showed the application of robust multivariate statistical methods to evaluate data generated from CPTAC studies. This model utilized signals from LC-
MS/MS analyses in place of protein/peptide identification data to assess the mass spectrometer variability and batch effects. Robust PCA and nested ANOVA analyses were
14 used to identify outliers and generate comprehensive information on the experimental variability (X. Wang et al., 2014).
1.2.4.2. CPTAC studies from 2011-2016:
Improved statistical framework is needed to design clinical proteomics studies to attain a decent probability to successfully pass a biomarker from discovery and verification to clinical validation. A study by the CPTAC investigators described a statistical model that considered the plasma protein dynamic range, biological variability and instrument variation and described calculations of biospecimen cohort size needed to achieve good statistical power at various stages of the clinical biomarker discovery pipelines(Skates et al., 2013).
The authors uncovered unanticipated effects that tumor collection protocols including nature and duration of tissue freezing/harvesting/processing can have over the pTyr signaling. This study also highlighted the challenges associated with accurately defining tyrosine phosphorylation dynamics from intertumoral heterogeneity leading to unforeseen patient specific responses to ischemia (Gajadhar et al., 2015). This study was complementary to an earlier report published by the same group that investigated the ischemia-driven effects on pSer and pThr signaling of the same ovarian tumor samples
(Mertins et al., 2014). In this study, the authors found perturbations in only 6% of the 9000 pSer/pThr sites in contrast to 62% of the 217 pTyr sites in the ovarian specimens. The authors connected this finding to the highly regulated biological pTyr regulation when compared to pSer/pThr sites.
A first of its kind large-scale integrated proteogenomic study investigated 95 gnomically annotated TCGA colon and rectal tumors identified potentially relevant chromosome 20q
15
amplification and the role of HNF4α (Hepatocyte nuclear factor 4, alpha) in colon and
rectal cancer (CRC). Although the correlation between the mRNA and protein level
changes was low, the proteomics associated CRC subtypes were similar to the
transcriptomic profiles, but further identified features not seen in the transcript profiles.
The proteomic subtypes had functional signatures related to poor prognosis and EMT
activation. The authors argued translating these proteome subtypes into the laboratory for
tumor stratification after validation in other independent cohorts(B. Zhang et al., 2014). In
an adjunct report, the authors provided a comprehensive description of the proteomic data and the novel analysis strategy for this large CRC proteomics dataset. The authors built a customized database that integrated individual sequence variation identified from RNA- seq to detect single amino acid variations (SAAVs) in the proteome. All the primary and secondary datafiles were made available to the research community for further exploration of the CRC biology(Slebos et al., 2015).
In another study, the authors used an LC-MS based proteomic and phosphoproteomic technique coupled with iTRAQ relative quantification to profile 105 genomically TCGA annotated breast cancer tumors. Correlation of RNA and protein levels identified the list of potential trans-regulating proteins. The loss of CETN3 and SKP1 related to the upregulation of EGFR. Phosphoproteomics profiling identified novel kinase substrates of
PIK3CA and TP53. The authors also identified candidate genes that exhibited gene- amplification-driven patterns. These include CDK12, TLK2, PAK1 and RIPK2 with stronger evidence for CDK12 because of its location in the ERBB2 amplicon. These conclusions led to the generation of several testable hypothesis for further orthogonal validations (Mertins et al., 2016). In another study, the same group of scientists utilized a
16
proteogenomic approach to investigate protein and phosphosite expression changes across
24 breast cancer patient-derived xenograft samples. The authors showed a good correlation
between the proteomic profiles and mRNA upregulations and identified several druggable
candidates e.g. HER2 expression which was treated by lapatinib. Alternate druggable
targets were also identified including, PIK3CA mutation, AKT protein expression coupled
with elevated AKT phosphorylation. The study showed the feasibility of this using a
combination of PI3K and MTOR inhibitor- based lowering of tumor proliferation in two
breast cancer samples(Huang et al., 2017).
Another group carried out an integrated proteogenomic analysis of the ovarian high-grade
serous carcinoma of 174 ovarian tumors to complement TCGA’s genomic and
transcriptomic profiling and showed a good mRNA-protein expression correlation. MS-
based proteomic spectra searched against a custom database consisting of variant peptides
seen in TCGA data identified several single amino acid variants (SAAVs). The authors
further validated this finding by synthesizing 20 of these variant peptides and matching the
tandem spectra to their observed variant peptides. Combining proteomic, genomic and
phosphoproteomic measurements identified several differentially regulated pathways with
functional implications that predicted and differentiated patient outcomes, e.g. PDGFR-β
signaling associated with angiogenesis, RhoA and integrin-associated cell mobility and invasion, pathways associated with adaptive immunity. Interrogating PTMs e.g. specific histone acetylation provided evidence for alternate biomarkers for homologous recombinant deficiency (HRD) thereby providing a basis for patient stratification in the clinical testing of HDAC inhibitors (H. Zhang et al., 2016).
Such large-scale quantitative clinical proteomic studies with comparative analysis over
17
100s of human bio specimens require extended periods of instrument time where
experimental and analytical factors can be critical sources of variability. A quality
assessment study evaluated the long-term performance of traditional 2DLC-MS/MS platforms for analysis of large sample cohorts for testing clinical hypothesis. The authors used a reference iTRAQ sample for normalization across the entire sample set and robust confidence score filtering strategy based on the quality of PSMs for reliable protein candidate quantitation. This approach reproducibly quantified proteins over a period of 7 months from complex breast cancer xenograft tissue samples with almost 0 FDRs (J.-Y.
Zhou et al., 2017).
1.3 Kinase signaling in cancer
Kinases are the key signaling molecules that regulate cell function and make the largest and most functionally versatile proteins. Upon stimulation, kinases activate signaling cascades by phosphorylating their substrates and thereby regulate the activity, movement and overall activity of many proteins. Aberrations in theses signaling networks brought on by mutations or abnormal protein expression has been linked to several disorders including cancer.
EGFR is a member of the transmembrane receptor tyrosine kinase family which upon ligand stimulation (EGF) undergoes homodimerization or heterodimerization followed by receptor phosphorylation and activation of downstream effectors (Fig 1.5). Other EGFR ligands are TGF-alpha, epigen, betacellulin, epiregulin. The activation of downstream signaling cascades include PI3K→AKT→mTOR and Ras→ Raf→ MEK→ ERK.
Overactivated EGFR signaling has been extensively linked to cancer cell proliferation, evasion or programmed cell death, angiogenesis and metastasis (Mitsudomi & Yatabe,
18
2010; I. B. Weinstein, 2002) (Fig. 1.5).
The extracellular signal regulated kinases (ERK1 and ERK2) are important members of
Mitogen activated kinases (MAPK) family and play a crucial role in normal cell adhesion,
cell cycle progression, cell migration, survival, proliferation and differentiation and are
also involved in cancer cell growth. The activity of Ras→ Raf→ MEK→ ERK cascade is
enhanced in over one third of all human cancers.
Of the four major MAPK signaling pathways, the role of ERK signaling cascade has been thought to be parallel to the importance of Krebs cycle in energy metabolism (Pages et al.,
1999). The signaling pathway (Ras→ Raf→ MEK→ ERK) (Wortzel & Seger, 2011)
involves a series of phosphorylation steps starting with interaction of a great variety of
ligands like hormones, growth factors etc. to receptor tyrosine kinases (RTKs), integrins
and ion channels which in turn leads to the activation of Ras proteins that usually remain
anchored to the cytoplasmic face of the plasma membrane by C-terminal farnesylation
(Roberts & Der, 2007; Rodriguez et al., 2010; Wortzel & Seger, 2011). Ras is a GTPase
which when in proximity with nucleotide exchange factor Son of Sevenless (SOS) (Li et
al., 1993) exchanges bound GDP for GTP from the cytoplasm (Rodriguez et al., 2010). Tyr
autophosphorylation on RTKs facilitate identification and assembly of downstream
effectors by providing binding for Src Homology2 (SH2) and phosphotyrosine binding
(PTB) domains (Lemmon & Schlessinger, 2010).
This exchange activates Ras conformationally and initiates the central three-tiered core
phosphorylation cascade, which starts with Raf (MAPKKK) followed by MEK (MAPKK)
and finally ending in ERK (MAPK) (Seger & Krebs, 1995). The active Ras binds to Raf
Murine Sarcoma Viral Oncogene Homolog (Raf) kinases, which then migrate to the cell
19 membrane and are activated with the help of Prohibitin (PHB) (Kasid et al., 1996;
Rajalingam et al., 2005).
Phosphorylated Raf activates MEK by phosphorylating serine residues at 217 and 221 in the activation loop (Zheng & Guan, 1993). Activated ERK1 (MAPK3) and ERK2
(MAPK1) are 44 and 42 kDa serine/threonine kinases that regulate proteins in cytoplasm and even migrate to nucleus to activate transcription factors involved in gene expression
(Brunet et al., 1999). Hyper-activation, mutations and transforming overexpression of
RTKs, Ras and B-Raf has been linked to pathophysiology of many human cancers including mammary carcinomas, squamous carcinomas, glioblastomas etc (Fang &
Richardson, 2005; Lopez-Gines et al., 2008; Roberts & Der, 2007).
1.4 Dissertation objectives
The focus of the research presented in this dissertation is the discovery and validation of potential markers of targeted therapy resistance and response in melanoma and lung adenocarcinoma respectively. LC MS/MS based proteomics has been used as an analytical approach to carry out these research objectives.
Chapter 2 describes the application of LC MS based discovery proteomics to investigate the development of resistance to MAPK inhibitors (BRaf, MEK1/2 and ERK1/2 inhibitors) in cutaneous melanoma. DDA based shotgun proteomics using label free quantification has been used to profile differential protein expression in the melanoma cells resistant to
MAPK inhibitors upon comparison with drug sensitive cells. Along with mass spectrometry data, supporting bioinformatic analyses and simple western assays are also described.
Chapter 3 describes the utility of LC MS based targeted proteomics to validate Tyrosine
20
EGFR-targeted TGFa, EGF, TKI e.g. EGFR-targeted mAbs e.g. gefitinib, AREG EGFR cetuximab, pantimumab erlotinib
STAT3 P P SRC SHC SOS P P PI3K SRC RAS BRAF inhibitors e.g. AKT STAT3 RAF vemurafenib, dabrafenib GSK3 BAD STAT3P MEK1/2 MEK inhibitors e.g. selumetinib, trametinib STAT3 P ERK1/2 ERK inhibitors e.g. SCH772984
Survival, proliferation, invasion, angiogenesis, metastasis
Fig. 1. 5: Overview of the EGFR signaling involved in tumorigenesis and targeted kinase inhibitors. Kinase inhibitor (TKI) response in lung adenocarcinoma. The development and optimization of MRM based LC MS assays has been described in detail. The application of these assays in lung adenocarcinoma cells to validate the expression of a subset of tyrosine peptides is also presented.
Appendix 1 describes the supplementary methods associated with Chapter 3.
Chapter 4 presents a brief set of conclusions and future directions for the presented research.
21
Chapter 2: Differential Protein Expression Patterns in BRaf-Mutant Melanoma
Cells with Acquired Resistance to BRaf, MEK1/2, or ERK1/2 inhibitors
Abstract:
Constitutive activation of the ERK1/2 signaling pathway is a hallmark of many types of
cancer including melanoma. As such, drugs that inhibit upstream regulators, including
BRaf and/or MEK1/2 protein kinases, have emerged as promising therapies. While the
initial response to these kinase inhibitors is favorable, acquired drug resistance will
invariably occur and limit the therapeutic options. The re-activation of ERK1/2 associated
with acquired drug resistance has initiated the discovery of drugs that directly target
ERK1/2. To better identify the mechanisms of resistance to ERK1/2 pathway inhibitors,
we developed melanoma cells that were resistant to BRaf, MEK1/2, or ERK1/2 inhibitors.
We characterized and quantified global changes in the drug resistant proteomes and
phosphoproteomes using liquid chromatography coupled to tandem mass spectrometry
with label-free quantitation. Around 2800 proteins and 1500 phosphosites were identified
in all the cell lines and expression levels were compared to the drug sensitive parent cells.
Changes were most similar between cells resistant to MEK1/2 and ERK1/2 inhibitors.
Proteins that were downregulated included HLA class II antigens and tissue inhibitor of
metalloproteinases (TIMP3). Proteins that were upregulated included cathepsin D,
caveolin-1 and beta catenin. These findings provide a quantitative determination of
proteomic changes that occur in cells that are resistant to inhibitors of proteins in the
ERK1/2 pathway and identify potential biomarkers of drug sensitivity and resistance.
2.1 Introduction:
22
Mitogen activated protein kinases (MAPK) are an important family of enzymes that
participate in several important intracellular processes like proliferation, differentiation,
mitosis, gene expression and apoptosis in response to a diverse extracellular signal like
mitogens, cellular stress, heat shock. In case of Ras→ Raf→ MEK→ ERK pathway (Fig.
2.1), receptor tyrosine kinase (RTK) phosphorylates and activates the GTPase Ras, this
initiates the central three-tiered core phosphorylation cascade, which starts with Raf
(MAPKKK) followed by MEK (MAPKK) and finally ending in ERK (MAPK) (Hindley
& Kolch, 2002; Roskoski, 2012; Seger & Krebs, 1995). Activated ERK1 (MAPK3) and
ERK2 (MAPK1) are serine/threonine kinases that regulate proteins in cytoplasm and even
migrate to nucleus to activate transcription factors involved in gene expression (Brunet et
al., 1999). Hyper-activation, mutations and transforming overexpression of RTKs, Ras and
B-Raf has been linked to pathophysiology of many human cancers including melanomas, colorectal cancers, squamous carcinomas, glioblastomas (Millington, 2013).
In 2017, it was estimated that there were 87,110 cases of melanoma diagnoses that resulted
in 9,730 deaths in the US (R. L. Siegel, K. D. Miller, & A. Jemal, 2017). It is classified as
one of the most common types of cancer after breast, lung, prostate and colorectal cancers.
Even though invasive melanoma accounts for only about 1% of all skin cancer diagnoses
but is the major cause of all skin cancer mortality (Cancer Facts & Figures 2017 - American
Cancer Society). In melanoma, 75% of tumors harbor mutations in the ERK signaling, with
NRAS mutated in 20-25% and over 50% of melanoma tumors show a mutation in the
valine residue (V600E) in the coding region of BRAF, which leads to an aggressive
phenotype (Long et al., 2011; Sullivan, Lorusso, & Flaherty, 2013). This mutation causes
the tumors to exhibit constitutively active mitogen-activated protein kinase (MAPK)
23 signaling through MEK1/2 and ERK1/2 in turn driving the cell proliferation and tumor growth. Whole-exome studies have demonstrated that most melanomas contain at least one mutation that would potentially increase MAPK signaling (Krauthammer et al., 2012).
Therapeutic effort has been directed to achieve targeted inhibition of the MAPK proteins
(Fig. 2.1) like mutated BRAF protein with selective inhibitors like vemurafenib (Chapman et al., 2011) and dabrafenib (Hauschild et al., 2012) and MEK1/2 inhibitors like trametinib
(Flaherty et al., 2012).
BRAF inhibitors lead to successful disease treatment and progression free survival for
Imatinib, erlotinib, Receptor/non-receptor tyrosine kinase sorafenib, sunitinib, Activating dasatinib, gefitinib, Ras G-proteins mutations etc… vemurafenib, Raf (B-Raf) dabrafenib trametinib, selumetinib, pimasertib, MEK1/2 cobimetinib ERK1/2 inhibitors ERK1/2
Uncontrolled cell proliferation and survival Fig. 2. 1: ERK1/2 signaling pathway and targeted MAPK inhibitors. approximately 5-6 months after which all patients develop a drug resistant aggressive phenotype (Wagle et al., 2011). General ineffectiveness of MEK1/2 inhibitors as monotherapies led to the approval of combinations of BRaf and MEK1/2 inhibitors that have provided some improvement in therapeutic outcomes (Queirolo, Picasso, &
Spagnolo, 2015). Re-activation of ERK1/2 signaling prompted the discovery of ERK1/2
24
inhibitors (SCH772984) (Erick J. Morris et al., 2013). Mechanisms describing the development of resistance either intrinsic or acquired have been described in great detail
(Pritchard & Hayward, 2013; Wellbrock, 2014) but despite these efforts a complete understanding of resistance is yet to be achieved. It may be due to the plasticity and heterogeneity of the melanoma cells that multiple compensatory pathways may be involved in the process of resistance and therefore a single or a few biomarkers cannot indicate drug resistance. Development of resistance and relapse in melanomas are directed by reprogramming of signaling pathways that overcome inhibition by drug and re-establish the ERK1/2 signaling thus driving the cell proliferation.
Mechanisms explaining resistance to BRAF inhibitor vemurafenib include mutational
activation of NRAS (Nazarian et al., 2010), over-expression of RTKs including PDGFRβ
or insulin like growth factor receptor (IGFR) (Villanueva et al., 2010), dimerization of
aberrantly spliced BRAF (V600E) (Poulikakos et al., 2011), concurrent activation of the
PI3K-AKT pathway (Manzano et al., 2016) and over-amplification of COT (Mitogen-
activated protein kinase kinase kinase 8) (Johannessen et al., 2010). Mechanisms
explaining resistance to MEK inhibitors include overexpression of MITF
(Microphthalmia-associated transcription factor), a transcription factor that controls many
genes involved in melanogenesis (Smith et al., 2013) and several mutations in MEK1
(Emery et al., 2009). The comparative effect of these different mechanisms to the disease
progression is currently not fully understood, although most seem to cause ERK
reactivation. A recent study examined the development of ERK inhibitor SCH773984
resistance in KRAS-mutant cell line and concluded that a mutation in the conserved DFG
25 motif (ERK1G186D) causes the pathway reactivation and cancer proliferation, which facilitates the cells to acquire resistance to ERK inhibition (Jha et al., 2016).
A better elucidation of these resistance mechanisms can help in prevention of resistance development, offer better therapeutic options and identify pharmacodynamic biomarkers.
Since most of the current knowledge is derived from the genomics studies and may potentially miss important changes on the protein-level. Therefore, shotgun proteomics might help provide thorough insights into the pathophysiological conditions arising from changes in protein expression and help identify unique protein signatures of the MAPK pathway inhibitor resistant cells.
In the present study, we have carried out a global mass spectrometry based proteomic and phosphoproteomic analysis of ERK pathway inhibitor resistant melanoma cells and utilized bioinformatics to understand mechanisms of resistance development. The overall goal was to compare the resistant proteome to the drug sensitive proteome to determine potential resistant biomarker candidates that could be targeted to overcome drug resistance.
2.2. Methods and materials:
2.2.1. Cell lines and reagents
A375 cells with the homozygous BRaf (V600E) mutation were purchased from ATTC
(CRL-1619). The protocol for generating drug resistant cell lines has been described in previous studies (E. J. Morris et al., 2013). Briefly, cells were grown such that the final drug concentrations in the growth media for the A375 cells resistant to PLX4032,
AZD6244, or SCH772984 (Fig 2.2) were 10, 1.5, or 1 µM, respectively. All cell lines were authenticated at the University of Maryland-Baltimore Biopolymer-Genomics Core
26
Laboratory and shown to be 100% related (shared 12 out of 12 alleles) to the ATCC reference CRL-1619 (A-375) cell line.
2.2.2. Sample preparation and Trypsin digestion
After cells were 80% confluent, they were harvested by washing the plates with 5 ml of cold PBS twice followed by solubilization in 1 ml of lysis buffer (RIPA) for 5 minutes on ice with occasional swirling for uniform spreading of the buffer. The lysate was then centrifuged at 14,000 g for 15 minutes to pellet the cell debris and the supernatant was saved. Three biological replicates were prepared for the proteomics analysis. Protein concentration was determined using a Pierce BCA Protein Assay Kit (Thermo Scientific).
An equal amount of total cell lysates (100 µg) from each parent and resistant cell lines was precipitated by mixing at a ratio of 1:1 with 20% TCA (Trichloroacetic acid) followed by incubation on ice for 30 minutes. These were then centrifuged for 15 minutes at 14000 rpm at 4°C. The pellet was saved and washed with cold acetone twice followed by centrifugation for 15 minutes at 14000 rpm at 4°C. Finally, the protein digestion was carried out using the adapted FASP protein digestion protocol. Briefly, the pellet is suspended in denaturing buffer (7.5 mM TCEP, 8M Urea, 100 mM Ammonium bicarbonate) and loaded on a 3K MWCO filter (Millipore Amicon Ultra 0.5ml) and incubated at 37°C for 1 hour. This was followed by centrifugation of samples at 14000 g for 15 minutes to remove the salts. The samples are then treated with the alkylation buffer
(8M Urea, 100mM Ammonium bicarbonate, 50 mM Iodoacetamide) followed by incubation in dark for an hour. After centrifugation at 14000 g for 15 minutes, 25 µl of 500 mM DTT is added to deactivate the residual IAM and the samples are again centrifuged at
14000 g for 15 minutes to remove the salts. The filters are washed four times with 50 mM
27
Ammonium bicarbonate and finally inverted and centrifuged to collect the retentate in a
new eppendorf tube. Trypsin is added to the protein samples in a 50 protein to 1 trypsin
ratio followed by incubation at 37°C overnight.
PLX4032
AZD6244
SCH772984
Fig. 2. 2: Chemical structures of ERK1/2 pathway inhibitors: BRaf inhibitor (PLX4032), MEK1/2 inhibitor (AZD6244) and ERK1/2 inhibitor (SCH772984). The tryptic digests were then acidified with 10% TFA to a final concentration of 1% TFA and dried briefly in a vacuum concentrator. The dried peptides were then dissolved in 5%
28
ACN/ 0.1% formic acid and stored at -80°C until the mass spectrometry analysis was
performed.
2.2.3. LC MS/MS analysis for global proteomic profiling
The samples were analyzed on an Orbitrap Fusion™ Tribrid™ mass spectrometer (Thermo
Scientific Corp., San Jose, CA) coupled to a NanoAquity UPLC system (Waters
Corporation, Milford, MA). Peptides were trapped on a 100 µm i.d. x 20 mm long
precolumn in-house packed with 200 Å (5 µm) Magic C18 particles (C18AQ; Michrom
Bioresources Inc., Auburn, CA) (Fig 2.3 and Fig 2.4). Subsequent peptide separation was
on an in-house constructed 75 µm i.d. x 180 mm long analytical column pulled using a
Sutter Instruments P-2000 CO2 laser puller (Sutter Instrument Company, Novato, CA) and packed with 100 Å (5 µm) C18AQ particle. For each liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, an estimated amount of 600 ng of peptides (0.2
µg/µL) was loaded on the precolumn at 4 µL/min in water/acetonitrile (95/5) with 0.1%
(v/v) formic acid. Peptides were eluted using an acetonitrile gradient flowing at 250 nL/min using mobile phase gradient of 5-35% acetonitrile over 60 min with a total gradient time of 95 min. The eluting peptides were interrogated with an Orbitrap Fusion mass spectrometer using data-dependent acquisition method with the Top Speed decisions selection. FTMS1 spectra were collected using the following parameters: scan range 350–
1800 m/z, resolving power 120k, AGC target 4E5, and maximum injection time of 50ms.
ITMS2 spectra were collected using the following parameters: rapid scan rate, CID NCE
35, 1.6 m/z isolation window, AGC target 1E4, and maximum injection time of 50ms. MS2
precursors were selected for a 3-sec cycle. Precursors with an assigned monoisotopic m/z
29
and a charge state of 2–7 were interrogated. Precursors were filtered using a 60-sec
dynamic exclusion window. Samples were analyzed in biological triplicate.
2.2.4. Titanium dioxide (TiO2) enrichment
The enrichment of phospho-peptides was carried out using the TiO2 phosphopeptide
enrichment and clean-up kit (Pierce, Thermo Scientific) using the manufacturer’s protocol.
Briefly, the TiO2 spin-tip was conditioned in 20 µl of 80% ACN, 19.8% LC-MS water,
0.2% TFA (Buffer A) by centrifugation at 3000 × g for 2 minutes. This step was then repeated in 28% lactic acid,72% Buffer A (Buffer B). The dried tryptic peptides derived from 1 mg equivalent of the cell lysate were suspended in Buffer B and applied to the spin-
tip and centrifuged for 10 minutes at 10,000 × g. The peptide sample was then reapplied to
the spin-tip and centrifuged in a similar fashion. The column was then washed with 20 µl
of Buffer B by centrifuging at 3000 × g for 2 minutes. This was followed by wash with
Buffer A twice. The enriched phospho-peptides were eluted first in 5% ammonium
hydroxide, 95% LC-MS water followed by elution in 5% pyrrolidine, 95% LC-MS water.
The eluted phospho-peptides were acidified using 2.5% TFA and cleaned up using the
graphite spin columns (Pierce, Thermo Scientific).
2.2.5. LC MS/MS analysis for global phosphoproteomic profiling
The phospho-peptide enriched samples were analyzed on an LTQ-Orbitrap Elite interfaced with an Easy-nLC 1000 RPLC (Thermo Scientific) (Fig 2.3 and Fig 2.4). The enriched phospho-peptides were loaded onto a nano-trap column (Acclaim PepMap100 Nano Trap
Column, C18, 5 µm, 100 Å, 100 µm id × 2 cm) and separated on a nano-LC column
(Acclaim PepMap100, C18, 3 µm, 100 Å, 75 µm id × 25 cm, nanoViper). Mobile phases
30
A and B consisted of 0.1% formic acid in water and 0.1% formic acid in 90% ACN,
respectively. Peptides were eluted from the column at 300 nL/min using the following
linear gradient: from 2 to 8% B in 5 min, from 8 to 32% B in 100 min, from 32 to 100% B
in 10min, and held at 100% B for an additional 10 min. The heated capillary temperature
and spray voltage were 275°C and 2.2 kV, respectively. Full spectra were collected from
m/z 350 to 1800 in the Orbitrap analyzer at a resolution of 120,000, followed by data- dependent higher energy collisional dissociation MS/MS scans of the ten most abundant ions. MS/MS was carried out using 32% collision energy, dynamic exclusion time of 30 s and charge state +1 and >4 and unassigned charge states not subjected to MS/MS. Samples were analyzed in biological triplicate.
2.2.6. Protein and phosphopeptide quantification and bio-informatics
Raw MS files for total protein data sets were uploaded and searched against the Uniprot
human proteome database (downloaded on 07/19/2013) in Maxquant v.1.5.0.25 using the
Andromeda search engine for label-free quantitation. For the global proteomics analysis,
raw files were searched with cysteine carbamidomethylation as a fixed modification and
N-term acetylation and methionine oxidation as variable modifications. For the global
phosphoproteomic analysis, phosphorylation (STY) was added as a variable modification.
The digestion mode was set to specific with trypsin as the digestion enzyme and two missed
cleavages were allowed. Mass tolerances were set to 6 ppm for precursor ions and 0.05 Da
for-fragment ions. The search criteria further included false discovery rates of 0.01 for both
protein and peptide identifications. The minimum peptide length was 7 amino acids. Decoy
database search was activated and the database searching was supplemented with the
31
common contaminants often found in cell culture and proteomics sample preparation
experiments, these were later identified and removed. All the other settings were set to
default except the “match between runs” feature was enabled with the default settings.
A375 parent A375-PLX(R) A375-AZD(R) A375-SCH(R)
Protein extraction via TCA precipitation
Trypsin digestion
Desalt
1 µg lysate 1 mg lysate
Total Protein TiO2 enrichment Phosphoproteomics
LC-MS LC-MS
Label Free Quantification
Fig. 2. 3: Outline of the experimental design: three biological replicates were prepared for each sample type for global proteomic and phosphoproteomic profiling.
Protein quantitation was carried out based on the LFQ intensities and phosphopeptide
quantitation was carried out peptide intensities for the modified (STY) P-sites, which were then uploaded into the statistical package Perseus (v.1.5.3.1) for further analysis.
Intensities were pre-processed by log2 transformation and filtered by a minimum three
identifications per protein.
32
Cell culture Whole cell lysates Protein extraction via TCA precipitation Generation of LC MS/MS on Orbitrap-Data peptides Dependent Acquisition Protein identification and Label Free Quantitation
Phosphopeptide enrichment
(TiO2 chromatography)
Fig. 2. 4: Overview of the sample preparation and LC MS/MS analysis.
Differentially expressed proteins and phosphosites in the resistant cells were examined by carrying out a two-sided t-test and p-values were filtered via multiple hypothesis testing using a FDR of 0.01.
2.2.7. Gene ontology annotations and pathway analysis
The functional annotation analysis of the drug sensitive and resistant proteins and phosphopetides was carried out using database for annotation, visualization, and integrated discovery (DAVID) online platform. Significantly enriched categories in the gene ontology annotations were filtered using a permutation-based p-value. Experimentally identified, verified and published protein-protein interactions maps were also generated using
Ingenuity pathway analysis.
33
2.2.8. Simple Western assays
Simple Western (ProteinSimple, San Jose, CA) assays were performed with cell lysates
(40 ng protein) over three biological replicates. The lysate was combined with 1x SDS
master mix containing sample buffer, DTT and fluorescently labeled standards
(ProteinSimple, catalog # PS-ST01) and were heated at 70 ˚C for 10 minutes before being
loaded into Peggy Sue instrument for analysis.
During electrophoresis, proteins were separated by MW while migrating through the
separation matrix (ProtinSimple, catalog# 042-512). Separated proteins were immobilized
on the capillary wall using UV light, and incubated with a blocking reagent (ProteinSimple,
catalog # 042-514), followed by immunoprobing with respective primary antibodies and
HRP conjugated anti-rabbit or anti-mouse secondary antibodies (Jackson
Immuno Research). A 1:1 mixture of luminol and peroxide (ProteinSimple catalog# 042-
521and 042-552 respectively) was added to generate chemiluminescence, which was captured by a CCD camera. The digital image was analyzed by Compass software
(ProteinSimple, Santa Clara, CA). Protein quantities were estimated by correlating signal
strength on digital images represented as peak areas. The primary antibodies used in the
study were: anti-Raf-B, anti-ERK1/2, anti-phospho-ERK1/2, anti-AKT, anti-
AKTpS473, anti-MEK1, anti-MEK2, anti-MEKpS29, anti-MEKpT292, anti-
MEKpS217/221, anti-MEK1pT386, anti-MEK2pS394, anti- Catenin-β and anti-CAV-1.
2.3. Results and discussion:
There are several clinical candidates under investigation for inhibition of mutated and overexpressed proteins in the MAPK pathway to limit and inhibit the proliferation of cancers. The inhibitors normally are synthetic small molecules that inhibit Raf or MEK or
34
ERK. The discovery of the oncogenic mutation in BRAF at codon 600 led to the identification of the first true actionable target in cutaneous melanoma.
2.3.1. Characterization of the drug resistant cells:
The cell line used in this study is A375 human melanoma cell line that expresses
BRAFV600E and constitutively active ERK signaling. We have used the parental drug sensitive A375 cells as control. For this study, we have utilized three resistant cell lines, derived from the parental cells, each resistant to a kinase inhibitor in the ERK signaling pathway. The resistant cells were generated by growing the parental cells in increasing doses of drugs for 2-3 months. The IC50 values show the effect of the drug treatments in the drug sensitive and resistant cells (Table 2.1) on cell viability.
Cell line PLX4032 AZD6244 SCH772984 A375 0.4 0.05 0.03 A375-PLX(R) 18 0.57 0.06 A375-AZD(R) 9.6 21 8.2 A375-SCH(R) >50 >50 40 Table 2. 1: IC50 values (µM) of the drug sensitive (A375) and drug resistant cell lines (A375-PLX(R), A375-AZD(R) and A375-SCH(R)). A375-PLX(R) has an IC50 of 18 µM for PLX4032 while it still retains some sensitivity for
AZD6244 and SCH772984. A375-AZD(R) has an IC50 of 21 µM for AZD6244 and is also resistant to PLX4032 and SCH772984. A375-SCH(R) has an IC50 of 40 µM for
SCH772984 and is similar to A375-AZD(R) in exhibiting resistant to PLX4032 and
AZD6244. The general cell morphology of the drug resistant cells compared to drug sensitive cells is presented in Fig. 2.5.
The expression of MAPK pathway proteins in the resistant cells was evaluated using
35
simple western assays over three biological replicates (Fig 2.6). ERK2 was
hyperphosphorylated in all the resistant cells, although the difference was statistically
significant only in A375-PLX(R). PAK1 substrate S298 on MEK1 was significantly
hyperphosphorylated in A375-AZD(R) and A375-SCH(R) suggesting a role of PAK1 in
the survival of these resistant cells. Fig. 2. 5: Cell morphology of the drug sensitive parent (A375) and drug resistant cell lines (A375-PLX(R), A375-AZD(R) and A375-SCH(R)).
Parent A375-PLX(R) A375-AZD(R)
60x
Scale Bar =50 mm
20x
Scale Bar = 100 mm
Parent
A375- SCH(R)
Scale bar ~50 μm Stain: α-tubulin and DAPI (40x mag)
36
Fig. 2. 6: Simple western characterization of the cell lines for Braf, phosphorylated ERK2 and phosphorylation sites of MEK1 and MEK2. The phosphorylated data was normalized to total protein data. The error bars show +/- standard deviations. * p- value < 0.05, ** p-value < 0.01, *** p-value <0.005
37
2.3.2. Protein and phospho-peptide identifications in the drug sensitive and resistant
cell lines:
The average number of proteins identified in A375-parent cells, A375-PLX(R), A375-
AZD(R) and A375-SCH(R) are 2770, 2882, 2940 and 2886 respectively (Fig. 2.7). These
Fig. 2. 7: Total number of LC MS protein and phosphosite identifications in the drug sensitive and resistant cells. The error bars show +/- standard deviations. numbers were filtered by the number of identified peptides, with each true protein
identification requiring 2 peptide identifications. The biological replicates for global
proteomic analysis showed good overlap between samples with an average overlap of
65.1% (Fig. 2.8). The average number of phosphosites identified in A375-parent cells,
38
A375-PLX(R), A375-AZD(R) and A375-SCH(R) are 1517, 1530, 1491 and 1474
respectively (Fig. 2.7). Overall, we identified 1739 singly phosphorylated peptides
(68.3%), 702 doubly phosphorylated peptides (27.5%) and 105 triply phosphorylated peptides (4.1%) (Fig. 2.9). The distribution for the identified phosphorylation residues amongst all the samples was 92.5% for phospho-serines (pS), 7% for phospho-threonines
(pT) and 0.003% for phospho-tyrosines (pY) (Fig. 2.9). Overall, the phosphosite
identifications in the biological replicates for the global phosphoproteomic analysis sh
owed an average overlap of 48.1% (Fig. 2.10). The phosphosite intensities between the
biological replicates showed a good correlation (Fig. 2.11). The overall enrichment
efficiency of the TiO2 spin-tips was around 68%.
2.3.3. Quantitative analysis of the drug resistant proteome and phosphoproteome:
The relative quantitation in this study was carried out using normalized intensities for
protein and phosphopeptide identifications. For label-free quantitation, significantly
differentially expressed proteins were filtered at a false discovery rate of 1% and a fold
change of 2 or -2 (log2 fold change = 1 or -1). Based on this cut-off, we identified 35, 196
and 89 proteins to be significantly upregulated and 42, 165, 86 proteins to be significantly
downregulated in the A375-PLX(R), A375-AZD(R) and A375-SCH(R) cells respectively
(Fig. 2.12). For the phosphosite quantitation, we identified 65 and 63 phosphosites to be
significantly upregulated and 147 and 49 phosphosites to be significantly downregulated
39
A375 A375 control AZD(R)
A375 A375 SCH(R) PLX(R)
Fig. 2. 8: Experimental repeatability and overlap for the three biological replicates (Experiments) for the LC MS based protein identifications. in A375-AZD(R) and A375-SCH(R) phosphoproteomes respectively (Fig. 2.13).
Triply pSTY sites: pY: 5 105 pT: 106 0.003%
7%
Doubly pSTY sites: 702
Singly pSTY pS: 1384 sites: 92.5% 1739
Fig. 2. 9: Distribution of the phosphosite multiplicity and percentages for the number of pS, pT and pY sites identified.
We didn’t identify any phosphosites that passed the significant testing threshold for A375-
40
PLX(R) cells.
Fig. 2. 10: Experimental repeatability and overlap for the three biological replicates for the LC MS based phosphosite identifications.
41
Fig. 2. 11: Pearson correlation coefficients (blue numbers) for the overlap of the biological replicates for the phosphosite normalized intensities.
42
Fig. 2. 12: Label free proteomic quantification: heat maps showing the log 2 transformed LFQ intensities for the differentially expressed proteins in the resistant cells. The proteins have been clustered via unsupervised hierarchical clustering.
43
AZD (R)
SCH (R)
Fig. 2. 13: Label free phosphoproteomic quantification: volcano plots showing log2 fold change on the x-axis and the -log p value on the y-axis for A375-AZD(R) and A375- SCH(R) cells.
44
2.3.2. Differential protein expression patterns common to all MAPK inhibitor
resistant cells (Fig 2.15):
In all three MAPK inhibitor resistant cells, the expression of HLA class II antigens was found to be significantly downregulated. Although the role of these proteins in melanoma disease progression has been under investigation for the last few decades, its functional implications are not clear(Bernsen et al., 2003; Brocker, Suter, & Sorg, 1984). Some studies have shown correlations between HLA-DR mediated signaling and inhibition of
Fas-mediated apoptosis in the A375 melanoma cells (Aoudjit et al., 2004) and increase in melanoma cell migration and invasion(Costantini & Barbieri, 2017). A recent study found the tumor-specific expression of HLA-DR in 60 melanoma cell lines to be a potential response biomarker for PDL-1-targeted therapy(D. B. Johnson et al., 2016). On the other hand, decline in the MHC Class II protein expression has been linked to incidence of metastasis in colorectal cancers(Warabi, Kitagawa, & Hirokawa, 2000). Another study found the expression of MHC molecules at the mRNA level to be tumor stage specific. In this study, the authors argued that the overexpression of MHC may promote initial tumor progression in nonmetastasis stages like vertical growth phase but its downregulation in the metastatic stage may promote invasion due to “immune escape”(Degenhardt et al.,
2010). Our results point to the possibility that these therapy resistant cells have lost the ability to present tumor antigens to the T-cells and antigen presenting cells thereby leading to immune suppression.
The fine balance in the expression of matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs) has been shown to regulate the degradation of extracellular matrix
(ECM) and thereby facilitating cell migration, invasion and metastasis (Hofmann,
45
Westphal, Van Muijen, & Ruiter, 2000). It has been shown that TIMP-3 overexpression
inhibited melanoma growth(M. Ahonen, Baker, & Kahari, 1998), led to induction of
apoptosis(Matti Ahonen et al., 2003) and inhibited melanoma cell migration(Das et al.).
We found a significant downregulation of TIMP3 in all the three drug resistant cells which
is in alignment with the previous findings about its role to control the progression of
melanoma.
2.3.3. Differential protein expression patterns common to BRaf and ERK1/2 inhibitor resistant cells:
Cathepsin D was found to be upregulated in both BRaf and ERK1/2 inhibitor resistant cells
with log2 fold changes of 1.4 and 1.8 respectively. Cathepsin D is an aspartyl protease that
has been extensively investigated for its role in melanoma progression(Podhajcer et al.,
1995; Westhoff, Fox, & Otto, 1998) and epithelial ovarian cancer (Pranjol, Gutowski,
Hannemann, & Whatmore, 2015). An in-vivo study showed a significant overexpression
of Cathepsin D in melanoma tumor cytosols when compared to normal tissues (Bartenjev
et al., 2000). Another study showed the upregulation of this enzyme in the malignant transformation and progression of melanoma (Zhu et al., 2013).
Fig. 2. 14: Venn diagram showing the overlap of the number of differentially expressed proteins in the three resistant cell lines (BRAF(R): A375-PLX(R), MEK (R): A375- AZD(R) and ERK(R): A375-SCH(R)).
46
2.3.4. Differential protein expression patterns common to BRaf and MEK1/2 inhibitor resistant cells:
Glucose-6-phosphate 1-dehydrogenase (G6PD) was found to be upregulated in BRaf and
MEK1/2 inhibitor resistant cells with log2 fold changes of 1.2 and 2.9 respectively.
Glucose-6-phosphate 1-dehydrogenase has been shown to be a diagnostic marker of cancer
because of its role in cell proliferation, angiogenesis, cyto-protection and metastasis (C.
Zhang, Zhang, Zhu, & Qin, 2014). Flavin reductase (NADPH) was also found to be
upregulated in both of these resistant proteomes with log2 fold changes of 3 and 4.1 for
BRaf and MEK1/2 inhibitor resistant cells respectively. It is plausible that a higher level of NADPH and G6PD may protect cancer cells from oxidative stress and thereby rendering therapy ineffective (C. Zhang et al., 2014).
Another protein that was upregulated in both of these resistant proteomes includes major vault protein (MVP) (PLX(R) log2 fold change = 1.9 and AZD (R) log2 fold change = 2.5).
MVP upregulation has been shown in multidrug-resistant cancer cells (Berger, Steiner,
Grusch, Elbling, & Micksche, 2009; Scheffer, Schroeijers, Izquierdo, Wiemer, & Scheper,
2000) and has been linked to EGFR targeted therapy resistance in human hepatoma cells
(Losert et al., 2012)
2.3.5. Analysis of the protein-protein interaction networks and gene ontology annotation clustering:
Bioinformatic analysis of the differentially expressed proteins was used to investigate the
protein-protein interactions that may be playing a role in driving the resistance. IPA
analysis identified several canonical pathways that were enriched in A375-AZD(R) and
A375-SCH(R) cells (Table 2.2). Several functional annotations were found to be enriched
47
in the resistant proteomes (Fig. 2.15). Analysis of the functional annotations identified
“proliferation of cells” category to be over-represented (z-score > 2) in all the drug
resistant cells. Gene ontology analysis of the molecular function category (GOMF) carried
out through DAVID identified “actin filament binding” proteins to be over-represented in
the A375-AZD(R) and A375-SCH(R) cells (Fig. 2.16). This finding points to the increased
cell motility and invasive character of these resistant cells. Table 2. 2: IPA shows the top canonical pathways represented in the A375-AZD(R) and A375-SCH(R) cells.
AZD Resistant Canonical Pathways -log (p-value) z-score Molecules Actin Nucleation by ARP- 1.78E00 2.000 ITGA3, PPP1R12A, RRAS, ARPC2 WASP Complex Chemokine Signaling 1.5E00 2.000 GNAI2, MAPK1, PPP1R12A, RRAS Mouse Embryonic Stem Cell 1.08E00 2.000 MAPK1, RRAS, STAT3, CTNNB1 Pluripotency Pancreatic Adenocarcinoma 9.35E-01 2.000 HMOX1, RALA, MAPK1, STAT3 Signaling Endothelin-1 Signaling 4.68E-01 2.000 GNAI2, HMOX1, MAPK1, RRAS Rac Signaling 1.46E00 2.236 ITGA3, MAPK1, RRAS, ARPC2, IQGAP1 Wnt/β-catenin Signaling 1.14E00 2.236 PPP2R4, SOX10, HDAC1, SFRP1, CTNNB1, SOX5 cAMP-mediated signaling 7.45E-01 2.236 GNAI2, AKAP8, MAPK1, PRKAR2A, PDE5A, STAT3 Cdc42 Signaling 4.76E00 2.646 ITGA3, HLA-DRB4, RALA, HLA- DRB1, MAPK1, PPP1R12A, HLA-A, ARPC2, HLA-DRA, IQGAP1, MYL1 NRF2-mediated Oxidative Stress 3.52E00 2.646 HMOX1, MGST1, AKR1A1, Response MAPK1, RRAS, DNAJB11, DNAJA2, SQSTM1, TXNRD1, GSTK1, FTH1 PTEN Signaling 8.14E-01 -2.000 ITGA3, MAPK1, INPP5F, RRAS SCH Resistant Cdc42 Signaling 5.57E00 2.000 HLA-G, MAPK14, HLA-DRB4, HLA-DRB1, MYL6, ARPC2, HLA- DRA, HLA-DPA1, MYL12A Actin Cytoskeleton Signaling 1.37E00 2.236 MYH9, MYL6, ARPC2, GSN, MYL12A EIF2 Signaling 4.56E00 -2.646 RPL24, RPL27A, RPL23A, RPL17, RPS23, RPS8, RPL19, AKT3, RPL31 Protein Kinase A Signaling 2.17E00 -2.646 AKAP12, GNAI2, HIST1H1C, MYL6, NGFR, HIST1H1D, PYGB, CTNNB1, MYL12A
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Fig. 2. 15: Protein-protein interaction maps showing the top functional annotations from IPA in A375-PLX(R), A375-AZD(R) and A375-SCH(R) cells. The p-value is a measure of the likelihood that the association between the set of proteins with the given annotation is due to random chance. Red arrows signify activation and green arrows signify inhibition. Black arrows signify that the manner of interaction is undetermined. The red and green colored proteins mean upregulated and downregulated proteins respectively. The color intensities correlate linearly to the extent of regulation (high or low log2 fold change).
PLX (R)
Cellular growth, function and maintenance Functional annotation p-Value Autophagy 6.40E-04 Proliferation of cells 4.93E-04 Apoptosis of tumor cells 4.61E-03 Colony formation of cells 1.83E-03
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AZD (R)
Cellular movement Functional annotation p-Value Invasion of cells 5.13E-31 Migration of cells 5.33E-21
50
AZD (R)
Cellular assembly, organization and Maintenance Functional annotation p-Value Organization of cytoplasm 4.36E-07 Formation of actin stress fibers 7.83E-04 Organization of actin cytoskeleton 7.48E-04
51
SCH (R)
Cellular development, Cellular growth and Proliferation Functional annotation p-Value Proliferation of cells 3.10E-10 Colony formation of cells 1.09E-04 Proliferation of fibroblast cells 1.84E-03
52
SCH (R)
Cardiovascular cell development, function and survival Functional annotation p-Value Angiogenesis 1.25E-04 Apoptosis of cardiomyocytes 8.09E-03
53
AZD (R)
SCH (R)
Fig. 2. 16: Gene ontology molecular function (GOMF) enrichment analysis via DAVID for A375-AZD(R) and A375-SCH(R) cells.
54
2.3.5. Validation of proteomic findings
The LC MS based global proteomic quantitation identified β-catenin (CTNNB1) to be
upregulated in A375-AZD(R) (CTNNB1, log2 fold change = 4.9) and A375-SCH(R)
(CTNNB1, log2 fold change = 2.7) cells. The overexpression of β-catenin has been identified in a variety of cancers and has implicated the WNT/ β-catenin pathway in tumorigenesis. The increase in transcrptional activity of β-catenin has been shown to be an important survival factor for aggressive melanoma in vitro (Tobias Sinnberg et al., 2011).
This also corelates with the overexpression of MITF, an important transcriptional target of
β-catenin and a well established mechamisn that explains targeted therapy resistance
(Widlund et al., 2002). Tumor-intrinsic activated WNT/ β-catenin signaling has been linked to T-cell exclusion and resistance to immunotherapies (Spranger, Bao, & Gajewski,
2015). Moreover, studies on melanoma patients show that increased β-catenin significantly correlates with decreased survival (Chien et al., 2014; Massi et al., 2014). We confirmed the overexpression of β-catenin in the A375-AZD(R) and A375-SCH(R) cells
(Fig. 2.17) through simple western assays.
Fig. 2. 17: Simple western analysis of the three biological replicates for β-catenin. The β- catenin data has been normalized to the total protein data for each replicate.
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The proteomic data also showed the upregulation of Signal transducer and activator of
transcription 3 (STAT3) in A375-AZD(R) (STAT3, log2 fold change = 3.3) and A375-
SCH(R) (STAT3, log2 fold change = 1.8) cells (Fig. 2.18).
Fig. 2. 18: Maxquant output LFQ intensities for STAT3 for A375-control, A375-AZD(R) and A375-SCH(R) cells. *** p-value <0.005 The interaction of β-catenin and STAT3 has been previously associated with acquisition
of resistance to vemurafenib (T. Sinnberg et al., 2016). Our results are consistent with these
previous findings and point to a regulatory function of β-catenin in STAT3 signaling.
Furthermore, both β-catenin and STAT3 share interacting partner proteins that are known to be involved in re-activation of MAPK signaling or overactivation of PI3K/Akt signaling
during the development of BRAF inhibitor reistance in melanoma. Simple western assays
showed hyperphosphorylation of Akt in A375-SCH(R) cells (Fig. 2.19)
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Fig. 2. 19: Simple western analysis of the three biological replicates for phospho AKT. The phospho AKT data has been normalized to the total protein data for each replicate.
Our results also show upregulation of Caveolin-1 (CAV1) in A375-AZD(R) (CAV1, log2
fold change = 4.6). CAV1 has been shown to be a playing a role in transduction of signals
through Src, EGFR, HER2 and MAPK (Boscher & Nabi, 2012). The loss of CAV1 has
been linked to inactivation of PTEN tumor suppressor signaling (Table 2.2, z-score = -2) and activation of pro-survival signaling PI3K/Akt (Fig. 2.19) and MAPK signaling (Quest,
Gutierrez-Pajares, & Torres, 2008). Genetic and pharmacological studies have shown a link between CAV1 expression and drug resistance (Lavie, Fiucci, & Liscovitch, 1998;
Yang, Galbiati, Volonte, Horwitz, & Lisanti, 1998). Moreover, clinical data show an inverse correlation between CAV1 expression and chemotherapy response in NSCLC (D.
Chen, Shen, Du, Zhou, & Che, 2014) and gastric cancer patients (Yuan et al., 2013).
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We confirmed the overexpression of CAV1 in A375-AZD(R) cells via simple western
assays (Fig. 2.20). This data also showed an upregulation of CAV1 on A375-SCH(R), although this was not found to be statistically significant.
Fig. 2. 20: Simple western analysis of the three biological replicates for CAV1. The CAV1 data has been normalized to the total protein data for each replicate.
2.4. Conclusions
The present work describes the application of quantitative LC MS based proteomics to probe the acquisition of resistance to targeted MAPK inhibitors in melanoma in vitro.
Examination of the expression patterns of several proteins in the resistant cells were found
to be consistent with evidence in literature for causing metastasis, malignancy and therapy
resistance in cancer. Pathway analysis showed an enrichment of proteins associated with
“actin cytoskeleton signaling” in MEK and EKR inhibitor resistant cells which points to
phenotypic transitions, causing tumor progression, and invasiveness. Overexpression of β-
catenin and CAV1 is consistent with previous research and reinforces the importance of
these proteins in development of therapy resistance in melanoma and potential of being
58 novel targets for co-therapy. Furthermore, this data presents a list of candidate proteins associated with metastatic potential and holds promise for further investigations into the mechanisms of reistance development. Although further efforts are needed to fully elucidate the biological implications of the identified protein patterns in vivo, the information derived from this data may provide useful insights for clarifying the role of these proteins in cancer progression.
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Chapter 3: Quantitative targeted proteomic analysis of potential markers of
Tyrosine Kinase Inhibitor (TKI) sensitivity in EGFR mutated lung
adenocarcinoma1
Abstract
Lung cancer causes the highest mortality among all cancers. Kinase domain mutations in the epidermal growth factor receptor (EGFR) account for 10-15% of lung cancers in the
Western world and 50-60% in Southeast Asia. Patients harboring these mutations respond
to EGFR tyrosine kinase inhibitors (TKIs), however, all patients inevitably acquire
secondary resistance. Moreover, 30-40% of patients with EGFR mutations exhibit primary resistance. Hence, there is an unmet need for additional biomarkers of TKI sensitivity that complement EGFR mutation testing and predict treatment response. We previously identified downstream hypophosphorylation of mutant EGFR targets mediated by the TKIs erlotinib and afatinib in TKI sensitive cells, but not in resistant cells. These phosphosites are potential biomarkers of TKI sensitivity. Here, we sought to develop modified immuno- multiple reaction monitoring (immuno-MRM)-based quantitation assays for select phosphosites (Fig. 3.1) including EGFR-pY1197, pY1172, pY998, AHNAK-pY160, pY715, DAPP1-pY139, CAV1-pY14, INPPL1-pY1135, NEDD9-pY164, NF1-pY2579, and STAT5A-pY694. These sites were significantly hypophosphorylated by erlotinib and osimertinib in TKI-sensitive H3255 cells, which harbor the TKI-sensitizing EGFRL858R
mutation. However, in H1975 cells, which harbor the TKI-resistant EGFRL858R/T790M
1 Adapted from Quantitative targeted proteomic analysis of potential markers of TKI sensitivity in EGFR mutated lung adenocarcinoma. Awasthi S, Maity T, Oyler BL, Zhang X, Goodlett DR, Guha U. (Journal of Proteomics, https://doi.org/10.1016/j.jprot.2018.04.005)
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mutant, osimertinib (which targets EGFRT790M), but not erlotinib, could significantly
inhibit phosphorylation of EGFR-pY-1197, STAT5A-pY694 and CAV1-pY14, suggesting these sites also predict TKI response in TKI-resistant cells.
Endogenous peptide Heavy labelled internal standard
EGFR-pY-1197 15 300 25 20 10 200 15 5 100 10 5 0 0 Intensity (10^3)
Intensity (10^3) 0 Intensity (10^3)
H3255 Time Time Time L858R (EGFR ) Control Erlotinib Osimertinib treatment treatment Target list
EGFR-pY-1197
20 10 10 15 10 5 5 5 0 0 Intensity (10^3)
0 Intensity (10^3) Intensity (10^3) 60 65 70 60 65 70 Time Time Time H1975 Control Osimertinib L858R/T790M Erlotinib (EGFR ) treatment treatment Targeted MRM assay method development
Fig 3. 1: Graphical abstract
Significance:
In this report, we have shown the development and optimization of MRM assays coupled with global phosphotyrosine enrichment (modified immuno-MRM) for a list of 11 phosphotyrosine candidates. Our optimized assays identified the targets reproducibly in
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biological samples with good selectivity. We also developed and characterized quantitation
methods to determine endogenous abundance of these targets and correlated the results of
the relative quantification with amounts estimated from the calibration curves. This
approach represents a way to validate and verify biomarker candidates discovered from
large-scale global phospho-proteomics analysis. The application of these modified
immuno-MRM assays in lung adenocarcinoma cells provides proof-of concept for the feasibility of clinical applications. These assays may be used in prospective clinical studies of EGFR TKI treatment of EGFR mutant lung cancer to correlate treatment response and other clinical endpoints.
3.1. Introduction
Lung cancer is a major public health problem and a leading cause of mortality in the United
States for both men and women. It is estimated that there will be 222,500 new cases and
155,870 deaths from lung cancer in 2017(Rebecca L. Siegel et al., 2017). Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and makes up 85% of all lung cancer diagnoses, with adenocarcinoma being the most common histology. Aberrant regulation of EGFR causes increased intracellular signaling through tyrosine kinase activation leading to cell growth, angiogenesis, inhibition of apoptosis, invasion and metastasis. Somatic mutations in the kinase domain of EGFR include a L858R point mutation in exon 21(Xuewu Zhang, Gureasko, Shen, Cole, & Kuriyan, 2006) and exon 19 in-frame deletions, such as Del E746-A750 which collectively make-up over 90% of known activating EGFR mutations. Mutations in the kinase domain of EGFR provide sensitivity to EGFR-tyrosine kinase inhibitor (EGFR-TKI) therapy (Lynch et al., 2004;
Paez et al., 2004; Pao, Miller, & Kris, 2004). The first-generation EGFR-TKIs, erlotinib
62 and gefitinib, and the second-generation TKI, afatinib, were approved by the FDA for front-line therapy to treat patients harboring kinase domain EGFR mutations(Copeman,
2008; Dowell, Minna, & Kirkpatrick, 2005; W. Pao et al., 2004). Unfortunately, all patients developed secondary resistance to EGFR-TKIs within 9-14 months of treatment. Several mechanisms of development of acquired resistance have been elucidated. The most common acquired resistance mechanism that occurs in 50-60% of patients is the acquisition of a second site mutation at the gatekeeper site residue in exon 20, T790M (Kobayashi et al., 2005; Pao et al., 2005). Other mechanisms of resistance include MET amplification
(Bean et al., 2007; Engelman et al., 2007; Turke et al., 2010) and small cell lung cancer
(SCLC) transformation (Niederst et al., 2015; Suda et al., 2015). However, in 20-25% of estimated cases, the mechanisms of acquired resistance to 1st and 2nd generation EGFR
TKIs remain unknown. Moreover, approximately 30-40% of patients harboring TKI- sensitizing mutations exhibit intrinsic resistance to TKI treatment(Maemondo et al., 2010;
Rosell et al., 2012; Wu et al., 2014). Therefore, it is essential to identify early treatment biomarkers and characterize the mechanisms of intrinsic and acquired resistance to EGFR
TKIs to better guide the development of new drugs or combination strategies to help overcome TKI- resistance.
We have previously employed a liquid chromatography-tandem mass spectrometry (LC
MS/MS) shotgun phosphoproteomics approach that used Stable isotope labeling with amino acids in cell culture (SILAC) to identify novel targets of mutant EGFR signaling in isogenic human bronchial epithelial cells (Guha et al., 2008), to characterize global dynamic phosphorylation (pSTY) changes upon immediate ligand stimulation, with or without prior EGFR 1st-generation TKI treatment (X. Zhang et al., 2015), and to investigate
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the effect of erlotinib and afatinib on downstream tyrosine phosphorylation of several drug-
sensitive and -resistant human adenocarcinoma cell lines (X. Zhang et al., 2017). These
studies showed that the degree of inhibition of tyrosine phosphorylation on several
phosphosites correlates well with the extent of TKI-sensitivity. We have identified several tyrosine phosphorylation sites downstream of mutant EGFRs that are hyperphosphorylated in cells expressing mutant EGFRs, compared to those expressing wild type EGFR, and phosphorylation targets that are inhibited by EGFR TKIs, such as erlotinib and afatinib, in
TKI sensitive cells, but not in resistant cells, suggesting these are potential biomarkers of
TKI sensitivity. Here, we sought to develop multiple reaction monitoring (MRM) quantitation assays for a subset of candidate phosphopeptides from downstream protein targets of mutant EGFRs based on the above criteria.
Although, the advances made in MS based “discovery” shotgun proteomics technologies have revolutionized the pace of protein identification and relative quantification in complex samples, however, several technical limitations exist that make it unsuitable for absolute quantification, i.e. only the highly abundant part of the proteome is reproducibly identified, protein targets present in lower stoichiometric amounts are stochastically identified leading to missing data and unreliable quantification. The discovery proteomics have led to the generation of long lists of biomarker candidates that change in abundance relative to specific disease states but subsequent hypothesis testing in patient specimens need to be performed to verify the proposed clinical utility. To overcome some of the limitations of these data dependent acquisition methods, we have utilized a more robust method of targeted proteomics via multiple reaction monitoring to carry out quantification of these potential biomarkers in the lung adenocarcinoma cells. Going from the “discovery” phase
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of the proteomics to the “verification” step, the goal of the present study was to carry out
validation of these potential makers in 1st generation TKI resistant adenocarcinoma cells.
The MS method most often used to complement shotgun methods is selected/multiple
reaction monitoring (SRM/MRM) because of its high sensitivity, multiplexing capabilities
and dynamic range. A previous study has shown the feasibility of MRM assays to
reproducibly quantify temporal tyrosine phosphorylation profiles upon EGF stimulation in
EGFR signaling (Wolf-Yadlin, Hautaniemi, Lauffenburger, & White, 2007). Another
study has shown utilization of quantitative targeted proteomics to monitor phosphorylation
dynamics of the PI3K-mTOR and MAPK signaling pathways (de Graaf et al., 2015).
Development of sensitive MRM assays (especially for posttranslational modifications like
phosphorylation) that detect low amounts of endogenous peptides in complex biological
matrices usually involves incorporation of an additional enrichment step through an anti-
peptide antibody (N. L. Anderson et al., 2004; Jeffrey R. Whiteaker et al., 2017). The
multiplexed immuno-MRM assays (peptide immunoaffinity enrichment coupled to MRM)
have been utilized previously to investigate phospho-signaling in the DNA damage
response network (J. R. Whiteaker et al., 2015).
In this study, we developed sensitive MRM assays for a subset of phosphotyrosine sites
(Table 3.1) and measured their basal phosphorylation levels in the 1st / 2nd generation
EGFR TKI-sensitive H3255 lung adenocarcinoma cell line, harboring the L858R mutation,
and TKI–resistant cell line H1975, harboring the L858R/T790M mutation, and compared
those to expression levels after treatment with the 1st generation EGFR TKI, erlotinib, and the 3rd generation EGFR TKI, osimertinib. Third generation EGFR TKIs, such as
osimertinib and rociletinib, have shown promising results in overcoming the T790M
65
mutated resistant tumors (Jänne et al., 2015). Based on the SILAC ratios from our previously published studies, we hypothesized that these phosphotyrosine sites are the markers of sensitivity to 1st and 3rd generation TKIs. To test this hypothesis, we quantified the tyrosine phosphorylation patterns of these sites in drug-sensitive and -resistant cells.
Our results indicate that, of the 11 sites for which quantitative MRM based assays were designed, EGFR-pY1197, can be used as potential biomarker of EGFR TKI sensitivity, regardless of the EGFR TKI used.
Gene Protein names Targets Sequence names
Epidermal growth factor receptor EGFR Y-998 MHLPSPTDSNFY(P)R Y-1172 GSHQISLDNPDY(P)QQDFFPK Y-1197 GSTAENAEY(P)LR Signal transducer and activator of STAT5A Y-694 AVDGY(P)VKPQIK transcription 5A Caveolin-1 CAV1 Y-14 YVDSEGHLY(P)TVPIR ERBB receptor feedback inhibitor 1 ERRFI1 Y-394 VSSTHY(P)YLLPERPPYLDK Dual adapter for phosphotyrosine and 3- DAPP1 Y-139 KVEEPSIY(P)ESVR phosphotyrosine and 3-phosphoinositide
Neuroblast differentiation-associated protein AHNAK Y-160 RVTAY(P)TVDVTGR Y-715 VKGEY(P)DMTVPK Enhancer of filamentation 1 NEDD9 Y-164 TGHGYVY(P)EYPSR Neurofibromin NF1 Y-2579 RVAETDY(P)EMETQR Phosphatidylinositol 3,4,5-trisphosphate 5- INPPL1 Y-1135 TLSEVDY(P)APAGPAR phosphatase 2
Table 3. 1: The list of phosphotyrosine tryptic peptide targets used for the development of the modified immuno-MRM assays.
3.2. Materials and methods
3.2.1 Cell culture and lysate preparation
The two lung adenocarcinoma cell lines used in this study were H3255, harboring the
EGFR L858R mutation, and H1975, harboring both EGFR L858R and T790M mutations.
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H1975 was purchased from American Type Culture Collection (ATCC) and H3255 was a
kind gift from Dr. Bruce Johnson. These cells were cultured in RPMI media 1640 (Thermo
Scientific, San Jose, CA) containing 10% dialyzed fetal bovine serum (Invitrogen,
Carlsbad, CA) and 1% penicillin/streptomycin. The cells were grown in several 15 cm
7 dishes (2-3 × 10 cells/dish) at 37 ˚C, 5% CO2 and high humidity. H3255 and H1975 cells
were treated with DMSO (vehicle), and EGFR TKIs, erlotinib and osimertinib (both
prepared in the vehicle) at 100 nM for 1 hour. Following treatment, cells were washed with
cold PBS and lysed in urea lysis buffer containing 20 mM HEPES pH 8.0, 8 M urea and
protease and phosphatase inhibitor mixture tablets (Roche, Indianapolis, IN). Cells were
sonicated three times on ice using a Branson 250 sonicator (Hampton, NH) at 20% pulse
for 30 seconds. The samples were then centrifuged at 20,000×g for 15 minutes at 4˚C and the protein concentration of the supernatant was measured via a modified Lowry assay
(BioRad, Hercules, CA).
3.2.2 Protease digestion, peptide clean-up and IP-enrichment of phosphotyrosine peptides
Lysates prepared in the urea buffer from the three treatments were reduced by 4.5 mM
dithiothreitol (DTT) solution at 60˚C for 20 minutes. This was followed by cysteine
alkylation by 10 mM iodoacetamide (IAA) stock solution for 15 minutes in the dark. The
samples were diluted four-fold to a final concentration of 2 M urea, 20 mM HEPES pH 8.0
and subsequently digested with trypsin (Worthington, Lakewood, NJ) at a ratio of 1:100
(trypsin: protein, w/w) for 16 hours at 30˚C. The digests were then acidified to a final
concentration of 1% trifluoroacetic acid (TFA) to quench the protease digestion. The
digested peptides were desalted using a solid-phase extraction C18 column (40 µm, 1 gram,
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Supelco, Bellefonte, PA). The desalted peptides were lyophilized and stored at -80˚C until
further use. The endogenous phosphotyrosine peptides were enriched using a PhosphoScan
Kit with modified antibodies (combination of pY100 and pY1000 antibody slurries, 1:1
v/v, Cell Signaling, Danvers, MA). For each sample, a total of 8mg digested protein extract
was suspended in IAP buffer (50 mM MOPS, pH 7.2, 10 mM sodium phosphate, 50 mM
NaCl) and incubated with 80 µL of the immobilized anti-Tyrosine antibody for 2 hours at
4˚C. After incubation, the antibody bound beads were centrifuged for 30 seconds at 2000×g
and the supernatant was pipetted out and stored. The antibody beads were then washed
twice with 1 mL IAP buffer and once with 1 mL chilled water. The phosphotyrosine
peptides were eluted off the antibody beads by incubating twice with 55 µL of 0.15% TFA
in water at room temperature for 10 minutes and mixing gently and intermittently. The
eluted phosphopeptides were desalted using Pierce C18 spin tips (Thermo Scientific,
Rockford, IL).
3.2.3 Synthetic peptide standards
The initial development of MRM assays was carried out using heavy isotope-labeled
15 13 13 15 synthetic peptides with a C-terminal N- and C-labeled arginine ( C6, N4) or lysine
13 15 ( C6, N2) residues, resulting in a mass shift of +10 or +8 Da, respectively. Unlabeled
12 C6 (light) versions of the peptides were also synthesized and used as internal standards for building the calibration curves. These were purchased from New England Peptides Inc.
(+95% purity by HPLC, Gardner, MA). The peptides were quantified by amino acid analysis by the company, aliquoted and stored at -80˚C until use.
3.2.4 Nano-LC-MRM method development and analysis
68
For assay development, the synthetic peptides were reconstituted in 0.1% formic acid and
were analyzed on a nano-chip-LC using a 1260 Infinity Series HPLC-Chip cube interface
(Agilent, Palo Alto, CA) coupled to a 6495-triple quadrupole mass spectrometer (Agilent,
Palo Alto, CA). A large capacity chip system (G4240-62010) consisting of a 160 nL
enrichment column and a 150 mm × 75 µm i.d. analytical column (Zorbax 300SB-C18, 5
µm, 300 Å pore size) was used. Mobile phase A consisted of 95% water, 4.9% acetonitrile
and 0.1% FA, and mobile phase B consisted of 95% acetonitrile, 4.9% water with 0.1%
FA. A flow rate of 3 µL/min was applied for sample loading by the capillary pump and
600 nL/min for the analytical separation through the nano pump. A 25-minute linear
gradient (0 min, 0% B; 1 min, 5% B; 17 min, 40% B; 20 min, 70% B; 21 min, 80% B; 25
min 0% B) was used for the chromatographic separation of the target peptides. An
electrospray ionization voltage of 1850 V was applied and quadrupoles 1 and 3 were run
at 0.7 m/z FWHM resolution.
Individual injections were carried out to select transitions (defined as fragmentation of a
precursor into a specific product ion for a given peptide). The optimum transitions were selected based on the presence of the top 5 most intense y-ions at m/z greater than the precursor m/z, to minimize crosstalk from potential interfering transitions occurring from singly charged precursor ions. When there were no y-ions present at a m/z greater than the precursor, then other more abundant b- and y-ions were chosen. The raw data were imported into Skyline 3.7 (MacLean et al., 2010) and manually reviewed to delete transitions with interferences. The MRM assay for each peptide consisted of two to five transitions. The collision energies were calculated by Skyline for the monoisotopic
precursor and product masses for the Agilent 6495 system. An equation, CE= 0.04 × m/z
69
+ y-intercept was used, and intercepts of -5.5664 and -4.8 were applied for doubly and
triply charged precursors, respectively (CE = collision energy, m/z = mass to charge ratio
of the precursor ion). The optimum transitions were combined in one scheduled MRM method with a 25-min run time and 2-minute retention time windows, using retention times extracted during the MRM assay refinement stage. To minimize carryover, calibration samples were run from lower to higher concentrations and two blank injections were performed in between each sample injection. The sample carryover amount was estimated by determining the percentage intensity in the second blank compared to the sample intensities.
3.2.5 MRM assay refinement, generation of calibration curves and statistical data analysis
The initial development of the MRM assays was carried out using stable-isotope labelled standards to select optimum transitions. The synthetic peptide standards were reconstituted in 0.1% formic acid, pooled and used for method optimization. A spectral library (Fig. 3.2) was generated with our discovery data (X. Zhang et al., 2017) to help select these transitions. All the raw data files were imported into Skyline 3.7 and data annotations were manually inspected. For endogenous target peptide identifications, we required that all the monitored transitions had nearly identical elution profiles, as well as good correlation with the relative transition intensities of the spiked-in internal standards, which were defined by ratio dot-product (rdotp) values, and that the observed elution times were consistent with the hydrophobicity of the peptides (Fig. 3.3). We used SSRCalc 3.0 (Krokhin, 2006) to predict retention times of the targets for the scheduled method building. The synthetic peptides were spiked in the samples right before LC-MRM analysis. Peak area ratios (PAR)
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of isotopically normal (i.e. light) endogenous signals to isotopically enriched (i.e. heavy)
internal standards were exported to Microsoft Excel for calculation of means, standard
deviations and coefficients of variation. The generation of linear regression was carried out
by building a reverse calibration curve (Campbell et al., 2011) in H1975 DMSO treated,
digested and immunoprecipitated cell lysate (matrix-matching) wherein the amount of the
heavy labelled standard was varied over a range (0.01, 0.1, 0.5, 2, 8, 50, 100, 500, 100
fmol) and a constant amount of the light EGFR-pY-1172, 3+ EGFR-pY-1197, 2+ EGFR-pY-998, 3+ precursor MS/MS precursor MS/MS precursor MS/MS 1.2 y6 200 b5 y10 2 1
150 y6 b3 y5 0.8 b9 1.5 y5
y7 y8 100 b2 0.6 1 y4 y4 y2 Intensity (10^3) y2 z3 0.4 Intensity (10^6) Intensity (10^6) y3 50 y8++ y7 b5 b8 y8 0.5 z6 z6 y3 y6++ y9 b2 a4++ z5 z12++ 0.2 b6 x7 y1 y1 z4 z7 b7 y9 b8++ b10 b11 y2 y1 b3 b10++ b15++ b12 b5 z8 0 0 0 200 400 600 800 1000 1200 200 400 600 800 1000 1200 1400 200 400 600 800 1000 1200 1 m/z m/z m/z STAT5A-pY -694 , 3+ CAV1-pY-14, 3+ precursor MS/MS precursor MS/MS 11 y9 DAPP1-pY-139, 3+ y5 10 y12 200 x2++ a2 precursor MS/MS 150 9 8 7 150 100 6 5 100 y4 4 b6 Intensity (10^3)
3 Intensity (10^3) 50 y5 y10++ y8 Intensity (10^6) b4 2 y11 50 y3 y10 y7 b7 y1 y1 a3++ y3 +++ z1 1 y8 y7 z3 y5 y1 y7 y2 b8 0 0 0 200 400 600 800 1000 1200 200 400 600 800 1000 1200 1400 200 400 600 800 1000 1200 m/z m/z m/z
INNPL1-pY-1135, 2+ NEDD9-pY-164, 3+ ERRFI1-pY-394 , 3+ precursor MS/MS precursor MS/MS precursor MS/MS 900 y3 y9 800 y9 150 700 150 y12 a2 600 y6 500 y10 100 100 400 b8 b9 300 b2 y8 y14++ b15++ y7 b5 Intensity (10^3)
Intensity (10^3) 50 Intensity (10^3) 50 y1 200 y2 y4 y11 y5 b7 b7 b3 b8 100 y6 y3 b4 y1 z6 z2 z1 y6 y10 y5 z4 b5 y15++ 0 0 0 200 400 600 800 1000 1200 1400 200 400 600 800 1000 1200 200 400 600 800 1000 1200 1400 m/z m/z m/z Fig 3. 2: Spectral library built from previously performed Data Dependent Acquisition (DDA) experiments to facilitate selection of optimal transitions
71
peptide (2 fmol) was used as an internal standard. Linear regression was used to fit the data
points using a 1/y weighting for each calibration standard. Coefficient of variation was
used to determine the precision. The standard deviations (y-axis) of the three lowest
calibrators (x-axis) were plotted and used to calculate the limits of detection (LOD) and
lower limits of quantification (LOQ) for each peptide, where LOD = 3 × y-intercept and
LOQ = 10 × y-intercept of those plots (D. J. Anderson, 1989). Quantitation was carried out by comparing the cells following TKI treatment to the controls using a two-tailed t-test
(p-values < 0.05 were considered statistically significant). To identify differentially expressed phosphotyrosine sites, the mean PAR was used for relative quantitation. The endogenous abundance of the targets was estimated by the amounts calculated from the linear regression obtained from the calibration curves.
Regression Predictor Peptides 12 slope = 0.23, intercept = 2.62 window = 1.2 r = 0.9789 10 slope = 0.23, intercept = 2.99 window = 1.1 r = 0.9789 8
6
4 Measured Time 2
0 15 20 25 30 35 40 SSRCalc 3.0 (300A) Fig 3. 3: The linear regression obtained for retention time prediction using the SSRCalc 3.0 calculator.
3.2.6 cBioPortal analysis:
The TCGA lung adenocarcinoma dataset that consisted of the maximum number of
sequenced cases (520 patients) was interrogated through the cBioPortal (Cerami et al.,
2012; Gao et al., 2013) for alterations in our gene list (Table 3.1) for missense, truncating
and in-frame mutations, amplification and deletions, mRNA up- and downregulation and
72
protein up- and down regulation by RPPA assay. Most of these were early stage lung cancer patients who underwent surgery. A survival analysis was also carried out comparing the cases with alterations in our query list to the ones without alterations to look at the median disease-free survival.
3.3. Results
The overall goal of this study was to develop modified immuno-MRM-based quantitative assays to detect and quantify tyrosine phosphorylation on specific tyrosine phosphorylated peptides of key signaling proteins involved in lung cancer-specific mutant EGFR signaling.
The tyrosine phosphorylated peptides selected for this study were identified in our previous
SILAC-based quantitative DDA (Data Dependent Acquisition) studies (Guha et al., 2008;
X. Zhang et al., 2015; X. Zhang et al., 2017) (Table 3.1). Phosphorylation at these sites was higher in cells expressing mutant EGFRs and/or inhibited upon treatment with 1st and
2nd generation EGFR TKIs, erlotinib and afatinib, respectively, in TKI-sensitive but not
TKI-resistant lung adenocarcinoma cells.
3.3.1 LC-MRM method development and optimization:
For each peptide target, two to five MRM transitions were chosen to build the optimized
MRM assays for targeted proteomics. We synthesized these peptides with heavy amino
acid-labeled Arginine or Lysine at the C-terminus and first, determined the optimal
chromatographic separation of the target peptides (Fig. 3.4). Chromatographic co-elution
of the target endogenous peptides with the stable-isotope labelled internal standards was
required for the highest confidence of detection (Fig. 3.5). For quantitation, noisy transitions and transitions with co-eluting interferences were
73
Table 3. 2: The list of precursor and product ions, their m/z values, charge and collision energies for the optimized assays. Spectral library and individual heavy isotope labelled peptide injections were carried out to choose optimally performing precursors and product ions for each target.
74
Table 3. 3 continued
Gene Precursor Precursor Product Product Collision Targets Fragment Ion names m/z Charge m/z Charge energy EGFR Y-998 548.9 3 881.3 1 y6 17.2 548.9 3 679.3 1 y4 17.2 548.9 3 565.2 1 y3 17.2 548.9 3 382.2 1 b3 (quantifier) 17.2 548.9 3 566.3 1 b5 17.2 552.2 3 891.3 1 y6 17.2 552.2 3 689.3 1 y4 17.2 552.2 3 575.2 1 y3 17.2 552.2 3 382.2 1 b3 17.2 552.2 3 566.3 1 b5 17.2 Y-1172 772.7 3 538.3 1 y4 26.1 772.7 3 391.2 1 y3 26.1 772.7 3 410.2 1 b4 26.1 772.7 3 523.3 1 b5 26.1 772.7 3 952.4 1 b9 (quantifier) 26.1 775.3 3 546.3 1 y4 26.1 775.3 3 399.2 1 y3 26.1 775.3 3 410.2 1 b4 26.1 775.3 3 523.3 1 b5 26.1 775.3 3 952.4 1 b9 26.1 Y-1197 645.8 2 845.4 1 y6 (quantifier) 20.3 645.8 2 731.3 1 y5 20.3 645.8 2 660.3 1 y4 20.3 645.8 2 531.2 1 y3 20.3 650.8 2 855.4 1 y6 20.3 650.8 2 741.3 1 y5 20.3 650.8 2 670.3 1 y4 20.3 650.8 2 541.2 1 y3 20.3 STAT5A Y-694 433.2 3 712.5 1 y6 12.5 433.2 3 613.4 1 y5 12.5 433.2 3 485.3 1 y4 (quantifier) 12.5 435.9 3 720.5 1 y6 12.5 435.9 3 621.4 1 y5 12.5 435.9 3 493.3 1 y4 12.5 CAV1 Y-14 576.9 3 1078.5 1 y8 18.3 576.9 3 941.5 1 y7 (quantifier) 18.3 576.9 3 828.4 1 y6 18.3 576.9 3 385.3 1 y3 18.3 580.3 3 1088.6 1 y8 18.3 580.3 3 951.5 1 y7 18.3 580.3 3 838.4 1 y6 18.3 580.3 3 395.3 1 y3 18.3
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DAPP1-pY-139
40 NEDD9-pY-164
KVE… EGFR-pY-1172
TGH…
EGFR-pY-1197 GSH… 30 RVT… GST… AHNAK-pY-160
20 STAT5A-pY-694 CAV1-pY-14 NF1-pY-2579 AHNAK-pY-715 Intensity (10^3) INNPL1 -pY-1135 EGFR-pY-998 10 RVA… AVD… TLS… YVD… ERRFI1-pY-394 VKG… VSS… MHL… 0 6 7 8 9 10 11 12
Fig 3. 4: Optimized chromatographic separation of the target phosphopeptides from method development stage. A) The final scheduled method showing the elution of the targets with 2-minute retention time windows in DMSO/vehicle treated H1975 cells.
discarded. The final method after the refinement stage consisted of 12 peptides comprising
87 transitions (Table 3.2).
The SSRCalc linear regression was used for retention time prediction for scheduling the
MRM assays (Fig. 3.3). Given the average chromatographic peak width was around 30 seconds, a cycle time of 1.3 seconds for the scheduled methods allowed for sufficient sampling across the chromatographic peak. For any given peptide, the single transition that was free of interfering co-eluting ions and had good signal intensity was chosen as the
“quantifier” for the quantitative assay while the remaining transitions were used as
“qualifiers” to allow for maximum specificity. At any given time during the scheduled
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EGFR-pY-998 EGFR-pY-1172 EGFR-pY-1197 2500 cted 9.1 30 Predicted 7.0 9.7 Predicted 10.7 6.8 25 2000 80 10.8 20 1500 60 9.7 15 1000 40 10 6.8
Intensity 500 20 10.8 5 Intensity (10^3) 0 0 Intensity (10^3) 0 9.0 9.5 10.0 10.5 11.0 11.5 6.5 7.0 Retention Retention Retention
STAT5A-pY-694 CAV1-pY-14 DAPP1-pY-139 35 16 Predicted 7.5 Predicted8.3 8.2 15 Predicted9.3 9.6 30 12 7.4 25 10 20 8 15 5 4 9.3 10 7.4 7.1 7.8 8.9 10.0 5
Intensity (10^3) 8.3 Intensity (10^3) 0 Intensity (10^3) 0 0 7.0 7.2 7.4 7.6 7.8 9.0 9.5 10.0 7.0 7.5 8.0 8.5 9.0 Retention Retention Retention
AHNAK-pY-160 AHNAK-pY-715 rdotp=1 NEDD9-pY-164 35 Predicted 7.4 40 Predicted 7.5 6000 Predicted 7.4 30 7.7 30 5000 25 7.8 7.6 7.7 4000 20 20 3000 15 10 2000 10 Intensity 7.7 1000 7.6 5 7.4 Intensity (10^3) 7.3 Intensity (10^3) 0 0 0 7.5 8.0 7.5 8.0 7.0 7.2 7.4 7.6 7.8 8.0 Retention Retention Retention
NF1-pY-2579 INNPL1-pY-1135
Predicted 8.1 10 Predicted 7.1 6000 6.9 8 5000 8.2 4000 6 3000 4 2000 Intensity 2 1000 7.3 9.0 6.5 6.9 7.37.4
Intensity (10^3) 0 0 6.5 7.0 7 7.5 8.0 8.5 9.0 Retention Retention Fig 3. 5: The representative chromatograms for each target is marked by a black arrow. The red peak represents the endogenous peptide chromatogram and the blue is the heavy labelled internal standard spiked-in at a constant amount of 2 fmol in all samples. The grey bars are the predicted retention times from SSRCalc 3.0. rdotp represents the correlation between the endogenous and internal standard chromatograms.
77
method, the dwell time varied from 80 to 160 milliseconds with 8 to 16 concurrent transitions being measured in any specified retention time window (Fig. 3.6).
The resulting scheduled MRM methods with a 120 second window allowed for over 20 points across the chromatographic peak for the “quantifier” transitions for all of the peptide targets. The scheduled MRM method resulted in superior chromatographic profiles for the final assays showing clearly defined peak areas for endogenous peptides and the heavy labelled standards in both H3255 experiments (Fig. 3.7) and H1975 experiments (Fig. 3.8).
20 15
10 5
Number of 0
concurrentMRMs 6.8 7.4 7.6 8.2 9.3 10.8 Retention time
Fig 3. 6: Graphs showing number of concurrent transitions being measured and estimated dwell time across the chromatographic elution of the targets for the final optimized scheduled MRM assays using a 2-minute retention time window. We tested our standardized assays to interrogate the tyrosine phosphorylation dynamics in
EGFR TKI-sensitive cell line H3255, and TKI-resistant cell line H1975, upon treatment with the 1st generation EGFR TKI, erlotinib and the 3rd generation EGFR TKI, osimertinib.
78
Erlotinib is expected to inhibit phosphorylation of these targets in H3255, but not in H1975, since these cells are resistant to erlotinib. In contrast, osimertinib is expected to inhibit
Fig 3. 7: Chromatographic profile for the endogenous (left) and the heavy
labelled internal standards (right) for H3255 (DMSO/vehicle treated) cells.
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Fig 3. 8: Chromatographic profile for the endogenous (left) and the heavy labelled internal standards (right) for H1975 (DMSO/vehicle treated) cells.
phosphorylation of these targets in both H3255 and H1975 cells.
Tyrosine phosphorylated peptides were enriched using pY antibodies. To optimize the enrichment of the phospho-tyrosine peptides, a combination of two pY antibodies, pY-100
80 and pY-1000 were used. Analysis of the enriched phosphopeptides by DDA-based LC-
MS/MS using the two antibodies showed on average, an overlap of ~60% in the enriched peptides (Fig. 3.9). Hence, a combination of the two antibodies was used for the enrichment of pY peptides. The median retention time CVs among the three process replicates for
H1975 DMSO/vehicle treated cells was 0.28% for the endogenous peptides and 0.23% for the heavy-labelled peptides (Fig. 3.10). All the monitored transitions for each peptide co- eluted. The phosphotyrosine target ERRFI1-pY-394 was not chosen for further optimizations owing to poor stability of the heavy labelled standard. The peak area ratios
(PAR) were calculated by normalizing the peak intensity of the endogenous peptide transitions to that of the heavy labelled standard peptide. The CV values for the PARs for the three process replicates in H1975 DMSO/vehicle treated cells ranged from 13.9% to
56.4% with a median CV of 27.47% (Fig. 3.11).
Mouse liver 54 Mouse liver 29 pY100 (61%) 40 pY1000
H1975 85 H1975 pY100 63 (58.5%) 56 pY1000
Fig 3. 9: Venn diagrams showing the number of unique and commonly identified phosphotyrosine sites from P-Tyr-100 and P-Tyr-1000 immunoprecipitations in control digested peptides from mouse liver extracts and digested protein extracts from H1975 cells.
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Fig 3. 10: Reproducibility of the optimized MRM assays from three independent proteolytic digestions and immunoprecipitations of DMSO/vehicle treated H1975 cells. A) Retention time repeatability; the red and blue bars represent endogenous and internal standard peptides, respectively. The median CV was 0.28% for the endogenous peptides and 0.23% for the internal standards.
Fig 3. 11: Peak area ratio CVs as determined by normalizing the signal intensities of the endogenous to the internal standards. The median CV was 27.47%.
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3.3.2 Relative quantitation of the phosphotyrosine sites upon TKI treatment in the lung adenocarcinoma cells:
The quantitative analysis of tyrosine phosphorylation of the peptides was carried out using
three biological replicates for the controls and the TKI treated cells. The average of the
median PAR CVs across three biological replicates of the targets analyzed upon
DMSO/vehicle, erlotinib and osimertinib treatment was 26.7% (range 17-35%) in H3255
experiments (Fig. 3.12) and 26.29% (range 19.2-35.5%) in H1975 experiments (Fig. 3.12).
The drug sensitive cells, H3255, showed significant downregulation of phosphorylation for
all the phosphotyrosine sites with p-values < 0.005 (Fig. 3.13 A). In H1975 cells, there was
no statistically significant change in the tyrosine phosphorylation of the targets upon
erlotinib treatment. The MRM ratios of phosphorylation at AHNAK-pY715, DAPP1- pY139 and CAV1-pY14 were less than 0.5, but these changes were not statistically significant (p > 0.05) (Fig. 3.13 B). In contrast, relative quantitation showed that the treatment with osimertinib in these cells led to statistically significant downregulation of
STAT5A-pY694 (p-value =0.0006) and EGFR-pY1197 (p-value =0.004) over 6 biological replicates and CAV1-pY14 (p-value =0.01) over three biological replicates. (Fig. 3.14).
Unsupervised clustering analysis of the log2-transformed MRM ratios of erlotinib and osimertinib showed that the H3255 experiments clustered together and so did the H1975 experiments. EGFR-pY1197, pY1172 and STAT5A-pY694 clustered together (Fig. 3.15).
83
H3255
H1975
Fig 3. 12: Peak area ratio coefficient of variations obtained from three biological replicates for the relative quantification inH3255 and H1975 cells for DMSO/vehicle, erlotinib and osimertinib treatments.
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EGFR-pY-998 EGFR-pY-1172 EGFR-pY-1197
*** *** *** *** **** ****
STAT5A-pY-694 DAPP1-pY-139 AHNAK-pY-160
*** *** *** *** *** ***
AHNAK-pY-715 NEDD9-pY-164 NF1-pY-2579
***
*** *** **** **** ****
INNPL1-pY-1135
**** ****
Fig 3. 13: A: Relative quantitation of phosphopeptides using peak area ratios. All the phosphotyrosine sites were significantly downregulated (p<0.005) in drug sensitive H3255 cells upon treatments with erlotinib (Erl) and osimertinib (Osi).
85
EGFR-pY-1172 EGFR-pY-998
AHNAK-pY-715 INPPL1-pY-1135
NEDD9-pY-164 NF1-pY-2579
AHNAK-pY-160 DAPP1-pY-139
Fig 3. 13: B: Relative quantitation of phosphopeptide abundance using peak area ratios from three biological replicates for H1975 cells treated with erlotinib and osimertinib. The changes in abundance of these phosphopeptides were not significant.
86
EGFR-pY-1197 STAT5A-pY-694 CAV1-pY-14
Fig 3. 14: Relative quantitation of phosphopeptides using peak area ratios. Phosphotyrosine sites EGFR-pY1197, STAT5A-pY694 and CAV1-pY14 in the H1975 cells show significant hypophosphorylation (p<0.05) with osimertinib, but not with erlotinib treatment (n.s.-not significant).
H1975 H1975 H3255 H3255 erlotinib H3255 H1975 erlotinib H1975
osimertinib osimertinib
CAV1-pY14
EGFR-pY1197
EGFR-pY1172
STAT5A-pY694
NF1-pY2579
EGFR-pY998
NEDD9-pY164
INNPL1-pY1135
DAPP1-pY139
AHNAK-pY715
AHNAK-pY160
87
Fig 3. 15: Unsupervised hierarchical clustering of the peak area ratios from relative quantitation of phosphorylation of the targets. Heat map shows the average log2 transformed MRM ratios for erlotinib and osimertinib treatments from three biological replicates in H3255 and H1975 cells.
3.3.3 Phosphosite abundance estimation in the lung adenocarcinoma cells from the
calibration curves:
After examining the relative changes in phosphorylation, we wanted to determine the
abundance of the tyrosine phosphorylated peptides in these cell lines upon erlotinib or
osimertinib treatment. In order to characterize the analytical performance of the
quantitative assays, we built the response curves in digested and phosphotyrosine enriched
matrix of H1975 cells treated with DMSO and processed in the same fashion as the TKI
treated samples (Fig. 3.16).
A reverse curve approach was used to eliminate the interferences from the endogenous
signals and to estimate the figures of merit like method dynamic range, limit of detection
(LOD) and limit of quantification (LOQ). Our matrix-matched external multi-point calibration curves were linear over 2-4 orders of magnitude. Precision was excellent with replicate CVs under 20%.
Of all the chosen targets, EGFR-pY1172 did not show a linear response to the dilution series, while the remaining peptide curves were linear with R2 > 0.9 (Table 3.3). For H3255
cells, the abundance changes in the phosphorylation levels of EGFR-pY998 and 1197 were
consistent with our results from relative quantification (Fig. 3.17). Although the amounts
determined for the DMSO-treated sample by the linear regression were above the linear
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dynamic range, the drug treatments showed a significant downregulation in the tyrosine phosphorylations of these sites, indicating the inhibitory effects of the TKIs.
Fig 3. 16: Response curves for the phosphotyrosine targets for quantitative analysis. Linear regression was used to fit the data points using a 1/y weighting for each concentration.
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In these cells, the expression of EGFR-pY998 was downregulated by 13.4-fold and 11.8- fold by osimertinib and erlotinib respectively. Also, the expression of EGFR-pY1197 was downregulated by 15-fold and 18.3-fold by osimertinib and erlotinib respectively. On the other hand, for the 1st generation TKI resistant H1975 cells, the expression of EGFR-pY998
was upregulated with drug treatments (erlotinib treatment, average amount = 4.38 fmol/mg,
p-value = 0.03 and osimertinib treatment, average amount = 3.73 fmol/mg, p-value = 0.1).
EGFR-pY1197 expression was not significantly changed with erlotinib treatment, but it was significantly downregulated with osimertinib treatment (p-value = 0.009) (Fig. 3.17).
Table 3. 4: Phosphopeptide abundance determined from three biological replicates using calibration curves. Mean abundance of target phospho-peptides expressed in fmol/mg protein obtained from reverse calibration curves following treatment with DMSO/vehicle, erlotinib and osimertinib in H3255 and H1975 cells. LOD, Limit of Detection, LOQ, Limit of Quantification. *under LOD; ** under LOQ; *** above linear dynamic range.
The average amount of INNPL1-pY1135 in the DMSO treated H3255 cells was 1.08 fmol/mg, while for the drug treatments, the amounts were determined to be significantly lower (osimertinib treatment, p-value =0.0002 and erlotinib treatment, p-value = 0.0004)
90 than the control (Fig. 3.17) as expected but were under the LOD, so could not be reliably calculated. The amounts calculated for the H1975 cells for INNPL1-pY1135 for all the treatments were found to be at LOD levels, therefore reliable quantitation could not be carried out.
The expression of AHNAK-pY160 in H3255 was significantly lowered by both erlotinib treatment (1.5-fold downregulation, p-value = 0.009) and osimertinib treatment (1.5-fold downregulation, p-value = 0.001) while the expression of AHNAK-pY160 in H1975 cells was not significantly affected with either of the TKI treatments (Fig. 3.17).
In H3255 cells, the quantitative data for STAT5A-pY694 showed a significant downregulation by erlotinib (p-value = 0.001) and osimertinib (p-value = 0.001) treatments. Similarly, the expression of NEDD9-pY164 in H3255 was significantly lowered by both erlotinib treatment (p-value = 0.001) and osimertinib treatment (p-value
= 0.001). Although, it should be noted that average amounts of STAT5A-pY694 and
NEDD9-pY164 in the DMSO treated H3255 cells was 0.65 fmol/mg and 0.6 fmol/mg respectively, which is just above LOD for their respective curves and the average amounts calculated for the TKI treatments for these sites were under LOD, therefore these results may not be quantitatively reliable. On the other hand, the expression of STAT5A-pY694 in H1975 cells couldn’t be determined owing to all values falling under LOD. The expression of NEDD9-pY164 in H1975 cells was not significantly affected with either of the TKI treatments (these expression values were above LOD but below LOQ).
The quantification for the remainder of sites was unreliable as the amounts calculated fell outside the domain of their respective standard curves.
91
H3255
Control Erlotinib Osimertinib
H1975 Control Erlotinib Osimertinib
92
Fig 3. 17: Bar charts showing the quantitative data of phosphopeptide abundance obtained with and without TKI treatments from three biological replicates in H3255 and H1975 cells. Error bars are +/- standard deviations. 3.4. Discussion
Kinase domain mutations in EGFR have been used to predict response to treatment with
EGFR TKIs in lung adenocarcinoma patients. Accordingly, FDA has approved companion diagnostics for several of these mutations to select patients to be treated with different generations of EGFR TKIs. However, the partial response rates (>30% of shrinkage of tumors using RECIST criteria) are in the range of 70-80% with different generations of
EGFR TKIs for treatment of newly diagnosed patients and even falls to around 60-70% in patients who have developed resistance to 1st or 2nd generation EGFR TKIs and harbor the most common TKI-resistant mutation, T790M. Here, we developed modified immuno-
MRM assays for enrichment of tyrosine phosphorylated peptides using a combination of pY100 and pY1000 antibodies, and carried out the quantitation of tyrosine phosphorylation of specific phosphopeptides of key mutant EGFR signaling proteins. These phosphosites exhibited increased phosphorylation in human bronchial epithelial cells expressing mutant
EGFRs. In addition, phosphorylation at these sites was inhibited in human lung adenocarcinoma cells that were sensitive to 1st generation EGFR TKI, erlotinib, and 2nd generation EGFR TKI, afatinib. We validated these assays in two lung adenocarcinoma cell lines with varying sensitivities to the 1st and 3rd generation EGFR TKIs, erlotinib and osimertinib respectively.
H3255 human lung adenocarcinoma cell line, harboring the EGFR TKI sensitizing mutation L858R is sensitive to both erlotinib and osimertinib. Osimertinib targets the TKI- sensitizing mutations (sensitive to 1st generation EGFR TKIs), as well as the most common
93 acquired resistance mutation in EGFR, T790M. Recently, a large randomized double-blind phase 3 trial demonstrated that the median progression free survival was longer with osimertinib than with the 1st generation EGFR TKIs, gefitnib or erlotinib, in newly diagnosed patients with EGFR mutations (Soria et al.). Treatment of H3255 with erlotinib or osimertinib represents “front-line” treatment of patients in this published study. Relative quantification carried out by comparing PARs through our validated MRM assays showed that tyrosine phosphorylation of the phosphopeptides chosen for this study was inhibited significantly (p<0.005) upon treatment of with either erlotinib or osimertinib in H3255 cells. Tyrosine phosphorylation of CAV1-pY14 was inhibited with erlotinib, but was not significant in this cell line. This is likely due to a lower endogenous level of CAV1 protein itself in H3255. The chromatographic peak for this phosphopeptide had low intensity in control lysates with DMSO treatment and was at background levels upon drug treatment in H3255. The hypophosphorylation of the sites EGFR-pY998, EGFR-pY1197, INNPL1- pY1135, AHNAK-pY160, STAT5A-pY694 and NEDD9-pY164 by both the TKI treatments was verified through the reverse calibration curves as well.
On the other hand, H1975 lung adenocarcinoma cells harboring the EGFR L858R and
T790M mutations are resistant to erlotinib, but are sensitive to osimertinib. Accordingly, there was no significant decrease in tyrosine phosphorylation of our chosen phosphosites upon erlotinib treatment in these cells. However, relative quantitation showed that osimertinib treatment significantly reduced tyrosine phosphorylation of EGFR-pY1197 (p- value = 0.009), STAT5A-pY694 (p-value = 0.02) and CAV1-pY14 (p-value = 0.01), suggesting that these phosphosites are indeed potential biomarkers of osimertinib sensitivity in cells resistant to 1st generation EGFR TKIs. An independent rerun of the
94
relative quantitation analyses for an additional three biological replicates in H1975 cells
showed that the hypophosphorylation of EGFR-pY1197 (p-value = 0.03) and STAT5A-
pY694 (p-value = 0.03) by osimertinib was reproducible but the same was not seen for the
target CAV1-pY14. This may be due to insufficient enrichment of CAV1-pY14 as the
PARs obtained for the H1975 DMSO treated cells went down from 1.5 from the first set
of experiments to 0.1 in the rerun. However, we were only able to confirm the
hypophosphorylation of EGFR-pY1197 by osimertinib through the calibration curve.
Previously we have shown significant downregulation of EGFR-pY1197 in lung
adenocarcinoma cells with TKI-sensitizing EGFR mutations upon treatment with either
erlotinib or the 2nd generation EGFR TKI, afatinib. More importantly, the degree to which
tyrosine phosphorylation was inhibited at this site was lower in 11-18 lung adenocarcinoma
cells with lesser sensitivity to the 1st and 2nd generation EGFR TKIs compared to H3255
cells, while both cell lines harbored the TKI sensitizing mutant EGFR-L858R (X. Zhang et al., 2017). Here, we have further validated EGFR-pY1197 as a potential biomarker of
TKI sensitivity by developing the pY1197 quantitative MRM assay. Interestingly, a quantitative phospho-tyrosine MS analysis combined with a partial least-squares regression (PLSR) analysis showed that EGFR-pY1197 and INPPL1-pY1135 were among the epidermal growth factor (EGF) and hepatocyte growth factor (HGF) network-specific markers of invasion in A549 lung adenocarcinoma cells that harbor the KRAS G12V mutation but wild type EGFR (H. Johnson, Lescarbeau, Gutierrez, & White, 2013). This suggests that the assays developed in this study may also be used in predicting the biology of even wild type EGFR-harboring NSCLC tumors.
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STAT5A is constitutively activated downstream of BCR-ABL, JAK2V617F, FLT3-ITD or
cKITD816V oncogenes in many hematological malignancies and myeloproliferative diseases(Benekli, Baer, Baumann, & Wetzler, 2003). Activated SRC family kinases
(SFKs) in BCR-ABL expressing cells can interact with STAT5A, inhibit their
dimerization, and phosphorylate STAT5A-pY394 leading to cytoplasmic localization of
STAT5A (Fahrenkamp, de Leur, Küster, Chatain, & Müller-Newen, 2015).
It has been previously shown that the STAT5A-pY694 phosphorylation was inhibited by ruxolitinib, a JAK1/2 inhibitor in JAK2V617F cells that resulted in prevention of cell proliferation in vitro and effective control of myeloproliferation in vivo (Bartalucci et al.,
2017). STAT5A-pY694 is also a downstream target of EGFRVIII mutation in glioblastoma
(Chumbalkar et al., 2011). Here, we show that STAT5A-pY694 is inhibited by both erlotinib and osimertinib in H3255 cells, but only by osimertinib in H1975 cells that are resistant to erlotinib, further demonstrating that this is indeed a mutant EGFR target and this site can be a novel biomarker of EGFR TKI response.
CAV1-pY14 phosphorylation and the specific extracellular matrix (ECM) proteins and integrin interaction are required for melanoma cell migration and metastasis. However, mutated CAV1-Y14F did not affect the tumor suppressor function of CAV1 (Ortiz et al.,
2016). Another study showed the involvement of the CAV1-pY14 in reinforcing the
activation of ERK or AKT kinases and thereby leading to increased proliferation,
migration, invasiveness and development of chemo-resistance in rhabdomyosarcoma
(RMS). This study also showed that the reduction of the pY14 levels by Src-kinase
inhibitors counteracted the malignant phenotype of RMS cells (Faggi et al., 2014). Y14
phosphorylated CAV1 functions as an effector of Rho/ROCK signaling in the focal
96
adhesion turnover and in turn promotes cancer cell invasion and migration. A feedback
loop was defined between Rho/ROCK, Src, CAV1-pY14 in metastatic tumor cell
protrusions (Joshi et al., 2008). The crosstalk between Mgat5/galectin lattice and CAV1-
pY14 was implicated in focal adhesions turnover due to the stabilization of focal adhesion
kinases (FAKs), elucidating the interdependent roles of galectin-3 and CAV1-pY14 in cancer cell migration (Goetz et al., 2008). In our study, CAV1-pY14 phosphorylation was significantly inhibited by osimertinib, but not erlotinib, in H1975 cells, suggesting that mutant EGFRs directly or indirectly through other tyrosine kinases, such as SFKs, can phosphorylate CAV1 at Y14, and that the dynamics of this phosphorylation can predict
TKI responsiveness.
We previously showed DAPP1-pY139 as a novel target of the mutant EGFRs. We also showed the involvement of DAPP1 in the survival of mutant EGFR-addicted cells (X.
Zhang et al., 2017). Here, the DAPP1-pY139 MRM assay reproducibly showed significant inhibition of phosphorylation by both erlotinib and osimertinib in H3255 cells.
The MRM assays developed in this study to detect and quantify tyrosine phosphorylation of specific targets allow us to add another dimension to the investigation of specific alterations in genes promoting tumorigenesis. Upon querying the TCGA provisional database for lung adenocarcinoma, our results indicate that our target list was found to be altered in 292 (56%) of the total 520 patients sequenced (Fig. 3.18). Interestingly, there was significantly lower disease-free survival among patients with combined alterations of our set of genes (Logrank test P-value:0.0369) (Fig. 3.18). However, with this limited retrospective data, we do not suggest that this list of phosphosites of mutant EGFR targets constitute a “signature” to predict survival of patients with lung adenocarcinoma. The
97
MRM assays described in this study can be used to validate prospectively differential phosphorylation of our list of phosphosites in patients with lung cancer, particularly EGFR mutant lung adenocarcinoma in the context of clinical trials.
Fig 3. 18: cBioPortal query of the TCGA lung adenocarcinoma dataset for alterations in genes of the list of targets and correlation with disease-free survival. Combined alterations of 9 genes for our assay targets were seen in 292 cases (56%) of lung adenocarcinoma in the TCGA dataset, and patients with alterations in our genes had a statistically significant lower disease-free survival when compared to cases with no alterations.
98
The target list from this study was queried against the TCGA lung adenocarcinoma dataset
(The Cancer Genome Atlas Research, 2014) through cBioPortal (Cerami et al., 2012; Gao et al., 2013) for alterations including missense, truncating and in-frame mutations, amplification and deletions, mRNA up- and downregulation and protein up- and down regulation by RPPA assay. The results showed that the target list was altered in 42% of the
230 sequenced patients (Fig. 3.19) and the disease-free survival among patients with alterations in the target genes was significantly lower (Logrank test P-value:0.00634) (Fig.
3.19).
Altered in 97 (42%) of 230 sequenced cases/patients (TCGA)
Cases with Alteration(s) in Query CasesG without Alteration(s) in Query
Logrank Test P-value: 0.00634
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Total Cases Disease free survival cases relapsed (median months) Cases with Alterations 86 36 37.68 in Query Genes Cases without Alterations 117 27 88.05 in Query Genes
Fig 3. 19: cBioPortal query of the TCGA lung adenocarcinoma dataset for alterations in the target list and correlation with disease-free survival. The query against the same patient database for the targets EGFR, CAV1 and STAT5A
identified alterations in 21% of the 230 sequenced patients (Fig. 3.20) and a significantly
lower disease-free survival (Logrank test P-value:0.00583) (Fig. 3.20).
Altered in 48 (21%) of 230 sequenced cases/patients (TCGA)
Cases with Alteration(s) in Query Genes Cases without Alteration(s) in Query Genes
Logrank Test P-value: 0.00583
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Total Cases Disease free survival cases relapsed (median months) Cases with Alterations 42 19 37.68 in Query Genes Cases without Alterations 161 44 49.24 in Query Genes
Fig 3. 20: cBioPortal query of the TCGA lung adenocarcinoma dataset for alterations in targets EGFR, CAV1 and STAT5A and correlation with disease-free survival.
3.5. Conclusions:
MRM-based proteomics is a powerful technique to verify potential biomarkers in complex samples with great selectivity. These assays routinely employ heavy labelled synthetic peptides for accurate quantification of the target endogenous peptides. In the present study, we performed quantitative MRM using stable isotope labelled synthetic peptides and a global phosphotyrosine peptide enrichment strategy to characterize dynamic changes of phosphorylation downstream of mutant EGFRs in lung adenocarcinoma cells harboring
EGFRL858R and EGFRL858R/T790M, the TKI-sensitive, and TKI-resistant mutants, respectively. A list of targets was selected from our previous SILAC-based quantitative mass spectrometry studies. We further validated these sites as mutant EGFR targets, and the dynamics of tyrosine phosphorylation at these sites correlated well with TKI sensitivity of lung adenocarcinoma cells. Lung adenocarcinoma patients with alterations in genes selected in our study had poorer disease-free survival, as revealed from the TCGA data.
We propose that alteration in tyrosine phosphorylation, as demonstrated by our modified immuno-MRM assays, will better predict disease prognosis as well as treatment response, and will add a new dimension to correlation of gene alteration with patient survival.
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Chapter 4: Conclusions and Future directions
The overall objective of this dissertation is the discovery and validation of potential markers of targeted therapy resistance and response in cancer. We have shown the application of LC MS based proteomics in the context of acquired therapy resistance to
MAPK (ERK1/2) inhibitors and EGFR TKI inhibitors in melanoma and lung adenocarcinoma respectively (Fig 4.1).
EGF EGFR
SRC SHC SOS RAS
RAF
MEK1/2
ERK1/2
Lung adenocarcinoma Cutaneous melanoma
Fig 4. 1: EGFR and ERK1/2 signaling
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4.1 Shotgun proteomics to investigate ERK1/2 therapy resistance in melanoma
In chapter 2, we have utilized global mass spectrometry based proteomic and
phosphoproteomic analysis to identify potential biomarkers of resistance to ERK1/2
pathway inhibitors. The oncogenic mutations of BRaf and RAS, leading to overactivation
of Ras→ Raf→ MEK→ ERK pathway are well established disease contributing factors in
melanoma. BRaf mutation V600E is found in 50% of the melanoma cases correlating with
poorer prognosis and aggressive disease.
We have used unbiased shotgun proteomic profiling to investigate three ERK1/2 pathway
inhibitor resistant cell lines (BRaf inhibitor (PLX4032), MEK1/2 inhibitor (AZD6244) and
ERK1/2 inhibitor (SCH772984)) and compared the protein expression patterns with the
drug sensitive cells. Several unique and common proteins were identified in the drug
resistant cells whose role in cancer progression and therapy resistance was corroborated by
literature mining. Analysis of the protein-protein interaction networks and gene ontology
annotation clustering revealed several annotations to be over-represented in the resistant
cells including “Proliferation of cells”, “Invasion of cells”, “Migration of cells”,
“Organization of actin cytoskeleton”, “Colony formation of cells”, “Angiogenesis”.
We also identified β-catenin (CTNNB1), Signal transducer and activator of transcription
3 (STAT3) and Caveolin-1 (CAV1) to be over-expressed in the MEK1/2 and ERK1/2 drug
resistant cells. The expression of CTNNB1 and CAV1 was verified by simple western
assays as well.
Although aberrant regulation of β-catenin in melanomas was established a long time ago,
its role is still not fully understood. Some in vitro studies point to increased β-catenin
activity causing melanoma proliferation and giving rise to invasive phenotypes while
103
others suggest a negative corelation to migration. This discrepency can be attributed to microenvironmental contexts and intercellular heterogeneity causing diverse melanoma subpopulations. These observations warrant further investigations to dissect the role of β-
catenin in melanoma progression.
Future studies will involve targeted orthogonal validation of potential candidate proteins
either via western blotting or targted MS analysis. We found an enrichment of proteins
associated with “actin cytoskeleton signaling” and overexpression of phosphorylated
MEK1-pS298 (PAK1 substrate), in MEK and EKR inhibitor resistant cells pointing to
PAK1 activity in these resistant proteomes. PAK1 inhibitors will also be tested as
combinatorial therapeutics to overcome resistance and restore sensitivity to therapy.
These studies involving proteome profiling and bioinformatic processing help in narrowing
down a list of candidate biomarkers and new drug targets that have the potential
applications in precison medicine for enabling patient stratification and guiding patient
tailored therapies.
4.2. Targeted proteomics to investigate EGFR TKI therapy resistance in lung
adenocarcinoma
In chapter 3, we elaborate the development of novel immuno-MRM assays using a global
phosphotyrosine enrichment strategy to quantify the degree of phosphorylation of a subset
of tyrosine phosphorylated peptides in EGFR mutant, TKI-sensitive and -resistant human
lung adenocarcinoma cells upon treatment with erlotinib, the first-generation EGFR TKI
and osimertinib, the third-generation EGFR TKI.
Lung cancer is the leading cause of cancer mortality worldwide with adenocarcinoma being
the predominant histopathology. EGFR mutations are frequent genetic abnormalities
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observed in NSCLC, accounting for 85% of cases of lung cancer. These drug sensitizing
mutations include L858R and E746-A750 deletion in the ATP-binding site of the protein.
These mutations lead to constitutively active EGFR signaling leading to uncontrolled cell
proliferation. In the U.S., 10-15% of patients with NSCLC carry EGFR mutations, but the
frequency in East Asians is much higher (30-45%). Most patients harbouring EGFR
sensitizing mutations respond to 1st generation EGFR-TKIs (erlotinib). However, a
significant proportion of patients do not respond to EGFR-TKIs and progress immediately or within 4 months of treatment (intrinsic resistance), and virtually all patients who respond invariably progress (acquired resistance), and the average duration of response is 1 year or less. Both intrinsic and acquired resistance to EGFR-TKIs limit the efficacy of these drugs in the treatment of advanced NSCLC. The T790M mutation in the kinase domain of the epidermal growth factor receptor accounts for almost 60% of all the acquired resistance to the first-generation EGFR TKIs. Second-generation TKIs like afatinib have shown some utility against T790M mediated resistance but have not been successful in the clinical trials due to toxicity seen due to its effect on the wild type EGFR.
Osimertinib has demonstrated impressive response rates for both EGFR sensitizing
mutations and the T790M resistant mutation, with reduced activity against wild type
EGFR. Given that osimertinib is being used more extensively based on a better tolerability
profile (markedly reduced skin rash and diarrhea) and efficacy in a broader spectrum of
EGFR mutations, studies investigating response markers for this TKI will have important
implications for the treatment of EGFR mutant NSCLC patients.
The goal of this chapter was to develop and characterize targeted proteomic assays to
validate potential biomarkers of TKI sensitivity. These assays were used to verify previous
105
findings with erlotinib and afatinib treatment. The cell lines used are well established
models of human lung adenocarcinoma and have been generated from patients with EGFR mutations and are therefore dependent on mutant EGFR signaling for survival. These optimized assays were validated in a TKI sensitive and -resistant cell line recapitulating
EGFR TKI sensitivity by known mechanisms, i.e. the nature of EGFR mutations.
We believe that development and validation of these quantitative assays provides proof-of
concept for the feasibility of clinical applications of such assays. The on-going clinical
treatment protocols (NCT02759835) with EGFR TKIs at the NIH Clinical Center may
benefit from addition of novel protein/peptide markers that predict TKI response. Although
presentation of genetic mutations predicts TKI response, additional peptide markers will
complement the genetic testing and allow patient selection to test TKI targeted therapy.
We believe this approach will speed up drug development and approval for the benefiting
specific patient subgroup.
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Appendix I: Development, optimization and application of SRM/MRM based
targeted proteomics strategy for quantification of potential biomarkers of EGFR
TKI sensitivity1
Abstract
We have investigated the use of targeted proteomic assays to quantify potential biomarkers of Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) sensitivity in lung adenocarcinoma as described in the accompanying chapter 3: “Quantitative targeted proteomic analysis of potential markers of tyrosine kinase inhibitor (TKI) sensitivity in
EGFR mutated lung adenocarcinoma”. This appendix describes the additional data associated with liquid chromatography coupled to multiple reaction monitoring (LC-
MRM) method development which includes selection of an optimal transition list, retention time prediction and building of reverse calibration curves. Sample preparation and optimization which includes enrichment via a combination of pan-phosphotyrosine antibodies is described. The dataset also consists of figures and tables describing the quantitative results of testing these optimized methods in two lung adenocarcinoma cell lines with EGFR mutations. The raw MS data associated with these results has been deposited in the Peptide Atlas database with a dataset identifier PASS01129.
1 Adapted from Development, optimization and application of SRM/MRM based targeted proteomics strategy for quantification of potential biomarkers of TKI sensitivity. Awasthi S, Maity T, Oyler BL, Zhang X, Goodlett DR, Guha U. (Data in Brief, https://doi.org/10.1016/j.dib.2018.04.086)
107
Specifications Table
Subject area Clinical Chemistry, Biology More specific subject Targeted therapy response biomarkers in lung area adenocarcinoma Type of data Tables, graphs, figures, raw MS data How data was acquired MS data acquired with LTQ-Orbitrap Elite (Thermo Scientific) and Agilent Triple Quadrupole 6495 Data format Raw MS (.d) files and analyzed (figures) Experimental factors Immunoprecipitation for enrichment of tyrosine phosphorylated peptides. Synthetic standard phosphorylated peptides spiked in for quantification. Experimental features Spectral library generation and retention time prediction for scheduled chromatography, optimization of enrichment strategy for phosphotyrsoine target peptides and external multi-point reverse response curve generation for quantitation Data source location Bethesda, MD, USA Data accessibility All the figures are provided with the article and MS data has been deposited in the Peptide Atlas database with a dataset identifier PASS01129.
Value of the data
• Optimization and method development for building quantitative targeted proteomic
assays for phosphotyrosine peptides.
• These validated MRM assays are useful for verification of phosphopeptide
abundance observed in large-scale LC-MS based phosphoproteomic experiments.
• Our approach of using the heavy labelled synthetic standards and immunoaffinity
enrichment of the tyrosine phosphorylated peptides can be applied to interrogate
these targets in other cell-based models and tumor tissue from patients.
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I.1 Data
The data presented here describe the development of LC-MRM based methods for quantification of tyrosine phosphorylated peptide biomarkers in lung adenocarcinoma cells. The experimental design consisted of development of robust MRM methods for each phosphorylated peptide candidate using synthetic phosphorylated peptides as “spike-in” standards. These assays were implemented in lung adenocarcinoma cells harboring TKI- sensitive EGFRL858R (H3255) and -resistant EGFRL858R/T790M (H1975) mutants, with and
without 1st generation TKI, erlotinib and 3rd generation TKI, osimertinib treatment in 3-6
biological replicates.
I.2 Experimental Design, Materials and Methods
I.2.1 Spectral library generation and retention time approximation
Previously published LC-MS output files (X. Zhang et al., 2017) based on data-dependent
acquisition (DDA) were used to generate a spectral library in Skyline. Briefly, enriched
phosphopeptide samples were analyzed on a LTQ-Orbitrap Elite (Thermo Scientific Corp.,
San Jose, CA) coupled to an Easy-nLC 1000 system (Thermo Scientific Corp., San Jose,
CA). Peptides were trapped on a 100 µm i.d. × 2 cm long precolumn (Acclaim PepMap100
Nano Trap column, C18, 5 µm, 100 Å). Subsequent peptide separation was carried out on a nano-LC column (Acclaim PepMap100, C18, 3 µm, 100 Å, 75 µm i.d. × 25 cm, nanoViper). Mobile phase A consisted of 0.1% formic acid in water (v/v) and mobile phase
B consisted of 0.1% formic acid in 90% acetonitrile. For each liquid chromatography- tandem mass spectrometry (LC-MS/MS) analysis, peptides were eluted from the column at 250 nL/min using an acetonitrile gradient of 2-8% B in 8 min, 8-32% B over 100 min,
109
32-100% B in 10 min and held at 100% B for an additional 10 min. The eluting peptides were interrogated with an Orbitrap analyzer with full scan spectra acquired between m/z
350 and 1800 at resolution of 120,000 followed by data-dependent HCD MS/MS acquisition for the top 10 most abundant ions at 32% normalized collision energy.
The resulting raw files were searched against the Uniprot human protein database using the
Maxquant software (version 1.3.0.5) with Andromeda search engine using previously described parameters (X. Zhang et al., 2017). The resulting search output file msms.txt was uploaded in Skyline to build the spectral library to pick optimal transitions for the construction of the LC-MRM transition list. The annotated MS/MS spectra for 9 of the 11- selected tyrosine phosphorylated peptide targets are shown in Fig 3.2. For the remaining three peptides containing DAPP1-pY139, AHNAK-pY715 and -pY160 phosphosites, individual injections of the heavy labelled peptide standard carried out on the nano-chip-
LC using a 1260 Infinity Series HPLC-Chip cube interface (Agilent, Palo Alto, CA) coupled to a 6495-triple quadrupole mass spectrometer (Agilent, Palo Alto, CA) identified a different charged precursor ion that was more abundant compared to the spectra obtained from HCD MS/MS in the Orbitrap Elite. Hence, for these three phosphopeptides, MRM data was used for the selection of optimal transitions.
Using the list of optimal transitions (Table 3.2), an unscheduled MRM method with a dwell time of 50 ms and a cycle time of 700 ms was used to determine the retention times of the targets and to generate scheduled MRM methods. The correlation between the peptide hydrophobicity and retention times was assessed using SSRCalc (version 3.0) (Krokhin,
2006) in-built in Skyline (Fig. 3.3).
I.2.2 Immunoaffinity enrichment, LC MS/MS and data analysis
110
The enrichment of the endogenous phosphotyrosine peptides in the samples was carried
out using PhosphoScan kits (Cell Signaling, Danvers, MA). Two antibody kits PTMScan
Phospho-Tyrosine Mouse mAb (P-Tyr-100) (product no. 5636) and PTMScan Phospho-
Tyrosine Rabbit mAb (P-Tyr-1000) (product no. 14478) were tested to optimize the phosphotyrosine enrichment. Four immunoprecipitations were carried out using the manufacturer’s protocol on the trypsin digested control peptides from mouse liver extracts
(product no. 12219, Cell Signaling, Danvers, MA) and 8 mg of digested protein extract from the H1975 cells using P-Tyr-100 and P-Tyr-1000 kits. The phosphorylated peptides eluted from the antibodies were analyzed on a LTQ-Orbitrap Elite (Thermo Scientific
Corp., San Jose, CA) mass spectrometer coupled to a Dionex nLC system (Thermo
Scientific Corp., San Jose, CA). Peptides were trapped on a 100 µm i.d. × 2 cm long precolumn (Acclaim PepMap100 Nano Trap column, C18, 5 µm, 100 Å). Subsequent peptide separation was carried out on a nano-LC column (Acclaim PepMap100, C18, 3
µm, 100 Å, 75 µm i.d. × 25 cm, nanoViper). Mobile phase A consisted of 0.1% formic acid in water (v/v) and mobile phase B consisted of 0.1% formic acid in 90% acetonitrile. For each liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, peptides were eluted from the column at 300 nL/min using an acetonitrile gradient of 4% B in 5 min, 4-25% B in 70 min, 25-35% B in 85 min, 35-45% B in 95 min and 45-90% B for 10 min. The eluting peptides were interrogated with an Orbitrap analyzer with full scan spectra acquired between m/z 350 and m/z 1800 at a resolution of 120,000 followed by data- dependent HCD FTMS2 acquisition for the top 15 most abundant ions at 35% normalized collision energy using resolution of 15,000.
111
Raw MS files were searched against the Uniprot human and mouse proteome database using the Maxquant software (version 1.5.7.4) with Andromeda search engine. Search parameters included cysteine carbamidomethylation as a fixed modification and
phosphorylation (STY) was added as a variable modification. The digestion mode was set
to specific with trypsin as the digestion enzyme and two missed cleavages were allowed.
Mass tolerances were set to 6 ppm for precursor ions and 20 ppm for product ions. The
search criteria further included false discovery rates of 0.01 for both protein and peptide
identifications. The minimum peptide length was 7 amino acid residues. Decoy database
search was activated and the database searching was supplemented with the common
contaminants often found in cell culture and proteomics sample preparation experiments;
these were later identified and removed. All the other settings were set to default except
that the “match between runs” feature was enabled with the default settings. There was
only 60% overlap in the phosphopeptides identified from the two kits (Fig. 3.9). Hence,
we used a combination of antibodies to enrich our samples. The final optimized enrichment
protocol comprised a combination of P-Tyr-100 and P-Tyr-1000 antibody slurries at 1:1 v/v.
I.2.3 Estimation of dwell times, quantitative data and analysis of the replicates
The final chromatographic scheduled methods consisted of a 25-minute gradient with 2- minute retention time windows. The number of concurrent transitions being measured in any retention time window varied from 8-16 (Fig. 4A). As the target peptides eluted, at any given time in the gradient, the dwell times were estimated to fall in the range of 80-
160 milliseconds (Fig. 4B). This allowed for excellent sensitivity as we acquired around
20 points across the chromatographic peak for all the “quantifier” transitions. The
112
chromatographic profiles obtained from these optimized methods in H1975 cells is shown
in Fig. 3.8. The CVs for the peak area ratios obtained from implementing these assays in
H3255 and H1975 lung adenocarcinoma cells with and without erlotinib and osimertinib
treatment are shown (Fig 3.12).
I.2.4 Quantitative assay characterization and calibration curve generation
Quantitation was carried out using synthetic peptide standards which were synthesized as
matched pairs of light and heavy stable isotope-labeled peptides (New England Peptide,
Gardner, MA). Heavy peptides were 13C and 15N labelled at the C-terminal lysine or
arginine position of the tryptic peptide target. A reverse response curve was generated in
digested and phosphotyrosine enriched matrix of H1975 cells treated with DMSO and
processed in a similar manner to the TKI treated samples. For the calibration samples, the
light peptide amount was held constant (2 fmol) and the heavy peptide was varied over a
range (0.01, 0.1, 0.5, 2, 8, 50, 100, 500, 100 fmol). The analytical performance of the
quantitative assays was characterized by determining the linear dynamic range and figures
of merit like limit of detection (LOD) and lower limit of quantification (LOQ) before their
application in the lung adenocarcinoma cells as described in chapter 3. The calibration
curves are shown in Fig. 3.16.
I.2.5 cBioPortal analysis of the target genes:
The target list from this study was queried against the TCGA lung adenocarcinoma dataset
(The Cancer Genome Atlas Research, 2014) through cBioPortal (Cerami et al., 2012; Gao
et al., 2013) for alterations including missense, truncating, in-frame mutations,
amplification, deletions, mRNA up- and downregulation and protein up- and down
113 regulation by RPPA assay. The results showed that the target list was altered in 42% of the
230 sequenced patients (Fig. 3.19) and the disease-free survival among patients with alterations in the target genes was significantly lower (Logrank test P-value:0.00634). The query against the same patient database for the targets EGFR, CAV1 and STAT5A identified alterations in 21% of the 230 sequenced patients (Fig. 3.20) and a significantly lower disease-free survival (Logrank test P-value:0.00583).
114
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