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Patient-Specific Genomic Profiling for Advanced Cancers in Young Adults

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저작자표시-비영리-변경금지 2.0 대한민국 이용자는 아래의 조건을 따르는 경우에 한하여 자유롭게 l 이 저작물을 복제, 배포, 전송, 전시, 공연 및 방송할 수 있습니다. 다음과 같은 조건을 따라야 합니다: 저작자표시. 귀하는 원저작자를 표시하여야 합니다. 비영리. 귀하는 이 저작물을 영리 목적으로 이용할 수 없습니다. 변경금지. 귀하는 이 저작물을 개작, 변형 또는 가공할 수 없습니다. l 귀하는, 이 저작물의 재이용이나 배포의 경우, 이 저작물에 적용된 이용허락조건 을 명확하게 나타내어야 합니다. l 저작권자로부터 별도의 허가를 받으면 이러한 조건들은 적용되지 않습니다. 저작권법에 따른 이용자의 권리는 위의 내용에 의하여 영향을 받지 않습니다. 이것은 이용허락규약(Legal Code)을 이해하기 쉽게 요약한 것입니다. Disclaimer 이학박사 학위논문 Patient-specific genomic profiling for advanced cancers in young adults 진행성 암을 지닌 젊은 성인 환자의 개별 유전체 분석 2016 년 8 월 서울대학교 대학원 협동과정 종양생물학과전공 차 수 진 A Thesis of the Degree of Doctor of Philosophy 진행성 암을 지닌 젊은 성인 환자의 개별 유전체 분석 Patient-specific genomic profiling for advanced cancers in young adults August 2016 The Department of Cancer biology Seoul National University College of Medicine Soojin Cha ABSTRACT 차 수 진 (Cha Soojin) 종양생물학과 (Cancer biology) The Graduate School Seoul National University Purpose: Although adolescent and young adult (AYA) cancers are characterized by biological features and clinical outcomes distinct from those of other age groups, the molecular profile of AYA cancers has not well been defined. In this study, I analyzed cancer genomes from rare types of metastatic AYA cancer patients to identify candidate driving and/or druggable genetic alterations. Methods: Pattern-based heuristic annotation was developed to identify candidate driving genetic alterations from processed sequencing data of individual cancer genome. The reliability of the approach was validated by applying to whole exome sequencing (WES) data of acute myeloid leukemia (AML) from The Cancer Genome Atlas (TCGA). Then, prospectively collected AYA tumor samples from seven different patients were analyzed using three different genomics platforms (whole-exome sequencing, whole- transcriptome sequencing or OncoScanTM). By using well-known bioinformatics tools (bwa, Picard, GATK, MuTect, and Somatic Indel i Detector) and the pattern-based heuristic annotation approach with open access databases (DAVID and DGIdb), I processed sequencing data and identified candidate driving genetic alterations with their druggability specific to each patient. Results: Using pattern-based heuristic annotation, all candidate genes were confirmed which was described in published data of TCGA AML study as well as additional candidates such as MYC, NF1 and ARID2. In AYA cancers, the mutation frequencies were lower than those of other adult cancers (median=0.56), except for a germ cell tumor with hypermutation. Patient- specific genetic alterations in candidate driving genes were idendified: RASA2 and NF1 (prostate cancer), TP53 and CDKN2C (olfactory neuroblastoma), FAT1, NOTCH1, and SMAD4 (head and neck cancer), KRAS (urachal carcinoma), EML4-ALK (lung cancer), and MDM2 and PTEN (liposarcoma). Under consult of clinicians, potential drugs were suggested for each patient according to his or her altered genes or related pathways. By comparing candidate driving genes between AYA cancers and those from all age groups for the same type of cancer, different driving genes were identified in prostate cancer and a germ cell tumor in AYAs compared with all age groups, whereas three common alterations (TP53, FAT1, and NOTCH1) in head and neck cancer were identified in both groups. Conclusion: This study identified patient-specific genetic alterations with druggability of seven rare types of AYA cancers and showed importance and ii feasibility of analysis of individual cancer genome. Additionally, it showed genetic alterations in cancers from AYA and those from all age groups varied by cancer type. ------------------------------------------------------------------------------------------- Key words: adolescent and young adult (AYA) cancer, next-generation sequencing (NGS), whole exome sequencing, precision medicine, genomics Student number: 2012-31154 iii CONTENTS Abstract .................................................................................................... i Contents ................................................................................................. iv List of tables and figures ...................................................................... vi General Introduction ........................................................................... 1 Chapter 1. Individual analysis of cancer genomes using next-generation sequencing Introduction ..................................................................................... 4 Material and Methods Pattern-based heuristic annotation: Alternative statistical method to select candidate driving genetic alterations in rare types of cancer ..................................................................................................... 6 Validation of pattern-based heuristic annotation using TCGA AML data .......................................................................................... 10 Results Pattern-based heuristic annotation could detect all candidate driving genes defined in published TCGA AML study and additional candidates ................................................................................ 13 Discussion ...................................................................................... 24 Chapter 2. Application of the individual analysis to adolescence and young adult (AYA) cancer genomes Introduction ................................................................................... 27 Material and Methods Study design and sample information ..................................... 29 Tumor samples and extraction of genomic DNA and RNA ... 29 iv Whole exome sequencing (WES) ........................................... 30 Processing WES data to analyze SNVs and indels ................. 31 Whole transcriptome sequencing (WTS) ................................ 32 Analysis of copy number variations (CNVs) by OncoScanTM .. 33 Analysis of CNVs by VarScan2 for AYA02 Results ................ 33 Analysis of mutation frequency and mutation spectrum ......... 34 Pathway-drug Analysis ........................................................... 34 Fusion analysis ........................................................................ 34 Concurrence of RasGAPs ....................................................... 35 Validation of RASA2 and NF1 in AYA01 ............................. 35 Validation of EML4-ALK in AYA09 ..................................... 35 Results AYA cancer samples, platforms and generation of data from WES ................................................................................................... 36 Mutation frequency and mutation spectrum ............................ 38 Individual AYA cancer analysis: patient-specific genetic alterations ................................................................................................... 41 AYA01, prostate cancer .................................................. 63 AYA02, olfactory neuroblastoma ................................... 65 AYA04, Head and neck squamous cell carcinoma ......... 66 AYA06, urachal carcinoma ............................................. 66 AYA07, germ cell tumor ................................................. 67 AYA09, lung cancer ........................................................ 67 AYA10, liposarcoma ....................................................... 69 Comparison of candidate driving genes from AYA cancers and cancers in other age groups ..................................................... 69 Discussion ...................................................................................... 71 References ...................................................................................... 73 Abstract in Korean ....................................................................... 81 v LIST OF TABLES AND FIGURES Chapter 1 Figure 1-1. CVE list ............................................................................ 7 Figure 1-2. Pattern-based heuristic annotation for single sample (SNV/Indel) ............................................................................................................... 8 Figure 1-3. Pattern-based heuristic annotation for single sample (CNVs) ............................................................................................................. 10 Figure 1-4. Pattern-based heuristic annotation for large-scale samples ............................................................................................................. 10 Figure 1-5. Summary of analysis of TCGA AML data using pattern-based heuristic annotation ............................................................................. 12 Table 1-1. Candidate driving genes defined in published AML study and assigned levels according to pattern-based heuristic annotation ......... 13 Table 1-2. Additional candidate driving genes identified by pattern-based heuristic annotation ............................................................................. 15 Table 1-3. Mutation pattern of TCGA AML study ............................. 16 Table 1-4. References of Class II candidate cancer genes of AML .... 17 Table 1-5. Patient-specific genetic alterations identified by published TCGA AML study (NEJM) and pattern-based heuristic annotation for large-scale samples (Pattern study) ....................................................................... 18 Table 1-6. Genetic alterations only defined by pattern-based heuristic annotation for single sample .............................................................. 23 vi Chapter 2 Figure 2-1 WES pipeline for
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    ΓΕΩΠΟΝΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ ΣΧΟΛΗ ΕΠΙΣΤΗΜΩΝ ΤΩΝ ΖΩΩΝ ΤΜΗΜΑ ΕΠΙΣΤΗΜΗΣ ΖΩΙΚΗΣ ΠΑΡΑΓΩΓΗΣ ΕΡΓΑΣΤΗΡΙΟ ΓΕΝΙΚΗΣ ΚΑΙ ΕΙΔΙΚΗΣ ΖΩΟΤΕΧΝΙΑΣ ΔΙΔΑΚΤΟΡΙΚΗ ΔΙΑΤΡΙΒΗ Εντοπισμός γονιδιωματικών περιοχών και δικτύων γονιδίων που επηρεάζουν παραγωγικές και αναπαραγωγικές ιδιότητες σε πληθυσμούς κρεοπαραγωγικών ορνιθίων ΕΙΡΗΝΗ Κ. ΤΑΡΣΑΝΗ ΕΠΙΒΛΕΠΩΝ ΚΑΘΗΓΗΤΗΣ: ΑΝΤΩΝΙΟΣ ΚΟΜΙΝΑΚΗΣ ΑΘΗΝΑ 2020 ΔΙΔΑΚΤΟΡΙΚΗ ΔΙΑΤΡΙΒΗ Εντοπισμός γονιδιωματικών περιοχών και δικτύων γονιδίων που επηρεάζουν παραγωγικές και αναπαραγωγικές ιδιότητες σε πληθυσμούς κρεοπαραγωγικών ορνιθίων Genome-wide association analysis and gene network analysis for (re)production traits in commercial broilers ΕΙΡΗΝΗ Κ. ΤΑΡΣΑΝΗ ΕΠΙΒΛΕΠΩΝ ΚΑΘΗΓΗΤΗΣ: ΑΝΤΩΝΙΟΣ ΚΟΜΙΝΑΚΗΣ Τριμελής Επιτροπή: Aντώνιος Κομινάκης (Αν. Καθ. ΓΠΑ) Ανδρέας Κράνης (Eρευν. B, Παν. Εδιμβούργου) Αριάδνη Χάγερ (Επ. Καθ. ΓΠΑ) Επταμελής εξεταστική επιτροπή: Aντώνιος Κομινάκης (Αν. Καθ. ΓΠΑ) Ανδρέας Κράνης (Eρευν. B, Παν. Εδιμβούργου) Αριάδνη Χάγερ (Επ. Καθ. ΓΠΑ) Πηνελόπη Μπεμπέλη (Καθ. ΓΠΑ) Δημήτριος Βλαχάκης (Επ. Καθ. ΓΠΑ) Ευάγγελος Ζωίδης (Επ.Καθ. ΓΠΑ) Γεώργιος Θεοδώρου (Επ.Καθ. ΓΠΑ) 2 Εντοπισμός γονιδιωματικών περιοχών και δικτύων γονιδίων που επηρεάζουν παραγωγικές και αναπαραγωγικές ιδιότητες σε πληθυσμούς κρεοπαραγωγικών ορνιθίων Περίληψη Σκοπός της παρούσας διδακτορικής διατριβής ήταν ο εντοπισμός γενετικών δεικτών και υποψηφίων γονιδίων που εμπλέκονται στο γενετικό έλεγχο δύο τυπικών πολυγονιδιακών ιδιοτήτων σε κρεοπαραγωγικά ορνίθια. Μία ιδιότητα σχετίζεται με την ανάπτυξη (σωματικό βάρος στις 35 ημέρες, ΣΒ) και η άλλη με την αναπαραγωγική
  • Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection

    Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection

    Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ......................................................
  • Mouse Mroh8 Knockout Project (CRISPR/Cas9)

    Mouse Mroh8 Knockout Project (CRISPR/Cas9)

    https://www.alphaknockout.com Mouse Mroh8 Knockout Project (CRISPR/Cas9) Objective: To create a Mroh8 knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Mroh8 gene (NCBI Reference Sequence: NM_001039557 ; Ensembl: ENSMUSG00000074627 ) is located on Mouse chromosome 2. 25 exons are identified, with the ATG start codon in exon 1 and the TAG stop codon in exon 23 (Transcript: ENSMUST00000143663). Exon 2~4 will be selected as target site. Cas9 and gRNA will be co-injected into fertilized eggs for KO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 2 starts from about 9.06% of the coding region. Exon 2~4 covers 12.42% of the coding region. The size of effective KO region: ~7013 bp. The KO region does not have any other known gene. Page 1 of 9 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 2 3 4 25 Legends Exon of mouse Mroh8 Knockout region Page 2 of 9 https://www.alphaknockout.com Overview of the Dot Plot (up) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section upstream of Exon 2 is aligned with itself to determine if there are tandem repeats. No significant tandem repeat is found in the dot plot matrix. So this region is suitable for PCR screening or sequencing analysis. Overview of the Dot Plot (down) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section downstream of Exon 4 is aligned with itself to determine if there are tandem repeats.