Somatic Alterations in Cancer Genomes

1. Somatic alterations in human cancer genomes Matthew Meyerson, M.D., Ph.D. Dana-Farber Cancer Institute Harvard Medical School Broad Institute Bioconductor Conference Dana-Farber Cancer Institute Boston, Massachusetts July 31, 2014

2. Somatic genome alterations and cancer therapy

3. Happy families are all alike; every unhappy family is unhappy in its own way. Leo Tolstoy, Anna Karenina Every cancer genome is uniquely altered from its host normal genome Normal human genomes are all (mostly) alike; every cancer genome is abnormal in its own way. Each cancer genome has a unique set of genome alterations from its normal host These alterations, however, are not random but act in common pathways and mechanisms

4. Somatic genome alterations are central to cancer pathogenesis While germ-line mutations can increase the risk of cancer, most cancer causing mutations are somatic Somatic mutations are present in the cancer DNA but not in the germ-line DNA Somatic alterations can provide a large therapeutic window Genome-targeted treatments can be selective for the genomically altered cancer cell and spare the rest of the body, which is genomically normal Somatic alterations are internally controlled Comparison between germ-line and cancer defines the cancer- specific alterations and allows precise diagnosis

5. Mutation-targeted therapies can be highly effective in cancer treatment Response to erlotinib (Tarceva) treatment of a patient with lung adenocarcinoma, with a somatic EGFR deletion mutant in exon 19 ( thanks to Bruce Johnson, M.D., DFCI) Before treatment After 2 months erlotinib treatment

6. Often, only patients whose cancers have mutated therapeutic targets will benefit from targeted therapy Patients with EGFR mutant lung cancer benefit from gefitinib While those with EGFR wild type lung cancer do not benefit Mok et al., NEJM , 2009

7. A growing armamentarium of genomically targeted cancer therapies Gene Mechanism of Activation Targeted Inhibitor ABL rearrangement imatinib, dasatinib, nilotinib, bosutinib ALK rearrangement, mutation crizotinib BRAF mutation, rearrangement vemurafenib, dabrafenib DDR2 mutation dasatinib EGFR mutation erlotinib, gefitinib, afatinib, cetuximab, panitumumab ERBB2 mutation, amplification trastuzumab, lapatinib, pertuzumab FGFR1 amplification, rearrangement ponatinib FGFR2 mutation, rearrangement ponatinib FGFR3 mutation ponatinib KIT mutation imatinib, sunitinib, regorafenib, pazopanib MET amplification, mutation crizotinib PDGFRA mutation, rearrangement imatinib, sunitinib, regorafenib, pazopanib RET rearrangement, mutation cabozantinib ROS1 rearrangement crizotinib

8. Application of high-throughput genomic analysis to cancer

9. Increasing power of genome sequencing technology

10. Genomic mechanisms of cancer (germline and somatic) Mutation GGT Gly G A T Asp G C T Ala G T T Val A GT Arg C GT Cys T GT Ser Amplification/ deletion Translocation Infection

11. Meyerson, Gabriel, Getz, Nat Rev Genet , 2010 Sequencing can discover all classes of cancer genome alteration

12. Approaches to cancer genome sequencing Whole genome Complete sequence of entire genome (3 billion basescurrently typically 30x coverage) Transcriptome Sequencing of all messenger RNAs Whole exome Complete sequence of all exons of coding genes (~30 million bases, currently typically 150x coverage) Targeted exome/plus Complete sequences of exons and rearrangement sites from selected cancer-related genes, such as oncogenes and tumor suppressor genes (can achieve up to 1000x coverage)

13. The Cancer Genome Atlas (TCGA) Clinical diagnosis Treatment history Histologic diagnosis Pathologic report/images Tissue anatomic site Surgical history Gene expression/RNA sequence Chromosomal copy number Loss of heterozygosity Methylation patterns miRNA expression DNA sequence RPPA (protein) Subset for Mass Spec Lung adenocarcinoma Lung squamous carcinoma Breast carcinoma Colorectal carcinoma Renal cell carcinoma Endometrial carcinoma Glioblastoma Ovarian carcinoma Bladder carcinoma HNSCC Acute myeloid leukemia Biospecimen Core Resource Cancer Genomic Characterization Centers Genome Sequencing Centers Genome Data Analysis Centers Data Coordinating Center More than 30 cancer histologies, incl 10,000 cancer/normal paired specimens Exome & transcriptome sequencing, copy number & methylome analysis, Whole genome sequencing underway for 1000 cancer/normal pairs

14. How do we find a cancer gene? How do we define a therapeutic target?

15. Genome alterations in squamous cell lung carcinoma: an illustration of computational and experimental issues in cancer gene discovery

16. Lung cancers are characterized by common chromosome arm level alterations Lung adenocarcinoma Squamous cell lung carcinoma Some differences between SqCC and AdC. Gain Loss Andrew Cherniack, TCGA

17. Arm-level chromosomal alterations are approximately the most common somatic genome alteration across all human cancers Most frequently somatically mutated genes (exome): TP53 : 36% PIK3CA : 14% PTEN : 8% Source: www.tumorportal.org Beroukhim et al., Nature, 2010

18. Athough there are tumor-type specific differences, most chromosome arms are either recurrently gained or recurrently lost, not both Beroukhim et al., Nature, 2010

19. Do chromosome arm level alterations contribute to cancer? And if so, how? Does the statistical recurrence imply that the chromosome arm-level gains and losses are important, or merely tolerated? If chromosome arm level copy changes are important, are they do to single genes or multiple genes per arm? Or are they due to systemic effects on the genome? On the computational level, what are effects of individual arm level copy changes, and total aneuploidy, on gene expression within tumors?

20. Focal chromosome alterations in lung cancers Lung adenocarcinoma Squamous cell lung carcinoma Gain Loss 9p loss Andrew Cherniack, TCGA 14q gain

21. Copy number structure of most common amplification in lung adenocarcinoma (14q13) mapping to NKX2-1 Barbara Weir & Gaddy Getz

22. Finding targets of focal genome alterations: Statistical recurrence is key to defining genome alterations but we need to find the right background model by understanding the biological variations in the genome

23. Evaluating significance of copy number alterations: Genomic Identification of Significant Targets In Cancer (GISTIC) Measure the amplitude of copy number gain or loss at each position in each sample Sum this amplitude across all samples Assign significance for the alteration (false discovery rate) by comparison to randomly permuted data Beroukhim, Getz et al. , PNAS, 2007

24. Focal copy number alterations in squamous cell lung carcinoma Amplification Deletion MYCL MCL1 REL NFE2L2 SOX2 PDGFRA EGFR FGFR1 CCND1 CRKL ERBB2 MDM2 LRP1B ERBB4 FOXP1 CSMD1 CDKN2A PTEN RB1 TCGA, Nature, 2012

25. Problem: can we build a statistical model for focal chromosomal alterations that allows us to identify all copy number altered oncogenes and tumor suppressor genes?

26. Challenge: genome is complex with many rearrangements Rearrangement junctions

27. A better model for determining significance of copy number alterations could be built from whole genome sequence data and would require understanding of genome structure

28. How to find significant mutations in cancer over background?

29. Squamous cell lung cancer has a very high rate of somatic mutations Hematologic Childhood Carcinogens

30. Top mutated genes in squamous cell lung cancer (crude analysis)

31. Top mutated genes in squamous cell lung cancer (expression-filtered significance) TCGA, Nature, 2012

32. The problem of mutation significance is even larger in whole genome sequence data The problem of background mutation rate is particularly high in regions of non-coding DNA/heterochromatin We see up to about 50-fold variation in mutation rates between regions of the genome What is the best model to correct for this Peter Hammerman, Akin Ojesina

33. Splicing factor alterations: what are their transcriptome consequences

34. Significantly mutated genes in lung adenocarcinoma Imielinski et al., Cell, 2012

35. 35 YYYYY Somatic mutations can disrupt mRNA splicing regulation Splicing factors U2AF1 (U2AF35) 5 ss 3 ss polypyrimidine tract Splicing regulatory sequences GU AG YUNAY branch point UGUGAA GAACCA SF3B1 enhancer enhancer

36. Alternative splicing of MET exon 14 in TCGA lung adenocarcinoma RNA sequencing data MET splice site mutation No MET splice site mutation Percent Spliced In, % 5 ss +3 3 ss 19bp del 5 ss 12bp del Y1003* Normal MET transcript: contains exon 14 in 220 samples Abnormal MET transcript: lacks exon 14 in 10 samples TCGA/Angela Brooks Kong-Beltran et al. 2006, Onozato et al. 2009; Seo et al., 2012

37. 37 All MET exon 14 skipping samples are, otherwise, oncogene negative MET splice site mutation No MET splice site mutation Percent Spliced In, % n=224 n=6, one sample has low expression TCGA/Alice Berger

38. Transcriptome / spliceome correlates to genome alterations Effects of cis mutations on transcriptomeboth near and far Effects of trans mutations (e.g. splicing factor mutations) on specific gene splicing On specific gene expression On global gene expression

39. Pathogen Discovery from Sequencing Data Alex Kostic Chandra Pedamallu Akin Ojesina Joonil Jung Ami Bhatt

40. Sequence-based computational subtraction for pathogen discovery Principle The human genome sequence is nearly complete Infected tissues contain human and microbial RNA and DNA Remainder is of non-human origin: disease-specific sequences can be validated experimentally Normal human sequences can be subtracted computationally Computational subtraction Generate & sequence libraries from human tissue 40 Weber et al., Nature Genetics, 2002

41. PathSeq : software to identify or discover microbes by deep sequencing of human tissue Kostic et al., Nature Biotechnology, 2011

42. PathSeq Pathogen analysis of 9 colorectal cancer/normal genome pairs

43. Initial analysis identifies tumor-enrichment of Fusobacterium and Streptococcaceae LEfSe: Linear Discriminant Analysis (LDA) coupled with effect size measurements Wilcoxon sum-rank test followed by LDA analysis Segata et al., 2012 Kostic et al., Genome Research, 2012

44. Idiopathic , antibiotic- responsive diarrheal syndrome Affected umbilical cord blood transplant patients between ~60d and 1y after transplantation 11 histopathologically confirmed cases between 2004-2011 at BWH All microbiology studies negative Cord Colitis Syndrome Herrera AF, Soriano G et al. NEJM 2011

45. Classification of the CCS-associated bacterium CCS organism CCS organism Comparison of B. enterica to B. japonicum Filamentous hemagglutinin genes Genes critical for Carbon fixation Phylogenetic analysis using the draft genome to classify the organism PhyloPhlAn N. Segata, C. Huttenhower

46. Challenges in sequence-based pathogen discovery How to analyze unclassified/unclassifiable reads Developing a fast algorithm for very large data sets Assignment of reads to nearest organisms

47. Summary: some challenges in somatic cancer genomics Whole genome and whole transcriptome sequencing provide unprecedented opportunities for understanding cancer development and evolution ...but require development of many computational tools New models for copy number significance (and rearrangement significant) using whole genome sequence data and developing appropriate background models Ways to determine significance of non-coding mutations with appropriate background models Finding non-human sequence data in large sequencing data sets to find new disease organisms

48. Meyerson laboratory Alice Berger Ami Bhatt Angela Brooks Scott Carter Andrew Cherniack Juliann Chmielecki Peter Choi Luc de Waal Josh Francis Hugh Gannon Heidi Greulich Elena Helman Bryan Hernadez Marcin Imielinski Joonil Jung Bethany Kaplan Nathan Kaplan Alex Kostic Rachel Liao Wenchu Lin Akinyemi Ojesina Chandra Pedamallu Trevor Pugh Tanaz Sharifnia Alison Taylor Hideo Watanabe Cheng-Zhong Zhang Selected alumni Jordi Barretina, Novartis Jeonghee Cho, Samsung Tom Laframboise, Case Western Se-Hoon Lee, Seoul National U. Katsuhiko Naoki, Keio U. Orit Rozenblatt-Rosen, Broad Institute Xiaojun Zhao, Novartis Dana-Farber Cancer Institute colleagues Adam Bass Rameen Beroukhim Michael Eck Levi Garraway Nathanael Gray Bill Hahn Peter Hammerman Pasi Janne Bruce Johnson Matt Kulke Keith Ligon David Pellman Scott Pomeroy Ramesh Shivdasani Kwok-kin Wong Dana-Farber CCGD Ravali Adusumili Marc Breineser Deniz Dolzen Matt Ducar Megan Hanna Robert Jones Jack Lepine Laura MacConaill Adri Mills Laura Schubert Ashwini Sunkavalli Aaron Thorner Paul van Hummelen Liuda Ziaugra Broad Institute colleagues Kristian Cibulskis Stacey Gabriel Gad Getz Todd Golub Jaegil Kim Eric Lander Mike Lawrence Tim Lewis Lee Lichtenstein Ben Munoz Beth Nickerson Mike Noble Mara Rosenberg Gordon Saksena Stuart Schreiber Carrie Sougnez Collaborators at other institutions Sylvia Asa, Toronto Jose Baselga, MSKCC Steve Baylin, Johns Hopkins David Carbone, Ohio State Eric Collisson, UCSF Aimee Crago, MSKCC Ramaswamy Govindan, Wash U Neil Hayes, UNC Santosh Kesari, UCSD Marc Ladanyi, MSKCC John Maris, UPenn Chris Love, MIT William Pao, Vanderbilt Harvey Pass, NYU Niki Schultz, MSKCC Sam Singer, MSKCC Josep Tabernero, Vall dHebron Roman Thomas, Koln Bill Travis, MSKCC Matt Wilkerson, UNC Thomas Zander, Koln Acknowledgements

49. Acknowledgements: The Meyerson Laboratory