Completion of The Cancer Genome Atlas, what did we learn?

Transcription

1 Charles M. Perou, Ph.D. Departments of Genetics Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Completion of The Cancer Genome Atlas, what did we learn?

2 TCGA Breast AWG Workshop University of North Carolina September 29-30, 2011

3 TCGA Breast Papers 1. Comprehensive molecular portraits of human breast tumours. TCGA et al., Nature, 2012 (PMID: ) 2. The molecular diversity of Luminal A breast tumors. Ciriello et al., BCRT, 2013 (PMID: ) 3. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Hoadley et al., Cell, 2014 (PMID: ) 4. Comprehensive molecular portraits of invasive lobular breast cancer. Ciriello et al., Cell (PMID: ) 5. DNA defects, epigenetics, and gene expression in cancer-adjacent breast: a study from The Cancer Genome Atlas. Troester et al., NPJ Breast Cancer, 2016 (PMID: ) 6. The molecular basis of breast cancer pathological phenotypes. Heng et al., J Pathology, 2017 (PMID: ) 7. Comparison of breast cancer molecular features and survival by African and European ancestry in The Cancer Genome Atlas. Huo et al., JAMA Oncol, 2017 (PMID: )

4 Comprehensive molecular portraits of human breast tumours. TCGA et al., Nature, 2012 (PMID: ) 466 Invasive Breast Carcinomas (348 common to all 6 platforms) 1. Whole Exome (all exons) capture and sequencing of tumor and normal (somatic coding sequence mutations and germline variation) WashU (n=507) 2. Gene expression analysis (Agilent microarrays and mrna-sequencing) - UNC 3. MicroRNA expression analysis (Illumina sequencing) - UBC 4. Tumor DNA copy number analysis (AFFY 6.0 microarrays and sequencing) - Broad 5. Tumor DNA methylation analysis (Illumina methylation arrays transitioning to sequencing)- USC 6. Reverse Phase Protein Arrays to detect proteins and phospho-proteins - MDACC 7. Integrated computational analyses (Genome Data Analysis Centers)- 6 Universities/sites 4

5 507 Breast Tumors Significantly Mutated Gene List by mrna intrinsic-subtype TCGA et al., Nature, 2012 (PMID: )

6 507 Breast Tumors Significantly Mutated Gene List # Mutated FDR TP E+00 PIK3CA E+00 TTN E-04 GATA E+00 MAP3K E+00 MLL E+00 CDH E+00 USH2A E-03 FLG E-03 RYR E-02 MAP2K E+00 FAT E-02 RUNX E+00 PTEN E+00 NCOR E-05 PIK3R E-11 NF E-03 SPTA E-02 TBX E-13 CTCF E-05 AFF E-03 SPEN E-02 CACNA1E E-02 AKT E-11 PTPRD E-02 MUC5B E-01 PREX E-02 ATM E-02 ARID1A E-01 SF3B E-04 RPGR E-03 PCDH E-02 FAM47C E-01 KIAA NA RB E-03 KIAA E-02 CBLB E-01 KCNT E-01 ATP1A E-01 MED23 9 NA CBFB E-08 TBL1XR E-06 FOXA E-02 NEK E-02 MYB E-02 SEMA5A E-02 ATN E-02 PIWIL E-02 UNC5D E-01 WNK3 8 NA ZFP36L E-04 PTPN E-03 WNT7A E-02 DCAF4L E-02 NLRC E-02 UBC E-01 TLR E-01 CPXM E-01 PPEF E-01 ZNF E-01 DSPP E-01 DGKG 7 NA PCDHGB6 7 NA ERBB2 7 NA GPS E-04 OR2G E-03 CHD1L E-01 BAI E-01 DALRD E-01 PRKG E-01 KCNB2 6 NA ITPKB 6 NA CTNND2 6 NA MST1P9 6 NA NPAS4 6 NA WSCD2 6 NA DENND1B 6 NA MLLT10 6 NA CDKN1B E-03 GPR E-02 LOC E-01 SRPR E-01 OR5I E-01 C2orf E-01 OR2T E-01 PIP4K2C E-01 PIWIL2 5 NA RGS7 5 NA GSPT1 5 NA PPP1R3A 5 NA CD5L 5 NA PRRX1 5 NA OR6A E-02 HIST1H2BC E-02 SEPT E-02 C12orf E-02 HIST1H3B E-02 OR2L E-02 HIST1H1C E-02 FAM166A E-01 HLF E-01 KRAS E-01 C8orf E-01 BID E-01 HLA-A E-01 GIMAP E-01 LYSMD E-01 ZNF266 4 NA GP1BA 4 NA HLA-DRB1 4 NA OR8H2 4 NA PIP5K1C 4 NA COPS3 4 NA C4orf40 4 NA TRIM6-TRIM34 4 NA TCGA et al., Nature, 2012 (PMID: )

7 Significantly Mutated Gene List according to mrna-subtype Basal-like (n=93) Mutated FDR TP TTN 24 NA USH2A E-02 PIK3CA E-07 FAT3 8 NA FLG 8 NA MLL3 6 NA MUC5B 5 NA KIAA E-01 SPTA1 4 NA RB E-02 AFF2 4 NA PREX2 4 NA RPGR E-01 BAI E-01 UNC5D 4 NA CACNA1E 3 NA PCDH19 3 NA CTNND2 3 NA PRKG2 3 NA OR5I E-01 TRIM6- TRIM E-02 ZNF841 3 NA HLF E-02 NPDC E-01 HER2-enriched (n=57) Mutated FDR TP PIK3CA E+00 TTN 7 NA FLG 5 NA ATP1A E-02 USH2A 4 NA MLL3 4 NA RYR2 4 NA PIK3R E-02 KIAA NA SRPR E-03 SPTA1 3 NA AFF2 3 NA PREX2 3 NA CACNA1E 3 NA ARID1A 3 NA CBLB 3 NA CDH1 3 NA PTPN22 3 NA ZNF E-01 KCNT2 2 NA CPXM1 2 NA C12orf36 2 NA C8orf31 2 NA PTPRD 2 NA FAM47C 2 NA OR2G3 2 NA SF3B1 2 NA ARMCX E-02 MAP3K1 2 NA RUNX1 2 NA ERBB2 2 NA Luminal A (n=225) Mutated FDR PIK3CA E+00 GATA E+00 MAP3K E+00 TP E+00 TTN 26 NA CDH E+00 MLL E-11 MAP2K E+00 RUNX E+00 NCOR E-09 PTEN E-11 CTCF E-06 AKT E-09 SPEN 8 NA SF3B E-05 USH2A 7 NA TBX E-05 NF1 6 NA RYR2 6 NA ATN E-03 MED E-02 CBFB E-05 TBL1XR E-03 FLG 5 NA FOXA E-02 NEK E-03 WNT7A E-03 MYB E-02 PCDH19 5 NA DGKG E-02 PIK3R E-01 GPS E-03 PTPRD 4 NA ATM 4 NA PPEF1 4 NA ARID1A 4 NA UNC5D 4 NA ITPKB E-01 GP1BA E-02 Luminal B (n=126) Mutated FDR PIK3CA E+00 TP E+00 GATA E+00 TTN 19 NA RYR E-01 FAT3 8 NA MLL E-01 MAP3K E-07 CDH E-03 PTEN E-07 TBX E-04 KCNB E-03 NF1 5 NA PTPRD E-02 FAM47C 5 NA WNK3 5 NA SEMA5A 5 NA MLLT E-02 USH2A 4 NA FLG 4 NA PIK3R1 4 NA ATM 4 NA PIWIL1 4 NA MUC5B 4 NA ZFP36L E-02 SPTA1 4 NA NLRC4 4 NA PCDHGB6 4 NA RB1 4 NA NPAS E-01 PIWIL E-01 GSPT E-02 MAP2K E-01 RUNX E-01 NCOR1 3 NA AKT1 3 NA ARID1A 3 NA AFF2 3 NA MST1P9 3 NA CBLB 3 NA KCNT2 3 NA PTPN22 3 NA DCAF4L2 3 NA PREX2 3 NA KIAA NA PRRX1 3 NA OR2G E-02 COPS E-02 CCDC E-01 CRTAP E-01 AGAP E-01 TCGA et al., Nature, 2012 (PMID: )

8 MAP3K1 (39) MAP2K4 (21) ~12% mutually exclusive TCGA et al., Nature, 2012 (PMID: )

9 PIK3CA Mutations TCGA et al., Nature, 2012 (PMID: )

10 Mutation of GATA3 in Human Breast Tumors Usary et al., Oncogene 46, (2004) (PMID: )

11 54 GATA3 mutant tumors TCGA et al., Nature, 2012 (PMID: )

12 A class of GATA3 mutation reprograms the breast cancer transcriptional network through gain and loss of function Mokoti Takaku et al. (Paul Wade Lab at NIEHS), Nature Com., In Press (2018) GATA3 mutations compiled from METABRIC, TCGA Nature 2012 & Nat Commun 2016

13 A class of GATA3 mutation reprograms the breast cancer transcriptional network through gain and loss of function Mokoti Takaku et al. (Paul Wade Lab at NIEHS), Nature Com., In Press (2018)

14 Comprehensive molecular portraits of human breast tumours. TCGA et al., Nature, 2012 (PMID: ) 466 Invasive Breast Carcinomas (348 common to all 6 platforms) 1. Whole Exome (all exons) capture and sequencing of tumor and normal (somatic coding sequence mutations and germline variation) WashU (n=507) 2. Gene expression analysis (Agilent microarrays and mrna-sequencing) - UNC 3. MicroRNA expression analysis (Illumina sequencing) - UBC 4. Tumor DNA copy number analysis (AFFY 6.0 microarrays and sequencing) - Broad 5. Tumor DNA methylation analysis (Illumina methylation arrays transitioning to sequencing)- USC 6. Reverse Phase Protein Arrays to detect proteins and phospho-proteins - MDACC 7. Integrated computational analyses (Genome Data Analysis Centers)- 6 Universities/sites 14

15 Unsupervised Hierarchical Cluster of DNA methylation data analyzed for 802 tumors using 574 methylation probes (5 subtypes) TCGA et al., Nature, 2012 (PMID: )

16 Comprehensive molecular portraits of human breast tumours. TCGA et al., Nature, 2012 (PMID: ) 466 Invasive Breast Carcinomas (348 common to all 6 platforms) 1. Whole Exome (all exons) capture and sequencing of tumor and normal (somatic coding sequence mutations and germline variation) WashU (n=507) 2. Gene expression analysis (Agilent microarrays and mrna-sequencing) - UNC 3. MicroRNA expression analysis (Illumina sequencing) - UBC 4. Tumor DNA copy number analysis (AFFY 6.0 microarrays and sequencing) - Broad 5. Tumor DNA methylation analysis (Illumina methylation arrays transitioning to sequencing)- USC 6. Reverse Phase Protein Arrays to detect proteins and phospho-proteins - MDACC 7. Integrated computational analyses (Genome Data Analysis Centers)- 6 Universities/sites 16

17 Hierarchical cluster analysis of 402 breast tumors using 171 proteins/phospho-proteins (7 subtypes) TCGA et al., Nature, 2012 (PMID: )

18 Comprehensive molecular portraits of human breast tumours. TCGA et al., Nature, 2012 (PMID: ) 466 Invasive Breast Carcinomas (348 common to all 6 platforms) 1. Whole Exome (all exons) capture and sequencing of tumor and normal (somatic coding sequence mutations and germline variation) WashU (n=507) 2. Gene expression analysis (Agilent microarrays and mrna-sequencing) - UNC 3. MicroRNA expression analysis (Illumina sequencing) - UBC 4. Tumor DNA copy number analysis (AFFY 6.0 microarrays and sequencing) - Broad 5. Tumor DNA methylation analysis (Illumina methylation arrays transitioning to sequencing)- USC 6. Reverse Phase Protein Arrays to detect proteins and phospho-proteins - MDACC 7. Integrated computational analyses (Genome Data Analysis Centers)- 6 Universities/sites 18

19 TCGA Breast Tumor RB Pathway Integrated Analysis Luminal A Luminal B HER2E Basal-like TCGA et al., Nature, 2012 (PMID: )

20 mrna microrna Protein/RPPA DNA Copy Number DNA Methylation Mutations/Exomes TCGA et al., Nature, 2012 (PMID: )

21 Cluster of Clusters (Consensus Clustering of 5 platform classification systems) TCGA et al., Nature, 2012 (PMID: )

22 Normal Breast Luminal B Luminal A Claudin-low HER2-enriched Basal-like MEMo Pathway Analyses of Basal-like Tumors. Giovanni Ciriello and others from MSKCC (Sanders Lab) if BRCA1 germline and somatic, and BRCA2 germline and somatic, deleterious variants are added up, then ~20% of Basal-like tumors have alterations in BRCA1/2

23 TCGA MEMo Comparison of Breast Luminal vs. Breast Basal-like vs. Serous Ovarian TCGA et al., Nature, 2012 (PMID: )

24 TCGA DNA Copy Number Comparison of Breast tumors vs. Serous Ovarian tumors TCGA et al., Nature, 2012 (PMID: )

25 TCGA Pan Cancer Study (12 tumor types representing 3527 specimens) Tumor Type # Samples GBM AML 173 AML Lung Adenocarcinoma Lung Squamous Ovarian Endometrial Katie Hoadley Head & Neck Breast Kidney Clear Cell Colon Rectum Bladder Bladder 122 Breast 845 Colon 190 Endometrial 370 GBM 168 Head & Neck 303 Kidney Clear Cell 480 Lung Adeno 355 Lung Squamous 259 Ovarian Serous 265 Rectum 72 Hoadley et al., Cell, (PMID: )

26 mrna microrna Protein/RPPA DNA Copy Number DNA Methylation Mutations/Exomes (-/+) tissue mut class Myeloid TP53-related PIK3-related Eclectic DNA-damage VHL-related Hoadley et al., Cell, (PMID: )

27 Consensus Clustering to define how many groups/subtypes are present within the 12 tumor types (3527 samples) when using all genomic platforms at once 11 Cluster of Cluster Assignment (COCA) subtypes are seen Hoadley et al., Cell, (PMID: )

28 Hoadley et al., Cell, (PMID: )

29 12 Tissue of Origin Sites Translate into 11 Genomic COCA Subtypes Bladder Head & Neck Rectum Lung Adeno Lung Squam Breast Renal Endometrial Colon Ovary GBM AML LUADenriched Squamous -like Breast Luminal (includes all HER2+ and all Non-basal TNBCs) Breast Basal-like Renal Endo Rectum & Colon 131/139 Basal-like tumors are in this group and nothing else Bladder Ovary GBM AML Hoadley et al., Cell, (PMID: )

30 TCGA Cluster Analysis of 10,000 tumors x 5000 genes 33 tumor types studied including breast (n=1100), bladder, colon, rectum, head & neck, gastric, lung squamous & adenocarcinoma, melanoma, renal clear cell & chromophobe, ovarian, glioblastoma, prostate, endometrial, thyroid, pancreas, testicular and others Luminal Basal-like

31 HOW MANY ETIOLOGICAL SUBTYPES OF BREAST CANCER: TWO, THREE, OR MORE? William F. Anderson, Philip S. Rosenberg, Aleix Prat, Charles M. Perou, and Mark E. Sherman. JNCI, (2014). PMID: Clemmesen s Hook (Br. J. Radiol, 1948) n=2000

32 Invasive Lobular Carcinoma (ILC) 10-15% Invasive Ductal Carcinoma (IDC) 70-75% Ciriello, Gatza et al., Cell PMID:

33 Comprehensive Molecular Portraits of Invasive Lobular Breast Cancer Ciriello, Gatza et al., Cell 2015 (PMID: ) 817 breast cancer samples with genomic data from: RNAseq, Exomes, DNA Copy Number, Methylation, micrornas, and RPPA (~200 proteins) Andy Beck (Harvard) and TCGA Breast Pathology Working Group Ciriello, Gatza et al., Cell PMID:

34 Lobular versus Ductal genomic alterations (within only Luminal A) Ductal Lum A (% altered samples) Lobular LumA (% altered samples) Ciriello, Gatza et al., Cell PMID:

35 Lobular versus Ductal genomic alterations (within only Luminal A) Ductal Lum A (% altered samples) ILC: 5% IDC: 20% ILC: 9% IDC: 2% Lobular LumA (% altered samples) Ciriello, Gatza et al., Cell PMID:

36 FOXA1 mutations in breast cancer All Lobular FOXA1 mutations fall within the Fork-head domain Ciriello, Gatza et al., Cell PMID:

37 FOXA1 mutations in breast cancer FOXA1 mutations correlates with increased FOXA1 mrna expression FOXA1 mutations PAM50 FOXA1 ESR1 ILC tumor only FOXA1 mrna expression correlates with decreased DNA methylation of FOXA1 binding sites FOXA1 binding sites Most variable probes Ciriello, Gatza et al., Cell PMID:

38 Identification of Molecular Subtypes of Invasive Lobular Cancers Reactive-like Immune-related Proliferative Ciriello, Gatza et al., Cell PMID:

39 Comparison of Breast Cancer Molecular Features and Survival by African and European Ancestry in The Cancer Genome Atlas. Huo, Hu, et al., JAMA Oncology, 2017 (PMID: )

40 Comparison of Breast Cancer Molecular Features and Survival by African and European Ancestry in The Cancer Genome Atlas. Huo, Hu, et al., JAMA Oncology, 2017 (PMID: ) subtype while most molecular differences were eliminated after adjusting for intrinsic subtype, the study found 16 DNA methylation probes, 4 DNA copy number segments, 1 protein, and 142 genes that were differentially expressed

41 TCGA Breast Cancer Summary 1. Comprehensive molecular portraits of human breast tumours. TCGA et al., Nature, 2012 (PMID: ); multi-platform analysis identifies 4 major subtypes of breast cancers 2. The molecular diversity of Luminal A breast tumors. Ciriello et al., BCRT, 2013 (PMID: ); identified 4 subtypes within Luminal A based on DNA copy number changes 3. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Hoadley et al., Cell, 2014 (PMID: ); showed that amongst 3500 tumors from 12 diverse tumor types, breast cancer is 2 diseases (basal and luminal) 4. Comprehensive Molecular Portraits of Invasive Lobular Breast Cancer. Ciriello et al., Cell (PMID: ); identified many unique genomic features of lobulars, and 3 subtypes 5. DNA defects, epigenetics, and gene expression in cancer-adjacent breast: a study from The Cancer Genome Atlas. Troester et al., NPJ Breast Cancer, 2016 (PMID: ); a molecular study of normal breast tissues adjacent to tumors identified occult tumor in many normals 6. The molecular basis of breast cancer pathological phenotypes. Heng et al., J Pathology, 2017 (PMID: ); genomic and genetic correlates to multiple pathology features including tubule formation and mitotic index 7. Comparison of Breast Cancer Molecular Features and Survival by African and European Ancestry in The Cancer Genome Atlas. Huo et al., JAMA Oncol, 2017 (PMID: ); molecular comparison of tumors from African Americans versus Whites shows little molecular differences once race associated subtype frequency % are accounted for 41

42 TCGA is over, what is next? 1. Its not over, we are working on the Special Histologies Paper, which includes the full set of 1101 tumors with ~10 different histological subtypes 2. Need to do similar multi-platform work on the metastatic setting. The AURORA project in the USA and EU is focused on this goal 3. Need to do similar multi-platform work on the DCIS and earlier lesion setting. In USA there is a call for new grants for the Pre-Cancer Atlas Project 4. Need to do more work with proteins. In USA we have the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which for breast cancer is lead by Matthew Ellis 5. Need to focus on data integration and using multiple data types together as tools for discovery, and as the possible biomarker(s) 42

43 TCGA Breast Cancer Analysis Working Group ( ) 43 University of North Carolina Chuck Perou (co-chair) Katie Hoadley Joel Parker Michael Gatza University of California Santa Cruz Buck Institute Chris Benz Christina Yau Sam Ng Ted Goldstein Kyle Ellrott Charlie Vaske Josh Stuart University of Southern California Peter Laird Swapna Mahurkar Simeen Malik Dan Weisenberger Nationwide Children s Hospital Jay Bowen Julie Gastier-Foster The University of Texas MD Anderson Cancer Center Roel Verhaak Rehan Akbani Nancy Shih Gordon Mills Memorial Sloan-Kettering Cancer Center Niki Schultz Giovanni Ciriello Ethan Cerami Arthur Goldberg Caitlin Byrne Anders Jacobsen Tari King Chris Sander National Cancer Institute Chunhua Yan John Demchok Laura Dillon Peter Fielding Margi Sheth Peter Good Jacqueline Palchik Heidi Sofia Kenna Shaw Baylor College of Medicine Chad Creighton Xiaosong Wang British Columbia Cancer Agency Andy Chu Elizabeth Chun Andy Mungall Gordon Robertson Dominik Stoll Broad Institute Andrew Cherniack Harvard Medical School Terrence Wu Yonghong Xiao Institute for Systems Biology Sheila Reynolds Ilya Shmulevich Lawrence Berkeley National Laboratory Paul Spellman Mayo Clinic Jim Ingle Windber Research Institute Hai Hu Richard Mural The Genome Institute at Washington University Matthew Ellis (co-chair) Li Ding Lucinda Fulton Daniel Koboldt Elaine Mardis