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Genome-wide Association Studies          in Cancer: A Step in the Right Direction        Stephen Chanock, M.D. Chief, Labo...
Genetic Predisposition to Breast Cancer                                                       European Population         ...
97 Genes Mutated in Cancer Susceptibility Syndromes SDHB MUTYH         ALK       FANCD2         MSH2         MSH6      VHL...
Genome-Wide Association Studies:          Age of Discovery• Discovery of New Regions in the Genome Associated  with Diseas...
Published Cancer GWAS Etiology Hits: 5.15.12   KIF1B    1p36                                                              ...
Lessons from GWAS Discovery• P values matter- to protect against a sea of  false positives• Size matters• Design “sort of”...
Lessons from GWAS Discovery• P values matter- to protect against a sea of  false positives• Size matters• Design “sort of”...
CGF & Data Sharing• Posted first public GWAS datasets for breast & prostate cancer  in 2006   • Aggregate data removed in ...
GWAS Regions for Testicular Cancer Point      Towards Alterations in a Common PathwayHigh Estimate for Heritability      S...
GWAS Studies:           Just the Start……“This is not the end. It is not even the  beginning of the end. But it is,  perhap...
Prostate Cancer Risk Factors                    2006• Age• Ethnic Background• Family History
Prostate Cancer: 48 as of May 1, 2012                                 > 24 More to come           2p24.1           THADA  ...
Prostate Cancer Risk Factors                 2012• Age• Ethnic background• Family history• Multiple common alleles-   48 ...
Prediction is difficult,Especially about the future.         Yogi Berra         Dan Quayle         Niels Bohr
Discriminatory Power of Genetic Risk      Score for Prostate Cancer                      Area Under the ROC               ...
Theoretical Limits of Risk Prediction         Crohn’s Disease                     Common cancers (Br or Pr)   Sibling rela...
Next Generation GWAS• Distinct Populations  – Prostate Cancer in Japan or in African Americans  – Esophageal Cancer in Chi...
Distinct Differences in the    Underlying Genetic Architecture of            Different CancersPreliminary Phase of Estimat...
Genetic Predisposition to Breast Cancer                                                       European Population         ...
Genomic Architecture of Prostate Cancer                                          Susceptibility Loci:                     ...
Genetic predisposition to neuroblastoma:                           Current status and future directions                   ...
Genomic Architecture of Lung Cancer                                   Susceptibility Loci                            5    ...
Investigation of GWAS Markers                                               Non-                                          ...
10q11.2 & Prostate Cancer                      Risk       rs10993994 within promoter of           MSMB= b-microseminoprote...
10q11.2 Could Be More Complex……            MSMB and NCOA4                                            Re-sequence          ...
Characterization of Chimeric Transcript at 10q11.23MSMB-NCOA4 Fusion   RT-PCR of MSMB-NCOA4 fusion transcripts   in human ...
11q13: Multi-Cancer Susceptibility RegionDiscovery by GWAS & MappingCGEMS (Thomas NG 2008)                                ...
Admixture Analysis of                          Native                                Asian    African   American       Eur...
Association testing                      Genome-wide                    association studiesBehavioral traits       Biometr...
Loci Associated with BMI Rethinking “Pathways”                 Opportunities for:                 1. Pathway Analyses     ...
Unexpected Findings     Genetic Mosaicism     & the Aging Genome              Genome-wide              association studies...
Looking at LRR/BAF graphs                                                                                          B-allel...
Definition of Genetic MosaicismCo-existence of distinct subpopulations of  cells regardless of the clonal or  developmenta...
Mosaicism in the Extreme• Age-old explanation for developmental disorders  and catastrophic diseases (NF)  • Trisomy 21, T...
Validation for 42 Events Observed                              100%1.7% OverallNo Association with Bladder Cancer Risk    ...
Genetic Mosaicism of the Autosomes• Analysis of 13 GWAS                      57,853   • Cancer cases                      ...
Genetic Mosaic Events                             0.4                             0.3        gain                         ...
Age at DNA Collection is the Strongest      3.0%      2.5% Predictor of Genetic Mosaicism            2.0%Frequency        ...
Higher Frequency in Men     Compared to Women              Females   MalesCancer Free     0.56%    0.87%Cancer          0....
Frequency of Mosaic Events by Type & Location                    Mosaic Chromosome Count     Mosaic Chromosome Frequency (...
Number of chromosomes with mosaic events• 69 individuals have two or more events   – 46 cancer cases   – 23 cancer-free   ...
Circos Plot of mosaic events in 57,583       individuals (681 events)                         0.4                         ...
Adjusted analysis of association between                mosaicism & cancer                                All cancer cases...
Hematological Cancers and “Mosaicism”         A Tale of Two StudiesNCI Study (Jacobs et al 2012)   GENEVA (Laurie et al 20...
Chromosome Y Mosaic Aneuploidy      Pre-Correction                      Post-CorrectionExample of whole chromosome loss in...
SRYRPS4Y1ZFY           qPCR Assay Panel for chrY loss           • 15 probes designed to determine copyAMELYTBL1YPRKY      ...
Calling whole chromosome Y-loss from Infinium GWAS                                                         data calibrated...
The Aging Genome: Implications for Cancer Studies• Importance of thorough characterization of  ‘germline’ DNA in parallel ...
Two Hypotheses for      Mosaicism in the Aging Genome         Early Event                          Late EventEmbryonic Pro...
Using Population Studies to Gain Biological     Insights into Genetic Mosaicism:• Map breakpoint sites (Mitelman/ENCODE)• ...
Detectable Genetic Mosaicism &     Hematological Cancers• PLCO heme cancers (> 700)  • Serial samples for ~25%• NHL GWAS  ...
Milestones at the Core Genotyping Facility    2001 2002 2004 2006 2008 2010 2012 2014 & beyond                            ...
Human Genetics:         Thresholds and Significance• Each new technology has brought us to a new ‘crisis’  Linkage and LOD...
Let’s not fool ourselves about             germline genetics…• First it is about……..Discovery    Biology    Targets    Pos...
Mapping Genetic Architecture• Comprehensive map will emerge across  spectrum of variation  • GWAS               Common Var...
In the not too distant future• We will look back and think of GWAS as the                “Golden Age”• The temptation is t...
Emerging Impact of Population Genetics   in the Search for ‘Elusive’ Variants• In GWAS era- we tolerated minor mismatching...
Advances will be accelerated by“Collective Intelligence”“I not only use all of the brainsI have, but all I can borrow”Wood...
Acknowledgements                        NCI-DCEG              HSPHLTG                     Joseph Fraumeni       David Hunt...
Acknowledgements                                                                               Glioma                     ...
61  Chromosomal mosaicism from birth to      old age and its relationship to          hematological cancerInvestigators:Ca...
Do mosaic subjects have increasedincidence of hematological cancer?                        Mosaic                  Non-mos...
CGF                         Metrics360,214 DNA profiles                         7.6 x 1013 SNP genotypes    (Identifiler) ...
Dr. Stephen Chanock: Genome-wide Association Studies
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Dr. Stephen Chanock: Genome-wide Association Studies

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On June 20, Dr. Stephen Chanock delivered a presentation titled "Genome-wide Association Studies: A Step in the Right Direction."

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Transcript of "Dr. Stephen Chanock: Genome-wide Association Studies"

  1. 1. Genome-wide Association Studies in Cancer: A Step in the Right Direction Stephen Chanock, M.D. Chief, Laboratory of Translational Genomics Director, Core Genotyping Facility June 20, 2012
  2. 2. Genetic Predisposition to Breast Cancer European Population 10 BRCA1 BRCA2Population genotype relative risk TP53 PTEN CHEK2 3 ATM PALB2 BRIP1 RAD51C ERCC2 1.5 1.4 1.3 1.2 > Doubling in 2012 1.1 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Population risk-allele frequency BCAC CGEMS/BCAC WTCCC Other
  3. 3. 97 Genes Mutated in Cancer Susceptibility Syndromes SDHB MUTYH ALK FANCD2 MSH2 MSH6 VHL XPC HRAS FANCL MLH1 PMS2 FANCF BAP1 TERT FANCE EGFR CDKN2 WT1 MITF FANCG A SDHA DDB2 PHOXB2 POLH EXT2 WRN TMEM127 CDK4 GATA2 SBDS NBS1 GALNT1 SDHAF2 PTPN11 HAX1 XPA 2 RET MEN1 SDHC ERCC3 TERC PTCH BMPR1 ATM MET PTEN A SDHD DIS3L2 KIT FANCC SUFO CBL HRPT2 TSC1 PDGFRA APC EXT1 RECQL4 T FH FANCB SLX4 TSC2 TP53 ELANE ERCC4 WRAP53 STK11 FANCN/BRIP FLCN 1 TINF2 CEBPA NF1 RUNX1 SMARCB1 BRCA2 FANCM ERCC2 CHEK2 BRCA1 RB1 CYLD FANCJ SMAD4 NF2 MAX BUB1B CDH1 RAD51C FANCI FANCA HOXB13 BLM DICER1 ERCC5 GPC3 DKC1 C Kratz
  4. 4. Genome-Wide Association Studies: Age of Discovery• Discovery of New Regions in the Genome Associated with Diseases/Traits • New “Candidate Genes & Regions”• Clues for Mechanistic Insights Into the Contribution of Common Genetic Variation to Cancer Biology• Challenge of Genetic Markers for Risk Prediction • Individual Risk • Public Health Decisions • Polygenic Risk Models
  5. 5. Published Cancer GWAS Etiology Hits: 5.15.12 KIF1B 1p36 ~240 Disease Loci marked by SNPs DDX1 IRF4 6p22 1 Locus marked by a CNV SLC4A7/ C2orf43 NEK10 6p21 THADA ITGA9 TACC3 TERT/ GPRC6A FOXP4 Another 85 coming soon… DMRT1 10p15.1 LSP1 11p15 TERT CLPTM1L DLG2 GSTM1 EHBP1 CLPTM1L GABBR1 breast, bladder, kidney, lung,10p14 JAZF1 NAT2 CDKN2A/ BNC2 LMO1 12p11.23 deletion REL 3p12.1 HLA-F 1p11.2 3p11.2 5p15 HLA-A ovary, prostate, TCGT IKZF1 8p21 CDKN2B GATA3 MSMB/ ATM ATF7IP 5p12 EGFR 6p21.33 NCOA4 FAM111A 1q22 2q13 5q11.2 11q13 KRT5 ARIDB5 3q13 6p21.32 1q21.1 PDLIM5 5q11 HSD17B12 EEFSEC BAK1 LMTK2 PLCE1 EPAS1 ADH1B 5q13 TYR KITLG 1q21.3 FOXE1 TET2 DDX4 ECHDC1/ ZNF365 11q23.1 12q13.13 1q32.1 ITGA6 RNF146 EIF3H 9q31.2 FGFR2 PHLDB1 3q26 C6orf97/ 7q32 ALDH2 SPRY4 8q24.21(x5) MYC CCDC26 8q24.21 ABO CTBP2 11q24.1 TARDBP 2q31 ESR1 7q32 TP63 SLC22A3 PSCA 10q21.2 1q41 CASP8 ERG2 1q42 BARD1 LINC00340 10q22.3 1q42.12 2q35 10q26 FARP2 2q37 19p13 C20orf54 RHPN2 BMP2 NUDT10/ CCNE1 15q15 22q12.25 NUDT11 CEBPE HNF1B x 2 SLC14A1 ASIP MX2 19q13.2 22q13 NKX2-1 GREM1 TOX3 COX11/ SMAD7 PRKD2 15q21.3 21q22 BIK BMP4 CDH1 STXBP4 RTEL1 KLK2/ 15q23 16q24.1 17q24.3 KLK3 KLF5/ RAD51L1 KLF12 CHRNA3/ MC1R 3 Ewing Sarcoma 3 Kidney 4 Thyroid 2 Non-Hodgkin 5 Ovary 3 Gastric 9 Multiple CHRNA5 13q12.125 3 Hodgkins 13q22 2 Wilms 1 Liver 10 CLL 6 Neuroblastoma 4 Pediatric Acute Lymphoblastic Leukemia 7 Esophageal Squamous48 Prostateate 26* Breast 13 Colorectal 7 Basal Cell Carcinoma11* Bladde7 Glioma 6 Lung 9 Melanoma 4 Pancreas 7 Nasopharyngeal 6 Testicular 7 Chung & Chanock 2011
  6. 6. Lessons from GWAS Discovery• P values matter- to protect against a sea of false positives• Size matters• Design “sort of” matters…….• Mapping is required to explore each region prior to functional work• Collaboration is central Epidemiology meets Genetics to Discover Biology Not for weak of heart or stomach…
  7. 7. Lessons from GWAS Discovery• P values matter- to protect against a sea of false positives• Size matters• Design “sort of” matters…….• Mapping is required to explore each region prior to functional work• Collaboration is central- Can’t do it alone Not for weak of heart or stomach…
  8. 8. CGF & Data Sharing• Posted first public GWAS datasets for breast & prostate cancer in 2006 • Aggregate data removed in 2008 in response to NIH policy change• Led development of standards for GWAS posting with dbGaP• Contributed all DCEG GWAS datasets to dbGaP• CGF was instrumental in addressing privacy issues with GWAS and other high-dimensional aggregate genomics data• 11 scans currently listed on dbGaP
  9. 9. GWAS Regions for Testicular Cancer Point Towards Alterations in a Common PathwayHigh Estimate for Heritability Strong Familial Component MZ Twins= 75 X DZ Twins= 20-25 XStrongest Effects Observed in GWAS KITLG Heterozygote OR= 2.5, Homozygote > 5.0
  10. 10. GWAS Studies: Just the Start……“This is not the end. It is not even the beginning of the end. But it is, perhaps, the end of the beginning.” Sir Winston Churchill @ Lord Mayors Luncheon, Mansion House following the victory at El Alameinin North Africa London, 10 November 1942.
  11. 11. Prostate Cancer Risk Factors 2006• Age• Ethnic Background• Family History
  12. 12. Prostate Cancer: 48 as of May 1, 2012 > 24 More to come 2p24.1 THADA EHBP1 No Clear Evidence of Specific Loci 5p15.33 RFX6 JAZF1 11p15 FOXP4 3 3p12.1 3p11 For Advanced Disease 8p21 MSMB/ FAM111A NCOA4 11q13 10q11.23 12q13 PDLIM5 EEFSEC LMTK2 TET2 ITGA6 8q24.21 CTBP2 SLC22A3 5 10q26 2q27.3 Suggests Distinct Regions Influence Etiology & Outcome NUDT10/ NUDT11 HNF1B 19q13.2 2 BIK KLK2/13q22.1 17q24.3 KLK3 Type 2 Diabetes PSA or Prostate Cancer or both??
  13. 13. Prostate Cancer Risk Factors 2012• Age• Ethnic background• Family history• Multiple common alleles-  48 published & more coming….  Each common variant explains a small proportion of risk  Together 15%
  14. 14. Prediction is difficult,Especially about the future. Yogi Berra Dan Quayle Niels Bohr
  15. 15. Discriminatory Power of Genetic Risk Score for Prostate Cancer Area Under the ROC Curve Model Under Over 65 75 FHx only 0.55 0.51 G only 0.66 0.60 G + FHx 0.68 0.60 PSA 0.87 0.84 P Kraft, S Lindstrom for the BPC3
  16. 16. Theoretical Limits of Risk Prediction Crohn’s Disease Common cancers (Br or Pr) Sibling relative-risk=20-35 Sibling relative risk=2-3 Park et al., Nat Genet ,2010 Different Diseases Display Distinct Architectures Random Using known loci Using all estimated loci Ideal (if we could explain all heritability) Park et al Nature Genetics 2010
  17. 17. Next Generation GWAS• Distinct Populations – Prostate Cancer in Japan or in African Americans – Esophageal Cancer in China• Meta-Analysis Yield Discoveries – Larger Scans – Large Scale Replication (iCOGS)• Shift to lower MAF – New Arrays vs Low-Pass Sequence Coverage – Imputation • Better for Mapping Regions – Larger Sample Sizes Required
  18. 18. Distinct Differences in the Underlying Genetic Architecture of Different CancersPreliminary Phase of Estimating Differential Contribution Common Variants Log additive Effects oEpistatic Effects oUncommon Variants oRare/Familial Mutations
  19. 19. Genetic Predisposition to Breast Cancer European Population 10 BRCA1 BRCA2Population genotype relative risk TP53 PTEN CHEK2 3 ATM PALB2 BRIP1 RAD51C ERCC2 1.5 1.4 1.3 1.2 > Doubling in 2012 1.1 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Population risk-allele frequency BCAC CGEMS/BCAC WTCCC Other
  20. 20. Genomic Architecture of Prostate Cancer Susceptibility Loci: BRCA2 5 3 ????Per allele relative risk 1.4 1.2 1 0 0.05 0.50 0.95 1 Risk-allele frequency
  21. 21. Genetic predisposition to neuroblastoma: Current status and future directions 100 Discovered mutations ALK Yet to be discovered mutations Discovered polymorphisms PHOX2B Additional mutations and Yet to be discovered polymorphisms rare polymorphisms to be TP53 discovered by NGS Effect size SDHB Additional polymorphisms 10 to be discovered by GWAS PTPN11 FLJ22536 5 DDX4 LMO1 BARD1 HSD17B12 NBPF23 NME7 1 0.001 0.01 0.1 DUSP12 0.5 IL31RAModified from Manolio, et al. Nature 2009 Allele Frequency
  22. 22. Genomic Architecture of Lung Cancer Susceptibility Loci 5 3 ????Per allele relative risk Smoking…. 1.4 Adenocarcinoma specific Stronger in nonsmokers 1.2 1 0 0.05 0.50 0.95 1 Risk-allele frequency
  23. 23. Investigation of GWAS Markers Non- CodingInitial Findings Protein Variant CodingBioinformatic Regulatory Unannotated Effect on Element Transcript ProteinAnalysis Alteration of Effect onFunctional Novel mRNA Allelic Gene Levels Epigenetic GenesElements Transcripts Stability Differences Elsewhere Expression Expression Gene Functional HistoneExperimental Elements Methylation Quantitative miRNA Quantitative ProductStrategy Trait RNASeq Trait Functional In vitro/vivo Elements Analysis Analysis Analysis
  24. 24. 10q11.2 & Prostate Cancer Risk rs10993994 within promoter of MSMB= b-microseminoprotein Prostate specific serum marker under study20,000 subjects Functional Analysis Risk Allele “T” Lower expression levels Reporter assays Electrophoretic Mobility Shifts Levels in Prostate Tissue Tumor Tissue
  25. 25. 10q11.2 Could Be More Complex…… MSMB and NCOA4 Re-sequence 454 FLX ~100 subjectsRNA Expression MSMB and NCOA4NormalTumor TissueAnchorage Independent Growth is Specific to ProstateMSMB- SuppressionNCOA4- Over-expression
  26. 26. Characterization of Chimeric Transcript at 10q11.23MSMB-NCOA4 Fusion RT-PCR of MSMB-NCOA4 fusion transcripts in human tissues and 9 prostate cancer cell lines Expression of MSMB-NCOA4 fusion protein in PC3 transfected cells organization of Genomic MSMB and NCOA4 Confirmed by Predicted fusion transcripts (UCSC) Western Blot Immunoprecipitation anti-N (MSMB) & anti-C (NCOA4) Fusion transcripts identified by 5’ RACE Lou et al in press Hum Genet 2012
  27. 27. 11q13: Multi-Cancer Susceptibility RegionDiscovery by GWAS & MappingCGEMS (Thomas NG 2008) GWAS 1-> 3 Loci (Chung et al HMG 2011) Australia/ iCOGS Analysis J French
  28. 28. Admixture Analysis of Native Asian African American European GWAS Data: Use of Differences in Allele Frequencies to Map Regions that Contribute to Differences in STRUCTURE IncidencePopulation Structure AnalysisCan 1000s of SNPs assist inIdentifying Individuals at Higher Risk for PoorResponse to Pediatric ALL?• GWAS Scan as a Preliminary Biomarker JJ Yang et al Nature Genetics 2011
  29. 29. Association testing Genome-wide association studiesBehavioral traits Biometrics Nutrient levels Tobacco Height, Weight, BMI, Vitamins D, B12 Caffeine Menarche/Menopause Carotene Alcohol >200 regions & Height/Weight
  30. 30. Loci Associated with BMI Rethinking “Pathways” Opportunities for: 1. Pathway Analyses 2. Polygenic Model GIANT CONSORTIUM
  31. 31. Unexpected Findings Genetic Mosaicism & the Aging Genome Genome-wide association studiesLarge chromosomal abnormalities, structural variation, aneuploidy in germline DNA Rodriguez-Santiago AJHG 2010 Jacobs et al Nature Genetics 2012 Laurie et al Nature Genetics 2012
  32. 32. Looking at LRR/BAF graphs B-allele Homozygous σLRR=0.24 σBAF=0.044 Heterozygous A-allele HomozygousLRR – log ratio of observed probe intensity to expected intensity – Significant deviation from zero is evidence for copy number changeBAF – B allele frequency – ratio of B probe intensity to total intensity - Expected values for diploid loci are 0, ½ and 1. Other values can indicate allelic imbalance and suggest copy number changes or mosaicism.
  33. 33. Definition of Genetic MosaicismCo-existence of distinct subpopulations of cells regardless of the clonal or developmental originPresence of large structural genomic events (> 2 Mb)Resulting in alteration of • Copy number (gain or loss) • Loss of heterozygosity
  34. 34. Mosaicism in the Extreme• Age-old explanation for developmental disorders and catastrophic diseases (NF) • Trisomy 21, Turners (XO)• Rare, Highly Penetrant Mutations lead to Variegated Aneuploidy • BUB1B • CEP57• Complex Syndromes • Proteus Syndrome & AKT1 (NEJM 2011) • Ollier Disease & IDH1/IDH2 (Nature Genetics 2011) • HRAS- Skin/Cancer (NEJM 2011)
  35. 35. Validation for 42 Events Observed 100%1.7% OverallNo Association with Bladder Cancer Risk Rodríguez-Santiago et al. Am J Hum Genet. 2010;87:129-38
  36. 36. Genetic Mosaicism of the Autosomes• Analysis of 13 GWAS 57,853 • Cancer cases 31,717 • Cancer-free controls 26,136• Mosaic events detected 681• Autosomal chromosomes 641• Individuals 517• Individuals with multiple events 69 Jacobs et. al. Nature Genetics 2012
  37. 37. Genetic Mosaic Events 0.4 0.3 gain neutral LOH 0.2 loss 0.1log2 intensity ratio (LRR) 0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Proportion of abnormal cells (p)
  38. 38. Age at DNA Collection is the Strongest 3.0% 2.5% Predictor of Genetic Mosaicism 2.0%Frequency 1.5% 1.0% 0.5% 0.0% <45 45‐49 50‐54 55‐59 60‐64 65‐69 70‐74 75‐ Age at DNA collection Cancer Free Cancer DX Jacobs et. al. Nature Genetics 2012
  39. 39. Higher Frequency in Men Compared to Women Females MalesCancer Free 0.56% 0.87%Cancer 0.79% 1.21%Overall 0.65% 1.04%
  40. 40. Frequency of Mosaic Events by Type & Location Mosaic Chromosome Count Mosaic Chromosome Frequency (%)Event Location gain loss cnloh mixed Total gain loss cnloh mixed Totalchromosome 62 11 42 5 120 9.7 1.7 6.6 0.8 18.7telomeric P 11 13 114 1 139 1.7 2.0 17.8 0.2 21.7telomeric Q 9 10 149 0 168 1.4 1.6 23.2 0.0 26.2interstitial 14 185 2 1 202 2.2 28.9 0.3 0.2 31.5span centromere 1 1 2 0 4 0.2 0.2 0.3 0.0 0.6complex 0 3 0 5 8 0.0 0.5 0.0 0.8 1.2 Total 97 223 309 12 641 15.1 34.8 48.2 1.9
  41. 41. Number of chromosomes with mosaic events• 69 individuals have two or more events – 46 cancer cases – 23 cancer-free Count of cancer mosaic Likely Possibly cancer- Cancer chromosomes Untreated Treated Total free type(s) 1 166 111 277 171 2 15 13 28 18 3 7 0 7 3 4 3 0 3 1 5 0 2 2 1 6 0 1 1 0 prostate 7 1 0 1 0 CLL & ovary 11 0 1 1 0 gastric 16 1 0 1 0 lung 20 0 2 2 0 gastric
  42. 42. Circos Plot of mosaic events in 57,583 individuals (681 events) 0.4 0.3 neutral LOH 0.2 gain 0.1 loss tio (LRR) 0
  43. 43. Adjusted analysis of association between mosaicism & cancer All cancer cases Likely Untreated Possibly TreatedSite of first cancer OR 95% CI p value OR 95% CI p value OR 95% CI p valuenon-hematologic cancer 1.27 (1.05-1.52) 0.012 1.45 (1.18-1.80) 5.4E-04 1.03 (0.81-1.30) 0.804 bladder 1.30 (0.90-1.89) 0.164 1.50 (1.01-2.23) 0.043 0.73 (0.32-1.68) 0.455 breast 0.72 (0.41-1.27) 0.256 0.49 (0.18-1.32) 0.159 0.90 (0.46-1.79) 0.770 endometrium 1.27 (0.64-2.50) 0.494 1.35 (0.42-4.30) 0.611 1.24 (0.54-2.82) 0.610 esophagus 0.86 (0.34-2.18) 0.751 3.51 (0.45-27.58) 0.232 0.76 (0.29-2.03) 0.590 glioma 0.88 (0.45-1.74) 0.717 0.95 (0.44-2.05) 0.892 0.70 (0.17-2.86) 0.622 kidney 1.98 (1.27-3.06) 2.3E-03 2.32 (1.46-3.69) 3.6E-04 0.95 (0.30-3.03) 0.931 lung 1.56 (1.18-2.08) 2.0E-03 1.69 (1.23-2.33) 1.3E-03 1.27 (0.81-1.96) 0.295 osteosarcoma 1.34 (0.39-4.59) 0.637 1.34 (0.39-4.59) 0.637 ovary 1.18 (0.48-2.93) 0.718 1.09 (0.27-4.47) 0.903 1.27 (0.40-4.04) 0.690 pancreas 0.89 (0.60-1.33) 0.574 0.55 (0.14-2.24) 0.406 0.93 (0.62-1.41) 0.735 prostate 1.14 (0.79-1.64) 0.485 1.28 (0.85-1.92) 0.243 0.92 (0.51-1.66) 0.781 stomach 1.43 (0.68-3.03) 0.345 3.35 (0.74-15.13) 0.116 1.32 (0.61-2.88) 0.481 testis 3.29 (0.59-18.46) 0.176 3.29 (0.59-18.46) 0.176 other sites 1.49 (0.55-4.05) 0.438 1.49 (0.55-4.05) 0.438
  44. 44. Hematological Cancers and “Mosaicism” A Tale of Two StudiesNCI Study (Jacobs et al 2012) GENEVA (Laurie et al 2012)43 Hematological cancers Subanalysis in 4 cohorts forHigher frequency Heme cancers 15.8% Myeloid Hazard ratio estimate for mosaic 26.3% CLL status =10.1 (95% CI=5.8 - 17.7)Untreated leukemia vs. p=3 x 10-10cancer-free controlsOR=35.4 (14.7-76.6 95% CI) p=3.8×10-11 Jacobs et al., 2012 Laurie et. al., 2012
  45. 45. Chromosome Y Mosaic Aneuploidy Pre-Correction Post-CorrectionExample of whole chromosome loss in ~60% of cells • Uncorrected appears to be segmental loss • Corrected results are clearly whole chromosome loss
  46. 46. SRYRPS4Y1ZFY qPCR Assay Panel for chrY loss • 15 probes designed to determine copyAMELYTBL1YPRKY number of single-copy genes relative to RNasePUSP9Y • Coverage across p- and q-armsDDX3YUTYTMSB4YNLGN4YCYorf15ACYorf15BKDM5DEIF1AY
  47. 47. Calling whole chromosome Y-loss from Infinium GWAS data calibrated using qPCR Ratios for 15 Regions 1.40 y = 0.8599x + 0.132 R² = 0.8121Uncalibrated Y-loss from Infinium LRR 1.20 1.00 Possible 0.80 Gain No Loss 0.60 Undetermined 0.40 Probable Loss 0.20 0.00 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 Y-loss from 15 qPCR Probes
  48. 48. The Aging Genome: Implications for Cancer Studies• Importance of thorough characterization of ‘germline’ DNA in parallel with somatic analyses• Insights into Genomic Instability Early vs. Late Events• Genetic biomarkers for early detection of hematological cancers
  49. 49. Two Hypotheses for Mosaicism in the Aging Genome Early Event Late EventEmbryonic Progenitors with Somatic Increase in Somatic Alterations with Alterations Are Below Threshold Age of Detection PLUSUnknown Events Trigger Survival Decreased Genomic Stability due to Bottleneck Telomere Attrition LEADS TO LEADS TO Proliferation of SuppressedPositive Selection with Rapid Populations of Somatically Expansion of Second Clonal Altered Clones Population Decreased Cellular Diversity with Aging and Cell Populations Become Increasingly Oligoclonal Co-existence of Distinct Clonal Populations
  50. 50. Using Population Studies to Gain Biological Insights into Genetic Mosaicism:• Map breakpoint sites (Mitelman/ENCODE)• Analyze paired tumors in identified studies• Investigate timing and dynamics (serial samples)• Proportions & cell type • Blood Bank Study at NIH • Australian Twin Registry (2000)• Confirm Non-hematological Association • New lung/kidney studies• Sex Chromosomes • Y chromosome in TGS • X has challenge of Lyonization
  51. 51. Detectable Genetic Mosaicism & Hematological Cancers• PLCO heme cancers (> 700) • Serial samples for ~25%• NHL GWAS • 9000 Cases (5 subtypes)• Aplastic Anemia (NHLBI- N Young) • 20% MDS/Leukemia• CLL/Waldenstrom families (GEB) • MBL/MGUS serial samples• CLL Study (L Staudt)
  52. 52. Milestones at the Core Genotyping Facility 2001 2002 2004 2006 2008 2010 2012 2014 & beyond Whole GenomeCandidate SNP SequencingFunctional Data Genome Wide Association Population-based Studies Exome Candidate Genes Sequencing Sequencing Biological Plausibility Genetic Markers Candidate Pathway Biological Plausibility Regional Sequencing GWAS & Linkage
  53. 53. Human Genetics: Thresholds and Significance• Each new technology has brought us to a new ‘crisis’ Linkage and LOD scores in too few families Candidate Gene/SNPS in small studies GWAS in insufficiently powered studies Required replication/validation CNVs and power outages together with unstable calling algorithms Exome sequencing, oligogenic models with insufficient number of families Whole genome sequencing and the rising tide of ‘uninterpretable’ variants…
  54. 54. Let’s not fool ourselves about germline genetics…• First it is about……..Discovery Biology Targets Possible risk variants• Then…………………….Validation• Followed by…………Characterization• Later…………………….Clinical application• Distant future………Targets Don’t forget lifestyle/environment…..
  55. 55. Mapping Genetic Architecture• Comprehensive map will emerge across spectrum of variation • GWAS Common Variants • Sequencing Uncommon and Rare Variants• Search for “Pathways” • Moderate penetrance breast cancer genes• Develop Risk Profiles in Age of Sequencing • Can Prevention or Early Detection Improve Outcome?
  56. 56. In the not too distant future• We will look back and think of GWAS as the “Golden Age”• The temptation is to continue to do GWAS – Should not be abandoned (esp with imputation) – Think bigger in size for comprehensively exploring MAF spaces • > 10% • 1-10% • < 1% Can it be done by association testing?? But the allure of sequencing is at hand…..
  57. 57. Emerging Impact of Population Genetics in the Search for ‘Elusive’ Variants• In GWAS era- we tolerated minor mismatching because sample sizes and MAFs are large• As MAFs fall, challenge of population private variants ‘sky-rockets’ • How do we know if a rare variant in one population is non-contributory whereas in a second population it influences risk/outcome? • Value of functional validation • Challenge of Environmental Exposure/Lifestyle
  58. 58. Advances will be accelerated by“Collective Intelligence”“I not only use all of the brainsI have, but all I can borrow”Woodrow Wilson
  59. 59. Acknowledgements NCI-DCEG HSPHLTG Joseph Fraumeni David HunterRenee Chen Peggy Tucker Pete KraftCharles Chung Gilles Thomas Sara LindstromJean- Nicolas Cornu Robert HooverJun Fang Meredith Yeager BPC3 & CGEMSPhoebe Lee Kevin Jacobs ACS (M Thun)Lea Jessop Sharon Savage ATBC (D Albanes-DCEG)Hye Kim Nilanjan Chatterjee CAPS (H Gronberg/J Xu)Joe Kovacs Nat Rothman CeRePP (O Cussenot)Tim Myers JuHyun Park CONOR (L Vatten)Nilabja Sikdar Sonja Berndt EPIC (E Riboli) Sharon Savage JHU (W Issacs/J Xu)Strategic Support Lindsay Morton MEC (B Henderson)MJ Horner Zhaoming Wang PLCO (R Hayes)Tammy Bell NCI-CCR WHI (R Prentiss) Mike DeanInvestigators Hong Lou DFCILaufey Amundadottir Institut Curie Matt FreedmanKevin Brown Olivier Delattre Mark PomerantzMila Prokunina-Olsson Carlo Lucchesi
  60. 60. Acknowledgements Glioma Preetha Rajamaran (NCI,) Laura Beane Freeman (NCI), Christine Berg (NCI), Julie Buring, Ulrika Andersson, Mary Butler, Tania Carreon, Maria Feychting, Anders Ahlbomm J Michael Gaziano, Graham Giles, Goran Hallmans, Wei Zheng, Susan E Hankinson,189 authors from 48 participating studies: Roger Henriksson, Peter D Inskip, Christoffer Johansen Annelie Landgren, Roberta McKean-Cowdin, Dominique Kevin Jacobs Michaud, Beatrice Melin, Ulrike Peters, Avima Ruder, Howard Upper GI Sesso, Gianluca Severi, Xiao-Ou Shu, Kala Visvanathan, Emily Meredith Yeager Christian Abnett, Alisa White, Alicja Wolk, Anne Zeleniuch-Jacquotte, Margaret Tucker Goldstein, Phil Taylor, Wei Zheng, Manolis Kogevinas Neal Freedman, Linda Nathaniel Rothman Liao, Ti Ding, You-Lin Sholom Wacholder Qiao, Yu-Tang Gao, African-American Lung Cancer Consortium Woon-Puay Koh, Yong- Luis Perez-Jurado Bing Xiang, Ze-Zhong Krista Zanetti (NCI), Melinda Aldrich, Chris Amos, Joseph Fraumeni Tang, Jin-Hu Fan, Jian- William Blot, Cathryn Bock, Elizabeth Gillanders, Curt Min Yuan Harris, Chris Haiman, Brian Henderson, Laurence Kolonel, Loic Le Marchand, Lorna McNeill, Benjamin Rybicki, Ann Schwartz, Lisa Signorello, Margaret Breast (CGEMS) Spitz, John Wiencke, Margaret Wrensch, Xifeng Wu Prostate Cancer (CGEMS) David Hunter Robert Hoover, Gilles Thomas, Peter Kraft Sonja Berndt, Weiyin Zhou, Xiang Louise A Brinton, Lung Deng, Chenwei Liu, Michael Cullen, Neal Caporaso, Teresa Landi, Lynn Goldin, Dario Jolanta Lissowska, Ann Hsing, Caroline Epstein, Laurie Consonni, Pier Alberto Bertazzi, Melissa Rotunno Beata Peplonska Burdett, Nilanjan Chatterjee, Joshua Regina Ziegler Sampson, Amanda Black, Michael PanScan Dean, Charles, Chung, Joseph Patricia Hartge, Laufey Amundadottir, Rachael Kovaks, Nan Hu, Kai Yu, MJ Horner Stolzenberg-Solomon (NCI), Demetrius Albanes (NCI), Renal Jarmo Virtamo, Zhaoming Wang, Amy Hutchinson, Alan American Cancer Society Mark Purdue, Wong- A Arslan, H Bas Bueno-de-Mesquita, Charles Fuchs, Ho Chow, Lee E Susan Gapstur, Victoria Stevens, Steven Gallinger, Myron D Gross, Elizabeth Holly, Alison Moore, Kendra Lauren Teras, Mia Gaudet Klein, Andrea LaCroix, Margaret Mandelson, Gloria Schwartz, Faith Davis Petersen, Marie-Christine Boutron-Ruault, Paige M Bracci, Federico Canzian, Kenneth Chang,Michelle Cottercho, Ed Giovannucci, Michael Goggins, Judith Bladder Hoffman Bolton, Mazda Jenab, Kay-Tee Khaw, Vittorio Montse Garcia-Closas, Debra Krogh, Robert Kurtz, Robert McWilliams, Julie B Silverman, B. Rodriguez-Santiago, Testis, Ovary & Mendelsohn, Kari Rabe Elio Riboli, Anne Tjonneland, Nuria Malats,, Francisco Real, Jonine Endometrium Geof Tobias, Dimitrios Trichopoulos, Joanne Elena, Figueroa, Ludmila Prokunina-Olsson, Christian Kratz, Katherine Herbert Yu, Fredrick Shumacher, Daniel Stram, Lisa Dalsu Baris, Gaelle Marenne, Manolis McGlynn, Mark Greene, Mirabello, Juan R Gonzalez, Olaya Villa, Donghui Li, Eric Kogevinas, Molly Schwenn, Alison Michael Cook, Barry J Duell, Harvey A Risch, Sara H Olson, Charles Johnson Graubard, Ralph Erickson, Kooperberg, Brian M Wolpin, Li Jiao, Manal Hassan, Nicolas Wentzensen William Wheeler Osteosarcoma Sharon Savage, Irene Andrulis, Jay Wunder, Ana Patiao-Garcia, Luis Sierrasesumaga, Donald A Barkauskas, Richard Gorlick
  61. 61. 61 Chromosomal mosaicism from birth to old age and its relationship to hematological cancerInvestigators:Cathy C. Laurie, Cecelia A. Laurie, Kenneth Rice, Kimberly F. Doheny, Leila R. Zelnick, Caitlin P.McHugh, Hua Ling, Kurt N. Hetrick, Elizabeth W. Pugh, Chris Amos, Qingyi Wei, Li-e Wang,Jeffrey E. Lee, Kathleen C. Barnes, Nadia N. Hansel, Rasika Mathias, Denise Daley, Terri H.Beaty, Alan F. Scott, Ingo Ruczinski, Rob B. Scharpf, Laura J. Bierut, Sarah M. Hartz, Maria TeresaLandi, Neal D. Freedman, Lynn R. Goldin, David Ginsburg, Jun Li, Karl C. Desch, Sara S. Strom,William J. Blot, Lisa B. Signorello, Sue A. Ingles, Stephen J. Chanock, Sonja I. Berndt, Loic LeMarchand, Brian E. Henderson, Kristine R Monroe, John A. Heit, Mariza de Andrade, Sebastian M.Armasu, Cynthia Regnier, William L. Lowe, M. Geoffrey Hayes, Mary L. Marazita, EleanorFeingold, Jeffrey C. Murray, Mads Melbye, Bjarke Feenstra, Jae Hee Kang, Janey L. Wiggs, GailJarvik, Andrew N. McDavid, Venkatraman E. Seshan, Daniel B. Mirel, Andrew Crenshaw, NataliyaSharopova, Anastasia Wise, Jess Shen, David R. Crosslin, David M. Levine, Xiuwen Zheng,Jenna I Udren, Siiri Bennett, Sarah C. Nelson, Stephanie M. Gogarten, Matthew P. Conomos,Patrick Heagerty, Teri Manolio, Louis R. Pasquale, Christopher A. Haiman, Neil Caporaso, BruceS. Weir
  62. 62. Do mosaic subjects have increasedincidence of hematological cancer? Mosaic Non-mosaicEvent 15 90No event 134 8,323Cox proportional hazards regression:Stratified analysis of the four cohortsTime to event ~ Age + non-hematological cancer status (time-dependent) + ethnicity + sex (within the PLCO stratum)The hazard ratio estimate for mosaic status is 10.1 (95% CI=5.8 - 17.7)and the p-value is 3 x 10-10Meta-analysis gave very similar estimate.
  63. 63. CGF Metrics360,214 DNA profiles 7.6 x 1013 SNP genotypes (Identifiler) 5.7 x 10-6 STRs 150 Gbps regional sequencing 80 whole genomes + 328 samples whole-exome 80 on the way sequencing
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