February 3-5, 2016 | Lansdowne Resort, Leesburg, VA

Stephen J Chanock, MD

Director, DCEG, NCI

“The Complexity of Susceptibility to Cancer”

Genetic Susceptibility & Cancer

The Human Genome Project has enabled unprecedented discovery

We should continue to work towards Discovery of a Comprehensive Catalog of Variants• From rare to common

Develop strategies for when and how to screen• Value of Polygenic Risk Score for Screening

• NGS for “Actionable” Mutations

Understand interactions with the environment• Targets for intervention and prevention

Architecture of Genetic Susceptibility of Cancer

Defining Distinct Spaces

Damaging Drivers

Perturbation

Key pathways

BRCA1,

TP53,

RB,PTCH

Polygenic

Models

SNPs &

SNPs

ATMPTCH

FLCN

BLM

BRCA1BRCA2

T

MLH1

MSH2

POT1

PMS2

MSH6HRAS

PTEN

DKC1

TERC

TERT

TINF2

WRAP53TP53

NF1

EXT1

EXT2

FANCA

FANCB

FANCC

FANCD2

FANCEFANCF

FANCG

PAX5

FANCI

FANCL

FANCJ

FANCM

FANCN/BRIP1

>115 Genes Mutated in Cancer Susceptibility Syndromes

RAD51C

TSC2

CDH1

KITPDGFRA

HRPT2

RUNX1CHEK2

CDKN2A

CDK4

BUB1B

MEN1RET

ALK

PHOXB2

NF2

NBS1

SDHDSDHC

SDHB

STK11

APC

BMPR1A

SMAD4

MUTYH

FH

MET

RB1

SMARCB1

RECQL4

GPC3

TSC1

ERCC4

VHL

WRN

WT1

XPAERCC3

XPC

ERCC2

DDB2

SLX4

ERCC5

POLH

GATA2

CEBPA

SDHA

SDHAF2

TMEM127

MAX

DICER1

BAP1

ELANE

HAX1

CBL

PTPN11

MITF

GALNT12

HOXB13

CYLD

EGFR

SUFODIS3L2

SBDS

Ascertained in Families

Rare Mutation with Strong Effect

>50% are known Oncogenes or

Tumor Suppressors (Knudsen Hypothesis)

Incomplete “Penetrance”Not all affected develop cancerModifiers - genetic & environmental

Published Cancer GWAS Etiology Hits: January 2016

42 Multiple

Basal Cell17 Bladder17 Breast102 Cervical7 CLL31 Colorectal39 Endometrial1 Esophageal Sq23 Ewing Sarcoma3 Gallbladder1

Gastric4 Glioma10 Hodgkins9 Kidney6 Liver7 Lung23 Melanoma19 Multiple Myeloma6 Nasopharyngeal11

Neuroblastoma9 Non-Hodgkin 15 Osteosarcoma2 Ovary18 Pancreas18 Pediatric ALL9 Prostate101 Thyroid5 Wilms3Testicular23

CASP8

SHROOM2

LSP1

SLC14A1

ERBB4

2q35

CHST9

18q11.2

IRF4

TSPAN32

15q23

BMF

15q21.3

SP140

PMAIP1

PTPN2

BAK1MICA

6p21.32

CDKN1A

15q15.1

GREM1

DCCSMAD7

MICB

EXOC2

HLA-DRA

SLC25A37

CTBP2TBX5

KLK2/

KLK3

NUDT11

AR

TRIM31

NOTCH4

FOXP4

MYO6

9p21

XAGE3

SLC7A

Xq13

MLPH

NEDD9

RSG17

GSDMB

CCHCR1HLA-DRB5

ARMC2HLA-DRB6

6q14.1

11p1510p15.1

GATA3

10p14

FGFR2 10q26

10q26.12

TCF7L2

TRIM8

FAS

ZMIZ1

ERG2 ZNF365

MSMB

MARCH8

11q24.3

GRAMD1B

PHLDB1 HTR3B

11q23.1

MMP7

FAM111A

OVOL1/

SNX32

11q13CCND1

POLD3

11q14

RPL6 12q24.21

ALDH2ACAD10

NTN4

KRT8 KRT5

12q13 12q13.13

TUBA1C

CCND2

CCDC88C

TTC9

RAD51L1 RAD51B

SIX1

BMP4

MC1R

IRF8

CDYL2

PHLPP2

CDH1

FTO

HEATR3TOX3

VPS53SMG6

TP53

17q12HNF1Bx2

SPOP

HAP1

SALL3

BCL2

BABAM1

ELL19p13

CCNE1 RHPN2

19q13C19orf61

PRKD2

LILRA3

C20orf54

BMP2 20p12.3

RALY

ADNP

GATASLAMA5

ZGPAT

RTEL1

EPAS1

ZEB2

TAF1B

2p24.1

C2orf43

2p22.2

THADA

EHBP1REL

GGCX/

VAMP8

12p13.1

ITPR2

PTHLH

SCARB1

KIF1B

CDCA7

ACOXL

2q14.2

STAT4

UGT1A

FARP2

DIRC3

ITGA6

DLX2

NABP1

HLA-DQ

RFX6ECHDC1/

RNF146

HBS1L

ESR1

IPCEF1

TAB2

6q25.1

SLC22A3

15q25.1

CPEB1

CHRNA3/5

17q24

BPTF

TBX1

CHEK2

EMID1

XBP1 MTMR3

22q13

22q13

VTI1A

12q23.16q22.1

6p22.1

UNC5CL BTNL2

BAT3

NOL10

DDX1

DLG2

NAT2

NKX3.1

NRG1

EBF2

8p12

HNF4G

8q21.11

EIF3H

MYC CCDC26

8q24

PSCA

9p21.3

CDKN2A/

CDKN2B

RAD23B

FOXE1

9q31.2

DAB21P

NKX2-1

PRDM14

DMRT1

KITLG

16q22.3

1p13.2

GSTM1

deletion

1p11.2

GOLPH3L

SLC25A44

KCNN3

UCK2

LGR6

MDM4

SLC41A1

DUSP10

ITPR1

TARDBP

PEX14

1p36

EOMES

DAZL

SLC4A7

TGFBR2

3p12.13p11.2

SIDT1

ZBTB20

EEFSEC

ZBTB38

MYNN

ST6GAL1

TP63

C3orf21

TACC3

EXOC1

AFM

COX18

PDLIM5 4q22.3

ADH1B

4q26

ADAM29

LEF1 CENP3

TET2

PRKAA1

5p12

FGF10

PDE4D

MAP3K1 RAB3C

5q31.1

SPRY4

EBF1

FAM44B

ATF7IP

MCM3AP

NRIP1

GRIK1

RUNX1

TMPRSS2

17q22

ZNF652

COX11

OR8U8

MAD1L1

SP8

JAZF1

TNS3IKZF1

EGFR

LMTK2

POT1

7q32

ARHGEF5

PIP4K2A

ARID5B

FERMT2

PAX9 MBIP

CEBPE

LINC-PINT

16q23.1

FAM19A5

NR5A2

DAB2

PRLHR

MIPEP

PDX1

BRCA2

13q22

UBAC2

13q22.1

BACH1

TFF1

ZNRF3

2q31.1

3q25.31

8q21.13

BNC2

10p12.31

17q21.32

BRIP1

2p25.2

GRM4

3q27.3

6p21.33

BARD1

1q21.1

DUSP12

DDX4

6p22.3

LIN28B

LMO1

HSD17B12

HACE11q22

ITGA9

MDS1

HLA-A/B/CGABBR1HLA-F

TNFRSF19

FOXQ1

RANBP9

ULK4DNAH11

CBX7

TNFRSF13B

MKL1

1q21.3

PARP1

10q25.1

TYR

ATM

CDC91L1

MX222q13.1

PLCE1

TGM3

RGS22 8q22.3

JAG1

ZC3H11A

ARRDC3

PRC1

PITX1

10q24.2

CLDN11/

SKIL

TERC

NCOA1

LPP

PVT1 CXCR5

ETS1

RSPO1PEX14

1p36

SYNPO2

GPX6

ATAD5

ABO

COL15A1

ANKRD30A

DNAJC1

ALS2CR12

ATP1B2

TMEM173FSTL5

TCF3

FAM49A

2q33.1

CLK3

MCF2L

20p12.2

EXO1

RHOU

3p21.3

5p13.3

5p15.1

TERT/

CLPTM1L

IRX4

ATG10

7q32.3

8p11.23

8q23.3

RIN3 17q25.3

SETBP1

3p14.1

3p22.1

12q24.12

12q24.22C12orf51

20q13.13

FADS1

AGR3

OCA2

ETAA1SUGCT

17q25.1

19p12

Virtually none are linked to outcomes

Distinct pathways for etiology compared to progression

Cumulative 30-year Absolute Risk for Bladder Cancer in a 50

Year Old Male in the U.S. (based on smoking + 12 SNPs)

Garcia-Closas et al, Cancer Research 2013

Furburg and Bochner, Nature Review Urology 2013

M Garcia-Closas

RD = risk differences for

current vs. never smokers

Thought Experiment:

If 100,000 smokers with high genetic risk stopped smoking

Eliminate 5,400 cases of bladder cancer

If 100,000 smokers with low genetic risk stopped smoking

Eliminate 1,500 cases of bladder cancer

Possible example of how genetic & environmental risk stratification

might translate into targeted prevention

Precision Prevention

Garcia-Closas & Rothman

8

Unexplained: 49%*

BRCA1BRCA2

CHEK2ATMPALB2

TP53PTENLKB1

27 pre-iCOGS SNPs(9%)

Contribution of Known Loci to Familial Relative Risk of Breast

Cancer (Europeans, all ages)

~66 post-iCOGS SNPs (14%)

~72 new SNPs OncoArray

(5%)

Known susceptibility loci explain about 50% of

familial relative risk

Courtesy D EastonCourtsey D Easton

Genetic Predisposition to Breast Cancer

European Population

1

1.1

10 0.1 0.9

3

10 BRCA1

BRCA2TP53

PTEN

ATMCHEK2

PALB2BRIP1

RAD51C

ERCC2

1.2

1.3

1.4

1.5

0.2 0.3 0.4 0.5 0.6 0.7 0.8

Po

pu

lati

on

gen

oty

pe

rela

tiv

e ri

sk

Population risk-allele frequency

> Doubled in

2014….now 100 loci

60 More with OncoArray

Explain ~30%

excess familial risk

15% Mutations

+ ~30% SNPs

Yields

Almost 50% Familial RR

77-SNP PRS can stratify groups for absolute risk assessment with age

Mavaddat E et al, JNCI 2015

Lifetime risk of breast cancer by percentiles of

77-SNP polygenic risk score (PRS)

Life

tim

e ab

solu

te r

isk

Age (years)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

Age (years)

All Breast Cancers

>99%

95-99%

90-95%

80-90%

60-80%

40-60%

20-40%

10-20%

5-10%

1-5%

<1%

11Maas et al, Under Review

Den

sity

Absolute Risk of Breast Cancer (%), Ages 30-80

Environmental factorsGenetics (78 SNPs)

Both

Addition of SNPs results in substantial improvements in

risk stratification

Distribution of lifetime risk of breast cancer, US Caucasians

12

The impact of changes in life-style may be larger for women at higher non-modifiable risk

Distribution of modifiable risk by deciles of non-modifiable risk

Maas et al, Submitted

Heavy drinker, smoker,

obese/HRT

Never drinker or,

smoker, no HRT use,

healthy weight,

Genetic Variation: Tension Between Utility for Individual & Public Health

Until we sequence 200,000,000, annotate and share whole genome

sequences:

How do we address the use of existing data for public health decisions?

• When to apply screening

• PSA or Mammography?

• Develop risk stratification approach

• Pennies on the dollar/pound

• SNP Chip far cheaper and easier to qc than an Exome

Somatic Mosaicism- ‘Dynamic & Aging’ Genome

Distinct

Genotypes

In

Subpopulations

Combined Sample Detected Events >2Mb (N=1,330

events in 127,417 individuals)

Mosaic Gain

Mosaic Copy Neutral

Mosaic Loss

* Combined GENEVA+TGSI+TGSII, N=127,417

Mitch Machiela AJHG 2015

Non-Heme Cancers

Cancer-free Controls

Age-related mutations associated

with clonal hematopoietic expansion

and malignancies

Xie et al Nat Med 2014

The Aging Genome: Implications for Cancer Studies

Importance of thorough characterization of ‘germline’ with somatic analyses

Insights into Genomic InstabilityEarly vs. Late Events

• Is it a matter of events instead of specific mutations?• Greater probability for accumulation of driver events

Early detection of hematological cancersDetection of CLL before 14 years before diagnosis

Somatic Clonal Expansion as the Tip of the Iceberg?

Detectable Genetic Mosaicism

“U” shape curve

Seen in very young &

aging population

Significance for aging

Diseases

Marker of ‘Genomic Stability’Question:

Can we define a cancer predisposition state based on

Genetic events in ‘normal’ tissue or circulating DNA?

Cancer Genomics

Numbers matter• Frequentism for discovery with mandatory replication

• Lump and then split

• Find the drivers (TCGA) or markers (GWAS)

• Interpret Later- Functional Studies

• Utility of using markers we don’t fully understand

Sets of markers are more powerful than individual markers

Challenge of moving from observation to action• Daunting Challenge of Conducting RCTs