Genomic Landscape of Breast Cancer in Women of

African Ancestry

Olufunmilayo I Olopade, MD, FACP, OONWalter L. Palmer Distinguished Service Professor

ACS Clinical Research ProfessorThe University of Chicago

Outline1. Introduction2. Define the genomic basis for

inherited breast cancer3. Describe ongoing research

integrating germline and somatic genomic research.

4. Discuss opportunities in Precision Medicine for All

2

Precision Cancer Care in the Genomic Era

This requires genetic information for the bestrisk assessment and the best therapies

Breast cancer rates by race/ethnicityU.S. 2008-2012, age-adjusted

Incidence rate, per 100,000 Mortality rate, per 100,000

Gene Expression Profiles in Hereditary Breast Cancer

Hedenfalk et al., NEJM, 344:539-548, 2001

Medullary and atypical medullary High mitotic rate Aneuploid High proliferation fraction high KI67 ER negative, PR negative No HER2 gene amplification Frequent Tp53 mutations BRCA1 associated basal like breast cancers

have the WORST outcomes African Americans have the worst overall

breast cancer related outcomesBreast Cancer Linkage ConsortiumCrook T et al., Lancet, 1997Grushko et al. Cancer Research, 2002

BRCA1 Tumors Have a Distinct Phenotype

Subgroups of Breast Cancer Molecular subtypes

Estrogen receptor -- druggable target Progesterone receptor HER-2/neu -- druggable target Gene expression profiling: 4 or more subtypes

Luminal A, luminal B, basal-like, her2-enriched

ER PR HER-2/neu

Breast Cancer Subtypes Across Populations(N=482 in Nigeria & Senegal) (N=203, Ilorin, Nigeria)

0% 20% 40% 60% 80% 100%

Japanese (median age = 54 y)

White in Poland (mean age = 56 y)

White in US (postmenopausal)

White in US (premenopausal)

African American (postmenopausal)

African American (premenopausal)

Nigeria & Senegal (mean age = 45 y)

Ilorin, Nigeria (mean age = 48 y)

Luminal A Luminal B Her2+/ER Basal-like Unclassified

Huo et al. J Clin Onc 2009

Chart1

Japanese (median age = 54 y)Japanese (median age = 54 y)Japanese (median age = 54 y)Japanese (median age = 54 y)Japanese (median age = 54 y)

White in Poland (mean age = 56 y)White in Poland (mean age = 56 y)White in Poland (mean age = 56 y)White in Poland (mean age = 56 y)White in Poland (mean age = 56 y)

White in US (postmenopausal)White in US (postmenopausal)White in US (postmenopausal)White in US (postmenopausal)White in US (postmenopausal)

White in US (premenopausal)White in US (premenopausal)White in US (premenopausal)White in US (premenopausal)White in US (premenopausal)

African American (postmenopausal)African American (postmenopausal)African American (postmenopausal)African American (postmenopausal)African American (postmenopausal)

African American (premenopausal)African American (premenopausal)African American (premenopausal)African American (premenopausal)African American (premenopausal)

Nigeria & Senegal (mean age = 45 y)Nigeria & Senegal (mean age = 45 y)Nigeria & Senegal (mean age = 45 y)Nigeria & Senegal (mean age = 45 y)Nigeria & Senegal (mean age = 45 y)

Ilorin, Nigeria (mean age = 48 y)Ilorin, Nigeria (mean age = 48 y)Ilorin, Nigeria (mean age = 48 y)Ilorin, Nigeria (mean age = 48 y)Ilorin, Nigeria (mean age = 48 y)

Luminal A

Luminal B

Her2+/ER

Basal-like

Unclassified

0.6330390921

0.1954602774

0.0693568726

0.0844892812

0.0176544767

0.6865671642

0.0597014925

0.0758706468

0.118159204

0.0597014925

0.665

0.107

0.06

0.093

0.075

0.574

0.124

0.056

0.145

0.101

0.563

0.087

0.077

0.16

0.113

0.414

0.073

0.084

0.272

0.157

0.2821576763

0.0248962656

0.1493775934

0.2634854772

0.2800829876

0.2047

0.1111

0.193

0.2515

0.2398

Sheet1

AsianEuropeanAfricanWhiteBlackNC update

JapanWhite in PolishWhite in NCAA in NCAA in DCWest AfricaNigerianCaliforniaCaliforniaWhiteAA

ER+ or PR+, HER2-50255216293206136118509287

ER+ or PR+, HER2+155485225441249245

ER-, PR-, HER2-8114369627927124174201

ER-, PR-, HER2+55611716437266848

793804300196372491152843581

ER+ or PR+, HER2-63%69%54%47%55%28%78%60%49%

ER+ or PR+, HER2+20%6%17%13%12%2%3%11%8%

ER-, PR-, HER2-10%18%23%32%21%55%16%10.80%24.60%21%35%

ER-, PR-, HER2+7%8%6%8%12%15%4%8%8%

Sampling adjusted

10841.441.5%

7027.226.9%

228.48.5%

197.37.3%

4115.715.8%

260

17956.355.8%

521616.2%

267.78.1%

268.78.1%

3811.311.8%

321

21657.457.1%

5414.514.3%

245.66.3%

4612.412.2%

3810.110.1%

378

29366.563.0%

499.310.5%

4469.5%

4610.79.9%

337.57.1%

465

Sheet1

ER+ or PR+, HER2-

ER+ or PR+, HER2+

ER-, PR-, HER2-

ER-, PR-, HER2+

Sheet2

Japanese (median age = 54 y)White in Poland (mean age = 56 y)White in US (postmenopausal)White in US (premenopausal)African American (postmenopausal)African American (premenopausal)Nigeria & Senegal (mean age = 45 y)Ilorin, Nigeria (mean age = 48 y)

Luminal A63.3%68.7%66.5%57.4%56.3%41.4%28.2%20.5%

Luminal B19.5%6.0%10.7%12.4%8.7%7.3%2.5%11.1%26%14%17%18%16%16%17%

Her2+/ER6.9%7.6%6.0%5.6%7.7%8.4%14.9%19.3%

Basal-like8.4%11.8%9.3%14.5%16.0%27.2%26.3%25.2%

Unclassified1.8%6.0%7.5%10.1%11.3%15.7%28.0%24.0%

Luminal A502552136subtype | Freq. Percent Cum.

Luminal B1554812-------------+-----------------------------------

Her2+/ER556172Luminal A | 35 20.47 20.47

Basal-like6795127Luminal B | 19 11.11 31.58

Unclassified1448135Basal-like | 43 25.15 56.73

Total793804465378321260482HER2+/ER- | 33 19.30 76.02

Unclassified | 41 23.98 100.00

-------------+-----------------------------------

Total | 171 100.00

Sheet2

Luminal A

Luminal B

Her2+/ER

Basal-like

Unclassified

Sheet3

Luminal A

Luminal B

Her2+/ER

Basal-like

Unclassified

Estrogen Receptor Status by RegionsRegion No. of studies ER+ (95% CI)North-eastern (Egypt, Sudan, and Libya)

30 0.63 (0.60-0.66)

North-western (Morocco, Algeria, and Tunisia)

24 0.54 (0.50-0.59)

Western (Ghana, Mali, Nigeria, and Senegal)

13 0.35 (0.23-0.46)

Eastern (Kenya, Uganda, Tanzania, and Madagascar)

7 0.41 (0.33-0.50)

Southern (South Africa) 6 0.60 (0.56-0.64)US SEER, African Americans 0.64US SEER, Caucasians 0.80

Eng et al. Plos Med 2014

Heterogeneity across studies can also be explained by age at diagnosis, study design (prospective, retrospective)

64 yr old white woman with interval TNBC

03/06/03 03/08/05Died 10/06

Aggressive ER Negative Breast Cancer

US Supreme Court --Opening the Pandoras Boxor closing it?

45 year old diagnosed with TNBC after self palpating a mass 6 months after normal MMG

Why not sequence everyones genome?

Adapted from Ian Foster, Computational Institute

Phenotypic Effect Size and Frequency of Occurrence of Cancer Susceptibility Genes

Stadler Z et al. J Clin Oncol 2010;28:4255-4267

BROCA: African Americans in Chicago

289 African American patients with primary invasive breast cancer and with personal or family cancer history or tumor characteristics associated with high genetic risk.

Sixty-eight damaging germline mutations were identified in 65 subjects (22%, 95% CI 1828%).

Breast Cancer Res Treat. 2015 Jan;149(1):31-9.

Fine-mapping of known susceptibility lociCases Controls P value

Age, mean SD 48.0 12.0 47.2 17.2 0.08

Age < 50 yrs, n (%) 878 (58.4%) 799 (57.9%) 0.76

% of African ancestry

Nigerian 0.980 0.012 0.981 0.010 0.08

Barbadian 0.856 0.104 0.857 0.098 0.93

African American 0.776 0.135 0.787 0.120 0.10

Baltimore 0.807 0.133 0.795 0.122

Pennsylvania 0.780 0.134 0.793 0.112

Chicago 0.773 0.141 0.796 0.115

Northern California 0.758 0.125 0.763 0.131

P = 0.42 P = 5.0E-15

.7

1

1.5

2

2.5

12-19 20-21 22-23 24-31 1-4 5 6 7-12

22 Index Markers 8 Best Markers in African Ancestry

Odd

s R

atio

, 95%

CI

Risk Allele Count

Zheng, et al. Carcinogenesis 2013

GWAS consortia of breast cancer in women of African Ancestry

Study consortium Cases Controls

AABC (discovery phase) 2120 1917

ROOT (discovery phase) 1657 2028

AMBER (validation phase) 2754 3698

Total 6522 7643

ER+ 2933

ER- 1876

Somatic Signatures in whole exome data

| Presentation Title | Presenter Name | Date | Subject | Business Use Only21

Significantly mutated genes in breast cancer

WABCS Update | OTR Lab Meeting | July 16, 2015

TCGA consortium, Nature 2012

22

Hereditary Breast Cancers (High risk)

Breast Imaging

Gierach GL et al. Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study. Breast Cancer Res. 2014;16(424):1-16.

All Breast Cancers (Average risk)

BRCA1/2

Routine Mammography

Genetic risk assessment,

advanced screening modalities

Radiogenomics classifier

Angelina JolieNot all women need risk reducing mastectomies

BRCA1+ women need risk reducing oophorectomies

BRCA-associated cancers benefit from Parp Inhibitors

Positive trials in ovarian cancer, prostate cancer and breast cancer etc

$700mm Center for Care and Discovery

Devoted to complex specialty care, with a focus on cancer and advanced surgical programs

Major advances will be driven by discoveries in genomics and personalized medicine

More effectively using genomics for:

Preventative care

Treatment

The Forefront of Oncology

27

Multidisciplinary Collaboration

Improved BiospecimenCollection and Repository System

Clinical Trial Awareness

Efficient Patient Recruitment

Clinical Trial Participation

Cutting Edge Research

1.

Achieving distinction in cancer genomics and personalized care requires:

Personalized Care3.

Innovative Clinical Trials

2.

Final Thoughts Physician Scientists can accelerate

discovery and translation of research that benefit patients 21st Century Medicine is interdisciplinary, patient

centered, community based and networked Diverse team of investigators --- basic science,

clinical trials, behavioral science, population science, policy, implementation science and health economics etc.

Address global burden of disease cancer predicted to be leading cause of death globally

Education mission is robust - prepare a diverse workforce to serve diverse populations

Leverage public private partnerships to accelerate progress

Univ. ChicagoOlopade LabDezheng HuoNkem ChinemeDominique SighokoYonglan ZhengGalina KhramtsovaLise Sveen

IGSB/White LabKevin WhiteJason GrundstadJason PittJigyasa Tuteja

Dept. Health StudiesDezheng Huo

Acknowledgement

Univ. IbadanDept. Ob/GynOladosu OjengbedeOmobolanle OyedeleStella OdedinaImaria Anetor

Dept. PathologyAbideen OluwasolaMustapha Ajani

Dept. SurgeryTemidayo OgundiranAdeyinka AdemolaKelechi WilliamsChibuzor Afolabi

IAMRATChinedum BabalolaAbayomi OdetundeIfeanyi NwosuJide OkedireOdunayo Akinyele

LASUTHJohn ObafunwaAbiodum PopoolaOlorunde IfeoluwaVictor AderojuAnne AyodeleMobolaji OludaraNasiru IbrahimAyodele SanniFelix SanniEsther Obasi

NovartisDimitris PapoutsakisJordi BarretinaScott Mahan

University of WashingtoonSeattleMC KingTom Walsh

GWAS Acknowledgement

Dezheng HuoYonglan ZhengStephen HaddadSong YaoYoo-Jeong HanFrank QianTemidayo O. OgundiranClement AdebamowoOladosu OjengbedeAdeyinka G. FalusiWilliam BlotWei ZhengQiuyin CaiLisa SignorelloKatherine L. NathansonSusan M. DomchekTimothy R. Rebbeck

Julie R. PalmerEdward N. RuizLara SuchestonJeannette BensenMichael S. SimonAnselm HennisBarbara NemesureSuh-Yuh WuM. Cristina LeskeStefan AmbsEric GamazonLin ChenKathyrn L. LunettaNancy J. CoxAndrew F. OlshanChristine B. AmbrosoneYe Feng

Christopher A. HaimanEsther M. JohnLeslie BernsteinJennifer J. HuRegina G. ZieglerSarah NyanteElisa V. BanderaSue A. InglesMichael F. PressSandra L. DemingJorge L. Rodriguez-GilStephen J. ChanockLaurence N. Kolonel

Acknowledgement

TCGA breast cancer analytical working groupDezheng Huo, U of ChicagoJason Pitt, U of ChicagoShengfeng Wang, U of ChicagoChuck Perou, U of North CarolinaKatherine A. Hoadley, U of North CarolinaHai Hu, Windber Research InstituteJianfang Liu, Windber Research InstituteSuhn Kyong Rhie, U of South CaliforniaPeter Laird, Van Andel InstituteAndrew D. Cherniack, Broad Institute

Hem/Onc Seminar 2016

Genomic Landscape of Breast Cancer in Women of African AncestrySlide Number 2Slide Number 3Breast cancer rates by race/ethnicityU.S. 2008-2012, age-adjustedSlide Number 5Slide Number 6Slide Number 7Slide Number 8Subgroups of Breast CancerBreast Cancer Subtypes Across Populations(N=482 in Nigeria & Senegal) (N=203, Ilorin, Nigeria)Estrogen Receptor Status by RegionsSlide Number 12US Supreme Court --Opening the Pandoras Boxor closing it?Slide Number 14Slide Number 15Phenotypic Effect Size and Frequency of Occurrence of Cancer Susceptibility GenesSlide Number 17Fine-mapping of known susceptibility lociSlide Number 19GWAS consortia of breast cancer in women of African AncestrySomatic Signatures in whole exome dataSignificantly mutated genes in breast cancerBreast ImagingSlide Number 24A Modified Definition of Precision MedicineSlide Number 26The Forefront of OncologyFinal ThoughtsSlide Number 29GWAS AcknowledgementAcknowledgement