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+/ER–6.9%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+/ER–556172Luminal 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 Pandora’s Box…or closing it?
45 year old diagnosed with TNBC after self palpating a mass 6 months after normal MMG
Why not sequence everyone’s 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 18–28%).
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 Jolie•Not 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 group•Dezheng Huo, U of Chicago•Jason Pitt, U of Chicago•Shengfeng Wang, U of Chicago•Chuck Perou, U of North Carolina•Katherine A. Hoadley, U of North Carolina•Hai Hu, Windber Research Institute•Jianfang Liu, Windber Research Institute•Suhn Kyong Rhie, U of South California•Peter Laird, Van Andel Institute•Andrew 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/ethnicity�U.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 Pandora’s Box�…or 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 Thoughts�Slide Number 29GWAS AcknowledgementAcknowledgement
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