Genetics and Genomics in African American Women
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This webinar will provide an overview of genetic risk and gene signatures that have been uncovered in recent years, which established unique molecular underpinnings of cancer growth that are specific to ancestry groups. Melissa B. Davis, PhD, Scientific Director of the International Center for the Study of Breast Cancer Subtypes, Weill Cornell Medical College, will go over a few examples and discuss the pending impact these have on cancer treatment and survival.
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Genetics and Genomics in African American Women
- 1. GENETICS AND GENOMICS IN AFRICAN AMERICAN WOMEN Melissa B Davis, PhD Department of Surgery Englander Institute for Precision Medicine
- 2. Financial Disclosures I HAVE NO FINANCIAL DISCLOSURES or CONFLICTS
- 3. Breast Cancer Disparities – Role of Genetic Ancestry • The emergence of breast cancer disparities – WHAT ARE THE DISPARITIES? • Distinct Tumor Biology in Breast Cancer Subtypes • Genomics in Disparities Research • KEY FINDINGS: o Distinct Tumor Gene Signatures o BrC across the African Diaspora – o African-specific risk of TNBC o Distinctions in TNBC phenotypes o Ancestry may impact Tumor Immune Responses
- 4. Background of Breast Cancer Disparities and Tumor Biology Emergence of Biased Outcomes
- 5. Cancer Etiology – Disparate Causes Cultural Differences in Environmental Exposures • Built/Structural Environment • Nutritional / Lifestyle Cultural Ethnicitycorrelateswith Self-ReportedRace Self-Reported Race correlateswith Genetic Ancestry Beyond Health Equity: How does SES translate into cancer biology? Victoria Seewaldt Most of these translate into RISK more than differences in tumor phenotypes… Socio-Economic Status often determines external environment • Housing • Pollution • Access to Health Care • Access to Nutrition Cultural Traditions often determine lifestyle • Physical Activity • Food choices and preparation Biological consequences of external exposures have been shown to impact cancer-related pathways • Mutagen exposures • Hormone Signaling • Aging • Epigenetic regulation of cell fate
- 6. 1940’s 1950’s 1970’s 1980’s 1990’s Trends in age-adjusted incidence and mortality of breast cancer in U.S. Black and White women, 1975–2013 [data from Surveillance, Epidemiology, and End Results (SEER) 9 sites]. Modified from: Katherine E. Reeder-Hayes, and Benjamin O. Anderson Clin Cancer Res 2017;23:2655-2664 ©2017 by American Association for Cancer Research First Endocrine Targeted Trial Targeted Therapy Is Standard of Care Targeted Therapies = Emergence of Survival Disparity Newman, et al 2016; JCO “Parsing the etiology of breast cancer disparities” Figure Credit: Melissa Davis, PhD
- 7. Racial Differences in Survival – Contemporary Mediating Effectors Warner ET, Tamimi RM, Hughes ME, et al. Racial and Ethnic Differences in Breast Cancer Survival: Mediating Effect of Tumor Characteristics and Sociodemographic and Treatment Factors. J Clin Oncol. 2015;33(20):2254–2261. doi:10.1200/JCO.2014.57.1349
- 8. Biased Incidence - Phenotypic Tumor SubtypesTherese Sørlie et al. PNAS 2001;98:19:10869-10874
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- 11. Genomics in Disparities Research Connections among African Ancestry, Evolution and Immune Response Davis MB, Newman LA. Surg Oncol Clin N Am. 2018. Review. PubMed PMID: 29132562.
- 12. Compendium of Tumor Phenotypes: The Cancer Genome Atlas Q Of European Ancestry
- 13. Not enough ‘power’ in non-white populations • 231 publications between 2010-2018 containing sequencing data from 15,721 unique patients. • Race was reported in 37% of studies compared with 84% of studies reporting age and 85% reporting gender. • Reporting of race was associated with cohort size, sequencing method, familial cancer, cancers with disparities, and reporting of age and gender. • Minority populations were significantly underpowered to detect recurrent pathogenic variants in most cancers.
- 14. Racial Differences in TNBC > QNBC (Quadruple Negative) Davis M, Tripathi S, Hughley R, et al. AR negative triple negative or "quadruple negative" breast cancers in African American women have an enriched basal and immune signature. PLoS One. 2018;13(6):e0196909. Published 2018 Jun 18. doi:10.1371/journal.pone.0196909
- 15. Clinical outcome disparities – related to gene regulation differences Racial Differences in the Association Between Luminal Master Regulator Gene Expression Levels and Breast Cancer Survival Byun et al. Clin Cancer Res February 24 2020 DOI: 10.1158/1078-0432.CCR-19-0875
- 16. 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 PercentageSurviving 0 20 40 60 80 100 120 140 160 180 Months to mortality event African Am. Caucasian ~30% disparity P=0.0001 5 yr survival line All Breast Tumors Survival associated with tumor biology is distinct among race groups
- 17. Emerging Findings from the ICSBCS and others Beyond Race – the DARC side of Breast Cancer
- 18. Ancestrally Informative Markers Geographic Ancestry
- 19. Global perspectives of Breast Cancer Incidence and Mortality Adapted from WHO data Africa has lowest incidence, but highest mortality burden
- 20. Health Disparities and Triple-Negative Breast Cancer in African American Women; A Review (2017) The African Diaspora—Population Migration Patterns Names in blue refer to alternative nomenclature describing regions, language groups, and populations associated with the migration patterns denoted by the arrows. Adapted from Campbell et al. Oncologic Anthropology Davis, et al – 2019, in preparation 20
- 21. 21 J Glob Oncol. 2018 Oct;4:1-8. doi: 10.1200/JGO.18.00056. Androgen Receptor and ALDH1 Expression Among Internationally Diverse Patient Populations. Jiagge E1, Jibril AS1, Davis M1, Murga-Zamalloa C1, Kleer CG1, Gyan K1, Divine G1, Hoenerhoff M1, Bensenhave J1, Awuah B1, Oppong J1, Adjei E1, Salem B1, Toy K1, Merajver S1, Wicha M1, Newman L1. Oncologic Anthropology
- 22. Trans-Atlantic Slave Trade Global Human Populations and African Ancestry Recombination of the founder alleles + drift Influx of parental (African) alleles • Hybrid vs Continuous Gene Flow Models • Identification of Population-Private Risk • Genetic Risk that transcends the African Diaspora African Genetic Diversity in African Americans Sarah A. Tishkoff et al. Science 2009;324:1035-1044
- 24. Genetic imprint of the West African Diaspora Impact on TNBC Risk? Duffy-null (Fy) allele Licensed to Reuse from Howes, R et al The global distribution of the Duffy blood group Nature Communications 2011 Hereditary Susceptibility for Triple Negative Breast Cancer Associated With Western Sub-Saharan African Ancestry: Results From an International Surgical Breast Cancer Collaborative. Newman, Lisa; MD, MPH; Jenkins, Brittany; Chen, Yalei; Oppong, Joseph; Adjei, Ernest; Jibril, Aisha; Hoda, Syed; Cheng, Esther; Chitale, Dhananjay; Bensenhaver, Jessica; Awuah, Baffour; Bekele, Mahteme; Abebe, Engida; Kyei, Ishmael; Aitpillah, Frances; Adinku, Michael; Nathanson, Saul; Jackson, LaToya; Jiagge, Evelyn; MD, PhD; Merajver, Sofia; MD, PhD; Petersen, Lindsay; Proctor, Erica; Gyan, Kofi; DVM, MPH; Martini, Rachel; Kittles, Rick; Davis, Melissa Annals of Surgery. 270(3):484-492, September 2019. DOI: 10.1097/SLA.0000000000003459
- 25. All subtypes high vs low FGFR2 has race and subtype -specific survival associations FGFR2 Expression African American White American TNBC Differences p,0.05 African American high vs low White American high vs low Basal subtype high vs low Ancestry groups Allele Allele Frequency African (AFR) A 0.661 (874) G 0.339 (448) European (EUR) A 0.451 (454) G 0.549 (552) Latin American (AMR) A 0.427 (296) G 0.573 (398)
- 26. Differences in TNBC Across the African Disapora
- 27. C AA EA Dimension2 Dimension 1 -2000 -1000 0 1000 2000 3000 4000 5000 -500 0 1000 2000 1500 500 EA AA AA EA B Ancestry % 0% 100% Row Expression Minimum Maximum 300 200 100 0 -100 -200 -300 -200 -150 -100 -50 0 50 100 150 200 Y X D Legend (D): Leads to activation Leads to inhibition Findings inconsistent with downstream state of molecule Effect not predicted Increased measurement Decreased measurement Complex Enzyme Group/Complex Growth factor Translation Regulator Transmembrane Receptor Transporter Other Kinase Peptidase Phosphatase Transcription Regulator A 0.0 0.2 0.4 0.6 0.8 1.0 %Ancestry Treatment Naïve Residual Tumor EA EA African American Vs Caucasian Manne Yates
- 28. Ghanaian Ethiopian John Carpten Lab West African Vs East African Increased in Ethiopian Reduced in Ethiopian predicted activation predicted inhibition
- 29. AACA Gene LogFold Change P-value S100A2 ↓ -4.31 7.37E-03 AC017101.1 ↓ -3.61 6.88E-03 ANTXR1 ↓ -2.09 2.69E-03 COL5A2 ↓ -2.01 6.15E-03 MRVI1 ↓ -2.01 4.88E-03 AL049840.4 ↓ -1.36 0.047254 TECPR2 ↓ -1.17 0.012084 Gene LogFold Change P-value PPAN ↑ 2.65 1.58E-03 RIMS3 ↑ 3.00 6.15E-03 NEK2 ↑ 3.03 1.13E-02 GPR37L1 ↑ 3.04 6.88E-03 DNAH11 ↑ 3.45 6.15E-03 ENSG204771 ↑ 3.78 8.52E-03 TNBC Residual Tumor signature
- 30. DARC Immune Tumor Phenotype Unique immune response – ancestry specific 30
- 31. T-cell (CD3) & Suppressor T-cell (FoxP3) expression • CD3 and FoxP3 counts higher in TNBC positive • CD3 counts highest in Ghanaians, lowest in White Americans • Fox P3 counts highest in African Americans, lowest in Whites and EthiopiansTotal CD3% Total FoxP3% CD3&FoxP3% Race African-American Ghanaian White American Ethiopian n=15 n=15 n=15 n=15 p=0.0008 p=0.0266 CD3&FoxP3% Yes No TNBC Status n=43 n=15 p=0.0356 p=0.0334 Total CD3% Total FoxP3% CD3/FoxP3 African-AmericanGhanaianWhiteAmericanEthiopian Distinctions in immune cell infiltration: • Multiplex panels of IHC immune cell markers • By TNBC status • By genetic ancestry groups
- 32. Human evolution genetics • African Ancestry is associated with a stronger immune response • Specific responses include wound healing, cytokine production and inflammatory response Rationale: Distinct Immune Response – Evolution Influences
- 33. 2 High Serum Chemokine Levels Chemokine Capture – return to homeostatic levels Buffer Effect ABSENT in Africans and African- Americans Duffy-null phenotype carriers lack critical immunological regulation Expression in Normal circulation context Expression in Cancer Context?? Transcytosis of chemokines Endothelial Cells Circulating Red blood Cells Chemokine Sink/Buffering Release into circulation Recruitment of leukocytes Sequestration of chemokines Blocks angiogenesis; slows tumor growth PRESENTin Africans and African- Americans ABSENTin Africans and African- Americans DARC: connection to African-Ancestry Disparities DARC/ ACKR1
- 34. DARC regulates dynamic inflammatory chemokine levels via vascular endothelial cells and concentrations in plasma
- 35. Luminal B Luminal A DARC Primary Tumors from North GA EPICS 0 1 2 3 4 5 6 7 8 9 10 R13-963-1(DARC+2) R13-959-1(DARC+1 S13-5613-2(DARC+1) S13-5745-6(DARC+1) S14-682-B5(DARC+2) R13-836-1(DARC-0) R13-961-1(DARC-0) S13-5929-7(DARC-0) S13-5929-9(TBD) IMMUNE CELL ‘SCORES’ CD68 (moncyte macrophage) CD208 (dendritic) CD79 (B-cell) CD3 (T-cell) Martini, Jenkins Nikolinakos Howerth Monteil
- 36. 60.8 14.9 24.3 45.8 12.5 41.7 22.4 38.3 39.3 50.0 46.6 3.4 AAWA 72.7 9.1 18.2 66.7 33.3 50.0 25.0 25.0 75.0 25.0 59.7 14.5 25.8 42.9 9.5 47.6 37.5 22.3 40.2 46.0 50.0 4.0 Basal-like HER2+ Lum A Lum B Allraces DARC/ACKR1 tumor phenotype (TCGA RNAseq) DARC/ACKR1 High DARC/ACKR1 Mid DARC/ACKR1 Low DARC/ACKR1 High DARC/ACKR1 Low Tumors n = 399 Genesn=67 Low High pro-inflammatory chemokines 0 20 40 60 80 100 80 100 DARC/ACKR1 A B C D E F Survival(%) Relapse-Free SurvivalOverall Survival All Subtypes LUM A BASAL-LIKELUM B HER2+ p = 2.2x10-6 p = 1.0x10-16 p = 9.1x10-8 p = 6.3x10-6 p = 0.011 p = 0.12 n=504 n=898 n=1312 n=2639 n=691 n=1242 n=364 n=785 n=351 n=267 n=181 n=70 n=145 n=106 n=178 n=440 n=714 n=435 n=1074 n=859 n=1117 n=2834 n=783 n=619 low, high, low, high, low, high, low, high, low, high, low, high, low, high, low, high, low, high, low, high, low, high, low, high,
- 37. Graph Builder DARC/ACKR1 Expression Status DARC/ACKR1 Low DARC/ACKR1 Mid DARC/ACKR1 High TALAbsoluteScore 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 Where(472 rows excluded) p < 0.0001 p < 0.0001 p < 0.0001 DARC/ACKR1 high vs low DARC/ACKR1 high vs mid DARC/ACKR1 mid vs low Bcellsnaïve Bcellsmemory TcellsCD8 TcellsCD4memoryresting Tcellsfollicularhelper Tcellsregulatory(Tregs) Tcellsgammadelta NKcellsresting Monocytes MacrophagesM1 MacrophagesM2 MastCellsResting >0.0001 DARC-associated tumor leukocyte associations in TCGA cohort Deconvolution of digitized RNA expression
- 38. Imaging mass cytometry to define spatial distinctions Characterizing the DARC immuno-tumor-type CD16 CD163 E-Cadherin DNA CD16 E-Cadherin Cytotoxic T-cells Ki-67 DNA White American – DARC low CD16 – neutrophil/NK/macrophage CD68 - macrophage Ki-67 – T-cell/proliferation CD163 - macrophage CD31- endothelial CD8a – cytotoxic T
- 39. CD8a E-Cadherin CD68 CD31 DNA DARC - positive immuno-tumor-type CD163 CD31 E-Cadherin T-Cells DNA CD163 CD31 E-Cadherin Cytotoxic T DNA CD16 E-Cadherin CD68 Ki-67 DNA CD16 CD68 Ki-67 DNA African American DARC HIGH CD16 – neutrophil/NK/macrophage CD68 - macrophage Ki-67 – T-cell/proliferation CD163 - macrophage CD31- endothelial CD8a – cytotoxic T CD3- T cell
- 40. “Image” Data abstraction - Single-Cell Statistics
- 41. Phenograph Clustering identifies unique subset of cells within tSNE plots Distinct phenotype of immune cells
- 42. Future Directions Breast Cancer Disparities Artificial Intelligence, In vivo models, Ex vivo screens and global clinical genomics 42
- 43. Mesenchymal Tumor Epithelial White Blood cells DNA Identify the spatial orientation of cells with unique phenotypes
- 44. Hypothesis: The distribution of Euclidean distances between all cells that express a given protein may identify biologically meaningful cellular “neighborhoods”. Quantifying Spatial Trends Distance (uM) Count Protein A Euclidean Distance Distribution Distance (uM) Count Protein B Euclidean Distance Distribution Distance (uM) Count Protein C Euclidean Distance Distribution Note: Cells positive for Protein A are uniformly distributed (dispersed). The distribution is wide and has one mode. Note: Cells positive for Protein B expression are localized to one neighborhood. The distribution is narrow and the mode distance is small. Note: Cells positive for Protein C expression are localized in two/muliple neighborhoods. The distribution is multimodal. Ariana Brenner Clerkin
- 45. CD20 CD45 Violin Plots Spatial Heat Maps Conclusions Euclidian Distance Analysis on Real Data The multimodal spatial distribution identified that CD20+ cells localize in “neighborhoods” The distribution of CD45+ cells is uniform and unimodal. The spatial heatmap further demonstrates that the distribution of CD45+ cells is uniform. Image rendered using histoCAT with 95% heatmap threshold Image rendered using histoCAT with 95% heatmap threshold
- 46. (Puca et al 2018, EIPM) 46 Olivier Elemento EIPM Organoid Team Precision Medicine Tools – Pan African Cohort Clinical genomic testing in Africa
- 47. SV40 DARC CD45 Merge DARC KO Mammary Tumors (3.5 months) DARC-KO : BrCa Mouse Plan Nancy Manley Mouse Model of DARC in Breast Cancer
- 48. Summary • Disparities in cancer mortality are driven by biological distinctions • Enrollment of genetically diverse populations will reveal distinct mechanisms and/or functionality of genes involved in tumor progression • Immune response is an evolutionary adaptation that may drive race/ancestry group differences in tumor immunology • DARC alleles are associated with TNBC-specific risk and immune cell landscape, correlated with its chemokine ligands • DARC tumor phenotypes have more infiltrating immune cells and distinct cell markers within immune cell groups • Precision medicine tools can help identify new ways of treating disease phenotypes that are biased to certain populations • Ex vivo and In vivo models of diverse tumor phenotypes will elucidate our understanding of tumor progression and microenvironment interactions.
- 49. The faces of Breast Cancer Janet – South Chicago Joan – North Chicago Janet died 3 years after diagnosis Joan is a 10 year survivor
- 50. Acknowledgements… Davis Lab Members Brittany Jenkins Rachel Martini Dorrah Deeb Rupali Hire (former) Lora Neves Hill (former) Andrea Walens Andrea Brown Inasia Brown SUNFIG students UGA AMAZING Students (Chris and Christina) An army of UGA undergraduates Weill Cornell Medicine Olivier Elemento Syed Hoda Esther Cheng Bing He Juan Miguel Mosquera Laura Martin Todd Evans (many many more!!) HFHS Breast Oncology Research Group Haythem Ali Yalei Chen Eleanor Walker Jessica Bensenhaver David Nathanson Ben Rybicki ICSBCS Lisa Newman, MD* Kofi Gyan, DVM Andrew Boyakele Cassandra Mills Laura Susick, PhD Sue Lynn Jenkins Komfo Anokye Teaching Hospital, Ghana St. Paul’s Hospital, Ethiopia Clinical Data and Biospecimen Teams John Carpten Lab* (USC) Kevin Gardner Lab (Columbia University) Other Mentors/Collaborators Michele Monteil (UGA Medical School) Nancy Manley (UGA Genetics) Clayton Yates Lab* (Tuskegee Univ) Upender Manne Lab Elizabeth Howerth Lab (UGA – Vet School) Rick Kittles Lab* (City of Hope) Barbara Schuster (Medical Partnership) Petros Nikolinakos,MD (UCBC, Athens, GA) Funding Sources: UGA Foundation HFHS Cancer Institute and Foundation NIH: National Cancer Institute (Cancer Disparities) New York Presbyterian Hospital Howard Hughes Medical Institute Susan G Komen for the Cure BE the RESEARCH
- 51. 52 ICSBCS 2018 Ghana Trip
Editor's Notes
- Before we get started, I have no disclosures… Looking forward to the day when I DO.
- Historically, incidence has been lower in AA women, until the last 5 years AND pre 1980, the mortality rates were the same in both races. There was a significant change in survival between whites and blacks IN THE US that correlates with the emergence of targeted therapies against hormone signalling…. Which inherently only benefited women with hormone sensitive tumors. This uncovered the existance of multiple phenotypes (unknowingly) Trends in age-adjusted incidence and mortality of breast cancer in U.S. Black and White women, 1975–2013 [data from Surveillance, Epidemiology, and End Results (SEER) 9 sites]. The first separation of these lines coincides with the introduction of targeted therapies…. Which unveiled some differences in tumor biology. 1940: First described in 1896 by Beatson (performing bilateral oophorectomy in 2 women with breast cancer), endocrine surgery was born in 1940 when Huggins first reported the dramatic effect of orchiectomy in men with prostate cancer… this intervention was based on the concept of ‘hormone-dependence”, in that when the cells of a normal tissue are dependent on hormonal support for metabolic activity, the cells of a neoplasm derived from these cells can be similarly dependent and involute or atrophy when endocrine support is withdrawn. Huggins went on to when the 1966 Nobel Prize for Medicine for these contributions (Shared with Peyton Rous for his insight on redirecting research from thinking cancer cells are anarchic or autonomous) WWII = World War II TSE=Tuskegee Syphilis Experiments CRA=Civil Rights Act 1950: Additive hormonal therapy 1970: Tamoxifen developed 1990’s Tamoxifen efficacy – leading to use as standard of care
- Moving forward in time… breast Cancer is NOT just a single disease but it can be subcategorized into specific subtypes Initial subtyping (beyond histology) was largely based on HR status. We can be confident that at least some of the disparities we see is related to biology / subtypes, as when we stratify our cancer cohort by HR status, in a hospital system where SOC is harmonized, the disparity of race, is greatly reduce. Other subtypes grouped into larger categories (ER-negative for instance) then might explain the persistence of disparities. Herein comes subtyping based upon polygenic gene signatures instead of a select few HRs… landmark perou paper established PAM50 and later Lehmann et al with TNBC subtypes (come back to this later in my talk) associated with these subtypes. We can see some of the disparities disappear when we stratify for subtypes that have targeted therapy options. However, in other subtypes (ER-) there may still a persisting disparity. (histological and gene expression)… there has been a long-standing accordance that hormone receptor status can distinguish the ‘behavior’ of tumor progression and predict the treatment course necessary to defeat it… Chemo vs targeted therapies Beyond these markers, Perou et al released a pivotal set of genes (PAM 50) that further delineates the spectrum of these subtypes (Luminal A vs B, Basal-like and HER2+) into a sets of targetable pathways for precision treatment. However the TNBC category retained the caveat of having NO targetable therapy and contradictions in therapy response data… because defining tumors by the absence of markers is NOT very informative with regard to characterizing there vulnerabilities. AND AA make up the largest proportion of TNBC
- Family history reflects transmission of an allele that increases risk. Depending on the allele, the risk of cancer increases greatly!! The biology that we know – the current paradigm: Study of Jewish families – led to discover of Jewish mutations by Dr Marie Claire King. Even carriers do not always present with the cancer, and this reduced penetrance of ‘risk’ genes is what complicated identifying the genes in unrelated populations – which is primarily our resource of genetic study currently.
- Family history reflects transmission of an allele that increases risk. Depending on the allele, the risk of cancer increases greatly!! The biology that we know – the current paradigm: Study of Jewish families – led to discover of Jewish mutations by Dr Marie Claire King. Even carriers do not always present with the cancer, and this reduced penetrance of ‘risk’ genes is what complicated identifying the genes in unrelated populations – which is primarily our resource of genetic study currently.
- And so to search for the genetic links we are fortunate to have a burst of technological advances that assist in our ‘needle in a haystack’ searches…
- Similar efforts as the 1000 genomes – with much more innovation and technology – created the TCGA. From driver mutations to systems biology of cancer progression – this huge project has been (and still is) a highly utilized source of cancer etiology information. It has laid down the premise of subtyping beyond a single set of genes, evolving into gene signatures and profiles. Intergrated data combines several data types to decipher the etiology, course of progression and treatment response for over 32 different types of cancer.
- (A). AA women have more AR-negative tumor types in each molecular subtype. (B). Within the AR-negative subtypes, there are significantly higher proportions of TNBC basal-like. (C). All TNBC samples were subjected to “Vanderbilt” subtypes. AAs, compared to White AR-negative QNBC patients, had more BL1 (24% v 17%), BL2 (16% v 12%), and IM (24% v 19%) subtypes. Inversely, AR-negative White QNBC patients had more mesenchymal (M) (25% v 20%), mesenchymal stem-like (MSL) (12% v 8%), and unstable (UNS) (14% vs 8%) subtypes compared to AR-negative QNBC AA TNBC patients.
- While traditional disparities attributed to SES…. During my postdoc, we analyzed survival in a large cohort treated at UofC, controlling for SES… So now, In my quest to discover biological mechanisms of cancer outcome disparities… at UofC we investigated a number of things, including epigenetic mechanisms… I decided to investigate whether the disparities persisted among subtypes… found that ER positive subtypes had very little difference (great treatment outcomes) Figure 4. ER status and subgroup survival trends. We see there is a 30% disparity in survival between ethnicity groups for all tumor types (inset on right), that decreases to 10% in the ER positive tumor category (top) and increases when assessed in the ER negative tumor category to 40% (bottom). While both ethnic groups show a lower survival rate in ER negative tumor groups, the survival is far worse in the African American group. Numbers of cases is shown in
- To truly address the distinct risk in an admixed populations, such as AA, which we presume is related to AFRICAN ancestry as we compare these populations to groups of EUROPEAN ancestry… we must seek to understand the risk/genetic underpinnings of disease bias in the African populations
- This migration and settling across the globe has over the centuries resulted in distinct subpopulations – geographic ancestry groups – over 50 distinct groups, defined by specific genetic/DNA alleles that are shared among the majority of a specific subpopulation, but rarely exist (if at all) outside of that group : population-private or Ancestrally Informative Markers/SNPs. One of the current standards of interrogation for diversity across these populations is 1000 Genomes. Representing populations from 5 general geographic regions, in creative ways – Indians in Houston, Mexicans in LA, Europeans in America. And the latter is by far the MOST referenced set.
- From a global perspective of incidence and mortality - we see that incidence is lowest in Africa, highest in US and contrarily the highest morality is in Africa. But what about the biological differences?? are all indicative of biological differences… distinct tumor phenotypes…. But what really underlies a phenotype of a breast tumor??
- To answer this fundamental question, we look to the source of shared African-American ancestry – Africa The first thought is, will we see the same subtype incidence bias in the ancestral group – YES! Work from Dr Lisa Newman and her team has over last 2 decades highlighted the frequency of TNBC across Africa Her work captures the dynamics of the many genetically distinct populations and their migration within the continent and the coupled distribution of TNBC incidence. Further indication that there is a genetic component, transmitted across the continent, and of course, beyond. Most recent work highlights the genetic mix within and out of Africa and the correlated frequencing of phentoypes there with hgier frequencies of TNBC in areas of AA descent.
- Our sites are strategically identified and recruited based upon our premise of genetic admixture and the original source of African Ancestry Recall the diversity of African genomes from my previous slide Here we further consider the implications on the recent admixed populations, allele frequency is a key component of having power to detect risk. If we predict allele frequencies to follow the hybrid model, which typically depicts a founder effect where two populations converge (small groups) single events (human migration) HOWEVER the dispersal of African people during the Transatlantic slave trade, which occurred continuously, over hundreds of years, displacing over 12 million Africans (who disembarked) A key Ancestral informative marker that traces this dispersion of African genomes is the duffy-null mutation. Ancient mutation Duffy null, acquired and fixed across subSaharan Africa – the global refrequency also correlates with TNBC and the African diaspora So now recall the frequency of TNBC, in the context of African ancestry dispersion and we see an imprint of the distribution of African ancestry coinciding with TNBC… across the entire diaspora.
- What is driving these distinctions and can it be traced globally? We think so…
- Another aspect of clinical signifance in functional alleles, opposing outcomes associated with the expression of a gene. There are several examples we are currently ‘chasing’ but I will show you one. FGFR2. Figure X. Preliminary evidence of race-specific risk allele and differential impact of alleles in the African cohort. A)In TNBC cases (5-yr), an opposing trend of higher FGFR2 gene expression in deceased AA patients but in surviving WA patients. This trend is replicated in survival curves from GEO datasets, where low FGFR2 expression is beneficial for AA (B), and high FGFR2 expression is beneficial for WA (C). We also see race-specific differences in FGFR2 expression among intrinsic subtypes, with higher expression in WA TNBC vs AA cases (D). Whereas high expression of FGFR2 has significantly longer survival overall (E), among basal subtypes, low expression yields lower probability of survival (F). G.) The FGFR2 SNP rs2981579 variant is associated with African Ancestry and specific risk of TNBC, in our Ghanaian and African American cohort
- To truly address the distinct risk in an admixed populations, such as AA, which we presume is related to AFRICAN ancestry as we compare these populations to groups of EUROPEAN ancestry… we must seek to understand the risk/genetic underpinnings of disease bias in the African populations
- Figure 1. Differentially Expressed Genes (DEGs) associated with Quantified Genetic Ancestry (QGA) in treatment naïve Triple Negative Breast Cancer RNAseq. (A) Quantified genetic ancestry estimates for each cancer case, derived from RNAseq variants. Geographic ancestry super-group categories are indicated as European (EUR, light blue), East Asian (EAS, dark blue), American (AMR, light green), South Asian (SAS, dark green) and African (AFR, pink). Samples are grouped by treatment status (treatment naïve or residual tumor) and self-reported race (SRR, African American (AA) or European American (EA)). (B) Clustergram heatmap of the 156 (p < 0.05) genes that show differential expression levels using QGA, where rows represent genes and columns represent individuals. SRR is shown in the top row of the color map (red indicating EA, and blue indicates AA), and the remaining color map rows indicate ancestry estimations for each individual. The red box indicates genes that are associated with non-European admixture (EAS, SAS and AMR). Constellation plot, right, represents the hierarchical structure of the individuals shown at the bottom of the heatmap. Red dots indicated SRR EA, while blue dots are SRR AA. Red arrow points to sub strata of EA individuals with increased admixture; this node is also indicated in the hierarchical structure below the heatmap by a blue arrow. (C) Multidimensional analysis using 156 ancestry-associated genes indicates that the expression patterns can separate individuals into SRR groups. Red indicates CA, and blue indicates AA. Blue arrow indicates a SRR AA individual that clusters towards the SRR EA group, and this AA individual also had the highest percentage of EUR ancestry of all AA individuals. (D) De novo network analysis using QGA DEGs. Molecules in green are upregulated in individuals with increased AFR ancestry, and those in blue are downregulated in individuals with AFR ancestry. Molecules in orange are drawn into the network and predicted to be activated based on the state of DEGs in the network, using published interactions from the curated Ingenuity Knowledge Base. Orange lines between molecules indicate relationships leading to activation, and blue lines indicate relationships leading to inhibition. Yellow lines indicate that the relationship between two molecules is not in the expected direction. For example, in this network TP53 is known to inhibit AKT1. TP53 is activated, and so it is expected that AKT1 would be downregulated, however it is not. Because of this, the line showing the interaction between these two molecules is shown as yellow. Red box highlights three central molecules to the network predicted to be activated in AA individuals: transcription regulator TP53, growth factor IL-6 and kinase AKT1.
- NFkb is central to this network, Complex is predicted to be activated in Ethiopians compared to Ghanaians AA vs CA cohort NFkB is also central Next steps will include analysis to leverage all 4 populations
- Figure X. IPA analysis of race-specific genes from residual tumors. (A) The top IPA network from our analysis was Cellular Growth and Proliferation, Cardiovascular System Development and Function, Organ Development. While TP53 was not a significantly differentially expressed gene, it was central to the top network in our analysis. In panel B, TP53 is shown to be an upstream regulator of our differentially expressed genes, and was shown to have predicted inhibition (P = 4.33E-03). (C) Expanded network from panel B, showing that the predicted inhibition of TP53 leads to activation of kinase NEK2, a top upregulated gene in AA compared to CA residual tumor cases.
- Stepping out of the cancer realm for a moment, I turn to an evolutionary human genetics study… where they highlight a scenario where the constitutional immunity of populations can be shaped over generations of exposure to endemic infectious diseases and parasites – which would certainly describe sub-Saharan Africa (those of you who may have traveled there know the vaccines… ) So in this study the investigators primed immune cells with an infectious agent and found distinct responses in immune cells from Africans, compared to Europeans. WOW… so if immune cells are responding DIFFERENTLY to the same infectious agents, might we find that in the cancer context?? And would it transcend to African Americans?? In a recent study:
- So, what does DARC do? Is it more than just a marker of ancestry. I’m betting “all-in” that it IS MORE! Fig 4. Hypothetical model of the role of DARC expression in tumors or in circulating blood cells. Based on our preliminary findings and observations, our current hypothesis addresses four major issues of tumor biology and lack of information as it relates to DARC expression and chemokine regulation. 1- Is the tumor microenvironment enriched by DARC expressing tumors excreting chemokines? 2- Do DARC expressing tumors recruit a distinct set of immune cells, relative to tumors that do not? 3- In tumors that express DARC vs those that do not, what chemokines are released to recruit specific immune cells? 4- Are altered circulating levels of chemokines modifying tumor behavior due to the status of DARC expression on tumors or on blood cells? First question to be answered is whether this assumption of circulating levels higher/lower
- What does DARC normally do? a | Atypical chemokine receptor 1 (ACKR1) regulates the dynamics of inflammatory chemokine presentation on vascular endothelial cells and chemokine concentrations in plasma. ACKR1 is thought to mediate chemokine transcytosis, carrying inflammatory chemokines from subluminal endothelial cell surfaces to luminal surfaces for presentation on endothelial surfaces to rolling leukocytes. The chemokines on the endothelial surfaces are in dynamic equilibrium with chemokines in the plasma, and erythrocyte-expressed ACKR1 buffers circulating inflammatory chemokines. b | ACKR2 and ACKR4 regulate leukocyte interactions with lymphatic endothelial cell (LEC) surfaces. ACKR2 (shown in light purple) is involved in clearing inflammatory CC-chemokines (light blue) from LEC surfaces during inflammation (left-hand side of the figure). This suppresses the interaction of lymphatic vessels with immature dendritic cells (DCs) and other inflammatory cells that have been recruited into inflamed tissue and that are expressing inflammatory CC-chemokine receptors. Similarly, in the lymph node, ACKR2 may help keep LEC surfaces chemokine-free to prevent lymph-borne inflammatory CC-chemokine receptor-expressing leukocytes from adhering to these surfaces. Semi-mature or mature DCs use CC-chemokine receptor 7 (CCR7) to sense and respond to CC-chemokine ligand 21 (CCL21) gradients (indicated in dark blue) produced by LECs that direct them from the interstitium into lymphatic vessels (right-hand side of the figure). ACKR4 expressed in the tissue (for example, by keratinocytes) may control the biodistribution of CCL19 and CCL21 (shown in dark blue) to aid CCR7-mediated DC migration. DCs also use CCR7 to efficiently enter the lymph node parenchyma from the subcapsular sinus. In this context, ACKR4-mediated scavenging by LECs may regulate CCL19 and CCL21 in the subcapsular sinus and adjacent lymph node parenchyma to maintain optimal chemokine signals for CCR7-mediated navigation
- In fact DARC in tumors – when stratified for levels of expression shows a similar trend of expression in race groups when all types of breast cancer are included however, there are distinctions within tumor subtypes. And a race-specific distinction emerges within molecular tumor phenotypes. From a systems biology perspective, when we interrogate the genome for associated/correlated gene networks, pro-inflammatory chemokines were a top hit. DARC high = high immune response. And what do these chemokines do? Direct infiltration of immune cells Figure 1. DARC/ACKR1 is significantly associated with VWF, breast cancer molecular subtypes, and pro-inflammatory chemokines in TCGA data. TCGA Breast Cancer RNAseq data (n = 838) was used to compare (A, top) the distribution of DARC/ACKR1 expression subgroups (high, n = 289, pink; medium, n = 268, yellow; low, n = 281, purple) and race (African-Americans, n = 167, AA; White Americans, n = 669, WA). (A, bottom) same as (A, top) but with VWF expression subgroups (VWF Low, n = 336; VWF Mid, n = 309; VWF High, n = 193, p = 0.06, ANOVA ) by race. (B, top) Distribution of DARC/ACKR1 expression subgroups compared to molecular breast cancer subtypes (Basal-like, n = 74; HER2+, n = 24; Luminal A, n = 196; Luminal B, n = 58, p<0.0001 ). (B, bottom) same as (B, top) but broken down by race. (C) Heat map (UCSC Xena Browser) of TCGA breast invasive carcinoma RNAseq gene expression data (IlluminaHiSeq) shows cytokine expression after creating dichotomized DARC/ACKR1 positive (red) and negative (blue) subgroups. Gene expression was assessed in 399 breast tumors against a panel of 67 genes associated with known cytokines, blue = low expression, red = high expression. Welch's t-test was performed on CCL2 (p = 0.0000, t = 10.28) and CXCL8 (p = 0.0003, t = -3.644). (D) DARC/ACKR1 high and low categories compared to CCL2 (left, student's t-test, p<0.0001, DF = 438.05) and CXCL8 (right, student's t-test, p<0.0001, DF = 376.25) gene expression (mean ± SEM).
- We therefore undertook a deconvolution of immune cell types within DARC strata and found a remarkable correlation with tumor associated leukocytes. “infiltrating??” I’ll tell you in April! Figure 3. DARC/ACKR1 expression levels are positively and significantly associated with TAL abundance in breast tumors. Using CIBERSORT absolute mode, the absolute score totals the quantitative abundance of each individual leukocyte population among TCGA breast primary cases. Those cases with significant CIBERSORT results (p<0.05) were included in this analysis (n=472). (A) The total TAL absolute score reported is a total of abundance scores across all 22 tumor-associated leukocyte populations. By plotting total TAL absolute score by DARC/ACKR1 expression status, we see significant increases in TAL abundance as DARC/ACKR1 expression is increasing between low, medium and high categories. (B) Looking at individual TAL populations, panel B shows the 12 leukocyte populations that are significantly different between DARC/ACKR1 expression levels. Student’s t-test was performed comparing each DARC/ACKR1 expression group, and the p-values for the associations are represented by the heat map in panel (C). Highly significant associations = dark red; Less significant associations = dark blue.
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4889274/ https://www.sciencedirect.com/science/article/pii/S1535610812005144?via%3Dihub#app3 https://pdfs.semanticscholar.org/3247/4279cbc116a6d2621ef4407cc1aaa3a7fd19.pdf http://ualcan.path.uab.edu/cgi-bin/TCGA-survival1.pl?genenam=NEK2&ctype=BRCA
- DARC +/- ; C3(1)Tag +/0 3.5 mo Upper mammary gland
- As clinicians and researchers, it is so overwhelming to think of every person with cancer as a “person” when you are bombarded with statistics (124,000 women will be diagnosed – 40% will die – 15% have positive family history – 60% of black women will die (unnecessarily) from breast cancer, prostate cancer is 80x more prevalent in blacks than whites…etc) but it’s SO important that we DO remember, these are wives, mothers, sisters, caregivers…etc. There are biological links to disparities in cancer, not just consequences of health care inequities, but the realities of LIVING and the situations of health that feed into subtle differences in risk.
- LS17-6515 DARC Positive AA (Score of 2) tSNE plots
- Our current paper – revised and awaiting approval… is the first of a cohort where we are collecting peripheral blood samples from newly diagnosed cases at each of our sites. We have characterized the phenotypic and genotypic status of DARC Excluded: HF272, EEG18 – more than 10 SD of mean
- Figure 2. DARC/ACKR1 phenotype and genotype associated with race and pro-inflammatory chemokines in case-control cohort (n=422). (A) Case-control comparison of circulating CCL2 by race (student’s t-test, p=0.0031, DF=182.09 (D) DARC/ACKR1 phenotype on RBCs compared to circulating CCL2 (left, student’s t-test, p = 0.0436, DF = 42.88) and CXCL8 levels (right); Inter. = intermediate. (E) DARC/ACKR1 genotype (rs2814778; CCL2, left, ANOVA, F = 0.0002, DF = 212, student’s t-test, p < 0.0001, DF = 137.31, student’s t-test, p = 0.0176, DF = 27.47, CXCL8, right).