Profiling flavonoid cytotoxicity in human breast cancer cell lines

Flavonoids are part of a large family of polyphenols that are found extensively in fruits and vegetables. This class of compounds has been of considerable medical interest due to their anti-inflammatory and anti-cancer activities. Although extensive effort has been made to identify the biological effects responsible for the chemopreventive activity of these compounds, the exact molecular mechanisms involved are not fully understood. In this study, we focused on the cytotoxic effects of fourteen different flavonoids against a series of breast cancer cell lines and evaluated the induction of cell cycle arrest at G1 or G2/M phase as result of such treatment. We also assessed a possible structure-function relationship for cellular cytotoxicity based on the various chemical structures of flavonoids. The results showed that several flavonoids were cytotoxic in all cell lines even in the absence of certain signaling pathways. In addition, only some flavonoids were able to induce cell cycle arrest, suggesting their cytotoxic potential may be independent of their ability to block cells at G1 or G2/M phases. Our results enabled identification of certain structural properties that are important for the anticancer activity of flavonoids. Finally, these results suggested that cytotoxicity does not depend on a particular signaling pathway

Quantitative, Architectural Analysis of Immune Cell Subsets in Tumor-Draining Lymph Nodes from Breast Cancer Patients and Healthy Lymph Nodes

Background: To date, pathological examination of specimens remains largely qualitative. Quantitative measures of tissue spatial features are generally not captured. To gain additional mechanistic and prognostic insights, a need for quantitative architectural analysis arises in studying immune cell-cancer interactions within the tumor microenvironment and tumor-draining lymph nodes (TDLNs). Methodology/Principal Findings: We present a novel, quantitative image analysis approach incorporating 1) multi-color tissue staining, 2) high-resolution, automated whole-section imaging, 3) custom image analysis software that identifies cell types and locations, and 4) spatial statistical analysis. As a proof of concept, we applied this approach to study the architectural patterns of T and B cells within tumor-draining lymph nodes from breast cancer patients versus healthy lymph nodes. We found that the spatial grouping patterns of T and B cells differed between healthy and breast cancer lymph nodes, and this could be attributed to the lack of B cell localization in the extrafollicular region of the TDLNs. Conclusions/Significance: Our integrative approach has made quantitative analysis of complex visual data possible. Our results highlight spatial alterations of immune cells within lymph nodes from breast cancer patients as an independent variable from numerical changes. This opens up new areas of investigations in research and medicine. Future application of this approach will lead to a better understanding of immune changes in the tumor microenvironment and TDLNs, and how they affect clinical outcome

Abstract PS7-41: Breast cancer outcomes among a diverse racial/ethnic south Florida population

Abstract Background: Breast cancer is the most common cancer diagnosed among Hispanic women in the US and is the leading cause of cancer-related death in this population. However, controversy remains as to whether this population has improved, or worse, overall survival (OS) outcomes compared to their non-Hispanic White (NHW) and non-Hispanic Black (NHB) counterparts. Given our location in South Florida, where Hispanics account for approximately 70% of the population we are perfectly poised to analyze breast cancer overall survival (OS) outcomes in a Hispanic population compared to a non-Hispanic population. Furthermore, given the diverse nature of our Hispanic population, this is the first study to also evaluate outcomes in Hispanic Whites (HW) compared to Hispanic Blacks (HB). Methods: Patients presenting to our medical campus with stage I-IV breast cancer from 2005-2017 were identified from the local tumor registry. Kaplan-Meier survival analysis was performed to identify patient, tumor, and NCCN-guideline based treatment characteristics associated with OS. Factors with a p $64,600649 (40.1%)658 (21.5%)91 (8.9%)7 (6.7%)1405 (24.2%)Insurancep<0.001Private1054 (64.0%)1111 (35.5%)397 (37.1%)24 (22.4%)2586 (43.5%)Medicare320 (19.4%)343 (11.0%)117 (10.9%)16 (15.0%)796 (13.4%)Medicaid94 (5.7%)716 (22.9%)276 (25.8%)34 (31.8%)1120 (18.8%)Uninsured72 (4.4%)667 (21.3%)187 (17.5%)28 (26.2%)954 (16.0%)TUMOR AND TREATMENT CHARACTERISTICSClinical Stagep<0.001I765 (46.4%)1137 (36.4%)281 (26.3%)28 (26.2%)2211 (37.2%)II512 (31.1%)1120 (35.8%)386 (36.1%)38 (35.5%)2056 (34.5%)III211 (12.8%)563 (18.0%)221 (20.7%)24 (22.4%)1019 (17.1%)IV122 (7.4%)226 (7.2%)141 (13.2%)14 (13.1%)503 (8.5%)Unknown37 (2.2%)81 (2.6%)41 (3.8%)3 (2.8%)162 (2.7%)Tumor Gradep<0.001Well diff.334 (20.3%)531 (17.0%)132 (12.3%)13 (12.1%)1010 (17.0%)Moderately diff.715 (43.4%)1341 (42.9%)370 (34.6%)46 (43.0%)2472 (41.5%)Poorly diff.415 (25.2%)959 (30.7%)450 (42.1%)37 (34.6%)1861 (31.3%)Anaplastic/Undifferentiated7 (0.4%)19 (0.6%)20 (1.9%)2 (1.9%)48 (0.8%)Unknown176 (10.7%)277 (8.9%)98 (9.2%)9 (8.4%)560 (9.4%)Receptor Statusp<0.001ER+/HER2+170 (10.3%)336 (10.7%)109 (10.2%)18 (16.8%)633 (10.6%)ER+/HER2-1078 (65.5%)1983 (63.4%)525 (49.1%)60 (56.1%)3646 (61.3%)ER-/HER2-315 (19.1%)571 (18.3%)335 (31.3%)22 (20.6%)1243 (20.9%)ER-/HER2+84 (5.1%)237 (7.6%)101 (9.4%)7 (6.5%)429 (7.2%)Pathologic Stagep<0.001012 (0.7%)20 (0.6%)7 (0.7%)1 (0.9%)40 (0.7%)I759 (46.2%)1086 (34.7%)281 (26.3%)31 (29.2%)2157 (36.3%)II406 (24.7%)859 (27.5%)268 (25.0%)26 (24.5%)1559 (26.2%)III146 (8.9%)340 (10.9%)106 (9.9%)12 (11.3%)604 (10.2%)IV44 (2.7%)81 (2.6%)37 (3.5%)9 (8.5%)171 (2.9%)Unknown277 (16.8%)740 (23.7%)371 (34.7%)27 (25.5%)1415 (23.8%)TreatmentsSurgery1494 (90.7%)2782 (89.0%)856 (80.0%)88 (82.2%)5220 (87.7%)p<0.001Chemotherapy854 (51.9%)1891 (60.5%)658 (61.5%)61 (57.0%)3464 (58.2%)p<0.001Radiation848 (51.5%)1761 (56.3%)528 (49.3%)56 (52.3%)3193 (53.7%)p<0.001Endocrine Therapy1121 (68.1%)1924 (61.5%)482 (45.0%)59 (55.1%)3586 (60.3%)p<0.001NCCN Guideline-Based Care (by stage and receptor)1311 (79.6%)2366 (75.7%)745 (69.6%)77 (72.0%)4499 (75.6%)p<0.001Treatment at Comprehensive Cancer Center1368 (83.1%)1445 (46.2%)432 (40.4%)37 (34.6%)3282 (55.2%)p<0.001 Citation Format: Sina Yadegarynia, Kristin Kelly, Seraphina Choi, Susan Kesmodel, Neha Goel. Breast cancer outcomes among a diverse racial/ethnic south Florida population [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS7-41

Abstract P3-12-08: Neighborhood disadvantage predicts worse breast cancer-specific survival

Abstract BackgroundAlthough advances in screening, detection, diagnosis, and treatment have reduced overall breast cancer mortality, well-documented socioeconomic and racial/ethnic disparities persist. The objective of this study was to utilize the area deprivation index (ADI), a compositive measure of neighborhood disadvantage, on breast cancer survival in South Florida, predominantly consisting of Miami-Dade County residents. The ADI is based on a measure created by the Health Resources & Services Administration (HRSA) over three decades ago, and has since been refined, adapted, and validated to the Census Block Group neighborhood level. The ADI score (1-10) includes factors from the domains of income/employment (e.g., median family income), education (e.g., % population &gt;25 with &lt;9 years of education), housing (e.g., % occupied housing without complete plumbing), and household characteristics (e.g., % single-parent households with children &lt;18). MethodsPatients treated at our medical campus, comprised of both a safety-net hospital and an adjacent academic cancer center, with stage I-IV breast cancer from 2005-2017 were identified from our local tumor registry. Our main outcome of interest was breast cancer-specific survival (BCSS). The ADI was calculated for each patient at the census block group level using the University of Wisconsin Neighborhood Atlas (https://www.neighborhoodatlas.medicine.wisc.edu/mapping) and categorized into tertiles. Random effects frailty models were conducted, controlling for patient and tumor characteristics [grade, stage, receptor status (ER+/HER-, ER+/HER2+, ER-HER2+, ER-/HER2-)], and NCCN-guideline appropriate treatment. ResultsThe study population was 5,377 breast cancer patients with 55.5% being Hispanic, 27.0% being non-Hispanic White (NHW), and 17.5% being non-Hispanic Black (NHB). The distribution of NHB was highest in the most disadvantaged neighborhoods compared to NHW and Hispanics (p&lt;0.001). In addition, more uninsured patients lived in the most disadvantaged neighborhoods compared to those with any type of insurance. After controlling for multiple covariates including comorbidities, race/ethnicity, insurance status, and tumor subtype, we found that those individuals living in the most disadvantaged neighborhoods (highest ADI tertile) had a significantly increased hazard of breast cancer specific death compared to those living in the most advantaged neighborhoods (T2: HR: 1.27 95% CI: 1.00, 1.63, p&lt;0.05 and T3: HR: 1.5 95% CI 1.17, 1.91, p&lt;0.05). ConclusionThis study is the first to evaluate BCSS through the lens of the ADI, a composite measure of neighborhood advantage and disadvantage using census block group data reflective of social determinants of health domains spanning income, education, employment, and housing quality. Our study suggests that breast cancer survival disparities are partly influenced by neighborhood disadvantage. Even when accounting for sociodemographics, tumor characteristics, and NCCN-guideline appropriate treatment, survival disparities remained, suggesting potential social and environmental factors impacting survival. To address these disparities, effective interventions are. needed that account for the social and environmental contexts in which cancer patients live and are treated. Table: ADI (Tertiles) and Breast Cancer-Specific SurvivalHR (95% CI)Area Deprivation Index (vs. most advantaged)ADI Tertile 21.27 (1.00, 1.63)ADI Tertile 3 (more disadvantaged)1.50 (1.17, 1.91)Race (vs. NHW)Hispanic0.94 (0.72, 1.22)NHB1.71 (1.27, 2.31)Age1.02 (1.01, 1.02)Insurance (vs. Private)Government1.49 (1.19, 1.86)Insurance, NOS0.97 (0.62, 1.51)Uninsured1.15 (0.87, 1.52)Unknown1.19 (0.78, 1.82)Receptor Status (vs. ER+/HER2-)ER+/HER2+1.40 (1.06, 1.86)ER-/HER2-2.11 (1.70, 2.60)ER-/HER2+1.20 (0.85, 1.69)Unknown0.88 (0.51, 1.51)Body Mass Index (vs. Normal Weight (18.5 – 24.9)Underweight (Less than 18.5)1.40 (0.72, 2.72)Overweight (25.0 – 29.9)0.70 (0.56, 0.88)Obese (&gt; 29.9)0.79 (0.63, 0.98)Hypertension0.84 (0.67, 1.03)Diabetes Mellitus1.02 (0.74, 1.40)NCCN-guideline concordant Treatment0.84 (0.75, 0.94) Citation Format: Neha Goel, Seraphina Choi, Sina Yadegarynia, Kristin Rojas, Susan Kesmodel, Erin Kobetz, Ashly Westrick. Neighborhood disadvantage predicts worse breast cancer-specific survival [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-12-08.</jats:p

Abstract SS1-01: Where you live matters: Impact of economic, racial/ethnic, and racialized economic residential segregation on breast cancer survival

Abstract Background:Racial and economic residential segregation remains a problem within the United States (US). Although advances in screening, detection, diagnosis, and treatment have reduced overall breast cancer mortality, well-documented socioeconomic and racial/ethnic survival disparities persist. The objective of this study was to analyze the effect of economic and racial/ethnic residential segregation as measured by the Index of Concentration at the Extremes (ICE) on breast cancer survival in South Florida. Methods:Patients treated at our medical campus with stage I-IV breast cancer from 2005-2017 were identified from our local tumor registry. Census tracts were used as neighborhood proxies. Using 5-year estimates from the American Community Survey, 5 ICE variables were computed: economic (high vs. low), race/ethnicity (non-Hispanic White (NHW) vs. non-Hispanic Black (NHB) and NHW vs. Hispanic) and racialized economic (low-income NHB vs high-income NHW and low-income Hispanics vs. high-income NHW) segregation. ICE captures spatial socioeconmic and racial/ethnic segregation by literally mapping a critical dimension of social inequality not otherwise captured by metrics that characterize areas solely in terms of the proportion of the population at a specified socioeconomic level or identified as belonging to a particular racial/ethnic group. Random effects frailty models were conducted for all patients and then stratified by race/ethnicity controlling for sociodemographics, tumor characteristics, and NCCN-guideline appropriate treatment. Results:The study population included 6,145 breast cancer patients. 52.6% were Hispanic, 26.3% were NHW, and 17.2% were NHB. After controlling for multiple covariates, those living in extreme economically disadvantaged neighborhoods had a statistically significant increased mortality compared to those living in more economically advantaged neighborhoods (HR: 1.58 95%CI: 1.29, 1.92, p<0.001), Table 1. Patients living in an economically disadvantaged NHB neighborhood also had a statistically significant increased mortality compared to those living in more economically advantaged NHW neighborhoods (HR: 2.0 95% CI: 1.54, 2.60, p<0.001). In race-stratified analyses, an NHW person living in a predominantly economically disadvantaged NHB neighborhood had increased mortality compared to a NHW person living in an economically advantaged NHW neighborhood (HR: 2.02 95%CI: 1.19-3.41, p< 0.0071) controlling for tumor subtype and NCCN-guideline appropriate treatment. Conclusion:This is the first study to evaluate breast cancer survival by ICE, which identifies inequitable associations by conveying extreme concentrations of both economic deprivation/privilege and racial/ethnic segregation. Our study suggests that breast cancer survival disparities is partly influenced by extreme racial/ethnic and economic segregation. Even when accounting for sociodemographics, tumor characteristics, and NCCN-guideline appropriate treatment, survival disparities remained, suggesting potential social and environmental factors impacting survival. To address these disparities, effective interventions are needed that account for the social and environmental contexts in which cancer patients live and are treated. Table 1: Breast Cancer Hazard Ratio by Economic, Racial/Ethnic, and Racialized Economic Residential Segregation Residential SegregationType of Segregation (ICE)QuartileModel 1Model 2Model3HR (95% CI)HR (95% CI)HR (95% CI)Economic SegregationQ11.83 (1.1, 3.03)*1.64 (0.89, 3.02)1.58 (1.29, 1.92)*Economic SegregationQ22.36 (1.48, 3.76)*2.45 (1.38, 4.34)*1.44 (1.16, 1.79)*Economic SegregationQ31.16 (0.72, 1.8)1.08 (0.61, 1.9)1.16 (0.94, 1.44)Economic SegregationQ4111NHB SegregationQ11.6 (0.9, 2.84)1.42 (0.72, 2.82)1.41 (0.96, 2.07)NHB SegregationQ20.92 (0.52, 1.6)0.91 (0.47, 1.77)1 (0.68, 1.48)NHB SegregationQ30.61 (0.29, 1.26)0.85 (0.37, 1.94)0.82 (0.52, 1.31)NHB SegregationQ4111Hispanic SegregationQ11.38 (0.83, 2.28)1.13 (0.61, 2.08)1.36 (1.12, 1.66)*Hispanic SegregationQ279 (0.47, 1.32)0.74 (0.4, 1.38)0.86 (0.67, 1.08)Hispanic SegregationQ30.94 (0.59, 1.49)0.98 (0.57, 1.67)1.05 (0.86, 1.29)Hispanic SegregationQ4111NHB Economic SegregationQ12.68 (1.6, 4.47)*2.02 (1.09, 3.74)2 (1.54, 2.6)*NHB Economic SegregationQ21.85 (1.15, 2.97)*1.39 (0.79, 2.44)1.56 (1.22, 2.02)*NHB Economic SegregationQ31.2 (0.69, 2.07)1.09 (0.58, 2.06)1.19 (0.88, 1.6)NHB Economic SegregationQ4111Hispanic Economic SegregationQ11.91 (1.19, 3.07)*1.45 (0.83, 2.54)1.64 (1.24, 2.15)*Hispanic Economic SegregationQ21.45 (0.8, 2.62)1.06 (0.52, 2.17)1.44 (1.06, 1.96)*Hispanic Economic SegregationQ31.26 (1.73, 2.18)0.95 (0.49, 1.84)1.11 (0.8, 1.54)Hispanic Economic SegregationQ4111Model 1: Adjusted for ICE, race/ethnicity, age, insuranceModel 2: Adjusted for Model 1 covariates plus receptor status, clinical stageModel 3: Adjusted for Model 1 and 2 covariates plus stage appropriate treatmentQ1: Most disadvantaged neighborhoods; Q4: Reference: most advantaged neighborhoods.*p < 0.05 Citation Format: Neha Goel, Kristin N Kelly, Sina Yadegarynia, Seraphina Choi, Susan B Kesmodel, Ashly Westrick. Where you live matters: Impact of economic, racial/ethnic, and racialized economic residential segregation on breast cancer survival [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr SS1-01

Abstract PO-005: Where you live matters: Impact of economic, racial/ethnic, and racialized economic residential segregation on breast cancer survival

Abstract Background Racial and economic residential segregation remains a problem within the United States (US). Although advances in screening, detection, diagnosis, and treatment have reduced overall breast cancer mortality, well-documented socioeconomic and racial/ethnic survival disparities persist. The objective of this study was to analyze the effect of economic and racial/ethnic residential segregation, as measured by the Index of Concentration at the Extremes (ICE), on breast cancer survival. Methods Patients treated at our medical campus, comprised of a safety-net hospital and an academic cancer center, with stage I-IV breast cancer from 2005-2017 were identified from our tumor registry. Census tracts were used as neighborhood proxies. Using 5-year estimates from the American Community Survey, 5 ICE variables were computed: economic (high vs. low), race/ethnicity (non- Hispanic White (NHW) vs. non-Hispanic Black (NHB) and NHW vs. Hispanic) and racialized economic (low-income NHB vs high-income NHW and low-income Hispanics vs. high-income NHW) segregation. ICE uniquely captures spatial economic and racial/ethnic segregation by mapping social inequality not otherwise captured by evaluating a population of a specific socioeconomic level or belonging to a particular racial/ethnic group. Random effects frailty models were conducted for all patients and then stratified by race/ethnicity controlling for demographics, tumor characteristics, and NCCN-guideline appropriate treatment subtype. Results The study population included 6,145 breast cancer patients. 52.6% were Hispanic, 26.3% were NHW, and 17.2% were NHB. After controlling for multiple covariates, those living in extreme economically disadvantaged neighborhoods had an increased hazard ratio (HR) of death compared to those living in more economically advantaged neighborhoods (HR: 1.58 95% CI: 1.29-1.92, p<0.001). Patients living in an economically disadvantaged NHB neighborhood also had an increased HR compared to those living in more economically advantaged NHW neighborhoods (HR: 2.0 95% CI:1.54-2.60, p<0.001). In race-stratified analyses, a NHW living in an economically disadvantaged NHB neighborhood had an increased HR compared to a NHW living in an economically advantaged NHW neighborhood (HR: 2.02 95% CI:1.19-3.41, p< 0.0071), even when controlling for demographics, tumor subtype, and appropriate treatment. Conclusion This study is the first to evaluate breast cancer survival by ICE, which brings social inequality to the forefront. Our study suggests that survival disparities persist at the extremes of economic deprivation/privilege and racial/ethnic residential segregation, even when accounting for demographics, tumor characteristics, and appropriate treatment, suggesting social/environmental factors are also impacting survival. To address these disparities, effective interventions are needed that account for the social and environmental contexts in which cancer patients live and are treated. Citation Format: Neha Goel, Sina Yadegarynia, Kristin N. Kelly, Susan B. Kesmodel, Erin N. Kobetz, Ashly Westrick. Where you live matters: Impact of economic, racial/ethnic, and racialized economic residential segregation on breast cancer survival [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-005

Abstract P3-13-05: Comprehensive analysis of global genetic ancestry and socioeconomic status on breast cancer outcomes

AbstractPurpose: Disparities in breast cancer outcomes have been a long-standing and persistent challenge. Earlier onset, advanced stage at diagnosis, aggressive tumor subtypes [triple negative breast cancer (TNBC)], and worse overall survival (OS) are some of the characteristic features of breast cancer in non-Hispanic Black (NHB) women compared to their non-Hispanic White (NHW) counterparts, denoting one of the most significant examples of racial/ethnic differences in oncology. Given our location in South Florida, gateway to Latin America and the Caribbean, we discovered that these disparities in tumor characteristics and outcomes among NHB and NHW also extend to Hispanic Blacks (HB) compared to Hispanic Whites (HW). Since Hispanics are the second largest ethnic group in the US and have a rich genetic architecture with contributions from European (EU), West African (WA), and Native American (NA) populations, we sought to investigate genomic associations between observed inter and intra-racial/ethnic differences and breast cancer characteristics and outcomes. Methods: Patients with stage I-IV breast cancer were included. Patient socioeconomnic status (SES), tumor and treatment characteristics, and follow-up data were collected for each patient. Genomic analysis was performed on the peripheral blood from a cohort of 309 patients with breast cancer. This breast cancer cohort was comprised of 192 self-reported HW, 12 HB, 46 NHW, 47 NHB, and 12 unknown (declined to report) patients. Leukocyte DNA from each patient was genotyped, generating whole genome single nucleotide polymorphism (SNP) profiles. Global ancestral estimates, using >100,000 SNPs, were calculated against reference samples from EU, WA, NA, and East Asian (EA) ancestral populations. A genomic diversity space was generated via principal component analysis and ADMIXTURE was used to estimate the ancestral proportions among the patients. Results: The genetic structure of individual patient sample revealed a diverse ancestral admixture where average EU, WA, NA, and EA ancestries were 64.5%, 21.8%, 11.2%, and 2.5%, respectively. Multinomial logistic regression revealed a significant association between increasing WA ancestry and aggressive tumor subtypes (ER-/HER2+ and TNBC), p=0.009 and p=0.031, respectively. These findings remained significant when correcting for patient age and tumor stage; however, when adjusting for income, the association between WA ancestry and ER-/HER2+ and TNBC was no longer significant. Kaplan Meier survival curves showed a significant difference in 5-year OS for patients with >70% WA ancestry compared to those with <70% WA ancestry, p=0.023. Conclusions: In this first integrative approach studying genetic ancestry and SES on breast cancer characteristics and outcomes, we found a significant association between increasing WA ancestry and aggressive breast cancer subtypes, even after adjusting for known covariates. More striking, this association was negated when adjusting for income, suggesting potential gene-environment interactions not accounted for by genetic race. We also discovered that Hispanics have a more complex genetic architecture than non-Hispanic patients, which may in-turn drive genetically-associated survival patterns of resiliency with improved survival in HW compared to NHB patients. Furthermore, the OS differences based on quantitative genetic ancestry cut-offs may serve as a future tool in patient prognosis. Collectively, our results show that genetic ancestry and SES influence breast cancer subtypes and survival. This lays a foundation for future studies to investigate complex genomic relationships between race/ethnicity, SES, and breast cancer characteristics and outcomes through the lens of gene-environment interactions.Citation Format: Daniel A Rodriguez, Sina Yadegarynia, J. William Harbour, Nipun B. Merchant, Erin N. Kobetz, Neha Goel. Comprehensive analysis of global genetic ancestry and socioeconomic status on breast cancer outcomes [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-13-05

Racial and Ethnic Disparities in Breast Cancer Survival Emergence of a Clinically Distinct Hispanic Black Population

OBJECTIVE: To understand the impact of Black race on breast cancer (BC) presentation, treatment, and survival among Hispanics. SUMMARY BACKGROUND DATA: It is well-documented that non-Hispanic Blacks (NHB) present with late-stage disease, less likely to complete treatment, and have worse survival compared to their non-Hispanic White (NHW) counterparts. However, no data evaluates whether this disparity extends to Hispanic Blacks (HB) and Hispanic Whites (HW). Given our location in Miami, gateway to Latin America and the Caribbean, we have the diversity to evaluate BC outcomes in HB and HW. METHODS: Retrospective cohort study of stage I-IV BC patients treated at our institution from 2005–2017. Kaplan-Meier survival curves were generated and compared using the log-rank test. Multivariable survival models were computed using Cox proportional hazards regression. RESULTS: Race/ethnicity distribution of 5,951 patients: 28% NHW, 51% HW, 3% HB, and 18% NHB. HB were more economically disadvantaged, had more aggressive disease, and less treatment compliant compared to HW. 5-year OS by race/ethnicity was: 85% NHW, 84.8% HW, 79.4% HB, and 72.7% NHB (p<0.001). After adjusting for covariates, NHB was an independent predictor of worse OS [HR:1.25 (95% CI: 1.01–1.52), p< 0.041)]. CONCLUSIONS: In this first comprehensive analysis of HB and HW, HB has worse OS compared to HW, suggesting that race/ethnicity is a complex variable acting as a proxy for tumor and host biology, as well as individual and neighborhood-level factors impacted by structural racism. This study identifies markers of vulnerability associated with Black race and markers of resiliency associated with Hispanic ethnicity to narrow a persistent BC survival gap

Axillary response rates to neoadjuvant chemotherapy in breast cancer patients with advanced nodal disease

Purpose Utilization of sentinel lymph node biopsy (SLNB) in breast cancer patients with positive nodes after neoadjuvant chemotherapy (NAC) has increased. We examine axillary response rates after NAC in patients with clinical N2‐3 disease to determine whether SLNB should be considered. Methods Breast cancer patients with clinical N2‐3 (AJCC 7th Edition) disease who received NAC followed by surgery were selected from our institutional tumor registry (2009–2018). Axillary response rates were assessed. Results Ninety‐nine patients with 100 breast cancers were identified: 59 N2 (59.0%) and 41 (41.0%) N3 disease; 82 (82.0%) treated with axillary lymph node dissection (ALND) and 18 (18.0%) SLNB. The majority (99.0%) received multiagent NAC. In patients undergoing ALND, cCR was observed in 20/82 patients (24.4%), pathologic complete response (pCR) in 15 patients (18.3%), and axillary pCR in 17 patients (20.7%). In patients with a cCR, pCR was identified in 60.0% and was most common in HER2+ patients (34.6%). Conclusion In this analysis of patients with clinical N2‐3 disease receiving NAC, 79.3% of patients had residual nodal disease at surgery. However, 60.0% of patients with a cCR also had a pCR. This provides the foundation to consider evaluating SLNB and less extensive axillary surgery in this select group