Abstract C012: Integration of social variables resolves subclasses of tumor microenvironment

Abstract

Abstract Intro: Recent work has identified subclasses of the tumor microenvironment, with most work emphasizing a robust adaptive immune response associated with ER-negative and/or basal-like tumors. Since adaptive immune responses may influence response to chemotherapy and may be targetable by immunotherapy, understanding population-level heterogeneity in immune response is important. Methods: We leveraged the Carolina Breast Cancer Study (CBCS), a large population-based study that oversampled young (≤50 years) and Black women with invasive breast cancer (BC). We performed NanoString gene expression profiling on tissue from 1,329 BC patients, including 704 (53%) Black and 625 (47%) non-Black women, and assayed genes representative of at least 10 individual immune cell types from both adaptive and innate arms of the immune system, homologous recombination deficiency signature and TP53 mutation status. Using a latent class approach that included variables for self-reported race, triple negative status, individual measures of socioeconomic status and community-level measures of advantage and disadvantage, we identified latent classes that integrated tumor microenvironment with tumor biology, individual and community level exposures under a Cells-to-Society conceptual model of breast cancer outcomes. Results: Latent Class Analysis identified 6 integrated classes of the tumor microenvironment, further subdividing a previously identified and validated adaptive immune class into two subgroups. The two adaptive classes were distinguished by different expression levels of B-cells, CD8+ T cells, cytotoxic cells and PD-L1, and differing frequency of basal-like subtype (Adaptive Class 1 RFD [95% CI]: 19.0 [11.9, 26.2]; Adaptive Class 2 RFD [95% CI]: 32.8 [23.3,42.3]) and presence of genomic instability gene expression subtypes (Adaptive Class-1 RFD [95% CI]: 22.1 [14.0, 30.1]; Adaptive Class-2 RFD [95% CI]: 37.5 [28.1,46.2]) relative to the immune-quiet tumors. Discussion: Social variables have complex effects on tumor biology and microenvironment, but latent class models and other model-based approaches may help to integrate biological data with other social factors to identify susceptible subgroups and to optimize precision medicine in diverse populations. Citation Format: Alina M. Hamilton, Kara McCormack, Marc A. Emerson, Melissa A. Troester, Terry Hyslop. Integration of social variables resolves subclasses of tumor microenvironment [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C012.