Abstract PO-140: Unsupervised clustering and data modeling reveals molecular signatures linked a distinct African American enriched cluster with higher probability of death in triple negative breast cancer
Authors: Gonzalez-Kozlova Edgar , Chalumeau Clelia , LaFace Ilaria , Shapiro Charles , Akturk Guray , Del Valle Diane Marie , Gnjatic Sacha 
Published: January 2022

Abstract
Triple-negative breast cancer is considered the most aggressive of all breast cancers, with the worst prognosis and increased risk in African Americans. This study aims to characterize proteomic, transcriptomic, and genomic signatures using tissue microarrays from resected TNBC tumors and correlate them with clinical data in an integrated approach to query immune mechanisms and predict patient survival. To this end, our cohort consisted of 118 localized, non-metastatic, resected TNBC patients with diverse demographics (African American 23% (n=28), Hispanic/Latino 9% (n=11), White/Caucasian 36% (n=43) and Other 30%(n=36)) with a median follow-up of 85 months. Here, we studied the patient transcriptomic profiles generated using NanoString sequencing. Immune cell subsets within the tumor microenvironment were assessed on tumor microarray slides by Multiplex Immunohistochemistry Consecutive on Single Slide (MICSSS). Protein markers measured include PD-L1, CD3, and CD8, CD20, CD66b, FOXP3, DC-LAMP, TLS, CD68, and CD163. We utilized unsupervised clustering of the patient transcriptomes as a data-driven approach to identify patient clusters defined by co-expressed RNA molecules. Then, we performed differential expression using mixed linear models for both data types (RNA & Protein), to compare these clusters to each other identifying cluster-specific molecular signatures and association to outcomes. This unbiased modeling strategy enabled us to include critical covariates in the analysis such as demographics, neoadjuvant treatment, clinical tumor parameters, and quantify the effect of potential confounders. Finally, these results revealed a cluster of patients with the best survival associated with high expression of genes such as GZMK/A, CCR5, IL10RA, IL2RG, and the highest levels of CD8, PD-L1, CD163, FOXP3, and CD68 protein tissue markers. By contrast, the cluster with the worst outcomes was enriched in African Americans (44% n=8/18, p.value&lt;0.05, compared to an average of 16% (n=8/25) in other clusters). Furthermore, this cluster included increased BRCA1, MYC, KIF2C, BIRC5, and HMGA1 gene expression, with an absence of immune proteomic markers, and it was independent of chemotherapy type, histology scores, tumor status, or lymphatic vessel invasion. Genomic alteration analyses are pending. In conclusion, we are using a data-driven approach to characterize patients with triple-negative breast cancer and patient clusters by integrating proteomic and transcriptomic molecular tumor profiles, and we identified a gene cluster enriched in African Americans associated with worse outcomes and poor immune infiltration. By combining a wider immune tissue characterization using an extended panel of MICSSS markers together with genetic mutational and expression profiling of tumors, we expect to refine and distinguish subsets of high-risk TNBC patients for whom more aggressive tailored treatment regimens may be indicated.
Citation Format: Edgar Gonzalez-Kozlova, Clelia Chalumeau, Ilaria LaFace, Charles Shapiro, Guray Akturk, Diane Marie Del Valle, Sacha Gnjatic. Unsupervised clustering and data modeling reveals molecular signatures linked a distinct African American enriched cluster with higher probability of death in triple negative breast cancer [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-140.