Project 3: Understanding genomic and external drivers of response-predictive inflammatory states underlying differences in clinical outcomes in patients with breast cancer

University Of California, San Francisco, San Francisco CA

Investigators

Linked publications, trials & patents

Abstract

ABSTRACT: The tumor immune microenvironment (TME) is a strong prognostic factor for many tumor types including breast cancer. Breast cancer mortality is higher among Black women and Hispanic/Latina women compared to White women. Various features of the TME vary across populations of different genetic ancestry. For example, the ISPY trial identified differences in interferon (IFN) signatures between Black, East Asian and White women; IFN signatures were higher in Black and East Asian ancestry women compared to White women. The study also identified an interaction between TGF-b1 signatures, Black patients and treatment outcomes. Other studies found a higher proportion of inhibitory macrophages in Hispanic/Latina women which were associated with poorer outcomes. Our hypotheses are that (a) TME varies across different genetic ancestry populations, (b) that these differences are due, in part, to genetic variants that differ by ancestry and in part to external factors (c) and that these differences in TME lead to differences in treatment response. We have already performed genetic studies on the effects of TME and identified variants that have large allele frequency differences and are candidates for effects on outcomes. One of these variants affects IFN signatures in the TME. The allele that increases IFN signatures is most common in African and East Asian ancestry populations and therefore, may, in part, affect the difference in IFN signatures in ISPY. Another genetic variant found most with T-lymphocyte depleted phenotype is only common in Hispanic/Latinas and affects expression of a gene which is expressed only on macrophages. External factors likely also affect the TME and account for differences in TME across different populations. For example, the conserved transcriptional response to adversity (CTRA) is a validated measure of response to adverse environmental conditions and includes IRF and STAT, which are both part of IFN signaling. Therefore, we will employ a strategy of understanding both germline genetic and external factors underlying TME. We will expand our existing matched germline genotype and tumor gene expression dataset to unravel genetic variants that affect TME. We will examine how individual variants and combinations of variants, polygenic scores (PGS), vary according to ancestry and whether the resultant TME differences affect treatment outcomes. We will prioritize the study of two genetic variants with large allele frequency differences across populations for detailed TME analyses using spatial transcriptomics. We will also leverage tumor organoid models from different populations to analyze the effects of the genotypes on therapeutic responses in vitro. In addition, we will analyze the effects of external factors on TME of breast cancer using traditional association approaches and machine learning approaches. At the conclusion of these studies, we will have identified genetic variants that affect TME resulting in cancer treatment outcomes, and developed specific predictors of breast cancer outcomes in these populations that can be evaluated in clinical trials.