Abstract B2-55: Critical role of immune spatial heterogeneity and the molecular scaffold in estrogen receptor-negative breast cancer

Abstract

Abstract Purpose: The abundance of tumor-infiltrating lymphocytes has been associated with a favorable prognosis in estrogen receptor-negative breast cancer. However, a high degree of spatial heterogeneity in lymphocytic infiltration is often observed in histology samples and its clinical implications and underpinning molecular scaffold remain unclear. Materials and methods: To quantify spatial heterogeneity in the distribution of tumor infiltrating lymphocytes, we combined automated histological image processing with methods of spatial statistics used in ecological data analysis. Hematoxylin and eosin-stained sections from two cohorts of estrogen receptor-negative breast cancer patients (discovery: n=120; validation: n=125) were processed with our automated cell classification algorithm to identify the location of lymphocytes and cancer cells. Subsequently, hotspot analysis (Getis-Ord Gi*) was applied to identify statistically significant hotspots of cancer and immune cells, defined as tumor regions with a significantly high number of cancer or immune cells respectively. To identify molecular aberrations that explain tumor spatial heterogeneity, we integrated our image-based hotspots results with microarray gene expression and copy number data profiled for the same set of tumors. Molecular data were generated with tumor materials sandwiched between these sections, thereby maximizing the biological relevance of multiple data types being generated. Results: We found that the amount of colocalized cancer and immune hotspots weighted by tumor area, rather than number of cancer or immune hotspots, significantly correlates with a better prognosis in estrogen receptor-negative breast cancer in uni- and multivariate Cox analysis. Moreover, colocalization of cancer and immune hotspots further stratified patients with immune cell-rich tumors. Subsequently, we developed a bioinformatics tool, iMmune hOTspoT Omics (iMOTTO), to explain the hotspots as a clinically relevant phenotype using molecular profiling data. Our preliminary analysis revealed significant correlations between this phenotype and expression of immune-specific genes such as CD79, CCL19 and SLAMF1, as well as an immunotherapy target, CTLA4. By incorporating the expression of hotspots-associated genes and copy number alteration data into a multivariate regression model, we aim to define a minimal set of genes to explain the observed degree of cancer-immune hotspot colocalization. Conclusion: Taken together, our study demonstrates the importance of quantifying not only the abundance of lymphocytes but also their spatial variation in the tumor specimen for which methods from other disciplines such as spatial statistics can be successfully applied. Systematic integration of histology and omics data revealed key immune regulators as well as novel genes which warrant further investigation to help elucidate the biological processes underlying immune spatial heterogeneity with potentially important clinical implications. Furthermore, our computational approach can be adapted for studying other cancer types for which immunotherapy has been applied, such as melanoma and non-small cell lung cancer, where our hotspot measures can potentially serve as prognostic and predictive biomarkers. Citation Format: Sidra Nawaz, Andreas Heindl, Andrea Agostinelli, Yinyin Yuan. Critical role of immune spatial heterogeneity and the molecular scaffold in estrogen receptor-negative breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-55.