Abstract IA024: Unraveling potential genetic drivers of intratumor transcriptional heterogeneity and phenotypic plasticity in malignant cells

Abstract

Abstract The concepts of transcriptional intratumor heterogeneity and phenotypic plasticity have gained significant attention in relation to the development of tumor resistance to cancer treatments [1]. In several cancer types, recent studies have linked certain somatic genomic aberrations to the presence of specific transcriptional states in malignant cells [2,3]. However, our general understanding of drivers of epigenetic and transcriptional plasticity stays somewhat fragmented. In theory, this gap could be filled through the generation and analysis of single-cell multi-omics datasets of human tumors coupled with clinical information. In practice, such datasets are pretty scarce, and existing data certainly do not allow for a global analysis of the genetic drivers of cell plasticity and heterogeneity across cancer types. On the other hand, the scientific community has already generated rich bulk transcriptomics datasets coupled with genetic and clinical information (e.g., TCGA) that could be used to answer the question about the relationship between genetic and epigenetic landscapes if proportions of cells in different malignant states could be extracted via a computational deconvolution of the transcriptomic signal. Unfortunately, available methods for accurate bulk data deconvolution require reference single-cell transcriptomics datasets, which limits the applicability of such approaches to study large pan-cancer datasets. We provide a solution to this problem by creating and benchmarking an algorithmic approach that allows extracting the transcriptional heterogeneity information from bulk tumor profiles without the need for a single-cell reference when a matched DNA-sequencing experiment is available. The latter is true for thousands of tumor samples, including all TCGA datasets. We present results of a pan-cancer analysis linking genetic aberrations with proportions of malignant cells in specific transcriptional states that shed light on the potential genetic drivers underlying malignant cell heterogeneity.