Title:  A Roadmap to Transcriptomic Deconvolution in Cancer

Abstract:  Cancer is characterized by vast transcriptional variations in genes and pathways. Cancer tissues are complex heterogeneous mixtures of epithelial, stromal and immune cells, with each group comprising multiple distinct cell types and states. This heterogeneity has likely led to numerous contradictory findings in the literature over the past 30 years of high-throughput transcriptomic profiling of tumor tissues, thereby impeding a clearer understanding of cancer biology. Two approaches to address this issue are single-cell RNA-seq profiling and bulk RNA-seq deconvolution. Due to the higher cost and sample quality requirements of single-cell profiling, bulk RNA sequencing remains widely used for a vast amount of patient tissues. An important analytical challenge is how to integrate information from the two technologies to fully uncover the tumor-microenvironment (TME) landscape of cancer. In this talk, I will present our recent development of DeMixSC for single-cell based bulk deconvolution, and a pan-cancer biomarker tumor-cell specific total mRNA expression score or TmS, which is calculated through an integrative deconvolution model. Both DeMixSC and TmS have opened up untapped opportunities in understanding the dynamics of TME in relation to metastasis and resistance to treatment. We envisage that transcriptomic deconvolution will continue to empower cancer researchers, deepening our understanding of tumor heterogeneity and informing clinical decision-making.