Abstract 2439: A semi-supervised deconvolution method for quantifying the composition and activity of tumor-infiltrating cell types

Tumor microenvironment (TME) plays a key role in tumorigenesis, disease progression and the acquirement of drug resistance. An accurate assessment of TME cellular compositions may not only shed light on how TME interacts with cancer cells, but also bring new insights for translational researchers in studying the non-responding mechanism of immunotherapy. Traditional deconvolution methods infer the relative proportions of predefined cell types based on a tissue omics data through either regression- or enrichment- based approaches. However, there are several challenges that remain unsolved in the current formulations, including (1) identifying the Immune/Stromal (I/S) cell types that truly exists in a TME, (2) identifying the marker genes for each cell type that are specifically expressed by one or a few I/S cell types in a TME, (3) co-linearity among to-be-assessed I/S proportions due to their co-infiltration. We have developed a novel semi-supervised deconvolution method namely ICTD (Inference of Cell types and Deconvolution), addressing the three challenges via (i) developing a Bi-Cross Validation (BCV) based matrix rank test to assess the significance level of the existence of cell types and signature genes, (ii) utilizing a constrained Non-negative Matrix Factorization (NMF) to eliminate the effect of co-linearity. We validated ICTD on bulk tumor datasets simulated using single-cell RNA-seq data. Our analysis suggested that ICTD has a largely improved prediction accuracy of TME compositions comparing to existing methods, and particularly, it is capable of identifying novel or sub-cell types. We applied our method to TCGA and other gene expression data of breast and prostate cancer. Subsets of CD8+ T cells with varied cytotoxicity levels and subtypes of fibroblast cells were identified. Moreover, integrated with an analysis of an independent single-cell and cell line gene expression dataset, we identified genes specifically expressed by cancer cells that are associated with decreased T cell cytotoxicity level. Citation Format: Wennan Chang, Changlin Wan, Yifan Sun, Yan Han, Siyuan Qi, Xiongbin Lu, Sha Cao, Chi Zhang. A semi-supervised deconvolution method for quantifying the composition and activity of tumor-infiltrating cell types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2439.