Decomprolute: A benchmarking platform designed for multiomics-based tumor deconvolution

Abstract

Tumor deconvolution is a reliable way to disentangle the diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA-seq) rather than protein levels in tumor cells. While gene expression is less expensive to measure, protein levels provide a more accurate view of immune markers. To facilitate the development as well as improve the reproducibility and reusability of multi-omic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic data sets. Decomprolute incorporates the large-scale multiomic data sets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. The platform consists of modular architecture and it comes with well-defined input and output formats at each module. As a result, it is robust and extendable easily with additional algorithms or analyses. The platform is available for access and uses at http://pnnl-compbio.github.io/decomprolute.

ProteoMixture: A Cell Type Deconvolution Tool for Bulk Tissue Proteomics Data

“…Thus, there remains a paucity of data investigating the applicability of these signatures for characterizing cellular admixture within proteome data in HGSOC tissues. Very recent efforts by Feng et al (21) described the Decomprolute tool which enables prediction of immune cell signatures using proteomic data and established deconvolution tools across various organ site malignancies, including ovarian cancer. Motivated by this work as well as recent efforts by our group correlating stromal cell admixture with prediction of the mesenchymal (MES) subtype (7), a molecular subtype correlating with poor disease prognosis in HGSOC, we examined proteomic signatures of tumor, stroma, and immune cell admixture in HGSOC.…”

“…ESTIMATE tumor purity scores positively correlated with percent OVCAR-3 and tumor cell collections between proteomic and transcriptomic data (Pearson's r > 0.9, p value < 0.05) (Supplementary Figure 2B, Supplementary Table 19). Global proteome data collected from LMD enriched HGSOC tissue admixtures was also analyzed using a proteomebased immune cell deconvolution tool, Decomprolute (21). Decomprolute scores for CD8+ T cells and B cells were highly correlated with the percent immune cell HGSOC tissue admixture conditions (Pearson's r > 0.94, p value < 0.01) (Supplementary Figure 3).…”

“…RNA-seq data (quartile cell percentages) from HGSOC tissue cell admixture was used for subtype classification with consensusOV (version 1.20.0) (10). Proteomic data from tissue admixture models were assessed by Decomprolute (21).…”

ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data

“…Very recent efforts by Feng et al. 21 described the Decomprolute tool, which enables the prediction of immune cell signatures using proteomic data, and established deconvolution tools across various organ site malignancies, including ovarian cancer. Motivated by this work as well as recent efforts by our group correlating stromal cell admixture with the prediction of the mesenchymal (MES) subtype, 7 a molecular subtype correlating with poor disease prognosis in HGSOC, we examined proteomic signatures of tumor, stroma, and immune cell admixture in HGSOC.…”

Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data

“…Most deconvolution algorithms have been initially developed for transcriptomic data (RNA-Seq data) (Newman et al 2015; Racle et al 2017; Finotello et al 2019; Monaco et al 2019; Newman et al 2019; T. Li et al 2020; Jimenez-Sanchez, Cast, and Miller 2019; Gong and Szustakowski 2013). More recently they have been adapted for other omics layers such as methylation (Chakravarthy et al 2018; Teschendorff et al 2020; Arneson, Yang, and Wang 2020; H. Zhang et al 2021) and proteomics (Feng et al 2023) or chromatin accessibility. For the latter, a specific framework called DeconPeaker (H. Li et al 2020) was developed to estimate cell-type proportions from bulk samples.…”