DeSide: A unified deep learning approach for cellular deconvolution of tumor microenvironment

Xin Xiong, Yerong Liu, Dandan Pu, Zhu Yang, Zedong Bi, Liang Tian*, Xuefei Li*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

Cellular deconvolution via bulk RNA sequencing (RNA-seq) presents a cost-effective and efficient alternative to experimental methods such as flow cytometry and single-cell RNA-seq (scRNA-seq) for analyzing the complex cellular composition of tumor microenvironments. Despite challenges due to heterogeneity within and among tumors, our innovative deep learning–based approach, DeSide, shows exceptional accuracy in estimating the proportions of 16 distinct cell types and subtypes within solid tumors. DeSide integrates biological pathways and assesses noncancerous cell types first, effectively sidestepping the issue of highly variable gene expression profiles (GEPs) associated with cancer cells. By leveraging scRNA-seq data from six cancer types and 185 cancer cell lines across 22 cancer types as references, our method introduces distinctive sampling and filtering techniques to generate a high-quality training set that closely replicates real tumor GEPs, based on The Cancer Genome Atlas (TCGA) bulk RNA-seq data. With this model and high-quality training set, DeSide outperforms existing methods in estimating tumor purity and the proportions of noncancerous cells within solid tumors. Our model precisely predicts cellular compositions across 19 cancer types from TCGA and proves its effectiveness with multiple additional external datasets. Crucially, DeSide enables the identification and analysis of combinatorial cell type pairs, facilitating the stratification of cancer patients into prognostically significant groups. This approach not only provides deeper insights into the dynamics of tumor biology but also highlights potential therapeutic targets by underscoring the importance of specific cell type or subtype interactions.

Original languageEnglish
Article numbere2407096121
Number of pages12
JournalProceedings of the National Academy of Sciences of the United States of America
Volume121
Issue number46
DOIs
Publication statusPublished - 12 Nov 2024

Scopus Subject Areas

  • General

User-Defined Keywords

  • bulk RNA sequencing
  • cellular deconvolution
  • deep learning
  • single-cell RNA sequencing
  • tumor microenvironment

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