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Community assessment of methods to deconvolve cellular composition from bulk gene expression.

ABSTRACT: We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.

SUBMITTER: White BS

PROVIDER: S-EPMC11350143 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Community assessment of methods to deconvolve cellular composition from bulk gene expression.

White Brian S BS de Reyniès Aurélien A Newman Aaron M AM Waterfall Joshua J JJ Lamb Andrew A Petitprez Florent F Lin Yating Y Yu Rongshan R Guerrero-Gimenez Martin E ME Domanskyi Sergii S Monaco Gianni G Chung Verena V Banerjee Jineta J Derrick Daniel D Valdeolivas Alberto A Li Haojun H Xiao Xu X Wang Shun S Zheng Frank F Yang Wenxian W Catania Carlos A CA Lang Benjamin J BJ Bertus Thomas J TJ Piermarocchi Carlo C Caruso Francesca P FP Ceccarelli Michele M Yu Thomas T Guo Xindi X Bletz Julie J Coller John J Maecker Holden H Duault Caroline C Shokoohi Vida V Patel Shailja S Liliental Joanna E JE Simon Stockard S Saez-Rodriguez Julio J Heiser Laura M LM Guinney Justin J Gentles Andrew J AJ

Nature communications 20240827 1

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community ...[more]

PMID: 39191725

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Project description:BackgroundTo facilitate the investigation of the pathogenic roles played by various immune cells in complex tissues such as tumors, a few computational methods for deconvoluting bulk gene expression profiles to predict cell composition have been created. However, available methods were usually developed along with a set of reference gene expression profiles consisting of imbalanced replicates across different cell types. Therefore, the objective of this study was to create a new deconvolution method equipped with a new set of reference gene expression profiles that incorporate more microarray replicates of the immune cells that have been frequently implicated in the poor prognosis of cancers, such as T helper cells, regulatory T cells and macrophage M1/M2 cells.MethodsOur deconvolution method was developed by choosing ε-support vector regression (ε-SVR) as the core algorithm assigned with a loss function subject to the L1-norm penalty. To construct the reference gene expression signature matrix for regression, a subset of differentially expressed genes were chosen from 148 microarray-based gene expression profiles for 9 types of immune cells by using ANOVA and minimizing condition number. Agreement analyses including mean absolute percentage errors and Bland-Altman plots were carried out to compare the performances of our method and CIBERSORT.ResultsIn silico cell mixtures, simulated bulk tissues, and real human samples with known immune-cell fractions were used as the test datasets for benchmarking. Our method outperformed CIBERSORT in the benchmarks using in silico breast tissue-immune cell mixtures in the proportions of 30:70 and 50:50, and in the benchmark using 164 human PBMC samples. Our results suggest that the performance of our method was at least comparable to that of a state-of-the-art tool, CIBERSORT.ConclusionsWe developed a new cell composition deconvolution method and the implementation was entirely based on the publicly available R and Python packages. In addition, we compiled a new set of reference gene expression profiles, which might allow for a more robust prediction of the immune cell fractions from the expression profiles of cell mixtures. The source code of our method could be downloaded from https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets.

Dataset Information

Community assessment of methods to deconvolve cellular composition from bulk gene expression.

Publications

Community assessment of methods to deconvolve cellular composition from bulk gene expression.

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