Community assessment of methods to deconvolve cellular composition from bulk gene expression.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
27 Aug 2024
27 Aug 2024
Historique:
received:
28
08
2023
accepted:
11
07
2024
medline:
28
8
2024
pubmed:
28
8
2024
entrez:
27
8
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 39191725
doi: 10.1038/s41467-024-50618-0
pii: 10.1038/s41467-024-50618-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
7362Subventions
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : CA209971
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : CA209923
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : CA209971
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : CA209923
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : CA209923
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : CA209923
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : CA209988
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : CA209923
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : U24CA224309
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : R01GM122085
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : R01GM122085
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : R01GM122085
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : U24CA224309
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : U24CA224309
Investigateurs
Aurélien de Reyniès
(A)
Informations de copyright
© 2024. The Author(s).
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