Details

Autor(en) / Beteiligte

• Murillo, Oscar D
• Petrosyan, Varduhi
• LaPlante, Emily L
• Dobrolecki, Lacey E
• Lewis, Michael T
• Milosavljevic, Aleksandar
• Zhang, Zhaolei

Titel

Deconvolution of cancer cell states by the XDec-SM method

Ist Teil von

• PLoS computational biology, 2023-08, Vol.19 (8), p.e1011365-e1011365

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2023

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Proper characterization of cancer cell states within the tumor microenvironment is a key to accurately identifying matching experimental models and the development of precision therapies. To reconstruct this information from bulk RNA-seq profiles, we developed the XDec Simplex Mapping (XDec-SM) reference-optional deconvolution method that maps tumors and the states of constituent cells onto a biologically interpretable low-dimensional space. The method identifies gene sets informative for deconvolution from relevant single-cell profiling data when such profiles are available. When applied to breast tumors in The Cancer Genome Atlas (TCGA), XDec-SM infers the identity of constituent cell types and their proportions. XDec-SM also infers cancer cells states within individual tumors that associate with DNA methylation patterns, driver somatic mutations, pathway activation and metabolic coupling between stromal and breast cancer cells. By projecting tumors, cancer cell lines, and PDX models onto the same map, we identify in vitro and in vivo models with matching cancer cell states. Map position is also predictive of therapy response, thus opening the prospects for precision therapy informed by experiments in model systems matched to tumors in vivo by cancer cell state.