Evidence map›Paper›PMID 37578979›Full record

ArticlePLoS computational biology2023

Deconvolution of cancer cell states by the XDec-SM method.

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

Abstract read
In one paragraph

Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Oscar D MurilloTranslational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.ORCID 0000-0003-1938-3905
Varduhi PetrosyanDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America.
Emily L LaPlanteDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America.
Lacey E DobroleckiLester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, United States of America.
Michael T LewisLester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, United States of America.
Aleksandar MilosavljevicDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America.

Funding

Tumor BiologyP30CA125123 · NCI · BAYLOR COLLEGE OF MEDICINE · PI Suzanne AW Fuqua · 2007 to 2026
$73.9M
SOC CoreU54DA036134 · NIDA · BAYLOR COLLEGE OF MEDICINE · PI GALAS, DAVID J., GERSTEIN, MARK BENDER · 2013 to 2018
$15.0M
Tolinapant efficacy in a subset of triple negative breast cancersU54CA224076 · NCI · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Alana L. Welm · 2017 to 2026
$13.5M
NCI NIH HHS P30 CA125123NCI NIH HHS U54 CA224076NIDA NIH HHS U54 DA036134
6 · The paper itself

Abstract

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.

Indexed as

Breast NeoplasmsCell LineDNA MethylationFemaleGene Expression ProfilingHumansRNA-SeqTumor Microenvironment

Identifiers

PMID37578979
PMCPMC10449115

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.