Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data.
deconvolution
drug resistance
drug screening
mechanistic modeling
multiple myeloma
tumor heterogeneity
tumor profiling
Journal
Cell reports methods
ISSN: 2667-2375
Titre abrégé: Cell Rep Methods
Pays: United States
ID NLM: 9918227360606676
Informations de publication
Date de publication:
27 03 2023
27 03 2023
Historique:
received:
23
06
2022
revised:
10
12
2022
accepted:
08
02
2023
medline:
15
4
2023
entrez:
14
4
2023
pubmed:
15
4
2023
Statut:
epublish
Résumé
Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses and estimates their drug sensitivities and frequencies within the bulk population. We apply PhenoPop to synthetically generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response.
Identifiants
pubmed: 37056380
doi: 10.1016/j.crmeth.2023.100417
pii: S2667-2375(23)00028-0
pmc: PMC10088094
doi:
Substances chimiques
Antineoplastic Agents
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Pagination
100417Informations de copyright
© 2023 The Author(s).
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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