Genotype-Fitness Maps of EGFR-Mutant Lung Adenocarcinoma Chart the Evolutionary Landscape of Resistance for Combination Therapy Optimization.
Acrylamides
/ administration & dosage
Adenocarcinoma of Lung
/ drug therapy
Aniline Compounds
/ administration & dosage
Antineoplastic Combined Chemotherapy Protocols
/ pharmacology
Benzimidazoles
/ administration & dosage
Drug Resistance, Neoplasm
/ genetics
ErbB Receptors
/ genetics
Erlotinib Hydrochloride
/ administration & dosage
Genetic Fitness
Genotype
Humans
Lung Neoplasms
/ drug therapy
MAP Kinase Signaling System
Mutation
EGFR inhibitor
MEK inhibitor
cancer resistance
combination therapy
evolutionary dynamics
genotype-fitness map
lung adenocarcinoma
targeted-therapy resistance
tumor evolution
tumor heterogeneity
Journal
Cell systems
ISSN: 2405-4720
Titre abrégé: Cell Syst
Pays: United States
ID NLM: 101656080
Informations de publication
Date de publication:
22 01 2020
22 01 2020
Historique:
received:
09
10
2018
revised:
21
05
2019
accepted:
30
09
2019
pubmed:
2
11
2019
medline:
9
6
2021
entrez:
1
11
2019
Statut:
ppublish
Résumé
Cancer evolution poses a central obstacle to cure, as resistant clones expand under therapeutic selection pressures. Genome sequencing of relapsed disease can nominate genomic alterations conferring resistance but sample collection lags behind, limiting therapeutic innovation. Genome-wide screens offer a complementary approach to chart the compendium of escape genotypes, anticipating clinical resistance. We report genome-wide open reading frame (ORF) resistance screens for first- and third-generation epidermal growth factor receptor (EGFR) inhibitors and a MEK inhibitor. Using serial sampling, dose gradients, and mathematical modeling, we generate genotype-fitness maps across therapeutic contexts and identify alterations that escape therapy. Our data expose varying dose-fitness relationship across genotypes, ranging from complete dose invariance to paradoxical dose dependency where fitness increases in higher doses. We predict fitness with combination therapy and compare these estimates to genome-wide fitness maps of drug combinations, identifying genotypes where combination therapy results in unexpected inferior effectiveness. These data are applied to nominate combination optimization strategies to forestall resistant disease.
Identifiants
pubmed: 31668800
pii: S2405-4712(19)30347-3
doi: 10.1016/j.cels.2019.10.002
pmc: PMC6981068
mid: NIHMS1545192
pii:
doi:
Substances chimiques
Acrylamides
0
Aniline Compounds
0
Benzimidazoles
0
binimetinib
181R97MR71
osimertinib
3C06JJ0Z2O
Erlotinib Hydrochloride
DA87705X9K
EGFR protein, human
EC 2.7.10.1
ErbB Receptors
EC 2.7.10.1
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
52-65.e7Subventions
Organisme : NCI NIH HHS
ID : DP2 CA239065
Pays : United States
Informations de copyright
Copyright © 2019 Elsevier Inc. All rights reserved.
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