Predicting and affecting response to cancer therapy based on pathway-level biomarkers.
Animals
Antineoplastic Agents
/ pharmacology
BRCA1 Protein
/ genetics
BRCA2 Protein
/ genetics
Biomarkers, Tumor
/ genetics
Breast Neoplasms
/ drug therapy
Cell Line, Tumor
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Humans
Lung Neoplasms
/ drug therapy
Mice
Neoplasms
/ drug therapy
Precision Medicine
/ methods
RNA Interference
Signal Transduction
/ drug effects
Xenograft Model Antitumor Assays
/ methods
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
03 07 2020
03 07 2020
Historique:
received:
05
10
2019
accepted:
12
06
2020
entrez:
5
7
2020
pubmed:
6
7
2020
medline:
1
9
2020
Statut:
epublish
Résumé
Identifying robust, patient-specific, and predictive biomarkers presents a major obstacle in precision oncology. To optimize patient-specific therapeutic strategies, here we couple pathway knowledge with large-scale drug sensitivity, RNAi, and CRISPR-Cas9 screening data from 460 cell lines. Pathway activity levels are found to be strong predictive biomarkers for the essentiality of 15 proteins, including the essentiality of MAD2L1 in breast cancer patients with high BRCA-pathway activity. We also find strong predictive biomarkers for the sensitivity to 31 compounds, including BCL2 and microtubule inhibitors (MTIs). Lastly, we show that Bcl-xL inhibition can modulate the activity of a predictive biomarker pathway and re-sensitize lung cancer cells and tumors to MTI therapy. Overall, our results support the use of pathways in helping to achieve the goal of precision medicine by uncovering dozens of predictive biomarkers.
Identifiants
pubmed: 32620799
doi: 10.1038/s41467-020-17090-y
pii: 10.1038/s41467-020-17090-y
pmc: PMC7335104
doi:
Substances chimiques
Antineoplastic Agents
0
BRCA1 Protein
0
BRCA1 protein, human
0
BRCA2 Protein
0
BRCA2 protein, human
0
Biomarkers, Tumor
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3296Références
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