Clinical Validation of a Machine-learning-derived Signature Predictive of Outcomes from First-line Oxaliplatin-based Chemotherapy in Advanced Colorectal Cancer.
Adult
Aged
Aged, 80 and over
Antineoplastic Combined Chemotherapy Protocols
/ pharmacology
Bevacizumab
/ pharmacology
Biomarkers, Tumor
/ genetics
Clinical Trials as Topic
Colorectal Neoplasms
/ drug therapy
Datasets as Topic
Drug Resistance, Neoplasm
/ genetics
Female
Follow-Up Studies
High-Throughput Nucleotide Sequencing
Humans
Machine Learning
Male
Middle Aged
Models, Genetic
Oxaliplatin
/ pharmacology
Prognosis
Progression-Free Survival
Prospective Studies
Risk Assessment
/ methods
Young Adult
Journal
Clinical cancer research : an official journal of the American Association for Cancer Research
ISSN: 1557-3265
Titre abrégé: Clin Cancer Res
Pays: United States
ID NLM: 9502500
Informations de publication
Date de publication:
15 02 2021
15 02 2021
Historique:
received:
19
08
2020
revised:
30
10
2020
accepted:
03
12
2020
pubmed:
10
12
2020
medline:
21
1
2022
entrez:
9
12
2020
Statut:
ppublish
Résumé
FOLFOX, FOLFIRI, or FOLFOXIRI chemotherapy with bevacizumab is considered standard first-line treatment option for patients with metastatic colorectal cancer (mCRC). We developed and validated a molecular signature predictive of efficacy of oxaliplatin-based chemotherapy combined with bevacizumab in patients with mCRC. A machine-learning approach was applied and tested on clinical and next-generation sequencing data from a real-world evidence (RWE) dataset and samples from the prospective TRIBE2 study resulting in identification of a molecular signature, FOLFOX A 67-gene signature was cross-validated in a training cohort ( Application of FOLFOX
Identifiants
pubmed: 33293373
pii: 1078-0432.CCR-20-3286
doi: 10.1158/1078-0432.CCR-20-3286
doi:
Substances chimiques
Biomarkers, Tumor
0
Oxaliplatin
04ZR38536J
Bevacizumab
2S9ZZM9Q9V
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
1174-1183Informations de copyright
©2020 American Association for Cancer Research.
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