Dilution of Molecular-Pathologic Gene Signatures by Medically Associated Factors Might Prevent Prediction of Resection Status After Debulking Surgery in Patients With Advanced Ovarian Cancer.
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:
01 01 2020
01 01 2020
Historique:
received:
30
05
2019
revised:
08
08
2019
accepted:
11
09
2019
pubmed:
19
9
2019
medline:
11
7
2020
entrez:
19
9
2019
Statut:
ppublish
Résumé
Predicting surgical outcome could improve individualizing treatment strategies for patients with advanced ovarian cancer. It has been suggested earlier that gene expression signatures (GES) might harbor the potential to predict surgical outcome. Data derived from high-grade serous tumor tissue of FIGO stage IIIC/IV patients of AGO-OVAR11 trial were used to generate a transcriptome profiling. Previously identified molecular signatures were tested. A theoretical model was implemented to evaluate the impact of medically associated factors for residual disease (RD) on the performance of GES that predicts RD status. A total of 266 patients met inclusion criteria, of those, 39.1% underwent complete resection. Previously reported GES did not predict RD in this cohort. Similarly, The Cancer Genome Atlas molecular subtypes, an independent Previously identified GES cannot be generalized. Medically associated factors for RD may be the main obstacle to predict surgical outcome in an all-comer population of patients with advanced ovarian cancer. If biomarkers derived from tumor tissue are used to predict outcome of patients with cancer, selection bias should be focused on to prevent overestimation of the power of such a biomarker.
Identifiants
pubmed: 31527166
pii: 1078-0432.CCR-19-1741
doi: 10.1158/1078-0432.CCR-19-1741
pmc: PMC7722525
mid: NIHMS1639505
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Comment
Langues
eng
Sous-ensembles de citation
IM
Pagination
213-219Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR002494
Pays : United States
Commentaires et corrections
Type : CommentIn
Type : CommentOn
Informations de copyright
©2019 American Association for Cancer Research.
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