RNA Biomarkers as a Response Measure for Survival in Patients with Metastatic Castration-Resistant Prostate Cancer

Autor: Emmy Boerrigter, Guillemette E. Benoist, Inge M. van Oort, Gerald W. Verhaegh, Anton F. J. de Haan, Onno van Hooij, Levi Groen, Frank Smit, Irma M. Oving, Pieter de Mol, Tineke J. Smilde, Diederik M. Somford, Paul Hamberg, Vincent O. Dezentjé, Niven Mehra, Nielka P. van Erp, Jack A. Schalken
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Cancers
Cancers, Vol 13, Iss 6279, p 6279 (2021)
Cancers, 13
Cancers, 13, 24
Cancers; Volume 13; Issue 24; Pages: 6279
ISSN: 2072-6694
Popis: Simple Summary Despite the increasing number of treatments for advanced prostate cancer, the evaluation of the added value of each line of treatment on survival outcome is challenging. Therefore, biomarkers to discriminate short-term survivors from long-term survivors shortly after start of treatment are urgently needed. We demonstrate that in 93 patients with mCRPC treated with first-line abiraterone acetate or enzalutamide the addition of KLK3 and miR-375 (at baseline and 1 month) to standard clinical parameters resulted in the best prediction model for survival assessment. Abstract Treatment evaluation in metastatic castration-resistant prostate cancer is challenging. There is an urgent need for biomarkers to discriminate short-term survivors from long-term survivors, shortly after treatment initiation. Thereto, the added value of early RNA biomarkers on predicting progression-free survival (PFS) and overall survival (OS) were explored. The RNA biomarkers: KLK3 mRNA, miR-375, miR-3687, and NAALADL2-AS2 were measured in 93 patients with mCRPC, before and 1 month after start of first-line abiraterone acetate or enzalutamide treatment, in two prospective clinical trials. The added value of the biomarkers to standard clinical parameters in predicting PFS and OS was tested by Harell’s C-index. To test whether the biomarkers were independent markers of PFS and OS, multivariate Cox regression was used. The best prediction model for PFS and OS was formed by adding miR-375 and KLK3 (at baseline and 1 month) to standard clinical parameters. Baseline miR-375 and detectable KLK3 after 1 month of therapy were independently related to shorter PFS, which was not observed for OS. In conclusion, the addition of KLK3 and miR-375 (at baseline and 1 month) to standard clinical parameters resulted in the best prediction model for survival assessment.
Databáze: OpenAIRE
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