A combination of molecular and clinical parameters provides a new strategy for high-grade serous ovarian cancer patient management
Bradbury, Melissa (Hospital Universitari Vall d'Hebron)
Borràs, Eva (Universitat Pompeu Fabra)
Vilar, Marta (Hospital Universitari Vall d'Hebron)
Castellvi, Josep (Hospital Universitari Vall d'Hebron)
Sanchez Iglesias, Jose Luis (Hospital Universitari Vall d'Hebron)
Pérez-Benavente, Assumpció (Hospital Universitari Vall d'Hebron)
Gil-Moreno, Antonio 1965- (Hospital Universitari Vall d'Hebron)
Fàbrega-Santamaria, Anna (Hospital Universitari Vall d'Hebron)
Sabidó, Eduard (Universitat Pompeu Fabra)
Universitat Autònoma de Barcelona

Date:	2022
Abstract:	High-grade serous carcinoma (HGSC) is the most common and deadly subtype of ovarian cancer. Although most patients will initially respond to first-line treatment with a combination of surgery and platinum-based chemotherapy, up to a quarter will be resistant to treatment. We aimed to identify a new strategy to improve HGSC patient management at the time of cancer diagnosis (HGSC-1LTR). A total of 109 ready-available formalin-fixed paraffin-embedded HGSC tissues obtained at the time of HGSC diagnosis were selected for proteomic analysis. Clinical data, treatment approach and outcomes were collected for all patients. An initial discovery cohort (n = 21) were divided into chemoresistant and chemosensitive groups and evaluated using discovery mass-spectrometry (MS)-based proteomics. Proteins showing differential abundance between groups were verified in a verification cohort (n = 88) using targeted MS-based proteomics. A logistic regression model was used to select those proteins able to correctly classify patients into chemoresistant and chemosensitive. The classification performance of the protein and clinical data combinations were assessed through the generation of receiver operating characteristic (ROC) curves. Using the HGSC-1LTR strategy we have identified a molecular signature (TKT, LAMC1 and FUCO) that combined with ready available clinical data (patients' age, menopausal status, serum CA125 levels, and treatment approach) is able to predict patient response to first-line treatment with an AUC: 0. 82 (95% CI 0. 72-0. 92). We have established a new strategy that combines molecular and clinical parameters to predict the response to first-line treatment in HGSC patients (HGSC-1LTR). This strategy can allow the identification of chemoresistance at the time of diagnosis providing the optimization of therapeutic decision making and the evaluation of alternative treatment strategies. Thus, advancing towards the improvement of patient outcome and the individualization of HGSC patients' care. The online version contains supplementary material available at 10. 1186/s12967-022-03816-7.
Grants:	Instituto de Salud Carlos III PI18/01017
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Biomarker ; High-grade serous ovarian cancer ; Prediction ; Proteomics ; Treatment
Published in:	Journal of translational medicine, Vol. 20 (december 2022) , ISSN 1479-5876

DOI: 10.1186/s12967-022-03816-7
PMID: 36544142

13 p, 1.7 MB

The record appears in these collections:
Articles > Research articles
Articles > Published articles