A combination of molecular and clinical parameters provides a new strategy for high-grade serous ovarian cancer patient management

Journal of Translational Medicine

Research

Anna Santamaria¹  José Luis Sánchez-Iglesias²  Assumpció Pérez-Benavente²  Antonio Gil-Moreno³  Marta Vilar⁴  Eva Borràs⁵  Eduard Sabidó⁵  Melissa Bradbury⁶  Josep Castellví⁷ 

[1] Biomedical Research Group in Gynecology, Vall d’Hebron Institut de Recerca, Universitat Autònoma de Barcelona, Vall, d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035, Barcelona, Spain;Cell Cycle and Cancer Laboratory, Biomedical Research Group in Urology, Vall Hebron Institut de Recerca, Vall d’Hebron Barcelona Hospital Campus, Universitat Autònoma de Barcelona, Passeig Vall d’Hebron 119-129, 08035, Barcelona, Spain;Biomedical Research Group in Gynecology, Vall d’Hebron Institut de Recerca, Universitat Autònoma de Barcelona, Vall, d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035, Barcelona, Spain;Department of Gynecology, Hospital Universitari Vall d’Hebron, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035, Barcelona, Spain;Biomedical Research Group in Gynecology, Vall d’Hebron Institut de Recerca, Universitat Autònoma de Barcelona, Vall, d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035, Barcelona, Spain;Department of Gynecology, Hospital Universitari Vall d’Hebron, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035, Barcelona, Spain;Centro de Investigación Biomédica en Red (CIBERONC), Instituto de Salud Carlos III, Avenida de Monforte de Lemos 3-5, 28029, Madrid, Spain;Centre de Regulació Genòmica, Barcelona Institute of Science and Technology (BIST), Dr Aiguader 88, 08003, Barcelona, Spain;Biomedical Research Group in Gynecology, Vall d’Hebron Institut de Recerca, Universitat Autònoma de Barcelona, Vall, d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035, Barcelona, Spain;Centre de Regulació Genòmica, Barcelona Institute of Science and Technology (BIST), Dr Aiguader 88, 08003, Barcelona, Spain;Universitat Pompeu Fabra, Dr Aiguader 88, 08003, Barcelona, Spain;Centre de Regulació Genòmica, Barcelona Institute of Science and Technology (BIST), Dr Aiguader 88, 08003, Barcelona, Spain;Universitat Pompeu Fabra, Dr Aiguader 88, 08003, Barcelona, Spain;Biomedical Research Group in Gynecology, Vall d’Hebron Institut de Recerca, Universitat Autònoma de Barcelona, Vall, d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035, Barcelona, Spain;Department of Gynecology, Hospital Universitari Vall d’Hebron, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035, Barcelona, Spain;Department of Pathology, Hospital Universitari Vall d’Hebron, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035, Barcelona, Spain;

关键词: High-grade serous ovarian cancer;    Proteomics;    Biomarker;    Prediction;    Treatment;   

DOI  :  10.1186/s12967-022-03816-7

 received in 2022-08-30, accepted in 2022-12-07,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundHigh-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).MethodsA 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.ResultsUsing 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).ConclusionsWe 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.

【 授权许可】

CC BY   
© The Author(s) 2022

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