BRCA1 mutations in high-grade serous ovarian cancer are associated with proteomic changes in DNA repair, splicing, transcription regulation and signaling

Bradbury, Melissa; Borràs, Eva; Castellví, Josep; Méndez, Olga; Sánchez-Iglesias, José Luís; Pérez-Benavente, Assumpció; Sabidó, Eduard; Santamaría, Anna

doi:10.1038/s41598-022-08461-0

Cited by 2 publications

(1 citation statement)

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“…In addition, the use of proteomic approaches (i.e. mass spectrometry) enable the systematic interrogation of proteomes from complex clinical samples which can later be translated into immunoassays for clinical use [15,16]. Despite the numerous studies being carried out to identify novel protein biomarkers in ovarian cancer, there are currently no validated markers used in the clinic to predict response to first-line treatment and guide the management of patients with newly diagnosed HGSC.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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Background 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). Methods 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. Results 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). Conclusions 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.

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Section: Introductionmentioning

confidence: 99%