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.
J Transl Med. 2022 Dec 21;20(1):611. doi: 10.1186/s12967-022-03816-7.
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.
高级别浆液性癌(HGSC)是卵巢癌最常见和最致命的亚型。尽管大多数患者最初会对手术联合铂类化疗的一线治疗产生反应,但多达四分之一的患者会对治疗产生耐药性。我们旨在确定一种新的策略,以改善癌症诊断时(HGSC-1LTR)的 HGSC 患者管理。
共选择了 109 例在 HGSC 诊断时获得的现成福尔马林固定石蜡包埋 HGSC 组织进行蛋白质组学分析。收集了所有患者的临床数据、治疗方法和结果。初始发现队列(n=21)分为耐药组和敏感组,并使用发现质谱(MS)-基于蛋白质组学进行评估。使用靶向 MS 基于蛋白质组学在验证队列(n=88)中验证了显示组间差异丰度的蛋白质。使用逻辑回归模型选择能够正确将患者分为耐药和敏感组的蛋白质。通过生成接收者操作特征(ROC)曲线评估蛋白质和临床数据组合的分类性能。
使用 HGSC-1LTR 策略,我们已经确定了一个分子特征(TKT、LAMC1 和 FUCO),与现成的临床数据(患者年龄、绝经状态、血清 CA125 水平和治疗方法)相结合,能够预测患者对一线治疗的反应,AUC:0.82(95%CI 0.72-0.92)。
我们已经建立了一种新的策略,该策略结合了分子和临床参数来预测 HGSC 患者对一线治疗的反应(HGSC-1LTR)。该策略可以在诊断时识别耐药性,从而优化治疗决策并评估替代治疗策略。因此,朝着改善患者预后和个性化 HGSC 患者护理的方向前进。