Kilim Oz, Olar Alex, Biricz András, Madaras Lilla, Pollner Péter, Szállási Zoltán, Sztupinszki Zsofia, Csabai István
Eötvös Loránd University, Department of Physics of Complex Systems, Budapest, Hungary.
Semmelweis University, Data-Driven Health Division of National Laboratory for Health Security, Health Services Management Training Centre, Budapest, Hungary.
NPJ Precis Oncol. 2025 Jan 26;9(1):27. doi: 10.1038/s41698-025-00808-w.
Patients with High-Grade Serous Ovarian Cancer (HGSOC) exhibit varied responses to treatment, with 20-30% showing de novo resistance to platinum-based chemotherapy. While hematoxylin-eosin (H&E)-stained pathological slides are used for routine diagnosis of cancer type, they may also contain diagnostically useful information about treatment response. Our study demonstrates that combining H&E-stained whole slide images (WSIs) with proteomic signatures using a multimodal deep learning framework significantly improves the prediction of platinum response in both discovery and validation cohorts. This method outperforms the Homologous Recombination Deficiency (HRD) score in predicting platinum response and overall patient survival. Our study suggests that histology and proteomics contain complementary information about biological processes determining response to first line platinum treatment in HGSOC. This integrative approach has the potential to improve personalized treatment and provide insights into the therapeutic vulnerabilities of HGSOC.
高级别浆液性卵巢癌（HGSOC）患者对治疗的反应各不相同，20%-30%的患者对铂类化疗表现出原发性耐药。苏木精-伊红（H&E）染色的病理切片虽用于癌症类型的常规诊断，但也可能包含有关治疗反应的诊断有用信息。我们的研究表明，使用多模态深度学习框架将H&E染色的全切片图像（WSIs）与蛋白质组学特征相结合，可显著提高在发现队列和验证队列中对铂反应的预测。该方法在预测铂反应和患者总体生存方面优于同源重组缺陷（HRD）评分。我们的研究表明，组织学和蛋白质组学包含有关决定HGSOC一线铂治疗反应的生物学过程的互补信息。这种综合方法有潜力改善个性化治疗，并深入了解HGSOC的治疗脆弱性。
