Historically, ovarian cancer (OC) was thought to metastasize by surface-to-surface spread, but recent developments have yielded a new understanding of the paths of metastatic spread. Given the histologic and molecular heterogeneity of OC, we will focus on high-grade serous carcinoma (HGSC). Here, we provide a critical and more holistic view of the evidence supporting various routes of metastasis, including peritoneal, hematogenous, lymphatic, and nerve-related. Understanding the underlying mechanisms is necessary to improve treatment strategies for this challenging disease.
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| http://dx.doi.org/10.1016/j.ccell.2024.08.016 | DOI Listing |
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Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China.
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J Ovarian Res
January 2025
Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, #128 Shenyang Road, Shanghai, 200090, People's Republic of China.
Background: Ovarian cancers (OC) and cervical cancers (CC) have poor survival rates. Tumor-infiltrating lymphocytes (TILs) play a pivotal role in prognosis, but shared immune mechanisms remain elusive.
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The development of resistance remains one of the biggest challenges in clinical cancer patient care and it comprises all treatment modalities from chemotherapy to targeted or immune therapy. In solid malignancies, drug resistance is the result of adaptive processes occurring in cancer cells or the surrounding tumor microenvironment (TME). Future therapy attempts will therefore benefit from targeting both, tumor and stroma compartments and drug targets which affect both sides will be highly appreciated.
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Eötvös Loránd University, Department of Physics of Complex Systems, Budapest, Hungary.
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.
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