High-grade serous ovarian cancer (HGSC) remains the most common and deadly subtype of ovarian cancer. It is characterized by its late diagnosis and frequent relapse despite standardized treatment with cytoreductive surgery and platinum-based chemotherapy. The past decade has seen significant advances in the clinical management and molecular understanding of HGSC following the publication of the Cancer Genome Atlas (TCGA) researchers and the introduction of targeted therapies with anti-angiogenic drugs and poly(ADP-ribose) polymerase inhibitors in specific subgroups of patients. We provide a comprehensive review of HGSC, focusing on the most important molecular advances aimed at providing a better understanding of the disease and its response to treatment. We emphasize the role that proteomic technologies are now playing in these two aspects of the disease, through the identification of proteins and their post-translational modifications in ovarian cancer tumors. Finally, we highlight how the integration of proteomics with genomics, exemplified by the work performed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), can guide the development of new biomarkers and therapeutic targets.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123279PMC
http://dx.doi.org/10.3390/cancers13092067DOI Listing

Publication Analysis

Top Keywords

ovarian cancer
16
high-grade serous
8
serous ovarian
8
cancer
5
proteomic studies
4
studies management
4
management high-grade
4
ovarian
4
cancer patients
4
patients mini-review
4

Similar Publications

Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of two canonical assumptions: (i) a homogeneous graph with a common network for all subjects or (ii) an assumption of normality, especially in the context of Gaussian graphical models. Both assumptions are restrictive and can fail to hold in certain applications such as proteomic networks in cancer.

View Article and Find Full Text PDF

Hypothesis: To evaluate how comorbidities affect mortality benefits of lung cancer screening (LCS) with low-dose computed-tomography (LDCT).

Methods: We developed a comorbidity index (PLCO-ci) using LCS-eligible participants' data from the Prostate Lung Colorectal and Ovarian (PLCO) trial (training set) and the National Lung Screening Trial (NLST) (validation set). PLCO-ci predicts 5-year non-lung cancer (LC) mortality using a regularized Cox model; with performance evaluated by the area under the ROC curve (ROC).

View Article and Find Full Text PDF

Self-interactive learning: Fusion and evolution of multi-scale histomorphology features for molecular traits prediction in computational pathology.

Med Image Anal

January 2025

Nuffield Department of Medicine, University of Oxford, Oxford, UK; Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford, UK. Electronic address:

Predicting disease-related molecular traits from histomorphology brings great opportunities for precision medicine. Despite the rich information present in histopathological images, extracting fine-grained molecular features from standard whole slide images (WSI) is non-trivial. The task is further complicated by the lack of annotations for subtyping and contextual histomorphological features that might span multiple scales.

View Article and Find Full Text PDF

Background: Melittin, a major peptide component of bee venom, has demonstrated promising anti-cancer activity across various preclinical cell models, making it a potential candidate for cancer therapy. However, its molecular mechanisms, particularly in ovarian cancer, remain largely unexplored. Ovarian cancer is a life-threatening gynecological malignancy with poor clinical outcomes and limited treatment options.

View Article and Find Full Text PDF

Objective: To provide up-to-date European Society of Urogenital Radiology (ESUR) guidelines for staging and follow-up of patients with ovarian cancer (OC).

Methods: Twenty-one experts, members of the female pelvis imaging ESUR subcommittee from 19 institutions, replied to 2 rounds of questionnaires regarding imaging techniques and structured reporting used for pre-treatment evaluation of OC patients. The results of the survey were presented to the other authors during the group's annual meeting.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!