ArticleJournal of proteome research2016
Predicting Ovarian Cancer Patients' Clinical Response to Platinum-Based Chemotherapy by Their Tumor Proteomic Signatures.
Article in Journal of proteome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 4 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics.Frontiers in oncology · 2024Pooled it
- Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.BioMed research international · 2022Pooled it
- Application of Artificial Intelligence Methods to Pharmacy Data for Cancer Surveillance and Epidemiology Research: A Systematic Review.JCO clinical cancer informatics · 2020Pooled it
- Relating transcriptomics to protein abundance reveals self-driven versus interactor-driven proteins.Protein science : a publication of the Protein Society · 2026Article
- The Proteomic Profile of Adults With a Fontan Circulation.JACC. Advances · 2026Article
- Ensemble Machine Learning Predicts Platinum Resistance in Ovarian Cancer Using Laboratory Data.Cancers · 2026Article
- Spatially resolved proteomics surveys the chemo-refractory proteins related to high-grade serous ovarian cancer.Clinical and translational medicine · 2025Article
- Artificial intelligence and anti-cancer drugs' response.Acta pharmaceutica Sinica. B · 2025Review
- Enhancing Personalized Chemotherapy for Ovarian Cancer: Integrating Gene Expression Data with Machine Learning.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- Categorizing high-grade serous ovarian carcinoma into clinically relevant subgroups using deep learning-based histomic clusters.Journal of pathology and translational medicine · 2025Article
- Histopathology and proteomics are synergistic for high-grade serous ovarian cancer platinum response prediction.NPJ precision oncology · 2025Article
- Cracking the code: predicting tumor microenvironment enabled chemoresistance with machine learning in the human tumoroid models.npj biomedical innovations · 2025Article
- Histopathology and proteomics are synergistic for High-Grade Serous Ovarian Cancer platinum response prediction.medRxiv : the preprint server for health sciences · 2024Article
- Evaluating Ovarian Cancer Chemotherapy Response Using Gene Expression Data and Machine Learning.BioMedInformatics · 2024Article
- Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer.Nature communications · 2024Article
- Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system.The EPMA journal · 2024Article
- Radioproteomics in patients with ovarian cancer.The British journal of radiology · 2021Review
- Comprehending the Proteomic Landscape of Ovarian Cancer: A Road to the Discovery of Disease Biomarkers.Proteomes · 2021Review
- Proteomic Studies on the Management of High-Grade Serous Ovarian Cancer Patients: A Mini-Review.Cancers · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Ovarian cancer is the deadliest gynecologic malignancy in the United States with most patients diagnosed in the advanced stage of the disease. Platinum-based antineoplastic therapeutics is indispensable to treating advanced ovarian serous carcinoma. However, patients have heterogeneous responses to platinum drugs, and it is difficult to predict these interindividual differences before administering medication. In this study, we investigated the tumor proteomic profiles and clinical characteristics of 130 ovarian serous carcinoma patients analyzed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), predicted the platinum drug response using supervised machine learning methods, and evaluated our prediction models through leave-one-out cross-validation. Our data-driven feature selection approach indicated that tumor proteomics profiles contain information for predicting binarized platinum response (P < 0.0001). We further built a least absolute shrinkage and selection operator (LASSO)-Cox proportional hazards model that stratified patients into early relapse and late relapse groups (P = 0.00013). The top proteomic features indicative of platinum response were involved in ATP synthesis pathways and Ran GTPase binding. Overall, we demonstrated that proteomic profiles of ovarian serous carcinoma patients predicted platinum drug responses as well as provided insights into the biological processes influencing the efficacy of platinum-based therapeutics. Our analytical approach is also extensible to predicting response to other antineoplastic agents or treatment modalities for both ovarian and other cancers.
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