Predicting Ovarian Cancer Patients' Clinical Response to Platinum-Based Chemotherapy by Their Tumor Proteomic Signatures.
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.
Kun-Hsing
KH
Yu
Department of Surgery, Memorial Sloan Kettering Cancer Center , New York, New York 10065, United States.
Douglas A
DA
Levine
Department of Pathology, Johns Hopkins Medical Institutions , Baltimore, Maryland 21287, United States.
Hui
H
Zhang
Department of Pathology, Johns Hopkins Medical Institutions , Baltimore, Maryland 21287, United States.
Daniel W
DW
Chan
Department of Pathology, Johns Hopkins Medical Institutions , Baltimore, Maryland 21287, United States.
Zhen
Z
Zhang
Michael
M
Snyder
D000368:Aged; D000970:Antineoplastic Agents / Q000494:pharmacology / Q000627:therapeutic use; D002945:Cisplatin; D018284:Cystadenocarcinoma, Serous / Q000737:chemistry / Q000188:drug therapy; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008875:Middle Aged; D009363:Neoplasm Proteins / Q000032:analysis*; D010051:Ovarian Neoplasms / Q000188:drug therapy*; D017671:Platinum Compounds / Q000494:pharmacology / Q000627:therapeutic use*; D057285:Precision Medicine / Q000379:methods*; D011237:Predictive Value of Tests; D040901:Proteomics; D000069553:Supervised Machine Learning / Q000706:statistics & numerical data; D016896:Treatment Outcome
D000970:Antineoplastic Agents; D009363:Neoplasm Proteins; D017671:Platinum Compounds; D002945:Cisplatin
bioinformatics; cancer biomarkers; drug resistance; ovarian cancer; tandem mass spectrometry