Predicting Ovarian Cancer Patients' Clinical Response to Platinum-Based Chemotherapy by Their Tumor Proteomic Signatures - PubMed (original) (raw)

Predicting Ovarian Cancer Patients' Clinical Response to Platinum-Based Chemotherapy by Their Tumor Proteomic Signatures

Kun-Hsing Yu et al. J Proteome Res. 2016.

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.

Keywords: bioinformatics; cancer biomarkers; drug resistance; ovarian cancer; tandem mass spectrometry.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1.

Figure 1.

Protein expression levels were associated with platinum response of ovarian serous carcinoma patients. (A) Patients with similar platinum responses shared certain proteomic signatures. In this heatmap, each row is a patient, and each column is a protein. Hierarchical clustering results showed that platinum-resistant patients formed clusters, indicating that these protein signatures are correlated with platinum status of ovarian serous carcinoma patients. (B) Log expression levels of proteins associated with platinum response status. Abundance levels of 24 proteins, including KRT19, KRT4, ACTN4, RANBP1, IGLL5, and TPMT, were strongly associated with the clinical platinum response in ovarian serous carcinoma patients.

Figure 2.

Figure 2.

Protein-protein interaction network revealed that proteins indicative of platinum response were significantly enriched in Ran-GTPase binding, ATP synthesis pathways, and regulation mechanisms of the cell cycle. The top proteins associated with platinum response were shown in color, and their interacting proteins were shown in gray.

Figure 3.

Figure 3.

Receiver operating characteristic (ROC) curve for platinum response prediction. Integrating the abundance levels of the top protein features identified in the training set, random forests, support vector machines, bagging, and naïve Bayes classifiers weakly predicted patients with different platinum responses on leave-one-out cross-validation with areas under the curves of approximately 0.58—0.64. These classifiers are significantly better than a null classifier (P = 7.96 × 10−9 for random forest with conditional inference trees, P = 2.10 × 10−7 for SVM with Gaussian kernel, P = 8.01 × 10−9 for SVM with polynomial kernel, P = 4.68 × 10−10 for naïve Bayes classifiers, P = 7.99 × 10−9 for Breiman’s random forest, and P = 7.99 × 10−9 for bagging). The black line indicates the performance of the null classifier. CIT: conditional inference trees.

Figure 4.

Figure 4.

Proteomic signatures informed the platinum-free interval of patients. A LASSO-Cox proportional hazards model stratified ovarian serous carcinoma patients in the test set into two predicted relapse groups with a statistically significant difference in their platinum free interval (P = 0.00013 ± 0.00012).

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