Project description:Purpose: Despite advances in radical surgery and chemotherapy delivery, ovarian cancer is the most lethal gynecologic malignancy. Most of these patients are treated with platinum-based chemotherapies, but there is no biomarker model to guide their responses to these therapeutic agents. We have developed and independently tested our novel multivariate molecular predictors for forecasting patients' responses to individual drugs on a cohort of 58 ovarian cancer patients. Experimental Design: We adapted and applied the previously-published COXEN algorithm to develop molecular predictors for therapeutic responses of patients' tumors based on expression signatures derived from the NCI-60 in vitro drug activities and genomic expression data. Genome-wide candidate biomarkers were first triaged by examining expression patterns of frozen and formalin-fixed paraffin embedded (FFPE) tissue samples. We then identify initial drug sensitivity biomarkers for carboplatin and paclitaxel, respectively. These biomarkers were further narrowed by examining concordant expression patterns between cell lines and a historical set of ovarian cancer patients. Multivariate predictors were obtained from the NCI-60 cell lines and refined using historical patient cohorts. To independent validate these molecular predictors, we performed genome-wide profiling on FFPE samples of 58 ovarian cancer patients obtained prior to adjuvant chemotherapy. Results: Carboplatin predictor significantly stratified platinum sensitive and resistant patients (p = 0.019) with sensitivity = 93%, specificity = 33%, PPV = 65%, and NPV = 78%. Paclitaxel predictor also significantly stratified patients' responses (p = 0.033) with sensitivity = 96%, specificity = 26%, PPV = 61%, and NPV = 86%. The combination predictor for platinum-taxane combination demonstrated a significant survival difference between the predicted responders and nonresponders with median survival of 12.9 months vs. 8.1 months (p = 0.045). Conclusions: COXEN predictors successfully stratified platinum resistance and taxane response in this retrospective cohort, especially based on their FFPE tumor samples. Accurate prediction of chemotherapeutic response, especially to platinum agents is highly clinically relevant and could alter primary management of ovarian cancer. Gene expression data from 58 stage III-IV ovarian cancer patients treated with Carboplatin and Taxol agents

Project description:Purpose: Despite advances in radical surgery and chemotherapy delivery, ovarian cancer is the most lethal gynecologic malignancy. Most of these patients are treated with platinum-based chemotherapies, but there is no biomarker model to guide their responses to these therapeutic agents. We have developed and independently tested our novel multivariate molecular predictors for forecasting patients' responses to individual drugs on a cohort of 58 ovarian cancer patients. Experimental Design: We adapted and applied the previously-published COXEN algorithm to develop molecular predictors for therapeutic responses of patients' tumors based on expression signatures derived from the NCI-60 in vitro drug activities and genomic expression data. Genome-wide candidate biomarkers were first triaged by examining expression patterns of frozen and formalin-fixed paraffin embedded (FFPE) tissue samples. We then identify initial drug sensitivity biomarkers for carboplatin and paclitaxel, respectively. These biomarkers were further narrowed by examining concordant expression patterns between cell lines and a historical set of ovarian cancer patients. Multivariate predictors were obtained from the NCI-60 cell lines and refined using historical patient cohorts. To independent validate these molecular predictors, we performed genome-wide profiling on FFPE samples of 58 ovarian cancer patients obtained prior to adjuvant chemotherapy. Results: Carboplatin predictor significantly stratified platinum sensitive and resistant patients (p = 0.019) with sensitivity = 93%, specificity = 33%, PPV = 65%, and NPV = 78%. Paclitaxel predictor also significantly stratified patients' responses (p = 0.033) with sensitivity = 96%, specificity = 26%, PPV = 61%, and NPV = 86%. The combination predictor for platinum-taxane combination demonstrated a significant survival difference between the predicted responders and nonresponders with median survival of 12.9 months vs. 8.1 months (p = 0.045). Conclusions: COXEN predictors successfully stratified platinum resistance and taxane response in this retrospective cohort, especially based on their FFPE tumor samples. Accurate prediction of chemotherapeutic response, especially to platinum agents is highly clinically relevant and could alter primary management of ovarian cancer.

Project description:BackgroundOvarian cancer (OC) is the deadliest tumor in the female reproductive tract. And increased resistance to platinum-based chemotherapy represents the major obstacle in the treatment of OC currently. Robust and accurate gene expression models are crucial tools in distinguishing platinum therapy response and evaluating the prognosis of OC patients.MethodsIn this study, 230 samples from The Cancer Genome Atlas (TCGA) OV dataset were subjected to mRNA expression profiling, single nucleotide polymorphism (SNP), and copy number variation (CNV) analysis comprehensively to screen out the differentially expressed genes (DEGs). An SVM classifier and a prognostic model were constructed using the Random Forest algorithm and LASSO Cox regression model respectively via R. The Gene Expression Omnibus (GEO) database was applied as the validation set.ResultsForty-eight differentially expressed genes (DEGs) were figured out through integrated analysis of gene expression, single nucleotide polymorphism (SNP), and copy number variation (CNV) data. A 10-gene classifier was constructed which could discriminate platinum-sensitive samples precisely with an AUC of 0.971 in the training set and of 0.926 in the GEO dataset (GSE638855). In addition, 8 optimal genes were further selected to construct the prognostic risk model whose predictions were consistent with the actual survival outcomes in the training cohort (p = 9.613e-05) and validated in GSE638855 (p = 0.04862). PNLDC1, SLC5A1, and SYNM were then identified as hub genes that were associated with both platinum response status and prognosis, which was further validated by the Fudan University Shanghai cancer center (FUSCC) cohort.ConclusionThese findings reveal a specific risk model that could serve as effective biomarkers to identify patients' platinum response status and predict survival outcomes for OC patients. PNLDC1, SLC5A1, and SYNM are the hub genes that may serve as potential biomarkers in OC treatment.

Dataset Information

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

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