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:BackgroundPancreatic ductal adenocarcinoma (PDAC) is a lethal cancer that is characterized by its poor prognosis, rapid progression and development of drug resistance. Chemotherapy is a vital treatment option for most of PDAC patients. Stratification of PDAC patients, who would have a higher likelihood of responding to chemotherapy, could facilitate treatment selection and patient management.MethodsA quantitative proteomic study was performed to characterize the protein profiles in the plasma of PDAC patients undergoing chemotherapy to determine if specific biomarkers could be used to predict likelihood of therapeutic response.ResultsBy comparing the plasma proteome of the PDAC patients with positive therapeutic response and longer overall survival (Good-responders) to those who did not respond as well with shorter survival time (Limited-responders), we identified differential proteins and protein variants that could effectively segregate Good-responders from Limited-responders. Functional clustering and pathway analysis further suggested that many of these differential proteins were involved in pancreatic tumorigenesis. Four proteins, including vitamin-K dependent protein Z (PZ), sex hormone-binding globulin (SHBG), von Willebrand factor (VWF) and zinc-alpha-2-glycoprotein (AZGP1), were further evaluated as single or composite predictive biomarker with/without inclusion of CA 19-9. A composite biomarker panel that consists of PZ, SHBG, VWF and CA 19-9 demonstrated the best performance in distinguishing Good-responders from Limited-responders.ConclusionBased on the cohort investigated, our data suggested that systemic proteome alterations involved in pathways associated with inflammation, immunoresponse, coagulation and complement cascades may be reporters of chemo-treatment outcome in PDAC patients. For the majority of the patients involved, the panel consisting of PZ, SHBG, VWF and CA 19-9 was able to segregate Good-responders from Limited-responders effectively. Our data also showed that dramatic fluctuations of biomarker concentration in the circulating system of a PDAC patient, which might result from biological heterogeneity or confounding complications, could diminish the performance of a biomarker. Categorization of PDAC patients in terms of their tumor stages and histological types could potentially facilitate patient stratification for treatment.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets