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Genomic Multivariate Predictors of Response to Adjuvant Chemotherapy in Ovarian Carcinoma: Predicting Platinum Resistance

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE30161 | GEO | 2012/08/21

SECONDARY ACCESSION(S): PRJNA143927

REPOSITORIES: GEO

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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:Although preoperative chemoradiotherapy (CRT) and surgical mesorectal resection is the standard of care for locally advanced rectal carcinomas, it is still difficult to predict which patients will respond to treatment. We explored how differential stromal transcriptomic profiles from microdissected pretreatment rectal biopsies can be used to define an immunohistochemical score based on two CAF-specific proteins for predicting neoadjuvant treatment response. The analysis of differentially expressed genes (DEGs) of stroma and tumour glands from responder and non-responder patients shows that most changes were associated with the stromal compartment. Gene ontology analysis revealed that the DEGs codify mainly for extracellular matrix and ribosomal components. We built a CAF-specific classifier with genes showing monotonic changes in expression according to the tumour regression grade (coefficient >1; FN1, COL3A1, COL1A1, MMP2 and IGFBP5). These are the genes that display the biggest a priori differences in expression between non-responder and responder stroma. We translated these five genes at the protein level by means of immunohistochemical staining in a cohort of 38 patients. For predictive purposes we used a leave-one-out cross-validated (LOOCV) model with a positive predictive value (PPV) of 80%. Classifier optimisation with Random Forest identified FN1 and COL3A1 as the best predictors. Rebuilding the LOOCV regression model improved the classification performance with a PPV of 89.5% and a negative predictive value (NPV) of 73.7%. An independent cohort of 36 patients was used to validate the classifier performance, which had a PPV of 84.2% and an NPV of 70.6%. In a multivariate analysis the two-protein classifier proved to be the only independent predictor of response (HR=2.58; P=0.003). We also explored a pharmacogenomic approach to gather information about possible therapeutic strategies for non-responder patients. In conclusion, we developed a two-protein immunohistochemical classifier that performs well at predicting the absence of response to neoadjuvant treatment in rectal cancer.

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Genomic Multivariate Predictors of Response to Adjuvant Chemotherapy in Ovarian Carcinoma: Predicting Platinum Resistance

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