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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: BACKGROUND, RESULTS. Meshes: Adenocarcinoma, Adenocarcinoma of Lung, Antineoplastic Agents, Biomarkers, Tumor, Breast Neoplasms, Cohort Studies, Estrogen Receptor alpha, Female, Gene Expression Profiling, Humans, Lung Neoplasms, Neoadjuvant Therapy, Neoplasms, Ovarian Neoplasms, Prognosis, Receptor, ErbB-2.
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Do consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response? Context: Genome scale expression profiling of human tumor samples is likely to yield improved cancer treatment decisions. However, identification of clinically predictive or prognostic classifiers can be challenging when a large number of genes are measured in a small number of tumors. We describe an unsupervised method to extract robust, consistent metagenes from multiple analogous data sets. We applied this method to expression profiles from five "double negative breast cancer" (DNBC) (not expressing ESR1 or HER2) cohorts and derived four metagenes. We assessed these metagenes in four similar but independent cohorts and found strong associations between three of the metagenes and agent-specific response to neoadjuvant therapy. Furthermore, we applied the method to ovarian and early stage lung cancer, two tumor types that lack reliable predictors of outcome, and found that the metagenes yield predictors of survival for both.
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These results suggest that the use of multiple data sets to derive potential biomarkers can filter out data set-specific noise and can increase the efficiency in identifying clinically accurate biomarkers. Final Decision: yes