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- W2589894312 abstract "e21111 Background: A challenge of cancer therapy is to optimize therapeutical options to individual patients. Cancers with similar histology may show dramatically different responses to therapy, indicating that a refined approach needs to be developed to classify tumors by intrinsic characteristics that may predict response to chemotherapy. Global expression profile-based classification has the potential to identify such tumor-intrinsic subclasses. Methods: Ninety-one non-small cell lung cancer (NSCLC) tumors were profiled using mRNA microarray. Unsupervised clustering was performed to identify molecular subclasses of NSCLC. The relevance of these molecular subclasses was linked to other clinical and histopathological characteristics. Results: The global expression profiles classified NSCLC into six subclasses with mixed histology. One of the subclasses with adenocarcinoma (ADC) and large cell carcinoma (LCC) was correlated to putative resistance to pyrimidine synthesis inhibitor (pemetrexed). Conversely, a subset of squamous cell carcinoma (SCC)had predicted therapeutic benefit from this agent. The distinct molecular attributes of NSCLCs resistant to pemetrexed were bioinformatically characterized. For instant clinical selection of putative responders to Pemetrexed, we tested the expression of eight routine histological markers and identified that the expression of these markers, cooperated with refined molecular classification of NSCLC, can predict response to pemetrexed. Conclusions: We identified six distinct subgroups of NSCLC based on gene expression profiles. This molecular classification may aid in reliably stratifying patients with respect to the choice of therapeutic agents." @default.
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- W2589894312 date "2011-05-20" @default.
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- W2589894312 title "Expression profiling-based subtyping of NSCLC and its putative association with response to chemotherapy." @default.
- W2589894312 doi "https://doi.org/10.1200/jco.2011.29.15_suppl.e21111" @default.
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