Recently, the TCGA research network identified 193 prognostic gene signatures predictive of OS, but the gene association with chemotherapy response remains unexplored. Here we used a large sample set for identification of molecular and morphologic signatures that are associated with chemotherapy response. The predictive model on the basis of gene signature revealed an accuracy of 87.9% in correctly classifying refractory from responsive tumors in the TCGA training set and stratified patients in both the TCGA validation set and the Australian data set into groups that demonstrated significant discrepancy in tumor progression, suggesting the capacity of the gene signature to serve as a mechanism to stratify patients with respect to MK-2206 2HCl treatment. The imaging approach stratifies the cells into 10 bins based on nuclear size and accounts for the heterogeneity of cells in a tumor population. Our stratification revealed that most significant morphologic features differed between the chemosensitive and chemoresistant groups in the larger nuclei. However, nuclei within this size range account for a very small percentage, and the majority of the nuclei do not show a significant difference in chemotherapy response. This observation not only is consistent with the Goldie-Coldman hypothesis that only a small cell population may contribute to differential response to chemotherapy, but also suggests the difficulty of a conventional approach of simply correlating the overall morphologic differences with chemotherapy response, owing to the “dilution” effect. Therefore, our imaging approach allows us to interrogate different cell populations separated on the basis of nuclear size in a high throughput and automated fashion. In addition, none of the image features calculated from the entire nucleus per sample, the way similar to those used in other studies, show significant difference between the chemoresistant and chemosensitive patients. This discrepancy from the previous studies likely results from the number of nuclei used in the feature calculation. We used approximately 4000 nuclei per sample for feature value calculation, almost 80 times more than the amount used in the other studies. Taken together, our approach of binning the nucleus size and then assessing the image feature in each individual bin improves the image feature resolution and enhances the discriminating power. Furthermore, our approach of calculating the morphologic features in separate bins is capable of alleviating the size dependence of some of the features, such as circularity and roundness. Aside from the potentially practical value, the morphologic features also provide insights into cancer morphogenesis. The chemosensitive patients exhibit a smaller value of nuclear roundness in Bin 8, but with a larger variability and a larger aspect ratio. Such morphologic differences likely result from the active response of the cells to their environment and heightened cellular metabolism, that is contributable from different molecular regulations. This is further corroborated by pathway analysis, which revealed the gene enrichment in the morphologic function at cellular, tissue, and tumor levels. The gene content of this table offers potential insight into the structural and molecular mechanisms of the chemotherapy response.