PubMed · 11295589
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R Lieberman, W G Nelson. 2001. Introduction.. https://doi.org/10.1016/s0090-4295(01)00932-3
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Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tumor microenvironment, has been shown playing a significant role in shaping the actual treatment outcome. In this study, we develop a deep variational autoencoder (VAE) model to compress thousands of genes into latent vectors in a low-dimensional space. We then demonstrate that these encoded vectors could accurately impute drug response, outperform standard signature-gene based approaches, and appropriately control the overfitting problem. We apply rigorous quality assessment and validation, including assessing the impact of cell line lineage, cross-validation, cross-panel evaluation, and application in independent clinical data sets, to warrant the accuracy of the imputed drug response in both cell lines and cancer samples. Specifically, the expression-regulated component (EReX) of the observed drug response achieves high correlation across panels. Using the well-trained models, we impute drug response of The Cancer Genome Atlas data and investigate the features and signatures associated with the imputed drug response, including cell line origins, somatic mutations and tumor mutation burdens, tumor microenvironment, and confounding factors. In summary, our deep learning method and the results are useful for the study of signatures and markers of drug response.
DNA-microarray technology can be used to assess the expression of several thousands of genes at the same time. The identification of the gene expression profiles may help to better characterize human cancer. These studies may reveal subclasses of tumor types with similar histopathologic profile but different clinical courses.Furthermore,such studies could help to define therapeutic sensitivity and to estimate prognosis of various cancers. Identification of gene expression profiles of cancer can identify new therapeutic targets or cancer susceptibility genes. The DNA-microarray technology may write a new chapter in molecular oncology.
The influence of tetramerisation on the properties of the p53 mutants is poorly understood. We describe here the influence of the tetramerisation on the properties of the oncogenic mutant Asp281Gly. We show that despite being both nuclear the tetrameric Asp281Gly and the monomeric Asp281GlyLeu344Pro proteins have different properties: only Asp281Gly stimulates the transcription of the multidrug resistance-1 gene promoter and induces cisplatin resistance in Saos-2 cells. Moreover, we identify a 130-kDa protein that specifically interacts with Asp281Gly but not with Asp281GlyLeu344Pro. This suggests that tetramerisation is important for the properties of the p53 mutants and that these properties might be mediated via protein-protein interactions.