PubMed · 8742840
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L Y Kuo, A H Liu, T J Marks. 1996. Metallocene interactions with DNA and DNA-processing enzymes.. https://pubmed.ncbi.nlm.nih.gov/8742840/
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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.
Asiatic acid (AA), a triterpene, decreased viability and induced apoptosis of HepG2 human hepatoma cells in a dose-dependent manner. AA also markedly increased intracellular Ca(2+) level, which was blocked by TMB-8 and dantrolene, intracellular Ca(2+) release blockers, but not by EGTA, an extracellular Ca(2+) chelator. Moreover, AA-induced apoptosis was significantly suppressed by treatment with TMB-8 and dantrolene, suggesting that intracellular Ca(2+) release may play an essential role in the AA-induced apoptosis. In addition, AA profoundly increased protein level of p53, which was also inhibited by BAPTA/AM, an intracellular Ca(2+) chelator, TMB-8 and dantrolene. Treatment with A23187, a Ca(2+) ionophore, or thapsigargin, a Ca(2+)-ATPase inhibitor, alone enhanced p53 nuclear accumulation, indicating that p53 accumulation is dependent on intracellular Ca(2+) increase. Furthermore, the viability of Hep3B, p53-null cells, was much higher than that of HepG2, p53-wild type cells, when treated with AA. Taken together, these results suggest that AA induced apoptosis through increased intracellular Ca(2+), which, in turn, enhanced p53 expression in HepG2 cells. These results further suggest that AA may be a valuable agent for the therapeutic intervention of human hepatomas.