PubMed · 10918248
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I H Krakoff. 2000. Predawn.. https://doi.org/10.1002/1096-911x(200008)35%3A2%3C157%3A%3Aaid-mpo20%3E3.0.co%3B2-q
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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.
Genistein is a soy isoflavone that has attracted much attention for its chemopreventive properties. Baicalein is a flavone found in Scutellaria species, and its structure is analogous to that of genistein. We examined the two flavones' antiestrogenicity by using a transient transfection model. Our results indicated that baicalein could suppress 17beta-estradiol-induced transactivation in cells expressing estrogen receptor alpha, whereas no antagonistic activity was seen in genistein. Cell death assay and flow cytometry analysis demonstrated that baicalein was a stronger apoptosis inducing agent than genistein. Considering all the criteria tested, baicalein could be a better chemopreventive agent than genistein.