PubMed · 9082153
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S Heinzl. 1997. [Optimizing the dosage].. https://pubmed.ncbi.nlm.nih.gov/9082153/
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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.
Multiple biologic effects of interferon-alpha (IFN-alpha), including cell growth inhibition and antiviral protection, are initiated by tyrosine phosphorylation of STAT proteins. Although this signal pathway has been intensively investigated, the relevance of STAT signal persistence has received scant attention. Using paired isogenic lymphoma cells (Daudi), which either are sensitive or resistant to growth inhibition by IFN-alpha, we found comparable initial tyrosine phosphorylation of multiple STAT proteins; however, the phosphorylation durations and associated DNA-binding activities diverged. Phosphorylation and DNA-binding capacity of STAT1 decreased after 4 to 8 hours in resistant cells, as compared with 24 to 32 hours in sensitive cells, whereas phosphorylation of STAT3 and STAT5b was briefer in both lines. Functional significance of the prolonged STAT1 signal, therefore, was explored by experimental interruption of tyrosine phosphorylation, either by premature withdrawal of the IFN-alpha or deferred addition of pharmacologically diverse antagonists: staurosporine (protein kinase inhibitor), phorbol 12-myristate 13-acetate (growth promoter), or aurintricarboxylic acid (ligand competitor). Results indicated that an approximately 18-hour period of continued STAT1 phosphorylation was associated with growth arrest, but that antiviral protection developed earlier. These differences provide novel evidence of a temporal dimension to IFN-alpha signal specificity and show that duration of STAT1 activation may be a critical variable in malignant cell responsiveness to antiproliferative therapy.
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