PubMed · 14051074
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L M HUSSEY. 1963. ANTICANCER AGENTS.. https://pubmed.ncbi.nlm.nih.gov/14051074/
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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.
Disseminated lymphangiomatosis is a rare disease mostly observed in children and young adults. If no surgical removal can be achieved, the prognosis is poor, especially for patients with thoracic localization and pleural effusions. Next to pleural drainage, therapeutic options include radiotherapy, local, and systemic pharmacotherapy. An 11-year-old boy presented with disseminated lymphangiomatosis involving thorax with massive pleural effusions, retroperitoneum, and bones. In immunohistochemical analysis, the tissue biopsy stained positive for vascular endothelial growth factor-receptor 3 (VEGFR-3). The patient has been treated with interferon alpha 2b for 2 years, and achieved a good clinical and radiological response.
To quantify the excess effect of an experimental treatment over a placebo group in clinical trials, we often consider use of the relative difference, defined as the proportion of patients who would respond to the experimental treatment among those who would not otherwise if they were assigned to the placebo group. To control the effects due to confounders on the response of interest, we frequently employ stratified analysis in practice. Before obtaining a summary estimate of the relative difference, it is desirable to assess whether this measure is constant across strata. Based on the beta-binomial model, we develop simple procedures for testing the homogeneity of relative difference for sparse data in which we have many strata but few patients per stratum. Using Monte Carlo simulations, we demonstrate that the proposed test procedures can generally perform well with respect to type I error in a variety of situations. We further evaluate and study the power of these test procedures. Finally, we note some robustness in using the test procedures proposed here.