PubMed · 8915725
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
H H Sharata, K H Katz. 1996. Liposomes.. https://doi.org/10.1111/j.1365-4362.1996.tb02969.x
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
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.
The bone marrow micro-environment produces a number of different survival factors that are important for the malignant growth and drug resistance of multiple myeloma (MM) cells. One of the main factors reported to be essential for survival and growth of MM cells in some experimental systems is IL-6. Therefore, the development and testing of substances that interfere with IL-6 or IL-6 receptor (IL-6R) function might have therapeutic value for the treatment of MM. We analyzed the effect of the IL-6R antagonist SANT-7 on growth and survival of the IL-6--dependent MM cell lines INA-6 and XG-1 as well as primary MM cells from 7 patients co-cultured with bone marrow stromal cells (BMSCs). In particular, we were interested in whether SANT-7 enhances the growth-inhibitory effects of dexamethasone (Dex) and all-trans-retinoic acid (ATRA). None of the drugs when tested as a single substance, including SANT-7, induced major growth inhibition if MM cells were co-cultured with primary human BMSCs. However, when Dex and ATRA were given in combination with SANT-7, strong growth inhibition was achieved in cell lines and primary MM cells. This effect was due to cell-cycle arrest and induction of apoptosis.
Glutathione S-transferases (GSTs) are metabolic phase II enzymes that promote reactive metabolite elimination by conjugating them to glutathione (GSH). Because of their important role in xenobiotic metabolism and detoxification, they have been implicated in carcinogenesis processes, especially epithelium transformation. Moreover, their influence on response to chemotherapy in cancer patients has been demonstrated. Genetic polymorphisms for GSTM1, GSTT1 and GSTP1 have been found in human populations and have been shown to have phenotypic consequences. To investigate the role of GST enzymes in carcinogenesis and in response to chemotherapy in patients with head and neck squamous cell carcinoma (HNSCC), GSTP1, GSTM1 and GSTT1 were studied prospectively in a large series of HNSCC patients. Correlations between GST alterations, p53 mutation status and clinical response to chemotherapy were investigated. We showed that the risk of developing laryngeal cancer was increased by 2.6-fold [95% CI 1.6--6.1] in patients with the GSTM1 null genotype and by 2.8-fold [95% CI 0.9--8.1] in patients with the homozygous GSTP1 val105 genotype. Furthermore, individuals with this latter genotype were over-represented in the p53 mutation group (p = 0.05). After storage duration and hemolysis adjustment, a significantly lower plasmatic GSTP1 level was observed in complete responders compared with partial and non-responders (mean: 4.4 +/- 0.06 microg/l, 4.7 +/- 0.06 microg/l and 4.7 +/- 0.07 microg/l; p = 0.05), respectively. The prevalence of p53-mutated tumors was significantly higher in the group of non-responders (81%) compared with partial (60%) and complete responders (64%) (p = 0.05). Two types of multivariate analysis were performed including parameters that have been shown to influence response to chemotherapy significantly in univariate analysis. p53 mutations and high tumor stage are independent factors of non-response to chemotherapy, whereas plasmatic GSTP1 levels and low tumor stage are independent factors of complete response. Our data suggest that GST enzymes are associated with larynx cancer and that their use as predictive factors and treatment targets should be further explored.