Keywords: multiomics, drug screen, blood cancer, personalized medicine, shiny
Webpages:
https://github.com/lujunyan1118/DrugScreenExplorer Better tools for response prediction would improve quality of cancer care. To gain further insight into the pathogenesis of blood cancers as well as to understand determinants of drug response, we measured the sensitivity of primary tumor samples from a large cohort of leukemia/lymphoma patients to marketed drugs and chemical probes. Alongside, genome, transcriptome, DNA methylome and metablome data were obtained for the same set of patient samples, providing a valuable multidimensional resource for blood cancer study.
To facilitate the query and analysis of our dataset, we have created an R and Shiny based online platform – DrugScreenExplorer. This platform incorporates various tools for quality assessment, data visualization, exploratory data analysis and association test. For example, the drug screening quality can be readily examined by interactive heatmap plots of the screening plates and outlier samples and drugs can be detected by unsupervised clustering methods, such as principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). Moreover, associations among different omics datasets can be analyzed and visualized within this platform, facilitating hypothesis generation and subsequent experimental validation.
Those handy tools enable us to achieve seamless and efficient collaboration between dry lab and wet lab groups and to extract useful information from out multi-layer structure dataset in order to gain insight into the complexity of drug response and genotype-phenotype relationships in cancer. Currently, this Shiny platform are customized for our in-house data. But with further extensions, such as allowing users to upload their own data, it can be used as general-purpose tools to streamline the pre-processing, quality control, data visualization and reporting for other drug screening projects as well.
