Background: Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. Results: In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC₅₀) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://manticore.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. Conclusions: We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

Quantifying cell-type proportions and their corresponding gene expression profiles in tissue samples would enhance understanding of the contributions of individual cell types to the physiological states of the tissue. Current approaches that address tissue heterogeneity have drawbacks. Experimental techniques, such as fluorescence-activated cell sorting, and single cell RNA sequencing are expensive. Computational approaches that use expression data from heterogeneous samples are promising, but most of the current methods estimate either cell-type proportions or cell-type-specific expression profiles by requiring the other as input. Although such partial deconvolution methods have been successfully applied to tumor samples, the additional input required may be unavailable. We introduce a novel complete deconvolution method, CDSeq, that uses only RNA-Seq data from bulk tissue samples to simultaneously estimate both cell-type proportions and cell-type-specific expression profiles. Using several synthetic and real experimental datasets with known cell-type composition and cell-type-specific expression profiles, we compared CDSeq’s complete deconvolution performance with seven other established deconvolution methods. Complete deconvolution using CDSeq represents a substantial technical advance over partial deconvolution approaches and will be useful for studying cell mixtures in tissue samples. CDSeq is available at GitHub repository (MATLAB and Octave code): https://github.com/kkang7/CDSeq. Understanding the cellular composition of bulk tissues is critical to investigate the underlying mechanisms of many biological processes. Single cell sequencing is a promising technique, however, it is expensive and the analysis of single cell data is non-trivial. Therefore, tissue samples are still routinely processed in bulk. To estimate cell-type composition using bulk gene expression data, computational deconvolution methods are needed. Many deconvolution methods have been proposed, however, they often estimate only cell type proportions using a reference cell type gene expression profile, which in many cases may not be available. We present a novel complete deconvolution method that uses only bulk gene expression data to simultaneously estimate cell-type-specific gene expression profiles and sample-specific cell-type proportions. We showed that, using multiple RNA-Seq and microarray datasets where the cell-type composition was previously known, our method could accurately determine the cell-type composition. By providing a method that requires a single input to determine both cell-type proportion and cell-type-specific expression profiles, we expect that our method will be beneficial to biologists and facilitate the research and identification of mechanisms underlying many biological processes.

Recent studies suggested that human/mammalian genomes are divided into large, discrete domains that are units of chromosome organization. CTCF, a CCCTC binding factor, has a diverse role in genome regulation including transcriptional regulation, chromosome-boundary insulation, DNA replication, and chromatin packaging. It remains unclear whether a subset of CTCF binding sites plays a functional role in establishing/maintaining chromatin topological domains.

Introduction: The objective of this study is to determine if multiple systemic autoimmune diseases (SAID) share gene expression pathways that could provide insights into pathogenic mechanisms common to these disorders. Methods: RNA microarray analyses (Agilent Human 1A(V2) 20K oligo arrays) were used to quantify gene expression in peripheral blood cells from 20 monozygotic (MZ) twin pairs discordant for SAID. Six affected probands with systemic lupus erythematosus (SLE), six with rheumatoid arthritis (RA), eight with idiopathic inflammatory myopathies (IIM), and their same-gendered unaffected twins, were enrolled. Comparisons were made between discordant twin pairs and these were also each compared to 40 unrelated control subjects (matched 2:1 to each twin by age, gender and ethnicity) using statistical and molecular pathway analyses. Relative quantitative PCR was used to verify independently measures of differential gene expression assessed by microarray analysis. Results: Probands and unrelated, matched controls differed significantly in gene expression for 104 probes corresponding to 92 identifiable genes (multiple-comparison adjusted P values < 0.1). Differentially expressed genes involved several overlapping pathways including immune responses (16%), signaling pathways (24%), transcription/translation regulators (26%), and metabolic functions (15%). Interferon (IFN)-response genes (IFI27, OASF, PLSCR1, EIF2AK2, TNFAIP6, and TNFSF10) were up-regulated in probands compared to unrelated controls. Many of the abnormally expressed genes played regulatory roles in multiple cellular pathways. We did not detect any probes expressed differentially in comparisons among the three SAID phenotypes. Similarly, we found no significant differences in gene expression when comparing probands to unaffected twins or unaffected twins to unrelated controls. Gene expression levels for unaffected twins appeared intermediate between that of probands and unrelated controls for 6535 probes (32% of the total probes) as would be expected by chance. By contrast, in unaffected twins intermediate ordering was observed for 84 of the 104 probes (81%) whose expression differed significantly between probands and unrelated controls. Conclusions: Alterations in expression of a limited number of genes may influence the dysregulation of numerous, integrated immune response, cell signaling and regulatory pathways that are common to a number of SAID. Gene expression profiles in peripheral blood suggest that for genes in these critical pathways, unaffected twins may be in a transitional or intermediate state of immune dysregulation between twins with SAID and unrelated controls, perhaps predisposing them to the development of SAID given the necessary and sufficient environmental exposures.