Darshan Singh

The accurate mapping of reads that span splice junctions is a critical component of all analytic techniques that work with RNA-seq data. We introduce a second generation splice detection algorithm, MapSplice, whose focus is high sensitivity and specificity in the detection of splices as well as CPU and memory efficiency. MapSplice can be applied to both short (<75 bp) and long reads (≥ 75 bp). MapSplice is not dependent on splice site features or intron length, consequently it can detect novel canonical as well as non-canonical splices. MapSplice leverages the quality and diversity of read alignments of a given splice to increase accuracy. We demonstrate that MapSplice achieves higher sensitivity and specificity than TopHat and SpliceMap on a set of simulated RNA-seq data. Experimental studies also support the accuracy of the algorithm. Splice junctions derived from eight breast cancer RNA-seq datasets recapitulated the extensiveness of alternative splicing on a global level as well as the differences between molecular subtypes of breast cancer. These combined results indicate that MapSplice is a highly accurate algorithm for the alignment of RNA-seq reads to splice junctions. Software download URL: http://www.netlab.uky.edu/p/bioinfo/MapSplice.

Comprehensive sequencing of human cancers has identified recurrent mutations in genes encoding chromatin regulatory proteins. For clear cell renal cell carcinoma (ccRCC), three of the five commonly mutated genes encode the chromatin regulators PBRM1, SETD2, and BAP1. How these mutations alter the chromatin landscape and transcriptional program in ccRCC or other cancers is not understood. Here, we identified alterations in chromatin organization and transcript profiles associated with mutations in chromatin regulators in a large cohort of primary human kidney tumors. By associating variation in chromatin organization with mutations in SETD2, which encodes the enzyme responsible for H3K36 trimethylation, we found that changes in chromatin accessibility occurred primarily within actively transcribed genes. This increase in chromatin accessibility was linked with widespread alterations in RNA processing, including intron retention and aberrant splicing, affecting ∼25% of all expressed genes. Furthermore, decreased nucleosome occupancy proximal to misspliced exons was observed in tumors lacking H3K36me3. These results directly link mutations in SETD2 to chromatin accessibility changes and RNA processing defects in cancer. Detecting the functional consequences of specific mutations in chromatin regulatory proteins in primary human samples could ultimately inform the therapeutic application of an emerging class of chromatin-targeted compounds.

Abstract Targeting the dysregulated BRAF–MEK–ERK pathway in cancer has increasingly emerged in clinical trial design. Despite clinical responses in specific cancers using inhibitors targeting BRAF and MEK, resistance develops often involving nongenomic adaptive bypass mechanisms. Inhibition of MEK1/2 by trametinib in patients with triple-negative breast cancer (TNBC) induced dramatic transcriptional responses, including upregulation of receptor tyrosine kinases (RTK) comparing tumor samples before and after one week of treatment. In preclinical models, MEK inhibition induced genome-wide enhancer formation involving the seeding of BRD4, MED1, H3K27 acetylation, and p300 that drives transcriptional adaptation. Inhibition of the P-TEFb–associated proteins BRD4 and CBP/p300 arrested enhancer seeding and RTK upregulation. BRD4 bromodomain inhibitors overcame trametinib resistance, producing sustained growth inhibition in cells, xenografts, and syngeneic mouse TNBC models. Pharmacologic targeting of P-TEFb members in conjunction with MEK inhibition by trametinib is an effective strategy to durably inhibit epigenomic remodeling required for adaptive resistance. Significance: Widespread transcriptional adaptation to pharmacologic MEK inhibition was observed in TNBC patient tumors. In preclinical models, MEK inhibition induces dramatic genome-wide modulation of chromatin, in the form of de novo enhancer formation and enhancer remodeling. Pharmacologic targeting of P-TEFb complex members at enhancers is an effective strategy to durably inhibit such adaptation. Cancer Discov; 7(3); 302–21. ©2017 AACR. This article is highlighted in the In This Issue feature, p. 235

The RNA transcriptome varies in response to cellular differentiation as well as environmental factors, and can be characterized by the diversity and abundance of transcript isoforms. Differential transcription analysis, the detection of differences between the transcriptomes of different cells, may improve understanding of cell differentiation and development and enable the identification of biomarkers that classify disease types. The availability of high-throughput short-read RNA sequencing technologies provides in-depth sampling of the transcriptome, making it possible to accurately detect the differences between transcriptomes. In this article, we present a new method for the detection and visualization of differential transcription. Our approach does not depend on transcript or gene annotations. It also circumvents the need for full transcript inference and quantification, which is a challenging problem because of short read lengths, as well as various sampling biases. Instead, our method takes a divide-and-conquer approach to localize the difference between transcriptomes in the form of alternative splicing modules (ASMs), where transcript isoforms diverge. Our approach starts with the identification of ASMs from the splice graph, constructed directly from the exons and introns predicted from RNA-seq read alignments. The abundance of alternative splicing isoforms residing in each ASM is estimated for each sample and is compared across sample groups. A non-parametric statistical test is applied to each ASM to detect significant differential transcription with a controlled false discovery rate. The sensitivity and specificity of the method have been assessed using simulated data sets and compared with other state-of-the-art approaches. Experimental validation using qRT-PCR confirmed a selected set of genes that are differentially expressed in a lung differentiation study and a breast cancer data set, demonstrating the utility of the approach applied on experimental biological data sets. The software of DiffSplice is available at http://www.netlab.uky.edu/p/bioinfo/DiffSplice.