BET Inhibition Enhances TNF-Mediated Antitumor Immunity

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Abstract

Targeting chromatin binding proteins and modifying enzymes can concomitantly affect tumor cell proliferation and survival, as well as enhance antitumor immunity and augment cancer immunotherapies. By screening a small-molecule library of epigenetics-based therapeutics, BET (bromo- and extra-terminal domain) inhibitors (BETi) were identified as agents that sensitize tumor cells to the antitumor activity of CD8+ T cells. BETi modulated tumor cells to be sensitized to the cytotoxic effects of the proinflammatory cytokine TNF. By preventing the recruitment of BRD4 to p65-bound cis-regulatory elements, BETi suppressed the induction of inflammatory gene expression, including the key NF-κB target genes BIRC2 (cIAP1) and BIRC3 (cIAP2). Disruption of prosurvival NF-κB signaling by BETi led to unrestrained TNF-mediated activation of the extrinsic apoptotic cascade and tumor cell death. Administration of BETi in combination with T-cell bispecific antibodies (TCB) or immune-checkpoint blockade increased bystander killing of tumor cells and enhanced tumor growth inhibition in vivo in a TNF-dependent manner. This novel epigenetic mechanism of immunomodulation may guide future use of BETi as adjuvants for immune-oncology agents.

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The Sequence Alignment/Map format and SAMtools

Heng Li, Bob Handsaker, Alec Wysoker … (2009)

Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk

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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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voom: precision weights unlock linear model analysis tools for RNA-seq read counts

Charity Law, Yunshun Chen, Wei Shi … (2014)

New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.