Pathway-guided analysis identifies Myc-dependent alternative pre-mRNA splicing in aggressive prostate cancers

Abstract

We sought to define the landscape of alternative pre-mRNA splicing in prostate cancers and the relationship of exon choice to known cancer driver alterations. To do so, we compiled a metadataset composed of 876 RNA-sequencing (RNA-Seq) samples from five publicly available sources representing a range of prostate phenotypes from normal tissue to drug-resistant metastases. We subjected these samples to exon-level analysis with rMATS-turbo, purposebuilt software designed for large-scale analyses of splicing, and identified 13,149 high-confidence cassette exon events with variable incorporation across samples. We then developed a computational framework, pathway enrichment-guided activity study of alternative splicing (PEGASAS), to correlate transcriptional signatures of 50 different cancer driver pathways with these alternative splicing events. We discovered that Myc signaling was correlated with incorporation of a set of 1,039 cassette exons enriched in genes encoding RNA binding proteins. Using a human prostate epithelial transformation assay, we confirmed the Myc regulation of 147 of these exons, many of which introduced frameshifts or encoded premature stop codons. Our results connect changes in alternative pre-mRNA splicing to oncogenic alterations common in prostate and many other cancers. We also establish a role for Myc in regulating RNA splicing by controlling the incorporation of nonsense-mediated decay-determinant exons in genes encoding RNA binding proteins.alternative splicing | prostate cancer | Myc | rMATS | PEGASAS A lternative pre-mRNA splicing is a regulated process that governs exon choice and greatly diversifies the proteome. It is an essential process that contributes to development, tissue specification, and homeostasis and is often dysregulated in disease states (1). In cancer, this includes growth signaling, epithelial-tomesenchymal transition, resistance to apoptosis, and treatment resistance (2). In prostate cancer, our area of interest, the most notable splicing change is the emergence of the ligand-independent androgen receptor ARV7 isoform in response to hormone deprivation (3). Other examples include proangiogenic splice variants of VEGFA (4), tumorigenic variants of the transcription factors ERG and KLF6 (5, 6), and antiapoptotic splicing of BCL2L2 (7, 8). However, the intersection of upstream oncogenic signaling, pre-mRNA splicing, and the biological processes affected by those splicing events has not been defined at a global level.Prostate cancers progress from hormone-responsive, localized disease to hormone-independent, metastatic disease accompanied by changes in gene expression and mutations that confer cellautonomous growth and therapeutic resistance (9). The study of disease progression from primary prostate adenocarcinoma (PrAd) to metastatic, castration-resistant prostate cancer (mCRPC) and treatment-related neuroendocrine prostate cancer (NEPC) has been aided by large-scale genomic and transcriptomic studies of patient samples representing each form of the disease (10-13). E...

Poison Exon Splicing Regulates a Coordinated Network of SR Protein Expression during Differentiation and Tumorigenesis

“…Empty vector control contains the HA-tag but no CDS sequence . SRSF1-, SRSF2-, SRSF3-, SRSF4-, SRSF5-, SRSF6- (Sundararaman et al, 2016) was downloaded from ENCODE (https://www.encode.org/) and processed using rMATS (v4.0.1) (Phillips et al, 2020). For each alternative splicing event, the inclusion level is calculated as PSI for RBP-KD and control samples and averaged across the two replicates for each condition.…”

“…Sample IDs are listed in Table S1B. PSI values for each splicing event were quantified using the rMATS turbo v 4.0.2 (Phillips et al, 2020) docker (https://github.com/nuno-agostinho/rmats4/blob/master/Dockerfile) with GENCODE v32 reference transcriptome. Each sample was run against itself, and PSI values from each sample were then extracted from the unfiltered rMATS output for further analysis in R. Patient matched DPSI values ( Figure 1G) were calculated as PSI Tumor -PSI Normal by matching TCGA IDs.…”

“…Reads were mapped to the human reference genome using STAR in 2-pass mode (v 2.7.3a) (Dobin et al, 2013) and the Gencode GRCh38 v.32 reference transcript annotation. We utilized an in-house pipeline that implemented rMATS (v 4.1.0) (Phillips et al, 2020) to detect splicing events using both splice junction read counts and exon body counts and calculated a percent spliced in (PSI) score for each splicing event. PSI values were extracted from the unfiltered rMATS output for plotting and analysis in R.…”

Alcohol use disorder causes global changes in splicing in the human brain

“…The alignment was performed with the STAR aligner (v.2.5.2a) against the hg19 human genome. Resulting bam files from the STAR alignment were indexed with samtools for use by rMATS, as described elsewhere 18 , 19 . Briefly, rMATS pipeline used RNA sequencing reads which were mapped to different splice variants to estimate the isoform proportion, and a hierarchical framework was employed to simultaneously account for estimation uncertainty in individual replicates and variability among replicates.…”

Differential contribution of transcriptomic regulatory layers in the definition of neuronal identity

“…Importantly, surrogate variable analysis reveals that these results are independent of batch effects (Supplementary Fig. 4a, b , Supplementary Results, and Supplementary Methods) minimizing the potential impact of ratio-based metrics 48 . Similar to the hippocampal sub-region analysis, GE accounted for the most variance ( R 2 = 0.55).…”