Detection of recurrent alternative splicing switches in tumor samples reveals novel signatures of cancer

Exploring transcriptional switches from pairwise, temporal and population RNA-Seq data using deepTS

Transcriptional switch (TS) is a widely observed phenomenon caused by changes in the relative expression of transcripts from the same gene, in spatial, temporal or other dimensions. TS has been associated with human diseases, plant development and stress responses. Its investigation is often hampered by a lack of suitable tools allowing comprehensive and flexible TS analysis for high-throughput RNA sequencing (RNA-Seq) data. Here, we present deepTS, a user-friendly web-based implementation that enables a fully interactive, multifunctional identification, visualization and analysis of TS events for large-scale RNA-Seq datasets from pairwise, temporal and population experiments. deepTS offers rich functionality to streamline RNA-Seq-based TS analysis for both model and non-model organisms and for those with or without reference transcriptome. The presented case studies highlight the capabilities of deepTS and demonstrate its potential for the transcriptome-wide TS analysis of pairwise, temporal and population RNA-Seq data. We believe deepTS will help research groups, regardless of their informatics expertise, perform accessible, reproducible and collaborative TS analyses of large-scale RNA-Seq data.

HBA-DEALS: accurate and simultaneous identification of differential expression and splicing using hierarchical Bayesian analysis

We present Hierarchical Bayesian Analysis of Differential Expression and ALternative Splicing (HBA-DEALS), which simultaneously characterizes differential expression and splicing in cohorts. HBA-DEALS attains state of the art or better performance for both expression and splicing and allows genes to be characterized as having differential gene expression, differential alternative splicing, both, or neither. HBA-DEALS analysis of GTEx data demonstrated sets of genes that show predominant DGE or DAST across multiple tissue types. These sets have pervasive differences with respect to gene structure, function, membership in protein complexes, and promoter architecture.

Reference genome and annotation updates lead to contradictory prognostic predictions in gene expression signatures: a case study of resected stage I lung adenocarcinoma

RNA-sequencing enables accurate and low-cost transcriptome-wide detection. However, expression estimates vary as reference genomes and gene annotations are updated, confounding existing expression-based prognostic signatures. Herein, prognostic 9-gene pair signature (GPS) was applied to 197 patients with stage I lung adenocarcinoma derived from previous and latest data from The Cancer Genome Atlas (TCGA) processed with different reference genomes and annotations. For 9-GPS, 6.6% of patients exhibited discordant risk classifications between the two TCGA versions. Similar results were observed for other prognostic signatures, including IRGPI, 15-gene and ORACLE. We found that conflicting annotations for gene length and overlap were the major cause of their discordant risk classification. Therefore, we constructed a prognostic 40-GPS based on stable genes across GENCODE v20-v30 and validated it using public data of 471 stage I samples (log-rank P < 0.0010). Risk classification was still stable in RNA-sequencing data processed with the newest GENCODE v32 versus GENCODE v20-v30. Specifically, 40-GPS could predict survival for 30 stage I samples with formalin-fixed paraffin-embedded tissues (log-rank P = 0.0177). In conclusion, this method overcomes the vulnerability of existing prognostic signatures due to reference genome and annotation updates. 40-GPS may offer individualized clinical applications due to its prognostic accuracy and classification stability.

Ribosome profiling at isoform level reveals evolutionary conserved impacts of differential splicing on the proteome

The differential production of transcript isoforms from gene loci is a key cellular mechanism. Yet, its impact in protein production remains an open question. Here, we describe ORQAS (ORF quantification pipeline for alternative splicing), a pipeline for the translation quantification of individual transcript isoforms using ribosome-protected mRNA fragments (ribosome profiling). We find evidence of translation for 40-50% of the expressed isoforms in human and mouse, with 53% of the expressed genes having more than one translated isoform in human, and 33% in mouse. Differential splicing analysis revealed that about 40% of the splicing changes at RNA level are concordant with changes in translation. Furthermore, orthologous cassette exons between human and mouse preserve the directionality of the change, and are enriched in microexons in a comparison between glia and glioma. ORQAS leverages ribosome profiling to uncover a widespread and evolutionarily conserved impact of differential splicing on translation, particularly of microexon-containing isoforms.

An atlas of transposable element-derived alternative splicing in cancer

Transposable element (TE)-derived sequences comprise more than half of the human genome, and their presence has been documented to alter gene expression in a number of different ways, including the generation of alternatively spliced transcript isoforms. Alternative splicing has been associated with tumorigenesis for a number of different cancers. The objective of this study was to broadly characterize the role of human TEs in generating alternatively spliced transcript isoforms in cancer. To do so, we screened for the presence of TE-derived sequences co-located with alternative splice sites that are differentially used in normal versus cancer tissues. We analysed a comprehensive set of alternative splice variants characterized for 614 matched normal-tumour tissue pairs across 13 cancer types, resulting in the discovery of 4820 TE-generated alternative splice events distributed among 723 cancer-associated genes. Short interspersed nuclear elements (Alu) and long interspersed nuclear elements (L1) were found to contribute the majority of TE-generated alternative splice sites in cancer genes. A number of cancer-associated genes, including MYH11, WHSC1 and CANT1, were shown to have overexpressed TE-derived isoforms across a range of cancer types. TE-derived isoforms were also linked to cancer-specific fusion transcripts, suggesting a novel mechanism for the generation of transcriptome diversity via trans-splicing mediated by dispersed TE repeats. This article is part of a discussion meeting issue 'Crossroads between transposons and gene regulation'.

Edgetic perturbation signatures represent known and novel cancer biomarkers

Isoform switching is a recently characterized hallmark of cancer, and often translates to the loss or gain of domains mediating protein interactions and thus, the re-wiring of the interactome. Recent computational tools leverage domain-domain interaction data to resolve the condition-specific interaction networks from RNA-Seq data accounting for the domain content of the primary transcripts expressed. Here, we used The Cancer Genome Atlas RNA-Seq datasets to generate 642 patient-specific pairs of interactomes corresponding to both the tumor and the healthy tissues across 13 cancer types. The comparison of these interactomes provided a list of patient-specific edgetic perturbations of the interactomes associated with the cancerous state. We found that among the identified perturbations, select sets are robustly shared between patients at the multi-cancer, cancer-specific and cancer sub-type specific levels. Interestingly, the majority of the alterations do not directly involve significantly mutated genes, nevertheless, they strongly correlate with patient survival. The findings (available at EdgeExplorer: “http://webclu.bio.wzw.tum.de/EdgeExplorer”) are a new source of potential biomarkers for classifying cancer types and the proteins we identified are potential anti-cancer therapy targets.

Putatively cancer-specific exon-exon junctions are shared across patients and present in developmental and other non-cancer cells

This study probes the distribution of putatively cancer-specific junctions across a broad set of publicly available non-cancer human RNA sequencing (RNA-seq) datasets. We compared cancer and non-cancer RNA-seq data from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) Project and the Sequence Read Archive. We found that (i) averaging across cancer types, 80.6% of exon-exon junctions thought to be cancer-specific based on comparison with tissue-matched samples (σ = 13.0%) are in fact present in other adult non-cancer tissues throughout the body; (ii) 30.8% of junctions not present in any GTEx or TCGA normal tissues are shared by multiple samples within at least one cancer type cohort, and 87.4% of these distinguish between different cancer types; and (iii) many of these junctions not found in GTEx or TCGA normal tissues (15.4% on average, σ = 2.4%) are also found in embryological and other developmentally associated cells. These findings refine the meaning of RNA splicing event novelty, particularly with respect to the human neoepitope repertoire. Ultimately, cancer-specific exon-exon junctions may have a substantial causal relationship with the biology of disease.

Prognostic Value and Potential Regulatory Mechanism of Alternative Splicing in Geriatric Breast Cancer

Breast cancer has the highest mortality and morbidity among women, especially in elderly women over 60 years old. Abnormal alternative splicing (AS) events are associated with the occurrence and development of geriatric breast cancer (GBC), yet strong evidence is lacking for the prognostic value of AS in GBC and the regulatory network of AS in GBC, which may highlight the mechanism through which AS contributes to GBC. In the present study, we obtained splicing event information (SpliceSeq) and clinical information for GBC from The Cancer Genome Atlas, and we constructed a GBC prognosis model based on AS events to predict the survival outcomes of GBC. Kaplan–Meier analysis was conducted to evaluate the predictive accuracy among different molecular subtypes of GBC. We conducted enrichment analysis and constructed a splicing network between AS and the splicing factor (SF) to examine the possible regulatory mechanisms of AS in GBC. We constructed eight prognostic signatures with very high statistical accuracy in predicting GBC survival outcomes from 45,421 AS events of 10,480 genes detected in 462 GBC patients; the prognostic model based on exon skip (ES) events had the highest accuracy, indicating its significant value in GBC prognosis. The constructed regulatory SF–AS network may explain the potential regulatory mechanism between SF and AS, which may be the mechanism through which AS events contribute to GBC survival outcomes. The findings confirm that AS events have a significant prognostic value in GBC, and we found a few effective prognostic signatures. We also hypothesized the mechanism underlying AS in GBC and discovered a potential regulatory mechanism between SF and AS.

Leptin, Adiponectin, and Sam68 in Bone Metastasis from Breast Cancer

The most serious aspect of neoplastic disease is the spread of cancer cells to secondary sites. Skeletal metastases can escape detection long after treatment of the primary tumour and follow-up. Bone tissue is a breeding ground for many types of cancer cells, especially those derived from the breast, prostate, and lung. Despite advances in diagnosis and therapeutic strategies, bone metastases still have a profound impact on quality of life and survival and are often responsible for the fatal outcome of the disease. Bone and the bone marrow environment contain a wide variety of cells. No longer considered a passive filler, bone marrow adipocytes have emerged as critical contributors to cancer progression. Released by adipocytes, adipokines are soluble factors with hormone-like functions and are currently believed to affect tumour development. Src-associated in mitosis of 68 kDa (Sam68), originally discovered as a protein physically associated with and phosphorylated by c-Src during mitosis, is now recognised as an important RNA-binding protein linked to tumour onset and progression of disease. Sam68 also regulates splicing events and recent evidence reports that dysregulation of these events is a key step in neoplastic transformation and tumour progression. The present review reports recent findings on adipokines and Sam68 and their role in breast cancer progression and metastasis.

Telomerase Reverse Transcriptase (TERT) Regulation in Thyroid Cancer: A Review

Telomerase reverse transcriptase (TERT) is the catalytic subunit of the enzyme telomerase and is essential for telomerase activity. Upregulation of TERT expression and resulting telomerase activity occurs in the large majority of malignancies, including thyroid cancer. This upregulation results in continued cellular proliferation and avoidance of cellular senescence and cell death. In this review we will briefly introduce TERT and telomerase activity as it pertains to thyroid cancer and, highlight the effects of TERT on cancer cells. We will also explore in detail the different TERT regulatory strategies and how TERT is reactivated in thyroid cancer cells, specifically. These regulatory mechanisms include both activating single base pair TERT promoter mutations and epigenetic changes at the promoter, including changes in CpG methylation and histone modifications that affect chromatin structure. Further, regulation includes the allele-specific regulation of the TERT promoter in thyroid cancer cells harboring the TERT promoter mutation. These entail allele-specific transcriptional activator binding, DNA methylation, histone modifications, and mono-allelic expression of TERT. Lastly, TERT copy number alterations and alternative splicing are also implicated. Both amplifications of the TERT locus and increased full-length transcripts and decreased inactive and dominant negative isoforms result in active telomerase. Finally, the clinical significance of TERT in thyroid cancer is also reviewed.

Survival-associated alternative splicing signatures in esophageal carcinoma

Alternative splicing (AS), a major mechanism for the enhancement of transcriptome and proteome diversity, has been widely demonstrated to be involved in the full spectrum of oncogenic processes. High-throughput sequencing technology and the rapid accumulation of clinical data sets have provided an opportunity to systemically analyze the association between messenger RNA AS variants and patient clinical outcomes. Here, we compared differentially spliced AS transcripts between esophageal carcinoma (ESCA) and non-tumor tissues, profiled genome-wide survival-associated AS events in 87 patients with esophageal adenocarcinoma (EAC) and 79 patients with esophageal squamous cell carcinoma (ESCC) using The Cancer Genome Atlas (TCGA) RNA-seq data set, and constructed predictive models as well as splicing regulation networks by integrated bioinformatic analysis. A total of 2326 AS events in 1738 genes and 1812 AS events in 1360 genes were determined to be significantly associated with overall survival (OS) of patients in the EAC and ESCC cohorts, respectively, including some essential participants in the oncogenic process. The predictive model of each splice type performed reasonably well in distinguishing good and poor outcomes of patients with esophageal cancer, and values for the area under curve reached 0.942 and 0.815 in the EAC exon skip predictive model and the ESCC alternate acceptor site predictive model, respectively. The splicing regulation networks revealed an interesting correlation between survival-associated splicing factors and prognostic AS genes. In summary, we created prognostic models for patients with esophageal cancer based on AS signatures and constructed novel splicing correlation networks.

Aberrant alternative splicing in breast cancer

Alternative splicing is critical for human gene expression regulation, which plays a determined role in expanding the diversity of functional proteins. Importantly, alternative splicing is a hallmark of cancer and a potential target for cancer therapeutics. Based on the statistical data, breast cancer is one of the top leading causes of cancer-related deaths in women worldwide. Strikingly, alternative splicing is closely associated with breast cancer development. Here, we seek to provide a general review of the relationship between alternative splicing and breast cancer. We introduce the process of alternative splicing and its regulatory role in cancers. In addition, we highlight the functions of aberrant alternative splicing and mutations of splicing factors in breast cancer progression. Moreover, we discuss the role of alternative splicing in cancer drug resistance and the potential of being targets for cancer therapeutics.

Construction of a model to predict the prognosis of patients with cholangiocarcinoma using alternative splicing events

Cholangiocarcinoma (CCA) is a type of malignant tumor that originates in the mucosal epithelial cells of the biliary system. It is a highly aggressive cancer that progresses rapidly, has low surgical resection rates and a high recurrence. At present, no prognostic molecular biomarker for CCA has been identified. However, CCA progression is affected by mRNA precursors that modify gene expression levels and protein structures through alternative splicing (AS) events, which create molecular indicators that may potentially be used to predict CCA outcomes. The present study aimed to construct a model to predict CCA prognosis based on AS events. Using prognostic data available from The Cancer Genome Atlas, including the percent spliced index of AS events obtained from TCGASpliceSeq in 32 CCA cases, univariate and multivariate Cox regression analyses were performed to assess the associations between AS events and the overall survival (OS) rates of patients with CCA. Additional multivariate Cox regression analyses were used to identify AS events that were significantly associated with prognosis, which were used to construct a prediction model with a prognostic index (PI). A receiver operating characteristic (ROC) curve was used to determine the predictive value of the PI, and Pearson's correlation analysis was used to determine the association between OS-related AS events and splicing factors. A total of 38,804 AS events were identified in 9,673 CCA genes, among which univariate Cox regression analysis identified 1,639 AS events associated with OS (P<0.05); multivariate Cox regression analysis narrowed this list to 23 CCA AS events (P<0.001). The final PI model was constructed to predict the survival of patients with CCA; the ROC curve demonstrated that it had a high predictive power for CCA prognosis, with a highest area under the curve of 0.986. Correlations between 23 OS-related AS events and splicing factors were also noted, and may thus, these AS events may be used to improve predictions of OS. In conclusion, AS events exhibited potential for predicting the prognosis of patients with CCA, and thus, the effects of AS events in CCA required further examination.

Uncovering and characterizing splice variants associated with survival in lung cancer patients

Splice variants have been shown to play an important role in tumor initiation and progression and can serve as novel cancer biomarkers. However, the clinical importance of individual splice variants and the mechanisms by which they can perturb cellular functions are still poorly understood. To address these issues, we developed an efficient and robust computational method to: (1) identify splice variants that are associated with patient survival in a statistically significant manner; and (2) predict rewired protein-protein interactions that may result from altered patterns of expression of such variants. We applied our method to the lung adenocarcinoma dataset from TCGA and identified splice variants that are significantly associated with patient survival and can alter protein-protein interactions. Among these variants, several are implicated in DNA repair through homologous recombination. To computationally validate our findings, we characterized the mutational signatures in patients, grouped by low and high expression of a splice variant associated with patient survival and involved in DNA repair. The results of the mutational signature analysis are in agreement with the molecular mechanism suggested by our method. To the best of our knowledge, this is the first attempt to build a computational approach to systematically identify splice variants associated with patient survival that can also generate experimentally testable, mechanistic hypotheses. Code for identifying survival-significant splice variants using the Null Empirically Estimated P-value method can be found at https://github.com/thecodingdoc/neep. Code for construction of Multi-Granularity Graphs to discover potential rewired protein interactions can be found at https://github.com/scwest/SINBAD.

P120 Catenin Isoforms Differentially Associate with Breast Cancer Invasion and Metastasis

Tumor metastasis is the endpoint of tumor progression and depends on the ability of tumor cells to locally invade tissue, transit through the bloodstream and ultimately to colonize secondary organs at distant sites. P120 catenin (p120) has been implicated as an important regulator of metastatic dissemination because of its roles in cell–cell junctional stability, cytoskeletal dynamics, growth and survival. However, conflicting roles for p120 in different tumor models and steps of metastasis have been reported, and the understanding of p120 functions is confounded by the differential expression of p120 isoforms, which differ in N-terminal length, tissue localization and, likely, function. Here, we used in silico exon expression analyses, in vitro invasion assays and both RT-PCR and immunofluorescence of human tumors. We show that alternative exon usage favors expression of short isoform p120-3 in 1098 breast tumors and correlates with poor prognosis. P120-3 is upregulated at the invasive front of breast cancer cells migrating as collective groups in vitro. Furthermore, we demonstrate in histological sections of 54 human breast cancer patients that p120-3 expression is maintained throughout the metastatic cascade, whereas p120-1 is differentially expressed and diminished during invasion and in metastases. These data suggest specific regulation and functions of p120-3 in breast cancer invasion and metastasis.

Abi1 loss drives prostate tumorigenesis through activation of EMT and non-canonical WNT signaling

Background

Prostate cancer development involves various mechanisms, which are poorly understood but pointing to epithelial mesenchymal transition (EMT) as the key mechanism in progression to metastatic disease. ABI1, a member of WAVE complex and actin cytoskeleton regulator and adaptor protein, acts as tumor suppressor in prostate cancer but the role of ABI1 in EMT is not clear.

Methods

To investigate the molecular mechanism by which loss of ABI1 contributes to tumor progression, we disrupted the ABI1 gene in the benign prostate epithelial RWPE-1 cell line and determined its phenotype. Levels of ABI1 expression in prostate organoid tumor cell lines was evaluated by Western blotting and RNA sequencing. ABI1 expression and its association with prostate tumor grade was evaluated in a TMA cohort of 505 patients and metastatic cell lines.

Results

Low ABI1 expression is associated with biochemical recurrence, metastasis and death (p = 0.038). Moreover, ABI1 expression was significantly decreased in Gleason pattern 5 vs. pattern 4 (p = 0.0025) and 3 (p = 0.0012), indicating an association between low ABI1 expression and highly invasive prostate tumors. Disruption of ABI1 gene in RWPE-1 cell line resulted in gain of an invasive phenotype, which was characterized by a loss of cell-cell adhesion markers and increased migratory ability of RWPE-1 spheroids. Through RNA sequencing and protein expression analysis, we discovered that ABI1 loss leads to activation of non-canonical WNT signaling and EMT pathways, which are rescued by re-expression of ABI1. Furthermore, an increase in STAT3 phosphorylation upon ABI1 inactivation and the evidence of a high-affinity interaction between the FYN SH2 domain and ABI1 pY421 support a model in which ABI1 acts as a gatekeeper of non-canonical WNT-EMT pathway activation downstream of the FZD2 receptor.

Conclusions

ABI1 controls prostate tumor progression and epithelial plasticity through regulation of EMT-WNT pathway. Here we discovered that ABI1 inhibits EMT through suppressing FYN-STAT3 activation downstream from non-canonical WNT signaling thus providing a novel mechanism of prostate tumor suppression.

Electronic supplementary material

The online version of this article (10.1186/s12964-019-0410-y) contains supplementary material, which is available to authorized users.

Prognostic index of aberrant mRNA splicing profiling acts as a predictive indicator for hepatocellular carcinoma based on TCGA SpliceSeq data

Alternative splicing in tumor cells may be used as a molecular marker for the differential diagnosis of certain tumor types and assessment of prognosis. The aim of the present study was to investigate the associations among alternative splicing events, splicing factors, and the survival of patients with hepatocellular carcinoma (HCC). The alternative splicing event profiles of 371 patients with HCC were downloaded from The Cancer Genome Atlas (TCGA) SpliceSeq data, and the percent-splice-in value for each splicing event was calculated. The association between alternative splicing events and overall survival was evaluated. The most significant prognosis-related splicing events were used to build up a prognostic index (PI). A total of 3,082 survival-associated alternative splicing events were detected in HCC. The final PI based on all of the most significant candidate alternative splicing events exhibited better performance in distinguishing good or poor survival in patients compared to the PI based on a single type of splicing event. Receiver operating characteristic curves confirmed the high efficiency of the PI in predicting the survival of HCC patients, with an area under the curve of 0.914. The overexpression of 32 prognosis-related splicing factor genes could also predict poor prognosis in patients with HCC. In conclusion, the constructed computational prognostic model based on HCC-specific alternative splicing events may be used as a molecular marker for the prognosis of HCC.

Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer

Motivation

Current bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches.

Results

We introduce Splice Expression Variability Analysis (SEVA) to model increased heterogeneity of splice variant usage between conditions (e.g. tumor and normal samples). SEVA uses a rank-based multivariate statistic that compares the variability of junction expression profiles within one condition to the variability within another. Simulated data show that SEVA is unique in modeling heterogeneity of gene isoform usage, and benchmark SEVA's performance against EBSeq, DiffSplice and rMATS that model differential isoform usage instead of heterogeneity. We confirm the accuracy of SEVA in identifying known splice variants in head and neck cancer and perform cross-study validation of novel splice variants. A novel comparison of splice variant heterogeneity between subtypes of head and neck cancer demonstrated unanticipated similarity between the heterogeneity of gene isoform usage in HPV-positive and HPV-negative subtypes and anticipated increased heterogeneity among HPV-negative samples with mutations in genes that regulate the splice variant machinery. These results show that SEVA accurately models differential heterogeneity of gene isoform usage from RNA-seq data.

Availability and implementation

SEVA is implemented in the R/Bioconductor package GSReg.

Contact

bahman@jhu.edu or favorov@sensi.org or ejfertig@jhmi.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Alternative splicing-derived intersectin1-L and intersectin1-S exert opposite function in glioma progression

Intersectin1 (ITSN1) contains two isoforms: ITSN1-S and ITSN1-L, which is highly regulated by alternative splicing. However, the alteration of alternative splicing and its importance in cancer is still unknown. In this study, our transcriptome analysis by using a large glioma cohort indicated the two isoforms exerted opposite function in glioma progression. Our previous results had shown ITSN1-S could promote glioma development; however, the function of ITSN1-L remained unknown. In this study, we first confirmed that ITSN1-L exerted an inhibitory role in glioma progression both in vivo and in vitro, which was contrary to the function of ITSN1-S. In additional, we also elucidated the mechanisms of ITSN1-L in inhibiting tumor progression. First, we revealed ITSN1-L could interact with α-tubulin to promote HDAC6-dependent deacetylation of ac-tubulin leading to decreased cell motility. Second, ITSN1-L could attenuate cell-substrate adhesion through FAK/integrin β3 pathway. Third, ITSN1-L was able to strengthen cell-cell adhesion by upregulating N-cadherin expression and its re-localization to membrane by ANXA2 and TUBB3/TUBB4. In conclusion, we found for the first time that two isoforms produced by alternative splicing exerted opposite functions in glioma development. Therefore, upregulation of ITSN1-L expression as well as downregulation of ITSN1-S expression probably was a better strategy in glioma treatment. Our present study laid a foundation for the importance of alternative splicing in glioma progression and raised the possibility of controlling glioma development completely at an alternative splicing level to be a more effective strategy.

Alternative splicing in lung cancer

Lung cancer has the highest mortality rate of all cancers worldwide. Lung cancer is a very heterogeneous disease that is often diagnosed at later stages which have a poor prognosis. Aberrant alternative splicing patterns found in lung cancer contribute to important cell functions. These include changes in splicing for the BCL2L1, MDM2, MDM4, NUMB and MET genes during lung tumourigenesis, to affect pathways involved in apoptosis, cell proliferation and cellular cohesion. Global analyses of RNASeq datasets suggest there may be many more potentially influential aberrant splicing events that need to be investigated in lung cancer. Changes in expression of the splicing factors that regulate alternative splicing events have also been identified in lung cancer. Of these, changes in expression of QKI, RBM4, RBM5, RBM6, RBM10 and SRSF1 proteins regulate many of the most frequently referenced aberrant splicing events in lung cancer. The expanding list of genes known to be aberrantly spliced in lung cancer along with the altered expression of splicing factors that regulate them are providing new clues as to how lung cancer develops, and how these events can be exploited for better treatment. This article is part of a Special Issue entitled: RNA structure and splicing regulation edited by Francisco Baralle, Ravindra Singh and Stefan Stamm.

RNA Sequencing of the NCI-60: Integration into CellMiner and CellMiner CDB

CellMiner (http://discover.nci.nih.gov/cellminer) and CellMinerCDB (https://discover.nci.nih.gov/cellminercdb/) are web-based applications for mining publicly available genomic, molecular, and pharmacologic datasets of human cancer cell lines including the NCI-60, Cancer Cell Line Encyclopedia, Genomics of Drug Sensitivity in Cancer, Cancer Therapeutics Response Portal, NCI/DTP small cell lung cancer, and NCI Almanac cell line sets. Here, we introduce our RNA sequencing (RNA-seq) data for the NCI-60 and their access and integration with the other databases. Correlation to transcript microarray expression levels for identical genes and identical cell lines across CellMinerCDB demonstrates the high quality of these new RNA-seq data. We provide composite and isoform transcript expression data and demonstrate diversity in isoform composition for individual cancer- and pharmacologically relevant genes, including HRAS, PTEN, EGFR, RAD51, ALKBH2, BRCA1, ERBB2, TP53, FGFR2, and CTNND1. We reveal cell-specific differences in the overall levels of isoforms and show their linkage to expression of RNA processing and splicing genes as well as resultant alterations in cancer and pharmacologic gene sets. Gene-drug pairings linked by pathways or functions show specific correlations to isoforms compared with composite gene expression, including ALKBH2-benzaldehyde, AKT3-vandetanib, BCR-imatinib, CDK1 and 20-palbociclib, CASP1-imexon, and FGFR3-pazopanib. Loss of MUC1 20 amino acid variable number tandem repeats, which is used to elicit immune response, and the presence of the androgen receptor AR-V4 and -V7 isoforms in all NCI-60 tissue of origin types demonstrate translational relevance. In summary, we introduce RNA-seq data to our CellMiner and CellMinerCDB web applications, allowing their exploration for both research and translational purposes. SIGNIFICANCE: The current study provides RNA sequencing data for the NCI-60 cell lines made accessible through both CellMiner and CellMinerCDB and is an important pharmacogenomics resource for the field.

IsoformSwitchAnalyzeR: analysis of changes in genome-wide patterns of alternative splicing and its functional consequences

Summary

Alternative splicing is an important mechanism involved in health and disease. Recent work highlights the importance of investigating genome-wide changes in splicing patterns and the subsequent functional consequences. Current computational methods only support such analysis on a gene-by-gene basis. Therefore, we extended IsoformSwitchAnalyzeR R library to enable analysis of genome-wide changes in specific types of alternative splicing and predicted functional consequences of the resulting isoform switches. As a case study, we analyzed RNA-seq data from The Cancer Genome Atlas and found systematic changes in alternative splicing and the consequences of the associated isoform switches.

Availability and implementation

Windows, Linux and Mac OS: http://bioconductor.org/packages/IsoformSwitchAnalyzeR.

Supplementary information

Supplementary data are available at Bioinformatics online.

Relative Abundance of Transcripts ( RATs): Identifying differential isoform abundance from RNA-seq

The biological importance of changes in RNA expression is reflected by the wide variety of tools available to characterise these changes from RNA-seq data. Several tools exist for detecting differential transcript isoform usage (DTU) from aligned or assembled RNA-seq data, but few exist for DTU detection from alignment-free RNA-seq quantifications. We present the RATs, an R package that identifies DTU transcriptome-wide directly from transcript abundance estimates. RATs is unique in applying bootstrapping to estimate the reliability of detected DTU events and shows good performance at all replication levels (median false positive fraction < 0.05). We compare RATs to two existing DTU tools, DRIM-Seq & SUPPA2, using two publicly available simulated RNA-seq datasets and a published human RNA-seq dataset, in which 248 genes have been previously identified as displaying significant DTU. RATs with default threshold values on the simulated Human data has a sensitivity of 0.55, a Matthews correlation coefficient of 0.71 and a false discovery rate (FDR) of 0.04, outperforming both other tools. Applying the same thresholds for SUPPA2 results in a higher sensitivity (0.61) but poorer FDR performance (0.33). RATs and DRIM-seq use different methods for measuring DTU effect-sizes complicating the comparison of results between these tools, however, for a likelihood-ratio threshold of 30, DRIM-Seq has similar FDR performance to RATs (0.06), but worse sensitivity (0.47). These differences persist for the simulated drosophila dataset. On the published human RNA-seq dataset the greatest agreement between the tools tested is 53%, observed between RATs and SUPPA2. The bootstrapping quality filter in RATs is responsible for removing the majority of DTU events called by SUPPA2 that are not reported by RATs. All methods, including the previously published qRT-PCR of three of the 248 detected DTU events, were found to be sensitive to annotation differences between Ensembl v60 and v87.

A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine

The development and application of marine current energy are attracting more and more attention around the world. Due to the hardness of its working environment, it is important and difficult to study the fault diagnosis of a marine current generation system. In this paper, an underwater image is chosen as the fault-diagnosing signal, after different sensors are compared. This paper proposes a diagnosis method based on the sparse autoencoder (SA) and softmax regression (SR). The SA is used to extract the features and SR is used to classify them. Images are used to monitor whether the blade is attached by benthos and to determine its corresponding degree of attachment. Compared with other methods, the experiment results show that the proposed method can diagnose the blade attachment with higher accuracy.

Network-based approach to identify principal isoforms among four cancer types

Protein isoforms are structurally similar proteins produced by alternative splicing of a single gene or genes from the same family. Isoforms of a protein can perform the same, similar, or even opposite biological functions. A previous study identified principal isoforms of proteins based on the extent of interactions per isoform in a functional relationship network, focusing on data from normal tissues. Additionally, the expression levels of specific isoforms of various genes associated with tumorigenesis and prognosis are frequently altered in tumors compared with those in normal tissues. In this study, we aimed to identify higher degree isoforms (HDIs) of multi-isoform genes (MIGs) in cancer by applying a meta-analytical framework to calculate co-expression between each pair of isoforms in two large datasets of RNA-seq profiles from breast cancer, lung cancer, leukemia, and colon cancer cell lines. Then, we compared HDIs with isoforms identified by proteomic data and prognostic and predictive evidence in various cancers. In addition, we separately analyzed the associations between HDIs and non-HDIs (nHDIs) of the same genes according to transcript expression and drug responses in various cancer type cell lines. Collectively, these results indicated the complex properties of HDIs per gene identified by cancer type-based isoform-isoform co-expression networks and showed the potential of HDIs as novel therapeutic targets for cancer treatment.

Functional characterization of alternatively spliced GSN in head and neck squamous cell carcinoma

We have recently performed the characterization of alternative splicing events (ASEs) in head and neck squamous cell carcinoma, which allows dysregulation of protein expression common for cancer cells. Such analysis demonstrated a high ASE prevalence among tumor samples, including tumor-specific alternative splicing in the GSN gene.In vitro studies confirmed that overall expression of either ASE-GSN or wild-type GSN (WT-GSN) isoform inversely correlated with cell proliferation, whereas the high ratio of ASE-GSN to WT-GSN correlated with increased cellular invasion. Additionally, a change in expression of either isoform caused compensatory changes in expression of the other isoform. Our results suggest that the overall expression and the balance between GSN isoforms are mediating factors in proliferation, while increased overall expression of ASE-GSN is specific to cancer tissues. As a result, we propose ASE-GSN can serve not only as a biomarker of disease and disease progression, but also as a neoantigen for head and neck squamous cell carcinoma treatment, for which only a limited number of disease-specific targeted therapies currently exist.

Genome-wide isoform-level analysis reveals tumor-specific isoforms for lung adenocarcinoma diagnosis and prognosis

Last decades have witnessed the great progress in exploration of tumor transcriptome. However, most researches were restricted in gene-level expression. mRNA isoforms, especially tumor-specific isoforms have not been fully explored in tumor. Here, by analyzing RNA-seq data derived from hundreds of samples in TCGA projects, we comprehensively characterized the expression variations of mRNA isoforms in adenocarcinoma of lung (LUAD), which is one of leading causes of cancer-related death. Our analysis found that a variety of mRNA isoforms showed differential expression in LUAD tumor samples. Some of them even showed distinct variations compared to their host genes. Further analysis of functional enrichment revealed that up- and down-regulated mRNA isoforms took part in different types of biological process. In addition, we also identified hundreds of isoforms that expressed exclusively in LUAD tumor samples. Furthermore, the expression level of several isoforms, such as uc001kuk.3 and uc003yls.2, could separate tumor patients by overall survival periods. Our study provided new candidates for the diagnosis and prognosis of lung cancer.

Dietary cholesterol promotes steatohepatitis related hepatocellular carcinoma through dysregulated metabolism and calcium signaling

The underlining mechanisms of dietary cholesterol and nonalcoholic steatohepatitis (NASH) in contributing to hepatocellular carcinoma (HCC) remain undefined. Here we demonstrated that high-fat-non-cholesterol-fed mice developed simple steatosis, whilst high-fat-high-cholesterol-fed mice developed NASH. Moreover, dietary cholesterol induced larger and more numerous NASH-HCCs than non-cholesterol-induced steatosis-HCCs in diethylnitrosamine-treated mice. NASH-HCCs displayed significantly more aberrant gene expression-enriched signaling pathways and more non-synonymous somatic mutations than steatosis-HCCs (335 ± 84/sample vs 43 ± 13/sample). Integrated genetic and expressional alterations in NASH-HCCs affected distinct genes pertinent to five pathways: calcium, insulin, cell adhesion, axon guidance and metabolism. Some of the novel aberrant gene expression, mutations and core oncogenic pathways identified in cholesterol-associated NASH-HCCs in mice were confirmed in human NASH-HCCs, which included metabolism-related genes (ALDH18A1, CAD, CHKA, POLD4, PSPH and SQLE) and recurrently mutated genes (RYR1, MTOR, SDK1, CACNA1H and RYR2). These findings add insights into the link of cholesterol to NASH and NASH-HCC and provide potential therapeutic targets.

Defective transcription elongation in a subset of cancers confers immunotherapy resistance

The nature and role of global transcriptional deregulations in cancers are not fully understood. We report that a large proportion of cancers have widespread defects in mRNA transcription elongation (TE). Cancers with TE defects (TEdeff) display spurious transcription and defective mRNA processing of genes characterized by long genomic length, poised promoters and inducible expression. Signaling pathways regulated by such genes, such as pro-inflammatory response pathways, are consistently suppressed in TEdeff tumors. Remarkably, TEdeff correlates with the poor response and outcome in immunotherapy, but not chemo- or targeted therapy, -treated renal cell carcinoma and metastatic melanoma patients. Forced pharmacologic or genetic induction of TEdeff in tumor cells impairs pro-inflammatory response signaling, and imposes resistance to the innate and adaptive anti-tumor immune responses and checkpoint inhibitor therapy in vivo. Therefore, defective TE is a previously unknown mechanism of tumor immune resistance, and should be assessed in cancer patients undergoing immunotherapy.

Splicing dysregulation as a driver of breast cancer

Breast cancer is known to be a heterogeneous disease driven by a large repertoire of molecular abnormalities, which contribute to its diverse clinical behaviour. Despite the success of targeted therapy approaches for breast cancer patient management, there is still a lack of the molecular understanding of aggressive forms of the disease and clinical management of these patients remains difficult. The advent of high-throughput sequencing technologies has paved the way for a more complete understanding of the molecular make-up of the breast cancer genome. As such, it is becoming apparent that disruption of canonical splicing within breast cancer governs its clinical progression. In this review, we discuss the role of dysregulation of spliceosomal component genes and associated factors in the progression of breast cancer, their role in therapy resistance and the use of quantitative isoform expression as potential prognostic and predictive biomarkers with a particular focus on oestrogen receptor-positive breast cancer.

RANK-c attenuates aggressive properties of ER-negative breast cancer by inhibiting NF-κB activation and EGFR signaling

The RANK/RANKL axis emerges as a key regulator of breast cancer initiation, progression, and metastasis. RANK-c is a RANK receptor isoform produced through alternative splicing of the TNFRSF11A (RANK) gene and a dominant-negative regulator of RANK-induced nuclear factor-κB (NF-κB) activation. Here we report that RANK-c transcript is expressed in 3.2% of cases in The Cancer Genome Atlas breast cancer cohort evenly between ER-positive and ER-negative cases. Nevertheless, the ratio of RANK to RANK-c (RANK/RANK-c) is increased in ER-negative breast cancer cell lines compared to ER-positive breast cancer cell lines. In addition, forced expression of RANK-c in ER-negative breast cancer cell lines inhibited stimuli-induced NF-κB activation and attenuated migration, invasion, colony formation, and adhesion of cancer cells. Further, RANK-c expression in MDA-MB-231 cells inhibited lung metastasis and colonization in vivo. The RANK-c-mediated inhibition of cancer cell aggressiveness and nuclear factor-κB (NF-κB) activation in breast cancer cells seems to rely on a RANK-c/TNF receptor-associated factor-2 (TRAF2) protein interaction. This was further confirmed by a mutated RANK-c that is unable to interact with TRAF2 and abolishes the ability to attenuate NF-κB activation, migration, and invasion. Additional protein interaction characterization revealed epidermal growth factor receptor (EGFR) as a novel interacting partner for RANK-c in breast cancer cells with a negative effect on EGFR phosphorylation and EGF-dependent downstream signaling pathway activation. Our findings further elucidate the complex molecular biology of the RANKL/RANK system in breast cancer and provide preliminary data for RANK-c as a possible marker for disease progression and aggressiveness.

Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?

RNA sequencing (RNA-seq) is becoming a prevalent approach to quantify gene expression and is expected to gain better insights into a number of biological and biomedical questions compared to DNA microarrays. Most importantly, RNA-seq allows us to quantify expression at the gene or transcript levels. However, leveraging the RNA-seq data requires development of new data mining and analytics methods. Supervised learning methods are commonly used approaches for biological data analysis that have recently gained attention for their applications to RNA-seq data. Here, we assess the utility of supervised learning methods trained on RNA-seq data for a diverse range of biological classification tasks. We hypothesize that the transcript-level expression data are more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment utilizes multiple data sets, organisms, lab groups, and RNA-seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-seq data sets and include over 2000 samples that come from multiple organisms, lab groups, and RNA-seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes, and pathological tumor stages for the samples from the cancerous tissue. For each problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the transcript-based classifiers outperform or are comparable with gene expression-based methods. The top-performing techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-seq based data analysis.

Serine/arginine rich splicing factor 2 expression and clinic pathological features indicating a prognostic factor in human hepatocellular carcinoma patients

BACKGROUND

This research was aimed to study the expression of Serine/arginine rich splicing factor 2 (SRSF2) in tissues of hepatocellular carcinoma, and explore the relationship between the expression and the clinic pathological and prognosis of human hepatocellular carcinoma (HCC).

METHODS

One hundred and fifty-three pairs HCC tissues and adjacent normal tissue were collected from January 2010 to March 2013. The expression of SRSF2 gene was detected by immunohistochemistry, western blotting and real-time quantitative polymerase chain reaction (PCR), and the relationship between the expression and the clinic pathological and prognosis of HCC being analyzed.

RESULTS

In 153 cases of hepatocellular carcinoma, SRSF2 was highly expressed in 93 cases, low expression of 60 cases, immunohistochemistry score (6.50 ± 2.82), which was significantly higher than that in adjacent normal tissues (2.94 ± 1.23) (P< 0.05). The expression of SRSF2 in HCC was not associated with gender (χ2= 0.014, P= 0.906), age (χ2= 0.007, P= 0.931), tumor size (χ2= 3.566, P= 0.059) and T stage (χ2= 2.708, P= 0.100), and was significantly correlated with tumor differentiation (χ2= 9.687, P= 0.007), lymph node metastasis (χ2= 4.827, P= 0.028), distal metastasis (χ2= 9.235, P= 0.002), tumor, node, metastasis (TNM) stage (χ2= 3.978, P= 0.046), portal vein invasion and serum alpha-fetoprotein (χ2= 14.919, P= 0.000). The expression of SRSF2 protein in hepatocellular carcinoma was positively correlated (r = 0.704, P< 0.05) with serum alpha-fetoprotein through Pearson analysis. The survival rates of SRSF2 overexpressing hepatocellular carcinoma were 74.19%, 44.09%, 26.88%, 24.73% and 21.51% at 1 year, 2 years, 3 years, 4 years and 5 years respectively, which were lower than those of SRSF2 low expression group (93.33%, 71.67%, 56.67%, 51.67% and 50.00%).

CONCLUSION

SRSF2 is highly expressed in hepatocellular carcinoma and its expression increases with the degree of tumor differentiation and TNM staging. It is related to lymph node metastasis and metastasis of tumor cells, and is positively related to serum alpha fetoprotein content, and affects the postoperative survival time of HCC patients.

Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients

Our comprehensive analysis of alternative splicing across 32 The Cancer Genome Atlas cancer types from 8,705 patients detects alternative splicing events and tumor variants by reanalyzing RNA and whole-exome sequencing data. Tumors have up to 30% more alternative splicing events than normal samples. Association analysis of somatic variants with alternative splicing events confirmed known trans associations with variants in SF3B1 and U2AF1 and identified additional trans-acting variants (e.g., TADA1, PPP2R1A). Many tumors have thousands of alternative splicing events not detectable in normal samples; on average, we identified ≈930 exon-exon junctions ("neojunctions") in tumors not typically found in GTEx normals. From Clinical Proteomic Tumor Analysis Consortium data available for breast and ovarian tumor samples, we confirmed ≈1.7 neojunction- and ≈0.6 single nucleotide variant-derived peptides per tumor sample that are also predicted major histocompatibility complex-I binders ("putative neoantigens").

TSIS: an R package to infer alternative splicing isoform switches for time-series data

Summary

An alternative splicing isoform switch is where a pair of transcript isoforms reverse their relative expression abundances in response to external or internal stimuli. Although computational methods are available to study differential alternative splicing, few tools for detection of isoform switches exist and these are based on pairwise comparisons. Here, we provide the TSIS R package, which is the first tool for detecting significant transcript isoform switches in time-series data. The main steps of TSIS are to search for the isoform switch points in the time-series, characterize the switches and filter the results with user input parameters. All the functions are integrated into a Shiny App for ease of implementation of the analysis.

Availability and implementation

The TSIS package is available on GitHub: https://github.com/wyguo/TSIS.

Gene Regulatory Network Perturbation by Genetic and Epigenetic Variation

Gene regulatory networks underlie biological function and cellular physiology. Alternative splicing (AS) is a fundamental step in gene regulatory networks and plays a key role in development and disease. In addition to the identification of aberrant AS events, an increasing number of studies are focusing on molecular determinants of AS, including genetic and epigenetic regulators. We review here recent efforts to identify various deregulated AS events as well as their molecular determinants that alter biological functions, and discuss clinical features of AS and their druggable potential.

Identification and characterization of host proteins bound to dengue virus 3' UTR reveal an antiviral role for quaking proteins

The four dengue viruses (DENV1-4) are rapidly reemerging infectious RNA viruses. These positive-strand viral genomes contain structured 3' untranslated regions (UTRs) that interact with various host RNA binding proteins (RBPs). These RBPs are functionally important in viral replication, pathogenesis, and defense against host immune mechanisms. Here, we combined RNA chromatography and quantitative mass spectrometry to identify proteins interacting with DENV1-4 3' UTRs. As expected, RBPs displayed distinct binding specificity. Among them, we focused on quaking (QKI) because of its preference for the DENV4 3' UTR (DENV-4/SG/06K2270DK1/2005). RNA immunoprecipitation experiments demonstrated that QKI interacted with DENV4 genomes in infected cells. Moreover, QKI depletion enhanced infectious particle production of DENV4. On the contrary, QKI did not interact with DENV2 3' UTR, and DENV2 replication was not affected consistently by QKI depletion. Next, we mapped the QKI interaction site and identified a QKI response element (QRE) in DENV4 3' UTR. Interestingly, removal of QRE from DENV4 3' UTR abolished this interaction and increased DENV4 viral particle production. Introduction of the QRE to DENV2 3' UTR led to QKI binding and reduced DENV2 infectious particle production. Finally, reporter assays suggest that QKI reduced translation efficiency of viral RNA. Our work describes a novel function of QKI in restricting viral replication.

SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions

Despite the many approaches to study differential splicing from RNA-seq, many challenges remain unsolved, including computing capacity and sequencing depth requirements. Here we present SUPPA2, a new method that addresses these challenges, and enables streamlined analysis across multiple conditions taking into account biological variability. Using experimental and simulated data, we show that SUPPA2 achieves higher accuracy compared to other methods, especially at low sequencing depth and short read length. We use SUPPA2 to identify novel Transformer2-regulated exons, novel microexons induced during differentiation of bipolar neurons, and novel intron retention events during erythroblast differentiation.

A human-specific switch of alternatively spliced AFMID isoforms contributes to TP53 mutations and tumor recurrence in hepatocellular carcinoma

Pre-mRNA splicing can contribute to the switch of cell identity that occurs in carcinogenesis. Here, we analyze a large collection of RNA-seq data sets and report that splicing changes in hepatocyte-specific enzymes, such as AFMID and KHK, are associated with HCC patients' survival and relapse. The switch of AFMID isoforms is an early event in HCC development and is associated with driver mutations in TP53 and ARID1A The switch of AFMID isoforms is human-specific and not detectable in other species, including primates. Finally, we show that overexpression of the full-length AFMID isoform leads to a higher NAD+ level, lower DNA-damage response, and slower cell growth in HepG2 cells. The integrative analysis uncovered a mechanistic link between splicing switches, de novo NAD+ biosynthesis, driver mutations, and HCC recurrence.

Human PDE4D isoform composition is deregulated in primary prostate cancer and indicative for disease progression and development of distant metastases

Phosphodiesterase 4D7 was recently shown to be specifically over-expressed in localized prostate cancer, raising the question as to which regulatory mechanisms are involved and whether other isoforms of this gene family (PDE4D) are affected under the same conditions.We investigated PDE4D isoform composition in prostatic tissues using a total of seven independent expression datasets and also included data on DNA methylation, copy number and AR and ERG binding in PDE4D promoters to gain insight into their effect on PDE4D transcription.We show that expression of PDE4D isoforms is consistently altered in primary human prostate cancer compared to benign tissue, with PDE4D7 being up-regulated while PDE4D5 and PDE4D9 are down-regulated. Disease progression is marked by an overall down-regulation of long PDE4D isoforms, while short isoforms (PDE4D1/2) appear to be relatively unaffected. While these alterations seem to be independent of copy number alterations in the PDE4D locus and driven by AR and ERG binding, we also observed increased DNA methylation in the promoter region of PDE4D5, indicating a long lasting alteration of the isoform composition in prostate cancer tissues.We propose two independent metrics that may serve as diagnostic and prognostic markers for prostate disease: (PDE4D7 - PDE4D5) provides an effective means for distinguishing PCa from normal adjacent prostate, whereas PDE4D1/2 - (PDE4D5 + PDE4D7 + PDE4D9) offers strong prognostic potential to detect aggressive forms of PCa and is associated with metastasis free survival. Overall, our findings highlight the relevance of PDE4D as prostate cancer biomarker and potential drug target.

Alternative Splicing as a Target for Cancer Treatment

Alternative splicing is a key mechanism determinant for gene expression in metazoan. During alternative splicing, non-coding sequences are removed to generate different mature messenger RNAs due to a combination of sequence elements and cellular factors that contribute to splicing regulation. A different combination of splicing sites, exonic or intronic sequences, mutually exclusive exons or retained introns could be selected during alternative splicing to generate different mature mRNAs that could in turn produce distinct protein products. Alternative splicing is the main source of protein diversity responsible for 90% of human gene expression, and it has recently become a hallmark for cancer with a full potential as a prognostic and therapeutic tool. Currently, more than 15,000 alternative splicing events have been associated to different aspects of cancer biology, including cell proliferation and invasion, apoptosis resistance and susceptibility to different chemotherapeutic drugs. Here, we present well established and newly discovered splicing events that occur in different cancer-related genes, their modification by several approaches and the current status of key tools developed to target alternative splicing with diagnostic and therapeutic purposes.

The effects of alternative splicing on miRNA binding sites in bladder cancer

Eukaryotic organisms have developed a variety of mechanisms to regulate translation post-transcriptionally, including but not limited to the use of miRNA silencing in many species. One method of post-transcriptional regulation is through miRNAs that bind to the 3′ UTRs to regulate mRNA abundance and influence protein expression. Therefore, the diversity of mRNA 3′ UTRs mediating miRNA binding sites influence miRNA-mediated regulation. Alternative polyadenylation, by shortening mRNA isoforms, increases the diversity of 3′ UTRs; moreover, short mRNA isoforms elude miRNA-medicated repression. Because no current prediction methods for putative miRNA target sites consider whether or not 1) splicing-informed miRNA binding sites and/or 2) the use of 3′ UTRs provide higher resolution or functionality, we sought to identify not only the genome-wide impact of using exons in mRNA 3′ UTRs but also their functional connection to miRNA regulation and clinical outcomes in cancer. With a genome-wide expression of mRNA and miRNA quantified by 395 bladder cancer cases from The Cancer Genome Atlas (TCGA), we 1) demonstrate the diversity of 3′ UTRs affecting miRNA efficiency and 2) identify a set of genes clinically associated with mRNA expression in bladder cancer. Knowledge of 3′ UTR diversity will not only be a useful addition to current miRNA target prediction algorithms but also enhance the clinical utility of mRNA isoforms in the expression of mRNA in cancer. Thus, variability among cancer patient’s variability in molecular signatures based on these exon usage events in 3′ UTR along with miRNAs in bladder cancer may lead to better prognostic/treatment strategies for improved precision medicine.

Genome-wide characterization of differential transcript usage in Arabidopsis thaliana

Alternative splicing and the usage of alternate transcription start- or stop sites allows a single gene to produce multiple transcript isoforms. Most plant genes express certain isoforms at a significantly higher level than others, but under specific conditions this expression dominance can change, resulting in a different set of dominant isoforms. These events of differential transcript usage (DTU) have been observed for thousands of Arabidopsis thaliana, Zea mays and Vitis vinifera genes, and have been linked to development and stress response. However, neither the characteristics of these genes, nor the implications of DTU on their protein coding sequences or functions, are currently well understood. Here we present a dataset of isoform dominance and DTU for all genes in the AtRTD2 reference transcriptome based on a protocol that was benchmarked on simulated data and validated through comparison with a published reverse transciptase-polymerase chain reaction panel. We report DTU events for 8148 genes across 206 public RNA-Seq samples, and find that protein sequences are affected in 22% of the cases. The observed DTU events show high consistency across replicates, and reveal reproducible patterns in response to treatment and development. We also demonstrate that genes with different evolutionary ages, expression breadths and functions show large differences in the frequency at which they undergo DTU, and in the effect that these events have on their protein sequences. Finally, we showcase how the generated dataset can be used to explore DTU events for genes of interest or to find genes with specific DTU in samples of interest.

Targeting splicing abnormalities in cancer

Recently splicing has been recognized as a key pathway in cancer. Although aberrant splicing has been shown to be a consequence of mutations or the abnormal expression of splicing factors (trans-effect changes) or mutations in the splicing sequences (cis-effect mutations), the connections between aberrant splicing and cancer initiation or progression are still not well understood. Here we review the mutational landscape of splicing factors in cancer and associated splicing consequences, along with the most important examples of the therapeutic approaches targeting the spliceosome currently being investigated in oncology.

Depletion of somatic mutations in splicing-associated sequences in cancer genomes

Background

An important goal of cancer genomics is to identify systematically cancer-causing mutations. A common approach is to identify sites with high ratios of non-synonymous to synonymous mutations; however, if synonymous mutations are under purifying selection, this methodology leads to identification of false-positive mutations. Here, using synonymous somatic mutations (SSMs) identified in over 4000 tumours across 15 different cancer types, we sought to test this assumption by focusing on coding regions required for splicing.

Results

Exon flanks, which are enriched for sequences required for splicing fidelity, have ~ 17% lower SSM density compared to exonic cores, even after excluding canonical splice sites. While it is impossible to eliminate a mutation bias of unknown cause, multiple lines of evidence support a purifying selection model above a mutational bias explanation. The flank/core difference is not explained by skewed nucleotide content, replication timing, nucleosome occupancy or deficiency in mismatch repair. The depletion is not seen in tumour suppressors, consistent with their role in positive tumour selection, but is otherwise observed in cancer-associated and non-cancer genes, both essential and non-essential. Consistent with a role in splicing modulation, exonic splice enhancers have a lower SSM density before and after controlling for nucleotide composition; moreover, flanks at the 5’ end of the exons have significantly lower SSM density than at the 3’ end.

Conclusions

These results suggest that the observable mutational spectrum of cancer genomes is not simply a product of various mutational processes and positive selection, but might also be shaped by negative selection.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1337-5) contains supplementary material, which is available to authorized users.

Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism

Alternative splicing plays important roles in generating different transcripts from one gene, and consequently various protein isoforms. However, there has been no systematic approach that facilitates characterizing functional roles of protein isoforms in the context of the entire human metabolism. Here, we present a systematic framework for the generation of gene-transcript-protein-reaction associations (GeTPRA) in the human metabolism. The framework in this study generated 11,415 GeTPRA corresponding to 1,106 metabolic genes for both principal and nonprincipal transcripts (PTs and NPTs) of metabolic genes. The framework further evaluates GeTPRA, using a human genome-scale metabolic model (GEM) that is biochemically consistent and transcript-level data compatible, and subsequently updates the human GEM. A generic human GEM, Recon 2M.1, was developed for this purpose, and subsequently updated to Recon 2M.2 through the framework. Both PTs and NPTs of metabolic genes were considered in the framework based on prior analyses of 446 personal RNA-Seq data and 1,784 personal GEMs reconstructed using Recon 2M.1. The framework and the GeTPRA will contribute to better understanding human metabolism at the systems level and enable further medical applications.

Alternative Splicing in Breast Cancer and the Potential Development of Therapeutic Tools

Alternative splicing is a key molecular mechanism now considered as a hallmark of cancer that has been associated with the expression of distinct isoforms during the onset and progression of the disease. The leading cause of cancer-related deaths in women worldwide is breast cancer, and even when the role of alternative splicing in this type of cancer has been established, the function of this mechanism in breast cancer biology is not completely decoded. In order to gain a comprehensive view of the role of alternative splicing in breast cancer biology and development, we summarize here recent findings regarding alternative splicing events that have been well documented for breast cancer evolution, considering its prognostic and therapeutic value. Moreover, we analyze how the response to endocrine and chemical therapies could be affected due to alternative splicing and differential expression of variant isoforms. With all this knowledge, it becomes clear that targeting alternative splicing represents an innovative approach for breast cancer therapeutics and the information derived from current studies could guide clinical decisions with a direct impact in the clinical advances for breast cancer patients nowadays.