Splicing mutation analysis reveals previously unrecognized pathways in lymph node-invasive breast cancer

Screening and Identification of Survival-Associated Splicing Factors in Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LUSC) is a disease with high morbidity and mortality. Many studies have shown that aberrant alternative splicing (AS) can lead to tumorigenesis, and splicing factors (SFs) serve as an important function during AS. In this research, we propose an analysis method based on synergy to screen key factors that regulate the initiation and progression of LUSC. We first screened alternative splicing events (ASEs) associated with survival in LUSC patients by bivariate Cox regression analysis. Then an association network consisting of OS-ASEs, SFs, and their targeting relationship was constructed to identify key SFs. Finally, 10 key SFs were selected in terms of degree centrality. The validation on TCGA and cross-platform GEO datasets showed that some SFs were significantly differentially expressed in cancer and paracancer tissues, and some of them were associated with prognosis, indicating that our method is valid and accurate. It is expected that our method would be applied to a wide range of research fields and provide new insights in the future.

A prometastatic splicing program regulated by SNRPA1 interactions with structured RNA elements

Aberrant alternative splicing is a hallmark of cancer, yet the underlying regulatory programs that control this process remain largely unknown. Here, we report a systematic effort to decipher the RNA structural code that shapes pathological splicing during breast cancer metastasis. We discovered a previously unknown structural splicing enhancer that is enriched near cassette exons with increased inclusion in highly metastatic cells. We show that the spliceosomal protein small nuclear ribonucleoprotein polypeptide A' (SNRPA1) interacts with these enhancers to promote cassette exon inclusion. This interaction enhances metastatic lung colonization and cancer cell invasion, in part through SNRPA1-mediated regulation of PLEC alternative splicing, which can be counteracted by splicing modulating morpholinos. Our findings establish a noncanonical regulatory role for SNRPA1 as a prometastatic splicing enhancer in breast cancer.

Widespread Alternative Splicing Changes in Metastatic Breast Cancer Cells

Aberrant alternative splicing (AS) is a hallmark of cancer and a potential target for novel anti-cancer therapeutics. Breast cancer-associated AS events are known to be linked to disease progression, metastasis, and survival of breast cancer patients. To identify altered AS programs occurring in metastatic breast cancer, we perform a global analysis of AS events by using RNA-mediated oligonucleotide annealing, selection, and ligation coupled with next-generation sequencing (RASL-seq). We demonstrate that, relative to low-metastatic, high-metastatic breast cancer cells show different AS choices in genes related to cancer progression. Supporting a global reshape of cancer-related splicing profiles in metastatic breast cancer we found an enrichment of RNA-binding motifs recognized by several splicing regulators, which have aberrant expression levels or activity during breast cancer progression, including SRSF1. Among SRSF1-regulated targets we found DCUN1D5, a gene for which skipping of exon 4 in its pre-mRNA introduces a premature termination codon (PTC), thus generating an unstable transcript degraded by nonsense-mediated mRNA decay (NMD). Significantly, distinct breast cancer subtypes show different DCUN1D5 isoform ratios with metastatic breast cancer expressing the highest level of the NMD-insensitive DCUN1D5 mRNA, thus showing high DCUN1D5 expression levels, which are ultimately associated with poor overall and relapse-free survival in breast cancer patients. Collectively, our results reveal global AS features of metastatic breast tumors, which open new possibilities for the treatment of these aggressive tumor types.

Aberrant RNA Splicing Events Driven by Mutations of RNA-Binding Proteins as Indicators for Skin Cutaneous Melanoma Prognosis

The worldwide incidence of skin cutaneous melanoma (SKCM) is increasing at a more rapid rate than other tumors. Aberrant alternative splicing (AS) is found to be common in cancer; however, how this process contributes to cancer prognosis still remains largely unknown. Mutations in RNA-binding proteins (RBPs) may trigger great changes in the splicing process. In this study, we comprehensively analyzed DNA and RNA sequencing data and clinical information of SKCM patients, together with widespread changes in splicing patterns induced by RBP mutations. We screened mRNA expression-related and prognosis-related mutations in RBPs and investigated the potential affections of RBP mutations on splicing patterns. Mutations in 853 RBPs were demonstrated to be correlated with splicing aberrations (p < 0.01). Functional enrichment analysis revealed that these alternative splicing events (ASEs) may participate in tumor progress by regulating the modification process, cell-cycle checkpoint, metabolic pathways, MAPK signaling, PI3K-Akt signaling, and other important pathways in cancer. We also constructed a prediction model based on overall survival-related AS events (OS-ASEs) affected by RBP mutations, which exhibited a good predict efficiency with the area under the curve of 0.989. Our work highlights the importance of RBP mutations in splicing alterations and provides effective biomarkers for prediction of prognosis of SKCM.

Expression Changes Confirm Genomic Variants Predicted to Result in Allele-Specific, Alternative mRNA Splicing

Splice isoform structure and abundance can be affected by either noncoding or masquerading coding variants that alter the structure or abundance of transcripts. When these variants are common in the population, these nonconstitutive transcripts are sufficiently frequent so as to resemble naturally occurring, alternative mRNA splicing. Prediction of the effects of such variants has been shown to be accurate using information theory-based methods. Single nucleotide polymorphisms (SNPs) predicted to significantly alter natural and/or cryptic splice site strength were shown to affect gene expression. Splicing changes for known SNP genotypes were confirmed in HapMap lymphoblastoid cell lines with gene expression microarrays and custom designed q-RT-PCR or TaqMan assays. The majority of these SNPs (15 of 22) as well as an independent set of 24 variants were then subjected to RNAseq analysis using the ValidSpliceMut web beacon (http://validsplicemut.cytognomix.com), which is based on data from the Cancer Genome Atlas and International Cancer Genome Consortium. SNPs from different genes analyzed with gene expression microarray and q-RT-PCR exhibited significant changes in affected splice site use. Thirteen SNPs directly affected exon inclusion and 10 altered cryptic site use. Homozygous SNP genotypes resulting in stronger splice sites exhibited higher levels of processed mRNA than alleles associated with weaker sites. Four SNPs exhibited variable expression among individuals with the same genotypes, masking statistically significant expression differences between alleles. Genome-wide information theory and expression analyses (RNAseq) in tumor exomes and genomes confirmed splicing effects for 7 of the HapMap SNP and 14 SNPs identified from tumor genomes. q-RT-PCR resolved rare splice isoforms with read abundance too low for statistical significance in ValidSpliceMut. Nevertheless, the web-beacon provides evidence of unanticipated splicing outcomes, for example, intron retention due to compromised recognition of constitutive splice sites. Thus, ValidSpliceMut and q-RT-PCR represent complementary resources for identification of allele-specific, alternative splicing.

Alternative Splicing Events as Indicators for the Prognosis of Uveal Melanoma

Growing evidence has revealed that abnormal alternative splicing (AS) events are closely related to carcinogenic processes. However, the comprehensive study on the prognostic value of splicing events involved in uveal melanoma (UM) is still lacking. Therefore, splicing data of 80 UM patients were obtained from the Cancer Genome Atlas (TCGA) SpliceSeq and RNA sequence data of UM and patient clinical features were downloaded from the Cancer Genome Atlas (TCGA) database to identify survival related splicing events in UM. As a result, a total of 37996 AS events of 17911 genes in UM were detected, among which 5299 AS events of 3529 genes were significantly associated with UM patients’ survival. Functional enrichment analysis revealed that this survival related splicing genes are corelated with mRNA catabolic process and ribosome pathway. Based on survival related splicing events, seven types of prognostic markers and the final overall prognostic signature could independently predict the overall survival of UM patients. Finally, an 11 spliced gene was identified in the final signature. On the basis of these 11 genes, we constructed a Support Vector Machine (SVM) classifier and evaluated it with leave-one-out cross-validation. The results showed that the 11 genes could determine short- and long-term survival with a predicted accuracy of 97.5%. Besides, the splicing factors and alternative splicing events correlation network was constructed to serve as therapeutic targets for UM treatment. Thus, our study depicts a comprehensive landscape of alternative splicing events in the prognosis of UM. The correlation network and associated pathways would provide additional potential targets for therapy and prognosis.

Three-microRNA expression signature predicts survival in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a specific type of breast cancer with poor overall survival (OS) time. Previous studies revealed that microRNAs (miRNAs/miRs) serve important roles in the pathogenesis, progression and prognosis of TNBC. The present study analyzed the miRNA expression and clinical data of patients with TNBC downloaded from The Cancer Genome Atlas. A total of 194 differentially expressed miRNAs were identified between TNBC and matched normal tissues using the cut-off criteria of P<0.05 and |log2 fold change|>2. Of these miRNAs, 65 were downregulated and 129 were upregulated. Using Kaplan-Meier survival analysis, a total of 77 miRNAs that were closely associated with OS time were identified (P<0.05). The intersection of the 77 miRNAs and 194 differentially expressed miRNAs revealed six miRNAs. Log-rank tests based on survival curves were performed and two miRNAs were eliminated. The prognostic value of the remaining four miRNAs was evaluated with a Cox proportional hazards model using multiple logistic regression with forward stepwise selection of variables. Three miRNAs (miR-21-3p, miR-659-5p and miR-200b-5p) were subsequently identified as independent risk factors associated with OS time in the model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed that the target genes of these three miRNAs were mainly involved in ‘cell protein metabolism’, ‘RNA transcriptional regulation’, ‘cell migration’, ‘MAPK signaling pathway’, ‘ErbB signaling pathway’, ‘prolactin signaling pathway’ and ‘adherens junctions’. Taken together, the results obtained in the present study suggested that the three-miRNA signature may serve as a prognostic biomarker for patients with TNBC.

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.

T5224, RSPO2 and AZD5363 are novel drugs against functional pituitary adenoma

We tested whether the drugs T5224, RSPO2, and AZD5363 exert therapeutic effects against functioning pituitary adenoma (FPA). We analysed the gene expression profiles of four FPA mRNA microarray datasets (GSE2175, GSE26966, GSE36314, and GSE37153) from the Gene Expression Omnibus database and identified genes differentially expressed in FPA vs control tissues. We then carried out Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction network analyses. We also measured the difference in expression of hub genes between human normal pituitary cells and FPA cells using qRT-PCR. Our in vitro colony-formation and MTT assays showed that cell viability, number, and the size of clonogenicities were all lower in the presence of T5224, RSPO2, or AZD536 than in controls. Moreover, flow cytometry experiments showed that the incidence of apoptosis was higher in the presence of T5224, RSPO2, or AZD5363 than among controls, and was increased by increasing the doses of the drugs. This suggests these drugs could be used as therapeutic agents to treat FPA. Finally, we found that cFos, WNT5A, NCAM1, JUP, AKT3, and ADCY1 are abnormally expressed in FPA cells compared to controls, which highlights these genes as potential prognostic and/or therapeutic targets.

Pan-cancer repository of validated natural and cryptic mRNA splicing mutations

We present a major public resource of mRNA splicing mutations validated according to multiple lines of evidence of abnormal gene expression. Likely mutations present in all tumor types reported in the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) were identified based on the comparative strengths of splice sites in tumor versus normal genomes, and then validated by respectively comparing counts of splice junction spanning and abundance of transcript reads in RNA-Seq data from matched tissues and tumors lacking these mutations. The comprehensive resource features 341,486 of these validated mutations, the majority of which (69.9%) are not present in the Single Nucleotide Polymorphism Database (dbSNP 150). There are 131,347 unique mutations which weaken or abolish natural splice sites, and 222,071 mutations which strengthen cryptic splice sites (11,932 affect both simultaneously). 28,812 novel or rare flagged variants (with <1% population frequency in dbSNP) were observed in multiple tumor tissue types. An algorithm was developed to classify variants into splicing molecular phenotypes that integrates germline heterozygosity, degree of information change and impact on expression. The classification thresholds were calibrated against the ClinVar clinical database phenotypic assignments. Variants are partitioned into allele-specific alternative splicing, likely aberrant and aberrant splicing phenotypes. Single variants or chromosome ranges can be queried using a Global Alliance for Genomics and Health (GA4GH)-compliant, web-based Beacon "Validated Splicing Mutations" either separately or in aggregate alongside other Beacons through the public Beacon Network, as well as through our website. The website provides additional information, such as a visual representation of supporting RNAseq results, gene expression in the corresponding normal tissues, and splicing molecular phenotypes.

Contribution of alternative splicing to breast cancer metastasis

Alternative splicing is a major contributor to transcriptome and proteome diversity in eukaryotes. Comparing to normal samples, about 30% more alternative splicing events were recently identified in 32 cancer types included in The Cancer Genome Atlas database. Some alternative splicing isoforms and their encoded proteins contribute to specific cancer hallmarks. In this review, we will discuss recent progress regarding the contributions of alternative splicing to breast cancer metastasis. We plan to dissect the role of MTDH, CD44 and their interaction with other mRNA splicing factors. We believe an in-depth understanding of the mechanism underlying the contribution of splicing to breast cancer metastasis will provide novel strategies to the management of breast cancer.

Comparative genomic analysis of collagen gene diversity

Collagen gene family, comprising 30% of the total protein mass in mammals, is the major part of extracellular matrix. To understand the complexity of collagen gene family, detailed sequence, phylogenetic and synteny analyses of 44 collagen genes were performed. According to sequence analysis results, Fibril-associated collagen with interrupted triple helices (FACITs) were identified as the most recently evolved vertebrate-specific collagens while Fibril-forming collagens and Collagen VI, VII, XXVI, and XXVIII were the most ancient collagens, originating at the time of choanoflagellates. Network-forming collagens were entirely conserved from arthopods to homo sapiens, except one gene loss event. Of note, bird specific gene dispensability of COL1A1, COL3A1, COL5A3 and COL11A2 genes was observed in Fibril-forming collagens. According to phylogenetic analysis, gene duplications in collagen family occurred at variable time points during invertebrate to vertebrate evolution. However, majority of gene duplications in FACITs and network-forming collagens occurred at fish time point, suggesting large scale duplications at the root of vertebrate lineage. Lastly, synteny analysis identified 12 conserved blocks containing 27 collagen genes in vertebrate species. Interestingly, dysregulation of seven conserved blocks including block1 (COL11A1), block3 (COL3A1, COL5A2), block5 (COL6A5, COL6A6), block7 (COL1A2), block9 (COL4A1, COL4A2), block11 (COL6A1, COL6A2, COL18A1) and block12 (COL4A5, COL4A6) were also reported in different diseases including cancer. The current study revealed many critical insights into sequence, structural and functional diversity of collagen gene family. In future, by using this information we may be able to establish the clinical and pathological relevance of these conserved collagen blocks in different diseases.

RNA Splicing and Disease: Animal Models to Therapies

Alternative splicing of pre-mRNA increases genetic diversity, and recent studies estimate that most human multiexon genes are alternatively spliced. If this process is not highly regulated and accurate, it leads to mis-splicing events, which may result in proteins with altered function. A growing body of work has implicated mis-splicing events in a range of diseases, including cancer, neurodegenerative diseases, and muscular dystrophies. Understanding the mechanisms that cause aberrant splicing events and how this leads to disease is vital for designing effective therapeutic strategies. In this review, we focus on advances in therapies targeting splicing, and highlight the animal models developed to recapitulate disease phenotypes as a model for testing these therapies.

Pan-cancer repository of validated natural and cryptic mRNA splicing mutations

We present a major public resource of mRNA splicing mutations validated according to multiple lines of evidence of abnormal gene expression. Likely mutations present in all tumor types reported in the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) were identified based on the comparative strengths of splice sites in tumor versus normal genomes, and then validated by respectively comparing counts of splice junction spanning and abundance of transcript reads in RNA-Seq data from matched tissues and tumors lacking these mutations. The comprehensive resource features 341,486 of these validated mutations, the majority of which (69.9%) are not present in the Single Nucleotide Polymorphism Database (dbSNP 150). There are 131,347 unique mutations which weaken or abolish natural splice sites, and 222,071 mutations which strengthen cryptic splice sites (11,932 affect both simultaneously). 28,812 novel or rare flagged variants (with <1% population frequency in dbSNP) were observed in multiple tumor tissue types. An algorithm was developed to classify variants into splicing molecular phenotypes that integrates germline heterozygosity, degree of information change and impact on expression. The classification thresholds were calibrated against the ClinVar clinical database phenotypic assignments. Variants are partitioned into allele-specific alternative splicing, likely aberrant and aberrant splicing phenotypes. Single variants or chromosome ranges can be queried using a Global Alliance for Genomics and Health (GA4GH)-compliant, web-based Beacon "Validated Splicing Mutations" either separately or in aggregate alongside other Beacons through the public Beacon Network, as well as through our website. The website provides additional information, such as a visual representation of supporting RNAseq results, gene expression in the corresponding normal tissues, and splicing molecular phenotypes.

Insights from structures of cancer-relevant pre-mRNA splicing factors

Pre-mRNA splicing factors recognize consensus signals within preliminary transcripts, and as cogs of the spliceosome machine, orchestrate the excision and rejoining of pre-mRNA regions for gene expression. Large-scale sequencing has demonstrated that mutations in key genes encoding pre-mRNA splicing factors are common among myeloid neoplasms and also occur in a variety of other cancers. This revelation offers new therapeutic opportunities to target pre-mRNA splicing vulnerabilities in hematologic and other malignancies. The mutated residues typically alter 3' splice site choice for a subset of transcripts. In this review, we highlight mechanistic insights from recent 3D structures that reveal the affected residues poised for pre-mRNA recognition.

Mutations of RNA splicing factors in hematological malignancies

Systematic large-scale cancer genomic studies have produced numerous significant findings. These studies have not only revealed new cancer-promoting genes, but they also have identified cancer-promoting functions of previously known "housekeeping" genes. These studies have identified numerous mutations in genes which play a fundamental role in nuclear precursor mRNA splicing. Somatic mutations and copy number variation in many of the splicing factors which participate in the formation of multiple spliceosomal complexes appear to play a role in many cancers and in particular in myelodysplastic syndromes (MDS). Mutated proteins seem to interfere with the recognition of the authentic splice sites (SS) leading to utilization of suboptimal alternative splicing sites generating aberrantly spliced mRNA isoforms. This short review is focusing on the function of the splice factors involved in the formation of splicing complexes and potential mechanisms which affect usage of the authentic splice site recognition.

RNA processing as an alternative route to attack glioblastoma

Genomic analyses have become an important tool to identify new avenues for therapy. This is especially true for cancer types with extremely poor outcomes, since our lack of effective therapies offers no tangible clinical starting point to build upon. The highly malignant brain tumor glioblastoma (GBM) exemplifies such a refractory cancer, with only 15 month average patient survival. Analyses of several hundred GBM samples compiled by the TCGA (The Cancer Genome Atlas) have produced an extensive transcriptomic map, identified prevalent chromosomal alterations, and defined important driver mutations. Unfortunately, clinical trials based on these results have not yet delivered an improvement on outcome. It is, therefore, necessary to characterize other regulatory routes known for playing a role in tumor relapse and response to treatment. Alternative splicing affects more than 90% of the human coding genes and it is an important source for transcript variation and gene regulation. Mutations and alterations in splicing factors are highly prevalent in multiple cancers, demonstrating the potential for splicing to act as a tumor driver. As a result, numerous genes are expressed as cancer-specific splicing isoforms that are functionally distinct from the canonical isoforms found in normal tissue. These include genes that regulate cancer-critical pathways such as apoptosis, DNA repair, cell proliferation, and migration. Splicing defects can even induce genomic instability, a common characteristic of cancer, and a driver of tumor evolution. Importantly, components of the splicing machinery are targetable; multiple drugs can inhibit splicing factors or promote changes in splicing which could be exploited to begin improving clinical outcomes. Here, we review the current literature and present a case for exploring RNA processing as therapeutic route for the treatment of GBM.

Somatic TP53 Mutations in the Era of Genome Sequencing

Amid the complexity of genetic alterations in human cancer, TP53 mutation appears as an almost invariant component, representing by far the most frequent genetic alteration overall. Compared with previous targeted sequencing studies, recent integrated genomics studies offer a less biased view of TP53 mutation patterns, revealing that >20% of mutations occur outside the DNA-binding domain. Among the 12 mutations representing each at least 1% of all mutations, five occur at residues directly involved in specific DNA binding, four affect the tertiary fold of the DNA-binding domain, and three are nonsense mutations, two of them in the carboxyl terminus. Significant mutations also occur in introns, affecting alternative splicing events or generating rearrangements (e.g., in intron 1 in sporadic osteosarcoma). In aggressive cancers, mutation is so common that it may not have prognostic value (all these cancers have impaired p53 function caused by mutation or by other mechanisms). In several other cancers, however, mutation makes a clear difference for prognostication, as, for example, in HER2-enriched breast cancers and in lung adenocarcinoma with EGFR mutations. Thus, the clinical significance of TP53 mutation is dependent on tumor subtype and context. Understanding the clinical impact of mutation will require integrating mutation-specific information (type, frequency, and predicted impact) with data on haplotypes and on loss of heterozygosity.

Genome-wide association studies and epigenome-wide association studies go together in cancer control

Completion of the human genome a decade ago laid the foundation for: using genetic information in assessing risk to identify individuals and populations that are likely to develop cancer, and designing treatments based on a person's genetic profiling (precision medicine). Genome-wide association studies (GWAS) completed during the past few years have identified risk-associated single nucleotide polymorphisms that can be used as screening tools in epidemiologic studies of a variety of tumor types. This led to the conduct of epigenome-wide association studies (EWAS). This article discusses the current status, challenges and research opportunities in GWAS and EWAS. Information gained from GWAS and EWAS has potential applications in cancer control and treatment.

SURVIV for survival analysis of mRNA isoform variation

The rapid accumulation of clinical RNA-seq data sets has provided the opportunity to associate mRNA isoform variations to clinical outcomes. Here we report a statistical method SURVIV (Survival analysis of mRNA Isoform Variation), designed for identifying mRNA isoform variation associated with patient survival time. A unique feature and major strength of SURVIV is that it models the measurement uncertainty of mRNA isoform ratio in RNA-seq data. Simulation studies suggest that SURVIV outperforms the conventional Cox regression survival analysis, especially for data sets with modest sequencing depth. We applied SURVIV to TCGA RNA-seq data of invasive ductal carcinoma as well as five additional cancer types. Alternative splicing-based survival predictors consistently outperform gene expression-based survival predictors, and the integration of clinical, gene expression and alternative splicing profiles leads to the best survival prediction. We anticipate that SURVIV will have broad utilities for analysing diverse types of mRNA isoform variation in large-scale clinical RNA-seq projects.

Large-scale analysis of genome and transcriptome alterations in multiple tumors unveils novel cancer-relevant splicing networks

Alternative splicing is regulated by multiple RNA-binding proteins and influences the expression of most eukaryotic genes. However, the role of this process in human disease, and particularly in cancer, is only starting to be unveiled. We systematically analyzed mutation, copy number, and gene expression patterns of 1348 RNA-binding protein (RBP) genes in 11 solid tumor types, together with alternative splicing changes in these tumors and the enrichment of binding motifs in the alternatively spliced sequences. Our comprehensive study reveals widespread alterations in the expression of RBP genes, as well as novel mutations and copy number variations in association with multiple alternative splicing changes in cancer drivers and oncogenic pathways. Remarkably, the altered splicing patterns in several tumor types recapitulate those of undifferentiated cells. These patterns are predicted to be mainly controlled by MBNL1 and involve multiple cancer drivers, including the mitotic gene NUMA1 We show that NUMA1 alternative splicing induces enhanced cell proliferation and centrosome amplification in nontumorigenic mammary epithelial cells. Our study uncovers novel splicing networks that potentially contribute to cancer development and progression.

Prioritizing Variants in Complete Hereditary Breast and Ovarian Cancer Genes in Patients Lacking Known BRCA Mutations

BRCA1 and BRCA2 testing for hereditary breast and ovarian cancer (HBOC) does not identify all pathogenic variants. Sequencing of 20 complete genes in HBOC patients with uninformative test results (N = 287), including noncoding and flanking sequences of ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, EPCAM, MLH1, MRE11A, MSH2, MSH6, MUTYH, NBN, PALB2, PMS2, PTEN, RAD51B, STK11, TP53, and XRCC2, identified 38,372 unique variants. We apply information theory (IT) to predict and prioritize noncoding variants of uncertain significance in regulatory, coding, and intronic regions based on changes in binding sites in these genes. Besides mRNA splicing, IT provides a common framework to evaluate potential affinity changes in transcription factor (TFBSs), splicing regulatory (SRBSs), and RNA-binding protein (RBBSs) binding sites following mutation. We prioritized variants affecting the strengths of 10 splice sites (four natural, six cryptic), 148 SRBS, 36 TFBS, and 31 RBBS. Three variants were also prioritized based on their predicted effects on mRNA secondary (2°) structure and 17 for pseudoexon activation. Additionally, four frameshift, two in-frame deletions, and five stop-gain mutations were identified. When combined with pedigree information, complete gene sequence analysis can focus attention on a limited set of variants in a wide spectrum of functional mutation types for downstream functional and co-segregation analysis.

Aberrant RNA splicing in cancer; expression changes and driver mutations of splicing factor genes

Alternative splicing is a widespread process contributing to structural transcript variation and proteome diversity. In cancer, the splicing process is commonly disrupted, resulting in both functional and non-functional end-products. Cancer-specific splicing events are known to contribute to disease progression; however, the dysregulated splicing patterns found on a genome-wide scale have until recently been less well-studied. In this review, we provide an overview of aberrant RNA splicing and its regulation in cancer. We then focus on the executors of the splicing process. Based on a comprehensive catalog of splicing factor encoding genes and analyses of available gene expression and somatic mutation data, we identify cancer-associated patterns of dysregulation. Splicing factor genes are shown to be significantly differentially expressed between cancer and corresponding normal samples, and to have reduced inter-individual expression variation in cancer. Furthermore, we identify enrichment of predicted cancer-critical genes among the splicing factors. In addition to previously described oncogenic splicing factor genes, we propose 24 novel cancer-critical splicing factors predicted from somatic mutations.

Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning

Increasingly, the effectiveness of adjuvant chemotherapy agents for breast cancer has been related to changes in the genomic profile of tumors. We investigated correspondence between growth inhibitory concentrations of paclitaxel and gemcitabine (GI50) and gene copy number, mutation, and expression first in breast cancer cell lines and then in patients. Genes encoding direct targets of these drugs, metabolizing enzymes, transporters, and those previously associated with chemoresistance to paclitaxel (n = 31 genes) or gemcitabine (n = 18) were analyzed. A multi-factorial, principal component analysis (MFA) indicated expression was the strongest indicator of sensitivity for paclitaxel, and copy number and expression were informative for gemcitabine. The factors were combined using support vector machines (SVM). Expression of 15 genes (ABCC10, BCL2, BCL2L1, BIRC5, BMF, FGF2, FN1, MAP4, MAPT, NFKB2, SLCO1B3, TLR6, TMEM243, TWIST1, and CSAG2) predicted cell line sensitivity to paclitaxel with 82% accuracy. Copy number profiles of 3 genes (ABCC10, NT5C, TYMS) together with expression of 7 genes (ABCB1, ABCC10, CMPK1, DCTD, NME1, RRM1, RRM2B), predicted gemcitabine response with 85% accuracy. Expression and copy number studies of two independent sets of patients with known responses were then analyzed with these models. These included tumor blocks from 21 patients that were treated with both paclitaxel and gemcitabine, and 319 patients on paclitaxel and anthracycline therapy. A new paclitaxel SVM was derived from an 11-gene subset since data for 4 of the original genes was unavailable. The accuracy of this SVM was similar in cell lines and tumor blocks (70-71%). The gemcitabine SVM exhibited 62% prediction accuracy for the tumor blocks due to the presence of samples with poor nucleic acid integrity. Nevertheless, the paclitaxel SVM predicted sensitivity in 84% of patients with no or minimal residual disease.

Interpretation of mRNA splicing mutations in genetic disease: review of the literature and guidelines for information-theoretical analysis

The interpretation of genomic variants has become one of the paramount challenges in the post-genome sequencing era. In this review we summarize nearly 20 years of research on the applications of information theory (IT) to interpret coding and non-coding mutations that alter mRNA splicing in rare and common diseases. We compile and summarize the spectrum of published variants analyzed by IT, to provide a broad perspective of the distribution of deleterious natural and cryptic splice site variants detected, as well as those affecting splicing regulatory sequences. Results for natural splice site mutations can be interrogated dynamically with Splicing Mutation Calculator, a companion software program that computes changes in information content for any splice site substitution, linked to corresponding publications containing these mutations. The accuracy of IT-based analysis was assessed in the context of experimentally validated mutations. Because splice site information quantifies binding affinity, IT-based analyses can discern the differences between variants that account for the observed reduced (leaky) versus abolished mRNA splicing. We extend this principle by comparing predicted mutations in natural, cryptic, and regulatory splice sites with observed deleterious phenotypic and benign effects. Our analysis of 1727 variants revealed a number of general principles useful for ensuring portability of these analyses and accurate input and interpretation of mutations. We offer guidelines for optimal use of IT software for interpretation of mRNA splicing mutations.

Interpretation of mRNA splicing mutations in genetic disease: review of the literature and guidelines for information-theoretical analysis

The interpretation of genomic variants has become one of the paramount challenges in the post-genome sequencing era. In this review we summarize nearly 20 years of research on the applications of information theory (IT) to interpret coding and non-coding mutations that alter mRNA splicing in rare and common diseases. We compile and summarize the spectrum of published variants analyzed by IT, to provide a broad perspective of the distribution of deleterious natural and cryptic splice site variants detected, as well as those affecting splicing regulatory sequences. Results for natural splice site mutations can be interrogated dynamically with Splicing Mutation Calculator, a companion software program that computes changes in information content for any splice site substitution, linked to corresponding publications containing these mutations. The accuracy of IT-based analysis was assessed in the context of experimentally validated mutations. Because splice site information quantifies binding affinity, IT-based analyses can discern the differences between variants that account for the observed reduced (leaky) versus abolished mRNA splicing. We extend this principle by comparing predicted mutations in natural, cryptic, and regulatory splice sites with observed deleterious phenotypic and benign effects. Our analysis of 1727 variants revealed a number of general principles useful for ensuring portability of these analyses and accurate input and interpretation of mutations. We offer guidelines for optimal use of IT software for interpretation of mRNA splicing mutations.