A pan-cancer transcriptome analysis of exitron splicing identifies novel cancer driver genes and neoepitopes.

Exitron splicing (EIS) creates a cryptic intron (called an exitron) within a protein-coding exon to increase proteome diversity. EIS is poorly characterized, but emerging evidence suggests a role for EIS in cancer. Through a systematic investigation of EIS across 33 cancers from 9,599 tumor transcriptomes, we discovered that EIS affected 63% of human coding genes and that 95% of those events were tumor specific. Notably, we observed a mutually exclusive pattern between EIS and somatic mutations in their affected genes. Functionally, we discovered that EIS altered known and novel cancer driver genes for causing gain- or loss-of-function, which promotes tumor progression. Importantly, we identified EIS-derived neoepitopes that bind to major histocompatibility complex (MHC) class I or II. Analysis of clinical data from a clear cell renal cell carcinoma cohort revealed an association between EIS-derived neoantigen load and checkpoint inhibitor response. Our findings establish the importance of considering EIS alterations when nominating cancer driver events and neoantigens.

Comparative analyses of gene networks mediating cancer metastatic potentials across lineage types.

Studies have identified genes and molecular pathways regulating cancer metastasis. However, it remains largely unknown whether metastatic potentials of cancer cells from different lineage types are driven by the same or different gene networks. Here, we aim to address this question through integrative analyses of 493 human cancer cells' transcriptomic profiles and their metastatic potentials in vivo. Using an unsupervised approach and considering both gene coexpression and protein-protein interaction networks, we identify different gene networks associated with various biological pathways (i.e. inflammation, cell cycle, and RNA translation), the expression of which are correlated with metastatic potentials across subsets of lineage types. By developing a regularized random forest regression model, we show that the combination of the gene module features expressed in the native cancer cells can predict their metastatic potentials with an overall Pearson correlation coefficient of 0.90. By analyzing transcriptomic profile data from cancer patients, we show that these networks are conserved in vivo and contribute to cancer aggressiveness. The intrinsic expression levels of these networks are correlated with drug sensitivity. Altogether, our study provides novel comparative insights into cancer cells' intrinsic gene networks mediating metastatic potentials across different lineage types, and our results can potentially be useful for designing personalized treatments for metastatic cancers.

RAS internal tandem duplication disrupts GTPase-activating protein (GAP) binding to activate oncogenic signaling.

The oncogene RAS is one of the most widely studied proteins in cancer biology, and mutant active RAS is a driver in many types of solid tumors and hematological malignancies. Yet the biological effects of different RAS mutations and the tissue-specific clinical implications are complex and nuanced. Here, we identified an internal tandem duplication (ITD) in the switch II domain of NRAS from a patient with extremely aggressive colorectal carcinoma. Results of whole-exome DNA sequencing of primary and metastatic tumors indicated that this mutation was present in all analyzed metastases and excluded the presence of any other clear oncogenic driver mutations. Biochemical analysis revealed increased interaction of the RAS ITD with Raf proto-oncogene Ser/Thr kinase (RAF), leading to increased phosphorylation of downstream MAPK/ERK kinase (MEK)/extracellular signal-regulated kinase (ERK). The ITD prevented interaction with neurofibromin 1 (NF1)-GTPase-activating protein (GAP), providing a mechanism for sustained activity of the RAS ITD protein. We present the first crystal structures of NRAS and KRAS ITD at 1.65-1.75 Å resolution, respectively, providing insight into the physical interactions of this class of RAS variants with its regulatory and effector proteins. Our in-depth bedside-to-bench analysis uncovers the molecular mechanism underlying a case of highly aggressive colorectal cancer and illustrates the importance of robust biochemical and biophysical approaches in the implementation of individualized medicine.

LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data.

BACKGROUND: Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs. RESULTS: As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancer patient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types. CONCLUSIONS: LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA .

ScanNeo: identifying indel-derived neoantigens using RNA-Seq data.

SUMMARY: Insertion and deletion (indels) have been recognized as an important source generating tumor-specific mutant peptides (neoantigens). The focus of indel-derived neoantigen identification has been on leveraging DNA sequencing such as whole exome sequencing, with the effort of using RNA-seq less well explored. Here we present ScanNeo, a fast-streamlined computational pipeline for analyzing RNA-seq to predict neoepitopes derived from small to large-sized indels. We applied ScanNeo in a prostate cancer cell line and validated our predictions with matched mass spectrometry data. Finally, we demonstrated that indel neoantigens predicted from RNA-seq were associated with checkpoint inhibitor response in a cohort of melanoma patients. AVAILABILITY AND IMPLEMENTATION: ScanNeo is implemented in Python. It is freely accessible at the GitHub repository (https://github.com/ylab-hi/ScanNeo). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HLA-IMPUTER: an easy to use web application for HLA imputation and association analysis using population-specific reference panels.

SUMMARY: HLA allele imputation from SNP genotypes has become increasingly useful, but its accuracy is heavily dependent on the reference panels used. HLA-IMPUTER implements HIBAG algorithm for HLA imputation with different population specific reference panels, including a new Han Chinese reference panel derived from 10 689 samples. We provide a convenient platform for researchers to impute HLA alleles and perform association analysis. AVAILABILITY AND IMPLEMENTATION: http://wyanglab.org: 3838/RefPanelWebsite/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of ST3AGL4, MFHAS1, CSNK2A2 and CD226 as loci associated with systemic lupus erythematosus (SLE) and evaluation of SLE genetics in drug repositioning.

OBJECTIVES: Systemic lupus erythematosus (SLE) is a prototype autoimmune disease with a strong genetic component in its pathogenesis. Through genome-wide association studies (GWAS), we recently identified 10 novel loci associated with SLE and uncovered a number of suggestive loci requiring further validation. This study aimed to validate those loci in independent cohorts and evaluate the role of SLE genetics in drug repositioning. METHODS: We conducted GWAS and replication studies involving 12 280 SLE cases and 18 828 controls, and performed fine-mapping analyses to identify likely causal variants within the newly identified loci. We further scanned drug target databases to evaluate the role of SLE genetics in drug repositioning. RESULTS: We identified three novel loci that surpassed genome-wide significance, including ST3AGL4 (rs13238909, pmeta=4.40E-08), MFHAS1 (rs2428, pmeta=1.17E-08) and CSNK2A2 (rs2731783, pmeta=1.08E-09). We also confirmed the association of CD226 locus with SLE (rs763361, pmeta=2.45E-08). Fine-mapping and functional analyses indicated that the putative causal variants in CSNK2A2 locus reside in an enhancer and are associated with expression of CSNK2A2 in B-lymphocytes, suggesting a potential mechanism of association. In addition, we demonstrated that SLE risk genes were more likely to be interacting proteins with targets of approved SLE drugs (OR=2.41, p=1.50E-03) which supports the role of genetic studies to repurpose drugs approved for other diseases for the treatment of SLE. CONCLUSION: This study identified three novel loci associated with SLE and demonstrated the role of SLE GWAS findings in drug repositioning.

Detecting Medium and Large Insertions and Deletions with transIndel.

Insertions and deletions (indels) are primarily detected from DNA sequencing (DNA-seq) data, but their transcriptional consequences remain unexplored due to challenges in distinguishing medium- and large-sized indels from RNA splicing events in RNA-seq data. We introduce transIndel, a splice-aware algorithm that parses the chimeric alignments predicted by a short read aligner and reconstructs the mid-sized insertions and large deletions based on the linear alignments of split reads from DNA-seq or RNA-seq data. Here, we describe the method and provide a tutorial on the installation and application of transIndel.

Integrated protocol for exitron and exitron-derived neoantigen identification using human RNA-seq data with ScanExitron and ScanNeo.

Exitron splicing (EIS) events in cancers can disrupt functional protein domains to cause cancer driver effects. EIS has been recognized as a new source of tumor neoantigens. Here, we describe an integrated protocol for EIS and EIS-derived neoantigen identification using RNA-seq data. The protocol constitutes a step-by-step guide from data collection to neoantigen prediction. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021).

Opposing transcriptional programs of KLF5 and AR emerge during therapy for advanced prostate cancer.

Endocrine therapies for prostate cancer inhibit the androgen receptor (AR) transcription factor. In most cases, AR activity resumes during therapy and drives progression to castration-resistant prostate cancer (CRPC). However, therapy can also promote lineage plasticity and select for AR-independent phenotypes that are uniformly lethal. Here, we demonstrate the stem cell transcription factor Krüppel-like factor 5 (KLF5) is low or absent in prostate cancers prior to endocrine therapy, but induced in a subset of CRPC, including CRPC displaying lineage plasticity. KLF5 and AR physically interact on chromatin and drive opposing transcriptional programs, with KLF5 promoting cellular migration, anchorage-independent growth, and basal epithelial cell phenotypes. We identify ERBB2 as a point of transcriptional convergence displaying activation by KLF5 and repression by AR. ERBB2 inhibitors preferentially block KLF5-driven oncogenic phenotypes. These findings implicate KLF5 as an oncogene that can be upregulated in CRPC to oppose AR activities and promote lineage plasticity.

Identification of 38 novel loci for systemic lupus erythematosus and genetic heterogeneity between ancestral groups.

Systemic lupus erythematosus (SLE), a worldwide autoimmune disease with high heritability, shows differences in prevalence, severity and age of onset among different ancestral groups. Previous genetic studies have focused more on European populations, which appear to be the least affected. Consequently, the genetic variations that underlie the commonalities, differences and treatment options in SLE among ancestral groups have not been well elucidated. To address this, we undertake a genome-wide association study, increasing the sample size of Chinese populations to the level of existing European studies. Thirty-eight novel SLE-associated loci and incomplete sharing of genetic architecture are identified. In addition to the human leukocyte antigen (HLA) region, nine disease loci show clear ancestral differences and implicate antibody production as a potential mechanism for differences in disease manifestation. Polygenic risk scores perform significantly better when trained on ancestry-matched data sets. These analyses help to reveal the genetic basis for disparities in SLE among ancestral groups.

ScanITD: Detecting internal tandem duplication with robust variant allele frequency estimation.

BACKGROUND: Internal tandem duplications (ITDs) are tandem duplications within coding exons and are important prognostic markers and drug targets for acute myeloid leukemia (AML). Next-generation sequencing has enabled the discovery of ITD at single-nucleotide resolution. ITD allele frequency is used in the risk stratification of patients with AML; higher ITD allele frequency is associated with poorer clinical outcomes. However, the ITD allele frequency data are often unavailable to treating physicians and the detection of ITDs with accurate variant allele frequency (VAF) estimation remains challenging for short-read sequencing. RESULTS: Here we present the ScanITD approach, which performs a stepwise seed-and-realignment procedure for ITD detection with accurate VAF prediction. The evaluations on simulated and real data demonstrate that ScanITD outperforms 3 state-of-the-art ITD detectors, especially for VAF estimation. Importantly, ScanITD yields better accuracy than general-purpose structural variation callers for predicting ITD size range duplications. CONCLUSIONS: ScanITD enables the accurate identification of ITDs with robust VAF estimation. ScanITD is written in Python and is open-source software that is freely accessible at https://github.com/ylab-hi/ScanITD.

Identification of Regulatory Modules That Stratify Lupus Disease Mechanism through Integrating Multi-Omics Data.

Although recent advances in genetic studies have shed light on systemic lupus erythematosus (SLE), its detailed mechanisms remain elusive. In this study, using datasets on SLE transcriptomic profiles, we identified 750 differentially expressed genes (DEGs) in T and B lymphocytes and peripheral blood cells. Using transcription factor (TF) binding data derived from chromatin immunoprecipitation sequencing (ChIP-seq) experiments from the Encyclopedia of DNA Elements (ENCODE) project, we inferred networks of co-regulated genes (NcRGs) based on binding profiles of the upregulated DEGs by significantly enriched TFs. Modularization analysis of NcRGs identified co-regulatory modules among the DEGs and master TFs vital for each module. Remarkably, the co-regulatory modules stratified the common SLE interferon (IFN) signature and revealed SLE pathogenesis pathways, including the complement cascade, cell cycle regulation, NETosis, and epigenetic regulation. By integrative analyses of disease-associated genes (DAGs), DEGs, and enriched TFs, as well as proteins interacting with them, we identified a hierarchical regulatory cascade with TFs regulated by DAGs, which in turn regulates gene expression. Integrative analysis of multi-omics data provided valuable molecular insights into the molecular mechanisms of SLE.

Regulatory and evolutionary signatures of sex-biased genes on both the X chromosome and the autosomes.

BACKGROUND: Sex is an important but understudied factor in the genetics of human diseases. Analyses using a combination of gene expression data, ENCODE data, and evolutionary data of sex-biased gene expression in human tissues can give insight into the regulatory and evolutionary forces acting on sex-biased genes. METHODS: In this study, we analyzed the differentially expressed genes between males and females. On the X chromosome, we used a novel method and investigated the status of genes that escape X-chromosome inactivation (escape genes), taking into account the clonality of lymphoblastoid cell lines (LCLs). To investigate the regulation of sex-biased differentially expressed genes (sDEG), we conducted pathway and transcription factor enrichment analyses on the sDEGs, as well as analyses on the genomic distribution of sDEGs. Evolutionary analyses were also conducted on both sDEGs and escape genes. RESULTS: Genome-wide, we characterized differential gene expression between sexes in 462 RNA-seq samples and identified 587 sex-biased genes, or 3.2% of the genes surveyed. On the X chromosome, sDEGs were distributed in evolutionary strata in a similar pattern as escape genes. We found a trend of negative correlation between the gene expression breadth and nonsynonymous over synonymous mutation (dN/dS) ratios, showing a possible pleiotropic constraint on evolution of genes. Genome-wide, nine transcription factors were found enriched in binding to the regions surrounding the transcription start sites of female-biased genes. Many pathways and protein domains were enriched in sex-biased genes, some of which hint at sex-biased physiological processes. CONCLUSIONS: These findings lend insight into the regulatory and evolutionary forces shaping sex-biased gene expression and their involvement in the physiological and pathological processes in human health and diseases.

A predicted protein-protein interaction network of the filamentous fungus Neurospora crassa.

The filamentous fungus Neurospora crassa is a leading model organism for circadian clock studies. Computational identification of a protein-protein interaction (PPI) network (also known as an interactome) in N. crassa can provide new insights into the cellular functions of proteins. Using two well-established bioinformatics methods (the interolog method and the domain interaction-based method), we predicted 27,588 PPIs among 3006 N. crassa proteins. To the best of our knowledge, this is the first identified interactome for N. crassa, although it remains problematic because of incomplete interactions and false positives. In particular, the established PPI network has provided clues to further decipher the molecular mechanism of circadian rhythmicity. For instance, we found that clock-controlled genes (ccgs) are more likely to act as bottlenecks in the established PPI network. We also identified an important module related to circadian oscillators, and some functional unknown proteins in this module may serve as potential candidates for new oscillators. Finally, all predicted PPIs were compiled into a user-friendly database server (NCPI), which is freely available at .