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

The RNA isoform repertoire is regulated by splicing factor (SF) expression, and alterations in SF levels are associated with disease. SFs contain ultraconserved poison exon (PE) sequences that exhibit greater identity across species than nearby coding exons, but their physiological role and molecular regulation is incompletely understood. We show that PEs in serine-arginine-rich (SR) proteins, a family of 14 essential SFs, are differentially spliced during induced pluripotent stem cell (iPSC) differentiation and in tumors versus normal tissues. We uncover an extensive cross-regulatory network of SR proteins controlling their expression via alternative splicing coupled to nonsense-mediated decay. We define sequences that regulate PE inclusion and protein expression of the oncogenic SF TRA2ß using an RNA-targeting CRISPR screen. We demonstrate location dependency of RS domain activity on regulation of TRA2ß-PE using CRISPR artificial SFs. Finally, we develop splice-switching antisense oligonucleotides to reverse the increased skipping of TRA2ß-PE detected in breast tumors, altering breast cancer cell viability, proliferation, and migration.

SRSF1-Regulated Alternative Splicing in Breast Cancer.

Splicing factor SRSF1 is upregulated in human breast tumors, and its overexpression promotes transformation of mammary cells. Using RNA-seq, we identified SRSF1-regulated alternative splicing (AS) targets in organotypic three-dimensional MCF-10A cell cultures that mimic a context relevant to breast cancer. We identified and validated hundreds of endogenous SRSF1-regulated AS events. De novo discovery of the SRSF1 binding motif reconciled discrepancies in previous motif analyses. Using a Bayesian model, we determined positional effects of SRSF1 binding on cassette exons: binding close to the 5' splice site generally promoted exon inclusion, whereas binding near the 3' splice site promoted either exon skipping or inclusion. Finally, we identified SRSF1-regulated AS events deregulated in human tumors; overexpressing one such isoform, exon-9-included CASC4, increased acinar size and proliferation, and decreased apoptosis, partially recapitulating SRSF1's oncogenic effects. Thus, we uncovered SRSF1 positive and negative regulatory mechanisms, and oncogenic AS events that represent potential targets for therapeutics development.

Splicing-factor alterations in cancers.

Tumor-associated alterations in RNA splicing result either from mutations in splicing-regulatory elements or changes in components of the splicing machinery. This review summarizes our current understanding of the role of splicing-factor alterations in human cancers. We describe splicing-factor alterations detected in human tumors and the resulting changes in splicing, highlighting cell-type-specific similarities and differences. We review the mechanisms of splicing-factor regulation in normal and cancer cells. Finally, we summarize recent efforts to develop novel cancer therapies, based on targeting either the oncogenic splicing events or their upstream splicing regulators.

Isolated pseudo-RNA-recognition motifs of SR proteins can regulate splicing using a noncanonical mode of RNA recognition.

Serine/arginine (SR) proteins, one of the major families of alternative-splicing regulators in Eukarya, have two types of RNA-recognition motifs (RRMs): a canonical RRM and a pseudo-RRM. Although pseudo-RRMs are crucial for activity of SR proteins, their mode of action was unknown. By solving the structure of the human SRSF1 pseudo-RRM bound to RNA, we discovered a very unusual and sequence-specific RNA-binding mode that is centered on one α-helix and does not involve the ß-sheet surface, which typically mediates RNA binding by RRMs. Remarkably, this mode of binding is conserved in all pseudo-RRMs tested. Furthermore, the isolated pseudo-RRM is sufficient to regulate splicing of about half of the SRSF1 target genes tested, and the bound α-helix is a pivotal element for this function. Our results strongly suggest that SR proteins with a pseudo-RRM frequently regulate splicing by competing with, rather than recruiting, spliceosome components, using solely this unusual RRM.

OLego: fast and sensitive mapping of spliced mRNA-Seq reads using small seeds.

A crucial step in analyzing mRNA-Seq data is to accurately and efficiently map hundreds of millions of reads to the reference genome and exon junctions. Here we present OLego, an algorithm specifically designed for de novo mapping of spliced mRNA-Seq reads. OLego adopts a multiple-seed-and-extend scheme, and does not rely on a separate external aligner. It achieves high sensitivity of junction detection by strategic searches with small seeds (~14 nt for mammalian genomes). To improve accuracy and resolve ambiguous mapping at junctions, OLego uses a built-in statistical model to score exon junctions by splice-site strength and intron size. Burrows-Wheeler transform is used in multiple steps of the algorithm to efficiently map seeds, locate junctions and identify small exons. OLego is implemented in C++ with fully multithreaded execution, and allows fast processing of large-scale data. We systematically evaluated the performance of OLego in comparison with published tools using both simulated and real data. OLego demonstrated better sensitivity, higher or comparable accuracy and substantially improved speed. OLego also identified hundreds of novel micro-exons (<30 nt) in the mouse transcriptome, many of which are phylogenetically conserved and can be validated experimentally in vivo. OLego is freely available at http://zhanglab.c2b2.columbia.edu/index.php/OLego.

Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma.

Resistance of BRAF-mutant melanomas to targeted therapy arises from the ability of cells to enter a persister state, evade treatment with relative dormancy, and repopulate the tumor when reactivated. Using spatial transcriptomics in patient derived xenograft models, we capture clonal lineage evolution during treatment, finding the persister state to show increased oxidative phosphorylation, decreased proliferation, and increased invasive capacity, with central-to-peripheral gradients. Phylogenetic tracing identifies intrinsic- and acquired-resistance mechanisms (e.g. dual specific phosphatases, Reticulon-4, CDK2) and suggests specific temporal windows of potential therapeutic efficacy. Using deep learning to analyze histopathological slides, we find morphological features of specific cell states, demonstrating that juxtaposition of transcriptomics and histology data enables identification of phenotypically-distinct populations using imaging data alone. In summary, we define state change and lineage selection during melanoma treatment with spatiotemporal resolution, elucidating how choice and timing of therapeutic agents will impact the ability to eradicate resistant clones. Statement of Significance: Tumor evolution is accelerated by application of anti-cancer therapy, resulting in clonal expansions leading to dormancy and subsequently resistance, but the dynamics of this process are incompletely understood. Tracking clonal progression during treatment, we identify conserved, global transcriptional changes and local clone-clone and spatial patterns underlying the emergence of resistance.

RNA splicing as a biomarker and phenotypic driver of meningioma DNA methylation groups.

BACKGROUND: Advances in our understanding of the molecular biology of meningiomas have led to significant gains in the ability to predict patient prognosis and tumor recurrence and to identify novel targets for therapeutic design. Specifically, classification of meningiomas based on DNA methylation has greatly improved our ability to risk stratify patients, however new questions have arisen in terms of the underlying impact these DNA methylation signatures have on meningioma biology. METHODS: This study utilizes RNA-seq data from 486 meningioma samples corresponding to three meningioma DNA methylation groups (Merlin-intact, Immune-enriched, and Hypermitotic), followed by in vitro experiments utilizing human meningioma cell lines. RESULTS: We identify alterations in RNA splicing between meningioma DNA methylation groups including individual splicing events that correlate with Hypermitotic meningiomas and predict tumor recurrence and overall patient prognosis and compile a set of splicing events that can accurately predict DNA methylation classification based on RNA-seq data. Furthermore, we validate these events using RT-PCR in patient samples and meningioma cell lines. Additionally, we identify alterations in RNA binding proteins and splicing factors that lie upstream of RNA splicing events, including upregulation of SRSF1 in Hypermitotic meningiomas which we show drives alternative RNA splicing changes. Finally, we design splice switching antisense oligonucleotides to target RNA splicing changes in NASP and MFF observed in Hypermitotic meningiomas, providing a rationale for RNA-based therapeutic design. CONCLUSIONS: RNA splicing is an important driver of meningioma phenotypes that can be useful in prognosticating patients and as a potential exploit for therapeutic vulnerabilities.

Autoregulated splicing of TRA2ß programs T cell fate in response to antigen-receptor stimulation.

T cell receptor (TCR) sensitivity to peptide-major histocompatibility complex (MHC) dictates T cell fate. Canonical models of TCR sensitivity cannot be fully explained by transcriptional regulation. In this work, we identify a posttranscriptional regulatory mechanism of TCR sensitivity that guides alternative splicing of TCR signaling transcripts through an evolutionarily ultraconserved poison exon (PE) in the RNA-binding protein (RBP) TRA2ß in mouse and human. TRA2ß-PE splicing, seen during cancer and infection, was required for TCR-induced effector T cell expansion and function. Tra2ß-PE skipping enhanced T cell response to antigen by increasing TCR sensitivity. As antigen levels decreased, Tra2ß-PE reinclusion allowed T cell survival. Finally, we found that TRA2ß-PE was first included in the genome of jawed vertebrates that were capable of TCR gene rearrangements. We propose that TRA2ß-PE splicing acts as a gatekeeper of TCR sensitivity to shape T cell fate.

RNA splicing dysregulation and the hallmarks of cancer.

Dysregulated RNA splicing is a molecular feature that characterizes almost all tumour types. Cancer-associated splicing alterations arise from both recurrent mutations and altered expression of trans-acting factors governing splicing catalysis and regulation. Cancer-associated splicing dysregulation can promote tumorigenesis via diverse mechanisms, contributing to increased cell proliferation, decreased apoptosis, enhanced migration and metastatic potential, resistance to chemotherapy and evasion of immune surveillance. Recent studies have identified specific cancer-associated isoforms that play critical roles in cancer cell transformation and growth and demonstrated the therapeutic benefits of correcting or otherwise antagonizing such cancer-associated mRNA isoforms. Clinical-grade small molecules that modulate or inhibit RNA splicing have similarly been developed as promising anticancer therapeutics. Here, we review splicing alterations characteristic of cancer cell transcriptomes, dysregulated splicing's contributions to tumour initiation and progression, and existing and emerging approaches for targeting splicing for cancer therapy. Finally, we discuss the outstanding questions and challenges that must be addressed to translate these findings into the clinic.

Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology.

Three-dimensional (3D) culture models, such as organoids, are flexible systems to interrogate cellular growth and morphology, multicellular spatial architecture, and cell interactions in response to drug treatment. However, new computational methods to segment and analyze 3D models at cellular resolution with sufficiently high throughput are needed to realize these possibilities. Here we report Cellos (Cell and Organoid Segmentation), an accurate, high throughput image analysis pipeline for 3D organoid and nuclear segmentation analysis. Cellos segments organoids in 3D using classical algorithms and segments nuclei using a Stardist-3D convolutional neural network which we trained on a manually annotated dataset of 3,862 cells from 36 organoids confocally imaged at 5 µm z-resolution. To evaluate the capabilities of Cellos we then analyzed 74,450 organoids with 1.65 million cells, from multiple experiments on triple negative breast cancer organoids containing clonal mixtures with complex cisplatin sensitivities. Cellos was able to accurately distinguish ratios of distinct fluorescently labelled cell populations in organoids, with ≤3% deviation from the seeding ratios in each well and was effective for both fluorescently labelled nuclei and independent DAPI stained datasets. Cellos was able to recapitulate traditional luminescence-based drug response quantifications by analyzing 3D images, including parallel analysis of multiple cancer clones in the same well. Moreover, Cellos was able to identify organoid and nuclear morphology feature changes associated with treatment. Finally, Cellos enables 3D analysis of cell spatial relationships, which we used to detect ecological affinity between cancer cells beyond what arises from local cell division or organoid composition. Cellos provides powerful tools to perform high throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging.

Alternative splicing is coupled to gene expression in a subset of variably expressed genes.

Numerous factors regulate alternative splicing of human genes at a co-transcriptional level. However, how alternative splicing depends on the regulation of gene expression is poorly understood. We leveraged data from the Genotype-Tissue Expression (GTEx) project to show a significant association of gene expression and splicing for 6874 (4.9%) of 141,043 exons in 1106 (13.3%) of 8314 genes with substantially variable expression in ten GTEx tissues. About half of these exons demonstrate higher inclusion with higher gene expression, and half demonstrate higher exclusion, with the observed direction of coupling being highly consistent across different tissues and in external datasets. The exons differ with respect to sequence characteristics, enriched sequence motifs, RNA polymerase II binding, and inferred transcription rate of downstream introns. The exons were enriched for hundreds of isoform-specific Gene Ontology annotations, suggesting that the coupling of expression and alternative splicing described here may provide an important gene regulatory mechanism that might be used in a variety of biological contexts. In particular, higher inclusion exons could play an important role during cell division.

High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology with Cellos.

Three-dimensional (3D) organoid cultures are flexible systems to interrogate cellular growth, morphology, multicellular spatial architecture, and cellular interactions in response to treatment. However, computational methods for analysis of 3D organoids with sufficiently high-throughput and cellular resolution are needed. Here we report Cellos, an accurate, high-throughput pipeline for 3D organoid segmentation using classical algorithms and nuclear segmentation using a trained Stardist-3D convolutional neural network. To evaluate Cellos, we analyze ~100,000 organoids with ~2.35 million cells from multiple treatment experiments. Cellos segments dye-stained or fluorescently-labeled nuclei and accurately distinguishes distinct labeled cell populations within organoids. Cellos can recapitulate traditional luminescence-based drug response of cells with complex drug sensitivities, while also quantifying changes in organoid and nuclear morphologies caused by treatment as well as cell-cell spatial relationships that reflect ecological affinity. Cellos provides powerful tools to perform high-throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging.

Comprehensive single cell aging atlas of mammary tissues reveals shared epigenomic and transcriptomic signatures of aging and cancer.

Aging is the greatest risk factor for breast cancer; however, how age-related cellular and molecular events impact cancer initiation is unknown. We investigate how aging rewires transcriptomic and epigenomic programs of mouse mammary glands at single cell resolution, yielding a comprehensive resource for aging and cancer biology. Aged epithelial cells exhibit epigenetic and transcriptional changes in metabolic, pro-inflammatory, or cancer-associated genes. Aged stromal cells downregulate fibroblast marker genes and upregulate markers of senescence and cancer-associated fibroblasts. Among immune cells, distinct T cell subsets (Gzmk+, memory CD4+, γδ) and M2-like macrophages expand with age. Spatial transcriptomics reveal co-localization of aged immune and epithelial cells in situ. Lastly, transcriptional signatures of aging mammary cells are found in human breast tumors, suggesting mechanistic links between aging and cancer. Together, these data uncover that epithelial, immune, and stromal cells shift in proportions and cell identity, potentially impacting cell plasticity, aged microenvironment, and neoplasia risk.

Challenges and opportunities for modeling aging and cancer.

Age is among the main risk factors for cancer, and any cancer study in adults is faced with an aging tissue and organism. Yet, pre-clinical studies are carried out using young mice and are not able to address the impact of aging and associated comorbidities on disease biology and treatment outcomes. Here, we discuss the limitations of current mouse cancer models and suggest strategies for developing novel models to address these major gaps in knowledge and experimental approaches.

MYC regulates a pan-cancer network of co-expressed oncogenic splicing factors.

MYC is dysregulated in >50% of cancers, but direct targeting of MYC has been clinically unsuccessful. Targeting downstream MYC effector pathways represents an attractive alternative. MYC regulates alternative mRNA splicing, but the mechanistic links between MYC and the splicing machinery in cancer remain underexplored. Here, we identify a network of co-expressed splicing factors (SF-modules) in MYC-active breast tumors. Of these, one is a pan-cancer SF-module correlating with MYC activity across 33 tumor types. In mammary cell models, MYC activation leads to co-upregulation of pan-cancer module SFs and to changes in >4,000 splicing events. In breast cancer organoids, co-overexpression of the pan-cancer SF-module induces MYC-regulated splicing events and increases organoid size and invasiveness, while knockdown decreases organoid size. Finally, we uncover a MYC-activity pan-cancer splicing signature correlating with survival across tumor types. Our findings provide insight into the mechanisms of MYC-regulated splicing and for the development of therapeutics for MYC-driven tumors.

A comprehensive long-read isoform analysis platform and sequencing resource for breast cancer.

Tumors display widespread transcriptome alterations, but the full repertoire of isoform-level alternative splicing in cancer is unknown. We developed a long-read (LR) RNA sequencing and analytical platform that identifies and annotates full-length isoforms and infers tumor-specific splicing events. Application of this platform to breast cancer samples identifies thousands of previously unannotated isoforms; ~30% affect protein coding exons and are predicted to alter protein localization and function. We performed extensive cross-validation with -omics datasets to support transcription and translation of novel isoforms. We identified 3059 breast tumor­specific splicing events, including 35 that are significantly associated with patient survival. Of these, 21 are absent from GENCODE and 10 are enriched in specific breast cancer subtypes. Together, our results demonstrate the complexity, cancer subtype specificity, and clinical relevance of previously unidentified isoforms and splicing events in breast cancer that are only annotatable by LR-seq and provide a rich resource of immuno-oncology therapeutic targets.

Comprehensive Analysis of Alternative Splicing in Gastric Cancer Identifies Epithelial-Mesenchymal Transition Subtypes Associated with Survival.

Alternatively spliced RNA isoforms are a hallmark of tumors, but their nature, prevalence, and clinical implications in gastric cancer have not been comprehensively characterized. We systematically profiled the splicing landscape of 83 gastric tumors and matched normal mucosa, identifying and experimentally validating eight splicing events that can classify all gastric cancers into three subtypes: epithelial-splicing (EpiS), mesenchymal-splicing (MesS), and hybrid-splicing. These subtypes were associated with distinct molecular signatures and epithelial-mesenchymal transition markers. Subtype-specific splicing events were enriched in motifs for splicing factors RBM24 and ESRP1, which were upregulated in MesS and EpiS tumors, respectively. A simple classifier based only on RNA levels of RBM24 and ESRP1, which can be readily implemented in the clinic, was sufficient to distinguish gastric cancer subtypes and predict patient survival in multiple independent patient cohorts. Overall, this study provides insights into alternative splicing in gastric cancer and the potential clinical utility of splicing-based patient classification. SIGNIFICANCE: This study presents a comprehensive analysis of alternative splicing in the context of patient classification, molecular mechanisms, and prognosis in gastric cancer.

Splicing alterations in healthy aging and disease.

Alternative RNA splicing is a key step in gene expression that allows generation of numerous messenger RNA transcripts encoding proteins of varied functions from the same gene. It is thus a rich source of proteomic and functional diversity. Alterations in alternative RNA splicing are observed both during healthy aging and in a number of human diseases, several of which display premature aging phenotypes or increased incidence with age. Age-associated splicing alterations include differential splicing of genes associated with hallmarks of aging, as well as changes in the levels of core spliceosomal genes and regulatory splicing factors. Here, we review the current known links between alternative RNA splicing, its regulators, healthy biological aging, and diseases associated with aging or aging-like phenotypes. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Processing > Splicing Regulation/Alternative Splicing.