Splicing dysregulation as a driver of breast cancer

Multi-tissue transcriptome-wide association studies identified 235 genes for intrinsic subtypes of breast cancer

BACKGROUND

Although genome-wide association studies (GWAS) of breast cancer (BC) identified common variants which differ between intrinsic subtypes, genes through which these variants act to impact BC risk have not been fully established. Transcriptome-wide association studies (TWAS) have identified genes associated with overall BC risk, but subtype-specific differences are largely unknown.

METHODS

We performed two multi-tissue TWAS for each BC intrinsic subtype, including an expression-based approach that collated TWAS signals from expression quantitative trait loci (eQTLs) across multiple tissues and a novel splicing-based approach that collated signals from splicing QTLs (sQTLs) across intron clusters and subsequently across tissues. We used summary statistics for five intrinsic subtypes including Luminal A-like, Luminal B-like, Luminal B/HER2-negative-like, HER2-enriched-like, and triple-negative BC, generated from 106 278 BC cases and 91 477 controls in the Breast Cancer Association Consortium.

RESULTS

Overall, we identified 235 genes in 88 loci that were associated with at least one of the five intrinsic subtypes. Most genes were subtype-specific, and many have not been reported in previous TWAS. We discovered common variants that modulate expression of CHEK2 confer increased risk to Luminal A-like BC, in contrast to the viewpoint that CHEK2 primarily harbors rare, penetrant mutations. Additionally, our splicing-based TWAS provided population-level support for MDM4 splice variants that increased the risk of triple-negative BC.

CONCLUSION

Our comprehensive, multi-tissue TWAS corroborated previous GWAS loci for overall BC risk and intrinsic subtypes, while underscoring how common variation that impacts expression and splicing of genes in multiple tissue types can be used to further elucidate the etiology of BC.

Proteomics Analysis of Polyphyllin D-Treated Triple-Negative Breast Cancer Cells Reveal the Anticancer Mechanisms of Polyphyllin D

Polyphyllin D (PD), one of the important steroid saponins in traditional medicinal herb Paris polyphylla, has been demonstrated to have anticancer activity both in vitro and in vivo. However, the mechanisms through which PD exerts its anticancer effects in triple-negative breast cancer (TNBC) remain unclear. Our study was presented to evaluate the anticancer effect and the potential mechanisms of PD in two TNBC cell lines, BT-549 and MDA-MB-231. Through comprehensively comparing the liquid chromatography-tandem mass spectrometry (LC-MS/MS) data of PD-treated and untreated BT-549 and MDA-MB-231 cells, we found that PD could induce apoptosis of TNBC cells by activating oxidative phosphorylation pathway in BT-549 cells, as well as inhibiting spliceosome function alteration in MDA-MB-231 cells. These results suggested that the mechanisms underlying the pro-apoptotic effect of PD on TNBC may be cell type-specificity-dependent. Moreover, we found that nodal modulator 2/3 (NOMO2/3) were downregulated both in PD-treated BT-549 and MDA-MB-231 cells, suggesting that NOMO2/3 may be the potential target of PD. Verification experiments revealed that PD deceased NOMO2/3 expression at protein level, rather than mRNA level. Whether NOMO2/3 are the upstream modulators of oxidative phosphorylation pathway and spliceosome needs further validation. In conclusion, a comprehensive proteomics study was performed on PD-treated or untreated TNBC cells, revealing the anticancer mechanisms of PD.

Precision unveiled: Synergistic genomic landscapes in breast cancer-Integrating single-cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics

BACKGROUND

Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment.

METHODS

Two frameworks, T-cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T-cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a "Mixed" class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses.

RESULTS

Utilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions.

CONCLUSION

Utilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype-specific characteristics essential for prognostic assessment, clinical decision-making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions.

Expression- and splicing-based multi-tissue transcriptome-wide association studies identified multiple genes for breast cancer by estrogen-receptor status

BACKGROUND

Although several transcriptome-wide association studies (TWASs) have been performed to identify genes associated with overall breast cancer (BC) risk, only a few TWAS have explored the differences in estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) breast cancer. Additionally, these studies were based on gene expression prediction models trained primarily in breast tissue, and they did not account for alternative splicing of genes.

METHODS

In this study, we utilized two approaches to perform multi-tissue TWASs of breast cancer by ER subtype: (1) an expression-based TWAS that combined TWAS signals for each gene across multiple tissues and (2) a splicing-based TWAS that combined TWAS signals of all excised introns for each gene across tissues. To perform this TWAS, we utilized summary statistics for ER + BC from the Breast Cancer Association Consortium (BCAC) and for ER- BC from a meta-analysis of BCAC and the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA).

RESULTS

In total, we identified 230 genes in 86 loci that were associated with ER + BC and 66 genes in 29 loci that were associated with ER- BC at a Bonferroni threshold of significance. Of these genes, 2 genes associated with ER + BC at the 1q21.1 locus were located at least 1 Mb from published GWAS hits. For several well-studied tumor suppressor genes such as TP53 and CHEK2 which have historically been thought to impact BC risk through rare, penetrant mutations, we discovered that common variants, which modulate gene expression, may additionally contribute to ER + or ER- etiology.

CONCLUSIONS

Our study comprehensively examined how differences in common variation contribute to molecular differences between ER + and ER- BC and introduces a novel, splicing-based framework that can be used in future TWAS studies.

Exploring the mechanism of resistance to vincristine in breast cancer cells using transcriptome sequencing technology

Breast cancer has replaced lung cancer as the leading cancer globally, but various chemotherapy drugs for breast cancer are prone to resistance, especially in patients with distant metastases who are susceptible to multiple chemotherapy drug resistance often leading to treatment failure. Vincristine (VCR) is an alkaloid extracted from Catharanthus roseus, and is often used in combination with other chemotherapy drugs to treat various types of cancer, including breast cancer. Research on the development of resistance to VCR has been carried out using transcriptome sequencing technology. Firstly, gradient increase of VCR concentration was used to produce a VCR-resistant breast cancer cell line. Mechanistically, RNA was extracted from the VCR-resistant breast cancer cell line, and the transcriptome was sequenced. Further analysis showed changes in the expression levels of various genes in the aforementioned VCR-resistant breast cancer cell line. Meanwhile, the analysis of splicing events also indicated a change in variable splicing events. Further validation showed that the expression levels of multiple genes, including interleukin-1β, were altered in the VCR-resistant breast cancer cell line, and these gene expression changes were related to VCR resistance. The results of the present study provide a theoretical basis for exploring the mechanism of VCR resistance clinically.

Splicing alterations in pancreatic ductal adenocarcinoma: a new molecular landscape with translational potential

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal cancers worldwide, mainly due to its late diagnosis and lack of effective therapies, translating into a low 5-year 12% survival rate, despite extensive clinical efforts to improve outcomes. International cooperative studies have provided informative multiomic landscapes of PDAC, but translation of these discoveries into clinical advances are lagging. Likewise, early diagnosis biomarkers and new therapeutic tools are sorely needed to tackle this cancer. The study of poorly explored molecular processes, such as splicing, can provide new tools in this regard. Alternative splicing of pre-RNA allows the generation of multiple RNA variants from a single gene and thereby contributes to fundamental biological processes by finely tuning gene expression. However, alterations in alternative splicing are linked to many diseases, and particularly to cancer, where it can contribute to tumor initiation, progression, metastasis and drug resistance. Splicing defects are increasingly being associated with PDAC, including both mutations or dysregulation of components of the splicing machinery and associated factors, and altered expression of specific relevant gene variants. Such disruptions can be a key element enhancing pancreatic tumor progression or metastasis, while they can also provide suitable tools to identify potential candidate biomarkers and discover new actionable targets. In this review, we aimed to summarize the current information about dysregulation of splicing-related elements and aberrant splicing isoforms in PDAC, and to describe their relationship with the development, progression and/or aggressiveness of this dismal cancer, as well as their potential as therapeutic tools and targets.

Supervised Relevance-Redundancy assessments for feature selection in omics-based classification scenarios

BACKGROUND AND OBJECTIVE

Many classification tasks in translational bioinformatics and genomics are characterized by the high dimensionality of potential features and unbalanced sample distribution among classes. This can affect classifier robustness and increase the risk of overfitting, curse of dimensionality and generalization leaks; furthermore and most importantly, this can prevent obtaining adequate patient stratification required for precision medicine in facing complex diseases, like cancer. Setting up a feature selection strategy able to extract only proper predictive features by removing irrelevant, redundant, and noisy ones is crucial to achieving valuable results on the desired task.

METHODS

We propose a new feature selection approach, called ReRa, based on supervised Relevance-Redundancy assessments. ReRa consists of a customized step of relevance-based filtering, to identify a reduced subset of meaningful features, followed by a supervised similarity-based procedure to minimize redundancy. This latter step innovatively uses a combination of global and class-specific similarity assessments to remove redundant features while preserving those differentiated across classes, even when these classes are strongly unbalanced.

RESULTS

We compared ReRa with several existing feature selection methods to obtain feature spaces on which performing breast cancer patient subtyping using several classifiers: we considered two use cases based on gene or transcript isoform expression. In the vast majority of the assessed scenarios, when using ReRa-selected feature spaces, the performances were significantly increased compared to simple feature filtering, LASSO regularization, or even MRmr - another Relevance-Redundancy method. The two use cases represent an insightful example of translational application, taking advantage of ReRa capabilities to investigate and enhance a clinically-relevant patient stratification task, which could be easily applied also to other cancer types and diseases.

CONCLUSIONS

ReRa approach has the potential to improve the performance of machine learning models used in an unbalanced classification scenario. Compared to another Relevance-Redundancy approach like MRmr, ReRa does not require tuning the number of preserved features, ensures efficiency and scalability over huge initial dimensionalities and allows re-evaluation of all previously selected features at each iteration of the redundancy assessment, to ultimately preserve only the most relevant and class-differentiated features.

DNA Repair and Therapeutic Strategies in Cancer Stem Cells

First-line cancer treatments successfully eradicate the differentiated tumour mass but are comparatively ineffective against cancer stem cells (CSCs), a self-renewing subpopulation thought to be responsible for tumour initiation, metastasis, heterogeneity, and recurrence. CSCs are thus presented as the principal target for elimination during cancer treatment. However, CSCs are challenging to drug target because of numerous intrinsic and extrinsic mechanisms of drug resistance. One such mechanism that remains relatively understudied is the DNA damage response (DDR). CSCs are presumed to possess properties that enable enhanced DNA repair efficiency relative to their highly proliferative bulk progeny, facilitating improved repair of double-strand breaks induced by radiotherapy and most chemotherapeutics. This can occur through multiple mechanisms, including increased expression and splicing fidelity of DNA repair genes, robust activation of cell cycle checkpoints, and elevated homologous recombination-mediated DNA repair. Herein, we summarise the current knowledge concerning improved genome integrity in non-transformed stem cells and CSCs, discuss therapeutic opportunities within the DDR for re-sensitising CSCs to genotoxic stressors, and consider the challenges posed regarding unbiased identification of novel DDR-directed strategies in CSCs. A better understanding of the DDR mediating chemo/radioresistance mechanisms in CSCs could lead to novel therapeutic approaches, thereby enhancing treatment efficacy in cancer patients.

Alternative mRNA Splicing and Promising Therapies in Cancer

Cancer is among the leading causes of mortality worldwide. While considerable attention has been given to genetic and epigenetic sources of cancer-specific cellular activities, the role of alternative mRNA splicing has only recently received attention as a major contributor to cancer initiation and progression. The distribution of alternate mRNA splicing variants in cancer cells is different from their non-cancer counterparts, and cancer cells are more sensitive than non-cancer cells to drugs that target components of the splicing regulatory network. While many of the alternatively spliced mRNAs in cancer cells may represent "noise" from splicing dysregulation, certain recurring splicing variants have been shown to contribute to tumor progression. Some pathogenic splicing disruption events result from mutations in cis-acting splicing regulatory sequences in disease-associated genes, while others may result from shifts in balance among naturally occurring alternate splicing variants among mRNAs that participate in cell cycle progression and the regulation of apoptosis. This review provides examples of cancer-related alternate splicing events resulting from each step of mRNA processing and the promising therapies that may be used to address them.

Systematic proximal mapping of the classical RAD51 paralogs unravel functionally and clinically relevant interactors for genome stability

Homologous recombination (HR) plays an essential role in the maintenance of genome stability by promoting the repair of cytotoxic DNA double strand breaks (DSBs). More recently, the HR pathway has emerged as a core component of the response to replication stress, in part by protecting stalled replication forks from nucleolytic degradation. In that regard, the mammalian RAD51 paralogs (RAD51B, RAD51C, RAD51D, XRCC2, and XRCC3) have been involved in both HR-mediated DNA repair and collapsed replication fork resolution. Still, it remains largely obscure how they participate in both processes, thereby maintaining genome stability and preventing cancer development. To gain better insight into their contribution in cellulo, we mapped the proximal interactome of the classical RAD51 paralogs using the BioID approach. Aside from identifying the well-established BCDX2 and CX3 sub-complexes, the spliceosome machinery emerged as an integral component of our proximal mapping, suggesting a crosstalk between this pathway and the RAD51 paralogs. Furthermore, we noticed that factors involved RNA metabolic pathways are significantly modulated within the BioID of the classical RAD51 paralogs upon exposure to hydroxyurea (HU), pointing towards a direct contribution of RNA processing during replication stress. Importantly, several members of these pathways have prognostic potential in breast cancer (BC), where their RNA expression correlates with poorer patient outcome. Collectively, this study uncovers novel functionally relevant partners of the different RAD51 paralogs in the maintenance of genome stability that could be used as biomarkers for the prognosis of BC.

COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures

Precision medicine is a paradigm shift in healthcare relying heavily on genomics data. However, the complexity of biological interactions, the large number of genes as well as the lack of comparisons on the analysis of data, remain a tremendous bottleneck regarding clinical adoption. In this paper, we introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. It offers modularity as concerns metric functions and scalability, while being able to automatically determine the best number of clusters. Our evaluation includes both mathematical and biological criteria to define a quantitative metric. The recovered signature is applied to a variety of biological tasks, including screening of biological pathways and functions, and characterization relevance on tumor types and subtypes. Quantitative comparisons among different distance metrics, commonly used clustering methods and a referential gene signature used in the literature, confirm state of the art performance of our approach. In particular, our signature, based on 27 genes, reports at least 30 times better mathematical significance (average Dunn's Index) and 25% better biological significance (average Enrichment in Protein-Protein Interaction) than those produced by other referential clustering methods. Finally, our signature reports promising results on distinguishing immune inflammatory and immune desert tumors, while reporting a high balanced accuracy of 92% on tumor types classification and averaged balanced accuracy of 68% on tumor subtypes classification, which represents, respectively 7% and 9% higher performance compared to the referential signature.

Dysregulation of splicing variants and spliceosome components in breast cancer

The dysregulation of the splicing process has emerged as a novel hallmark of metabolic and tumor pathologies. In breast cancer (BCa), which represents the most diagnosed cancer type among women worldwide, the generation and/or dysregulation of several oncogenic splicing variants have been described. This is the case of the splicing variants of HER2, ER, BRCA1, or the recently identified by our group, In1-ghrelin and SST5TMD4, which exhibit oncogenic roles, increasing the malignancy, poor prognosis, and resistance to treatment of BCa. This altered expression of oncogenic splicing variants has been closely linked with the dysregulation of the elements belonging to the macromolecular machinery that controls the splicing process (spliceosome components and the associated splicing factors). In this review, we compile the current knowledge demonstrating the altered expression of splicing variants and spliceosomal components in BCa, showing the existence of a growing body of evidence supporting the close implication of the alteration in the splicing process in mammary tumorigenesis.

NSrp70 suppresses metastasis in triple-negative breast cancer by modulating Numb/TβR1/EMT axis

Alternative splicing of mRNA precursors allows cancer cells to create different protein isoforms that promote growth and survival. Compared to normal cells, cancer cells frequently exhibit a higher diversity of their transcriptomes. A comprehensive understanding of splicing regulation is required to correct the splicing alterations for the future precision oncology. A quantitative proteomic screen was performed to identify the regulators associated the metastasis in triple-negative breast cancer. Multiple in vitro and in vivo functional analyses were used to study the effects of NSrp70 on breast cancer metastasis. Next, transcriptomic sequencing (RNA-seq) and alternative splicing bioinformatics analysis was applied to screen the potential targets of NSrp70. Moreover, in vitro splicing assays, RNA pull-down, and RNA immunoprecipitation assay were used to confirm the specific binding between NSrp70 and downstream target genes. Furthermore, the prognostic value of NSrp70 was analyzed in a cohort of patients by performing IHC. We uncovered NSrp70 as a novel suppressor of breast cancer metastasis. We discovered that NSrp70 inhibited the skipped exon alternative splicing of NUMB, promoted the degradation of transforming growth factor receptor 1 through lysosome pathway, and regulated TGFβ/SMAD-mediated epithelial-mesenchymal transition phenotype in breast cancer cells. Furthermore, high NSrp70 expression correlated with a better prognosis in breast cancer patients. Our findings revealed that splicing regulator NSrp70 serves as a metastasis suppressor.

Identifying novel transcript biomarkers for hepatocellular carcinoma (HCC) using RNA-Seq datasets and machine learning

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world owing to limitations in its prognosis. The current prognosis approaches include radiological examination and detection of serum biomarkers, however, both have limited efficiency and are ineffective in early prognosis. Due to such limitations, we propose to use RNA-Seq data for evaluating putative higher accuracy biomarkers at the transcript level that could help in early prognosis.

METHODS

To identify such potential transcript biomarkers, RNA-Seq data for healthy liver and various HCC cell models were subjected to five different machine learning algorithms: random forest, K-nearest neighbor, Naïve Bayes, support vector machine, and neural networks. Various metrics, namely sensitivity, specificity, MCC, informedness, and AUC-ROC (except for support vector machine) were evaluated. The algorithms that produced the highest values for all metrics were chosen to extract the top features that were subjected to recursive feature elimination. Through recursive feature elimination, the least number of features were obtained to differentiate between the healthy and HCC cell models.

RESULTS

From the metrics used, it is demonstrated that the efficiency of the known protein biomarkers for HCC is comparatively lower than complete transcriptomics data. Among the different machine learning algorithms, random forest and support vector machine demonstrated the best performance. Using recursive feature elimination on top features of random forest and support vector machine three transcripts were selected that had an accuracy of 0.97 and kappa of 0.93. Of the three transcripts, two were protein coding (PARP2-202 and SPON2-203) and one was a non-coding transcript (CYREN-211). Lastly, we demonstrated that these three selected transcripts outperformed randomly taken three transcripts (15,000 combinations), hence were not chance findings, and could then be an interesting candidate for new HCC biomarker development.

CONCLUSION

Using RNA-Seq data combined with machine learning approaches can aid in finding novel transcript biomarkers. The three biomarkers identified: PARP2-202, SPON2-203, and CYREN-211, presented the highest accuracy among all other transcripts in differentiating the healthy and HCC cell models. The machine learning pipeline developed in this study can be used for any RNA-Seq dataset to find novel transcript biomarkers. Code: www.github.com/rajinder4489/ML_biomarkers.

Genomic Mapping of Splicing-Related Genes Identify Amplifications in LSM1, CLNS1A, and ILF2 in Luminal Breast Cancer

Simple Summary

The alternative splicing (AS) process is highly relevant, affecting most of the hallmarks of cancer, such as proliferation, angiogenesis, and metastasis. Our study evaluated alterations in 304 splicing-related genes and their prognosis value in breast cancer patients. Amplifications in CLNS1A, LSM1, and ILF2 genes in luminal patients were significantly associated with poor outcome. Downregulation of these genes in luminal cell lines showed an antiproliferative effect. Pharmacological modulation of transcription and RNA regulation is key for the optimal development of therapeutic strategies against key proteins. Administration of a BET inhibitor and BET-PROTAC reduced the expression of these identified genes and displayed a significant antiproliferative effect on these cell models. In conclusion, we describe novel splicing genes amplified in luminal breast tumors that are associated with detrimental prognosis and can be modulated pharmacologically. It opens the door for further studies confirming the effect of these genes in patients treated with BET inhibitors.

Abstract

Alternative splicing is an essential biological process, which increases the diversity and complexity of the human transcriptome. In our study, 304 splicing pathway-related genes were evaluated in tumors from breast cancer patients (TCGA dataset). A high number of alterations were detected, including mutations and copy number alterations (CNAs), although mutations were less frequently present compared with CNAs. In the four molecular subtypes, 14 common splice genes showed high level amplification in >5% of patients. Certain genes were only amplified in specific breast cancer subtypes. Most altered genes in each molecular subtype clustered to a few chromosomal regions. In the Luminal subtype, amplifications of LSM1, CLNS1A, and ILF2 showed a strong significant association with prognosis. An even more robust association with OS and RFS was observed when expression of these three genes was combined. Inhibition of LSM1, CLNS1A, and ILF2, using siRNA in MCF7 and T47D cells, showed a decrease in cell proliferation. The mRNA expression of these genes was reduced by treatment with BET inhibitors, a family of epigenetic modulators. We map the presence of splicing-related genes in breast cancer, describing three novel genes, LSM1, CLNS1A, and ILF2, that have an oncogenic role and can be modulated with BET inhibitors.

Alternative RNA Splicing-The Trojan Horse of Cancer Cells in Chemotherapy

Almost all transcribed human genes undergo alternative RNA splicing, which increases the diversity of the coding and non-coding cellular landscape. The resultant gene products might have distinctly different and, in some cases, even opposite functions. Therefore, the abnormal regulation of alternative splicing plays a crucial role in malignant transformation, development, and progression, a fact supported by the distinct splicing profiles identified in both healthy and tumor cells. Drug resistance, resulting in treatment failure, still remains a major challenge for current cancer therapy. Furthermore, tumor cells often take advantage of aberrant RNA splicing to overcome the toxicity of the administered chemotherapeutic agents. Thus, deciphering the alternative RNA splicing variants in tumor cells would provide opportunities for designing novel therapeutics combating cancer more efficiently. In the present review, we provide a comprehensive outline of the recent findings in alternative splicing in the most common neoplasms, including lung, breast, prostate, head and neck, glioma, colon, and blood malignancies. Molecular mechanisms developed by cancer cells to promote oncogenesis as well as to evade anticancer drug treatment and the subsequent chemotherapy failure are also discussed. Taken together, these findings offer novel opportunities for future studies and the development of targeted therapy for cancer-specific splicing variants.

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.

Over-Expression of RNA Processing, Heat Shock, and DNA Repair Proteins in Breast Tumor Compared to Normal Tissue

This study identifies the main changes in protein expression in human breast tumors compared to normal breast tissue. Malignant tumors (32) and normal breast tissue samples (23), from formaldehyde-fixed, paraffin-embedded specimens are subjected to discovery proteomics using liquid chromatography/tandem mass spectrometry, with spectral counts for quantitation. The dataset contains 1406 proteins. Differential expression is measured using a method that takes advantage of estimates of the percentage of tumor on a slide. This analysis shows that the major classes of proteins over-expressed by tumors are RNA-binding, heat shock and DNA repair proteins. RNA-binding proteins, including heterogeneous nuclear ribonucleoproteins (HNRNPs), SR splice factors (SRSF) and elongation factors form the largest group. Comparison with results from another study demonstrates that the RNA-binding proteins are associated specifically with malignant transformation, rather than with cell proliferation. HNRNP and SRSF proteins help define splice sites in normal cells. Their over-expression may dysregulate splicing, which in turn has the potential to promote malignant transformation.

Wide-ranging analysis of survival-related alternative splicing events in invasive breast carcinoma

Invasive breast carcinoma (BRCA) is a serious disease that threatens the survival time of those affected. Alternative splicing (AS) involved in BRCA pathogenesis may be a potential therapeutic target. However, to the best of our knowledge, a systematic analysis of survival-related alternative splicing events (SREs) has not yet been reported. The aim of the present study was to identify SREs and analyze their potential biological functions as BRCA prognostic biomarkers. An UpSet plot demonstrated AS global characteristics. Cox's proportional hazards regression model quantitatively demonstrated the prognostic relevance of AS events. Functional enrichment analysis investigated the potential pathways through which AS events affect BRCA progression. The receiver operating characteristic curve model determined the clinical significance of AS events represented using percent-spliced-in (PSI) values. The regulatory network of splicing factors (SFs) and AS events laid the foundation for studying the role of SFs in BRCA. The present study identified 1,215 SREs and their distribution characteristics, suggesting that AS events in exon skipping (ES) primarily exerted normal physiological functions, while AS events in alternative terminator sites had the most significant prognostic effect. The present study demonstrated that survival-associated genes are involved primarily in certain biological processes of ribosomal proteins. In the diagnostic model, the alternative acceptor site, alternative donor site, alternative promoter site and ES performed well. ELAVL4 was the key gene associated with prognosis and SREs. In conclusion, a number of AS events affect BRCA initiation, progression and prognosis. The PSI value of AS events has the potential to diagnose BRCA and predict a prognosis; however, this must be confirmed in additional studies.

Novel Insights Into Triple-Negative Breast Cancer Prognosis by Comprehensive Characterization of Aberrant Alternative Splicing

Background

Alternative splicing (AS) is important in the regulation of gene expression and aberrant AS is emerging as a major factor in the pathogenesis of human conditions, including cancer. Triple-negative breast cancer (TNBC) is the most challenging subtype of breast cancer with strong invasion, high rate of metastasis, and poor prognosis. Here we report a systematic profiling of aberrant AS in TNBC.

Methods

The percent spliced in (PSI) values for AS events in 151 TNBC patients were obtained from The Cancer Genome Atlas (TCGA) SpliceSeq database. Univariate Cox and stepwise Multivariate Cox regression analyses were conducted to find the best prognostic AS model. Splicing regulatory networks were constructed by prognosis-related spliceosome and aberrant AS events. Additionally, pathway enrichment and gene set enrichment analysis (GSEA) were further employed to reveal the significant pathways for prognosis-related AS genes. Finally, splicing regulatory networks were constructed via Spearman's rank correlation coefficients between prognosis-related AS events and splicing factor expressions.

Results

A total of 1,397 prognosis-associated AS events were identified in TNBC. The majority of the parent genes of prognostic AS events exhibited direct interactions to each other in the STRING gene network. Pathways of focal adhesion (p < 0.001), RNA splicing (p = 0.007), homologous recombination (p = 0.042) and ECM-receptor interaction (p = 0.046) were found to be significantly enriched for prognosis-related AS. Additionally, the area under curve (AUC) of the best AS prognostic predictor model reached 0.949, showing a powerful capability to predict outcomes. The Exon Skip (ES) type of AS events displayed more robust and efficient capacity in predicting performance than any other specific AS events type in terms of prognosis. The ES AS signature might confer a strong oncogenic phenotype in the high-risk group with elevated activities in cell cycle and SUMOylating pathways of tumorigenesis, while programmed cell death and metabolism pathways were found to be enriched in the low-risk group of TNBC. The splicing correlation network also revealed a regulatory mode of prognostic splicing factors (SFs) in TNBC.

Conclusion

Our analysis of AS events in TNBC could not only contribute to elucidating the tumorigenesis mechanism of AS but also provide clues to uncovering underlying prognostic biomarkers and therapeutic targets for further study.

In Silico Analysis of a Highly Mutated Gene in Cancer Provides Insight into Abnormal mRNA Splicing: Splicing Factor 3B Subunit 1K700E Mutant

Cancer is the second leading cause of death worldwide. The etiology of the disease has remained elusive, but mutations causing aberrant RNA splicing have been considered one of the significant factors in various cancer types. The association of aberrant RNA splicing with drug/therapy resistance further increases the importance of these mutations. In this work, the impact of the splicing factor 3B subunit 1 (SF3B1) K700E mutation, a highly prevalent mutation in various cancer types, is investigated through molecular dynamics simulations. Based on our results, K700E mutation increases flexibility of the mutant SF3B1. Consequently, this mutation leads to i) disruption of interaction of pre-mRNA with SF3B1 and p14, thus preventing proper alignment of mRNA and causing usage of abnormal 3' splice site, and ii) disruption of communication in critical regions participating in interactions with other proteins in pre-mRNA splicing machinery. We anticipate that this study enhances our understanding of the mechanism of functional abnormalities associated with splicing machinery, thereby, increasing possibility for designing effective therapies to combat cancer at an earlier stage.

Cell-free transcription in Xenopus egg extract

Soluble extracts prepared from Xenopus eggs have been used extensively to study various aspects of cellular and developmental biology. During early egg development, transcription of the zygotic genome is suppressed. As a result, traditional extracts derived from unfertilized and early stage eggs possess little or no intrinsic transcriptional activity. In this study, we show that Xenopus nucleoplasmic extract (NPE) supports robust transcription of a chromatinized plasmid substrate. Although prepared from eggs in a transcriptionally inactive state, the process of making NPE resembles some aspects of egg fertilization and early embryo development that lead to transcriptional activation. With this system, we observed that promoter-dependent recruitment of transcription factors and RNA polymerase II leads to conventional patterns of divergent transcription and pre-mRNA processing, including intron splicing and 3' cleavage and polyadenylation. We also show that histone density controls transcription factor binding and RNA polymerase II activity, validating a mechanism proposed to regulate genome activation during development. Together, these results establish a new cell-free system to study the regulation, initiation, and processing of mRNA transcripts.

SpliceHetero: An information theoretic approach for measuring spliceomic intratumor heterogeneity from bulk tumor RNA-seq

Motivation

Intratumor heterogeneity (ITH) represents the diversity of cell populations that make up cancer tissue. The level of ITH in a tumor is usually measured by a genomic variation profile, such as copy number variation and somatic mutation. However, a recent study has identified ITH at the transcriptome level and suggested that ITH at gene expression levels is useful for predicting prognosis. Measuring ITH levels at the spliceome level is a natural extension. There are serious technical challenges in measuring spliceomic ITH (sITH) from bulk tumor RNA sequencing (RNA-seq) due to the complex splicing patterns.

Results

We propose an information-theoretic method to measure the sITH of bulk tumors to overcome the above challenges. This method has been extensively tested in experiments using synthetic data, xenograft tumor data, and TCGA pan-cancer data. As a result, we showed that sITH is closely related to cancer progression and clonal heterogeneity, along with clinically significant features such as cancer stage, survival outcome and PAM50 subtype. As far as we know, it is the first study to define ITH at the spliceome level. This method can greatly improve the understanding of cancer spliceome and has great potential as a diagnostic and prognostic tool.

Therapeutic approaches to treat human spliceosomal diseases

Mutated RNA splicing machinery drives many human diseases and is a promising therapeutic target for engineering and small molecule therapy. In the case of mutations in individual genes that cause them to be incorrectly spliced, engineered splicing factors can be introduced to correct splicing of these aberrant transcripts and reduce the effects of the disease phenotype. Mutations that occur in certain splicing factor genes themselves have been implicated in many cancers, particularly myelodysplastic syndromes. Small molecules that target splicing factors have been developed as therapies to preferentially induce apoptosis in these cancer cells. Specifically, drugs targeting the splicing factor SF3B1 have led to recent clinical trials. Here, we review the role of alternative splicing in disease, approaches to rescue incorrect splicing using engineered splicing factors, and small molecule splicing inhibitors developed to treat hematological cancers.

PUF60 accelerates the progression of breast cancer through downregulation of PTEN expression

Background

PUF60 is a splicing variant of far upstream element binding protein 1-interacting repressor, which is abnormally expressed in a variety of tumors and is closely involved in their progression. However, whether PUF60 participates in the occurrence and development of breast cancer remains unknown. Therefore, the objective of the current study is to explore the effects and mechanism of PUF60 in the progression of breast cancer.

Methods

PUF60 expression patterns in breast cancer tissues and cells were determined by RT-PCR and Western blotting. The relationship between PUF60 expression and patients' clinical features and outcome was evaluated to assess the potential of PUF60 as a marker for progression and prognosis prediction. CCK-8, flow cytometry, transwell and in vivo tumor formation assays were used to detect cell proliferation, apoptosis, migration, invasion and tumorigenesis. The effects of PUF60 on the activation of PTEN/PI3K/AKT were also evaluated by Western blotting and immunofluorescence assays.

Results

The expression of PUF60 was elevated in breast cancer tissue samples and cell lines, and its high expression was closely associated with the high incidence of lymph node metastasis and advanced TNM stage. Besides, upregulation of PUF60 with lentivirus infection significantly increased the growth, migration, and invasion and repressed the apoptosis of breast cancer HCC1937 and MDA-MB-231 cells, while silencing of PUF60 with shRNA showed the opposite results. Moreover, PUF60 upregulation promoted the expression of p-AKT, PI3K, and mTOR, while decreased PTEN expression through inhibiting its stability and enhancing its ubiquitination. Furthermore, upregulation of PUF60 promoted the tumorigenesis in vivo, whereas this effect was impaired when PTEN expression was upregulated in MDA-MB-231 and HCC1937 cells.

Conclusion

This study demonstrates that PUF60 is highly expressed in breast cancer; upregulation of PUF60 accelerates the progression of breast cancer through PTEN inhibition.

Alternative splicing of the vitamin D receptor modulates target gene expression and promotes ligand-independent functions

Alternative splicing modulates gene function by creating splice variants with alternate functions or non-coding RNA activity. Naturally occurring variants of nuclear receptor (NR) genes with dominant negative or gain-of-function phenotypes have been documented, but their cellular roles, regulation, and responsiveness to environmental stress or disease remain unevaluated. Informed by observations that class I androgen and estrogen receptor variants display ligand-independent signaling in human cancer tissues, we questioned whether the function of class II NRs, like the vitamin D receptor (VDR), would also respond to alternative splicing regulation. Artificial VDR constructs lacking exon 3 (Dex3-VDR), encoding part of the DNA binding domain (DBD), and exon 8 (Dex8-VDR), encoding part of the ligand binding domain (LBD), were transiently transfected into DU-145 cells and stably-integrated into Caco-2 cells to study their effect on gene expression and cell viability. Changes in VDR promoter signaling were monitored by the expression of target genes (e.g. CYP24A1, CYP3A4 and CYP3A5). Ligand-independent VDR signaling was observed in variants lacking exon 8, and a significant loss of gene suppressor function was documented for variants lacking exon 3. The gain-of-function behavior of the Dex8-VDR variant was recapitulated in vitro using antisense oligonucleotides (ASO) that induce the skipping of exon 8 in wild-type VDR. ASO targeting the splice acceptor site of exon 8 significantly stimulated ligand-independent VDR reporter activity and the induction of CYP24A1 above controls. These results demonstrate how alternative splicing can re-program NR gene function, highlighting novel mechanisms of toxicity and new opportunities for the use of splice-switching oligonucleotides (SSO) in precision medicine.