Development of a novel splice array platform and its application in the identification of alternative splice variants in lung cancer

The dual role of SUSD2 in cancer development

Sushi domain-containing protein 2 (SUSD2, also known as the complement control protein domain) is a representative and vital protein in the SUSD protein family involved in many physiological and pathological processes beyond complement regulation. Cancer is one of the leading causes of death worldwide. The complex role of SUSD2 in tumorigenesis and cancer progression has raised increasing concerns. Studies suggest that SUSD2 has different regulatory tendencies among different tumors and exerts its biological effects in a cancer type-specific manner; for instance, it has oncogenic effects on breast cancer, gastric cancer, and glioma and has tumor-suppression effects on lung cancer, bladder cancer, and colon cancer. Moreover, SUSD2 can be regulated by noncoding RNAs, its promoter methylation and other molecules, such as Galectin-1 (Gal-1), tropomyosin alpha-4 chain (TPM4), and p63. The therapeutic implications of targeting SUSD2 have already been preliminarily revealed in some malignancies, including melanoma, colon cancer, and breast cancer. This article reviews the role and regulatory mechanisms of SUSD2 in cancer development, as well as its structure and distribution. We hope that this review will advance the understanding of SUSD2 as a diagnostic and/or prognostic biomarker and provide new avenues for the development of novel cancer therapies.

Proteomics-Derived Biomarker Panel Facilitates Distinguishing Primary Lung Adenocarcinomas With Intestinal or Mucinous Differentiation From Lung Metastatic Colorectal Cancer

The diagnosis of primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) remains challenging due to the overlapping histomorphological, immunohistochemical (IHC), and genetic characteristics with lung metastatic colorectal cancer (lmCRC). This study aimed to explore the protein biomarkers that could distinguish between PAIM and lmCRC. To uncover differences between the two diseases, we used tandem mass tagging-based shotgun proteomics to characterize proteomes of formalin-fixed, paraffin-embedded tumor samples of PAIM (n = 22) and lmCRC (n = 17).Then three machine learning algorithms, namely support vector machine (SVM), random forest, and the Least Absolute Shrinkage and Selection Operator, were utilized to select protein features with diagnostic significance. These candidate proteins were further validated in an independent cohort (PAIM, n = 11; lmCRC, n = 19) by IHC to confirm their diagnostic performance. In total, 105 proteins out of 7871 proteins were significantly dysregulated between PAIM and lmCRC samples and well-separated two groups by Uniform Manifold Approximation and Projection. The upregulated proteins in PAIM were involved in actin cytoskeleton organization, platelet degranulation, and regulation of leukocyte chemotaxis, while downregulated ones were involved in mitochondrial transmembrane transport, vasculature development, and stem cell proliferation. A set of ten candidate proteins (high-level expression in lmCRC: CDH17, ATP1B3, GLB1, OXNAD1, LYST, FABP1; high-level expression in PAIM: CK7 (an established marker), NARR, MLPH, S100A14) was ultimately selected to distinguish PAIM from lmCRC by machine learning algorithms. We further confirmed using IHC that the five protein biomarkers including CDH17, CK7, MLPH, FABP1 and NARR were effective biomarkers for distinguishing PAIM from lmCRC. Our study depicts PAIM-specific proteomic characteristics and demonstrates the potential utility of new protein biomarkers for the differential diagnosis of PAIM and lmCRC. These findings may contribute to improving the diagnostic accuracy and guide appropriate treatments for these patients.

SUSD2 expression correlates with decreased metastasis and increased survival in a high-grade serous ovarian cancer xenograft murine model

The cause of death among high-grade serous ovarian cancer (HGSOC) patients involves passive dissemination of cancer cells within the peritoneal cavity and subsequent implantation of cancer spheroids into adjacent organs. Sushi DomainContaining 2 (SUSD2) encodes a type I transmembrane protein containing several functional domains inherent to adhesion molecules. Previous studies using in vitro methods have indicated that SUSD2 functions as a tumor suppressor in several cancers, including HGSOC. In this study, we generated a HGSOC xenograft mouse model to investigate SUSD2 expression in the context of HGSOC late-stage metastasis and overall survival. OVCAR3 cells with knock-down expression of SUSD2 (OVCAR3 SUSD2-KD) or endogenous expression of SUSD2 (OVCAR3-Non-Targeting (NT)) were injected into the peritoneal cavity of athymic nude mice. Immunohistochemistry analysis was utilized to identify infiltrating cancer cells and metastatic tumors in mouse ovaries, pancreas, spleen, omentum and liver. OVCAR3-NT mice developed significantly less cancer cell infiltrate and tumors in their pancreas and omentum compared to OVCAR3 SUSD2-KD mice. Furthermore, OVCAR3-NT mice displayed a longer median survival when compared to OVCAR3 SUSD2-KD mice (175 days and 185.5 days, respectively; p-value 0.0159). Altogether, the findings generated through the preclinical mouse model suggest that increased SUSD2 expression in HGSOC impedes in vivo metastasis to pancreas and omentum. These results correlate to longer median survival and prove to be consistent with previous findings showing prolonged survival of HGSOC patients with high SUSD2-expressing primary tumors.

Fusion of ALK to the melanophilin gene MLPH in pediatric Spitz nevi

Spitzoid neoplasms typically affect young individuals and include Spitz nevus, atypical Spitz tumor, and Spitzoid melanoma. Spitz tumors can exhibit gene fusions involving the receptor tyrosine kinases NTRK1, NTRK3, ALK, ROS1, RET, or MET, or the serine-threonine kinase BRAF. Because most studies have been based on adult cases, we studied ALK fusions in Spitz nevi occurring in pediatric patients. Twenty-seven cases were screened for ALK expression by immunohistochemistry, and 6 positive cases were identified. These cases were studied further using the TruSight RNA Fusion Panel, and in 4 cases, exon 20 of the ALK gene was found to be fused to exon 14 of the MLPH (melanophilin) gene, a gene fusion that has only been reported in a Spitz nevus in an adult. The remaining 2 cases showed no fusion of ALK with any gene. The cases with the MLPH-ALK fusion showed a similar histology to that described for Spitz nevi with ALK fusions, with spindle-shaped and epithelioid melanocytes in fusiform nests with a plexiform growth pattern and infiltrative border. We created a breakapart fluorescence in situ hybridization assay for MLPH, and all 4 cases with the MLPH-ALK fusion were positive, whereas the other 23 cases in the study were negative. Thus, ALK and MLPH were fused only to each other in our series. Melanophilin is part of the melanosome trafficking apparatus together with MYO5a, TPM3, and RAB27a, all constitutively expressed in melanocytes. Kinase fusions involving MYO5A and TPM3 have been reported in Spitz tumors, and our series adds MLPH to this group.

Environmental factors and risk of aggressive prostate cancer among a population of New Zealand men - a genotypic approach

Prostate cancer is one of the most significant health concerns for men worldwide. Numerous researchers carrying out molecular diagnostics have indicated that genetic interactions with biological and behavioral factors play an important role in the overall risk and prognosis of this disease. Single nucleotide polymorphisms (SNPs) are increasingly becoming strong biomarker candidates to identify susceptibility to prostate cancer. We carried out a gene × environment interaction analysis linked to aggressive and non-aggressive prostate cancer (PCa) with a number of SNPs. By using this method, we identified the susceptible alleles in a New Zealand population, and examined the interaction with environmental factors. We have identified a number of SNPs that have risk associations both with and without environmental interaction. The results indicate that certain SNPs are associated with disease vulnerability based on behavioral factors. The list of genes with SNPs identified as being associated with the risk of PCa in a New Zealand population is provided in the graphical abstract.

A large-scale analysis of alternative splicing reveals a key role of QKI in lung cancer

Increasing interest has been devoted in recent years to the understanding of alternative splicing in cancer. In this study, we performed a genome-wide analysis to identify cancer-associated splice variants in non-small cell lung cancer. We discovered and validated novel differences in the splicing of genes known to be relevant to lung cancer biology, such as NFIB, ENAH or SPAG9. Gene enrichment analyses revealed an important contribution of alternative splicing to cancer-related molecular functions, especially those involved in cytoskeletal dynamics. Interestingly, a substantial fraction of the altered genes found in our analysis were targets of the protein quaking (QKI), pointing to this factor as one of the most relevant regulators of alternative splicing in non-small cell lung cancer. We also found that ESYT2, one of the QKI targets, is involved in cytoskeletal organization. ESYT2-short variant inhibition in lung cancer cells resulted in a cortical distribution of actin whereas inhibition of the long variant caused an increase of endocytosis, suggesting that the cancer-associated splicing pattern of ESYT2 has a profound impact in the biology of cancer cells. Finally, we show that low nuclear QKI expression in non-small cell lung cancer is an independent prognostic factor for disease-free survival (HR = 2.47; 95% CI = 1.11-5.46, P = 0.026). In conclusion, we identified several splicing variants with functional relevance in lung cancer largely regulated by the splicing factor QKI, a tumor suppressor associated with prognosis in lung cancer.

EventPointer: an effective identification of alternative splicing events using junction arrays

Background

Alternative splicing (AS) is a major source of variability in the transcriptome of eukaryotes. There is an increasing interest in its role in different pathologies. Before sequencing technology appeared, AS was measured with specific arrays. However, these arrays did not perform well in the detection of AS events and provided very large false discovery rates (FDR). Recently the Human Transcriptome Array 2.0 (HTA 2.0) has been deployed. It includes junction probes. However, the interpretation software provided by its vendor (TAC 3.0) does not fully exploit its potential (does not study jointly the exons and junctions involved in a splicing event) and can only be applied to case–control studies. New statistical algorithms and software must be developed in order to exploit the HTA 2.0 array for event detection.

Results

We have developed EventPointer, an R package (built under the aroma.affymetrix framework) to search and analyze Alternative Splicing events using HTA 2.0 arrays. This software uses a linear model that broadens its application from plain case–control studies to complex experimental designs. Given the CEL files and the design and contrast matrices, the software retrieves a list of all the detected events indicating: 1) the type of event (exon cassette, alternative 3′, etc.), 2) its fold change and its statistical significance, and 3) the potential protein domains affected by the AS events and the statistical significance of the possible enrichment.

Our tests have shown that EventPointer has an extremely low FDR value (only 1 false positive within the tested top-200 events). This software is publicly available and it has been uploaded to GitHub.

Conclusions

This software empowers the HTA 2.0 arrays for AS event detection as an alternative to RNA-seq: simplifying considerably the required analysis, speeding it up and reducing the required computational power.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2816-x) contains supplementary material, which is available to authorized users.

A network-based analysis of colon cancer splicing changes reveals a tumorigenesis-favoring regulatory pathway emanating from ELK1

Splicing aberrations are prominent drivers of cancer, yet the regulatory pathways controlling them are mostly unknown. Here we develop a method that integrates physical interaction, gene expression, and alternative splicing data to construct the largest map of transcriptomic and proteomic interactions leading to cancerous splicing aberrations defined to date, and identify driver pathways therein. We apply our method to colon adenocarcinoma and non-small-cell lung carcinoma. By focusing on colon cancer, we reveal a novel tumor-favoring regulatory pathway involving the induction of the transcription factor MYC by the transcription factor ELK1, as well as the subsequent induction of the alternative splicing factor PTBP1 by both. We show that PTBP1 promotes specific RAC1,NUMB, and PKM splicing isoforms that are major triggers of colon tumorigenesis. By testing the pathway's activity in patient tumor samples, we find ELK1,MYC, and PTBP1 to be overexpressed in conjunction with oncogenic KRAS mutations, and show that these mutations increase ELK1 levels via the RAS-MAPK pathway. We thus illuminate, for the first time, a full regulatory pathway connecting prevalent cancerous mutations to functional tumor-inducing splicing aberrations. Our results demonstrate our method is applicable to different cancers to reveal regulatory pathways promoting splicing aberrations.

Putative Prostate Cancer Risk SNP in an Androgen Receptor-Binding Site of the Melanophilin Gene Illustrates Enrichment of Risk SNPs in Androgen Receptor Target Sites

Genome-wide association studies have identified genomic loci, whose single-nucleotide polymorphisms (SNPs) predispose to prostate cancer (PCa). However, the mechanisms of most of these variants are largely unknown. We integrated chromatin-immunoprecipitation-coupled sequencing and microarray expression profiling in TMPRSS2-ERG gene rearrangement positive DUCaP cells with the GWAS PCa risk SNPs catalog to identify disease susceptibility SNPs localized within functional androgen receptor-binding sites (ARBSs). Among the 48 GWAS index risk SNPs and 3,917 linked SNPs, 80 were found located in ARBSs. Of these, rs11891426:T>G in an intron of the melanophilin gene (MLPH) was within a novel putative auxiliary AR-binding motif, which is enriched in the neighborhood of canonical androgen-responsive elements. T→G exchange attenuated the transcriptional activity of the ARBS in an AR reporter gene assay. The expression of MLPH in primary prostate tumors was significantly lower in those with the G compared with the T allele and correlated significantly with AR protein. Higher melanophilin level in prostate tissue of patients with a favorable PCa risk profile points out a tumor-suppressive effect. These results unravel a hidden link between AR and a functional putative PCa risk SNP, whose allele alteration affects androgen regulation of its host gene MLPH.

TMPRSS4 induces cancer stem cell-like properties in lung cancer cells and correlates with ALDH expression in NSCLC patients

Metastasis involves a series of changes in cancer cells that promote their escape from the primary tumor and colonization to a new organ. This process is related to the transition from an epithelial to a mesenchymal phenotype (EMT). Recently, some authors have shown that migratory cells with an EMT phenotype share properties of cancer stem cells (CSCs), which allow them to form a new tumor mass. The type II transmembrane serine protease TMPRSS4 is highly expressed in some solid tumors, promotes metastasis and confers EMT features to cancer cells. We hypothesized that TMPRSS4 could also provide CSC properties. Overexpression of TMPRSS4 reduces E-cadherin and induces N-cadherin and vimentin in A549 lung cancer cells, supporting an EMT phenotype. These changes are accompanied by enhanced migration, invasion and tumorigenicity in vivo. TMPRSS4 expression was highly increased in a panel of lung cancer cells cultured as tumorspheres (a typical assay to enrich for CSCs). H358 and H441 cells with knocked-down TMPRSS4 levels were significantly less able to form primary and secondary tumorspheres than control cells. Moreover, they showed a lower proportion of ALDH+ cells (examined by FACS analysis) and lower expression of some CSC markers than controls. A549 cells overexpressing TMPRSS4 conferred the opposite phenotype and were also more sensitive to the CSC-targeted drug salinomycin than control cells, but were more resistant to regular chemotherapeutic drugs (cisplatin, gemcitabine and 5-fluorouracil). Analysis of 70 NSCLC samples from patients revealed a very significant correlation between TMPRSS4 expression and CSC markers ALDH (p = 0.0018) and OCT4 (p = 0.0004), suggesting that TMPRSS4 is associated with a CSC phenotype in patients' tumors. These results show that TMPRSS4, in addition to inducing EMT, can also promote CSC features in lung cancer; therefore, CSC-targeting drugs could be an appropriate treatment for TMPRSS4+ tumors.

RNA-seq analysis of lung adenocarcinomas reveals different gene expression profiles between smoking and nonsmoking patients

Lung adenocarcinoma is caused by the combination of genetic and environmental effects, and smoking plays an important role in the disease development. Exploring the gene expression profile and identifying genes that are shared or vary between smokers and nonsmokers with lung adenocarcinoma will provide insights into the etiology of this complex cancer. We obtained RNA-seq data from paired normal and tumor tissues from 34 nonsmoking and 34 smoking patients with lung adenocarcinoma (GEO: GSE40419). R Bioconductor, edgeR, was adopted to conduct differential gene expression analysis between paired normal and tumor tissues. A generalized linear model was applied to identify genes that were differentially expressed in nonsmoker and smoker patients as well as genes that varied between these two groups. We identified 2273 genes that showed differential expression with FDR < 0.05 and |logFC| >1 in nonsmoker tumor versus normal tissues; 3030 genes in the smoking group; and 1967 genes were common to both groups. Sixty-eight and 70% of the identified genes were downregulated in nonsmoking and smoking groups, respectively. The 20 genes such as SPP1, SPINK1, and FAM83A with largest fold changes in smokers also showed similar large and highly significant fold changes in nonsmokers and vice versa, showing commonalities in expression changes for adenocarcinomas in both smokers and nonsmokers for these genes. We also identified 175 genes that were significantly differently expressed between tumor samples from nonsmoker and smoker patients. Gene expression profile varied substantially between smoker and nonsmoker patients with lung adenocarcinoma. Smoking patients overall showed far more complicated disease mechanism and have more dysregulation in their gene expression profiles. Our study reveals pathogenetic differences in smoking and nonsmoking patients with lung adenocarcinoma from transcriptome analysis. We provided a list of candidate genes for further study for disease detection and treatment in both smoking and nonsmoking patients with lung adenocarcinoma.

SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples

Conventionally, overall gene expressions from microarrays are used to infer gene networks, but it is challenging to account splicing isoforms. High-throughput RNA Sequencing has made splice variant profiling practical. However, its true merit in quantifying splicing isoforms and isoform-specific exon expressions is not well explored in inferring gene networks. This study demonstrates SpliceNet, a method to infer isoform-specific co-expression networks from exon-level RNA-Seq data, using large dimensional trace. It goes beyond differentially expressed genes and infers splicing isoform network changes between normal and diseased samples. It eases the sample size bottleneck; evaluations on simulated data and lung cancer-specific ERBB2 and MAPK signaling pathways, with varying number of samples, evince the merit in handling high exon to sample size ratio datasets. Inferred network rewiring of well established Bcl-x and EGFR centered networks from lung adenocarcinoma expression data is in good agreement with literature. Gene level evaluations demonstrate a substantial performance of SpliceNet over canonical correlation analysis, a method that is currently applied to exon level RNA-Seq data. SpliceNet can also be applied to exon array data. SpliceNet is distributed as an R package available at http://www.jjwanglab.org/SpliceNet.

[Alternative splicing regulation: implications in cancer diagnosis and treatment]

The accurate expression of the genetic information is regulated by processes like mRNA splicing, proposed after the discoveries of Phil Sharp and Richard Roberts, who demonstrated the existence of intronic sequences, present in almost every structural eukaryotic gene, which should be precisely removed. This intron removal is called "splicing", which generates different proteins from a single mRNA, with different or even antagonistic functions. We currently know that alternative splicing is the most important source of protein diversity, given that 70% of the human genes undergo splicing and that mutations causing defects in this process could originate up to 50% of genetic diseases, including cancer. When these defects occur in genes involved in cell adhesion, proliferation and cell cycle regulation, there is an impact on cancer progression, rising the opportunity to diagnose and treat some types of cancer according to a particular splicing profile.

The RNA-binding protein QKI suppresses cancer-associated aberrant splicing

Lung cancer is the leading cause of cancer-related death worldwide. Aberrant splicing has been implicated in lung tumorigenesis. However, the functional links between splicing regulation and lung cancer are not well understood. Here we identify the RNA-binding protein QKI as a key regulator of alternative splicing in lung cancer. We show that QKI is frequently down-regulated in lung cancer, and its down-regulation is significantly associated with a poorer prognosis. QKI-5 inhibits the proliferation and transformation of lung cancer cells both in vitro and in vivo. Our results demonstrate that QKI-5 regulates the alternative splicing of NUMB via binding to two RNA elements in its pre-mRNA, which in turn suppresses cell proliferation and prevents the activation of the Notch signaling pathway. We further show that QKI-5 inhibits splicing by selectively competing with a core splicing factor SF1 for binding to the branchpoint sequence. Taken together, our data reveal QKI as a critical regulator of splicing in lung cancer and suggest a novel tumor suppression mechanism involving QKI-mediated regulation of the Notch signaling pathway.

Identification of alternative splicing events regulated by the oncogenic factor SRSF1 in lung cancer

Abnormal alternative splicing has been associated with cancer. Genome-wide microarrays can be used to detect differential splicing events. In this study, we have developed ExonPointer, an algorithm that uses data from exon and junction probes to identify annotated cassette exons. We used the algorithm to profile differential splicing events in lung adenocarcinoma A549 cells after downregulation of the oncogenic serine/arginine-rich splicing factor 1 (SRSF1). Data were generated using two different microarray platforms. The PCR-based validation rate of the top 20 ranked genes was 60% and 100%. Functional enrichment analyses found a substantial number of splicing events in genes related to RNA metabolism. These analyses also identified genes associated with cancer and developmental and hereditary disorders, as well as biologic processes such as cell division, apoptosis, and proliferation. Most of the top 20 ranked genes were validated in other adenocarcinoma and squamous cell lung cancer cells, with validation rates of 80% to 95% and 70% to 75%, respectively. Moreover, the analysis allowed us to identify four genes, ATP11C, IQCB1, TUBD1, and proline-rich coiled-coil 2C (PRRC2C), with a significantly different pattern of alternative splicing in primary non-small cell lung tumors compared with normal lung tissue. In the case of PRRC2C, SRSF1 downregulation led to the skipping of an exon overexpressed in primary lung tumors. Specific siRNA downregulation of the exon-containing variant significantly reduced cell growth. In conclusion, using a novel analytical tool, we have identified new splicing events regulated by the oncogenic splicing factor SRSF1 in lung cancer.

Bayesian detection of causal rare variants under posterior consistency

Identification of causal rare variants that are associated with complex traits poses a central challenge on genome-wide association studies. However, most current research focuses only on testing the global association whether the rare variants in a given genomic region are collectively associated with the trait. Although some recent work, e.g., the Bayesian risk index method, have tried to address this problem, it is unclear whether the causal rare variants can be consistently identified by them in the small-n-large-P situation. We develop a new Bayesian method, the so-called Bayesian Rare Variant Detector (BRVD), to tackle this problem. The new method simultaneously addresses two issues: (i) (Global association test) Are there any of the variants associated with the disease, and (ii) (Causal variant detection) Which variants, if any, are driving the association. The BRVD ensures the causal rare variants to be consistently identified in the small-n-large-P situation by imposing some appropriate prior distributions on the model and model specific parameters. The numerical results indicate that the BRVD is more powerful for testing the global association than the existing methods, such as the combined multivariate and collapsing test, weighted sum statistic test, RARECOVER, sequence kernel association test, and Bayesian risk index, and also more powerful for identification of causal rare variants than the Bayesian risk index method. The BRVD has also been successfully applied to the Early-Onset Myocardial Infarction (EOMI) Exome Sequence Data. It identified a few causal rare variants that have been verified in the literature.

Alternative Pre-mRNA Splicing, Cell Death, and Cancer

Alternative splicing is one of the most powerful mechanisms for generating functionally distinct products from a single genetic loci and for fine-tuning gene activities at the post-transcriptional level. Alternative splicing plays important roles in regulating genes critical for cell death. These cell death genes encode death ligands, cell surface death receptors, intracellular death regulators, signal transduction molecules, and death executor enzymes such as caspases and nucleases. Alternative splicing of these genes often leads to the formation of functionally different products, some of which have antagonistic effects that are either cell death-promoting or cell death-preventing. Differential alternative splicing can affect expression, subcellular distribution, and functional activities of the gene products. Molecular defects in splicing regulation of cell death genes have been associated with cancer development and resistance to treatment. Studies using molecular, biochemical, and systems-based approaches have begun to reveal mechanisms underlying the regulation of alternative splicing of cell death genes. Systematic studies have begun to uncover the multi-level interconnected networks that regulate alternative splicing. A global picture of the complex mechanisms that regulate cell death genes at the pre-mRNA splicing level has thus begun to emerge.

Detection of Alternatively Spliced or Processed RNAs in Cancer Using Oligonucleotide Microarray

Deregulation of gene expression plays a pivotal role in tumorigenesis, so the ability to detect RNA alterations is of great value in cancer diagnosis and management. DNA microarrays have been used to measure changes in mRNA or microRNA level, but less often the change of RNA isoforms. Here we appraise the utilization of microarray in detecting alternatively processed RNAs, which have alternative splice forms, retained introns, or altered 3' untranslated regions. We cover the methodology and focus on cancer studies. Recent development in parallel or deep sequencing used in transcriptome analysis is also discussed.

Identification of novel deregulated RNA metabolism-related genes in non-small cell lung cancer

Lung cancer is a leading cause of cancer death worldwide. Several alterations in RNA metabolism have been found in lung cancer cells; this suggests that RNA metabolism-related molecules are involved in the development of this pathology. In this study, we searched for RNA metabolism-related genes that exhibit different expression levels between normal and tumor lung tissues. We identified eight genes differentially expressed in lung adenocarcinoma microarray datasets. Of these, seven were up-regulated whereas one was down-regulated. Interestingly, most of these genes had not previously been associated with lung cancer. These genes play diverse roles in mRNA metabolism: three are associated with the spliceosome (ASCL3L1, SNRPB and SNRPE), whereas others participate in RNA-related processes such as translation (MARS and MRPL3), mRNA stability (PCBPC1), mRNA transport (RAE), or mRNA editing (ADAR2, also known as ADARB1). Moreover, we found a high incidence of loss of heterozygosity at chromosome 21q22.3, where the ADAR2 locus is located, in NSCLC cell lines and primary tissues, suggesting that the downregulation of ADAR2 in lung cancer is associated with specific genetic losses. Finally, in a series of adenocarcinoma patients, the expression of five of the deregulated genes (ADAR2, MARS, RAE, SNRPB and SNRPE) correlated with prognosis. Taken together, these results support the hypothesis that changes in RNA metabolism are involved in the pathogenesis of lung cancer, and identify new potential targets for the treatment of this disease.

Evaluation of alternative RNA labeling protocols for transcript profiling with Arabidopsis AGRONOMICS1 tiling arrays

Microarrays are routine tools for transcript profiling, and genomic tiling arrays such as the Arabidopsis AGRONOMICS1 arrays have been found to be highly suitable for such experiments because changes in genome annotation can be easily integrated at the data analysis level. In a transcript profiling experiment, RNA labeling is a critical step, most often initiated by oligo-dT-primed reverse transcription. Although this has been found to be a robust and reliable method, very long transcripts or non-polyadenylated transcripts might be labeled inefficiently. In this study, we first provide data handling methods to analyze AGRONOMICS1 tiling microarrays based on the TAIR10 genome annotation. Second, we describe methods to easily quantify antisense transcripts on such tiling arrays. Third, we test a random-primed RNA labeling method, and find that on AGRONOMICS1 arrays this method has similar general performance as the conventional oligo-dT-primed method. In contrast to the latter, however, the former works considerably better for long transcripts and for non-polyadenylated transcripts such as found in mitochondria and plastids. We propose that researchers interested in organelle function use the random-primed method to unleash the full potential of genomic tiling arrays.

Development of a multiplexed PCR-coupled liquid bead array assay for vascular endothelial growth factor (VEGF) splice variants

OBJECTIVES

To develop a multiplex PCR-coupled liquid bead array assay for the expression of VEGF splice variants.

DESIGN AND METHODS

The assay was based on the combination of multiplex PCR with liquid bead array technology, and optimized and evaluated in terms of analytical sensitivity, specificity, and reproducibility using the MCF-7 cell line. Clinical performance was evaluated in 16 pairs of fresh frozen cancerous and corresponding noncancerous adjacent tissues from NSCLC patients.

RESULTS

The assay is highly sensitive, reproducible and can detect specifically VEGF splice variants in clinical samples. When applied in 32 clinical samples it gave comparable results to RT-qPCR (concordance of 81%, 75%, 88% and 81% for PBGD, VEGF(121), VEGF(165), and VEGF(189) respectively).

CONCLUSIONS

The developed assay can simultaneously detect three VEGF splice variants with high specificity and sensitivity and can be further used to evaluate the role of each individual VEGF splice variant in molecular therapies targeted against VEGF.

Splicing programs and cancer

Numerous studies report splicing alterations in a multitude of cancers by using gene-by-gene analysis. However, understanding of the role of alternative splicing in cancer is now reaching a new level, thanks to the use of novel technologies allowing the analysis of splicing at a large-scale level. Genome-wide analyses of alternative splicing indicate that splicing alterations can affect the products of gene networks involved in key cellular programs. In addition, many splicing variants identified as being misregulated in cancer are expressed in normal tissues. These observations suggest that splicing programs contribute to specific cellular programs that are altered during cancer initiation and progression. Supporting this model, recent studies have identified splicing factors controlling cancer-associated splicing programs. The characterization of splicing programs and their regulation by splicing factors will allow a better understanding of the genetic mechanisms involved in cancer initiation and progression and the development of new therapeutic targets.