Functional microRNA screening using a comprehensive lentiviral human microRNA expression library

ABSTRACT: BACKGROUND: MicroRNAs (miRNAs) are a class of small regulatory RNAs that target sequences in messenger RNAs (mRNAs) to inhibit their protein output. Dissecting the complexities of miRNA function continues to prove challenging as miRNAs are predicted to have thousands of targets, and mRNAs can be targeted by dozens of miRNAs. RESULTS: To systematically address biological function of miRNAs, we constructed and validated a lentiviral miRNA expression library containing 660 currently annotated and 422 candidate human miRNA precursors. The miRNAs are expressed from their native genomic backbone, ensuring physiological processing. The arrayed layout of the library renders it ideal for high-throughput screens, but also allows pooled screening and hit picking. We demonstrate its functionality in both short- and long-term assays, and are able to corroborate previously described results of well-studied miRNAs. CONCLUSIONS: With the miRNA expression library we provide a versatile tool for the systematic elucidation of miRNA function.

Fusion transcripts and their genomic breakpoints in polyadenylated and ribosomal RNA-minus RNA sequencing data

BACKGROUND: Fusion genes are typically identified by RNA sequencing (RNA-seq) without elucidating the causal genomic breakpoints. However, non–poly(A)-enriched RNA-seq contains large proportions of intronic reads that also span genomic breakpoints. RESULTS: We have developed an algorithm, Dr. Disco, that searches for fusion transcripts by taking an entire reference genome into account as search space. This includes exons but also introns, intergenic regions, and sequences that do not meet splice junction motifs. Using 1,275 RNA-seq samples, we investigated to what extent genomic breakpoints can be extracted from RNA-seq data and their implications regarding poly(A)-enriched and ribosomal RNA–minus RNA-seq data. Comparison with whole-genome sequencing data revealed that most genomic breakpoints are not, or minimally, transcribed while, in contrast, the genomic breakpoints of all 32 TMPRSS2-ERG–positive tumours were present at RNA level. We also revealed tumours in which the ERG breakpoint was located before ERG, which co-existed with additional deletions and messenger RNA that incorporated intergenic cryptic exons. In breast cancer we identified rearrangement hot spots near CCND1 and in glioma near CDK4 and MDM2 and could directly associate this with increased expression. Furthermore, in all datasets we find fusions to intergenic regions, often spanning multiple cryptic exons that potentially encode neo-antigens. Thus, fusion transcripts other than classical gene-to-gene fusions are prominently present and can be identified using RNA-seq. CONCLUSION: By using the full potential of non–poly(A)-enriched RNA-seq data, sophisticated analysis can reliably identify expressed genomic breakpoints and their transcriptional effects

Consensus molecular subtype classification of colorectal adenomas

Consensus molecular subtyping is an RNA expression-based classification system for colorectal cancer (CRC). Genomic alterations accumulate during CRC pathogenesis, including the premalignant adenoma stage, leading to changes in RNA expression. Only a minority of adenomas progress to malignancies, a transition that is associated with specific DNA copy number aberrations or microsatellite instability (MSI). We aimed to investigate whether colorectal adenomas can already be stratified into consensus molecular subtype (CMS) classes, and whether specific CMS classes are related to the presence of specific DNA copy number aberrations associated with progression to malignancy. RNA sequencing was performed on 62 adenomas and 59 CRCs. MSI status was determined with polymerase chain reaction-based methodology. DNA copy number was assessed by low-coverage DNA sequencing (n = 30) or array-comparative genomic hybridisation (n = 32). Adenomas were classified into CMS classes together with CRCs from the study cohort and from The Cancer Genome Atlas (n = 556), by use of the established CMS classifier. As a result, 54 of 62 (87%) adenomas were classified according to the CMS. The CMS3 ‘metabolic subtype’, which was least common among CRCs, was most prevalent among adenomas (n = 45; 73%). One of the two adenomas showing MSI was classified as CMS1 (2%), the ‘MSI immune’ subtype. Eight adenomas (13%) were classified as the ‘canonical’ CMS2. No adenomas were classified as the ‘mesenchymal’ CMS4, consistent with the fact that adenomas lack invasion-associated stroma. The distribution of the CMS classes among adenomas was confirmed in an independent series. CMS3 was enriched with adenomas at low risk of progressing to CRC, whereas relatively more high-risk adenomas were observed in CMS2. We conclude that adenomas can be stratified into the CMS classes. Considering that CMS1 and CMS2 expression signatures may mark adenomas at increased risk of progression, the distribution of the CMS classes among adenomas is consistent with the proportion of adenomas expected to progress to CRC

Identification of differentially expressed splice variants by the proteogenomic pipeline splicify

Proteogenomics, i.e. comprehensive integration of genomics and proteomics data, is a powerful approach identifying novel protein biomarkers. This is especially the case for proteins that differ structurally between disease and control conditions. As tumor development is associated with aberrant splicing, we focus on this rich source of cancer specific biomarkers. To this end, we developed a proteogenomic pipeline, Splicify, which can detect differentially expressed protein isoforms. Splicify is based on integrating RNA massive parallel sequencing data and tandem mass spectrometry proteomics data to identify protein isoforms resulting from differential splicing between two conditions. Proof of concept was obtained by applying Splicify to RNA sequencing and mass spectrometry data obtained from colorectal cancer cell line SW480, before and after siRNA-mediated downmodulation of the splicing factors SF3B1 and SRSF1. These analyses revealed 2172 and 149 differentially expressed isoforms, respectively, with peptide confirmation upon knock-down of SF3B1 and SRSF1 compared with their controls. Splice variants identified included RAC1, OSBPL3, MKI67, and SYK. One additional sample was analyzed by PacBio Iso-Seq full-length transcript sequencing after SF3B1 downmodulation. This analysis verified the alternative splicing identified by Splicify and in addition identified novel splicing events that were not represented in the human reference genome annotation. Therefore, Splicify offers a validated proteogenomic data analysis pipeline for identification of disease specific protein biomarkers resulting from mRNA alternative splicing. Splicify is publicly available on GitHub (https://github.com/NKI-TGO/ SPLICIFY) and suitable to address basic research questions using pre-clinical model systems as well as translational research questions using patient-derived samples, e.g. allowing to identify clinically relevant biomarkers

Consensus molecular subtype classification of colorectal adenomas

Consensus molecular subtyping is an RNA expression-based classification system for colorectal cancer (CRC). Genomic alterations accumulate during CRC pathogenesis, including the premalignant adenoma stage, leading to changes in RNA expression. Only a minority of adenomas progress to malignancies, a transition that is associated with specific DNA copy number aberrations or microsatellite instability (MSI). We aimed to investigate whether colorectal adenomas can already be stratified into consensus molecular subtype (CMS) classes, and whether specific CMS classes are related to the presence of specific DNA copy number aberrations associated with progression to malignancy. RNA sequencing was performed on 62 adenomas and 59 CRCs. MSI status was determined with polymerase chain reaction-based methodology. DNA copy number was assessed by low-coverage DNA sequencing (n = 30) or array-comparative genomic hybridisation (n = 32). Adenomas were classified into CMS classes together with CRCs from the study cohort and from The Cancer Genome Atlas (n = 556), by use of the established CMS classifier. As a result, 54 of 62 (87%) adenomas were classified according to the CMS. The CMS3 ‘metabolic subtype’, which was least common among CRCs, was most prevalent among adenomas (n = 45; 73%). One of the two adenomas showing MSI was classified as CMS1 (2%), the ‘MSI immune’ subtype. Eight adenomas (13%) were classified as the ‘canonical’ CMS2. No adenomas were classified as the ‘mesenchymal’ CMS4, consistent with the fact that adenomas lack invasion-associated stroma. The distribution of the CMS classes among adenomas was confirmed in an independent series. CMS3 was enriched with adenomas at low risk of progressing to CRC, whereas relatively more high-risk adenomas were observed in CMS2. We conclude that adenomas can be stratified into the CMS classes. Considering that CMS1 and CMS2 expression signatures may mark adenomas at increased risk of progression, the distribution of the CMS classes among adenomas is consistent with the proportion of adenomas expected to progress to CRC.Pattern Recognition and Bioinformatic