Alternative splicing is regulated by multiple RNA-binding proteins and influences the expression of most eukaryotic genes. However, the role of this process in human disease, and particularly in cancer, is only starting to be unveiled. We systematically analyzed mutation, copy number, and gene expression patterns of 1348 RNA-binding protein (RBP) genes in 11 solid tumor types, together with alternative splicing changes in these tumors and the enrichment of binding motifs in the alternatively spliced sequences. Our comprehensive study reveals widespread alterations in the expression of RBP genes, as well as novel mutations and copy number variations in association with multiple alternative splicing changes in cancer drivers and oncogenic pathways. Remarkably, the altered splicing patterns in several tumor types recapitulate those of undifferentiated cells. These patterns are predicted to be mainly controlled by MBNL1 and involve multiple cancer drivers, including the mitotic gene NUMA1. We show that NUMA1 alternative splicing induces enhanced cell proliferation and centrosome amplification in nontumorigenic mammary epithelial cells. Our study uncovers novel splicing networks that potentially contribute to cancer development and progression.

Alternative splicing plays an essential role in many cellular processes and bears major relevance in the understanding of multiple diseases, including cancer. High-throughput RNA sequencing allows genome-wide analyses of splicing across multiple conditions. However, the increasing number of available data sets represents a major challenge in terms of computation time and storage requirements. We describe SUPPA, a computational tool to calculate relative inclusion values of alternative splicing events, exploiting fast transcript quantification. SUPPA accuracy is comparable and sometimes superior to standard methods using simulated as well as real RNA-sequencing data compared with experimentally validated events. We assess the variability in terms of the choice of annotation and provide evidence that using complete transcripts rather than more transcripts per gene provides better estimates. Moreover, SUPPA coupled with de novo transcript reconstruction methods does not achieve accuracies as high as using quantification of known transcripts, but remains comparable to existing methods. Finally, we show that SUPPA is more than 1000 times faster than standard methods. Coupled with fast transcript quantification, SUPPA provides inclusion values at a much higher speed than existing methods without compromising accuracy, thereby facilitating the systematic splicing analysis of large data sets with limited computational resources. The software is implemented in Python 2.7 and is available under the MIT license at https://bitbucket.org/regulatorygenomicsupf/suppa.

C/D box small nucleolar RNAs (SNORDs) are small noncoding RNAs, and their best-understood function is to target the methyltransferase fibrillarin to rRNA (for example, SNORD27 performs 2′-Omethylation of A27 in 18S rRNA). Unexpectedly, we found a subset of SNORDs, including SNORD27, in soluble nuclear extract made under native conditions, where fibrillarin was not detected, indicating that a fraction of the SNORD27 RNA likely forms a protein complex different from canonical snoRNAs found in the insoluble nuclear fraction. As part of this previously unidentified complex, SNORD27 regulates the alternative splicing of the transcription factor E2F7 pre-mRNA through direct RNA-RNA interaction without methylating the RNA, likely by competing with U1 small nuclear ribonucleoprotein (snRNP). Furthermore, knockdown of SNORD27 activates previously "silent" exons in several other genes through base complementarity across the entire SNORD27 sequence, not just the antisense boxes. Thus, some SNORDs likely function in both rRNA and pre-mRNA processing, which increases the repertoire of splicing regulators and links both processes.alternative splicing | gene regulation | snoRNAs | pre-mRNA processing S mall nucleolar RNAs (snoRNAs) are 60-to 300-nt-long noncoding RNAs that accumulate in the nucleolus. Based on conserved sequence elements, snoRNAs are classified as C/D box small nucleolar RNAs (SNORDs) or H/ACA box snoRNAs (SNORAs). SNORDs contain sequence elements termed C (RUGAUGA) and D (CUGA) boxes, usually present in duplicates (C′ and D′ boxes), and up to two antisense boxes that hybridize to the target RNA (1). In humans, SNORDs are usually derived from introns. After the splicing reaction, introns are excised as lariats, which are then opened by the debranching enzyme and subsequently degraded. Intronic SNORDs escape this degradation by forming a protein complex that consists of non-histone chromosome protein 2-like 1 (NHP2L1, 15.5K, SNU13), nucleolar protein 5A (NOP56), nucleolar protein 5 (NOP58), and fibrillarin (2-4). The SNORD protein complex forms through the entry of the snoRNA and fibrillarin to a complex containing NHP2L1, NOP58, and at least five assembly factors (5). The SNORD acts as a scaffold for the final protein complex formation and also controls recognition of other RNAs using the antisense boxes. The antisense boxes recognize sequences in rRNA, resulting in the fifth nucleotide upstream of the D or D′ box being 2′-O-methylated by fibrillarin (1). Structural studies indicate that the active form of SNORDs is dimeric (6).The conserved overall structure of SNORDs allows the identification of their putative target RNA binding sites. However, numerous SNORDs without obvious target RNAs have been identified (7-10) and are termed "orphan snoRNAs." Genome-wide deep sequencing experiments identified shorter but stable SNORD fragments that were found in all species tested, ranging from mammals to the protozoan Giardia lamblia (11) and EpsteinBarr virus (12). Fragments longer than 27 nt generated by SNORDs will ...

Alternative splicing is regulated by multiple RNA-binding proteins and influences the expression of most eukaryotic genes. However, the role of this process in human disease, and particularly in cancer, is only starting to be unveiled. We systematically analyzed mutation, copy number, and gene expression patterns of 1348 RNA-binding protein (RBP) genes in 11 solid tumor types, together with alternative splicing changes in these tumors and the enrichment of binding motifs in the alternatively spliced sequences. Our comprehensive study reveals widespread alterations in the expression of RBP genes, as well as novel mutations and copy number variations in association with multiple alternative splicing changes in cancer drivers and oncogenic pathways. Remarkably, the altered splicing patterns in several tumor types recapitulate those of undifferentiated cells. These patterns are predicted to be mainly controlled by MBNL1 and involve multiple cancer drivers, including the mitotic gene NUMA1. We show that NUMA1 alternative splicing induces enhanced cell proliferation and centrosome amplification in nontumorigenic mammary epithelial cells. Our study uncovers novel splicing networks that potentially contribute to cancer development and progression.