Abstract B6: Human genome contains a large number of very long intergenic transcribed regions, some of which are associated with cancer outcome
Authors: Mitra Anirban P. , Triche Timothy J. , Mitra Sheetal A. , Buckley Jonathan D. , Sorensen Poul H. B. , Reynolds C Patrick , Arceci Robert J. , Milos Patrice , St. Laurent Georges , Kapranov Philipp 
Published: April 2013

Abstract
Introduction: Transcriptional output of human genome is far more complex than predicted by the current set of protein-coding annotations. However, the fraction of the genome that is utilized to produce cellular RNA whose function is hitherto not completely defined, the so called “dark matter RNA”, and its role in regulating cancer progression remains an open issue. Furthermore, our understanding of the repertoire of human RNAs is far from complete, and almost all RNA-Seq studies have missed this complexity due to the limited view obtained when only interrogating the polyA+ RNA fraction confounded by biases due to PCR amplifications. This study aimed to assess the complexity of RNAs produced by cancerous and normal human tissues in an unbiased fashion, and examine any potential roles of such transcripts in modulating cancer outcome using pediatric rhabdomyosarcoma as a model malignancy.
Methods: Total RNA was profiled from a number of tissues using single-molecule sequencing (SMS) to examine the complexity of transcriptome in the human genome in an unbiased fashion. To minimize methodological biases, no PCR amplification, ligation, or size selection were used. In parallel, to examine the relevance of different RNAs in predicting cancer outcome, expression profiles on 79 intermediate-risk childhood rhabdomyosarcoma primary tumors were generated on Affymetrix Human Exon 1.0 ST microarrays and compared to prospectively-obtained outcome information.
Results: SMS analysis revealed that “dark matter RNAs” comprised up to two-thirds of total non-ribosomal, non-mitochondrial RNA in human cells. PolyA+ RNA fraction had a significantly lower complexity then the total RNA, especially in non-exonic regions. Strikingly, several hundreds of very long (100′s of kbs) abundant intergenic transcribed regions (vlinc's) were identified in areas of the genome that were devoid of protein-coding annotations. Most (~80%) of the genomic sequences covered by vlincs did not overlap with previously documented long intergenic non-coding (linc) RNAs, suggesting large number of RNAs in intergenic space are yet to be uncovered. To address the question whether vlincs may be associated with prognosis in cancer, specifically childhood rhabdomyosarcoma, array-based whole-genome expression profiles were analyzed. Interestingly, probe sets that were the best predictors of cancer outcome were found to be outside boundaries of exons of protein-coding transcripts. Strikingly, most of the top-ranked probe sets were found to cluster together and defined a &gt;230 kb vlinc region on chromosome 2, previously found by the SMS analysis. This vlinc region was singularly able to predict survival for the entire cohort (p=0.007).
Conclusions: Our data suggest that many genomic regions currently defined as intergenic give rise to very long transcribed regions that can modulate ultimate cancer behavior. This indicates that a great number of the “dark matter” RNAs uncharacterized thus far may be involved in tumorigenesis, and can be used as diagnostic, prognostic and therapeutic-response indicators. In turn, this argues for functional importance of the genome's “dark matter”.
Citation Format: Anirban P. Mitra, Timothy J. Triche, Sheetal A. Mitra, Jonathan D. Buckley, Poul H. B. Sorensen, C Patrick Reynolds, Robert J. Arceci, Patrice Milos, Georges St. Laurent, Philipp Kapranov. Human genome contains a large number of very long intergenic transcribed regions, some of which are associated with cancer outcome [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer; 2012 Jan 8-11; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Res 2012;72(2 Suppl):Abstract nr B6.