Abstract 5438: Knowledge of which miRNA isoforms are expressed in a sample can successfully discriminate amongst the 32 TCGA cancer types
Authors: Aristeidis G. Telonis, Rogan Magee, Phillipe Loher, Inna Chervoneva, Eric Londin, Isidore Rigoutsos
Journal: Cancer Research (2017)

Abstract Background: Next-generation sequencing (NGS) technologies made possible the generation of large amounts of data quickly and inexpensively process. As a result, the challenge nowadays is how best to manipulate and analyze Big Data. Among non-coding RNAs, microRNAs (miRNAs) are the best studied to date. MiRNAs have attracted a lot of attention because they are centrally involved in homeostasis and can regulate thousands of coding and non-coding transcripts. The complexity of miRNA-mediated regulation increased further with the recent discovery that miRNA isoforms (isomiRs) are produced constitutively and that isomiR expression depends on a person’s sex, population origin, race and also on tissue type, tissue state, and disease subtype. Methods: We computed and analyzed isomiR profiles across 10,271 short RNA-seq datasets from The Cancer Genome Atlas (TCGA) repository, which represent 32 cancer types. Focusing on the isomiRs of each dataset, we built a classifier that relied solely on “binarized” isomiR profiles: in a dataset, an isomiR was labeled ‘present,’ if its expression exceeded a threshold that depended on the depth of sequencing, and ‘absent’ otherwise. This differs radically from previous methods in that it attempts to classify by relying on the presence or absence of isomiRs, intentionally discarding all information about the isomiRs’ expression level. We also built a classifier that relied solely on “binarized” miRNA arm profiles: a miRNA arm was labeled ‘present’ if at least one of the isomiRs it produces is ‘present’ in the same dataset. Results: The classifier we built using binarized isomiR profiles discriminated among all 32 TCGA cancer types and classified datasets with sensitivity &amp;gt;90% and a false discovery rate (FDR) &amp;lt; 3.0%. The ability of this classifier to correctly distinguish among the 32 cancers persisted even after a 15x reduction in the number of isomiRs used. The classifier we built using binarized miRNA arm profiles could also discriminate among all 32 TCGA cancers albeit with a lower sensitivity (83%) and a slightly higher FDR (6%). We also examined how well the various miRNA loci have been studied over time by measuring the corresponding number of published articles. Surprisingly, the miRNAs whose isoforms have the highest ability to classify tumors are not those with the most articles in PubMed. Conclusions: The presence or absence of a specific isomiR provides adequate discriminatory power to accurately classify a sample as belonging to one of the 32 TCGA cancers. The findings suggest that isomiRs can be used as potent, novel, cancer-specific biomarkers. By ignoring the expression levels of isomiRs, we effectively show that the same miRNA produces different clouds of isoforms in each cancer type. And, since distinct isomiRs target distinct mRNAs, it follows that a miRNA locus will target different mRNAs in different cancers simply by expressing different clouds of isoforms each time. Citation Format: Aristeidis G. Telonis, Rogan Magee, Phillipe Loher, Inna Chervoneva, Eric Londin, Isidore Rigoutsos. Knowledge of which miRNA isoforms are expressed in a sample can successfully discriminate amongst the 32 TCGA cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5438. doi:10.1158/1538-7445.AM2017-5438