Circular RNAs (circRNAs) are stable RNA molecules that can drive cancer through interactions with microRNAs and proteins and by the expression of circRNA encoded peptides. The aim of the study was to define the circRNA landscape and potential impact in T-cell acute lymphoblastic leukemia (T-ALL). Analysis by CirComPara of RNA-sequencing data from 25 T-ALL patients, immature, HOXA overexpressing, TLX1, TLX3, TAL1, or LMO2 rearranged, and from thymocyte populations of human healthy donors disclosed 68 554 circRNAs. Study of the top 3447 highly expressed circRNAs identified 944 circRNAs with significant differential expression between malignant T cells and normal counterparts, with most circRNAs displaying increased expression in T-ALL. Next, we defined subtype-specific circRNA signatures in molecular genetic subgroups of human T-ALL. In particular, circZNF609, circPSEN1, circKPNA5, and circCEP70 were upregulated in immature, circTASP1, circZBTB44, and circBACH1 in TLX3, circHACD1, and circSTAM in HOXA, circCAMSAP1 in TLX1, and circCASC15 in TAL-LMO. Backsplice sequences of 14 circRNAs ectopically expressed in T-ALL were confirmed, and overexpression of circRNAs in T-ALL with specific oncogenic lesions was substantiated by quantification in a panel of 13 human cell lines. An oncogenic role of circZNF609 in T-ALL was indicated by decreased cell viability upon silencing in vitro. Furthermore, functional predictions identified circRNA-microRNA gene axes informing modes of circRNA impact in molecular subtypes of human T-ALL.

Juvenile myelomonocytic leukemia (JMML), a rare myelodysplastic/myeloproliferative neoplasm of early childhood, is characterized by clonal growth of RAS signaling addicted stem cells. JMML subtypes are defined by specific RAS pathway mutations and display distinct gene, microRNA (miRNA) and long non-coding RNA expression profiles. Here we zoom in on circular RNAs (circRNAs), molecules that, when abnormally expressed, may participate in malignant deviation of cellular processes. CirComPara software was used to annotate and quantify circRNAs in RNA-seq data of a “discovery cohort” comprising 19 JMML patients and 3 healthy donors (HD). In an independent set of 12 JMML patients and 6 HD, expression of 27 circRNAs was analyzed by qRT-PCR. CircRNA-miRNA-gene networks were reconstructed using circRNA function prediction and gene expression data. We identified 119 circRNAs dysregulated in JMML and 59 genes showing an imbalance of the circular and linear products. Our data indicated also circRNA expression differences among molecular subgroups of JMML. Validation of a set of deregulated circRNAs in an independent cohort of JMML patients confirmed the down-regulation of circOXNAD1 and circATM, and a marked up-regulation of circLYN, circAFF2, and circMCTP1. A new finding in JMML links up-regulated circMCTP1 with known tumor suppressor miRNAs. This and other predicted interactions with miRNAs connect dysregulated circRNAs to regulatory networks. In conclusion, this study provides insight into the circRNAome of JMML and paves the path to elucidate new molecular disease mechanisms putting forward circMCTP1 up-regulation as a robust example.

Circular RNAs (circRNAs) are a large class of covalently closed RNA molecules originating by a process called back-splicing. CircRNAs are emerging as functional RNAs involved in the regulation of biological processes as well as in disease and cancer mechanisms. Current computational methods for circRNA identification from RNA-seq experiments are characterized by low discovery rates and performance dependent on the analysed data set. We developed CirComPara2 (https://github.com/egaffo/CirComPara2), a new automated computational pipeline for circRNA discovery and quantification, which consistently achieves high recall rates without losing precision by combining multiple circRNA detection methods. In our benchmark analysis, CirComPara2 outperformed state-of-the-art circRNA discovery tools and proved to be a reliable and robust method for comprehensive transcriptome characterization.

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The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed by computational detection tools. During the last decade, a plethora of such tools have been developed, but a systematic comparison is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools were used and detected over 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were empirically validated using three orthogonal methods. Generally, tool-specific precision values are high and similar (median of 98.8%, 96.3%, and 95.5% for qPCR, RNase R, and amplicon sequencing, respectively) whereas the number of predicted circRNAs is the largest tool differentiator (ranging from 1,372 to 58,032). Furthermore, we demonstrate the complementarity of tools through the increase in detection sensitivity by considering the union of highly-precise tool combinations while keeping the number of false discoveries low. Finally, based on the benchmarking results, recommendations are put forward for circRNA detection and validation.