Genomic mutations in key genes are known to drive tumorigenesis and have been the focus of much attention in recent years. However, genetic content also may change farther downstream. RNA editing alters the mRNA sequence from its genomic blueprint in a dynamic and flexible way. A few isolated cases of editing alterations in cancer have been reported previously. Here, we provide a transcriptome-wide characterization of RNA editing across hundreds of cancer samples from multiple cancer tissues, and we show that A-to-I editing and the enzymes mediating this modification are significantly altered, usually elevated, in most cancer types. Increased editing activity is found to be associated with patient survival. As is the case with somatic mutations in DNA, most of these newly introduced RNA mutations are likely passengers, but a few may serve as drivers that may be novel candidates for therapeutic and diagnostic purposes.

Human and chimpanzee genomes are almost identical, yet humans express higher brain capabilities. Deciphering the basis for this superiority is a long sought-after challenge. Adenosine-to-inosine (A-to-I) RNA editing is a widespread modification of the transcriptome. The editing level in humans is significantly higher compared with nonprimates, due to exceptional editing within the primate-specific Alu sequences, but the global editing level of nonhuman primates has not been studied so far. Here we report the sequencing of transcribed Alu sequences in humans, chimpanzees, and rhesus monkeys. We found that, on average, the editing level in the transcripts analyzed is higher in human brain compared with nonhuman primates, even where the genomic Alu structure is unmodified. Correlated editing is observed for pairs and triplets of specific adenosines along the Alu sequences. Moreover, new editable species-specific Alu insertions, subsequent to the humanchimpanzee split, are significantly enriched in genes related to neuronal functions and neurological diseases. The enhanced editing level in the human brain and the association with neuronal functions both hint at the possible contribution of A-to-I editing to the development of higher brain function. We show here that combinatorial editing is the most significant contributor to the transcriptome repertoire and suggest that Alu editing adapted by natural selection may therefore serve as an alternate information mechanism based on the binary A/I code.

BackgroundAdenosine to inosine (A-to-I) RNA-editing is an essential post-transcriptional mechanism that occurs in numerous sites in the human transcriptome, mainly within Alu repeats. It has been shown to have consistent levels of editing across individuals in a few targets in the human brain and altered in several human pathologies. However, the variability across human individuals of editing levels in other tissues has not been studied so far.ResultsHere, we analyzed 32 skin samples, looking at A-to-I editing level in three genes within coding sequences and in the Alu repeats of six different genes. We observed highly consistent editing levels across different individuals as well as across tissues, not only in coding targets but, surprisingly, also in the non evolutionary conserved Alu repeats.ConclusionsOur findings suggest that A-to-I RNA-editing of Alu elements is a tightly regulated process and, as such, might have been recruited in the course of primate evolution for post-transcriptional regulatory mechanisms.

Background: Interpretation of genetic variation remains an impediment to cost-effective application of genomics to medicine. An advanced artificial intelligence (AI)-based Variant Classification Engine (aiVCE), rooted in ACMG/AMP guidelines, employs data-driven methods to expedite gene-specific classification (franklin.genoox.com). In this blinded study, the aiVCE’s overall and rule-level performances were evaluated using ClinVar (v. 2018-10) variants with creation dates after 5/01/2017. By removing any prior knowledge of these variants from the aiVCE training data, they were treated as novel variants. Using a ‘Full’ dataset (75,801 variants with ≥1 star) and an ‘Increased-Certainty’ dataset (3,993 variants with ≥2 stars), the aiVCE classified variants as pathogenic (P), likely-pathogenic (LP), uncertain significance (VUS), likely-benign (LB), or benign (B). VUS with sufficient supporting data were subclassified as VUS-leaning benign or VUS-leaning pathogenic. aiVCE results were evaluated to determine concordance with final ClinVar classification and rule-level determinations. Results: The aiVCE demonstrated >97% concordance among Increased-Certainty variants. Concordance was >95% across variant effects (e.g., missense, null, splice region), and was >93.5% for the Full dataset. When assessing the aiVCE’s application of specific ACMG rules, significant differences were observed between ClinVar P/LP and B/LB variants rule-met proportions (all P<0.00001), thus supporting gene-specific rule selections. Evaluation of discordance between the aiVCE and ClinVar uncovered evidences that might have been unavailable to submitting laboratories, highlighting AI utility in variant classification. Conclusions: The aiVCE exhibited robust performance, despite lacking past evidence, in determining whether variants would be categorized as P/LP. Applying latest computational advances to existing guidelines may assist scientists and clinicians interpret variants with limited clinical information and greatly reduce analytical bottlenecks.