Picardi et al., 2017 Picardi E.

D'Erchia A.M.

Lo Giudice C.

Pesole G. REDIportal: a comprehensive database of A-to-I RNA editing events in humans.

Figure 4 Clinical Relevance of A-to-I RNA Editing Events with Peptide Support Show full caption (A) Normal-tumor comparison of RNA editing levels. Paired t test was used to assess statistical significance. (B) The upper quartile values of RNA editing levels of four RNA editing sites. For each editing site, only the top five cancer types with the highest editing levels are shown. (C) Clinically relevant patterns of RNA editing sites with peptide evidence in different cancer types. For each cancer type, gray boxes indicate not significant, red boxes indicate significantly differential editing levels among tumor subtypes (Kruskal-Wallis or Wilcoxon rank-sum test, FDR <0.05, editing level difference >3%), orange boxes indicate significantly differential editing levels among stage (Kruskal-Wallis or Wilcoxon rank-sum test, FDR <0.05, editing level difference >3%), and blue boxes indicate significant associations of editing level with progression-free survival times (log rank or Cox model test, FDR <0.05, editing level difference >3%). (D) Differential editing level of COPA_I164V (left) and IGFBP7_R78G (right) in stomach adenocarcinoma (STAD) subtypes (left) and lung adenocarcinoma (LUAD) subtypes (right). Kruskal-Wallis test was used to assess statistical significance. (E) Correlations of editing level in COPA_I164V (left) and IGFBP7_R78G (right) with patient progression-free survival time in kidney renal clear cell carcinoma (KIRC). Log rank test was used to assess statistical significance. (F) The association of editing level at COG3_I635V with the drug sensitivity of fluorouracil and austocystin D. (G) The association of editing level at COPA_I164V with the sensitivity to austocystin D and lapatinib. (F and G) Wilcoxon rank-sum test was used to assess statistical significance. In (A), (D), (F), and (G), the horizontal line in the box is the median, the bottom and top of the box are the first and third quartiles, and the whiskers extend to 1.5 interquartile range of the lower quartile and the upper quartile, respectively. In (D)–(G), numbers in parentheses indicate the sample numbers included in each comparison group. Figure S4. See also Tables S4 and S5

For the eight unique RNA editing events with variant peptides detected in patient samples, one fundamental question is whether they, like “driver mutations,” can play active roles in tumor pathophysiology or simply represent “passenger” events. We carried out several analyses to address this question. First, unlike somatic mutations that are by definition cancer specific, RNA editing usually occurs in both normal and tumor samples. To assess whether these RNA editing events are dysregulated in cancer, we compared their editing levels in tumor samples relative to the matched normal samples using TCGA RNA-seq data. Although the observed RNA editing patterns often varied in different tumor contexts, six of the eight RNA editing sites showed significant overediting patterns in some cancer types ( Figure 4 A and Table S4 ). However, it should be noted that such tumor-normal comparisons could be misleading because tumor and normal samples usually contain very different cell compositions. For example, most cells in a breast tumor are epithelial cells, whereas the epithelial proportion in normal breast tissues is typically low (e.g., a few percent). We also detected RNA editing signals in the Genotype-Tissue Expression (GTEx) RNA-seq data of normal tissues ( Figure S4 A) (). Second, somatic mutations usually occur at a high allele frequency in tumor cells (e.g., 50% for heterozygous mutations and 100% for homozygous mutations for a diploid cancer genome). To assess the editing level (equivalent to the allele frequency) of these RNA editing sites in tumor samples, we performed a pan-cancer analysis using TCGA RNA-seq data of >8,000 tumor samples of 24 cancer types ( Table S5 ). We found that RNA editing events for these sites generally could be detected in a broad range of cancer types, but the editing level varied greatly from site to site and from cancer type to cancer type. Importantly, four RNA editing sties (COG3_I635V, COPA_I164V, FLNB_Q2327R, and IGFBP7_R78G) showed relatively high editing levels in a large portion of patients (e.g., >25% of patients) of multiple cancer types ( Figure 4 B). Although it is under debate about what variant level (%) is required for gain-of-function activity in cancer, our results clearly showed that the functional effects for amino acid changes caused by RNA editing events cannot be simply dismissed due to low editing level. Third, to assess their clinical relevance more thoroughly, we examined the correlations of these four RNA editing events with key clinical features using TCGA pan-cancer data and identified extensive significant patterns in different cancer types (FDR <0.05, editing level difference >3%; Figure 4 C, Table S4 ). For example, the editing level of COPA_I164V was increased in stomach adenocarcinoma subtypes, from intestinal, mixed, to diffuse (p = 3.1 × 10, editing level difference = 12.1%; Figure 4 D, Table S4 ). RNA editing at both COG3_I635V and COPA_I164V correlated with worse progression-free patient survival time in kidney renal clear cell carcinoma (COG3, log rank p = 1.2 × 10, Cox model p = 6.0 × 10, editing level difference = 18.1%; COPA, log rank p = 6.4 × 10, Cox model p = 1.9 × 10, editing level difference = 15.0%; Figure 4 E, Table S4 ). Notably, ADAR1 and ADAR2 expression levels did not show significant correlations with patient survival times in this disease ( Figure S4 B), suggesting that the signals at individual RNA editing sites contain independent prognostic information from the ADAR enzymes responsible for their generation. Finally, we examined correlations between RNA editing levels and drug sensitivity using Cancer Cell Line Encyclopedia (CCLE) cell lines and identified that editing at COG3 and COPA was significantly associated with drug sensitivity ( Figures 4 F and 4G, Table S4 ). For example, higher RNA editing at COG3_I635V was significantly associated with resistance to fluorouracil (Rs = 0.21, p = 4.6 × 10, FDR <0.01; Wilcoxon rank-sum test p = 7.8 × 10 Figure 4 F); and higher RNA editing at COPA_I164V was significantly associated with resistance to austocystin D (Rs = 0.33, p = 2.3 × 10, FDR <0.01, Wilcoxon rank-sum test p = 4.6 × 10 Figure 4 G). Thus, the identified RNA editing events may be involved in tumorigenesis and have potential clinical implications.