1. Introduction
The diversification of messenger RNA (mRNA) sequences from genomic DNA relies on posttranscriptional mechanisms, such as alternative splicing and RNA editing [
1,
2]. In mammals, RNA editing involves the deamination of adenosines (A) or cytidines (C) generating in situ inosine (I) or uridine (U), respectively. While adenosine deaminase acting on RNA (ADAR) [
3] and adenosine deaminase acting on tRNA (ADAT) [
4] deaminate A residues, enzymes from the APOBEC family [
5] deaminate C residues. In the following, we focus on ADAR activities, as A-to-I modifications globally represent 97% of RNA editing events [
6], in mammals and especially in humans. A-to-I editing events can occur in coding and non-coding sequences of mRNA [
7]. In general, they are prominent in double-stranded RNA stretches formed by inverted non-coding repeats such as Arthrobacter luteus (Alu) and long interspersed element (LINE) located in mRNA untranslated regions (UTRs) and introns [
8,
9,
10]. RNA editing in non-coding sequences can influence alternative splicing, nuclear retention, and transcript degradation (e.g., recognition by miRNA) [
11]. In addition, RNA editing can affect sites in the protein coding region of mRNAs, leading to potential amino acid changes, known as recoding editing [
12]. In humans, recoding editing is rare while A-to-I in Alu elements is abundant and accounts for 97% of all available events [
13]. Both ADAR1 and ADAR2 can perform recoding or non-recoding editing. A third ADAR protein called ADAR3 is expressed only in the brain and its deaminase activity has not been yet proven [
7].
A-to-I RNA editing is essential in maintaining cellular homeostasis [
14,
15,
16] and has been implicated in several diseases ([
17] reviewed in [
18,
19,
20]). In particular, the disruption of the controlled expression of ADAR1 and ADAR2 has been shown to contribute to cancer pathogenesis. Based on current reports, recoding and non-coding editomes with hypo- or hyper-editing levels appear to be dependent on cancer-types and genes [
21,
22]. For example, the DNA base excision repair glycosylase enzyme NEI-like protein 1 (NEIL1), encoding antizyme inhibitor 1 (AZIN1), Ras homologue family member Q (RHOQ), and protein tyrosine phosphatase non-receptor type 6 (PTPN6) are found hyper-edited in cancer compared to healthy tissues, while gamma-aminobutyric acid type A receptor alpha 3 subunit (Gabra3), glutamate receptor subunit B Glur-B (also known as GRIA2), and insulin-like growth factor-binding protein 7 (IGFBP7) are found to be hypo-edited in cancer compared to normal tissue. These editing events have been reported to impact protein functions. Other recoding editing, such as filamin B (FLNB), cyclin I (CCNI), coatomer protein complex subunit α (COPA), and the component of oligomeric Golgi complex 3 (COG3) have not been functionally characterized. Non-coding editomes in cancer can also be different when compared to the corresponding healthy tissues, such as with hypo-editing for melanoma and glioma or hyper-editing (of microRNA) in non-small cell lung carcinoma (NSCLC). This editome is involved in cancer development, progression, invasion, metastatic potential, recurrence, and resistance (reviewed by [
18]).
ADARs are also associated with immunity in several ways. First, the elongated form of ADAR1 is located in the cytosol and is induced by type I interferon, while constitutive ADAR2 and the short form of ADAR1 are nuclear. Second, by editing dsRNA, ADARs avoid the stimulation of innate immune responses by endogenous dsRNA [
23] and thereby, lowering ADAR1 increases tumor inflammation [
24,
25]. Third, adaptive immunity is impacted by ADARs; a recent report shows that peptides containing amino acids generated through ADAR-recoding events are human leukocyte antigen (HLA) ligands. In particular, CCNI-edited peptides act as cancer antigens capable of activating tumor infiltrating lymphocytes (TILs) and thereby mediate cancer cell death in melanoma [
26]. Thus, ADAR-recoding can impact the Major Histocompatibility Complex (MHC) ligandome and thereby the specific anti-cancer T-cell response.
Although ADARs affect immunity, studies investigating the potential relationship between editome and cancer immune evasion are lacking. Melanoma is the most aggressive form of skin cancer and is currently best treated through the administration of immune checkpoint inhibitors: monoclonal antibodies blocking negative signals generated by cytotoxic T-lymphocyte-associated protein 4 (Ipilimumab) or programmed cell death-1 (e.g., Pembrolizumab or Nivolumab) in activated T lymphocytes [
27]. Since such therapies rely on the potential for the immune system to fight cancer cells, and since ADARs are involved in the immune response in multiple ways (see above), we used RNA-Seq datasets to analyze the editome of melanoma cell lines made from tumors obtained before immunotherapy and from Ipilimumab and Pembrolizumab-resistant (relapsing) metastasis.
3. Results
We analyzed 23 RNA seq; 12 datasets were collected from cell lines made from biopsies before the administration of immune checkpoint inhibitors (“before IT”), and 11 datasets were collected from cell lines made from biopsies of relapsing melanoma in patients treated with Ipilimumab and Pembrolizumab (“relapsed”) (
Table 1). Clinical features of the melanoma patients from which cell lines were derived are indicated for each patient in
Table 1 and for both groups in
Table 2. Our methods to establish cell lines were optimized so that melanoma cells were more effectively retrieved and that the cell culture represented the full range of in vivo tumor heterogeneity [
28]. It is not possible to verify that the cell lines will have similar gene expressions or editing profiles than tumor cells in the biopsies, however as all cell lines analyzed here were produced in the exact same conditions, we would expect that if the cell culture derivation would affect gene expression or editing, it would be similar for all, thus not affecting the comparison between the two groups. From a multivariant analysis of limma and voom, no gene was found to be significantly up- or downregulated in one group or the other when
p values of less than 0.05 and q values of less than 0.05 were used (
Supplementary Figure S1A and
Supplementary Table S1). In addition, no clear clustering of the two groups can be evidenced using PCA (
Supplementary Figure S1B). Thus, we extended our transcriptome analysis by exploring the role of ADARs in checkpoint inhibitor immunotherapies against melanoma (
Supplementary Table S2).
3.1. Expression of the ADAR Genes before IT Versus in Relapsing Tumours
We evaluated changes in gene expression between the two groups for the
Adar1 and
Adarb1 genes corresponding to the ADAR1 and ADAR2 enzymes, respectively. The analysis shows that ADAR1 gene expression (long and short forms of ADAR1 combined) remained unchanged between the groups (
Figure 1A), while ADAR2 was significantly enhanced in the “relapsed” group (
Figure 1B) based on an unpaired
t-test. This result was unexpected, as ADAR2 was previously reported to act as a tumor suppressor [
35,
36] while ADAR1 has been preferably deemed a tumor oncogene (reviewed by [
18]). Thus, the role of ADARs in relapse during therapy by immune checkpoint inhibitors may not be the same as the role of such enzymes in the natural course of melanoma development.
3.2. Recoding Editing
Clinically relevant recoding editing in cancer has been reported. Corresponding levels of editing are shown in
Supplementary Figure S2 and
Supplementary Table S3. ADAR2 is responsible for most recoding editing events including COG3_I635V and COPA_I164V [
37]. While ADAR2 is upregulated in “relapsed”, the two forms of recoding editing were not significantly different between “before IT” and “relapsed” (
Figure 2A). These forms of recoding editing show a strong positive correlation with the expression of ADAR2, confirming their ADAR2 dependence (
Figure 2B). The recoding editing of COG3 and COPA was first identified as clinically relevant in more than one form of cancer in Han et al. [
21]. The editing of COG3 and COPA has been shown to correlate with shortened progression-free survival in renal clear cell carcinoma, and high levels of COG3 editing have been associated with resistance to fluorouracil and austocystin D while high levels of COPA editing have been significantly associated with resistance to austocystin D and lapatinib [
37]. Meanwhile, it has been reported that RNA editing at both CCNI R75G and COPA I164V generates MHC-associated epitopes [
26]. Our data suggest that in patients treated with immune checkpoint inhibitors, MHC epitopes derived from edited COPA are not strongly immunogenic, as otherwise they would be downregulated when the immune response is enhanced by blockade of immune checkpoint inhibitors.
3.3. Non-Recoding Editing in Alu Elements
The global noncoding editome represented by the Alu editing indexes shows no differences between the two groups (
Figure 3A and
Supplementary Table S4). Additionally, in line with previous reports, we find a positive correlation between ADAR1 expression and Alu editing indexes (
Figure 3B), confirming ADAR1’s major contribution to the noncoding editome. However, a focus on non-recoding single editing sites based on a qPhred score of ≥10 indicates Alu hyper-editing in relapsed samples of Gap junction gamma-1 protein (GJC1) at site 42877641 (
Figure 3C and
Supplementary Tables S5 and S6). Interestingly, GJC1 Alu editing frequency at this site shows no correlation with ADAR1 expression, but it correlates positively with ADAR2 expression (
Figure 3D). This editing of Alu elements has been observed for nuclear-retained Cat2 transcribed nuclear RNA [
38]. Furthermore, Alu editing in GJC1 is reported as clinically relevant, showing differential editing levels across tumor subtypes and tumor stages and correlating with patients’ overall survival rates [
21]. However, further studies must be conducted to determine the contributions of this Alu editing to cancer progression and to responses to therapy.
3.4. Principal Component Analysis
To obtain an overview of potential differences in editing patterns observed between “Before IT” tumors and “Relapsed” during treatment with checkpoint inhibitors, we applied a principal component analysis. It was done using the two group comparison statistical analysis (
t-test) provided by the Qlucore software and the following parameters: filtering by variance 0.15,
p value was 0.05, and q value was 0.775. The outcome by the software was 57 Alu editing sites in 57 genes for the sample representation in a 3D PCA plot and heatmap representation of Alu editing sites (
Figure 4 and
Supplementary Table S7). Editing events were not normalized as a single editing site was used as a unique identifier for variable analysis and not the complete gene with all the editing events. From the PCA plot including the 57 Alu editing events, we can clearly separate the two groups of patients based on the corresponding cell lines. This separation is confirmed in a hierarchically clustered heatmap showing that Alu signatures can differentiate cell lines from patients who relapse from cell lines taken from patients before IT (
Figure 4A,B). Conversely, no recoding editing signatures could be identified to separate the two groups (
Supplementary Figure S2).
Collectively, our study reveals an editing signature in Alu elements that characterizes tumors relapsing during treatment with immune checkpoint inhibitors (
Figure 3C and
Figure 4). Surprisingly, this special signature may be associated with the higher expression of ADAR2 (at least for GJC1,
Figure 3D). While ADAR2 has been envisioned as a tumor suppressor, its increased expression in “relapses” would indicate that under treatment with immune checkpoint inhibitors, inhibiting ADAR2 could help prevent tumor recurrence. Some targets of ADAR2, mostly recoding editing events may be of relevance, but they could not be identified in the present study (non-significant changes in the recoding editing of COPA and COG3 and no grouping of recoding events,
Supplementary Figure S2). Meanwhile, the known recoding editing that generates MHC epitopes that are recognized by anti-cancer T-cells was not downregulated, indicating the weak significance of these epitopes for the immune control of cancer. The combination of several recoding and non-coding editing events (Alu editing of GJC1) eventually mediated by ADAR2 may be responsible for the potential advantages that the overexpression of this gene provides for recurrence during treatment with immune checkpoint inhibitors. Although RNAseq data from whole tumor tissues are publicly available (for example from Liu et al. [
39]), an analysis of A to G editing and its eventual correlation with clinical outcome would require the generation of tumor cell lines. Indeed, in whole tumor tissues, RNA sequences are originating not solely from tumor cells but also from non-tumor stroma cells, immune cells, etc. Thus, editing analysis of these sequencing files would not provide information exclusively on melanoma cells but would primarily reflect the heterogeneity of the tumor samples (percentages of immune cells, non-tumor cells, blood vessels, etc.) that varies from one sample to another. We foresee that our results will galvanize the analysis of A to G editing in tumor cell lines made from patients with a precisely known cancer history and will enable the identification of further correlations between editing and cancer outcome. Our study does not address the biological or biochemical phenomenon that connects the Alu-editing signature to the immunotherapeutic treatment. It however is the very first study that shows a correlation between RNA editing in melanoma and clinical outcome. We foresee that based on those observational results, further studies can be undertaken to more precisely decipher the mechanisms leading to the differential RNA editing in tumor cells during immunotherapy of cancer.