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Frontiers in RNA Research ·

Volume 4 - 2026 | https://doi.org/10.3389/frnar.2026.1779364

Beyond mutations: personalising oncology via RNA-editing

  • Ivo Fierro-Monti1*corresponding author
  1. European Molecular Biology Laboratory’s European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, United Kingdom
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Abstract

Adenosine-to-inosine (A-to-I) RNA-editing, catalysed by ADAR enzymes, is a dynamic layer of post-transcriptional regulation that significantly diversifies the cancer transcriptome and proteome. A-to-I editing, the most frequent and well-established form of editing in human cancer, holds greater immediate relevance for personalised oncology than the less mapped C-to-U “editome” landscape. By recoding oncogenes and tumour suppressors (e.g., AZIN1, NEIL1), modulating drug response, and generating immunogenic RNA-editing-derived neoantigens, A-to-I events directly influence tumour progression, heterogeneity, and immune recognition. These roles establish RNA-editing profiles as promising biomarkers for patient stratification and prognosis. Furthermore, the integration of editing data with multi-omics and machine learning is accelerating the development of personalised therapeutic strategies. These include ADAR-guided RNA correction, mRNA vaccines encoding edited neoantigens, and RNA interference, offering reversible and tailored intervention points. Despite challenges in delivery, specificity, and clinical translation, A-to-I RNA-editing represents both a fundamental cancer mechanism and a versatile toolkit, driving the next generation of precision oncology beyond the constraints of the genomic blueprint.

Keywords

1 Introduction

RNA-editing (RE) is an important layer of post-transcriptional gene regulation that diversifies the transcriptome and proteome beyond the static information encoded by DNA. Among the various types of editing, adenosine-to-inosine (A-to-I) editing catalysed by adenosine deaminases acting on RNA (ADARs) is the most prevalent in mammals and has profound implications for health and disease, including cancer (Han et al., 2015; Paz-Yaacov et al., 2015). ADAR1 and ADAR2 convert adenosine residues within double-stranded RNA (dsRNA) regions into inosine, which is recognised as guanosine during translation and splicing (Chen et al., 2013; Han et al., 2015). This substitution can alter codons (Figure 1A), splice sites, RNA secondary structures, and RNA–protein interactions, thereby reshaping gene and protein expression in tumour cells. Single amino acid variants arise from RE and genomic substitutions; both affect amino acid sequences and structures that differ from their wild-type matches (Figure 1A).

RNA-editing and personalised oncology. **(A)** Adenosine-to-Inosine substitution catalysed by Adenosine Deaminase Acting on double-stranded (ds)RNA, ADAR hydrolytic deamination at the C6 position of adenine (depicted on the left side of the top panel). On the top right side (of the panel), an A–to-I RE-derived single lysine (Lys) substitution for an arginine (Arg), followed by a genomic missense-derived lysine (Lys) to arginine (Arg) substitution, and the wild-type expression of lysine. An A-to-I RE event without genomic substitution or a genomic missense single nucleotide substitution (C-to-G) leads to a single amino acid variant (SAAV) Lys-to-Arg substitution. Alternatively, when no change occurs at the DNA or RNA level, a lysine is produced (far top right side of the panel) (Fierro-Monti, 2024). **(B)** RBP NEIL1 pre-mRNA forms a specific hairpin duplex secondary structure, which is recognised by ADAR. The unedited (left side of the bottom panel) and edited (right side of the bottom panel) NEIL1 Alphafold2 predicted 3D protein structure. The unedited NEIL1 K242 and the edited R242 residues are shown (yellow circles), including a diagram depicting the original K and substituted R amino acid structures (centre, bottom of the panel). **(C)** Schematic representation of how A-to-I RE integrates with multi-omics profiling, computational modelling and therapeutic innovation in personalised oncology. RE profiling across diverse patient cohorts aids in the identification of editing-derived biomarkers and tumour-specific signatures. The integration of multi-omics data with machine learning enables the prediction of drug response, tumour progression, and immune recognition (represented by an immune synapse, between T cell receptor, peptide antigen, and Human Leukocyte Antigen). Therapeutic applications include ADAR-guided RE, mRNA vaccines and RNA interference strategies. **(D)** Key outstanding questions concern the prediction of clinically actionable editing events, optimisation of delivery and specificity for RE-based therapeutics, and enhancement of biomarker discovery through multi-omics integration.
Figure 1. RNA-editing and personalised oncology. (A) Adenosine-to-Inosine substitution catalysed by Adenosine Deaminase Acting on double-stranded (ds)RNA, ADAR hydrolytic deamination at the C6 position of adenine (depicted on the left side of the top panel). On the top right side (of the panel), an A–to-I RE-derived single lysine (Lys) substitution for an arginine (Arg), followed by a genomic missense-derived lysine (Lys) to arginine (Arg) substitution, and the wild-type expression of lysine. An A-to-I RE event without genomic substitution or a genomic missense single nucleotide substitution (C-to-G) leads to a single amino acid variant (SAAV) Lys-to-Arg substitution. Alternatively, when no change occurs at the DNA or RNA level, a lysine is produced (far top right side of the panel) (Fierro-Monti, 2024). (B) RBP NEIL1 pre-mRNA forms a specific hairpin duplex secondary structure, which is recognised by ADAR. The unedited (left side of the bottom panel) and edited (right side of the bottom panel) NEIL1 Alphafold2 predicted 3D protein structure. The unedited NEIL1 K242 and the edited R242 residues are shown (yellow circles), including a diagram depicting the original K and substituted R amino acid structures (centre, bottom of the panel). (C) Schematic representation of how A-to-I RE integrates with multi-omics profiling, computational modelling and therapeutic innovation in personalised oncology. RE profiling across diverse patient cohorts aids in the identification of editing-derived biomarkers and tumour-specific signatures. The integration of multi-omics data with machine learning enables the prediction of drug response, tumour progression, and immune recognition (represented by an immune synapse, between T cell receptor, peptide antigen, and Human Leukocyte Antigen). Therapeutic applications include ADAR-guided RE, mRNA vaccines and RNA interference strategies. (D) Key outstanding questions concern the prediction of clinically actionable editing events, optimisation of delivery and specificity for RE-based therapeutics, and enhancement of biomarker discovery through multi-omics integration.

Biochemically, ADAR proteins are modular enzymes containing double-stranded RNA-binding domains (dsRBDs) that recognise imperfectly paired RNA duplexes and a deaminase catalytic domain that mediates the hydrolytic deamination of adenosine to inosine. ADAR1 exists in two major isoforms: p110 (nuclear) and p150 (cytoplasmic and interferon-inducible), which enable context-dependent editing of both endogenous transcripts and viral RNAs. ADAR2 is primarily expressed in neurons but is also found in some cancers, where its dysregulation can drive tumour evolution through recoding and transcriptomic plasticity (Okugawa et al., 2018; Ghalali et al., 2022). ADAR3, the third member of this family, appears to be inactive and may act as a dominant-negative regulator of the editing process. (Yeo et al., 2010).

Cytidine deaminases mediate C-to-U RNA-editing, classically APOBEC1 on specific mRNAs (e.g., APOB in the intestine), and AID/APOBEC family members that can act on RNA or DNA (Baysal et al., 2017; Paz et al., 2007). C-to-U RE editing changes cytidine to uridine in transcripts and generates missense or nonsense codons and alters protein function, based on a principle similar to A-to-I (Baysal et al., 2017). Animal models indicate that cytidine deaminase–mediated editing can modulate cancer susceptibility: APOBEC1-dependent RE and (AID)-mediated deamination, AICDA have been linked to protection from hepatocellular carcinoma, HCC and retrovirus-induced leukaemia, respectively (Paz et al., 2007). Compared with A-to-I, the landscape of C-to-U RE in human tumours is far less mapped: transcriptome-wide surveys and cancer-wide atlases overwhelmingly focus on A-to-I sites (Baysal et al., 2017). Here, the focus is on A-to-I RE because of its relevance in cancer.

Programmable A-to-I RE refers to the use of engineered molecular tools to direct the A-to-I and C-to-U editing within a target RNA molecule. Because the translation machinery interprets inosine as guanosine (G), this technology effectively acts as a programmable A-to-G and C-to-U “search and replace” function for the transcriptome (Han et al., 2022). In humans, the only well-established, programmable RNA base-substitutions, A-to-G and C-to-U, are both catalysed by deaminases (Baysal et al., 2017).

A-to-I RE differs from other RNA modifications. While A-to-I editing alters RNA sequence identity, N6-methyladenosine (m6A) and related modifications (e.g., m5C, Ψ) regulate RNA stability, translation efficiency, and localisation without changing the underlying nucleotide sequence (Chen et al., 2021; Tang et al., 2023). In contrast to m6A “writers” such as METTL3 and METTL14, which place methyl groups on adenosine residues, ADAR enzymes act through deamination, leading to a direct base conversion that can recode codons or alter splicing decisions. Consequently, A-to-I editing can directly modify or recode protein products, or generate new antigenic peptides, known as RE-derived neoantigens, which influence tumour immunogenicity and therapeutic response (Zhang et al., 2018; Merdler-Rabinowicz et al., 2025a).

In cancer, dysregulated ADAR activity contributes to genomic instability, immune evasion, and drug resistance in cancer cells. Overexpression of ADAR1 in HCC cell lines, for instance, suppresses cytosolic double-stranded RNA sensing by MDA5 and dampens the interferon response, thereby allowing tumour cells to evade immune surveillance (Chan et al., 2022). In contrast, loss of ADAR function can lead to aberrant activation of innate immunity and inflammatory microenvironments. These effects place RE as a dynamic regulator of tumour phenotype and a promising target for personalised oncology, particularly when integrated with multi-omics (proteomics, Ribo-Seq, Genomics, Transcriptomics, Metabolomics) profiling and computational modelling approaches (An et al., 2021; Booth et al., 2023).

2 Editing mechanisms as drivers of personalised oncology

A-to-I RE mediated by ADAR enzymes introduces post-transcriptional changes that diversify the cancer transcriptome and influence tumour behaviour. These changes involve mechanisms contributing to personalised oncology by affecting tumour progression, drug response, and the development of individualised therapies.

RE alters mRNA and non-coding RNA sequences, resulting in isoforms of proteins and regulatory RNAs that are not encoded by the genome. This increases tumour heterogeneity, affects cell proliferation, metastasis, and drug resistance, and can create cancer-specific vulnerabilities or resistance mechanisms (Han et al., 2015; Frezza et al., 2023; Paz-Yaacov et al., 2015; Baysal et al., 2017; Bernal et al., 2024).

2.1 Dual RNA editing’s oncogenic and tumour-suppressive roles

Dysregulated RE can have both pro-tumorigenic and tumour-suppressive effects, presenting a complex dual nature in cancer biology. For instance, editing of the transcript encoding the hedgehog pathway transcription factor GLI1 introduces an I-to-M substitution (I803M) that impedes its nuclear localisation and transcriptional activity, thereby suppressing tumour growth in medulloblastoma and other cancers (Shimokawa et al., 2013). Similarly, editing in the 3′UTR of Focal Adhesion Kinase (FAK) can destabilise its mRNA and reduce FAK protein levels, consequently inhibiting cell invasion and metastasis (Paz et al., 2007). Editing of dihydropyridine receptor (CACNA1D) in melanoma can lead to a premature stop codon and a truncated, non-functional protein, impairing calcium signalling and cell proliferation (Fumagalli et al., 2015).

Alternatively, the editing of the RBP NEIL1 is an example of an oncogenic role, shown in Figure 1B. ADAR1 deaminates a specific adenosine (A) at codon position 242 into inosine, among other sites. During translation, the cellular machinery reads inosine as guanosine (G). The original, genomically encoded codon is AAA (lysine, K), whereas the edited codon is AGA (arginine, R). This results in two distinct NEIL1 protein isoforms: K242 (unedited) and R242 (edited). The editing process of NEIL1 transcript is regulated by extracellular signals, such as interferon-alpha (IFN-α) treatment, which increases ADAR1 activity and, thus, the level of the edited isoform. This dynamic regulation allows cells to fine-tune their DNA repair capacity in response to different types of genomic stress, and aberrant editing is implicated in various diseases, including cancer (Minko et al., 2020; Yeo et al., 2010). When developing editing-based diagnostics and therapies, it is important to understand the cellular context, specific transcripts being edited, and the overall editing landscape.

2.2 Oncogene and tumour suppressor activation

Activation of oncogenes and inactivation of tumour suppressors can occur as a result of editing events, which may act as driver mutations in certain cases. As an example of an editable transcript encoding an RNA-binding protein (RBP) that plays an important role in polyamine metabolism and cell cycle progression, AZIN1 is described below in normal and cancerous conditions.

In normal tissues, AZIN1 functions as a tumour suppressor. It helps maintain cellular homeostasis by modulating the activity of antizymes (AZs), which in turn regulate the levels of polyamines essential for normal cell growth and differentiation. In tumour tissues, AZIN1 acts as an oncogene, often overexpressed and/or functionally altered by RE to promote uncontrolled cell proliferation, invasion, and tumour growth. The underlying data mostly derive from HCC and multiple epithelial cancers such as colorectal cancer (CRC, Shigeyasu et al., 2018), gastric cancer (GC, Okugawa et al., 2018), non-small-cell lung cancer (NSCLC, Hu et al., 2017), esophageal squamous cell carcinoma (ESCC, Qin et al., 2014), endometrial (Nakamura et al., 2022), and prostate cancer (Ghalali et al., 2022), using patient tumour specimens, cancer cell lines with AZIN1 editing/overexpression or knockdown, and mouse xenograft models, plus biochemical assays of AZIN1–antizyme–polyamine regulation. In normal proliferating cells, AZIN1 primarily functions as a competitive inhibitor of antizymes (AZs) (Qiu et al., 2017).

AZIN1 plays a role in maintaining polyamine homeostasis. Intracellular polyamine levels are tightly controlled. Antizymes (OAZ1, OAZ2, and OAZ3) bind to ornithine decarboxylase (ODC), the rate-limiting enzyme in polyamine synthesis, to induce its degradation and inhibit polyamine uptake. AZIN1 binds to antizymes (AZs) with a higher affinity than ODC, effectively sequestering AZs and preventing them from inhibiting ODC or polyamine transport. This mechanism ensures appropriate polyamine levels to support normal cell cycle progression, embryonic development, and other physiological processes. AZIN1 also participates in polyamine-independent pathways, such as modulating the degradation of cell cycle-related proteins, such as cyclin D1 (CCND1). By binding to AZs, it prevents AZ-mediated degradation of CCND1, allowing for proper cell cycle progression. It is also involved in the regulation of centrosome duplication (Qiu et al., 2017; Ghalali et al., 2022).

In cancer, the expression and function of AZIN1 are often dysregulated, contributing to malignant transformation and aggressive tumour behaviour. Elevated levels of wild-type AZIN1, often due to gene amplification, result in increased ornithine decarboxylase (ODC) activity and higher polyamine levels, which are necessary for rapid tumour cell proliferation (Tulluri and Nemmara, 2021).

A-to-I RE of the AZIN1 transcript leads to a serine-to-glycine substitution (S367G) in the protein, which is associated with many cancers (hepatocellular carcinoma, prostate cancer, and colorectal cancer). This edited form (edAZIN1) is a “gain-of-function” protein that exhibits several pro-tumourigenic properties (Qiu et al., 2017; Ghalali et al., 2022; Chen et al., 2013).

In contrast to wild-type, edAZIN1 may have enhanced AZ binding characteristics, increasing protein stability and neutralising AZ tumour-suppressor functions more effectively. AZIN1 moves from the cytoplasm to the nucleus because of altered subcellular localisation. When AZIN1 is edited, it binds to myosin-9 and actin to move to the nucleus. Nuclear localisation of edAZIN1 is associated with poor prognosis and increased tumour aggressiveness. The RE of AZIN1 increases cancer cell proliferation, invasion, migration, and angiogenesis through mechanisms such as increased stability of ODC and cyclin D1 and upregulation of proangiogenic factors such as IL-8.

In essence, while normal AZIN1 is a tightly regulated protein that maintains cellular balance, its dysregulation or specific editing in cancer transforms it into a driver of tumour initiation and progression. It is worth mentioning that AZIN1 is both a prognostic marker and a therapeutic target in colorectal cancer (Shigeyasu et al., 2018; Han et al., 2015; Frezza et al., 2023).

2.3 Editing and neoantigens

By editing RNA, new tumour antigens, neoantigens, can be generated and presented to HLA molecules, inducing immune responses and offering new targets for immunotherapy and cancer vaccine development.

Zhang et al. demonstrated that A-to-I RE generates functional tumour antigens that can be recognised by the immune system. Using a proteogenomics approach, the authors combined mass spectrometry (MS)-based HLA immunopeptidomics with RNA sequencing to identify naturally presented edited peptides in tumour and normal tissues. Among the edited sites, a critical focus was the RBP Cyclin I (CCNI) R75G A-to-I editing event, catalysed primarily by ADAR1. The encoded amino acid (arginine) is modified to glycine, resulting in edited peptide variants that are processed and presented by tumour cells by HLA class I molecules.

Mechanistically, the study demonstrated that ADAR1-mediated editing drives the production of HLA-bound edited peptides, which can be recognised by CD8+ T cells. Tumour-infiltrating lymphocytes (TILs) from patients with malignant melanoma responded to CCNI-ED9/ED10 peptides based on mass spectrometry (MS)-based immunopeptidomics. These edited peptides are present at elevated levels in subsets of cancers such as ovarian, melanoma, and breast tumours (“over-editing”), correlating with increased edited RNA levels. Functionally, T cells recognising the edited peptide selectively killed tumour cells expressing the edited antigen, whereas ADAR1 knockdown reduced both editing and T-cell activation.

These findings provide direct evidence that A-to-I RE expands the immunopeptidome and creates tumour-associated neoepitopes, a fragment of a neoantigen recognised by the immune system, specifically by T cells, as a foreign target capable of inducing anti-tumour immunity. This mechanistic insight establishes RE as a source of immunogenic diversity beyond DNA mutations and highlights ADAR1-driven RE as a critical pathway for generating novel HLA-presented cancer neoantigens with therapeutic potential. Personalised RE-based approaches can engineer tumour-specific neoantigens tailored to a patient’s genetic and HLA profiles (Zhang et al., 2018; Merdler-Rabinowicz et al., 2025b).

3 RE as a source of tumour heterogeneity and biomarkers

RE, specifically A-to-I editing, is a major source of transcriptomic and proteomic diversity in tumours, creating unique molecular signatures that can serve as biomarkers for cancer diagnosis, prognosis, and patient stratification (Han et al., 2015; Wang et al., 2017; Christofi and Zaravinos, 2019; Shigeyasu et al., 2018; An et al., 2021). Editing patterns are often cancer-type-specific and correlate with clinical variables such as tumour subtype, stage, and patient survival (Wang et al., 2017; Han et al., 2015; Shigeyasu et al., 2018).

Editing events are often dysregulated in cancer, with both global increases and decreases observed depending on the tumour type, and can affect coding and non-coding regions, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) (Han et al., 2015; Baysal et al., 2017; Okugawa et al., 2018; Frezza et al., 2023; Peng et al., 2018; Zhang et al., 2024; Kurkowiak et al., 2021; Tang et al., 2023).

Single-cell and spatial analyses have revealed that increased RE is specific to cancer cells and correlates with drug resistance, immune evasion, and a higher mutation burden, driving intratumor heterogeneity (Romano et al., 2020; Stanley and Abdel-Wahab, 2022; Yan and Bu, 2021; Zhang et al., 2021).

A biomarker for prognosis and therapy response results from RE patterns that are distinct from tumour subtypes, stages, and survival rates. This supports the use of RE patterns as prognostic and therapeutic markers (Wang et al., 2017; Han et al., 2015; Hu et al., 2024; Okugawa et al., 2018; Peng et al., 2018; Chan et al., 2022; Zhang et al., 2024; Zhou et al., 2020). Specific editing events, such as those in AZIN1 and miR-200b, are linked to metastasis, poor prognosis, and treatment resistance in several cancers (Hu et al., 2024; Peng et al., 2018; Obeng et al., 2019; Bach et al., 2019).

RE-derived neoantigens can be presented by tumour cells, influencing immune recognition and serving as potential targets for immunotherapy (Zhou et al., 2020; Romano et al., 2020; Zhang et al., 2020; Bach et al., 2019). The data are derived from work on HCC, CRC, gastric, endometrial, thyroid, and ovarian cancers, using patient tumour cohorts (prognosis/metastasis), cancer cell lines with edited/WT constructs or mimics, and xenograft models, plus editing-specific molecular assays, invasion/migration and stemness readouts to link AZIN1 and miR-200b editing to metastasis, poor prognosis, and treatment-resistance–related phenotypes.

RE is a significant driver of tumour heterogeneity and holds strong promise as a biomarker for cancer prognosis, classification, and therapy. Ongoing research is refining detection methods and clarifying the clinical benefits of specific editing events in precision oncology.

4 Modulation of drug response and therapy resistance

Specific RE events can alter the sensitivity of cancer cells to targeted therapies. For example, RE in transcripts derived from genes such as AZIN1, COG3, and GRIA2 has been shown to either increase or decrease drug sensitivity, similar to the effects of DNA mutations (Han et al., 2015; Wang et al., 2017; Bernal et al., 2024; An et al., 2021). Editing signatures, such as the predictive score for chemotherapy response in patients with gastric cancer (GCRE), can predict chemotherapy response and guide treatment selection (An et al., 2021).

5 RE as a therapeutic target and tool

Programmable RE technologies, including ADAR-based editors and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) systems, are being developed to correct disease-causing mutations or modulate gene expression transiently and reversibly. Endogenous ADAR likely reverts a fifth of single-nucleotide variants in cancer predisposition transcript-derived genes, reducing the risk of cancer development during ageing. In over a third of cancer samples, RE has the potential to correct at least one driver mutation, suggesting it may be worth exploring as a treatment option (Booth et al., 2023; Merdler-Rabinowicz et al., 2024; Merdler-Rabinowicz et al., 2025a; Ullah et al., 2020; Yang et al., 2018; Mahato et al., 2023; Palaz et al., 2021). These approaches offer safer alternatives to permanent DNA editing and are being explored for cancer prevention and treatment (Merdler-Rabinowicz et al., 2024; Merdler-Rabinowicz et al., 2025b).

RE-based therapeutics are emerging as a promising approach for personalised cancer treatment. By enabling precise, reversible modifications at the RNA level, these strategies offer the potential to tailor therapies to individual tumour profiles, minimise off-target-effects, and address tumour heterogeneity. RE supports the development of personalised RNA-based therapies, such as mRNA vaccines encoding tumour-specific neoantigens, RNAi-based drugs, and engineered non-coding RNAs (Smith et al., 2022; Liu X. et al., 2023; Trivedi et al., 2024; Beck et al., 2021; Huang et al., 2022; Han G. et al., 2023; Li et al., 2022). These therapies can be tailored to the unique genetic and editing landscapes of individual tumours, enhancing efficacy and minimising off-target effects (Merdler-Rabinowicz et al., 2025a; Trivedi et al., 2024; Beck et al., 2021; Smith et al., 2022).

6 RNA-editing-based approaches for cancer therapeutics

mRNA vaccines encode a variety of tumour-specific antigens that can stimulate both humoral and cellular immunity and can be tailored for individual patients. They have entered multiple clinical studies in cancer types such as melanoma, pancreatic, and brain cancers (Yaremenko et al., 2025; Liu C. et al., 2023; Beck et al., 2021; Katopodi et al., 2024; Yao et al., 2024; Miao et al., 2021; Huang et al., 2022; Sayour et al., 2024; Bidram et al., 2021; Liu X. et al., 2023; Vishweshwaraiah and Dokholyan, 2022; Taibi et al., 2024; Trivedi et al., 2024; Deng et al., 2022; Laila et al., 2024; Di Trani et al., 2021; Pan et al., 2023).

Endogenous ADAR enzymes enable programmable A-to-I RE, correcting pathogenic mutations and generating personalised neoantigens in combination with computational pipelines that enable the design of patient-specific guide RNAs (Merdler-Rabinowicz et al., 2025b; Hussen et al., 2023). Preclinical studies have shown that systems such as CRISPR/Cas13 (using a guide RNA to find a specific target sequence on an RNA molecule and then using the Cas13 enzyme to cut or modify it) can produce precise transcriptome editing, targeting coding and non-coding RNAs, and can silence oncogenes or immune checkpoints (Fan et al., 2022; Granados-Riverón and Aquino-Jarquin, 2018; Zhang B. et al., 2023).

RNAi-based therapeutics (siRNA, miRNA, antimiRs) and lncRNA-targeting strategies are being developed to silence genes and modulate oncogenic networks, with several agents currently in clinical trials (Coan et al., 2024; Kara et al., 2022; Gilles et al., 2018; Barata et al., 2016; Ratti et al., 2020; Khaleel et al., 2024; Smith et al., 2022; Wu et al., 2014; Hattab et al., 2021; El Sayed et al., 2021; Arun et al., 2018).

7 Personalised strategies

The personalisation strategies outlined in Table 1, particularly neoantigen engineering and targeted editing, are being actively translated into clinical development. In neoantigen engineering, individualised mRNA vaccines encoding patient-specific somatic mutations are being clinically tested. In the phase 2b KEYNOTE-942 trial in resected high-risk melanoma, the personalised mRNA-4157 (V940) vaccine (encoding up to 34 tumour neoantigens) combined with pembrolizumab significantly improved 18-month recurrence-free survival (79% vs. 62%; HR 0.56) with mainly grade 1–2 toxicities (Weber et al., 2024). In a separate phase 1 study (KEYNOTE-603), mRNA-4157 given alone in resected NSCLC or with pembrolizumab in melanoma induced de novo and boosted pre-existing neoantigen-specific CD4+ and CD8+ T-cell responses without dose-limiting toxicities, providing mechanistic proof-of-concept for this personalised mRNA platform (Gainor et al., 2024). Another regimen, GRANITE, uses a chimpanzee adenovirus prime and self-amplifying mRNA boost encoding patient-specific neoantigens in metastatic microsatellite-stable colorectal cancer; interim phase 2 data show a trend toward improved progression-free survival (HR 0.73) and robust neoantigen-specific T-cell responses, particularly in patients with low circulating tumour DNA burden, with mainly flu-like grade 1–2 adverse events (Hecht et al., 2025). Early clinical experience with a lipopolyplex-formulated personalised mRNA neoantigen vaccine has also shown induction of strong neoantigen-specific T-cell responses and measurable clinical responses in two solid-tumour patients (Fan et al., 2024).

TABLE 1. Clinical applications and personalisation strategies.

Mechanism/StrategyPersonalisation potentialReferences
RNA-editing profilingPatient stratification, prognosis, therapy selectionHan et al. (2015), Frezza et al. (2023), Bernal et al. (2024)
Targeted RNA-editing (ADAR/CRISPR)Correction of mutations, modulation of drug responseYang et al. (2018), Booth et al. (2023), Merdler-Rabinowicz et al. (2025b), Palaz et al. (2021)
Neoantigen engineeringPersonalised immunotherapy and vaccine designZhang et al. (2018), Merdler-Rabinowicz et al. (2025a)
Non-coding RNA targetingOvercoming therapy resistance, biomarker discoveryCoan et al. (2024), Chen et al. (2022), Alqahtani et al. (2025)

Beyond DNA-encoded neoantigens, RE–derived (A-to-I) neoantigens are emerging as personalised targets: integrated pipelines now identify editing-derived peptides that are naturally presented and immunogenic across multiple tumour types, and higher burdens of RE neoantigens correlate with improved overall survival in melanoma patients receiving immune checkpoint blockade (Zhou et al., 2020; Lu et al., 2023). These RE signatures can therefore serve as biomarkers for patient stratification and vaccine design in addition to DNA-mutation–based profiles (Zhou et al., 2020; Lu et al., 2023; Zhang Z. et al., 2023).

Patient-derived models help optimise these personalised RNA-based strategies. Lipid- or lipopolyplex-packaged mRNA neoantigen vaccines encoding tandem patient-specific mutations demonstrate strong CD8+ T-cell responses, durable tumour control and protection on rechallenge in multiple murine tumour models, and have been translated into early-phase human use as noted above (Cho et al., 2025; Fan et al., 2024) Together, these clinical and preclinical data show that specific RNA modifications (somatic mutation–encoded mRNA and A-to-I RE–derived neoantigens) are already being exploited to tailor immunotherapy, improve patient selection and refine combination strategies with checkpoint inhibitors (Zhou et al., 2020; Lu et al., 2023; Weber et al., 2024; Gainor et al., 2024; Hecht et al., 2025; Zhang B. et al., 2023; Fan et al., 2024).

The integration of RE features with multi-omics data and machine learning has begun to refine patient stratification and therapeutic response prediction, as shown in recent models that combine A-to-I editing with genomic, transcriptomic and clinical data in breast cancer and lower-grade glioma (Bernal et al., 2024; Wang et al., 2025; Wang B. et al., 2023).

In breast cancer, adding A-to-I RE sites to DNA mutation, RNA expression and clinical data in machine-learning models improved or maintained drug-response prediction performance compared with models using classic omics alone (Bernal et al., 2024).

A comprehensive RNA-modification signature integrating nine RNA modification types (including A-to-I editing) with multi-omics and immune features was used to build a machine-learning prognostic and treatment-response model in breast cancer (Wang et al., 2025).

In lower-grade glioma, a machine-learning–based prognostic model built directly from thousands of RE sites demonstrates that editing data can be effectively integrated into ML pipelines for risk stratification and therapeutic inference (Wang S. et al., 2023).

Emerging studies indicate that integrating RE with multi-omics data and machine learning can enhance patient stratification and therapeutic response prediction, although broader validation across cancer types is still needed (Bernal et al., 2024; Wang et al., 2025; Wang B. et al., 2023; Mathema et al., 2023; Wei et al., 2023).

8 Clinical translation, challenges and limitations

Many RNA-based therapeutics are currently in clinical trials, such as siRNA agents and mRNA vaccines, some of which have already been approved by the FDA for non-cancer indications, as well as others that show promise in oncology (Yaremenko et al., 2025; Liu C. et al., 2023; Beck et al., 2021; Katopodi et al., 2024; Yao et al., 2024; Miao et al., 2021; Huang et al., 2022; Sayour et al., 2024). The successful clinical translation of these approaches depends on addressing several interconnected challenges, which might be described by the key outstanding questions presented in Figure 1D.

When it comes to maximising on-target effects and minimising off-target effects (editing at similar but undesired RNA sites), the efficiency and specificity of targeting, uptake of RNA therapeutics, and delivery and specificity remain major concerns. In addition, there is a challenge in avoiding competition with endogenous ADAR substrates (Kara et al., 2022; Mainini and Eccles, 2020; Dong et al., 2018; Liu X. et al., 2023; Han Y. et al., 2023; Li et al., 2022; Hattab et al., 2021; Eftekhari et al., 2024).

Some other challenges associated with RNA-based therapeutics, including tumour heterogeneity and adaptive resistance mechanisms, require ongoing combination therapy (Huang et al., 2022; Hussen et al., 2023; Gilles et al., 2018; Sayour et al., 2024; Pastor et al., 2018; Barata et al., 2016; Ratti et al., 2020; Merdler-Rabinowicz et al., 2025a). Consequently, long-term safety and immunogenicity concerns still exist about RE (Merdler-Rabinowicz et al., 2025b; Hussen et al., 2023; Fan et al., 2022; Granados-Riverón and Aquino-Jarquin, 2018; Zhang Z. et al., 2023).

Furthermore, significant analytical and computational hurdles must be overcome for the reliable clinical translation of RE biomarkers and signatures. Accurately identifying bona fide A-to-I editing events from bulk or single-cell RNA-seq data is non-trivial and requires sophisticated computational pipelines to distinguish them from single-nucleotide polymorphisms (SNPs), sequencing errors, and other technical artefacts. The inherent complexity is amplified by the need to detect low-abundance editing events in a transcript-specific manner against a noisy background. This emphasises the need for robust, standardised bioinformatic tools, high-quality reference databases of editing sites, and rigorous validation protocols to ensure that editing-based biomarkers and therapeutic decisions are founded on accurate and reproducible data (Peng et al., 2018; Picardi et al., 2017; Zhang et al., 2021).

9 Discussion

RE is a dynamic driver of tumour heterogeneity and a potential source of biomarkers for personalised oncology (Han et al., 2015; Wang et al., 2017; Christofi and Zaravinos, 2019; Shigeyasu et al., 2018; An et al., 2021). Editing events can modulate drug responses, influence resistance mechanisms, and serve as actionable targets for therapy (Han et al., 2015; Bernal et al., 2024; An et al., 2021; Booth et al., 2023; Ullah et al., 2020). The development of programmable RE tools and RNA-based therapeutics has accelerated the translation of these findings into clinical practice (Booth et al., 2023; Merdler-Rabinowicz et al., 2024; Merdler-Rabinowicz et al., 2025a; Yang et al., 2018; Mahato et al., 2023; Palaz et al., 2021).

While significant progress has been made, further research is needed to overcome the technical and clinical challenges and demonstrate the potential of RE in precision cancer medicine. More specific improvements are required in terms of delivery, specificity, and a comprehensive understanding of functional editing events (Relevant questions, Figure 1D) (Smith et al., 2022; Booth et al., 2023; Ullah, Akbar, and Yannarelli, 2020). mRNA vaccines and ADAR-mediated editing are at the forefront of RE-based therapeutics, with clinical trials showing promising immunogenicity and early efficacy, especially in hard-to-treat cancers (Yaremenko et al., 2025; Liu C. et al., 2023; Beck et al., 2021; Katopodi et al., 2024; Yao et al., 2024; Miao et al., 2021; Huang et al., 2022; Sayour et al., 2024; Bidram et al., 2021; Liu C. et al., 2023; Vishweshwaraiah and Dokholyan, 2022; Taibi et al., 2024; Trivedi et al., 2024; Deng et al., 2022; Laila et al., 2024; Di Trani et al., 2021; Pan et al., 2023; Merdler-Rabinowicz et al., 2025b).

The integration of RE features with multi-omics data and machine learning (Figure 1C) has begun to refine patient stratification and therapeutic response prediction, as shown in recent models that combine A-to-I editing with genomic, transcriptomic and clinical data in breast cancer and lower-grade glioma (Bernal et al., 2024; Wang et al., 2025; Wang S. et al., 2023).

Although most evidence is preclinical or early clinical, the field is advancing rapidly, with several RNA-based drugs and vaccines entering clinical trials (Smith et al., 2022; Liu X. et al., 2023; Beck et al., 2021; Huang et al., 2022; Han G. et al., 2023; Li et al., 2022).

10 Conclusion

RNA-editing, particularly A-to-I deamination mediated by ADAR enzymes, has emerged as a critical facilitator of personalised oncology. This review synthesises key insights that position RNA-editing at the forefront of precision cancer medicine.

Firstly, A-to-I editing exhibits a complex dual nature in cancer biology, functioning context-dependently as either an oncogenic driver or a tumour suppressor, as exemplified by the recoding of AZIN1 and NEIL1. This duality requires a refined, transcript-specific understanding for therapeutic exploitation.

Secondly, RNA-editing is a major, dynamic source of intratumor heterogeneity, generating unique molecular signatures that are cancer-type and stage-specific. These editing patterns serve as potent biomarkers for diagnosis, prognosis, and the prediction of therapy response, offering a layer of information distinct from and complementary to genomic data.

A third key insight is the role of A-to-I editing in expanding the cancer immunopeptidome. Editing-derived neoantigens, such as those from CCNI, are naturally presented on HLA molecules and can elicit functional T-cell responses. This establishes RNA-editing as a significant source of immunogenic diversity for personalised vaccine design and immunotherapy stratification, beyond DNA-encoded mutations.

Finally, the field is rapidly translating these biological insights into clinical strategies. Programmable essential tools (e.g., ADAR-guided editors, CRISPR-Cas13) enable precise, reversible transcriptome engineering for mutation correction or therapeutic modulation. Concurrently, personalised mRNA vaccines, informed by both genomic and “editome” landscapes, are showing promising clinical efficacy. The integration of RNA-editing profiles with multi-omics data through computational modelling is refining patient stratification and response prediction.

In conclusion, A-to-I RNA-editing shapes cancer biology through multiple, clinically actionable mechanisms. It provides a rich source of biomarkers, reveals novel therapeutic targets, and directly enables the design of individualised therapies. As research overcomes challenges in delivery and specificity, the systematic integration of the “editome” into clinical pipelines promises to advance the precision and personalisation of cancer care significantly.

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Footnotes

Edited by: Ashutosh P. Dubey, University at Buffalo, United States

Reviewed by: Agota Tuzesi, Garvan Institute of Medical Research, Australia

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. IF-M received support from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No. 945405.

Author contributions

IF-M: Writing – review and editing, Conceptualization, Investigation, Formal Analysis, Resources, Visualization, Writing – original draft, Funding acquisition.

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Citation

Fierro-Monti, I. (2026). Beyond mutations: personalising oncology via RNA-editing. Frontiers in RNA Research, Volume 4. https://doi.org/
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4 - 2026

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Copyright © 2026 Fierro-Monti.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Ivo Fierro-Monti

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