LncRNA antigens - a novel resource to improve immunotherapy efficacy predictions in melanoma

Sumaira Malik; Noor Kherreh; Aaron Golden

doi:10.12688/hrbopenres.14141.1

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Research Article

LncRNA antigens - a novel resource to improve immunotherapy efficacy predictions in melanoma

[version 1; peer review: awaiting peer review]

Sumaira Malik ^1,2, Noor Kherreh¹, Aaron Golden ³

PUBLISHED 30 Jun 2025

Author details Author details

¹ School of Mathematical and Statistical Sciences, University of Galway, Galway, County Galway, Ireland
² Walton Institute, South East Technological University, Waterford, County Waterford, Ireland
³ Physics, School of Natural Sciences, College of Science and Engineering, University of Galway, Galway, County Galway, Ireland

Sumaira Malik
Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – Original Draft Preparation

Noor Kherreh
Roles: Methodology, Software

Aaron Golden
Roles: Conceptualization, Supervision, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS AWAITING PEER REVIEW

Abstract

Background

ICI (immune checkpoint inhibitor) therapeutic response for melanoma varies among patients, emphasizing the importance of identifying genomic biomarkers to predict efficacy in advance of treatment. We hypothesised that a lncRNA based immunogenicity (lnc-IM) score could be used to predict individual response to ICI treatment, and that this could complement the existing criterion for ICI selection based on tumor mutation burden (TMB).

Methods

A lnc-IM score based on the number of lncRNA sORF derived peptides predicted to be presented by their tumor’s MHC-I genotype was derived using the clinical and transcriptomic data available in the TCGA-SKCM (n=101) and the ICI treated UCLA (n=25), MSKCC (n=16) and DFCI (n=40) melanoma cohorts. For the ICI treated cohorts, a combined antigen score was defined as a sum of neo-antigen load (derived from TMB) and lnc-IM score. A logistic regression based classifier was used to predict ICI responses based on these combined antigen scores.

Results

Low lnc-IM scores were associated with improved overall survival in the TCGA-SKCM cohort (HR = 0.39, p = 0.009) and higher inferred anti-tumor immune cell concentrations. The classifier was shown to consistently deliver an enhanced immunotherapeutic prediction performance compared to TMB prediction alone for the ICI treated cohorts (AUC = 0.89, accuracy = 0.79, recall = 0.5). We also demonstrated a reduced rate of false negatives (14%) by using a combined score as compared to the use of TMB alone (33%) in ICI treated cohorts.

Conclusions

Our findings suggest that the use of combined antigen scores (using lnc-IM scores along with TMB derived neoantigen load) have potential in improving immunotherapy efficacy predictions, in particular in reducing the rate of false negatives that would be the case if the clinical decision was based on the standard TMB derived antigen load alone. Prospective validation in larger cohort sizes is warranted.

Keywords

Immunotherapy efficacy, Biomarker, LncRNA sORFs, Prognostic value, Antigen load, Melanoma, Tumor immune response

Corresponding authors: Sumaira Malik, Aaron Golden

Competing interests: No competing interests were disclosed.

Grant information: This work was also supported by Hardiman Research Scholarship, University of Galway, Ireland.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2025 Malik S et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Malik S, Kherreh N and Golden A. LncRNA antigens - a novel resource to improve immunotherapy efficacy predictions in melanoma [version 1; peer review: awaiting peer review]. HRB Open Res 2025, 8:72 (https://doi.org/10.12688/hrbopenres.14141.1) First published: 30 Jun 2025, 8:72 (https://doi.org/10.12688/hrbopenres.14141.1) Latest published: 30 Jun 2025, 8:72 (https://doi.org/10.12688/hrbopenres.14141.1)

Introduction

Self versus non-self discrimination by T cells is a hallmark of cancer evasion by the immune response¹. T cells eliminate cancer cells by recognizing tumor-specific antigens presented on the tumor cell’s surface by MHC-I molecules². Immune and tumor cells possess “checkpoint proteins" such as PD-1, CTLA4 and PDL1 that keep such immune responses in check with the binding of such checkpoint proteins restricting T cells from killing tumor cells. Immune checkpoint inhibitor therapy (ICI) is a promising immunotherapy that restores the T cells’ capacity to attack and eliminate tumor cells by blocking such checkpoint proteins³.

The use of ICI therapy has been a significant achievement in the last decade for cancer treatment and has demonstrated clear improvements in the survival rate of cancer patients. Its use to date has been approved for multiple cancer types, including melanoma - one of the first cancers to be treated with this therapy⁴. Despite some remarkable successes, response to ICI therapy varies widely among individual patients, emphasizing the importance of identifying genomic biomarkers to try and predict an individual’s likely response in advance of treatment. Tumor mutation burden (TMB), programmed cell death ligand 1 (PDL1), and mismatch repair defect (dMMR)/microsatellites Instability (MSI) are some of the currently FDA-approved predictive biomarkers of ICI efficacy, of which TMB is the most widely used^5,6. High TMB has been shown to predict improved therapeutic efficacy to ICI therapy in various cancer types⁷. High TMB is associated with a high neoantigen burden⁷; hence, ICI therapy for patients whose cancers have a high TMB would be expected to elicit significant T cell responses against those antigens, resulting in enhanced tumor cell attrition. Although many trials have demonstrated the usability of TMB in clinical practices, a recent study has questioned the concept of the universal usage of TMB as a predictor of ICI efficacy⁸, arguing that TMB prediction power is only accurate to a subset of patients, and using TMB as a sole predictor of ICI response might deprive potential patients who might otherwise respond to ICI therapy.

TMB-associated antigens (neoantigens) are derived from non-synonymous missense mutations and originate from the coding region of the genome that encodes proteins⁹. However, TMB is not the only source of antigens. The Encyclopedia of DNA Elements (ENCODE) revealed that the human genome contains almost 80% of non-coding RNAs in contrast to the 1.5% that encodes for proteins¹⁰. Among non-coding RNAs, those longer than 200 nucleotides in length - collectively known as long non-coding RNAs or lncRNAs - are of great interest on account of their evident wide functional diversity¹¹. LncRNAs have been studied for their ability to control regulatory and cellular processes. LncRNAs regulate gene expression both as miRNA sponges and mRNA sponges¹². LncRNAs act as transcription regulators by modifying the chromatin complexes and can activate or repress gene expression. Moreover, they also control the binding of transcription regulatory factors leading to the activation of nearby genes¹³.

LncRNAs have also been shown to be implicated in the creation of MHC-I associated peptides with additional work demonstrating that a considerable number of tumor-specific antigens originate from non-coding regions of the genome¹⁴. LncRNAs' contribution to the cancer immunopeptidome is a topic of active research with a particular focus being an assessment of whether such lncRNA-associated antigens are associated with elevated cytotoxic T lymphocyte (CTL) responses. High CTL responses have been associated with better survival and treatment outcomes¹⁵. Many lncRNAs possess intact short open reading frames (sORF) that can result in the translation of short peptides in the dysregulated cancer transcriptome. Whilst short peptides have been detected in mass-spectrometry based proteomic studies¹⁶, direct association of lncRNA sORFs with tumor immune microenvironment (TIM) has yet to be fully characterised.

A preliminary version of this study has been posted as a preprint on medRxiv¹⁷. In this study we explore how the expression of lncRNA sORFs is associated with a patient's likely response to ICI therapy. Specifically, we derive a novel metric, the lnc-IM score, that estimates the level of sORF derived peptide presentation based on a tumour’s specific MHC-I genotype. In the first part of our study, we establish the association of lnc-IM scores with the tumor immune microenvironment – determined from known cellular biomarkers - and survival predictions in a melanoma cohort (TCGA-SKCM) without any ICI treatment. In the second part, we utilize three ICI-treated melanoma cohorts to examine how these lnc-IM scores can be used to improve ICI efficacy predictions by their integration with each patient’s TMB-associated antigen count.

Methods

Data acquisition

For this study, three different publicly available melanoma cohorts were utilized. Clinical information and transcriptomic profiles (RNA-seq) of the TCGA-SKCM cohort were downloaded from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov). HLA typing of the TCGA-SKCM cohort was acquired from a previous study¹⁸. A total of 101 patients were included in our analysis based on the availability of MHC-I genotype and transcriptomic data and having basic clinical information of age, gender, and overall survival. For ICI efficacy predictions, three ICI-treated cohorts involving metastatic melanoma (UCLA)¹⁹, melanoma (MSKCC)²⁰ and metastatic melanoma (DFCI)²¹ patient groups were utilized. Clinical information, including overall survival, treatment response, TMB, neoantigen load and immune cell concentrations were also downloaded from cBioPortal (https://www.cbioportal.org/)²². Based on the criteria mentioned above, a total of 16 patients from the MSKCC cohort, 25 from the UCLA cohort and 40 from DFCI cohort were selected for subsequent analysis. Tumor Immune Microenvironment (TIM) analysis for the TCGA-SKCM cohort was performed using the xCell algorithm²³, which estimates the the abundance of each immune cell type using expression profiles of specific gene signatures for each cell type.

Defining Lnc-IM scores

A lncRNA-immunogenicity score (lnc-IM) for a patient was defined as the total number of presentable lncRNA associated sORFs for that patient’s tumor. In the first step, all lncRNAs associated with short open reading frames (sORFs) were retrieved from sORFs.org²⁴. We selected our desired dataset from sORFs.org based on filters (species: humans and biotype = lncRNA). This initial search resulted in 425 lncRNAs and will be referred to as translatable lncRNAs in this work. These lncRNAs are associated with ~ 3000 sORFs, as experimentally proven by different Riboseq experiments²⁴. Among these translatable lncRNAs, only overexpressed lncRNAs were considered for subsequent analysis. The raw RNA-seq expression of translatable lncRNAs was acquired from GDC (TCGA, https://portal.gdc.cancer.gov) and cBioPortal (https://www.cbioportal.org/). Raw counts were converted to counts per million (cpm) and log normalized using the edgeR²⁵ package in R. A translatable lncRNA was considered for immunogenicity scoring if it passed the criteria of (log(cpm) > 6) (Supplemental Figure 1). For each translatable lncRNA, all of its sORFs were considered for lnc-IM scoring. The Patient Harmonic-mean Best Rank (PHBR) was then assigned to each sORF, which is an estimate of its derived peptide’s MHC-I presentation likelihood. The PHBR score represents the harmonic mean of best-ranked peptides (across a given patient’s HLA alleles)¹⁸. A sORF with PHBR < 0.5 was used to define a “presentable sORF". The resulting lnc-IM score (Supplemental Figure 2) represents the total number of presentable sORFs in a given patient’s tumor.

Combined antigen score

For the ICI-treated cohorts, a combined antigen score was derived as a sum of each patient’s TMB associated neoantigen burden and lnc-IM score. Any overlapping loci between sORFs and neoantigen loci were identified and removed to avoid the repetition of potential antigens.

Statistical analysis

All analyses were implemented using R (v.3.6.3). Patients were divided into high, low lnc-IM and combined antigen groups using the surv_cutpoint and surv_categorize functions from the survminer package in R²⁶. Cutpoints for dividing data into high/low immunogenic groups based on lnc-IM scores and combined antigen scores were chosen for each cohort separately using maximally ranked statistics (Supplemental Figure 2 and Supplemental Table 1). To determine the prognostic value of lnc-IM scores, Kaplan Meier survival plots were generated using the survival R package²⁷. Spearman’s correlation and Wilcoxon test were conducted using the ggpubr²⁸ R package to determine the association of lnc-IM scores with tumor immune cells. A significance level of 0.05 was used as the cutoff, with p < 0.05 considered as the statistically significant difference for all tests. Three ICI-treated cohorts were utilized to determine the predictive value of combined antigen score. Patients were stratified into the high and low combined immunogenic groups as described above, and a logistic regression-based classifier was utilized to predict immunotherapy responses using the stats²⁹ package in R. Patients were randomly assigned to training 70% and test 30% groups. For performance assessment, the evaluation metrics area under the curve (AUC), accuracy and recall were calculated using R packages caret and caTools^30,31. We compared the prediction power of combined antigen scores with TMB using overall response rates, true positive rates and false negative rates.

Functional and pathway analysis

Functional analysis of translatable lncRNAs was performed using LncSEA2.0³² with criteria; species: Human and p-value < 0.05. This analysis was performed for 7 major functional classes (cancer functional state, cancer immunology, cell markers, chromatin regulation, cancer metastasis, inflammation, subcellular localization, and tissue Spatial expression). Next, gene set enrichment analysis (GSEA) was performed using normalized RNA-seq expression data between patients with high and low combined antigen scores. This analysis was performed using Gene Ontology gene sets related to biological pathways using GSEA software version 4.3.2³³.

Results

Lnc-IM scores are associated with anti-tumor immune responses

We first performed functional analysis of those translatable lncRNAs used for deriving the lnc.IM scores. Most of the lncRNAs were found to be associated with cancer immunology pathways (Figure 1A). The tumor immune microenvironment (TIM) is critical in understanding disease progression and predicting treatment responses. Tumor infiltrating lymphocytes (TILs) comprises a complex set of cells in the TIM that play important roles in both tumor progression and suppression³⁴. Based on these roles, these cells can be divided into anti-tumor and pro-tumor immune cell groups. Tumor-associated antigens are known to enhance TILs associated immune responses against tumor cells³⁴. To this end, we investigated the association of lncRNA antigen load using lnc-IM scores with abundance of aDC, B cells, macrophages (M1 and M2), CD8-T cells, CD4 memory T-cells, T regulatory cells, Th1 and Th2 cells. Six of these cell types were characterized as anti-tumor immune cells based on their known involvement in tumor surveillance mechanisms, and three were characterized as pro-tumor immune cells. Three of six anti-tumor immune cells showed significant differences between the high and low lnc-IM groups. None of the pro-tumor immune cells showed a significant association between the two groups (Figure 1B, 1C). Taken together these data show a significant association between lnc-IM scores and elevated anti-tumor immune responses.

Figure 1. Comparison of immune cells abundance with lncRNA associated immunogenicity scores.

A) Functional analysis of translatable lncRNAs. Pathways were divided into 8 different categories and colours represent each class. Most of the lncRNAs were enriched in cancer immunology associated pathways. B) Association of pro-tumor immune cells with high and low Lnc.IM scores. C) Association of anti-tumor immune cells with Lnc.IM scores.

Lnc-IM scores are associated with survival predictions

High tumor infiltrating lymphocytes (TILs) levels have been associated with the immune system’s capacity to eliminate tumor cells³⁵. Hence, patients with high TILs show improved survival compared to those with low TILs. Knowing the value of TILs in survival prediction³⁶ and the association of lnc-IM scores with TILs (Figure 1), we next questioned if lnc-IM scores could be used to predict survival in the TCGA-SKCM cohort. The results showed that patients in the low lnc-IM category (n = 59) showed better overall survival (HR = 0.39, p = 0.009) than in the high lnc-IM category (n = 42) (Figure 2A). The overall median survival remained at 1070 days among the low lnc-IM group, while it reduced to 721 days in the high-IM group. The baseline characteristics of all patients are shown in Table 1. In order to check the association of any confounding variables with survival predictions, a multivariate analysis was performed using age, gender, and cancer stage and lnc-IM scores (Figure 2B). None except lnc-IM scores, were significantly associated with survival predictions.

Figure 2. Association of Lnc.IM scores with survival predictions in TCGA-SKCM cohort (n=101).

A) Low Lnc.IM group (n=59) is associated with better survival outcomes as compared to high Lnc.IM group (n=42). B) Multivariate analysis to identify any confounding variables. Only immunity count (Lnc.IM scores) showed significance with survival predictions (TCGA-SKCM).

Table 1. Baseline features of cohorts used in this study.

For ICI treated cohorts MSKCC, UCLA only subset of patients was selected for downstream analysis (as explained in Methods: Data acquisition).

	TCGA-SKCM	UCLA	MSKCC
Gender
Female	42	11	25
Male	59	27	39
Age
≤50	15	7	14
>50	86	31	47
Treatment
Iplimumab	-	-	60
Pembrolizumab	-	36	-
Nivolumab	-	2	-
Tremelimumab	-	-	4
Responders	-	21	14
Cancer stage
I/II	72	-	-
III/IV	29	-	-

Integrating lnc.IM scores to predict ICI efficacy

Based on the association of lnc.IM scores with both TILs and survival, we evaluated how such scoring can help improve immunotherapy outcomes prediction. We hypothesized that a model based on both lncRNA-associated antigen scores (lnc.IM) and TMB-associated neoantigen scores (neoantigen load) could capture a diverse source of the tumor’s immunopeptidome and so enhance the prediction of ICI efficacy. Such scoring could also help predict ICI outcomes for patients whose tumors are not hypermutated (i.e have a high TMB), and so would not be typically considered for such treatment. A logistic regression-based classifier was adopted to build a prediction model for immunotherapy outcomes in a combined cohort.

Our results showed an overall AUC of 0.89 in discriminating between responders and non-responders using both lncRNA derived antigen load (lnc.IM) and TMB derived antigen load (neoantigen load) as predictors. We compared this model with the TMB-based model (Figure 3A). The results showed an improved performance by incorporating lnc.IM as a predictor than using TMB alone. Other evaluation metrics are shown in Table 2. We also showed that both predictors (neoantigen load and lnc.IM) show significant differences among responders and non-responders (Figure 3B, 3C).

Figure 3.

A) Comparison of Lnc.IM and neoantigen load predictive power with TMB. B) Neoantigen load vary significantly between responders and non-responders. C) Lnc.IM show significant differences among responders and non-responders.

Table 2. Evaluation metrics for lnc.IM integrated (lnc.IM+Neoantigen load) and TMB based models.

Evaluation Metric	Lnc. IM+Neoantigen load	TMB
AUC	0.89	0.66
Recall	0.50	0.16
Accuracy	0.79	0.75

Combined antigen score performs better than TMB alone as biomarker

In our initial regression model, the significance of using both the neoantigen load and lncRNA load as predictors of ICI outcomes was established. Building upon these findings, we explored the efficacy of a combined antigen load, obtained by summing the neoantigen load and lnc.IM score. This approach allows us to consolidate the antigenic landscape by encompassing both traditional neoantigens and lnc.IM scores.To assess whether predictions based on combined antigen score performed better than using TMB alone, we compared overall response rates (ORR) between the high TMB group and high combined antigen score in all three ICI treated cohorts. The ORR improved in high combined antigen score as compared to high TMB among UCLA and MSKCC cohorts while remaining the same for DFCI cohort (Figure 4A). Among these ICI treated cohorts, 21 out of 81 patients responded to therapy. Using a high combined antigen score (cutoff criterion showed in Supplemental Material Table 1) as a classifier, 18 out of these 21 were correctly identified as responders. In contrast, using TMB alone using the same classifier formalism correctly categorizes only 14 responders. An additional advantage of using a combined strategy is the goal of minimizing the false negative rate to ensure a robust classification that would not deprive potential responders of therapy. In Figure 4B we show that the false negative rate decreased from 33% to 14% using a combined antigen score than TMB alone. These results demonstrate strong evidence that using a combined antigen score can help improve prediction efficacy for patients who might have a low mutation burden but still can benefit from treatment.

Figure 4.

A) Comparison of overall response rate between high TMB category and high combined antigen score among all ICI treated cohorts. B) Comparing true positive rate (TPR) and false negative rate (FNR) between high TMB and high combined antigen scores. High combined antigen scoring showed improved TPR of 85% as compared to high TMB (66%). FNR also showed improvement (14%) in high combined antigen classifier.

Molecular features association with combined antigen scoring

We further assessed the association of lnc.IM and combined antigen scoring with biological pathways in ICI treated UCLA cohort. First, a correlation analysis between lnc.IM scores, neoantigen load, TMB, and features associated with immune cells abundance was performed. We observed similar trends between lnc.IM scores and tumor immune cells infiltration in ICI treated cohort (UCLA) as previously observed for TCGA-SKCM cohort. Lnc.IM scores showed a significant positive correlation with anti-tumor immune cells. No such association was observed for neoantigen load or TMB. Both of these variables, neoantigen load, and TMB, also showed no correlation with lnc.IM scores (Figure 5). Next, we performed GSEA between high and low combined antigen scoring based groups. Results showed high enrichment of autophagy and inflammation induced apoptosis associated pathways among patients with high combined antigen load (Figure 6). These results, coupled with anti-tumor immune responses observed in TCGA-SKCM and overall functional analysis of translatable lncRNAs, show a strong association of lncRNA antigen load with inducing cytolytic responses.

Figure 5. Correlation anlysis between Lnc.IM scores, TMB, neoantigen and CBSET estimated tumor immune cells in ICI treated UCLA cohort.

Colour scale shows strength of correlation and asterisks show significance level.

Figure 6. GSEA enrichment analysis for gene ontology associated biological pathways that showed association with high combined antigen scoring (top 15, p-value <0.05) in ICI treated UCLA cohort.

Conclusions

In this bioinformatics-based study, we introduced a novel metric “lnc-IM scores” incorporating translatable lncRNA expression and patient MHC-I genotype and combined them with TMB-associated neoantigen load to create a predictive biomarker “combined antigen score” for ICI outcomes. TCGA SKCM data revealed the prognostic value and association of lnc-IM scores with the tumor immune microenvironment. Additionally, the value of combined antigen score as a predictive biomarker was investigated using ICI-treated cohorts (UCLA n=27, MSKCC n=21, DFCI n=40), previously used to investigate TMB-associated ICI response alone.

Among the TCGA SKCM cohort, lncRNA immunogenicity scores were significantly associated with anti-tumor immune responses. TILs associated with anti-tumor immune responses showed significant differences between high and low lnc-IM groups. Interestingly, all of these cells were upregulated in the low lnc-IM group. These findings appear to be in accordance with recent pan-cancer studies where high TMB/high antigen groups were associated with depressed immune cell infiltration in different cancer types³⁷. One of the possible explanations for such trends could be that the quality/immunogenicity of antigen being presented is responsible for a better immune response than the quantity of antigens³⁸. A study previously showed that certain antigens belonging to ERVs were associated with high antigen specific CD8-Tcell infiltrate as compared to other antigens that were associated with rare antigen specific CD8-T cells population¹⁵. Other critical factors are the tumor immune evasion mechanisms that are prevalent in high immunogenic groups. Cancers tend to evade immune responses by downregulating MHC molecules and TAP3 proteins, so these antigens are not efficiently presented on cancer cell surfaces³⁹. Hence, we see a decrease in immune cell infiltration in such cancers.

TILs have been linked to prognostic outcomes in various types of cancer. In general, elevated levels of TILs within a tumor correspond to a more aggressive anti-tumor immune microenvironment and are associated with better survival outcomes compared to tumors with low TILs, as previously reported⁴⁰. Our study (as illustrated in Figure 1 and Figure 2) supports this finding by demonstrating that patients with low lnc-IM tumors generally had greater anti-tumor TILs and better survival outcomes than those with high lnc-IM tumors.

We hypothesized that using lnc-IM scores along with conventional TMB might help improve efficacy predictions. We determined that the overall response rates improved using a combined antigen score as compared to using TMB alone. An essential factor is to ensure that the combined antigen score based classifier helps decrease the false negative rate to avoid depriving actual responders of ICI therapy. We have demonstrated that using the high combined antigen score compared to high TMB alone cannot only improve the true positive rate but also help decrease the false negative rate (Figure 4). In these cohorts, using high TMB alone as a biomarker could deprive 33% of potential responders of potentially efficacious ICI therapy. However, in our small study we demonstrated that using high combined antigen scores reduced this percentage to 14%. These findings have specifically highlighted the predictive power of combined antigen approach scores for responders with low TMB but high lnc-IM scores.

The antigenic properties of lnc.IM scores were confirmed in both TCGA-SKCM and ICI-treated cohort. Both cohorts showed a strong correlation of lnc.IM scores with anti-tumor immune cells. These patterns were further confirmed by functional annotation analysis of translatable lncRNAs used in our study. Most of these lncRNAs were found to be highly enriched in cancer immunology-associated pathways using LncSEA. LncRNAs have been previously associated with cancer immunology pathways^41,42; however, this study adds a layer of antigenic properties to these lncRNAs. Furthermore, GSEA analysis showed positive enrichment of autophagy, endocytosis, sialylation, and inflammatory cell apoptosis-associated pathways among patients with high combined antigen scores in ICI treated UCLA cohort. Autophagy, endocytosis, and sialylation have been associated with tumor antigen presentation pathways^43–46. Inflammatory cell apoptosis is studied to be an important factor in regulating immune responses and expansion of T-cells⁴⁷.

The key limitations of this study are the small sample size, the retrospective nature of cohorts used, and the variability of the type of ICI treatment administered. ICI treatments vary based on the type of checkpoint protein targeted (PD-1 or CTLA4). Both treatments show potential for melanoma treatment with 58% ORR for anti-PD-1 and 38% ORR for anti-CTLA4 therapy in melanoma⁴⁸. Using both primary (TCGA-SKCM) and metastatic cohorts (ICI treated) in our study may have advantages and limitations. On the one hand, the inclusion of both sample types increased the sample size of the study, enhancing our ability to identify more inclusive and robust prognostic and predictive biomarkers. On the other hand, using both cohorts may present challenges due to the potential differences in molecular profiles between primary and metastatic tumors. We note however, a recent study showing no significant difference between primary and metastatic melanoma TMB⁴⁹. We have demonstrated an association of the lnc-IM score with the TIM, survival, and ICI outcomes prediction in three different cohorts providing confidence as regards the added value of the lnc-IM scores as a biomarker. It is also worth noting that the predictive power of combined antigen scoring was evaluated specifically in metastatic tumors. Further validation in a larger cohort size is required to determine the optimal cutoff for the lnc-IM and combined antigen scores.

Future research would ideally focus on determining the value of lnc-IM scores in other cancer types that are not hypermutated yet still respond to ICI therapy, involving larger cohort sizes.

Ethics approval

All the data used in this study were derived from existing publicly available databases and previously published studies. All the data were pre anonymized. Thus, ethical approval was not required.

Data availability statement

All data used in this study are publicly available. The TCGA Skin Cutaneous Melanoma (SKCM) genomic and clinical data were obtained from the GDC data portal (https://portal.gdc.cancer.gov/). Immunotherapy-treated melanoma datasets were accessed through cBioPortal (https://www.cbioportal.org/).

Faculty Opinions recommended

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25. Robinson MD, McCarthy DJ, Smyth GK: edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26(1): 139–40. PubMed Abstract | Publisher Full Text | Free Full Text
26. Kassambara A, Kosinski M, Biecek P, et al.: Drawing survival curves using 'ggplot2' [R package survminer version 0.4.9]. The Comprehensive R Archive Network. Comprehensive R Archive Network (CRAN), 2021. Reference Source
27. Therneau TM: Survival analysis [R package survival version 3.5-3]. The Comprehensive R Archive Network. Comprehensive R Archive Network (CRAN), 2023. Reference Source
28. Kassambara A: 'Ggplot2' based publication ready plots [R package ggpubr version 0.6.0]. The Comprehensive R Archive Network. Comprehensive R Archive Network (CRAN), 2023. Reference Source
29. R Core Team: R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2013. Reference Source
30. Kuhn M: Classification and regression training [R package caret version 6.0-93]. The Comprehensive R Archive Network. Comprehensive R Archive Network (CRAN), 2022. Reference Source
31. Tuszynski J: Package catools. CRAN. Comprehensive R Archive Network (CRAN), 2021. Reference Source
32. Zhang G, Song C, Fan S, et al.: LncSEA 2.0: an updated platform for long non-coding RNA related sets and enrichment analysis. Nucleic Acids Res. 2024; 52(D1): D919–28. PubMed Abstract | Publisher Full Text | Free Full Text
33. Shi J, Walker MG: Gene Set Enrichment Analysis (GSEA) for interpreting gene expression profiles. Curr Bioinform. 2007; 2(2): 133–7. Publisher Full Text
34. He Z, Gu J, Luan T, et al.: Comprehensive analyses of a Tumor-Infiltrating Lymphocytes-related gene signature regarding the prognosis and immunologic features for immunotherapy in Bladder Cancer on the basis of WGCNA. Front Immunol. 2022; 13: 973974. PubMed Abstract | Publisher Full Text | Free Full Text
35. Brummel K, Eerkens AL, de Bruyn M, et al.: Tumour-Infiltrating Lymphocytes: from prognosis to treatment selection. Br J Cancer. 2022; 128(3): 451–8. PubMed Abstract | Publisher Full Text | Free Full Text
36. Maibach F, Sadozai H, Seyed Jafari SM, et al.: Tumor-Infiltrating Lymphocytes and their prognostic value in cutaneous melanoma. Front Immunol. 2020; 11: 2105. PubMed Abstract | Publisher Full Text | Free Full Text
37. Wang X, Li M: Correlate Tumor Mutation Burden with immune signatures in human cancers. BMC Immunol. 2019; 20(1): 4. PubMed Abstract | Publisher Full Text | Free Full Text
38. Laumont CM, Perreault C: Exploiting non-canonical translation to identify new targets for T cell-based cancer immunotherapy. Cell Mol Life Sci. 2018; 75(4): 607–21. PubMed Abstract | Publisher Full Text | Free Full Text
39. Dhatchinamoorthy K, Colbert JD, Rock KL: Cancer immune evasion through loss of MHC class I antigen presentation. Front Immunol. 2021; 12: 636568. PubMed Abstract | Publisher Full Text | Free Full Text
40. Gooden MJ, de Bock GH, Leffers N, et al.: The prognostic influence of Tumour-Infiltrating Lymphocytes in cancer: a systematic review with meta-analysis. Br J Cancer. 2011; 105(1): 93–103. PubMed Abstract | Publisher Full Text | Free Full Text
41. Pan X, Li C, Feng J: The role of lncRNAs in tumor immunotherapy. Cancer Cell Int. 2023; 23(1): 30. PubMed Abstract | Publisher Full Text | Free Full Text
42. Guo Y, Xie Y, Luo Y: The role of long non-coding RNAS in the tumor immune microenvironment. Front Immunol. 2022; 13: 851004. PubMed Abstract | Publisher Full Text | Free Full Text
43. Crotzer VL, Blum JS: Autophagy and its role in MHC-mediated antigen presentation. J Immunol. 2009; 182(6): 3335–41. PubMed Abstract | Publisher Full Text | Free Full Text
44. Burgdorf S, Kurts C: Endocytosis mechanisms and the cell biology of antigen presentation. Curr Opin Immunol. 2008; 20(1): 89–95. PubMed Abstract | Publisher Full Text
45. Montealegre S, van Endert PM: Endocytic recycling of MHC class I molecules in non-professional antigen presenting and Dendritic Cells. Front Immunol. 2019; 9: 3098. PubMed Abstract | Publisher Full Text | Free Full Text
46. Silva Z, Ferro T, Almeida D, et al.: MHC class I stability is modulated by cell surface sialylation in human Dendritic Cells. Pharmaceutics. 2020; 12(3): 249. PubMed Abstract | Publisher Full Text | Free Full Text
47. Zhang N, Hartig H, Dzhagalov I, et al.: The role of apoptosis in the development and function of T lymphocytes. Cell Res. 2005; 15(10): 749–69. PubMed Abstract | Publisher Full Text
48. Tie EN, Lai-Kwon J, Rtshiladze MA, et al.: Efficacy of Immune Checkpoint Inhibitors for In-Transit Melanoma. J Immunother Cancer. 2020; 8(1): e000440. PubMed Abstract | Publisher Full Text | Free Full Text
49. Gorris MAJ, van der Woude LL, Kroeze LI, et al.: Paired primary and metastatic lesions of patients with ipilimumab-treated melanoma: high variation in lymphocyte infiltration and HLA-ABC expression whereas tumor mutational load is similar and correlates with clinical outcome. J Immunother Cancer. 2022; 10(5): e004329. PubMed Abstract | Publisher Full Text | Free Full Text

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¹ School of Mathematical and Statistical Sciences, University of Galway, Galway, County Galway, Ireland
² Walton Institute, South East Technological University, Waterford, County Waterford, Ireland
³ Physics, School of Natural Sciences, College of Science and Engineering, University of Galway, Galway, County Galway, Ireland

Sumaira Malik
Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – Original Draft Preparation

Noor Kherreh
Roles: Methodology, Software

Aaron Golden
Roles: Conceptualization, Supervision, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

This work was also supported by Hardiman Research Scholarship, University of Galway, Ireland.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Malik S, Kherreh N and Golden A. LncRNA antigens - a novel resource to improve immunotherapy efficacy predictions in melanoma [version 1; peer review: awaiting peer review]. HRB Open Res 2025, 8:72 (https://doi.org/10.12688/hrbopenres.14141.1)

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[33] 33. Shi J, Walker MG: Gene Set Enrichment Analysis (GSEA) for interpreting gene expression profiles. Curr Bioinform. 2007; 2(2): 133–7. Publisher Full Text

[34] 34. He Z, Gu J, Luan T, et al.: Comprehensive analyses of a Tumor-Infiltrating Lymphocytes-related gene signature regarding the prognosis and immunologic features for immunotherapy in Bladder Cancer on the basis of WGCNA. Front Immunol. 2022; 13: 973974. PubMed Abstract | Publisher Full Text | Free Full Text

[35] 35. Brummel K, Eerkens AL, de Bruyn M, et al.: Tumour-Infiltrating Lymphocytes: from prognosis to treatment selection. Br J Cancer. 2022; 128(3): 451–8. PubMed Abstract | Publisher Full Text | Free Full Text

[36] 36. Maibach F, Sadozai H, Seyed Jafari SM, et al.: Tumor-Infiltrating Lymphocytes and their prognostic value in cutaneous melanoma. Front Immunol. 2020; 11: 2105. PubMed Abstract | Publisher Full Text | Free Full Text

[37] 37. Wang X, Li M: Correlate Tumor Mutation Burden with immune signatures in human cancers. BMC Immunol. 2019; 20(1): 4. PubMed Abstract | Publisher Full Text | Free Full Text

[38] 38. Laumont CM, Perreault C: Exploiting non-canonical translation to identify new targets for T cell-based cancer immunotherapy. Cell Mol Life Sci. 2018; 75(4): 607–21. PubMed Abstract | Publisher Full Text | Free Full Text

[39] 39. Dhatchinamoorthy K, Colbert JD, Rock KL: Cancer immune evasion through loss of MHC class I antigen presentation. Front Immunol. 2021; 12: 636568. PubMed Abstract | Publisher Full Text | Free Full Text

[40] 40. Gooden MJ, de Bock GH, Leffers N, et al.: The prognostic influence of Tumour-Infiltrating Lymphocytes in cancer: a systematic review with meta-analysis. Br J Cancer. 2011; 105(1): 93–103. PubMed Abstract | Publisher Full Text | Free Full Text

[41] 41. Pan X, Li C, Feng J: The role of lncRNAs in tumor immunotherapy. Cancer Cell Int. 2023; 23(1): 30. PubMed Abstract | Publisher Full Text | Free Full Text

[42] 42. Guo Y, Xie Y, Luo Y: The role of long non-coding RNAS in the tumor immune microenvironment. Front Immunol. 2022; 13: 851004. PubMed Abstract | Publisher Full Text | Free Full Text

[43] 43. Crotzer VL, Blum JS: Autophagy and its role in MHC-mediated antigen presentation. J Immunol. 2009; 182(6): 3335–41. PubMed Abstract | Publisher Full Text | Free Full Text

[44] 44. Burgdorf S, Kurts C: Endocytosis mechanisms and the cell biology of antigen presentation. Curr Opin Immunol. 2008; 20(1): 89–95. PubMed Abstract | Publisher Full Text

[45] 45. Montealegre S, van Endert PM: Endocytic recycling of MHC class I molecules in non-professional antigen presenting and Dendritic Cells. Front Immunol. 2019; 9: 3098. PubMed Abstract | Publisher Full Text | Free Full Text

[46] 46. Silva Z, Ferro T, Almeida D, et al.: MHC class I stability is modulated by cell surface sialylation in human Dendritic Cells. Pharmaceutics. 2020; 12(3): 249. PubMed Abstract | Publisher Full Text | Free Full Text

[47] 47. Zhang N, Hartig H, Dzhagalov I, et al.: The role of apoptosis in the development and function of T lymphocytes. Cell Res. 2005; 15(10): 749–69. PubMed Abstract | Publisher Full Text

[48] 48. Tie EN, Lai-Kwon J, Rtshiladze MA, et al.: Efficacy of Immune Checkpoint Inhibitors for In-Transit Melanoma. J Immunother Cancer. 2020; 8(1): e000440. PubMed Abstract | Publisher Full Text | Free Full Text

[49] 49. Gorris MAJ, van der Woude LL, Kroeze LI, et al.: Paired primary and metastatic lesions of patients with ipilimumab-treated melanoma: high variation in lymphocyte infiltration and HLA-ABC expression whereas tumor mutational load is similar and correlates with clinical outcome. J Immunother Cancer. 2022; 10(5): e004329. PubMed Abstract | Publisher Full Text | Free Full Text

LncRNA antigens - a novel resource to improve immunotherapy efficacy predictions in melanoma