Comprehensive analysis of prognostic biomarkers for immunotherapy response in patients with advanced malignant melanoma

Introduction

Malignant melanoma, originating from melanocytes, is highly aggressive, prone to early metastasis, and is associated with a poor prognosis (1). Based on etiology and genetics, melanoma is classified into four subtypes: acral, mucosal, chronic sun-damaged, and nonchronic sun-damaged (including unknown primary site). Cutaneous melanoma is predominant in Western populations, whereas acral and mucosal melanomas are more common in the Chinese population. In recent years, there has been a rapid increase in melanoma incidence in China, with about 20,000 new cases annually, and the mortality rate is also rising rapidly (2).

Complete surgical resection is considered the best option for cure in patients with early-stage melanoma. For patients with advanced melanoma, conventional chemotherapy was the main treatment before the advent of immunotherapy and targeted therapy. However, the efficacy of chemotherapy monotherapy is low. In randomized clinical trials, no single chemotherapy drug improved overall survival (OS), and the objective response rate (ORR) was 5–20% (3). A systematic review of 41 clinical trials reported that combination chemotherapy yields a higher ORR but usually at the cost of greater toxicity and without conferring significant survival benefits (4). A meta-analysis of biochemotherapy similarly found no improvement in survival time despite an increased ORR (5).

With the advent of immunotherapy and targeted therapy, the survival rate of patients with advanced melanoma has been substantially improved. Compared with chemotherapy, immunotherapy has relatively fewer side effects (6). Previous studies have reported that the median progression-free survival (mPFS) of patients with advanced cutaneous melanoma treated with anti-programmed cell death receptor 1 (anti-PD-1) monotherapy is 5–7 months, the median OS (mOS) is 33–36 months, and the ORR is about 30–40% (7-10). Although anti-PD-1 antibodies have improved the survival time of patients with advanced melanoma as compared with conventional chemotherapy, their efficacy as monotherapy is still limited, especially in the Chinese population, in which the acral and mucosal subtypes predominate (11). Therefore, combination therapy strategies based on immune checkpoint inhibitors (ICIs) such as anti-PD-1 and anti-cytotoxic T lymphocyte-associated antigen 4 (anti-CTLA-4) have become a hotspot in the field of tumor therapy.

Although chemotherapy has been supplanted by immune and targeted therapies to a certain extent, it still plays an important role in the treatment for melanoma. Chemotherapy can be used as a second- or third-line treatment after the failure of immunotherapy and targeted therapy. Evidence suggests that chemotherapy can provide clinical benefits to patients who have experienced disease progression after immunotherapy (12,13) or when immunotherapy fails.

Notably, most previous melanoma prognostic studies focused merely on single clinical or laboratory indicators. Comprehensive analyses combining baseline clinical characteristics, hematological inflammatory markers, and NGS genetic mutation data remain insufficient for Chinese advanced melanoma cohorts. Although several metastatic and hematological risk factors have been identified, real-world multi-dimensional prognostic evidence in Chinese patients is still lacking.

In this study, we conducted a comprehensive statistical analysis of the clinical factors and molecular biomarkers in 62 patients with advanced or unresectable malignant melanoma undergoing immunotherapy. The aim was to identify prognostic indicators related to melanoma immunotherapy in order to guide the treatment of patients with advanced melanoma and to improve the survival rate. We present this article in accordance with the REMARK reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0957/rc).

Methods

Patient population and treatment

Sixty-two patients with advanced or unresectable malignant melanoma who were admitted to Nanjing Drum Tower Hospital from February 2019 to October 2022 were retrospectively included in this study. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China (No. 2022-170-02) and informed consent was taken from all the patients. All enrolled patients had an Eastern Cooperative Oncology Group (ECOG) performance status of 0–2 and had received at least one line of systemic anti-tumor treatment. Patients with uncontrolled severe infections, autoimmune diseases, or incomplete clinical data were excluded. All patients received treatment regimens based on ICIs, including monotherapy with immunotherapy, combination of immunotherapy with chemotherapy, targeted therapy, antiangiogenic therapy, radiotherapy, and dual immunotherapy. Clinical characteristics, including age, sex, tumor-node-metastasis (TNM) stage (8^th edition of the American Joint Committee on Cancer), primary lesion site, subtype, metastatic site, and line of therapy, were recorded. Baseline peripheral blood biomarkers, including neutrophil-to-lymphocyte ratio (NLR) and serum albumin, were uniformly collected within 7 days prior to the initial ICI administration.

Endpoints

All radiological evaluations, including computed tomography (CT) and magnetic resonance imaging (MRI), were performed in strict accordance with the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. After treatment initiation, radiological re-examinations were conducted every 6–8 weeks to dynamically evaluate tumor lesions, ensuring consistent imaging intervals and minimizing lead-time bias in survival analysis. PFS was defined as the time from the date of the first treatment dose to the first occurrence of disease progression or death from any cause. OS was defined as the time from the date of the first treatment dose to death from any cause. All surviving patients received regular follow-up until the end of the follow-up cutoff date.

Sample collection

At baseline, tumor tissues from either biopsies or surgical resections were collected and formalin-fixed and paraffin-embedded. A next-generation sequencing (NGS)–based gene panel assay, performed by Geneseeq Technology (Nanjing, China)—a laboratory accredited under the Clinical Laboratory Improvement Amendments (CLIA), the College of American Pathologists (CAP), and ISO 15189—was employed to profile tumor mutation burden (TMB), clonal mutations, and other genetic alterations in accordance with the approved protocols.

Prior to treatment, peripheral blood was drawn for biomarker analyses, processed within 2 hours, and then used to compute the NLR and albumin levels.

Library preparation and sequencing

Cell-free DNA (cfDNA) was isolated from plasma via the QIAmp Circulating Nucleic Acid Kit (Qiagen, Hilden, Germany). Genomic DNA from surgical tumor specimens and leukocytes was extracted with the DNeasy Blood & Tissue Kit (Qiagen). The purity of the extracted genomic DNA was assessed on a Nanodrop 2000 instrument (Thermo Fisher Scientific, Waltham, MA, USA). Each DNA sample was quantified with a Qubit 3.0 fluorometer and the dsDNA HS Assay Kit (Life Technologies, Thermo Fisher Scientific) in accordance with the manufacturer’s instructions.

Sequencing libraries were constructed with a modified version of the KAPA Hyper Prep Kit (Roche, Basel, Switzerland). Briefly, for tumor tissue and normal control samples, 1–2 µg of genomic DNA was sheared into fragments of approximately 350 bp via a M220 Ultrasonicator (Covaris, Woburn, MA, USA). This material then underwent end-repair, A-tailing, and ligation to indexed sequencing adapters, which was followed by size selection with Agencourt AMPure XP beads (Beckman Coulter, Brea, CA, USA). For plasma samples, up to 50 ng of cfDNA was fragmented and processed via an adapted protocol involving end-repair, A-tailing, and ligation to a custom adapter carrying a unique molecular identifier. Subsequently, polymerase chain reaction (PCR) amplification was performed with demultiplexing index primers, and the resulting cfDNA libraries were recovered with Agencourt AMPure XP beads.

Distinctly indexed libraries were pooled at defined ratios to reach a total input of 2 µg. Human Cot-1 DNA (Life Technologies) and xGen Universal blocking oligos (Integrated DNA Technologies, Coralville, IA, USA) were added as blocking reagents. Hybridization capture was carried out with custom xGen Lockdown probes (Integrated DNA Technologies) targeting 437 genes. The capture procedure included Dynabeads M-270 (Life Technologies) with the xGen Lockdown hybridization and wash kit (Integrated DNA Technologies), which were conducted according to the manufacturers’ instructions. Captured libraries were then subjected to on‑bead PCR amplification in KAPA HiFi HotStart ReadyMix (KAPA Biosystems, Wilmington, MA, USA) and purified once more with Agencourt AMPure XP beads.

Library concentration was determined via quantitative PCR via the KAPA Library Quantification Kit (KAPA Biosystems), and fragment length was assessed with the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). After enrichment, libraries were sequenced on a HiSeq4000 platform (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions.

Mutation calling

For quality control of FASTQ files, Trimmomatic (14) was applied to trim low‑quality bases (leading/trailing quality below 30) and remove N bases. The filtered reads were then aligned to the human reference genome (hg19) via the BurrowsWheeler Aligner (15). In accordance with the local realignment around known insertions and deletions (indels) and base quality score recalibration with Genome Analysis Toolkit v.3.4.0, PCR duplicates were eliminated with Picard tools (Broad Institute, Cambridge, MA, USA).

Singlenucleotide variants (SNVs) and indels were called with VarScan2, with a variant allele frequency threshold of 0.01 being applied for tissue samples and 0.001 for cfDNA samples. Identified SNVs and indels were annotated via ANNOVAR (16) to obtain variant type, database of single nucleotide polymorphisms (dbSNP) ID, clinical annotations, and protein impact predictions from Sorting Intolerant from Tolerant (SIFT) (17) and PolyPhen (18). Germline mutations were excluded via comparison with matched wholeblood controls. Copy number variations (CNVs) were analyzed with CNVkit (19); gains were defined as a depth ratio >2.0 (tissue) or >1.6 (cfDNA) and losses as a depth ratio <0.6.

We defined the chromosomal instability score as the average percentage of the genome displaying aberrant copy number (log₂ depth ratio >0.2 or <−0.2) across all autosomes, weighted by the number of chromosomes analyzed. A stepwise approach was adopted to identify the optimal cutoff value: hazard ratios (HRs) were calculated at various confidence intervals (CIs) cutoffs, and the best cutoff was selected by comparing the HRs and the widths of their 95% CIs. To validate this cutoff, Kaplan‑Meier survival curves were generated to compare the PFS and OS between the high‑ and low‑risk groups.

TMB calculation

TMB was defined as the total count of nonsynonymous somatic mutations, including missense, nonsense, splice‑site, in‑frame, and frameshift alterations.

Statistical analysis

The Fisher’s exact test was used for the comparison of proportions between groups. Patient survival was analyzed via Kaplan-Meier curves, and statistical differences were analyzed with log-rank tests. Univariate and multivariate analyses were performed via Cox proportional hazards regression models in order to determine the relationship between variables and survival outcomes. All statistical analyses in this study were performed with R software (The R Foundation for Statistical Computing, Vienna, Austria), and a two-sided P value of <0.05 was considered significant.

Results

Patient characteristics and prognostic values of the clinicopathological features

The clinical characteristics of 62 patients with advanced or unresectable malignant melanoma treated with immunotherapy between February 2019 and October 2022 were statistically analyzed (Table 1). The median age at diagnosis was 59.5 years [interquartile range (IQR), 21–79 years]. The number of male and female patients was similar. The mucosal subtype predominated (54.8%), followed by the acral subtype (30.6%) and the cutaneous subtype (11.3%). The majority of patients had stage IV disease (93.5%). More than half of the patients had lymph node metastases (51.6%). The most common metastatic site was the lung (33.9%), followed by the liver (27.4%) and bone (14.5%). The majority of patients received first-line immunotherapy (82.3%). Among the patients receiving immunotherapy (Table 2), 32.3% had two metastatic sites, which was the largest proportion, and 8.1% had more than five metastatic sites.

Association of clinical factors with PFS in immunotherapy-treated patients

At the data cutoff point (February 4, 2023), 28 patients were evaluable for tumor response. The median follow-up duration was 25.85 months (95% CI: 24.31–27.47). The mPFS post-immunotherapy was 282 days, and the mOS was 1,012 days (Figure 1). The data maturity for PFS was sufficient, while that for OS was not. Therefore, the study focused mainly on analyzing the PFS.

Figure 1 Data on overall immunotherapy efficacy. CI, confidence interval; mOS, median overall survival; mPFS, median progression-free survival; NA, not available; OS, overall survival; PFS, progression-free survival.

There was no significant difference in PFS between the patients with different subtypes of malignant melanoma who received immunotherapy. The mPFS for the acral subtype, mucosal subtype, and cutaneous subtype groups was 359, 206, and 97 days, respectively (Figure 2).

Figure 2 PFS of the different melanoma subtype groups. CI, confidence interval; mPFS, median progression-free survival; NA, not available; PFS, progression-free survival.

The patients had 1–6 metastatic sites, and dichotomous analysis with 5 as the cutoff value revealed that patients with ≥5 metastatic sites had a significantly worse PFS [95% CI: 33–not available (NA), P=0.044] (Figure 3).

Figure 3 The association between metastatic site number and immunotherapy-related PFS. CI, confidence interval; mPFS, median progression-free survival; NA, not available; PFS, progression-free survival.

We analyzed the association between metastatic sites and immunotherapy efficacy and found that patients with liver or bone metastases had significantly shorter PFS (HR =2.12, 95% CI: 1.08–4.18, P=0.03 and HR =2.71, 95% CI: 1.16–6.36, P=0.02, respectively) (Figures 4,5).

Figure 4 The relationship between metastatic site and immunotherapy-related PFS. CI, confidence interval; HR, hazard ratio; PFS, progression-free survival.

Figure 5 The relationship between liver metastases, bone metastases, and immunotherapy-related PFS. CI, confidence interval; mPFS, median progression-free survival; NA, not available; PFS, progression-free survival.

The study analyzed the relationship between lymphocyte count, NLR, C-reactive protein (CRP) level, albumin level, lactate dehydrogenase level, and immunotherapy-related PFS. It was found that lymphocyte count, CRP level, and lactate dehydrogenase level were not associated with PFS (Figure 6). Analysis with 3 as the cutoff value for NLR indicated that patients with an NLR ≥3 had shorter immunotherapy-related PFS (HR =2.01, 95% CI: 1.03–3.92, P=0.04). In addition, patients with abnormal albumin levels also had a shorter PFS (HR =2.11, 95% CI: 1.09–4.09, P=0.02) (Figures 6,7).

Figure 6 The relationship between hematological indicators and immunotherapy-related PFS. CI, confidence interval; CRP, C-reactive protein; HR, hazard ratio; LDH, lactate dehydrogenase; NLR, neutrophil-to-lymphocyte ratio; PFS, progression-free survival.

Figure 7 The relationship between NLR, albumin levels and immunotherapy-related PFS. CI, confidence interval; mPFS, median progression-free survival; NA, not available; NLR, neutrophil-to-lymphocyte ratio; PFS, progression-free survival.

All 62 patients received immunotherapy, and 17 patients received chemotherapy, but the combination regimens varied widely. Further analysis showed that the PFS was significantly prolonged in patients administered immunotherapy combined with chemotherapy (HR =0.35, 95% CI: 0.14–0.90, P=0.02) (Figures 8,9).

Figure 8 Association of different combination regimens on immunotherapy-related PFS. CI, confidence interval; HR, hazard ratio; PFS, progression-free survival.

Figure 9 The relationship between combination chemotherapy and immunotherapy-related PFS. CI, confidence interval; mPFS, median progression-free survival; NA, not available; PFS, progression-free survival.

Genetic testing

The most frequently altered genes in the 62 patients who had evaluable NGS results are included in Figure 10. NRAS, CDKN2A, KIT, CDK4, and MDM2 were the most commonly altered genes, with respective alteration frequencies of 22.6%, 17.7%, 16.1%, 14.5%, and 14.5%.

Figure 10 The distribution of genetic variations. CNV, copy number variation.

To identify prognostic genetic biomarkers, we analyzed the relationship between each high-frequency mutation/CNV (counts ≥4) and PFS (Figure 11). Only the KMT2A mutation was found to be significantly associated with worse immunotherapy efficacy (HR =3.48, 95% CI: 1.20–10.10, P=0.01); meanwhile, mutations of MYC and MCL1 were non-significantly associated with immunotherapy efficacy (P<0.10) (Figure 12). EGFR mutation and MDM2 amplification were not associated with immunotherapy-related PFS.

Figure 11 The relationship between gene mutations and immunotherapy-related PFS. CI, confidence interval; HR, hazard ratio; Mut, mutation; PFS, progression-free survival; WT, wild type.

Figure 12 The relationship between KMT2A, MCL1, and MYC and immunotherapy-related PFS. CI, confidence interval; mPFS, median progression-free survival; Mut, mutation; NA, not available; PFS, progression-free survival; WT, wild type.

We examined the relationship between MDM2 amplification and the clinical factors associated with the efficacy of immunotherapy identified in previous analyses and found that the majority of patients with MDM2 amplification received immune-combination chemotherapy (P=0.10) (Figure 13). Further stratified analysis revealed that patients with MDM2 amplification and not treated with chemotherapy had the shortest PFS; meanwhile, those treated with a combination of immunotherapy and chemotherapy had a significantly prolonged PFS (Figure 14). This may explain why PFS was not shortened with immunotherapy in patients with MDM2 amplification. In addition, there was no significant survival difference between patients with TMB-high and TMB-low status (Figure 15).

Figure 13 Correlation of MDM2 amplification with predictors of immunotherapy efficacy. Amp, amplification; Chemo, chemotherapy; NLR, neutrophil-to-lymphocyte ratio; WT, wild type.

Figure 14 Association between MDM2 amplification, combination chemotherapy, and immunotherapy-related PFS. Amp, amplification; Chemo, chemotherapy; CI, confidence interval; mPFS, median progression-free survival; NA, not available; PFS, progression-free survival.

Figure 15 Association between TMB and immunotherapy-related PFS. CI, confidence interval; H, high; L, low; mPFS, median progression-free survival; NA, not available; PFS, progression-free survival; TMB, tumor mutation burden.

Discussion

With the development of immunotherapy, the survival rate of patients with advanced malignant melanoma has improved. However, along with enhanced efficacy, the incidence of adverse events related to ICIs is also increasing. Although most ICI-related adverse events are mild, treatment-related deaths are not uncommon (20), especially in dual immunotherapy (21,22). A portion of patients develop hyperprogression within a short time after immunotherapy, and this entails high mortality and a poor prognosis.

Given that immunotherapy may be associated with low response rates, significant toxicity, and a risk of rapid progression in a portion of patients in the short term, it is critical to find novel biomarkers that can predict treatment outcomes and screen patients most likely to respond to treatment. In this study, we analyzed the correlation between clinical factors, blood biomarkers, genetic features, and immunotherapy-related PFS. We found that ≥5 metastatic sites, liver metastasis, bone metastasis, an NLR ≥3, abnormal albumin levels, and KMT2A mutation were biomarkers for predicting the efficacy of immunotherapy in patients with advanced melanoma.

The NLR, an inflammatory marker, is considered to be associated with poor prognosis in various solid tumors (23). Neutrophils induce tumor angiogenesis and promote tumor cell proliferation, metastasis, and invasion through the expression of proangiogenic factors and chemokines (24). In addition, neutrophils can produce neutrophil extracellular traps, whose components can directly stimulate tumor cell migration and invasion when released in the tumor microenvironment (25). Recent studies have shown that programmed death-ligand 1 (PD-L1) expression on tumor-infiltrating neutrophils can also inhibit T-lymphocyte activation (26).

Lymphocytes are involved in antitumor response through cellular and humoral immunity, and lymphopenia can promote immune tolerance and escape (27). Baseline NLR is a simple, cost-effective, and easily obtainable biomarker, but the optimal cutoff value remains unclear, and in our study, the cutoff employed was 3. Future clinical trials should further clarify the role of NLR in the prediction of immunotherapy efficacy and establish the optimal critical value for baseline NLR in order to select appropriate populations for immunotherapy.

Liver metastasis may diminish the systemic efficacy of immunotherapy. Patients with melanoma and liver metastases but no other organ metastasis exhibit a weaker response to immunotherapy than do those without liver metastasis (28,29). In a preclinical study using mouse models, the number of tumor-specific CD8⁺ T cells in the peripheral blood of mice with colorectal cancer and liver metastasis was lower than in those without liver metastasis. In the liver, activated antigen-specific Fas⁺CD8⁺ T cells undergo apoptosis after interacting with FasL⁺CD11b⁺F4/80⁺ monocyte-derived macrophages, transforming the liver into an “immunosuppressive filter”. Patients with liver metastasis have a reduced number of peripheral blood T cells, and the cell diversity and function are weakened. Thus, liver metastasis can lead to acquired immunotherapy resistance through CD8⁺ T-cell deficiency (30). Our findings also suggest that liver metastasis is associated with poor prognosis and is an independent predictor of immunotherapy-related PFS.

Previous studies have suggested that bone metastases (31), hypoalbuminemia (32), and the number of metastatic sites at baseline (33) are associated with poor prognosis in patients with tumors. Our study, which analyzed the association of clinical data, blood samples, and other factors with immunotherapy-related PFS, produced findings consistent with these studies.

KMT2A, a member of the immune epigenetic factor, is generally considered to be associated with poor prognosis in patients with acute myelocytic leukemia or acute lymphoblastic leukemia (34), but it has been less studied in other tumor types. In this study, KMT2A mutation was associated with worse immunotherapy efficacy. One study found that KMT2A regulates the growth of melanoma cells by targeting hTERT-dependent signaling pathways and that its overexpression promotes the proliferation of melanoma cells, resulting in poor prognosis in patients with melanoma (35). This suggests that the KMT2A/hTERT signaling pathway may be used to find potential therapeutic targets for melanoma.

MDM2 is an oncogene whose core function is to inhibit the antioncogene p53. When MDM2 is amplified, it promotes the proteasomal degradation of p53, which facilitates tumorigenesis. ICIs can increase the production of interferon-γ (IFN-γ) at the tumor site (36), and IFN-γ upregulates the expression of MDM2, which suggests that the IFN-γ-MDM2-p53 axis may mediate the occurrence of hyperprogression (37). Interestingly, MDM2 amplification was not associated with treatment efficacy in our study. This may be due to the fact that the majority of patients with the MDM2 mutation in this study received combined chemotherapy. In the synergistic mechanism of the chemotherapy-immunotherapy regimen, chemotherapy enhances antitumor immunity by inhibiting tumor-induced immunosuppression, inducing immune tumor cell apoptosis, activating the innate immune system, and directly stimulating T cells or depleting immunosuppressive cells. Immunotherapy maintains the induction of chemotherapy and improves patients’ prognosis (38,39). The poor efficacy of ICI monotherapy in patients with advanced melanoma can be synergistically enhanced through combination with chemotherapy drugs to achieve a greater antitumor effect. Multiple studies have confirmed that chemotherapy combined with immunotherapy can significantly improve the survival time and disease control rate of patients with advanced melanoma (40,41). Consistent with previous research, our observational results indicated that ICI-based combination chemotherapy may partially counteract the potential adverse effect of MDM2 amplification and yield favorable immunotherapeutic outcomes in advanced melanoma patients. Further large-scale prospective investigations are warranted to validate this preliminary conclusion.

Conclusions

A biomarker model established in this study through an examination of clinical factors, blood biomarkers, and genetic characteristics may provide a basis for identifying an advanced melanoma population suited to immunotherapy. Although the sample size of this study was relatively small, we preliminarily identified that a number of metastatic sites ≥5, liver metastasis, bone metastasis, NLR ≥3, abnormal albumin level, and KMT2A mutation may be prognostic biomarkers for the efficacy of immunotherapy in patients with advanced melanoma. Of these, NLR ≥3 and the presence of liver metastases were found to be independent predictors.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0957/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0957/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0957/prf

Funding: This work was supported by the National Natural Science Foundation of China (Nos. 82073365 and 81872484), the Special Fund for Clinical Research of Nanjing Drum Tower Hospital Affiliated to Nanjing University Medical School (No. 2024-LCYJ-DBZ-01), and the Nanjing Municipal Science and Technology Bureau-Hengrui Medicine Clinical Basic Research Project (No. 202511072).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0957/coif). F.W. is from Nanjing Geneseeq Technology Inc., Nanjing, China. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China (No. 2022-170-02) and informed consent was taken from all the patients

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Joshi UM, Kashani-Sabet M, Kirkwood JM. Cutaneous Melanoma: A Review. JAMA 2025;334:2113-25. [Crossref] [PubMed]
2. Guo J, Qin S, Liang J, et al. Chinese Guidelines on the Diagnosis and Treatment of Melanoma (2015 Edition). Ann Transl Med 2015;3:322. [PubMed]
3. Bhatia S, Tykodi SS, Thompson JA. Treatment of metastatic melanoma: an overview. Oncology (Williston Park) 2009;23:488-96. [PubMed]
4. Eigentler TK, Caroli UM, Radny P, et al. Palliative therapy of disseminated malignant melanoma: a systematic review of 41 randomised clinical trials. Lancet Oncol 2003;4:748-59. [Crossref] [PubMed]
5. Ives NJ, Stowe RL, Lorigan P, et al. Chemotherapy compared with biochemotherapy for the treatment of metastatic melanoma: a meta-analysis of 18 trials involving 2,621 patients. J Clin Oncol 2007;25:5426-34. [Crossref] [PubMed]
6. Ascierto PA, Del Vecchio M, Merelli B, et al. Nivolumab for Resected Stage III or IV Melanoma at 9 Years. N Engl J Med 2026;394:333-42. [Crossref] [PubMed]
7. Robert C, Ribas A, Schachter J, et al. Pembrolizumab versus ipilimumab in advanced melanoma (KEYNOTE-006): post-hoc 5-year results from an open-label, multicentre, randomised, controlled, phase 3 study. Lancet Oncol 2019;20:1239-51. [Crossref] [PubMed]
8. Hodi FS, Chiarion-Sileni V, Gonzalez R, et al. Nivolumab plus ipilimumab or nivolumab alone versus ipilimumab alone in advanced melanoma (CheckMate 067): 4-year outcomes of a multicentre, randomised, phase 3 trial. Lancet Oncol 2018;19:1480-92. [Crossref] [PubMed]
9. D'Angelo SP, Larkin J, Sosman JA, et al. Efficacy and Safety of Nivolumab Alone or in Combination With Ipilimumab in Patients With Mucosal Melanoma: A Pooled Analysis. J Clin Oncol 2017;35:226-35. [Crossref] [PubMed]
10. Ribas A, Puzanov I, Dummer R, et al. Pembrolizumab versus investigator-choice chemotherapy for ipilimumab-refractory melanoma (KEYNOTE-002): a randomised, controlled, phase 2 trial. Lancet Oncol 2015;16:908-18. [Crossref] [PubMed]
11. Sheng X, Huang G, Fang M, et al. Toripalimab vs Dacarbazine as First-Line Therapy for Advanced Melanoma of Acral Subtype: The Phase 3 MELATORCH Randomized Clinical Trial. JAMA Oncol 2026;12:243-50. [Crossref] [PubMed]
12. Weber JS, D'Angelo SP, Minor D, et al. Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): a randomised, controlled, open-label, phase 3 trial. Lancet Oncol 2015;16:375-84. [Crossref] [PubMed]
13. Wolchok JD, Chiarion-Sileni V, Gonzalez R, et al. Long-Term Outcomes With Nivolumab Plus Ipilimumab or Nivolumab Alone Versus Ipilimumab in Patients With Advanced Melanoma. J Clin Oncol 2022;40:127-37. [Crossref] [PubMed]
14. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114-20. [Crossref] [PubMed]
15. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754-60. [Crossref] [PubMed]
16. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010;38:e164. [Crossref] [PubMed]
17. Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 2003;31:3812-4. [Crossref] [PubMed]
18. Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet 2013;Chapter 7:Unit7.20.
19. Talevich E, Shain AH, Botton T, et al. CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing. PLoS Comput Biol 2016;12:e1004873. [Crossref] [PubMed]
20. Chen C, Wu B, Zhang C, et al. Immune-related adverse events associated with immune checkpoint inhibitors: An updated comprehensive disproportionality analysis of the FDA adverse event reporting system. Int Immunopharmacol 2021;95:107498. [Crossref] [PubMed]
21. Lebbé C, Meyer N, Mortier L, et al. Evaluation of Two Dosing Regimens for Nivolumab in Combination With Ipilimumab in Patients With Advanced Melanoma: Results From the Phase IIIb/IV CheckMate 511 Trial. J Clin Oncol 2019;37:867-75. [Crossref] [PubMed]
22. Reschke R, Budde P, Zucht HD, et al. Autoantibodies as predictors for immune-related adverse events in checkpoint inhibition therapy of metastatic melanoma. J Immunother Cancer 2026;14:e013814. [Crossref] [PubMed]
23. Templeton AJ, McNamara MG, Šeruga B, et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J Natl Cancer Inst 2014;106:dju124. [Crossref] [PubMed]
24. Hawinkels LJ, Zuidwijk K, Verspaget HW, et al. VEGF release by MMP-9 mediated heparan sulphate cleavage induces colorectal cancer angiogenesis. Eur J Cancer 2008;44:1904-13. [Crossref] [PubMed]
25. Schedel F, Mayer-Hain S, Pappelbaum KI, et al. Evidence and impact of neutrophil extracellular traps in malignant melanoma. Pigment Cell Melanoma Res 2020;33:63-73. [Crossref] [PubMed]
26. He G, Zhang H, Zhou J, et al. Peritumoural neutrophils negatively regulate adaptive immunity via the PD-L1/PD-1 signalling pathway in hepatocellular carcinoma. J Exp Clin Cancer Res 2015;34:141. [Crossref] [PubMed]
27. Farhood B, Najafi M, Mortezaee K. CD8(+) cytotoxic T lymphocytes in cancer immunotherapy: A review. J Cell Physiol 2019;234:8509-21. [Crossref] [PubMed]
28. Bilen MA, Shabto JM, Martini DJ, et al. Sites of metastasis and association with clinical outcome in advanced stage cancer patients treated with immunotherapy. BMC Cancer 2019;19:857. [Crossref] [PubMed]
29. Tumeh PC, Hellmann MD, Hamid O, et al. Liver Metastasis and Treatment Outcome with Anti-PD-1 Monoclonal Antibody in Patients with Melanoma and NSCLC. Cancer Immunol Res 2017;5:417-24. [Crossref] [PubMed]
30. Yu J, Green MD, Li S, et al. Liver metastasis restrains immunotherapy efficacy via macrophage-mediated T cell elimination. Nat Med 2021;27:152-64. [Crossref] [PubMed]
31. Mannavola F, Mandala M, Todisco A, et al. An Italian Retrospective Survey on Bone Metastasis in Melanoma: Impact of Immunotherapy and Radiotherapy on Survival. Front Oncol 2020;10:1652. [Crossref] [PubMed]
32. Kato S, Goodman A, Walavalkar V, et al. Hyperprogressors after Immunotherapy: Analysis of Genomic Alterations Associated with Accelerated Growth Rate. Clin Cancer Res 2017;23:4242-50. [Crossref] [PubMed]
33. Ferrara R, Mezquita L, Texier M, et al. Hyperprogressive Disease in Patients With Advanced Non-Small Cell Lung Cancer Treated With PD-1/PD-L1 Inhibitors or With Single-Agent Chemotherapy. JAMA Oncol 2018;4:1543-52. [Crossref] [PubMed]
34. Piciocchi A, Messina M, Elia L, et al. Prognostic impact of KMT2A-AFF1-positivity in 926 BCR-ABL1-negative B-lineage acute lymphoblastic leukemia patients treated in GIMEMA clinical trials since 1996. Am J Hematol 2021;96:E334-8. [Crossref] [PubMed]
35. Zhang C, Song C, Liu T, et al. KMT2A promotes melanoma cell growth by targeting hTERT signaling pathway. Cell Death Dis 2017;8:e2940. [Crossref] [PubMed]
36. Zhao L, Ren Y, Zhang G, et al. Single-arm study of camrelizumab plus apatinib for patients with advanced mucosal melanoma. J Immunother Cancer 2024;12:e008611. [Crossref] [PubMed]
37. Sasaki A, Nakamura Y, Mishima S, et al. Predictive factors for hyperprogressive disease during nivolumab as anti-PD1 treatment in patients with advanced gastric cancer. Gastric Cancer 2019;22:793-802. [Crossref] [PubMed]
38. Galluzzi L, Zitvogel L, Kroemer G. Immunological Mechanisms Underneath the Efficacy of Cancer Therapy. Cancer Immunol Res 2016;4:895-902. [Crossref] [PubMed]
39. Ramakrishnan R, Huang C, Cho HI, et al. Autophagy induced by conventional chemotherapy mediates tumor cell sensitivity to immunotherapy. Cancer Res 2012;72:5483-93. [Crossref] [PubMed]
40. Patel SP, Kim DW, Bassett RL, et al. A phase II study of ipilimumab plus temozolomide in patients with metastatic melanoma. Cancer Immunol Immunother 2017;66:1359-66. [Crossref] [PubMed]
41. Si L, Zhang X, Shu Y, et al. A Phase Ib Study of Pembrolizumab as Second-Line Therapy for Chinese Patients With Advanced or Metastatic Melanoma (KEYNOTE-151). Transl Oncol 2019;12:828-35. [Crossref] [PubMed]

(English Language Editor: J. Gray)