Cutaneous melanoma (CM) is a malignant neoplasia characterized by rapid progression, metastasis to regional lymph nodes as well as distant organs, and limited responsiveness to therapeutics (MacKie et al., 2009). It contributes to more than 80% death of skin cancer patients (Miller and Mihm, 2006), and its incidence is one of the most rapidly increasing cancers in the United States where it is estimated that there will be 91,270 new cases and 9,320 deaths due to this disease in 2018 (Siegel et al., 2018). Although significant progress has been made in both understanding CM biology and genetics, and in therapeutic approaches, the prognosis remains poor due to the high potential for CM invasion and metastasis. This malignancy still represents a major public health problem worldwide. Primary localized melanoma has a relatively high survival rate. The 5- year survival rate prognosis of patients with lymph node or distant metastases was only 15–20% (Siegel et al., 2018; Weiss et al., 2015). Assessment of patients prior to therapy may aid in the identification of individuals at high risk for recurrence and may guide the development of future targeted treatment strategies. For example, for patients who are at high operative risk, conservative therapy may be helpful in the operative decision-making process. Molecular biomarkers could provide additional prognostic information and insight into the mechanisms of melanoma progression, as well as guide treatment selection. Consequently, new strategies for the identification of effective prognostic biomarkers may improve the clinical management of melanoma by providing more accurate prognostic information.

Aberrant DNA methylation is an epigenetic hallmark of cancer and actively contributes to cancer development and progression by inactivating tumor suppressor genes (Egger et al., 2004). Some characteristics of methylation markers render them particularly attractive for development of clinically applicable biomarkers, including high stability in biologic samples, limited susceptibility to tumor environmental factors, and ease of detection (Keeley et al., 2013). A growing number of studies have demonstrated that aberrant DNA methylation plays important roles in tumorigenesis and progression, and DNA methylation has great potential to act as a biomarker for predicting the prognosis of patients with a variety of malignancies (Egger et al., 2004; Guo et al., 2004; Roh et al., 2016). For instance, GATA4 is epigenetically silenced in gastrointestinal cancers (Akiyama et al., 2003) and lung cancer (Guo et al., 2004); OPCML promoter methylation has been identified as a biomarker for predicting ovarian cancer prognosis (Zhou et al., 2014); PCDH19 methylation is associated with poor hepatocellular carcinoma prognosis (Zhang et al., 2018) and Sigalotti et al. identified a 17-gene methylation signature as a molecular marker of survival in stage IIIC melanoma (Sigalotti et al., 2012). Unfortunately, many recent melanoma studies have several limitations including relatively small sample cohorts, lack of subsequent biomarker validation, narrow focus on patient specimens with specific clinical features, or investigation of only one or a few genes. These studies lack the comprehensive and systematic approach of genome-wide methylation analysis. Because of this, the identified methylation signatures do not have universal prognostic power, and their capability is limited by the specimen type. The Cancer Genome Atlas (TCGA) database (Hudson et al., 2010), a large well-annotated database with nearly 500 CM samples collected worldwide is a helpful resource for developing promising biomarkers with prospective studies into a methylation signature which can be of clinical utility.

Consequently, the purpose of this study was to identify DNA methylation biomarkers so as to explore the utility of DNA methylation analysis for cancer prognosis. The whole genome methylation profiles of tumor tissues from patients with CM in the TCGA database were analyzed, and the potential clinical significance of methylation biomarkers serving as molecular prognostic predictors was examined using the Kaplan–Meier method and receiver operating characteristic (ROC) analyses. Furthermore, the prognostic capacity of the identified methylation biomarker was evaluated with an independent cohort of patients in the Gene Expression Omnibus (GEO) database. Also, we investigated the independence and reproducibility in various clinical groups, as well as the possible role of the methylation biomarker in immune-checkpoint blockade (ICB) immunotherapy.

CM is the deadliest form of skin cancer and lead to 60,712 deaths in 2018 (Miller and Mihm, 2006). In recent years, the importance of DNA methylation in the biology of CM has been increasingly acknowledged. For instance, hypermethylated estrogen receptor alpha (ER-α) is a significant factor in melanoma progression (Mori et al., 2006); methylation-dependent SOX9 expression mediates invasion in human melanoma cells and is a negative prognostic factor in advanced melanoma (Cheng et al., 2015), and MGMT gene promoter methylation in metastatic CM is associated with longer survival (Cesinaro et al., 2012). However, these studies usually concentrated on single gene methylation or patient samples with specific clinical characteristics, and combinations of DNA methylation as biomarkers could achieve higher sensitivity and specificity than individual DNA methylation (Dai et al., 2011). Moreover, a limited overlap between signatures published by various studies can be observed, possibly due to variation in disease causes, tissue heterogeneity, or stage of disease at the point of analysis (Tremante et al., 2012). The TCGA database provides a large number of samples with a variety of clinical characteristics. Based on a TCGA dataset that included 461 CM samples, the current study identified a prognostic DNA methylation signature with potential clinical applicability, validated it in an independent cohort, and investigated its high reproducibility and utility in various clinical groups.

CM has a high degree of heterogeneity in terms of clinical, dermatological, and histopathological presentation (Coricovac et al., 2018). Several parameters, such as age, sex, stage of disease, Breslow thickness, and ulceration status have significant influence on CM patient prognosis, in which Breslow thickness has been demonstrated as the most important clinicopathological characteristic for predicting prognosis. In order to be clinically useful, a DNA methylation signature must be independent of clinical factors. Here we adopted a grouping method not influenced by our subjective, that is based on the TCGA series number of patients, without adjusting for clinical parameters. If detection, treatment and surgical excision occur when the tumor burden is restricted to a primary site, a patient’s prognostic survival is enhanced, but once the disease has metastasized to distant organs such as the brain and liver, prognosis is poor (Balch et al., 2009). Since patients with primary tumors had shorter follow-ups, none more than 5 years, we analyzed the independence and applicability of our four-DNA methylation signature in samples obtained from distant metastasis, subcutaneous tissue, and regional lymph node metastasis. The results showed that our signature was independent of tumor metastatic sites. Moreover, as patients suffering from early stages of the disease exhibit higher potential for healing, a prognostic signature that can also efficiently risk-stratify these patients would have higher clinical application value (Segura et al., 2010). Our signature has been demonstrated to be not only independent of all clinical factors, but also to have higher predictive performance for patients with tumor thickness of less than 2 mm and patients in early stages (AUCs were 0.830 and 0.814, respectively). Therefore, our signature was an independent applicable prognostic biomarker, which may be of high clinical value.

Considering that an ideal prognostic marker is one that can also efficiently risk-stratify in other independent cohorts, we employed GEO dataset (GSE51547) to further evaluate the practicality of our four-DNA methylation signature. Although the predictive accuracy in the GEO dataset is not as high as in the validation dataset due to the low number of samples (N = 47), the four-DNA methylation signature performed well in distinguishing low- and high-risk groups (AUC = 0.708, p<0.05). Furthermore, it was demonstrated that in both the validation and independent cohorts, our signature outperformed other known prognostic biomarkers, including mRNA, lncRNA, and DNA methylation, and statistical comparison using Z-test revealed that it has significantly higher (p<0.05) predictive performance than almost all the other known biomarkers. When further samples become available it will be important to analyze this methylation signature in another validation dataset.

Targeting immune checkpoints such as PD-1, PD-L1, and CTLA-4 have achieved noteworthy benefit in multiple cancers by blocking immunoinhibitory signals and enabling patients to produce an effective antitumor response, especially in patients with CM (Riaz et al., 2017). However, a significant limitation of ICB is that less than one-third of patients respond to ICB treatment, and identification of ICB response biomarkers and resistance regulators is a critical challenge (Sharma et al., 2017). DNA methylation plays a critical role in cell lineage specification and may serve as a specific molecular marker for measurement of immune responses. Recently, Jeschke et al highlighted the power of MeTIL to evaluate local and functional TIL-based tumor immune responses and the ability of this approach to improve prognosis (Jeschke et al., 2017). However, the identification of MeTIL was focused on CpGs that are highly differentially methylated between T lymphocytes and epithelial cells. Lymphocytes only account for a small fraction of TME (Pretscher et al., 2009); thus, there may be bias when using MeTIL as a prognostic marker to predict survival outcomes. Intriguingly, the correlation analyses and the observed predictive performance suggested that our four-DNA methylation signature was significantly correlated with the ICB immunotherapy-related signature. Additionally, our signature displayed higher predictive performance than other known signatures, including PD-1, PD-L1, PD-L2, CTLA-4, and MeTIL. These results demonstrate that our four-DNA methylation signature, although developed for accurate prognosis, may also have potential as a guide for precision cancer ICB immunotherapy.

Furthermore, epigenetic changes have been shown to alter gene expression, and epigenetic inactivation of tumor suppressor genes has been implicated in tumorigenesis of various malignancies, including CM (Herman and Baylin, 2003). Here, the expression of GBP5 and KLHL21 were significantly (p<0.001) negatively correlated with their methylation levels, and the other two genes show significant positive correlation (p<0.001) between the expression and their methylation levels (Figure 1—figure supplement 3). We also found that expression of this four-gene can also be used as a prognostic biomarker (Figure 2—figure supplement 1), but the four-DNA methylation biomarker offer a better potential to fulfill much more sensitive and specific prognostic test. For our four-DNA methylation sites, researchers have revealed that their corresponding genes may be crucial in immunity and cancer development, including CM. For instance, GBP5 promotes NLRP3 inflammasome assembly and immunity in mammals (Shenoy et al., 2012). GBP5 was induced by IFN-γ, could serve as a marker of IFN-γ-induced classically activated macrophages and the substitute indicator of IFN-γ, which can directly suppress tumorigenesis and infection and/or can modulate the immunological status in cancer cells (Chang et al., 2015; Yamamoto et al., 2012). Meanwhile, GBP5 expression in CM is associated with favorable prognosis (Wang et al., 2018). RAB37, as a tumor suppressor gene, promotes M1-like macrophage infiltration and suppresses tumor growth (Tzeng et al., 2018), and it was frequently down-regulated due to promoter hypermethylation in metastatic lung cancer, can serve as a potential predictive biomarker (Wu et al., 2009). RAB37-mediated SFRP1 secretion suppresses cancer stemness, and dysregulated RAB37-SFRP1 pathway confers cancer stemness via the activation of Wnt signaling (Cho et al., 2018). OCA2 is involved in the melanin biosynthetic process and mammalian pigmentation (Crawford et al., 2017), and the DNA variant in intron of OCA2 (rs4778138) has been found associated with CM risk (Law et al., 2015). The hypomethylation levels of cg18456782 (OCA2) was associated with lower expression of OCA2 and a lower risk. Meanwhile separating CM patients by median expression of OCA2, there is a significant differential survival (p<0.0001) with low expression favoring better survival. All these results suggest a risk pattern for OCA2 gene in CM. KLHL21 could affect cell migration and invasion, play an essential role in tumorigenesis and progression, and it might serve as a potential therapeutic target for cholangiocarcinoma (Chen et al., 2018) and hepatocellular carcinoma (Shi et al., 2016). Although the functional mechanism of these four genes in CM still needs further study, significant correlation between these four genes and OS or response to therapy of patients with CM, and DNA methylation might also be suitable as biomarkers for response to ICB therapy.

In addition, the results of the univariate Cox regression, Kaplan–Meier and ROC analyses for the four individual methylation sites show that each of the DNA methylation sites also could distinguish high- and low-risk patients, but the predictive performances were lower than the combination of these four DNA methylation sites in both the training and validation cohorts (Figure 2—figure supplements 2–3), indicating that single methylation sites are likely to play a role in the prognostic prediction, and a combination of methylation sites might offer better potential to fulfill much more sensitive and specific prognostic tests for patients with CM. To the best of our knowledge, the prognostic value of this multi-marker signature in melanoma has not been previously reported. Therefore, the current study provides new insight that a combination of epigenetic biomarkers could help to improve risk-stratification and prediction of survival in patients with melanoma.

In conclusion, we identified and verified a four-DNA methylation signature that was significantly associated with the OS of patients in TCGA and an independent cohort. The four-DNA methylation signature was not only independent of clinical factors including patient sex, age, stage, tumor location, and Breslow thickness, but also exhibited superior ability in predicting OS compared with known biomarkers. The four-DNA methylation signature was able to stratify patients with startling accuracy in survival differences, suggesting that it may be used to select patients for therapies, and help to determine whether patients may require more or less aggressive treatment. Furthermore, the four-DNA methylation signature was significantly correlated with the ICB immunotherapy-related signature. Therefore, though these exploratory findings are warranted to validate the potential role of this prognostic signature in clinical application and the functional characterization in CM development, these four-DNA methylation sites, or some of them, may participate in the progress of the cancer, and have great potential implications for both risk-stratification, adjuvant management and measures of response to ICB immunotherapy of patients with CM.

We downloaded the data from publicly available databases: The Cancer Genome Atlas database (https://cancergenome.nih.gov/) and GEO database (under accession code GSE51547).

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Thank you for submitting your article "A four-DNA methylation biomarker predicts survival of patients with cutaneous melanoma" for consideration by eLife. Your article has been reviewed by 3 peer reviewers and the evaluation has been overseen by a Reviewing Editor and Maarten van Lohuizen as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

In the current manuscript, Guo et al. use published data on cutaneous melanoma including DNA methylation status to develop a 4-DNA methylation biomarker that is predictive of overall survival based on historical case data.

Significant points:

1) The identification of a novel epigenetic four-DNA methylation signature that can successfully stratify patients into high- and low-risk groups, with AUC estimates exceeding 0.80 and 0.75 in training and validation cohorts.

2) Confirmation of the prognostic value of this signature in an independent (though smaller) cohort.

3) The finding that the four-DNA methylation signature was effective in distinguishing the high-risk patients from low-risk patients, independent of clinical parameters like Breslow thickness.

4) The finding that the 4-gene methylation signature is inversely correlated with expression of immune-checkpoint genes.

Essential revisions:

1) Absence of functional data: Does the methylation pattern of the 4 genes correlate with the expression of these genes? The authors are encouraged to analyze RNA Seq data from TCGA to address this issue. This issue is important because if methylation correlates with expression, then these four genes may (also) be important therapeutic targets. More specific questions to address at a basic level – Are the genes at/near the methylation sites expressed in melanomas at the RNA or protein level? Does expression (in at least a subset or other dataset) correlate with the methylation status at the loci? Some plausible roles for the genes GBP5, RAB37, etc. are offered in the discussion, but some functional analysis of these genes or the pathways hypothesized to be affected (as described in the Discussion) would substantially increase the degree to which this study could move the field forward.

2) Questions about the analysis: The authors should carefully control for clinical features from the very beginning of their analysis rather than estimating that clinical features seem to not have an effect on their predictor after the model is fully built. It is hard to evaluate and interpret any intermediate findings (are they driven by clinical biases or actual signal in methylation data?). Specifically: was univariate analysis in the training cohort adjusted for age, tumor stage/grade and tumor tissue site? To perform a search for markers independent from clinical features it seems to be critical to adjust for clinical differences first. Will this change marker selection for prediction model? The authors should introduce a comparison with known melanoma methylation signals in (e.g. MITF region, etc.) as a positive control for their model. Interestingly, they do cite and use the data from M. Lauss et al. who found the MITF signal, but never check whether their predictor-building approach captures the same signal.

Also: subsection “Statistical analysis”. "The univariate Cox proportional hazard analysis was first conducted in the training cohort to identify methylation markers significantly (P < 0.001) associated with patient survival."

How was the significance level was determined? According to the manuscript 461 samples with 485,577 DNA methylation sites were analyzed. Most-widely used Bonferroni correction (might be too aggressive cutoff, I am not insisting on using this particular one) should result in P < 0.05 / 485,577 ~ 1x10^-7. Subsection “Derivation of prognostic DNA methylation markers from the training cohort”: 4,454 markers were included into the multivariate regression analysis (above comment on significance level determination applies).

3) Validation: The four-DNA methylation signature "beats" the other biomarkers in Supplementary file 2/Figure 4A-C. Data may not be available for all of these studies, but for those based on DNA methylation, how does the four-DNA signature compare to the datasets used to generate other methylation predictive markers (Supplementary file 2 17-DNA methylation, CTLA-4, etc.)? Presumably these had validation datasets as well (it seems unfair to expect the four-DNA signature of this study to "beat" the training set for another marker, but other validation sets should be a reasonable comparator). The authors did include a comparison using their validation set, but additional validation datasets would strengthen the generalizability of their biomarker/signature.

4) It would be helpful to include a supplemental figure/panel illustrating distribution of the 4-site methylation risk predictor score value for the TCGA melanoma cohort and then plot the threshold dividing "low-risk" and "high-risk" groups (median). Subsequently, the Figure 3 (main text) and supplementary figure legends need to be explained better (e.g. in Figure 3A "low-risk (28/69)" what does (28/69) mean? I seem to be unable to find this in the text or figure caption.)

5) One additional panel in Figure 1 (or supplement) might be helpful: principal component analysis plot using methylation values at 4 selected biomarkers that illustrates separation between "high" and "low" survival groups. This should clearly illustrate direct effect of methylation level on survival differences.

6) Subsection “Association of the four-DNA methylation signature with ICB immunotherapy-related signature”: "significantly negatively correlated with PD-1, PD-L1, PD-L2, CTLA-4 (P < 0.05 [..,]". Multiple hypothesis testing significance adjustment should be considered. Based on Figure 4D – 4-meth signature correlation was estimated against 7 other signatures (or 6 if GBP5-meth is considered a part of 4-meth), thus significance should be determined using P-value threshold P < 0.05/7 (if Bonferroni correction is used). Please, report raw p-values for this analysis in the supplement.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "A four-DNA methylation biomarker is a superior predictor of survival of patients with cutaneous melanoma" for further consideration at eLife. Your revised article has been favorably evaluated by Maarten van Lohuizen (Senior Editor), a Reviewing Editor, and three reviewers.

The manuscript has been improved but there are some minor remaining issues that need to be addressed before acceptance, as outlined below:

The reviewers appreciate the effort made by the authors to address their concerns. The result is a strengthened manuscript.

1) Without another validation dataset (major point #3), the generalizability of the results will remain a potential weakness but publishing this study with this caveat noted in the text/discussion is of value.

It is recommended that the authors mention this, for example by adding the following sentence to the end of the third paragraph of the Discussion section: When further samples become available it will be important to analyze this methylation signature in another validation dataset.

2) The DNA variant in intron of OCA2 has been found to have a protective effect in melanoma GWAS (rs4778138, β = -0.18, PMID: 26237428). The authors state that cg18456782 (OCA2) marker "had positive coefficients, indicating a correlation between higher DNA methylation level and shorter overall survival". If one looks at the TCGA survival data and separates melanoma patients by median OCA2 expression, there is a significant differential survival (p~1e-5) with low expression favoring better survival. This is consistent with the presented data and suggests that while in GWAS the OCA2 variant has been found having a protective function, survival and expression data suggests a risk pattern for OCA2 gene.

Discussing this in the Discussion section would strengthen the manuscript because there is no sufficient experimental data on OCA2.

3) Prior to the analysis the training cohort was not adjusted for clinical parameters like age, tumor stage/grade and tumor tissue site; the authors have explained how they controlled for this, but this fact should be mentioned in the Discussion section so that results could be interpreted with the appropriate caution.

4) The Abstract should be modified as follows:

Cutaneous melanoma (CM) is a life-threatening form of skin cancer. Prognosticbiomarkers can reliably stratify patients at initial melanoma diagnosis according to risk, and may inform clinical decisions. Here, we performed a retrospective, cohort-based study analyzing genome-wide DNA methylation of 461 patients with CM from the TCGA database. Cox regression analyses were conducted to establish a four-DNA methylation signature that was significantly associated with the overall survival (OS) of patients with CM, and that was validated in an independent cohort. Corresponding Kaplan-Meier analysis displayed a distinct separation in OS. The ROC analysis confirmed that the predictive signature performed well. Notably, this signature exhibited much higher predictive accuracy in comparison with known biomarkers. This signature was significantly correlated with immune checkpoint blockade (ICB) immunotherapy-related signatures, and may have potential as a guide for measures of responsiveness to ICB immunotherapy.

Essential revisions:

1) Absence of functional data: Does the methylation pattern of the 4 genes correlate with the expression of these genes? The authors are encouraged to analyze RNA Seq data from TCGA to address this issue. This issue is important because if methylation correlates with expression, then these four genes may (also) be important therapeutic targets. More specific questions to address at a basic level – Are the genes at/near the methylation sites expressed in melanomas at the RNA or protein level? Does expression (in at least a subset or other dataset) correlate with the methylation status at the loci? Some plausible roles for the genes GBP5, RAB37, etc. are offered in the discussion, but some functional analysis of these genes or the pathways hypothesized to be affected (as described in the Discussion) would substantially increase the degree to which this study could move the field forward.

Thank you for noting this. Indeed, it is helpful to analyze the correlation between the methylation pattern of the 4 genes and the expression of these genes. It is generally believed that differential methylation can affect gene expression by influencing transcription factor binding (Medvedeva et al., 2014). In the manuscript, we have analyzed the expression of these genes and the correlation between the expression and their methylation levels, found that all these 4 genes were expressed in melanomas, the expression of KLHL21 and GBP5 were significantly (P < 0.001) negatively correlated with their methylation levels, and the other two genes show significant positive correlation (P < 0.001) between the expression and their methylation levels. In fact, we have investigated the role of the expression levels of these four genes in prognostic prediction, and found that their expression levels can also be used as prognostic markers (AUC: 0.662, 95%CI: 0.55-0.77), but the four-DNA methylation biomarker offer a better potential to fulfill much more sensitive and specific prognostic test (AUC: 0.754, 95%CI: 0.66-0.85). A possible explanation is that DNA methylation not only correlated with expression of corresponding genes, but also reflected upstream regulations. Therefore, this study focused on DNA methylation prognostic biomarkers. To show these results in more detail, we made changes in our manuscript and added two supplementary figures.

In the Discussion section: “Furthermore, epigenetic changes have been shown to alter gene expression, and epigenetic inactivation of tumor suppressor genes has been implicated in tumorigenesis of various malignancies, including CM (41). Here, the expression of GBP5 and KLHL21 were significantly (P < 0.001) negatively correlated with their methylation levels, and the other two genes show significant positive correlation (P < 0.001) between the expression and their methylation levels (Figure 1—figure supplement 3). We also found that expression of this four-gene can also be used as a prognostic biomarker (Figure 2—figure supplement 1), but the four-DNA methylation biomarker offer a better potential to fulfill much more sensitive and specific prognostic test.”

2) Questions about the analysis: The authors should carefully control for clinical features from the very beginning of their analysis rather than estimating that clinical features seem to not have an effect on their predictor after the model is fully built. It is hard to evaluate and interpret any intermediate findings (are they driven by clinical biases or actual signal in methylation data?). Specifically: was univariate analysis in the training cohort adjusted for age, tumor stage/grade and tumor tissue site? To perform a search for markers independent from clinical features it seems to be critical to adjust for clinical differences first. Will this change marker selection for prediction model? The authors should introduce a comparison with known melanoma methylation signals in (e.g. MITF region, etc.) as a positive control for their model. Interestingly, they do cite and use the data from M. Lauss et al. who found the MITF signal, but never check whether their predictor-building approach captures the same signal.

Also: subsection “Statistical analysis”. "The univariate Cox proportional hazard analysis was first conducted in the training cohort to identify methylation markers significantly (P < 0.001) associated with patient survival."

How was the significance level was determined? According to the manuscript 461 samples with 485,577 DNA methylation sites were analyzed. Most-widely used Bonferroni correction (might be too aggressive cutoff, I am not insisting on using this particular one) should result in P < 0.05 / 485,577 ~ 1x10^-7. Subsection “Derivation of prognostic DNA methylation markers from the training cohort”: 4,454 markers were included into the multivariate regression analysis (above comment on significance level determination applies).

Control for clinical features from the very beginning of the analysis may reduce or avoid the influence of clinical biases on the identification of predictor, but the clinicopathological parameters relevant to this study including sex, age at diagnosis, tumor tissue site, Breslow thickness, pathologic stage, ulceration status, and last clinical status, it was difficult to control all the clinical features from the very beginning, so we chose an grouping method based on the TCGA series number of patients, which is not influenced by our subjective. Chi-square test showed no significant difference in the distribution of patients’ sex (P = 0.194), age (P = 0.718), tumor stage (P = 0.597), and tumor tissue site (P = 0.337) between the training set and the validation set. Here we take a brute force approach, that is, exhaustively chose 2-5 sites from all DNA methylation sites that significantly (P < 0.001) correlated with the OS of patients as covariates in multivariate Cox regression analysis, and built billions of models, from which the four-DNA methylation was identified as prognostic biomarker. And after controlling clinicopathological parameters such as age, tumor stage and tumor tissue location, and so on, it was still proved that this biomarker had a high prediction performance (Figure 3, Figure 3—figure supplement 1 to Figure 3—figure supplement 7). These results indicated that the findings in this study were based on actual signal in methylation data rather than driven by clinical biases.

As for MITF, a melanocyte-specific modulator also recognized as a lineage addiction oncogene in melanoma, we are sorry for missing the comparison of our model with MITF, which is a favorable prognosticator of OS in melanoma patients (Garraway et al., 2005; Naffouje et al., 2015). According to the reviewer's suggestion, we have compared the sensitivity and specificity of our model and MITF expression. The result show that the AUC of MITF (AUC: 0.538, 95%CI: 0.42-0.65) was smaller than that of our model (AUC: 0.754, 95%CI: 0.66-0.854). And we also have made changes to Results section and Supplementary file 2.

In the Results section: “In addition, numerous prognostic markers have previously been identified for CM, utilizing archival tumor tissues or single institutional studies. MITF has been identified as a lineage survival oncogene amplified in malignant melanoma (Garraway et al., 2005).”

In addition, in our manuscript, the significance level was determined based on P values in univariate cox regression analysis with the cut-off of P value as 0.001. Here in univariate cox regression analysis, there were only three methylation markers with P less than 1x10^-7, which was not sufficient for subsequent analysis, so Bonferroni correction was not implemented. But as an alternative, Benjamini and Hochberg False Discovery Rate correction was performed, and the results indicated all of our four methylation sites were also significantly (P < 0.001) associated with patient survival. We also made changes to Supplementary file 1 in the revised manuscript.

3) Validation: The four-DNA methylation signature "beats" the other biomarkers in Supplementary file 2/Figure 4A-C. Data may not be available for all of these studies, but for those based on DNA methylation, how does the four-DNA signature compare to the datasets used to generate other methylation predictive markers (Supplementary file 2 17-DNA methylation, CTLA-4, etc.)? Presumably these had validation datasets as well (it seems unfair to expect the four-DNA signature of this study to "beat" the training set for another marker, but other validation sets should be a reasonable comparator). The authors did include a comparison using their validation set, but additional validation datasets would strengthen the generalizability of their biomarker/signature.

This is a valuable suggestion. For other known prognostic biomarkers, on the one hand, if the prognostic model of the biomarker is available in the previous research, we directly calculated the risk scores of patients in our validation dataset using their model, then verified their predictive performance through performing ROC analysis, and the AUCs were obtained, just as the analysis for our four-DNA methylation signature. For instance, the model for the four-lncRNA signature has been reported in the previous studies (Chen et al., 2017). On the other hand, if the biomarker was single gene or DNA methylation site, such as CTLA-4 expression and CTLA-4 methylation (cg08460026), we directly performed ROC analysis to verify their predictive performance using them as test variables in our validation dataset, and the AUCs were obtained. Meanwhile, if the biomarkers are known, but the model is unavailable, such as a 17-DNA methylation signature (Sigalotti et al., 2012), we would first perform multivariate Cox regression analysis to construct model in our training cohort. Then according to the model, we calculated the risk score for each patient in validation cohort, and performed ROC analysis to verify their predictive performance. Ultimately, Supplementary file 2/Figure 4A-C were obtained.

In addition, as suggested by the reviewer, additional validation datasets indeed would strengthen the generalizability of the biomarker. In fact, we have conducted a comprehensive search before the submission of our paper, but only one publicly available dataset (GSE51547) that meets the requirements, which had been used as an additional validation dataset to examine and compare our signature. Recently, we have launched another search in NCBI, National Cancer Database (NCDB), Surveillance Epidemiology and End Results Program (SEER) database, and so on, but other than the validation dataset we used in this study, there is no other publicly available dataset containing both the DNA methylation data and related clinical survival information of patients at present. Undoubtedly, we will make further validation when there are available independent datasets in the future.

4) It would be helpful to include a supplemental figure/panel illustrating distribution of the 4-site methylation risk predictor score value for the TCGA melanoma cohort and then plot the threshold dividing "low-risk" and "high-risk" groups (median). Subsequently, the Figure 3 (main text) and supplementary figure legends need to be explained better (e.g. in Figure 3A "low-risk (28/69)" what does (28/69) mean? I seem to be unable to find this in the text or figure caption.)

According to reviewer’s kind suggestion, we added a supplemental figure (Figure 1—figure supplement 2) illustrating distribution of the four-methylation risk predictor score value in both training cohort and validation cohort, and made changes in the manuscript as follows.

In the Results section: “To determine the potential predictive value of this four-DNA methylation signature in the prognosis, Kaplan–Meier curves along with the Wilcoxon test were used to visualize and compare the OS of patients in the low- versus high-risk group which were classified using the median risk score (3.69) of the training cohort as the cutoff point, and the distribution of the risk predictor scores for the training and validation cohort was illustrated in Figure 1—figure supplement 2.”

We are sorry for not giving the detailed explanation of "low-risk (28/69)" in figure 3 and other figures. In this study, based on the median risk score (3.69) of training cohort, the patients were classified into low- or high-risk groups, low-risk group with lower risk scores, and high-risk group with higher risk scores. In Figure 3A, “low-risk (28/69)” refers to that a total of 69 patients in the low-risk group, in which 28 with last clinical status “death”, and other figures are similar. To make this more clearly in all figures, all figure legends were updated.

5) One additional panel in Figure 1 (or supplement) might be helpful: principal component analysis plot using methylation values at 4 selected biomarkers that illustrates separation between "high" and "low" survival groups. This should clearly illustrate direct effect of methylation level on survival differences.

Thanks for the reviewer’s kind suggestion. Principal component analysis (PCA) was carried out using four methylation values at selected biomarkers. The PCA scores plots exhibited discrimination between short survival (OS < 5 years) and long survival (OS > 5 years) patients. We added a new supplementary figure as Figure1—figure supplement 1.

In the Results section: “Patients exhibiting long-term survival tended to have lower methylation levels of cg24670442, cg18456782 and higher methylation levels of the other two methylation sites, consistent with the results of multivariate Cox regression analysis. Moreover, principal component analysis (PCA) was carried out using four methylation values at selected biomarkers (Figure 1—figure supplement 1). The difference of PC1 and PC4 is 15.42%, indicating the continuous capturing of information. And the combination of four methylation markers can effectively distinguish patients with long- and short-term survival.”

6) Subsection “Association of the four-DNA methylation signature with ICB immunotherapy-related signature”: "significantly negatively correlated with PD-1, PD-L1, PD-L2, CTLA-4 (P < 0.05 […]". Multiple hypothesis testing significance adjustment should be considered. Based on Figure 4D – 4-meth signature correlation was estimated against 7 other signatures (or 6 if GBP5-meth is considered a part of 4-meth), thus significance should be determined using P-value threshold P < 0.05/7 (if Bonferroni correction is used). Please, report raw p-values for this analysis in the supplement.

We apologize for the lack of significance adjustment. According to the reviewer's suggestion, we have adjusted the P values by applying the Bonferroni correction and we have added raw P values and adjusted P values for this analysis in revised manuscript and Supplementary file 3.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

The manuscript has been improved but there are some minor remaining issues that need to be addressed before acceptance, as outlined below:

The reviewers appreciate the effort made by the authors to address their concerns. The result is a strengthened manuscript.

1) Without another validation dataset (major point #3), the generalizability of the results will remain a potential weakness, but publishing this study with this caveat noted in the text/discussion is of value.

It is recommended that the authors mention this, for example by adding the following sentence to the end of the third paragraph of the Discussion section: “When further samples become available it will be important to analyze this methylation signature in another validation dataset.”

Thanks for the reviewer’s kind suggestion. We made changes in the manuscript as follows.

In the Discussion section: “Furthermore, it was demonstrated that in both the validation and independent cohorts, our signature outperformed other known prognostic biomarkers, including mRNA, lncRNA, and DNA methylation, and statistical comparison using Z-test revealed that it has significantly higher (P < 0.05) predictive performance than almost all the other known biomarkers. When further samples become available it will be important to analyze this methylation signature in another validation dataset.”

2) The DNA variant in intron of OCA2 has been found to have a protective effect in melanoma GWAS (rs4778138, β = -0.18, PMID: 26237428). The authors state that cg18456782 (OCA2) marker "had positive coefficients, indicating a correlation between higher DNA methylation level and shorter overall survival". If one looks at the TCGA survival data and separates melanoma patients by median OCA2 expression, there is a significant differential survival (p~1e-5) with low expression favoring better survival. This is consistent with the presented data and suggests that while in GWAS the OCA2 variant has been found having a protective function, survival and expression data suggests a risk pattern for OCA2 gene.

Discussing this in the Discussion section would strengthen the manuscript because there is no sufficient experimental data on OCA2.

We deeply appreciate the reviewer’s insightful suggestion. We made changes in the manuscript as follows.

In the Discussion section: “OCA2 is involved in the melanin biosynthetic process and mammalian pigmentation (Crawford et al., 2017), and the DNA variant in intron of OCA2 (rs4778138) has been found associated with CM risk (Law et al., 2015). The hypomethylation levels of cg18456782 (OCA2) was associated with lower expression of OCA2 and a lower risk. Meanwhile separating CM patients by median expression of OCA2, there is a significant differential survival (P < 0.0001) with low expression favoring better survival. All these results suggest a risk pattern for OCA2 gene in CM.”

3) Prior to the analysis the training cohort was not adjusted for clinical parameters like age, tumor stage/grade and tumor tissue site; the authors have explained how they controlled for this, but this fact should be mentioned in the Discussion section so that results could be interpreted with the appropriate caution.

We agree with reviewer’s comments. To make this clear in our manuscript, we made changes as follows.

In the Discussion section: “In order to be clinically useful, a DNA methylation signature must be independent of clinical factors. Here we adopted a grouping method not influenced by our subjective, that is based on the TCGA series number of patients, without adjusting for clinical parameters.”

4) The Abstract should be modified as follows:

Cutaneous melanoma (CM) is a life-threatening form of skin cancer. Prognosticbiomarkers can reliably stratify patients at initial melanoma diagnosis according to risk, and may inform clinical decisions. Here, we performed a retrospective, cohort-based study analyzing genome-wide DNA methylation of 461 patients with CM from the TCGA database. Cox regression analyses were conducted to establish a four-DNA methylation signature that was significantly associated with the overall survival (OS) of patients with CM, and that was validated in an independent cohort. Corresponding Kaplan-Meier analysis displayed a distinct separation in OS. The ROC analysis confirmed that the predictive signature performed well. Notably, this signature exhibited much higher predictive accuracy in comparison with known biomarkers. This signature was significantly correlated with immune checkpoint blockade (ICB) immunotherapy-related signatures, and may have potential as a guide for measures of responsiveness to ICB immunotherapy.

Thanks for the reviewer’s improving comments, and we modified the abstract as suggested.

Author details

1. Wenna Guo

   State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China

   Contribution

   Data curation, Software, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Liucun Zhu

   Competing interests

   No competing interests declared

2. Liucun Zhu

   School of Life Sciences, Shanghai University, Shanghai, China

   Contribution

   Software, Formal analysis, Validation, Investigation, Methodology, Writing—review and editing

   Contributed equally with

   Wenna Guo

   Competing interests

   No competing interests declared

3. Rui Zhu

   School of Life Sciences, Shanghai University, Shanghai, China

   Contribution

   Validation, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

4. Qihan Chen

   State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China

   Contribution

   Funding acquisition, Validation, Writing—review and editing

   Competing interests

   No competing interests declared

5. Qiang Wang

   State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China

   Contribution

   Conceptualization, Software, Supervision, Funding acquisition, Validation, Project administration, Writing—review and editing

   For correspondence

   wangq@nju.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2907-9851

6. Jian-Qun Chen

   State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China

   Contribution

   Conceptualization, Supervision, Validation, Project administration, Writing—review and editing

   For correspondence

   chenjq@nju.edu.cn

   Competing interests

   No competing interests declared

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