{"paper_id":"0bc30b59-73c5-49c5-87f7-dca80d38ddf2","body_text":"Genomic and transcriptomic analyses of melanoma in Japanese patients reveal candidate biomarkers for immune checkpoint inhibitor responders | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genomic and transcriptomic analyses of melanoma in Japanese patients reveal candidate biomarkers for immune checkpoint inhibitor responders Satoshi Fukushima This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5997977/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Immune checkpoint inhibitors (ICIs) have greatly improved the prognosis of advanced melanoma. However, the efficacy of ICIs in Japanese patients has been found to be lower than that in Caucasians. We aimed to elucidate the genomic and transcriptomic features associated with the response to ICIs in Japanese patients with melanoma. Patients and methods A total of 129 tumor samples from 78 patients with melanoma who received therapeutic regimens with or without ICI treatment were collected from 13 institutions in Japan. We performed exome and RNA sequencing and investigated the association between genomic and transcriptomic factors and the clinical efficacy of ICI therapy. Time-course data were also analyzed. This is the first and largest genomic cohort study in Japanese patients with melanoma in which tumor samples were prospectively analyzed. Results The number of somatic SNVs in Japanese patients with melanoma was lower than that in TCGA Caucasian data owing to the biased distribution of WHO subtypes. The driver subtypes BRAF , NRAS, and NF1 were less prevalent, but triple wildtype predominantly existed in the Japanese cohort. An exome-wide survey revealed no significant association of mutated genes with ICI response; however, transcriptomic analysis revealed inflammation-associated genes, including several chemokines and cytokines, that were highly expressed in responders. Follicular helper T cells, estimated by immune cell composition analysis, were significantly enriched in responders ( p = 0.0422). Through time-course transcriptome analysis, in addition to several cytotoxic T-cell genes, MARCO on tumor-associated macrophages was found to be induced by ICI treatment in responders ( p = 0.0040). Protein expression of these genes was confirmed by immunohistochemical and multiplex immunofluorescence analyses. Conclusions Prospective genomic and transcriptomic analyses revealed candidate biomarkers for ICI treatment in Japanese patients with melanoma. Melanoma Immune checkpoint inhibitor Biomarker Follicular Helper T Cell MARCO Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Although immune checkpoint inhibitors (ICIs) have greatly improved the prognosis of advanced melanoma, response rates vary considerably among individuals and across ethnic groups. In Caucasians, the reported overall response rate (ORR) is 45% for anti-PD-1 antibody monotherapy and 58% for combination therapy with CTLA-4 inhibitors 1 . In contrast, Japanese patients showed markedly lower response rates, with ORR of only 34.8% for monotherapy 2 and 26.3–43.3% for combination therapy. 3 , 4 The distribution of melanoma subtypes based on the World Health Organization (WHO) classification 5 differs between Japanese and Caucasian patients. Specifically, cumulative sun damage (CSD)-type melanomas (low and high CSD), which tend to respond better to ICI treatment, are more common in Caucasians, whereas acral and mucosal melanomas, which tend to respond poorly to ICI treatment, are more prevalent in Japanese patients. These results suggest that the distribution of melanoma subtypes may explain the interethnic differences in ICI responses among patients with melanoma; however, no study has analyzed the genetic mutational profiles of Japanese patients with melanoma. In addition, many reports have indicated large individual differences in ICI responses, even among the same melanoma subtype. Several review articles focusing on melanoma in Caucasians have identified tumor tissue biomarkers associated with the efficacy of ICIs, including Tumor Infiltrating Lymphocytes (TILs) 6 , CD8 + TILs 7 , and Tumor Mutation Burden (TMB). 8 However, there are no established biomarkers for melanoma in Japanese patients because of the lack of studies with sufficient sample sizes. In this study, we analyzed the genomic and transcriptomic profiles of blood and tumor samples from Japanese patients with melanoma, before and after ICI therapy, from multiple facilities and examined their association with response to ICIs. Materials and Methods Study structure Patients included in the study were from 14 academic and cancer hospitals in Japan (Supplementary Table S1). Bioinformatical and statistical analyses were conducted at the Cancer Precision Medicine Center of the Japanese Foundation for Cancer Research (Ariake, Tokyo, Japan). These institutions participated in the “Project for Cancer Research and Therapeutic Evolution” (P-CREATE) program, granted by the Japan Agency for Medical Research and Development (AMED). The follow-up duration to determine clinical response was at least 1 year. Ethical approval Patients with melanoma who underwent surgery and/or biopsy at the participating hospitals/institutes between 2014 and 2021 were recruited for the study. Ethical approval was obtained from the internal review boards of the participating institutions. All patients provided written informed consent. Melanoma samples and histopathological diagnosis Melanoma samples were obtained by surgical excision or biopsy. Clinicopathological characteristics were obtained from medical records. Prior to the first sample collection, most patients had not received any ICIs, but might have received conventional chemotherapy (dacarbazine) or BRAF and MEK inhibitors (dabrafenib/trametinib). Ten patients whose prior ICI were ineffective and were sampled before receiving a second ICI treatment were included in the study. Sections from formalin-fixed paraffin-embedded (FFPE) tissues were used for pathological diagnoses. The tumors were classified based on the WHO classification. 5 Case information with therapeutic history is presented in Supplementary Table S2. Clinical response of the patients in the cohort to immune checkpoint inhibitors From the initial 129 tumor samples from 78 patients, 7 samples from 3 cases were excluded from subsequent analyses owing to poor tumor content (tumor content < 0.2). A total of 112 and 10 tumors from 65 and 10 patients who received therapeutic regimens with and without ICIs (ipilimumab, nivolumab, or pembrolizumab), respectively, were rendered for further analyses (Figure 1). The clinical response to ICIs was classified according to the immune-related Response Evaluation Criteria in Solid Tumors (irRECIST), as described previously. 9 After ICI treatment, some patients with stable disease (SD) or progressive disease (PD) have been observed to survive for longer than 1 year. 10 Based on this observation, SD or PD was subdivided into long-term survival (LS) and short-term survival using a cut-off of 1 year. The 5-tier classes of clinical response were then used as complete response (CR), partial response (PR), LS, SD, and PD in the current study. We further defined CR, PR, and LS as responders and SD and PR as non-responders for binary comparisons. Sample preparation for genomic and transcriptomic analysis Specimens were divided into multiple pieces and processed either to snap-frozen (fresh-frozen; FF) tissues for DNA and RNA sequencing or to FFPE tissues for histopathological and immunohistochemical examination. FF cancer tissues were cut into 10-µm thick sections, and cancer cells were selectively enriched by laser-capture microdissection with an LMD7000 (Leica, Wetzlar, Germany) following the manufacturer’s protocol. DNA samples were extracted using the QIAamp DNA Micro Kit (Qiagen, Valencia, CA, USA) (Supplementary Table S3). Matched normal DNA samples were used for exome sequencing in all germline and somatic variant calling. Normal DNA was extracted from the buffy coat using the RiboPure Blood Kit (Thermo Fisher Scientific, Carlsbad, CA, USA). The quantity and quality were confirmed using a NanoDrop 2000 (Thermo Fisher Scientific) and Qubit 2.0 fluorometer (Thermo Fisher Scientific). A hundred and twenty-eight DNA specimens from 78 patients met the criteria for purity (OD 260/280nm > 1.4) and concentration (ratio of dsDNA/ssDNA > 0.18). RNA was extracted from the FF tumor specimens using a RNeasy Micro Kit (Qiagen, Hilden, Germany). For RNA, 122 samples from 77 patients met the purity criteria (optical density at 260/280 nm > 1.69, RNA integrity number > 2.2, and DV200 > 54). Library preparation and sequencing for exome and RNA sequencing analyses The extracted DNA was subjected to exome analysis using SureSelect V5 or Customs V5 Kit (Agilent Technologies, Santa Clara, CA, USA). mRNA libraries were generated using the TruSeq Stranded mRNA Library Prep Kit (Illumina, San Diego, CA, USA), following the manufacturer’s instructions. Sequencing of the captured DNA was conducted with an in-house HiSeq2500 or HiSeq2000 (Illumina) or outsourced to Macrogen Japan Corp. (Aomi, Tokyo). Germline/somatic mutation call, single base substitution signature, and melanoma driver gene mutation Sequenced reads were aligned to the reference human genome (hg19) using the Burrows-Wheeler Aligner (ver. 0.7.12). 11 GATK (GenomeAnalysisTK, ver. 3.4-46) was used to recalibrate the variant quality score and perform local realignment. 12 Somatic SNVs were called using VarScan (ver. 2.3.7) 13 , MuTect (ver. 1.1.5) 14 , and Karkinos2 (ver.0.1; http://sourceforge.net/projects/karkinos/) as previously reported. 15 VarScan (ver. 2.3.7), SomaticIndelDetector (ver. 2.3-9), and Karkinos2 (ver.0.1) were used to detect somatic indels. Somatic SNVs and indels were considered as genuine mutations if they were found at least twice among the three callers. Somatic copy number variants (CNVs) were detected using EXCAVATOR (ver. 2.2) with GISTIC (ver. 2.0.23). 16 , 17 Single base substitution (SBS) signatures were computed using Rsolnp (ver. 1.1.6) by minimizing the differences between the model (COSMIC mutation signature v2; downloaded from https://cancer.sanger.ac.uk/signatures/signatures_v2) and observed substitutions. 18 Germline SNVs and indels were called according to a previously described method. 19 None of the patients had pathogenic variants in the causal genes of hereditary melanoma syndrome. Driver subtypes Melanoma driver genes were curated using TumorPortal (http://www.tumorportal.org/; Supplementary Table S4). 20 T-cell receptor repertoire The complement-determining region 3 (CDR3) of the T-cell receptor (TCR) b chain was amplified using the iRepertoire multiplex primer set (HTBI-M; iRepertoire, Huntsville, AL, USA) following the manufacturer’s instructions. 21 The Qiagen OneStep RT-PCR Kit (Qiagen) was used for reverse transcription. Amplification and library preparation were performed using the iR-Processor and iRock 2.0 (iRepertoire, Huntsville, AL, USA). Paired-end sequencing was performed on the purified libraries using the Illumina MiSeq v2 500-cycle Reagent Kit (Illumina), and a median read depth of 615,127 reads per sample was generated. Web tools provided by iRepertoire (http://www.irepertoire.com) were used to perform basic informatics analyses, such as barcode demultiplexing and filtering, V(D)J alignment, and CDR identification, and to calculate diversity indices. 21 HLA genotyping and mutational analysis HLA class I genotypes were determined via the OptiType algorithm (ver.1.3.1) 22 using targeted re-sequencing data of normal samples. SNVs, indels, and CNVs in HLA-A, -B, and -C genes were identified using POLYSOLVER (v4) and LOHHLA (ver. 20171108). 23 , 24 Neoantigen prediction Exome sequencing data were used to predict neoantigens. All 8- to 11-mer peptides, each harboring a substituted amino acid, were examined by filtering with a predicted binding affinity for HLA-A < 500 nM, as previously described. 25 , 26 Data were analyzed using NetMHCpanv2.8 software. 27 Tumor-infiltrating cell-type inference The RNA-seq data were analyzed using CIBERSORT (ver. 1.05) to infer the immune cell types infiltrating the tumor. 28 Immunohistochemistry For immunohistochemistry, 4-μm thick sections from FFPE tissue samples were stained using anti-MARCO polyclonal antibody (rabbit IgG, Invitrogen) with 1:100 dilution and counterstained with Giemsa stain. Multiplex immunofluorescence with PhenoCycler® (CODEX) Multiplex immunofluorescence was performed as previously described. 29 Briefly, 4-µm thick FFPE tissue sections were deparaffinized, rehydrated, and rendered to antigen retrieval in citrate buffer solution (10 mM, pH 6.0) using a pressure cooker. The tissues were subsequently stained with a cocktail of antibodies (CD20-BX007 with Alexa750, CD8-BX026 with Atto 550, CD4-BX003, CD68-BX015, PD-1-BX046, PD-L1-BX043 with Alexa Fluor 647 [Akoya Biosciences, Delaware, USA], MARCO-BX016, and MelanA-BX022 [Abcam, Cambridge, UK]) for 3 hours at room temperature. MelanA (EPR20380, Abcam) was conjugated in-house, according to the manufacturer’s recommendations (Akoya Biosciences). Multiplex imaging was performed using a microfluidics instrument (PhenoCycler, Akoya Biosciences) with a fluorescence microscope (BZ-X700, Keyence, Osaka, Japan) and CODEX Instrument Manager software (Akoya Biosciences). The raw image files were processed using CODEX processor version 1.8 (Akoya Biosciences). The cell composition was quantified as follows: 1) a region of tissue cellular neighborhood was defined based on cellularity using the CODEX processor, 2) the region was labelled according to the cellularity (from low to high) as 1 and 2 for the samples before ICI treatment, and as 1, 2 and 3 for the samples after ICI treatment, 3) the composition of each antigen expressing cell was computed as the fold change in cell numbers within the neighborhood, 4) cell density was computed as the average of cell composition per region, and 5) the relative density of each antigen-expressing cell was estimated as the ratio of cell density of region 2 (or 3) to that of region 1. Statistical analyses The Mann-Whitney U -test, Wilcoxon signed rank test, McNemar test, and Fisher’s exact test were used to statistically evaluate the correlations between genomic or transcriptomic parameters and ICI-sensitive and resistant groups using GraphPad Prism (ver. 9.4.1) and R software (ver. 4.0.3). Results Landscape of melanoma exomes in Japanese patients To delineate the landscape of melanoma genomes in Japanese patients and identify potential genomic biomarker(s) associated with clinical response to ICIs, we conducted exome and RNA sequencing analyses of 129 tumors from 78 patients from 13 institutions in Japan. After excluding 7 tumors from 3 cases owing to poor tumor content (tumor content < 0.2), 122 tumors from 75 cases were processed for subsequent analyses, among which 65 cases had been treated with adjuvant regimens containing an ICI. Samples collected before ICI treatment were available for 39 patients (Figure 1A). Fifty-eight of the 65 cases had sufficient information and evaluable follow-up duration (overall survival ≥ 60 days) for classification of clinical response to ICIs. For the 58 patients (responders, n = 39; non-responders, n = 19), we statistically evaluated several clinicopathological attributes, including sex, age, WHO classification, and ICI regimen, and found no association with clinical response (Figure 1A, Supplementary Table S2, and Table 1). The melanoma samples were classified based on the driver subtyping scheme using the mutational status of BRAF , RAS, and NF1 (Supplementary Figure S1 and Figure 2D), according to the method previously described. 30 As previously reported 30 , the triple wildtype subtype was significantly enriched in acral melanomas in the current cohort (Supplementary Figure S1). However, the driver subtypes were not associated with the clinical response ( p = 0.8726, Fisher’s exact test; Table 1). Figure 2A presents an overview of the genomic aberration profiles of the 75 Japanese patients with melanoma. As anticipated, the number of SNVs, indels, CNVs, and neoantigens, and the ratio of SBS7 signature, a mutational signature generated by ultraviolet (UV) exposure 18 , 31 , exhibited significant correlations with the WHO classification and driver subtypes (Figure 2B, upper and middle panels), indicating the robustness of our exome analyses and clinical information. Nevertheless, unlike previous genomic studies 32 , in which intimate links of TMBs (the total number of SNVs/indels in a sample) or neoantigens with clinical response to ICIs were observed, no clear association was observed in the current cohort (Figure 2B, lower panels). Melanoma samples from our cohort, Shizuoka Cancer Center, or TCGA of East Asian population, had significantly fewer SNVs than those from TCGA of Caucasian population (Figure 2C left). 33 , 30 Coincidently, the majority of melanomas from our cohort or TCGA of East Asian population had a significantly lower ratio of SBS7 signature than those from TCGA of Caucasian population (Figure 2C right). 30 These observations are consistent with previous etiological findings that UV-induced and non-UV-induced melanomas are predominant in Caucasian and East Asian populations, respectively. 34 The composition of driver subtypes also differed between Caucasians (TCGA of Caucasians) and East Asians (our cohort and TCGA of East Asians); BRAF and triple wildtype were the major subtypes in Caucasian and East Asian populations, respectively. (Supplementary Figure S1 and Figure 2D). These findings suggest different melanomagenic processes in different ethnicities. As a reflection of the biased compositions of driver subtypes in WHO classifications, driver events for melanomas in Japanese patients were represented by TERT amplification, NRAS missense SNVs, EP300 amplification, BRAF missense SNVs, and CDKN2A homozygous deletions (Figure 3 and Supplementary Table S4; n = 39; pre-ICI samples). However, no particular driver gene was detected to be associated with the clinical response to ICIs (Figure 3). Moreover, exome-wide comparisons did not show association between any gene mutation and clinical response. As such, exome analyses revealed the characteristic genomic features of melanoma in Japanese patients but did not identify any genomic markers associated with susceptibility to ICI therapies. HLA type and somatic mutation Using the exome data, HLA class I of the patients was genotyped with OptiType. There were 10, 19, and 9 genotypes for HLA-A , - B, and - C subclasses, respectively. Among the subclasses, only HLA-A *24:02 exhibited significant enrichment in ICI responders (Figure 3 and Table 2; p = 0.0282 and Odds ratio = 5.74 [95% confidence interval = 1.11–34.9] by Fisher’s exact test). No somatic SNV and one loss of heterozygosity on class I HLA genes was detected in the samples using POLYSOLVER 23 and LOHHLA 24 , and the somatic alteration was not associated with clinical response to ICIs ( p = 0.359 by Fisher’s exact test). Temporal pattern of genomic changes in the time-course analysis of treatment Time-course exome data were available for 21 cases, among which 15 and 4 cases had paired samples before/during and before/after ICI treatment, respectively (Supplementary Figure S2). In these 19 cases, ICI interventions did not significantly change the number of SNVs, indels, or CNVs, the ratio of mutational signatures, or the status of driver gene mutations. Nevertheless, in one patient (FM009) with long-term survival, the pair of samples exhibited substantial genomic changes (Figure 4). This patient had been treated with dacarbazine, an alkylating agent, for 12 months as 1st line chemotherapy prior to sampling for this study and subsequently received nivolumab because of tumor progression (Figure 4A). Although samples before dacarbazine treatment were not available, samples were obtained before and after nivolumab treatment (FM009T1 and FM009T2). Although FM009T1 had 3,772 SNVs, the number of SNVs decreased to 194 in FM009T2 (a 19.4-fold reduction; Figure 4B). The composition of mutational signatures drastically changed across the ICI treatment; the major component in FM009T1 was SBS11, a signature of alkylating agent exposure, whereas SBS7, a UV signature, became dominant in FM009T2 (Figure 4C). Diversity index, a parameter of the complexity of the T cell receptor repertoire, increased with nivolumab treatment (Figure 4D). These observations imply that dacarbazine and nivolumab treatment modified the melanoma genome by interacting with the immune microenvironment. Transcriptomic features of responders and non-responders to ICI treatment Using transcriptomic analysis with RNA-seq, we identified 1,135 and 735 genes whose expression levels correlated with responders and non-responders, respectively ( p < 0.05 by SAM; Figure 5A). Further gene ontology enrichment analyses using DAVID 2022 35 revealed 25 and 102 pathway associations with responders and non-responders, respectively. Whereas the ontologies for responders included ‘regulator of G protein signaling superfamily’ ( p < 0.0163; 4 genes such as RGS22 , ADRBK2 , RGS13, and RGS20 ) and ‘inflammatory response’ ( p < 0.0001; 13 genes such as IL1B , S100A9 , S100A8 , CHI3L1, and CCL13 ), in non-responders, ‘cell adhesion’ ( p = 0.0055; 18 genes such as PCDHGC5 , APLP1 , PCDHB2 , FN1, and LAMC24 ), and ‘metalloprotease’ ( p = 0.0058; 9 genes such as MMP1 , MMP3 , MMP10 , CPA4, and AMZ1 ) were significantly enriched. Immune cell composition analysis using CIBERSORT revealed that follicular helper T cells were significantly enriched in responders (Figures 5B and 5C; p = 0.0422 by Mann–Whitney U -test). Although the complexity of the T-cell population infiltrating the tumor has been reported to correlate with ICI response 10 , 36 , 37 , diversity indices or other related parameters of the complexity of the T-cell receptor repertoire did not exhibit significant differences between responders and non-responders. These findings indicate the relevance of infiltrating immune cells in the response to ICI treatment. Changes in expression by ICI treatment Taking advantage of the fact that nine responders and five non-responders had paired transcriptome data (before/during and before/after ICI treatment), we next sought genes that were induced in responders and non-responders by ICI treatment (Figure 6A). Using the Mann-Whitney U test with the fold changes in the expression values, 597 and 98 genes were identified as significantly induced in responders and non-responders, respectively (Figure 6A). The induced genes in responders included markers of cytotoxic T cells, such as CD3D , CD3E , CD8B , PRF1, and GZMA , and tumor-associated macrophages (TAMs) such as MARCO. However,genes presumably expressed in melanoma cells, including TCF15 and NKX2-2, were induced in non-responders. These findings imply that immune cells relevant for tumor cell killing were accumulated by ICI treatment in responders. Gene ontology analyses with DAVID revealed 310 and 4 pathways associated with responders and non-responders, which included ‘GO:0042110 T-cell activation’ ( p = 1.312 × 10 -9 ; 13 genes such as CD3E , CD2 , CD7 , CD8B , and CD48 ) and ‘KW-0010 Activator’ ( p = 0.0172; 5 genes such as NFE4 , TCF15 , and NKX2-2 ), respectively. Delta diversity indices (defined by fold changes in diversity indices of the T-cell receptor repertoire before and after/during ICI treatment) were significantly increased in responders (Figure 6B), which implied that the induced expression of T-cell markers was derived from the influx of multiple T-cell clones into the tissue, and not from the clonal expansion of T cells inside the tumor. Given the relevance of TAMs as regulators of the immune microenvironment in tumor cell killing, we focused on MARCO , a scavenger receptor expressed on TAMs. While MARCO expression was induced in responders, it remained unchanged or was even suppressed in non-responders by ICI treatment, with a significant difference in the expression change ( p = 0.0040 by Mann-Whitney U -test in Figure 6C, left). Immunohistochemical staining of the MARCO protein confirmed that MARCO gene expression correlated with MARCO protein expression ( p = 0.0095, Figure 6C right). The change in expression with ICI treatment at the RNA level seemed to be associated with the change in the number of infiltrated TAMs, with MARCO-expressing cells being increased and decreased in responders (KM041) and non-responders (KM011), respectively (Figure 6D). To further confirm the association between infiltrated TAMs and clinical response to ICI, we performed multiplex fluorescent immunostaining of the before- and after-ICI treatment samples from a responder (KM041). Following ICI treatment, PD-1 and PD-L1 were induced and colocalized on Melan-A + melanoma cells, with increased infiltration of CD4 + helper T cells, CD8 + cytotoxic T cells, CD68 + macrophages, and CD20 + B cells. Moreover, MARCO expression coincided with CD68 expression on the infiltrating TAMs in the post-treatment sample (Figure 7). Cellular neighborhood analyses using CODEX 29 revealed two and three regions with different compositions of infiltrating inflammatory cells in the before- and after-treatment samples, respectively (Supplementary Figure S4). Based on the density of the infiltrating cells, regions 1 (before) and 1 (after) and regions 2 (before), 2 (after), and 3 (after) were annotated as cold and hot regions, respectively. Compared to the cold regions, MARCO-expressing cells were more prevalent in the hot regions, with denser infiltration of CD4 + , CD8 + , and CD20 + cells. The relative density of each antigen-expressing cell was 1.403 (minimum 1.265–maximum 1.768) in the hot region of the pretreatment sample, and the relative density increased to 2.291 (1.973–5.485 in region 2) and 2.699 (1.7363–24.443 in region 3) in the hot regions of the post-treatment sample (Supplementary Figure S4E). Although CD20 + cells showed maximum accumulation among the cells, all antigen-expressing cells, including MARCO + cells, accumulated after ICI treatment (Supplementary Figure S4E). These findings support the collaborative role of MARCO-expressing TAMs and CD4 + , CD8 + , and CD20 + lymphocytes in the host immune response to melanoma cells 2023. 38 Discussion In this multicenter analysis of 129 tumor specimens from 78 patients, we found that, overall, melanoma in Japanese patients had lower TMB and fewer SNVs classified as the SBS7 signature compared to melanoma in Caucasians (Figure 2B), which aligns with previous findings in the East Asian population. 30 In our subtype analysis, the CSD subtype (both low-CSD and high-CSD) in this cohort showed higher TMB and more SNVs with the SBS7 signature, similar to Caucasian cases, than the acral and mucosal subtypes (Figure 2B and 2C). These results suggest that differences in genomic mutational profiles among racial groups may be influenced by the melanoma subtype. Moreover, the poor response to ICIs in Japanese patients with melanoma may be partly attributed to the high prevalence of acral and mucosal subtypes, which typically have lower TMBs and fewer driver mutations. This is supported by our observation that the four patients with high TMB in this study exhibited better clinical response to ICI therapy, although the sample size was limited. However, we also noticed many cases with low TMB in the responder group. Recently, Bai et al. reported that the prognosis with ICI treatment is poorer in Asians than in Caucasians, even for CSD-type melanoma (ORR; White 54% vs. East Asian/Hispanic/African 20%). 39 Considering these factors, the variation in clinical responses may not be solely attributable to TMB or subtype differences, suggesting the presence of other biomarkers in Japanese patients. Therefore, we conducted further screening for novel melanoma biomarkers in Japanese patients with low TMB, focusing on immune-related genes, including HLA and TCRs. Comparing the responder and non-responder groups, we found that HLA -A*24:02 allele carriers were significantly more among responders (Table 2). This finding is consistent with the data from Caucasian populations, which shows longer survival in melanoma patients with HLA-A24, HLA-B44, and HLA-B62. 40 However, in our cohort, no significant difference was found between responders and non-responders for HLA-B44 ( p = 1, odds ratio = 1.154 [95% confidence interval = 0.1959–8.567]), and none of the patients had HLA-B62. The lack of association in our cohort may be due to the low frequencies of HLA-B44 and HLA-B62 in the Japanese population, reported as 7.12% and 8.33%, respectively. 41 In the time-course analysis, gene expression of MARCO increased after ICI treatment, along with molecules associated with cytotoxic T cells, including CD3D , CD3E , CD8B , PRF1 , and GZMA, in the responder group (Figure 6A). Additionally, multiplex fluorescent immunostaining an increase in MARCO-positive macrophages after treatment, with high infiltration of CD4 + , CD8 + , and CD20 + cells, which may have favored tumor immunity. Although MARCO, mainly expressed in M2-type TAMs, is considered a poor prognostic factor in cancers like lung cancer 38 , it has been associated with good prognosis in melanoma, consistent with our findings. The molecular mechanisms underlying the interaction between these cells and tumor immunity remain unclear. Therefore, functional studies on immune regulation mediated by these cells are necessary for future research.Furthermore, we aim to validate in future studies, using a larger cohort of cases, whether these biomarkers could serve as predictors of immune checkpoint inhibitor efficacy. This is the largest study in Japan to prospectively analyze the genomic and transcriptomic profiles of tumor samples before and after ICI treatment for melanoma. Our findings indicate that TMB and neoantigens are not sufficient biomarkers for Japanese patients with melanoma owing to differences in genetic profiles between Japanese and Caucasian patients. In contrast, biomarkers such as HLA-A24 and infiltration of follicular helper T cells or MARCO-expressing macrophages have been suggested to be useful. Declarations Acknowledgements We thank Siew Kee Low, Yusuke Nakamura, Koichiro Inaki, and Yasuo Uemura for helpful discussions; Sayuri Amino, Rie Furuya, and Junko Kanayama for technical assistance; Minako Hoshida and Mariko Kawamura for administrative assistance; Mayuko Kosugi and Yuki Ota for analytical assistance and editing of figures and tables; Editage (www.editage.jp) for English language editing; and Rebecca Jackson for editing the draft of this manuscript. Ethics approval Ethical approval was obtained from the Internal Review Board of the National Cancer Center (approval number 2016-248). Written informed consent was obtained from all the recruited patients. Data availability statement The raw data generated in this study were submitted to the National Bioscience Database Center (NBDC; https://biosciencedbc.jp/en/; under accession numbers xxx; exome FASTQ files) and the NCBI Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/; under accession number GSE282471 (RNA-seq TPM file)). Funding This work was supported by the Japan Agency for Medical Research and Development (grant number JP cm0106301). This grant was allocated to S.F., R.O. and A.T. as a sub-research project for the “Establishment of predictive methods for cancer immunotherapy based on the evaluation of cancer cells and immune responses” in Hiroyoshi Nishikawa, M.D. Ph.D. (Division of Cancer Immunology, Exploratory Oncology Research and Clinical Trial Center (EPOC), National Cancer Center). Disclosure The authors have declared no conflicts of interest. Author contributions T.K., N.T., K. K., H.Y., S.F., and S.M. analyzed the data and wrote the paper. T.K., T.M., Y.K., R.O., J.A., S.M. H.U., H.K., K.N., A.K., A.T., T.F., Y.F., T.I., K.K., S.M., and S.F. collected the specimens and provided clinical information. T.K. and S.F. evaluated the immunohistochemical staining results. T.N., and S.F. conceived the study and wrote the manuscript. References Larkin J et al (2019) Five-Year Survival with Combined Nivolumab and Ipilimumab in Advanced Melanoma. N Engl J Med 381:1535–1546. https://doi.org/10.1056/NEJMoa1910836 Uhara H et al (2021) Five-year survival with nivolumab in previously untreated Japanese patients with advanced or recurrent malignant melanoma. J Dermatol 48:592–599. https://doi.org/10.1111/1346-8138.15804 Takahashi A et al (2020) Real-world efficacy and safety data of nivolumab and ipilimumab combination therapy in Japanese patients with advanced melanoma. J Dermatol 47:1267–1275. https://doi.org/10.1111/1346-8138.15521 Namikawa K et al (2018) Efficacy and safety of nivolumab in combination with ipilimumab in Japanese patients with advanced melanoma: An open-label, single-arm, multicentre phase II study. Eur J Cancer 105:114–126. https://doi.org/10.1016/j.ejca.2018.09.025 Bastian BC (2014) The molecular pathology of melanoma: an integrated taxonomy of melanocytic neoplasia. Annu Rev Pathol 9:239–271. https://doi.org/10.1146/annurev-pathol-012513-104658 Baltussen JC et al (2021) Predictive Biomarkers for Outcomes of Immune Checkpoint Inhibitors (ICIs) in Melanoma: A Systematic Review. Cancers (Basel) 13. https://doi.org/10.3390/cancers13246366 Li F et al (2021) The association between CD8 + tumor-infiltrating lymphocytes and the clinical outcome of cancer immunotherapy: A systematic review and meta-analysis. EClinicalMedicine 41:101134. https://doi.org/10.1016/j.eclinm.2021.101134 Ning B et al (2022) The Predictive Value of Tumor Mutation Burden on Clinical Efficacy of Immune Checkpoint Inhibitors in Melanoma: A Systematic Review and Meta-Analysis. Front Pharmacol 13:748674. https://doi.org/10.3389/fphar.2022.748674 Mizuki Nishino MG, Suda M, Nikhil H, Ramaiya F, Stephen Hodi (2014) Optimizing immune-related tumor response assessment: does reducing the number of lesions impact response assessment in melanoma patients treated with ipilimumab? J Immunother Cancer 2 Inoue H et al (2016) Intratumoral expression levels of PD-L1, GZMA, and HLA-A along with oligoclonal T cell expansion associate with response to nivolumab in metastatic melanoma. Oncoimmunology 5, e1204507 https://doi.org/10.1080/2162402X.2016.1204507 Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760. https://doi.org/10.1093/bioinformatics/btp324 DePristo MA et al (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43:491–498. https://doi.org/10.1038/ng.806 Koboldt DC et al (2012) VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 22:568–576. https://doi.org/10.1101/gr.129684.111 Cibulskis K et al (2013) Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol 31:213–219. https://doi.org/10.1038/nbt.2514 Gotoh O et al (2019) Clinically relevant molecular subtypes and genomic alteration-independent differentiation in gynecologic carcinosarcoma. Nat Commun 10:4965. https://doi.org/10.1038/s41467-019-12985-x Alberto Magi LT, Cifola I, Benelli RD’AurizioM, Mangano E, Bonora CBE, Kurg A, Seri M, Magini P, Giusti B, Romeo G (2013) Tommaso Pippucci, Gianluca De Bellis, Rosanna Abbate, & Gensini, G. F. EXCAVATOR: detecting copy number variants from whole-exome sequencing data. Genome Biol 14 Mermel CH et al (2011) GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 https://doi.org/10.1186/gb-2011-12-4-r41 Alexandrov LB et al (2013) Signatures of mutational processes in human cancer. Nature 500:415–421. https://doi.org/10.1038/nature12477 Kaneyasu T et al (2020) Prevalence of disease-causing genes in Japanese patients with BRCA1/2-wildtype hereditary breast and ovarian cancer syndrome. NPJ Breast Cancer 6:25. https://doi.org/10.1038/s41523-020-0163-1 Lawrence MS et al (2014) Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505:495–501. https://doi.org/10.1038/nature12912 Wang C et al (2010) High throughput sequencing reveals a complex pattern of dynamic interrelationships among human T cell subsets. Proc. Natl. Acad. Sci. U. S. A. 107, 1518–1523 https://doi.org/10.1073/pnas.0913939107 Szolek A et al (2014) OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics 30:3310–3316. https://doi.org/10.1093/bioinformatics/btu548 Shukla SA et al (2015) Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat Biotechnol 33:1152–1158. https://doi.org/10.1038/nbt.3344 McGranahan N et al (2017) Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution. Cell 171, 1259–1271 e1211 https://doi.org/10.1016/j.cell.2017.10.001 Kiyotani K, Chan HT, Nakamura Y (2018) Immunopharmacogenomics towards personalized cancer immunotherapy targeting neoantigens. Cancer Sci 109:542–549. https://doi.org/10.1111/cas.13498 Choudhury NJ et al (2016) Low T-cell Receptor Diversity, High Somatic Mutation Burden, and High Neoantigen Load as Predictors of Clinical Outcome in Muscle-invasive Bladder Cancer. Eur Urol Focus 2:445–452. https://doi.org/10.1016/j.euf.2015.09.007 Nielsen M et al (2007) NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS ONE 2:e796. https://doi.org/10.1371/journal.pone.0000796 Becht E et al (2016) Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol 17:218. https://doi.org/10.1186/s13059-016-1070-5 Kennedy-Darling J et al (2021) Highly multiplexed tissue imaging using repeated oligonucleotide exchange reaction. Eur J Immunol 51:1262–1277. https://doi.org/10.1002/eji.202048891 Atlas CG (2015) Genomic Classification of Cutaneous Melanoma. Cell 161:1681–1696. https://doi.org/10.1016/j.cell.2015.05.044 Alexandrov LB et al (2020) The repertoire of mutational signatures in human cancer. Nature 578:94–101. https://doi.org/10.1038/s41586-020-1943-3 Snyder A et al (2014) Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 371:2189–2199. https://doi.org/10.1056/NEJMoa1406498 Nagashima T et al (2020) Japanese version of The Cancer Genome Atlas, JCGA, established using fresh frozen tumors obtained from 5143 cancer patients. Cancer Sci 111:687–699. https://doi.org/10.1111/cas.14290 Chiu YJ et al (2022) Genomic profiling with whole-exome sequencing revealed distinct mutations and novel pathways in Asian melanoma. J Dermatol 49:1299–1309. https://doi.org/10.1111/1346-8138.16579 Sherman BT et al (2022) DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res 50:W216–W221. https://doi.org/10.1093/nar/gkac194 Hogan SA et al (2019) Peripheral Blood TCR Repertoire Profiling May Facilitate Patient Stratification for Immunotherapy against Melanoma. Cancer Immunol Res 7:77–85. https://doi.org/10.1158/2326-6066.CIR-18-0136 Yusko E et al (2019) Association of Tumor Microenvironment T-cell Repertoire and Mutational Load with Clinical Outcome after Sequential Checkpoint Blockade in Melanoma. Cancer Immunol Res 7:458–465. https://doi.org/10.1158/2326-6066.CIR-18-0226 Dong Q et al (2023) MARCO is a potential prognostic and immunotherapy biomarker. Int Immunopharmacol 116:109783. https://doi.org/10.1016/j.intimp.2023.109783 Bai X et al (2022) Benefit and toxicity of programmed death-1 blockade vary by ethnicity in patients with advanced melanoma: an international multicentre observational study. Br J Dermatol 187:401–410. https://doi.org/10.1111/bjd.21241 Diego Chowell LGTM, Claud M, Grigg JK, Weber RM, Samstein VM, Kuo F, Kendall SM, Requena D, Riaz N, Greenbaum B, Carroll J, Garon E, Zehir DMHA, Solit D, Michael Berger RZ, Naiyer A, Rizvi (2018) Timothy A. Chan. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359:582–587 Ikeda N et al (2015) Determination of HLA-A, -C, -B, -DRB1 allele and haplotype frequency in Japanese population based on family study. Tissue Antigens 85, 252–259 https://doi.org/10.1111/tan.12536 Hou D et al (2016) Immune Repertoire Diversity Correlated with Mortality in Avian Influenza A (H7N9) Virus Infected Patients. Sci Rep 6:33843. https://doi.org/10.1038/srep33843 Tables The main tables 1 and 2 are provided in the supplemental file KimuraMainTable240413.png. Additional Declarations The authors declare no competing interests. Supplementary Files KimuraMainTable240413.png main table MelanomaSupplementaryFigures241227.pptx supplementaryfigurelegends20241225.docx SupplementaryTable2412112.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-5997977\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":413546107,\"identity\":\"28c6a38c-59b7-4362-bf50-8c3b8cb58ff6\",\"order_by\":0,\"name\":\"Satoshi Fukushima\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Satoshi\",\"middleName\":\"\",\"lastName\":\"Fukushima\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-02-10 09:56:52\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":true,\"vertebrateSubjects\":false,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":true,\"humanSubjectConsent\":true,\"humanSubjectClinicalTrial\":true,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":false},\"doi\":\"10.21203/rs.3.rs-5997977/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5997977/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":76568609,\"identity\":\"ea617f65-a356-42bd-97d6-16b4034a02b0\",\"added_by\":\"auto\",\"created_at\":\"2025-02-18 13:17:11\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":24226,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eNumber of samples and cases with therapeutic response status and genomic or transcriptomic analyses. \\u003cstrong\\u003eA.\\u003c/strong\\u003e REMARK diagram. Therapeutic response of the patients was classified with irRECIST with modification. \\u003cstrong\\u003eB.\\u003c/strong\\u003e Schematic presentation of therapeutic histories of patients in the current study. Abbreviations: irRECIST, immune-related response evaluation criteria in solid tumors; QC, quality check; ICI, immune checkpoint inhibitor; CR, complete response; PR, partial response; LS, long-term survival; SD, stable disease; PD, progressive disease; IFNβ, Interferon-beta; DTIC, Dacarbazine; DAV-feron, the combination therapy of dacarbazine, nimusutine, vincristine, and local IFNβ; RT, radiation therapy; BRAF/MEKi, BRAF inhibitor and MEK inhibitor. “Other ICI” refers to an ICI agent different from that used in the prior systemic therapy.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5997977/v1/fd3a8281f56834505dee0612.png\"},{\"id\":76568227,\"identity\":\"d5dde931-8844-49be-bd4f-7df8138aa8ee\",\"added_by\":\"auto\",\"created_at\":\"2025-02-18 13:09:11\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":102103,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLandscape of Japanese melanoma exomes. \\u003cstrong\\u003eA.\\u003c/strong\\u003eOverview of genomic aberrations. Melanoma samples (\\u003cem\\u003en\\u003c/em\\u003e = 75; the results from the first sample for one case are presented) were sorted according to the number of SNVs. Panels from top to bottom: Bar plots for number of SNVs and indels; bar plots for number of segments with copy-number aberration (CN gain and loss); rates in percentage of nucleotide substitutions (C\\u0026gt;A, C\\u0026gt;G, C\\u0026gt;T, T\\u0026gt;A, T\\u0026gt;C, and T\\u0026gt;G); irRECIST; WHO classification; driver subtype; and the timing of tumor sampling. \\u003cstrong\\u003eB.\\u003c/strong\\u003e Group comparisons of number of SNVs, indels, CNVs, neoantigens, and ratio of SBS7 signature. Upper, middle, or lower panels indicate dot plots per WHO classification, driver subtype, or clinical response. \\u003cem\\u003eP\\u003c/em\\u003e-values were computed with the Mann–Whitney \\u003cem\\u003eU\\u003c/em\\u003e-test and are shown when significant (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt; 0.05). \\u003cstrong\\u003eC\\u003c/strong\\u003e. Ethnic differences in melanoma mutational profiles between Caucasian and Japanese or East Asian populations. \\u003cem\\u003eP\\u003c/em\\u003e-values were computed with the Mann–Whitney \\u003cem\\u003eU\\u003c/em\\u003e-test and are shown when significant (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05). \\u003cstrong\\u003eD.\\u003c/strong\\u003e Ethnic differences in driver subtypes between Caucasian and Japanese or East Asian populations. \\u003cem\\u003eP\\u003c/em\\u003e-values were computed with Fisher’s exact test and are shown when significant (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05). Abbreviations: CSD, cumulative sun-damaged; irRECIST, immune-related response evaluation criteria in solid tumors; CR, complete response; PR, partial response; LS, long-term survival; SD, stable disease; PD, progressive disease; WT, wildtype; ICI, immune checkpoint inhibitor; SBS, single-base substitution; TCGA, the Cancer Genome Atlas; Shizuoka CC, Shizuoka Cancer Center.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5997977/v1/bc10dcb6406d368849ab5944.png\"},{\"id\":76568225,\"identity\":\"1ab8b522-036f-40b2-943e-aa70483129c8\",\"added_by\":\"auto\",\"created_at\":\"2025-02-18 13:09:11\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":59628,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eOncoprint of driver gene mutations of pre-ICI treatment melanoma samples. Status of irRECIST, WHO classification, driver subtype, and HLA-24:02 allele zygosity are also shown with color codes above the oncoprint. Abbreviations: irRECIST, immune-related response evaluation criteria in solid tumors; CR, complete response; PR, partial response; LS, long-term survival; SD, stable disease; PD, progressive disease; CSD, cumulative sun-damaged; WT, wildtype; LOH, loss of heterozygosity.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5997977/v1/19af8a48ebdf952cf2d2b6ba.png\"},{\"id\":76568233,\"identity\":\"5a2a84bd-f84b-4c68-bd34-f162fa13c5b2\",\"added_by\":\"auto\",\"created_at\":\"2025-02-18 13:09:11\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":43005,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eClonal change by ICI treatment. Samples of FM009T1 and FM009T2 were collected before and after ICI treatment. \\u003cstrong\\u003eA.\\u003c/strong\\u003e The clinical course of FM009 and the site and timing of tumor specimen sampling. \\u003cstrong\\u003eB.\\u003c/strong\\u003e Number of SNVs, indels and CNVs. \\u003cstrong\\u003eC.\\u003c/strong\\u003e Proportion of SBS signatures. \\u003cstrong\\u003eD.\\u003c/strong\\u003e Complexity of T-cell receptor repertoire. D50\\u003csup\\u003e42\\u003c/sup\\u003e\\u003cstrong\\u003e \\u003c/strong\\u003eis used to indicate the complexity. Abbreviations: ICI, immune checkpoint inhibitor; LDH, Lactate Dehydrogenase; 5-S-CD, 5-S-cysteinyldopa; SD, stable disease; PD, progressive disease; Nivo, Nivolumab; Vem, Vemurafenib; SBS, single base substitution; POLE, polymerase e mutated; HRD, homologous recombination deficient; TCR; T-cell receptor.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5997977/v1/5f344bce37b5b73771c58ab8.png\"},{\"id\":76568230,\"identity\":\"a4e9ae8f-b01c-40d3-98c9-ab3f69834311\",\"added_by\":\"auto\",\"created_at\":\"2025-02-18 13:09:11\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":91989,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTranscriptomic features of tumor samples of responders and non-responders for ICIs. \\u003cstrong\\u003eA.\\u003c/strong\\u003e Expression heatmap of the genes correlated with clinical response to ICIs. Red and green colors are used to indicate over- and under-expressed genes. We show the genes exhibiting \\u003cem\\u003ep\\u003c/em\\u003e-value \\u0026lt; 0.05 in SAM of responders vs. non-responders. \\u003cstrong\\u003eB.\\u003c/strong\\u003e Heatmap for the scores of tumor-infiltrating immune cells inferred by CIBERSORT.\\u003csup\\u003e28\\u003c/sup\\u003e The asterisk indicates the immune cell type that exhibited statistically significant correlation with clinical response (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05; see also Figure 4C). \\u003cstrong\\u003eC.\\u003c/strong\\u003e CIBERSORT score of follicular helper T cells. \\u003cem\\u003eP\\u003c/em\\u003e-value was computed by Mann–Whitney \\u003cem\\u003eU\\u003c/em\\u003e-test. \\u003cstrong\\u003eD.\\u003c/strong\\u003e Complexity of T-cell receptor repertoire. As an index of the complexity, we show D50, percent of dominant and unique T-cell clones that account for the cumulative 50% of the total CDR3s (complementary determining region 3s) counted in the sample.\\u003csup\\u003e42\\u003c/sup\\u003e Regarding D50, no significant difference was detected for clinical response to ICIs. Responders and non-responders were defined as CR/PR/LS, and SD/PD cases, respectively. Abbreviations: ICI, immune checkpoint inhibitor; SAM, Significance Analysis of Microarrays; irRECIST, immune-related response evaluation criteria in solid tumors; CR, complete response; PR, partial response; LS, long-term survival; SD, stable disease; PD, progressive disease; TCR; T-cell receptor.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5997977/v1/7225d527ab3cbe06187e1613.png\"},{\"id\":76568245,\"identity\":\"e1256e89-13ac-4acd-bcbd-7c80769c48e5\",\"added_by\":\"auto\",\"created_at\":\"2025-02-18 13:09:14\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":108824,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAltered gene expression by ICI treatment. \\u003cstrong\\u003eA.\\u003c/strong\\u003eHeatmaps for gene expression changes. Status for irRECIST, timing of sampling and change in the tumor cell content are shown above the heatmaps. Upper and lower heatmaps represent the genes significantly induced in responder and non-responder samples, respectively (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05 by Mann-Whitney \\u003cem\\u003eU\\u003c/em\\u003e-test). Green and red colors are used for increased and decreased expression of genes. \\u003cstrong\\u003eB. \\u003c/strong\\u003eChanges in diversity index of TCR repertoire in responder and non-responder samples. \\u003cem\\u003eP\\u003c/em\\u003e-value was computed by Mann–Whitney \\u003cem\\u003eU\\u003c/em\\u003e-test. \\u003cstrong\\u003eC.\\u003c/strong\\u003e \\u003cem\\u003eMARCO\\u003c/em\\u003eexpression. Left. Changes in the TPM value of \\u003cem\\u003eMARCO\\u003c/em\\u003e gene in responder and non-responder samples. Right. TPM values of \\u003cem\\u003eMARCO\\u003c/em\\u003e gene in immunohistochemically positive and negative samples for MARCO protein. \\u003cstrong\\u003eD.\\u003c/strong\\u003eDistribution and expression level of MARCO protein in tumor samples from responder (KM041) and non-responder (KM011) cases.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5997977/v1/7b1ca4130545692a074cf06b.png\"},{\"id\":76568239,\"identity\":\"295ad5eb-934e-4554-8e2d-eaeeb8c24d88\",\"added_by\":\"auto\",\"created_at\":\"2025-02-18 13:09:12\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":176892,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMultiplex immunofluorescence of samples from a responder (KM041) before and after ICI treatment. Panels are images of immunofluorescent staining with labelled antibodies. DAPI (4’,6-diamidino-2-phenylindole) was used to stain the nucleus of the cells. ALL indicates a superimposed image made from all images, each of which were stained with an antibody or DAPI.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5997977/v1/adc94995619ecdf05dadf07f.png\"},{\"id\":76568614,\"identity\":\"2a7495fb-a12d-44b3-9693-428061c71d7c\",\"added_by\":\"auto\",\"created_at\":\"2025-02-18 13:17:16\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1470061,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5997977/v1/90af69c9-c3fc-4e8c-9093-8aac82f555fb.pdf\"},{\"id\":76568249,\"identity\":\"eb68296a-6379-45e2-8116-353e81dbfa96\",\"added_by\":\"auto\",\"created_at\":\"2025-02-18 13:09:14\",\"extension\":\"png\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":90526,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003emain table\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"KimuraMainTable240413.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5997977/v1/eef06d1d5e0654491fce1ca3.png\"},{\"id\":76568209,\"identity\":\"a17db25d-17a1-4d6c-9101-c552090057eb\",\"added_by\":\"auto\",\"created_at\":\"2025-02-18 13:09:07\",\"extension\":\"pptx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":2730765,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"MelanomaSupplementaryFigures241227.pptx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5997977/v1/1155b3176969dc12a2504f28.pptx\"},{\"id\":76568220,\"identity\":\"d9a69e44-c69b-4820-b938-0c2afc3c63df\",\"added_by\":\"auto\",\"created_at\":\"2025-02-18 13:09:09\",\"extension\":\"docx\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":17920,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"supplementaryfigurelegends20241225.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5997977/v1/806994f33e562341309e23ae.docx\"},{\"id\":76568247,\"identity\":\"c7f82a0a-89af-4f72-ac22-eb2063f61072\",\"added_by\":\"auto\",\"created_at\":\"2025-02-18 13:09:14\",\"extension\":\"xlsx\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":25330,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTable2412112.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5997977/v1/0f2b06c74aab08121b12b76a.xlsx\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003eGenomic and transcriptomic analyses of melanoma in Japanese patients reveal candidate biomarkers for immune checkpoint inhibitor responders\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eAlthough immune checkpoint inhibitors (ICIs) have greatly improved the prognosis of advanced melanoma, response rates vary considerably among individuals and across ethnic groups. In Caucasians, the reported overall response rate (ORR) is 45% for anti-PD-1 antibody monotherapy and 58% for combination therapy with CTLA-4 inhibitors\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e. In contrast, Japanese patients showed markedly lower response rates, with ORR of only 34.8% for monotherapy\\u003csup\\u003e2\\u003c/sup\\u003e and 26.3\\u0026ndash;43.3% for combination therapy.\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e The distribution of melanoma subtypes based on the World Health Organization (WHO) classification\\u003csup\\u003e\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e differs between Japanese and Caucasian patients. Specifically, cumulative sun damage (CSD)-type melanomas (low and high CSD), which tend to respond better to ICI treatment, are more common in Caucasians, whereas acral and mucosal melanomas, which tend to respond poorly to ICI treatment, are more prevalent in Japanese patients. These results suggest that the distribution of melanoma subtypes may explain the interethnic differences in ICI responses among patients with melanoma; however, no study has analyzed the genetic mutational profiles of Japanese patients with melanoma. In addition, many reports have indicated large individual differences in ICI responses, even among the same melanoma subtype. Several review articles focusing on melanoma in Caucasians have identified tumor tissue biomarkers associated with the efficacy of ICIs, including Tumor Infiltrating Lymphocytes (TILs) \\u003csup\\u003e\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u003c/sup\\u003e, CD8\\u003csup\\u003e+\\u003c/sup\\u003e TILs\\u003csup\\u003e7\\u003c/sup\\u003e, and Tumor Mutation Burden (TMB).\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e However, there are no established biomarkers for melanoma in Japanese patients because of the lack of studies with sufficient sample sizes.\\u003c/p\\u003e \\u003cp\\u003eIn this study, we analyzed the genomic and transcriptomic profiles of blood and tumor samples from Japanese patients with melanoma, before and after ICI therapy, from multiple facilities and examined their association with response to ICIs.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eStudy structure\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePatients included in the study were from 14 academic and cancer hospitals in Japan (Supplementary Table S1). Bioinformatical and statistical analyses were conducted at the Cancer Precision Medicine Center of the Japanese Foundation for Cancer Research (Ariake, Tokyo, Japan). These institutions participated in the \\u0026ldquo;Project for Cancer Research and Therapeutic Evolution\\u0026rdquo; (P-CREATE) program, granted by the Japan Agency for Medical Research and Development (AMED). The follow-up duration to determine clinical response was at least 1 year.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePatients with melanoma who underwent surgery and/or biopsy at the participating hospitals/institutes between 2014 and 2021 were recruited for the study. Ethical approval was obtained from the internal review boards of the participating institutions. All patients provided written informed consent.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMelanoma samples and histopathological diagnosis\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMelanoma samples were obtained by surgical excision or biopsy. Clinicopathological characteristics were obtained from medical records. Prior to the first sample collection, most patients had not received any ICIs, but might have received conventional chemotherapy (dacarbazine) or BRAF and MEK inhibitors (dabrafenib/trametinib). Ten patients whose prior ICI were ineffective and were sampled before receiving a second ICI treatment were included in the study. Sections from formalin-fixed paraffin-embedded (FFPE) tissues were used for pathological diagnoses. The tumors were classified based on the WHO classification.\\u003csup\\u003e5\\u003c/sup\\u003e Case information with therapeutic history is presented in Supplementary Table S2.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinical response of the patients in the cohort to immune checkpoint inhibitors\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFrom the initial 129 tumor samples from 78 patients, 7 samples from 3 cases were excluded from subsequent analyses owing to poor tumor content (tumor content \\u0026lt; 0.2). A total of 112 and 10 tumors from 65 and 10 patients who received therapeutic regimens with and without ICIs (ipilimumab, nivolumab, or pembrolizumab), respectively, were rendered for further analyses (Figure 1). The clinical response to ICIs was classified according to the immune-related Response Evaluation Criteria in Solid Tumors (irRECIST), as described previously.\\u003csup\\u003e9\\u003c/sup\\u003e After ICI treatment, some patients with stable disease (SD) or progressive disease (PD) have been observed to survive for longer than 1 year.\\u003csup\\u003e10\\u003c/sup\\u003e Based on this observation, SD or PD was subdivided into long-term survival (LS) and short-term survival using a cut-off of 1 year. The 5-tier classes of clinical response were then used as complete response (CR), partial response (PR), LS, SD, and PD in the current study. We further defined CR, PR, and LS as responders and SD and PR as non-responders for binary comparisons.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSample preparation for genomic and transcriptomic analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSpecimens were divided into multiple pieces and processed either to snap-frozen (fresh-frozen; FF) tissues for DNA and RNA sequencing or to FFPE tissues for histopathological and immunohistochemical examination. FF cancer tissues were cut into 10-\\u0026micro;m thick sections, and cancer cells were selectively enriched by laser-capture microdissection with an LMD7000 (Leica, Wetzlar, Germany) following the manufacturer\\u0026rsquo;s protocol. DNA samples were extracted using the QIAamp DNA Micro Kit (Qiagen, Valencia, CA, USA) (Supplementary Table S3). Matched normal DNA samples were used for exome sequencing in all germline and somatic variant calling. Normal DNA was extracted from the buffy coat using the RiboPure Blood Kit (Thermo Fisher Scientific, Carlsbad, CA,\\u0026nbsp;USA). The quantity\\u0026nbsp;and quality were confirmed\\u0026nbsp;using a NanoDrop\\u0026nbsp;2000 (Thermo Fisher Scientific) and Qubit 2.0 fluorometer (Thermo Fisher Scientific). A hundred and twenty-eight DNA specimens from 78 patients met the criteria for purity (OD\\u003csub\\u003e260/280nm\\u003c/sub\\u003e \\u0026gt; 1.4) and concentration (ratio of dsDNA/ssDNA \\u0026gt; 0.18). RNA was extracted from the FF tumor specimens using a RNeasy Micro Kit (Qiagen, Hilden, Germany). For RNA, 122 samples from 77 patients met the purity criteria (optical density at 260/280 nm \\u0026gt; 1.69, RNA integrity number \\u0026gt; 2.2, and DV200 \\u0026gt; 54).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLibrary preparation and sequencing for exome and RNA sequencing analyses\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe extracted DNA was subjected to exome analysis using SureSelect V5 or Customs V5 Kit (Agilent Technologies, Santa Clara, CA, USA). mRNA libraries were generated using the TruSeq Stranded mRNA Library Prep Kit (Illumina, San Diego, CA, USA), following the manufacturer\\u0026rsquo;s instructions. Sequencing of the captured DNA was conducted with an in-house HiSeq2500 or HiSeq2000 (Illumina) or outsourced to Macrogen Japan Corp. (Aomi, Tokyo).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eGermline/somatic mutation call,\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003esingle base substitution\\u003c/strong\\u003e \\u003cstrong\\u003esignature,\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;and melanoma driver gene mutation\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSequenced reads were aligned to the reference human genome (hg19) using the Burrows-Wheeler Aligner (ver. 0.7.12).\\u003csup\\u003e11\\u003c/sup\\u003e GATK (GenomeAnalysisTK, ver. 3.4-46) was used to recalibrate the variant quality score and perform local realignment.\\u003csup\\u003e12\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSomatic SNVs were called using VarScan (ver. 2.3.7)\\u003csup\\u003e13\\u003c/sup\\u003e, MuTect (ver. 1.1.5)\\u003csup\\u003e14\\u003c/sup\\u003e, and Karkinos2 (ver.0.1;\\u0026nbsp;http://sourceforge.net/projects/karkinos/) as previously reported.\\u003csup\\u003e15\\u003c/sup\\u003e VarScan (ver. 2.3.7), SomaticIndelDetector (ver. 2.3-9), and Karkinos2 (ver.0.1) were used to detect somatic indels. Somatic SNVs and indels were considered as genuine mutations if they were found at least twice among the three callers. Somatic copy number variants (CNVs) were detected using EXCAVATOR (ver. 2.2) with GISTIC\\u0026nbsp;(ver. 2.0.23).\\u003csup\\u003e16\\u003c/sup\\u003e\\u003csup\\u003e,\\u003c/sup\\u003e\\u003csup\\u003e17\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSingle base substitution (SBS) signatures were computed using Rsolnp (ver. 1.1.6) by minimizing the differences between the model (COSMIC mutation signature v2; downloaded from https://cancer.sanger.ac.uk/signatures/signatures_v2) and observed substitutions.\\u003csup\\u003e18\\u003c/sup\\u003e Germline SNVs and indels were called according to a previously described method.\\u003csup\\u003e19\\u003c/sup\\u003e None of the patients had pathogenic variants in the causal genes of hereditary melanoma syndrome.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDriver subtypes\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMelanoma driver genes were curated using TumorPortal (http://www.tumorportal.org/; Supplementary Table S4).\\u003csup\\u003e20\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eT-cell receptor repertoire\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe complement-determining region 3 (CDR3) of the T-cell receptor (TCR)\\u0026nbsp;b\\u0026nbsp;chain was amplified using the iRepertoire multiplex primer set (HTBI-M; iRepertoire, Huntsville, AL, USA) following the manufacturer\\u0026rsquo;s instructions.\\u003csup\\u003e21\\u003c/sup\\u003e The Qiagen OneStep RT-PCR Kit (Qiagen) was used for reverse transcription. Amplification and library preparation were performed using the iR-Processor and iRock 2.0 (iRepertoire, Huntsville, AL, USA). Paired-end sequencing was performed on the purified libraries using the Illumina MiSeq v2 500-cycle Reagent Kit (Illumina), and a median read depth of 615,127 reads per sample was generated. Web tools provided by iRepertoire (http://www.irepertoire.com) were used to perform basic informatics analyses, such as barcode demultiplexing and filtering, V(D)J alignment, and CDR identification, and to calculate diversity indices.\\u003csup\\u003e21\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHLA genotyping and mutational analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eHLA class I genotypes were determined via the OptiType algorithm (ver.1.3.1)\\u003csup\\u003e22\\u003c/sup\\u003e using targeted re-sequencing data of normal samples. SNVs, indels, and CNVs in HLA-A, -B, and -C genes were identified using POLYSOLVER (v4) and LOHHLA (ver. 20171108). \\u003csup\\u003e23\\u003c/sup\\u003e\\u003csup\\u003e,\\u003c/sup\\u003e \\u003csup\\u003e24\\u003c/sup\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNeoantigen prediction\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eExome sequencing data were used to predict neoantigens. All 8- to 11-mer peptides, each harboring a substituted amino acid, were examined by filtering with a predicted binding affinity for HLA-A \\u0026lt; 500 nM, as previously described.\\u003csup\\u003e25\\u003c/sup\\u003e\\u003csup\\u003e,\\u003c/sup\\u003e \\u003csup\\u003e26\\u003c/sup\\u003e Data were analyzed using NetMHCpanv2.8 software.\\u003csup\\u003e27\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTumor-infiltrating cell-type inference\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe RNA-seq data were analyzed using CIBERSORT (ver. 1.05) to infer the immune cell types infiltrating the tumor.\\u003csup\\u003e28\\u003c/sup\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eImmunohistochemistry\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFor immunohistochemistry, 4-\\u0026mu;m thick sections from FFPE tissue samples were stained using anti-MARCO polyclonal antibody (rabbit IgG, Invitrogen) with 1:100 dilution and\\u0026nbsp;counterstained with Giemsa stain.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMultiplex immunofluorescence with PhenoCycler\\u0026reg; (CODEX)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMultiplex immunofluorescence was performed as previously described.\\u003csup\\u003e29\\u003c/sup\\u003e Briefly, 4-\\u0026micro;m thick FFPE tissue sections were deparaffinized, rehydrated, and rendered to antigen retrieval in citrate buffer solution (10 mM, pH 6.0) using a pressure cooker. The tissues were subsequently stained with a cocktail of antibodies (CD20-BX007 with Alexa750, CD8-BX026 with Atto 550, CD4-BX003, CD68-BX015, PD-1-BX046, PD-L1-BX043 with Alexa Fluor 647 [Akoya Biosciences, Delaware, USA], MARCO-BX016, and MelanA-BX022 [Abcam, Cambridge, UK]) for 3 hours at room temperature. MelanA (EPR20380, Abcam) was conjugated in-house, according to the manufacturer\\u0026rsquo;s recommendations (Akoya Biosciences). Multiplex imaging was performed using a microfluidics instrument (PhenoCycler, Akoya Biosciences) with a fluorescence microscope (BZ-X700, Keyence, Osaka, Japan) and CODEX Instrument Manager software (Akoya Biosciences). The raw image files were processed using\\u0026nbsp;CODEX processor version 1.8 (Akoya Biosciences).\\u0026nbsp;The cell composition was quantified as follows: 1) a region of tissue cellular neighborhood was defined\\u0026nbsp;based on cellularity using the CODEX processor, 2) the region was labelled according to the cellularity (from low to high) as 1 and 2 for the samples before ICI treatment, and as 1, 2 and 3 for the samples after ICI treatment, 3) the composition of each antigen expressing cell was computed as the fold change in cell numbers within the neighborhood, 4) cell density was computed as the average of cell composition per region, and 5) the relative density of each antigen-expressing cell was\\u0026nbsp;estimated as the ratio of cell density of region 2 (or 3) to that of region 1.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStatistical analyses\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe Mann-Whitney\\u0026nbsp;\\u003cem\\u003eU\\u003c/em\\u003e-test, Wilcoxon signed rank test, McNemar test, and Fisher\\u0026rsquo;s exact test were used to statistically evaluate the correlations between genomic or transcriptomic parameters and ICI-sensitive and resistant groups using GraphPad Prism (ver. 9.4.1) and R software (ver. 4.0.3).\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eLandscape of melanoma exomes in Japanese patients\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo delineate the landscape of melanoma genomes in Japanese patients and identify potential genomic biomarker(s) associated with clinical response to ICIs, we conducted exome and RNA sequencing analyses of 129 tumors from 78 patients from 13 institutions in Japan. After excluding 7 tumors from 3 cases owing to poor tumor content (tumor content \\u0026lt; 0.2), 122 tumors from 75 cases were processed for subsequent analyses, among which 65 cases had been treated with adjuvant regimens containing an ICI. Samples collected before ICI treatment were available for 39 patients (Figure 1A).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eFifty-eight of the 65 cases had sufficient information and evaluable follow-up duration (overall survival \\u0026ge; 60 days) for classification of clinical response to ICIs. For the 58 patients (responders, \\u003cem\\u003en\\u003c/em\\u003e = 39; non-responders, \\u003cem\\u003en\\u003c/em\\u003e = 19), we statistically evaluated several clinicopathological attributes, including sex, age, WHO classification, and ICI regimen, and found no association with clinical response (Figure 1A, Supplementary Table S2, and Table 1). The melanoma samples were classified based on the driver subtyping scheme using the mutational status of \\u003cem\\u003eBRAF\\u003c/em\\u003e, \\u003cem\\u003eRAS,\\u003c/em\\u003e and \\u003cem\\u003eNF1\\u003c/em\\u003e (Supplementary Figure S1 and Figure 2D), according to the method previously described.\\u003csup\\u003e30\\u003c/sup\\u003e As previously\\u0026nbsp;reported\\u003csup\\u003e30\\u003c/sup\\u003e, the \\u003cem\\u003etriple wildtype\\u003c/em\\u003e subtype was significantly enriched in acral melanomas in the current cohort (Supplementary Figure S1).\\u0026nbsp;However, the driver subtypes were not associated with the clinical response (\\u003cem\\u003ep\\u003c/em\\u003e = 0.8726, Fisher\\u0026rsquo;s exact test;\\u0026nbsp;Table 1).\\u003c/p\\u003e\\n\\u003cp\\u003eFigure 2A presents an overview of the genomic aberration profiles of the 75 Japanese patients with melanoma. As anticipated, the number of SNVs, indels, CNVs, and neoantigens, and the ratio of SBS7 signature, a mutational signature generated by ultraviolet (UV) exposure\\u003csup\\u003e18\\u003c/sup\\u003e\\u003csup\\u003e,\\u003c/sup\\u003e \\u003csup\\u003e31\\u003c/sup\\u003e, exhibited significant correlations with the WHO classification and driver subtypes (Figure 2B, upper and middle panels), indicating the robustness of our exome analyses and clinical information. Nevertheless, unlike previous genomic studies\\u003csup\\u003e32\\u003c/sup\\u003e, in which intimate links of TMBs (the total number of SNVs/indels in a sample) or neoantigens with clinical response to ICIs were observed, no clear association was observed in the current cohort (Figure 2B, lower panels).\\u003c/p\\u003e\\n\\u003cp\\u003eMelanoma samples from our cohort, Shizuoka Cancer Center, or TCGA of East Asian population, had significantly fewer SNVs than those from TCGA of Caucasian population (Figure 2C left).\\u003csup\\u003e33\\u003c/sup\\u003e\\u003csup\\u003e,\\u003c/sup\\u003e \\u003csup\\u003e30\\u003c/sup\\u003e Coincidently, the majority of melanomas from our cohort or TCGA of East Asian population had a significantly lower ratio of SBS7 signature than those from TCGA of Caucasian population (Figure 2C right).\\u003csup\\u003e30\\u003c/sup\\u003e These observations are consistent with previous etiological findings that UV-induced and non-UV-induced melanomas are predominant in Caucasian and East Asian populations, respectively.\\u003csup\\u003e34\\u003c/sup\\u003e The composition of driver subtypes also differed between Caucasians (TCGA of Caucasians) and East Asians (our cohort and TCGA of East Asians); \\u003cem\\u003eBRAF\\u003c/em\\u003e and \\u003cem\\u003etriple wildtype\\u003c/em\\u003e were the major subtypes in Caucasian and East Asian populations, respectively. (Supplementary Figure S1 and Figure 2D). These findings suggest different melanomagenic processes in different ethnicities.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAs a reflection of the biased compositions of driver subtypes in WHO classifications, driver events for melanomas in Japanese patients were represented by \\u003cem\\u003eTERT\\u003c/em\\u003e amplification, \\u003cem\\u003eNRAS\\u003c/em\\u003e missense SNVs, \\u003cem\\u003eEP300\\u003c/em\\u003e amplification, \\u003cem\\u003eBRAF\\u003c/em\\u003e missense SNVs, and \\u003cem\\u003eCDKN2A\\u003c/em\\u003e homozygous deletions (Figure 3 and Supplementary Table S4; \\u003cem\\u003en\\u003c/em\\u003e = 39; pre-ICI samples). However, no particular driver gene was detected to be associated with the clinical response to ICIs (Figure 3). Moreover, exome-wide comparisons did not show association between any gene mutation and clinical response. As such, exome analyses revealed the characteristic genomic features of melanoma in Japanese patients but did not identify any genomic markers associated with susceptibility to ICI therapies.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHLA type and somatic mutation\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eUsing the exome data, HLA class I of the patients was genotyped with OptiType. There were 10, 19, and 9 genotypes for \\u003cem\\u003eHLA-A\\u003c/em\\u003e, -\\u003cem\\u003eB,\\u003c/em\\u003e and -\\u003cem\\u003eC\\u003c/em\\u003e subclasses, respectively. Among the subclasses, only \\u003cem\\u003eHLA-A\\u003c/em\\u003e*24:02 exhibited significant enrichment in ICI responders (Figure 3 and Table 2; \\u003cem\\u003ep\\u003c/em\\u003e = 0.0282 and Odds ratio = 5.74 [95% confidence interval = 1.11\\u0026ndash;34.9] by Fisher\\u0026rsquo;s exact test). No somatic SNV and one loss of heterozygosity on class I HLA genes was detected in the samples using POLYSOLVER\\u003csup\\u003e23\\u003c/sup\\u003e and LOHHLA\\u003csup\\u003e24\\u003c/sup\\u003e, and the somatic alteration was not associated with clinical response to ICIs (\\u003cem\\u003ep\\u003c/em\\u003e = 0.359 by Fisher\\u0026rsquo;s exact test).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTemporal pattern of genomic changes in the time-course analysis of treatment\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTime-course exome data were available for 21 cases, among which 15 and 4 cases had paired samples before/during and before/after ICI treatment, respectively (Supplementary Figure S2). In these 19 cases, ICI interventions did not significantly change the number of SNVs, indels, or CNVs, the ratio of mutational signatures, or the status of driver gene mutations. Nevertheless, in one patient (FM009) with long-term survival, the pair of samples exhibited substantial genomic changes (Figure 4). This patient had been treated with dacarbazine, an alkylating agent, for 12 months as 1st line chemotherapy prior to sampling for this study and subsequently received nivolumab because of tumor progression (Figure 4A). Although samples before dacarbazine treatment were not available, samples were obtained before and after nivolumab treatment (FM009T1 and FM009T2). Although FM009T1 had 3,772 SNVs, the number of SNVs decreased to 194 in FM009T2 (a 19.4-fold reduction; Figure 4B). The composition of mutational signatures drastically changed across the ICI treatment; the major component in FM009T1 was SBS11, a signature of alkylating agent exposure, whereas SBS7, a UV signature, became dominant in FM009T2 (Figure 4C). Diversity index, a parameter of the complexity of the T cell receptor repertoire, increased with nivolumab treatment (Figure 4D). These observations imply that dacarbazine and nivolumab treatment modified the melanoma genome by interacting with the immune microenvironment.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTranscriptomic features of responders and non-responders to ICI treatment\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eUsing transcriptomic analysis with RNA-seq, we identified 1,135 and 735 genes whose expression levels correlated with responders and non-responders, respectively (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05 by SAM; Figure 5A). Further gene ontology enrichment analyses using DAVID 2022\\u003csup\\u003e35\\u003c/sup\\u003e revealed 25 and 102 pathway associations with responders and non-responders, respectively. Whereas the ontologies for responders included \\u0026lsquo;regulator of G protein signaling superfamily\\u0026rsquo; (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.0163; 4 genes such as \\u003cem\\u003eRGS22\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;ADRBK2\\u003c/em\\u003e, \\u003cem\\u003eRGS13,\\u003c/em\\u003e and \\u003cem\\u003eRGS20\\u003c/em\\u003e) and \\u0026lsquo;inflammatory response\\u0026rsquo; (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.0001; 13 genes such as \\u003cem\\u003eIL1B\\u003c/em\\u003e, \\u003cem\\u003eS100A9\\u003c/em\\u003e, \\u003cem\\u003eS100A8\\u003c/em\\u003e, \\u003cem\\u003eCHI3L1,\\u0026nbsp;\\u003c/em\\u003eand \\u003cem\\u003eCCL13\\u003c/em\\u003e), in non-responders, \\u0026lsquo;cell adhesion\\u0026rsquo; (\\u003cem\\u003ep\\u003c/em\\u003e = 0.0055; 18 genes such as \\u003cem\\u003ePCDHGC5\\u003c/em\\u003e, \\u003cem\\u003eAPLP1\\u003c/em\\u003e, \\u003cem\\u003ePCDHB2\\u003c/em\\u003e, \\u003cem\\u003eFN1,\\u003c/em\\u003e and \\u003cem\\u003eLAMC24\\u003c/em\\u003e), and \\u0026lsquo;metalloprotease\\u0026rsquo; (\\u003cem\\u003ep\\u003c/em\\u003e = 0.0058; 9 genes such as \\u003cem\\u003eMMP1\\u003c/em\\u003e, \\u003cem\\u003eMMP3\\u003c/em\\u003e, \\u003cem\\u003eMMP10\\u003c/em\\u003e, \\u003cem\\u003eCPA4,\\u003c/em\\u003e and \\u003cem\\u003eAMZ1\\u003c/em\\u003e) were significantly enriched. Immune cell composition analysis using CIBERSORT revealed that follicular helper T cells were significantly enriched in responders (Figures 5B and 5C; \\u003cem\\u003ep\\u003c/em\\u003e = 0.0422 by Mann\\u0026ndash;Whitney \\u003cem\\u003eU\\u003c/em\\u003e-test). Although the complexity of the T-cell population infiltrating the tumor has been reported to correlate with ICI response\\u003csup\\u003e10\\u003c/sup\\u003e\\u003csup\\u003e,\\u003c/sup\\u003e \\u003csup\\u003e36\\u003c/sup\\u003e\\u003csup\\u003e,\\u003c/sup\\u003e \\u003csup\\u003e37\\u003c/sup\\u003e, diversity indices or other related parameters of the complexity of the T-cell receptor repertoire did not exhibit significant differences between responders and non-responders. These findings indicate the relevance of infiltrating immune cells in the response to ICI treatment.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eChanges in expression by ICI treatment\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTaking advantage of the fact that nine responders and five non-responders had paired transcriptome data (before/during and before/after ICI treatment), we next sought genes that were induced in responders and non-responders by ICI treatment (Figure 6A). Using the Mann-Whitney \\u003cem\\u003eU\\u003c/em\\u003e test with the fold changes in the expression values, 597 and 98 genes were identified as significantly induced in responders and non-responders, respectively (Figure 6A). The induced genes in responders included markers of cytotoxic T cells, such as \\u003cem\\u003eCD3D\\u003c/em\\u003e, \\u003cem\\u003eCD3E\\u003c/em\\u003e, \\u003cem\\u003eCD8B\\u003c/em\\u003e, \\u003cem\\u003ePRF1,\\u003c/em\\u003e and \\u003cem\\u003eGZMA\\u003c/em\\u003e, and tumor-associated macrophages (TAMs) such as \\u003cem\\u003eMARCO.\\u0026nbsp;\\u003c/em\\u003eHowever,genes presumably expressed in melanoma cells, including \\u003cem\\u003eTCF15\\u003c/em\\u003e and \\u003cem\\u003eNKX2-2,\\u003c/em\\u003e were induced in non-responders. These findings imply that immune cells relevant for tumor cell killing were accumulated by ICI treatment in responders. Gene ontology analyses with DAVID revealed 310 and 4 pathways associated with responders and non-responders, which included \\u0026lsquo;GO:0042110 T-cell activation\\u0026rsquo; (\\u003cem\\u003ep\\u003c/em\\u003e = 1.312 \\u0026times; 10\\u003csup\\u003e-9\\u003c/sup\\u003e; 13 genes such as \\u003cem\\u003eCD3E\\u003c/em\\u003e, \\u003cem\\u003eCD2\\u003c/em\\u003e, \\u003cem\\u003eCD7\\u003c/em\\u003e, \\u003cem\\u003eCD8B\\u003c/em\\u003e, and \\u003cem\\u003eCD48\\u003c/em\\u003e) and \\u0026lsquo;KW-0010 Activator\\u0026rsquo; (\\u003cem\\u003ep\\u003c/em\\u003e = 0.0172; 5 genes such as \\u003cem\\u003eNFE4\\u003c/em\\u003e, \\u003cem\\u003eTCF15\\u003c/em\\u003e, and \\u003cem\\u003eNKX2-2\\u003c/em\\u003e), respectively. Delta diversity indices (defined by fold changes in diversity indices of the T-cell receptor repertoire before and after/during ICI treatment) were significantly increased in responders (Figure 6B), which implied that the induced expression of T-cell markers was derived from the influx of multiple T-cell clones into the tissue, and not from the clonal expansion of T cells inside the tumor.\\u003c/p\\u003e\\n\\u003cp\\u003eGiven the relevance of TAMs as regulators of the immune microenvironment in tumor cell killing, we focused on \\u003cem\\u003eMARCO\\u003c/em\\u003e, a scavenger receptor expressed on TAMs. While \\u003cem\\u003eMARCO\\u003c/em\\u003e expression was induced in responders, it remained unchanged or was even suppressed in non-responders by ICI treatment, with a significant difference in the expression change (\\u003cem\\u003ep\\u003c/em\\u003e = 0.0040 by Mann-Whitney \\u003cem\\u003eU\\u003c/em\\u003e-test in Figure 6C, left). Immunohistochemical staining of the MARCO protein confirmed that \\u003cem\\u003eMARCO\\u003c/em\\u003e gene expression correlated with MARCO protein expression (\\u003cem\\u003ep\\u003c/em\\u003e = 0.0095, Figure 6C right). The change in expression with ICI treatment at the RNA level seemed to be associated with the change in the number of infiltrated TAMs, with MARCO-expressing cells being increased and decreased in responders (KM041) and non-responders (KM011), respectively (Figure 6D).\\u003c/p\\u003e\\n\\u003cp\\u003eTo further confirm the association between infiltrated TAMs and clinical response to ICI, we performed multiplex fluorescent immunostaining of the before- and after-ICI treatment samples from a responder (KM041). Following ICI treatment, PD-1 and PD-L1 were induced and colocalized on Melan-A\\u003csup\\u003e+\\u003c/sup\\u003e melanoma cells, with increased infiltration of CD4\\u003csup\\u003e+\\u003c/sup\\u003e helper T cells, CD8\\u003csup\\u003e+\\u003c/sup\\u003e cytotoxic T cells, CD68\\u003csup\\u003e+\\u003c/sup\\u003e macrophages, and CD20\\u003csup\\u003e+\\u003c/sup\\u003e B cells. Moreover, MARCO expression coincided with CD68 expression on the infiltrating TAMs in the post-treatment sample (Figure 7).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eCellular neighborhood analyses using CODEX\\u003csup\\u003e29\\u003c/sup\\u003e revealed two and three regions with different compositions of infiltrating inflammatory cells in the before- and after-treatment samples, respectively (Supplementary Figure S4). Based on the density of the infiltrating cells, regions 1 (before) and 1 (after) and regions 2 (before), 2 (after), and 3 (after) were annotated as cold and hot regions, respectively. Compared to the cold regions, MARCO-expressing cells were more prevalent in the hot regions, with denser infiltration of CD4\\u003csup\\u003e+\\u003c/sup\\u003e, CD8\\u003csup\\u003e+\\u003c/sup\\u003e, and CD20\\u003csup\\u003e+\\u003c/sup\\u003e cells. The relative density of each antigen-expressing cell was 1.403 (minimum 1.265\\u0026ndash;maximum 1.768) in the hot region of the pretreatment sample, and the relative density increased to 2.291 (1.973\\u0026ndash;5.485 in region 2) and 2.699 (1.7363\\u0026ndash;24.443 in region 3) in the hot regions of the post-treatment sample (Supplementary Figure S4E). Although CD20\\u003csup\\u003e+\\u003c/sup\\u003e cells showed maximum accumulation among the cells, all antigen-expressing cells, including MARCO\\u003csup\\u003e+\\u003c/sup\\u003e cells, accumulated after ICI treatment (Supplementary Figure S4E). These findings support the collaborative role of MARCO-expressing TAMs and CD4\\u003csup\\u003e+\\u003c/sup\\u003e, CD8\\u003csup\\u003e+\\u003c/sup\\u003e, and CD20\\u003csup\\u003e+\\u003c/sup\\u003e lymphocytes in the host immune response to melanoma cells 2023.\\u003csup\\u003e38\\u003c/sup\\u003e\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIn this multicenter analysis of 129 tumor specimens from 78 patients, we found that, overall, melanoma in Japanese patients had lower TMB and fewer SNVs classified as the SBS7 signature compared to melanoma in Caucasians (Figure 2B), which aligns with previous findings in the East Asian population.\\u003csup\\u003e30\\u003c/sup\\u003e In our subtype analysis, the CSD subtype (both low-CSD and high-CSD) in this cohort showed higher TMB and more SNVs with the SBS7 signature, similar to Caucasian cases, than the acral and mucosal subtypes (Figure 2B and 2C). These results suggest that differences in genomic mutational profiles among racial groups may be influenced by the melanoma subtype. Moreover, the poor response to ICIs in Japanese patients with melanoma may be partly attributed to the high prevalence of acral and mucosal subtypes, which typically have lower TMBs and fewer driver mutations. This is supported by our observation that the four patients with high TMB in this study exhibited better clinical response to ICI therapy, although the sample size was limited.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eHowever, we also noticed many cases with low TMB in the responder group. Recently, Bai et al. reported that the prognosis with ICI treatment is poorer in Asians than in Caucasians, even for CSD-type melanoma (ORR; White 54% vs. East Asian/Hispanic/African 20%).\\u003csup\\u003e39\\u003c/sup\\u003e Considering these factors, the variation in clinical responses may not be solely attributable to TMB or subtype differences, suggesting the presence of other biomarkers in Japanese patients. Therefore, we conducted further screening for novel melanoma biomarkers in Japanese patients with low TMB, focusing on immune-related genes, including HLA and TCRs.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Comparing the responder and non-responder groups, we found that \\u003cem\\u003eHLA\\u003c/em\\u003e\\u003cem\\u003e-A*24:02\\u003c/em\\u003e allele carriers were significantly more among responders (Table 2). This finding is consistent with the data from Caucasian populations, which shows longer survival in melanoma patients with HLA-A24, HLA-B44, and HLA-B62.\\u003csup\\u003e40\\u003c/sup\\u003e However, in our cohort, no significant difference was found between responders and non-responders for HLA-B44 (\\u003cem\\u003ep\\u003c/em\\u003e = 1, odds ratio = 1.154 [95% confidence interval = 0.1959\\u0026ndash;8.567]), and none of the patients had HLA-B62. The lack of association in our cohort may be due to the low frequencies of HLA-B44 and HLA-B62 in the Japanese\\u0026nbsp;population, reported as\\u0026nbsp;7.12% and 8.33%, respectively.\\u003csup\\u003e41\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn the time-course analysis, gene expression of \\u003cem\\u003eMARCO\\u003c/em\\u003e increased after ICI treatment, along with molecules associated with cytotoxic T cells, including \\u003cem\\u003eCD3D\\u003c/em\\u003e, \\u003cem\\u003eCD3E\\u003c/em\\u003e, \\u003cem\\u003eCD8B\\u003c/em\\u003e, \\u003cem\\u003ePRF1\\u003c/em\\u003e, and \\u003cem\\u003eGZMA,\\u003c/em\\u003e in the responder group (Figure 6A). Additionally, multiplex fluorescent immunostaining an increase in MARCO-positive macrophages after treatment, with high infiltration of CD4\\u003csup\\u003e+\\u003c/sup\\u003e, CD8\\u003csup\\u003e+\\u003c/sup\\u003e, and CD20\\u003csup\\u003e+\\u003c/sup\\u003e cells, which may have favored tumor immunity. Although MARCO, mainly expressed in M2-type TAMs, is considered a poor prognostic factor in cancers like lung cancer \\u003csup\\u003e38\\u003c/sup\\u003e, it has been associated with good prognosis in melanoma, consistent with our findings. The molecular mechanisms underlying the interaction between these cells and tumor immunity remain unclear. Therefore, functional studies on immune regulation mediated by these cells are necessary for future research.Furthermore, we aim to validate in future studies, using a larger cohort of cases, whether these biomarkers could serve as predictors of immune checkpoint inhibitor efficacy.\\u003c/p\\u003e\\n\\u003cp\\u003eThis is the largest study in Japan to prospectively analyze the genomic and transcriptomic profiles of tumor samples before and after ICI treatment for melanoma. Our findings indicate that TMB and neoantigens are not sufficient biomarkers for Japanese patients with melanoma owing to differences in genetic profiles between Japanese and Caucasian patients. In contrast, biomarkers such as HLA-A24 and infiltration of follicular helper T cells or MARCO-expressing macrophages have been suggested to be useful.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank Siew Kee Low, Yusuke Nakamura, Koichiro Inaki, and Yasuo Uemura for helpful discussions; Sayuri Amino, Rie Furuya, and Junko Kanayama for technical assistance; Minako Hoshida and Mariko Kawamura for administrative assistance; Mayuko Kosugi and Yuki Ota for analytical assistance and editing of figures and tables; Editage (www.editage.jp) for English language editing; and Rebecca Jackson for editing the draft of this manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eEthical approval was obtained from the Internal Review Board of the National Cancer Center (approval number 2016-248). Written informed consent was obtained from all the recruited patients.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe raw data generated in this study were submitted to the National Bioscience Database Center (NBDC; https://biosciencedbc.jp/en/; under accession numbers xxx; exome FASTQ files) and the NCBI Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/; under accession number GSE282471 (RNA-seq TPM file)).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by the Japan Agency for Medical Research and Development (grant number JP cm0106301). This grant was allocated to S.F., R.O. and A.T. as a sub-research project for the \\u0026ldquo;Establishment of predictive methods for cancer immunotherapy based on the evaluation of cancer cells and immune responses\\u0026rdquo; in Hiroyoshi Nishikawa, M.D. Ph.D. (Division of Cancer Immunology, Exploratory Oncology Research and Clinical Trial Center (EPOC), National Cancer Center).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDisclosure\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors have declared no conflicts of interest.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eT.K., N.T., K. K., H.Y., S.F., and S.M. analyzed the data and wrote the paper. T.K., T.M., Y.K., R.O., J.A., S.M. H.U., H.K., K.N., A.K., A.T., T.F., Y.F., T.I., K.K., S.M., and S.F. collected the specimens and provided clinical information. T.K. and S.F. evaluated the immunohistochemical staining results. T.N., and S.F. conceived the study and wrote the manuscript.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eLarkin J et al (2019) Five-Year Survival with Combined Nivolumab and Ipilimumab in Advanced Melanoma. N Engl J Med 381:1535\\u0026ndash;1546. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1056/NEJMoa1910836\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eUhara H et al (2021) Five-year survival with nivolumab in previously untreated Japanese patients with advanced or recurrent malignant melanoma. J Dermatol 48:592\\u0026ndash;599. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/1346-8138.15804\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eTakahashi A et al (2020) Real-world efficacy and safety data of nivolumab and ipilimumab combination therapy in Japanese patients with advanced melanoma. J Dermatol 47:1267\\u0026ndash;1275. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/1346-8138.15521\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eNamikawa K et al (2018) Efficacy and safety of nivolumab in combination with ipilimumab in Japanese patients with advanced melanoma: An open-label, single-arm, multicentre phase II study. Eur J Cancer 105:114\\u0026ndash;126. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.ejca.2018.09.025\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eBastian BC (2014) The molecular pathology of melanoma: an integrated taxonomy of melanocytic neoplasia. Annu Rev Pathol 9:239\\u0026ndash;271. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1146/annurev-pathol-012513-104658\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eBaltussen JC et al (2021) Predictive Biomarkers for Outcomes of Immune Checkpoint Inhibitors (ICIs) in Melanoma: A Systematic Review. Cancers (Basel) 13. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/cancers13246366\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eLi F et al (2021) The association between CD8\\u0026thinsp;+\\u0026thinsp;tumor-infiltrating lymphocytes and the clinical outcome of cancer immunotherapy: A systematic review and meta-analysis. EClinicalMedicine 41:101134. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.eclinm.2021.101134\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eNing B et al (2022) The Predictive Value of Tumor Mutation Burden on Clinical Efficacy of Immune Checkpoint Inhibitors in Melanoma: A Systematic Review and Meta-Analysis. Front Pharmacol 13:748674. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3389/fphar.2022.748674\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eMizuki Nishino MG, Suda M, Nikhil H, Ramaiya F, Stephen Hodi (2014) Optimizing immune-related tumor response assessment: does reducing the number of lesions impact response assessment in melanoma patients treated with ipilimumab? J Immunother Cancer 2\\u003c/li\\u003e\\n\\u003cli\\u003eInoue H et al (2016) Intratumoral expression levels of PD-L1, GZMA, and HLA-A along with oligoclonal T cell expansion associate with response to nivolumab in metastatic melanoma. \\u003cem\\u003eOncoimmunology\\u003c/em\\u003e 5, e1204507 \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1080/2162402X.2016.1204507\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eLi H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754\\u0026ndash;1760. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/bioinformatics/btp324\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eDePristo MA et al (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43:491\\u0026ndash;498. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/ng.806\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eKoboldt DC et al (2012) VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 22:568\\u0026ndash;576. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1101/gr.129684.111\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eCibulskis K et al (2013) Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol 31:213\\u0026ndash;219. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/nbt.2514\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eGotoh O et al (2019) Clinically relevant molecular subtypes and genomic alteration-independent differentiation in gynecologic carcinosarcoma. Nat Commun 10:4965. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41467-019-12985-x\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eAlberto Magi LT, Cifola I, Benelli RD\\u0026rsquo;AurizioM, Mangano E, Bonora CBE, Kurg A, Seri M, Magini P, Giusti B, Romeo G (2013) Tommaso Pippucci, Gianluca De Bellis, Rosanna Abbate, \\u0026amp; Gensini, G. F. EXCAVATOR: detecting copy number variants from whole-exome sequencing data. Genome Biol 14\\u003c/li\\u003e\\n\\u003cli\\u003eMermel CH et al (2011) GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. \\u003cem\\u003eGenome Biol.\\u003c/em\\u003e 12, R41 \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/gb-2011-12-4-r41\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eAlexandrov LB et al (2013) Signatures of mutational processes in human cancer. Nature 500:415\\u0026ndash;421. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/nature12477\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eKaneyasu T et al (2020) Prevalence of disease-causing genes in Japanese patients with BRCA1/2-wildtype hereditary breast and ovarian cancer syndrome. NPJ Breast Cancer 6:25. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41523-020-0163-1\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eLawrence MS et al (2014) Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505:495\\u0026ndash;501. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/nature12912\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eWang C et al (2010) High throughput sequencing reveals a complex pattern of dynamic interrelationships among human T cell subsets. \\u003cem\\u003eProc. Natl. Acad. Sci. U. S. A.\\u003c/em\\u003e 107, 1518\\u0026ndash;1523 \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1073/pnas.0913939107\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eSzolek A et al (2014) OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics 30:3310\\u0026ndash;3316. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/bioinformatics/btu548\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eShukla SA et al (2015) Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat Biotechnol 33:1152\\u0026ndash;1158. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/nbt.3344\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eMcGranahan N et al (2017) Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution. \\u003cem\\u003eCell\\u003c/em\\u003e 171, 1259\\u0026ndash;1271 e1211 \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.cell.2017.10.001\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eKiyotani K, Chan HT, Nakamura Y (2018) Immunopharmacogenomics towards personalized cancer immunotherapy targeting neoantigens. Cancer Sci 109:542\\u0026ndash;549. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/cas.13498\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eChoudhury NJ et al (2016) Low T-cell Receptor Diversity, High Somatic Mutation Burden, and High Neoantigen Load as Predictors of Clinical Outcome in Muscle-invasive Bladder Cancer. Eur Urol Focus 2:445\\u0026ndash;452. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.euf.2015.09.007\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eNielsen M et al (2007) NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS ONE 2:e796. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1371/journal.pone.0000796\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eBecht E et al (2016) Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol 17:218. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s13059-016-1070-5\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eKennedy-Darling J et al (2021) Highly multiplexed tissue imaging using repeated oligonucleotide exchange reaction. Eur J Immunol 51:1262\\u0026ndash;1277. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1002/eji.202048891\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eAtlas CG (2015) Genomic Classification of Cutaneous Melanoma. Cell 161:1681\\u0026ndash;1696. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.cell.2015.05.044\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eAlexandrov LB et al (2020) The repertoire of mutational signatures in human cancer. Nature 578:94\\u0026ndash;101. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41586-020-1943-3\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eSnyder A et al (2014) Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 371:2189\\u0026ndash;2199. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1056/NEJMoa1406498\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eNagashima T et al (2020) Japanese version of The Cancer Genome Atlas, JCGA, established using fresh frozen tumors obtained from 5143 cancer patients. Cancer Sci 111:687\\u0026ndash;699. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/cas.14290\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eChiu YJ et al (2022) Genomic profiling with whole-exome sequencing revealed distinct mutations and novel pathways in Asian melanoma. J Dermatol 49:1299\\u0026ndash;1309. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/1346-8138.16579\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eSherman BT et al (2022) DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res 50:W216\\u0026ndash;W221. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/nar/gkac194\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eHogan SA et al (2019) Peripheral Blood TCR Repertoire Profiling May Facilitate Patient Stratification for Immunotherapy against Melanoma. Cancer Immunol Res 7:77\\u0026ndash;85. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1158/2326-6066.CIR-18-0136\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eYusko E et al (2019) Association of Tumor Microenvironment T-cell Repertoire and Mutational Load with Clinical Outcome after Sequential Checkpoint Blockade in Melanoma. Cancer Immunol Res 7:458\\u0026ndash;465. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1158/2326-6066.CIR-18-0226\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eDong Q et al (2023) MARCO is a potential prognostic and immunotherapy biomarker. Int Immunopharmacol 116:109783. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.intimp.2023.109783\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eBai X et al (2022) Benefit and toxicity of programmed death-1 blockade vary by ethnicity in patients with advanced melanoma: an international multicentre observational study. Br J Dermatol 187:401\\u0026ndash;410. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/bjd.21241\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eDiego Chowell LGTM, Claud M, Grigg JK, Weber RM, Samstein VM, Kuo F, Kendall SM, Requena D, Riaz N, Greenbaum B, Carroll J, Garon E, Zehir DMHA, Solit D, Michael Berger RZ, Naiyer A, Rizvi (2018) Timothy A. Chan. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359:582\\u0026ndash;587\\u003c/li\\u003e\\n\\u003cli\\u003eIkeda N et al (2015) Determination of HLA-A, -C, -B, -DRB1 allele and haplotype frequency in Japanese population based on family study. \\u003cem\\u003eTissue Antigens\\u003c/em\\u003e 85, 252\\u0026ndash;259 \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/tan.12536\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eHou D et al (2016) Immune Repertoire Diversity Correlated with Mortality in Avian Influenza A (H7N9) Virus Infected Patients. Sci Rep 6:33843. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/srep33843\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003eThe main tables 1 and 2 are provided in the supplemental file KimuraMainTable240413.png.\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"Kumamoto University\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Melanoma, Immune checkpoint inhibitor, Biomarker, Follicular Helper T Cell, MARCO\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5997977/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5997977/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eImmune checkpoint inhibitors (ICIs) have greatly improved the prognosis of advanced melanoma. However, the efficacy of ICIs in Japanese patients has been found to be lower than that in Caucasians. We aimed to elucidate the genomic and transcriptomic features associated with the response to ICIs in Japanese patients with melanoma.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePatients and methods\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA total of 129 tumor samples from 78 patients with melanoma who received therapeutic regimens with or without ICI treatment were collected from 13 institutions in Japan. We performed exome and RNA sequencing and investigated the association between genomic and transcriptomic factors and the clinical efficacy of ICI therapy. Time-course data were also analyzed. This is the first and largest genomic cohort study in Japanese patients with melanoma in which tumor samples were prospectively analyzed.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe number of somatic SNVs in Japanese patients with melanoma was lower than that in TCGA Caucasian data owing to the biased distribution of WHO subtypes. The driver subtypes \\u003cem\\u003eBRAF\\u003c/em\\u003e, \\u003cem\\u003eNRAS,\\u003c/em\\u003e and \\u003cem\\u003eNF1\\u003c/em\\u003e were less prevalent, but \\u003cem\\u003etriple wildtype\\u003c/em\\u003e predominantly existed in the Japanese cohort. An exome-wide survey revealed no significant association of mutated genes with ICI response; however, transcriptomic analysis revealed inflammation-associated genes, including several chemokines and cytokines, that were highly expressed in responders. Follicular helper T cells, estimated by immune cell composition analysis, were significantly enriched in responders (\\u003cem\\u003ep\\u003c/em\\u003e= 0.0422). Through time-course transcriptome analysis, in addition to several cytotoxic T-cell genes, \\u003cem\\u003eMARCO\\u003c/em\\u003e on tumor-associated macrophages was found to be induced by ICI treatment in responders (\\u003cem\\u003ep\\u003c/em\\u003e = 0.0040). Protein expression of these genes was confirmed by immunohistochemical and multiplex immunofluorescence analyses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eProspective genomic and transcriptomic analyses revealed candidate biomarkers for ICI treatment in Japanese patients with melanoma.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Genomic and transcriptomic analyses of melanoma in Japanese patients reveal candidate biomarkers for immune checkpoint inhibitor responders\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-02-18 13:08:48\",\"doi\":\"10.21203/rs.3.rs-5997977/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"d9777487-5ee4-4217-8bd0-dab761c11492\",\"owner\":[],\"postedDate\":\"February 18th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-02-18T13:08:48+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-02-18 13:08:48\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5997977\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5997977\",\"identity\":\"rs-5997977\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}