Cited by 1,039Cancer Immunotherapy and BiomarkersCutaneous Melanoma Detection and ManagementImmunotherapy and Immune Responses

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Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma

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Liu, David, Schilling, Bastian, Liu, Derek, Sucker, Antje, Livingstone, Elisabeth, Jerby-Arnon, Livnat, Zimmer, Lisa, Gutzmer, Ralf, Satzger, Imke, Loquai, Carmen, Grabbe, Stephan, Vokes, Natalie, Margolis, Claire A., Conway, Jake, He, Meng Xiao, Elmarakeby, Haitham, Dietlein, Felix, Miao, Diana, Tracy, Adam, Gogas, Helen, Goldinger, Simone M., Utikal, Jochen, Blank, Christian U., Rauschenberg, Ricarda, von Bubnoff, Dagmar, Krackhardt, Angela, Weide, Benjamin, Haferkamp, Sebastian, Kiecker, Felix, Izar, Ben, Garraway, Levi, Regev, Aviv, Flaherty, Keith, Paschen, Annette, Van Allen, Eliezer M., Schadendorf, Dirk·2019·Nature Medicine

DOI 10.1038/s41591-019-0654-5PMC PMC6898788

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Abstract

Immune-checkpoint blockade (ICB) has demonstrated efficacy in many tumor types, but predictors of responsiveness to anti-PD1 ICB are incompletely characterized. In this study, we analyzed a clinically annotated cohort of patients with melanoma (n = 144) treated with anti-PD1 ICB, with whole-exome and whole-transcriptome sequencing of pre-treatment tumors. We found that tumor mutational burden as a predictor of response was confounded by melanoma subtype, whereas multiple novel genomic and transcriptomic features predicted selective response, including features associated with MHC-I and MHC-II antigen presentation. Furthermore, previous anti-CTLA4 ICB exposure was associated with different predictors of response compared to tumors that were naive to ICB, suggesting selective immune effects of previous exposure to anti-CTLA4 ICB. Finally, we developed parsimonious models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 ICB in individual tumors, with validation in smaller independent cohorts limited by the availability of comprehensive data. Broadly, we present a framework to discover predictive features and build models of ICB therapeutic response.

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Fig. 1 Cohort genomic and clinical characteristics and association of TMB with response. a, CoMut plot showing association between clinical and genomic characteristics. Each column represents a tumor. Tumors are ordered by best RECIST criteria response (CR, PR, PD, SD or MR), and within each response subgroup by decreasing nonsynonymous (Nonsyn) mutational load (top row). Nonsynonymous mutational burden is further subdivided into clonal (purple) and subclonal (light purple) mutational load. Mutational signatures (sig) refer to the inferred relative contribution of UV-induced mutations, alkylating DNA damage process and other mutational signatures (aging+). The primary type of melanoma (skin, occult, acral or mucosal) is indicated. Tumor purity is the inferred proportion of the tumor sample that is from cancer cells compared to other cell types (Methods). The dominant mutational signature (that is, the mutational signature associated with the highest proportion of mutations) is indicated. Mutations in BRAF, NRAS and NF1 are shown for each tumor. b, Mutational load (mut load) in progressors (n = 65 patients), responders (n = 55 patients) and patients with SD and MR (n = 24 patients). Nonsynonymous mutational load is higher in responders (CR and PR) than in progressors (two-sided MWW, P = 0.026), but is not significantly different between responders and patients having SD or MR as the best RECIST response (two-sided MWW, P = 0.14). c, Mutational load by melanoma type. Different melanoma types have different mutational loads (Kruskal–Wallis, P = 2.4 × 10−5): mutational load is higher in cutaneous and occult melanomas (n = 124 patients) than in acral and mucosal melanomas (n = 20 patients; median 297.5 versus 58; two-sided MWW, P = 1.1 × 10−6). d, Response to anti-PD1 ICB by melanoma type. Cutaneous and occult melanomas (n = 124 patients) have higher response rates (~40% CR and PR) versus acral and mucosal melanomas (n = 20 patients, 20%; two-sided Fisher’s exact test, P = 0.06). e, Mutational load in responders versus progressors stratified by melanoma type. There was no significant difference between responder and progressor mutational loads when melanomas were stratified by type (two-sided MWW; progressors versus responders (PD/R): skin (n = 42/43), P = 0.27; occult (n = 10/8), P = 0.35; acral (n = 7/2), P = 0.19; mucosal (n = 6/2), P = 0.40). Mutational load was also not a significant predictor of response in combined logistic regression after adjusting for melanoma type (P = 0.24). f, TMB in responders versus nonresponders, stratified by skin or occult melanomas versus mucosal or acral melanomas. Within each subgroup, responders trended toward having higher TMB than nonresponders (cutaneous/occult (n = 52 progressors and 51 responders): MWW, P = 0.14; mucosal/acral (n = 13 progressors and 4 responders): MWW, P = 0.08). Notably, responders with mucosal or acral melanoma (n = 4) had a lower mutational load than progressors with cutaneous or occult melanoma (n = 52; MWW, P = 0.03). Boxplots: box limits indicate the IQR (25th to 75th percentiles), with a center line indicating the median. Whiskers show the value ranges up to 1.5 × IQR above the 75th or below the 25th percentiles, with outliers beyond those ranges shown as individual points. *P < 0.05, **P < 0.01, ***P < 0.001. NS, not significant.CC BY · PubMed Central

Fig. 2 Genomic and transcriptomic features associated with response. All P values are unadjusted, unless otherwise indicated. a, Tumor heterogeneity, defined as the proportion of subclonal mutations in each tumor (Methods), in responders (CR or PR) versus progressors (PD). Progressors (n = 65 patients) had greater heterogeneity than responders (n = 55 patients; two-sided MWW, P = 0.02). b, Tumor ploidy, defined as the overall genomic copy number (a normal diploid cell has a copy number of 2; Methods), in responders versus progressors. Responders (n = 55 patients) had higher tumor ploidy than progressors (n = 65 patients; two-sided MWW, P = 0.04). c, Tumor purity, defined as the proportion of DNA from tumor versus other cells in the sample (Methods), in responders versus progressors. Progressors (n = 65 patients) had higher tumor purity than responders (n = 55 patients; two-sided MWW, P = 0.04). d, Response versus progression in TAP2-amplified tumors versus other tumors. TAP2 amplification (n = 6 patients) was associated with response (two-sided Fisher’s exact test, P = 0.008). e, Response versus progression in tumors with amplified MHC-I HLA genes (HLA-A, HLA-B or HLA-C) versus other tumors. MHC-I HLA amplification (n = 6 patients) was associated with response (two-sided Fisher’s exact test, P = 0.008). f, Venn diagram showing the overlap of TAP2-amplified tumors and tumors with amplification of MHC-I HLA genes. Four tumors had amplifications on chromosome 6, including the MHC-I genes HLA-A, HLA-B, HLA-C and TAP2 and two tumors each had amplifications in one but not the other region, for a total of eight tumors with amplifications in either. g, Difference in the median expression and two-sided MWW P value of association between 938 immune-related genes56 and features in responders versus progressors. Expression levels of MHC-II HLA genes (red), MHC-I HLA genes and antigen-presentation machinery (APM)-related genes (orange) are shown. h, Hierarchical clustering of the correlation matrix between genomic, clinical and transcriptomic features associated with response. Color indicates the Pearson correlation between features, from perfect negative correlation (Pearson, r = −1, blue) to perfect positive correlation (Pearson, r = 1, red). An immune-related cluster of MHC-I- and MHC-II-related gene expression is observed, with subclusters of MHC-I and MHC-II genes. Mutational and neoantigen load are highly correlated and form a cluster independently from the immune cluster (for example, Pearson correlation, r = 0.15, P = 0.11 between ssGSEA of MHC-II HLA genes and nonsynonymous mutational load). Purity is negatively correlated with the immune cluster and is independent of ploidy and heterogeneity. The sample size for each correlation depended on the number of available data points: correlations involving exclusively genomic or clinical data had n = 144 tumor samples, whereas correlations involving transcriptomic features had n = 121 tumor samples with data available. Tx, treatment. Boxplots: box limits indicate the IQR (25th to 75th percentile), with a center line indicating the median. Whiskers show the value ranges up to 1.5 × IQR above the 75th or below the 25th percentile, with outliers beyond those ranges shown as individual points. *P < 0.05, **P < 0.01, ***P < 0.001; NS, not significant.CC BY · PubMed Central

Fig. 3 Differential predictors of response and progression in ipilimumab-treated tumors versus ipilimumab-naive tumors. a, Timeline showing when sequenced biopsies were obtained from tumors that were treated with ipilimumab or naive to ipilimumab in the course of therapy. Subsequent analyses focused on comparing tumor biopsies obtained after ipilimumab treatment (n = 45 WES, n = 34 RNA-seq) to ipilimumab-naive tumor biopsies (n = 84 WES, n = 74 RNA-seq). b, Best RECIST response by ipilimumab pretreatment status. There was no difference between the distribution of responses in naive (n = 84) and pretreated (n = 60) patients (two-sided chi-squared test, P = 0.44; degrees of freedom (d.f.) = 3). c, ssGSEA of MHC-II HLA genes (Methods) in responders (n = 47 patients) versus progressors (n = 56 patients) in the overall cohort. MHC-II scores were higher in responders than in progressors (two-sided MWW, P = 0.03). d, ssGSEA of MHC-II HLA genes in responders versus progressors in the post-ipilimumab-treatment subgroup. MHC-II scores were higher in responders (n = 11 patients) than in progressors (n = 16 patients; two-sided MWW, P = 0.03). e, ssGSEA of MHC-II HLA genes in responders (n = 31 patients) versus progressors (n = 34 patients) in the ipilimumab-naive subgroup. There was no significant difference in MHC-II scores between responders and progressors (two-sided MWW, P = 0.31). f, MHC-II HLA gene set scores (ssGSEA) in responders (n = 10 patients) versus progressors (n = 22 patients) in a validation cohort (Methods; two-sided MWW, P = 0.34). g, MHC-II HLA gene set scores (ssGSEA) in responders (n = 4 patients) versus progressors (n = 11 patients) in the ipilimumab-treated subgroup of a validation cohort (two-sided MWW, P = 0.10). h, MHC-II HLA gene set scores (ssGSEA) in responders (n = 6 patients) versus progressors (n = 11 patients) in the ipilimumab-naive subgroup of a validation cohort (two-sided MWW, P = 0.80). i, Selected Cancer Hallmark gene sets (GSEA) enriched in responders versus progressors in the overall (n = 47 responders and 56 progressors), post-ipilimumab-treatment (n = 11 responders and 16 progressors), and ipilimumab-naive (n = 31 responders and 34 progressors) subgroups of our discovery cohort. IFN-γ and IFN-α response pathways were enriched in responders in the overall (FDR, q < 0.001 and q = 0.02, respectively) and ipilimumab-treated (q < 0.001, both) subgroups but not in the ipilimumab-naive subgroups (q = 0.13 and q = 0.997, respectively) in the discovery cohort (empiric, P = 0.183 and P = 0.18, respectively for the difference in q values between the subgroups; Methods). j, Selected Cancer Hallmark gene sets (GSEA) enriched in responders versus progressors in the overall, post-ipilimumab-treatment and ipilimumab-naive subgroups in an independent validation cohort. IFN-γ and IFN-α response pathways were enriched in responders in the overall (FDR, q < 0.0001 and q = 0.0034, respectively) and ipilimumab-treated (q < 0.0001, both) subgroups but not in the ipilimumab-naive subgroup (q = 0.87 and q = 0.03 (enriched in progressors), respectively) in the discovery cohort. Pathways favoring enrichment in progressors (as opposed to responders) are visualized here with an FDR q value of 1. All Hallmark pathways and their GSEA enrichment scores are shown in Supplementary Table 5. Boxplots: box limits indicate the IQR (25th to 75th percentile), with a center line indicating the median. Whiskers show the value ranges up to 1.5 × IQR above the 75th or below the 25th percentile, with outliers beyond those ranges shown as individual points. *P < 0.05, **P < 0.01, ***P < 0.001. NS, not significant.CC BY · PubMed Central

Fig. 4 Progression versus response by immune infiltrate and MHC-II HLA expression stratified by ipilimumab treatment. a, Proportion of responders versus progressors in subgroups with high and low MHC-II HLA score (ssGSEA; divided by the median). Overall, a high MHC-II HLA score was associated with higher response (MHC-II high: n = 29 responders and 20 progressors; MHC-II low: n = 18 responders and 36 progressors; two-sided Fisher’s exact test, P = 0.01; OR = 2.9, 95% CI 1.3–6.5). b, As in a but in the ipilimumab-treated subgroup. Tumors with low MHC-II HLA score (ssGSEA) were associated with PD (MHC-II high: n = 9 responders and 5 progressors; MHC-II low: n = 2 responders and 11 progressors; two-sided Fisher’s exact test, P = 0.02; OR = 9.9, 95% CI 1.5–63.7). c, As in a but in the ipilimumab-naive subgroup. There was no significant difference between tumors with high and low MHC-II HLA scores (ssGSEA; MHC-II high: n = 18 responders and 14 progressors; MHC-II low: n = 13 responders and 19 progressors; two-sided Fisher’s exact test, P = 0.32; OR = 1.9, 95% CI 0.7–5.1). d, Proportion of responders versus progressors in subgroups with high and low immune infiltrate scores (divided by the median). Overall, there was no statistically significant difference in the proportion of responders versus progressors in tumors with high versus low immune infiltrate (infiltrate high: n = 27 responders and 24 progressors; infiltrate low: n = 20 responders and 32 progressors; two-sided Fisher’s exact test, P = 0.17; OR = 1.8, 95% CI 0.8–3.9). e, As in d but in the ipilimumab-treated subgroup. Tumors with low immune infiltrate scores were strongly associated with PD (infiltrate high: n = 10 responders and 7 progressors; infiltrate low: n = 1 responder and 9 progressors; two-sided Fisher’s exact test, P = 0.02; OR = 12.9, 95% CI 1.3–125.8). f, As in d but in the ipilimumab-naive subgroup. There was no statistically significant difference between tumors with high and low immune infiltrate scores (infiltrate high: n = 14 responders and 15 progressors; infiltrate low: n = 17 responders and 18 progressors; two-sided Fisher’s exact test, P = 1.0; OR = 0.99, 95% CI 0.4–2.6). g, Schematic of the hypothesized effect of ipilimumab treatment on immune response as a predictor of subsequent response to anti-PD1 ICB. Tumors that were treated with ipilimumab but failed to have an immune response or infiltrate in the tumor microenvironment were strongly predicted to have intrinsic resistance to anti-PD1 ICB. However, having an immune response did not guarantee a subsequent response to anti-PD1 ICB. In ipilimumab-naive tumors, neither the presence nor the absence of immune infiltrate was a good predictor of anti-PD1 ICB response.CC BY · PubMed Central

Fig. 5 Integrative predictive modeling of intrinsic resistance to anti-PD1 ICB. a, CoMut plot showing the relationship between response and predictive features in the ipilimumab-treated subgroup. Each column represents a patient, and the top row indicates whether the patient had PD (intrinsic resistance) or non-PD (CR, PR, SD or MR) as the best response. Patients are sorted by MHC-II HLA score, which was the most predictive feature and was correlated with MHC-I and IFN response pathway scores. MHC-II HLA, LDH at treatment initiation and the presence of lymph node metastases (LN met) were features used for our logistic regression model, chosen using forward selection (Methods). b, A receiver–operator characteristic (ROC) curve for our predictive model for ipilimumab-treated tumors (n = 34 patients) using MHC-II HLA, LDH and lymph node metastases as features. The AUC was 0.9, and the log-likelihood ratio was P = 0.0003. The fivefold cross-validation mean AUC was 0.83. c, CoMut plot showing the relationship between response and predictive features in the ipilimumab-naive subgroup. Each column represents a patient, and the top row indicates whether the patient had PD (intrinsic resistance) or non-PD as the best response. Patients are sorted by tumor heterogeneity (top), tumor ploidy (middle) and tumor purity (bottom), which were the three features chosen in our predictive model (Methods). Purity, ploidy and heterogeneity were independent predictors in the multivariate model (Supplementary Table 9). d, The ROC curve for our predictive model for ipilimumab-naive tumors (n = 84 tumors) using heterogeneity, purity and ploidy as features. The AUC was 0.77, and the log-likelihood ratio was P = 0.0003. The average tenfold cross-validation mean AUC was 0.73. e, Survival (PFS and OS as indicated) stratified by high versus low predictive model score (split by the median) in ipilimumab-treated tumors in our discovery cohort. Tumors with high scores had worse PFS and OS (two-sided KM log-rank test, P = 0.0001 and P = 0.001, respectively). f, Survival (PFS and OS as indicated) stratified by high versus low predictive model score (split by the median) in ipilimumab-naive tumors in our discovery cohort. Tumors with high scores had worse PFS and OS (two-sided KM log-rank test, P = 0.003 and P = 6.3 × 10−5, respectively).CC BY · PubMed Central

Extended Data Fig. 1 Consort Diagram showing inclusion, exclusion, and quality control criteria for patients/tumors included in analysis.CC BY · PubMed Central

Extended Data Fig. 2 Survival and genomic copy number characteristics of responders (n = 55 patients) (defined as CR or PR as best response) vs. progressors (n = 65 patients) (defined as PD as best response). (a) PFS Kaplan Meier survival curves; two-sided log-rank p = 2.1e-28 (b) OS Kaplan Meier survival curves; two-sided log-rank p = 5.1e-18 (c) Proportion of tumor genome with copy number alterations (two-sided MWW p = 0.09). Boxplots: Box limits indicate the interquartile range (25th-75th percentile), with a center line indicating the median. Whiskers show the value ranges up to 1.5xIQR above the 75th or below the 25th percentile, with outliers beyond those ranges shown as individual points. PD = progressive disease. CR = complete response. PR = partial response.CC BY · PubMed Central

Extended Data Fig. 3 Association of Genomic Alterations in Genes with Response vs. Progression. For each gene, the association of a genomic alteration with response was tested using a two-sided Fisher’s Exact test, and the odds ratio (OR) and p-value calculated. The adjusted OR is a conservative estimate generated by adding 1 to both mutant gene responders and progressors, thus moving the OR estimate closer to 1. The x- and y-axis are on log scales, but labeled in the original units. (a) Association of gene mutations with response (n = 55 patients) vs. progression (n = 65 patients). (b) Association of gene amplifications with response (n = 55 patients) vs. progression (n = 65 patients).CC BY · PubMed Central

Extended Data Fig. 4 Amplification of Chromosome 6 Regions in TAP2, HLA-A/B/C amplified tumors. Patients with amplifications in TAP2 or HLA-A/B/C are shown, with inferred amplifications in the relevant section of Chromosome 6 shown. 6 patients had amplifications in TAP2, 6 patients had amplifications in HLA-A/B/C, and 4 patients had amplifications of a chromosomal region including all four genes.CC BY · PubMed Central

Extended Data Fig. 5 Hierarchical clustering of the correlation matrix between transcriptional signatures previously associated with immunotherapy response and hallmark gene-sets, genomic, clinical, and transcriptomic features associated with response in our cohort. The color indicates the Pearson correlation between features, from perfect negative correlation (Pearson r = -1, blue) to perfect positive correlation (Pearson r = 1, red). Most previously hypothesized signatures cluster within an immune-activity related group with immune-related gene-sets. Sample size for each correlation depends on the number of available data points: correlations involving exclusively genomic or clinical data had n = 144 tumor samples, while correlations involving transcriptomic features had n = 121 tumor samples with data available.CC BY · PubMed Central

Extended Data Fig. 6 Expression of Genomic Features in Responders vs. Progressors in ipilimumab-treated and -naive subsets. Two-sided Mann-Whitney-Wilcoxon tests were used to compare responders to progressors (ipilimumab-treated: n = 16 responders/20 progressors; ipilimumab-naïve: n = 34 responders/37 progressors). All p-values are unadjusted. Log Nonsyn Mutload: Ipi-treated p = 0.15; Ipi-naïve p = 0.15. Log Clonal Mutload: Ipi-treated p = 0.17; Ipi-naïve p = 0.08. Heterogeneity: Ipi-treated p = 0.51; Ipi-naïve p = 0.057. Ploidy: Ipi-treated p = 0.45; Ipi-naïve p = 0.002. Boxplots: Box limits indicate the interquartile range (25th-75th percentile), with a center line indicating the median. Whiskers show the value ranges up to 1.5xIQR above the 75th or below the 25th percentile, with outliers beyond those ranges shown as individual points.CC BY · PubMed Central

Extended Data Fig. 7 Survival of patients stratified by progressive disease (PD) or non-PD (SD, MR, PR, or CR) best response (RECIST 1.1) to PD-1 ICB. (a) PFS (two-sided logrank p = 4.0e-38 (b) OS (two-sided logrank p = 1.2e-19).CC BY · PubMed Central

Extended Data Fig. 8 Model selection criteria with increasing number of features by forward selection; cross-validation AUC distribution of final models. (a) Model AUC for the ipi-treated subgroup. Each point is labeled with the additional feature, and the final model includes MHC-II, Lymph node metastasis, and LDH below the median as features. (b) Model AUC for the ipi-naive subgroup. Each point is labeled with the additional feature, and the final model includes ploidy, heterogeneity, and purity as features. (c) Model AIC/BIC for the ipi-experienced subgroup. (d) Model AIC/BIC for the ipi-naïve subgroup. (e) Using 10-fold crossvalidation in the ipilimumab-naïve subset, mean crossvalidation AUC is 0.73, with a SD of 0.25. Each fold split the cohort into a training (n = 74 patients) and test set (n = 10 patients) ((f) In the ipi-experienced subset, mean crossvalidation AUC is 0.85, with a SD of 0.18. Each fold split the cohort into a training (n = 27 patients) and test set (n = 7 patients).CC BY · PubMed Central

Extended Data Fig. 9 Subgroup Model ROC curves applied to other subgroups. (a) Ipilimumab-treated subgroup model applied to ipilimumab-naive (n = 74 patients) subgroup; (b) Ipilimumab-naive model applied to ipilimumab-treated (n = 45 patients) subgroup; (c) Ipi-naïve model predicting PD as best response to PD-1 ICB applied to pre-ipilimumab-treated (n = 15 patients) tumors. (top) Comut plot overlaying best response and the ipi-naïve model score. Each column is one of 15 ipilimumab-treated tumors biopsied prior to ipilimumab therapy, ordered by the predictive model score. The 5 highest scoring tumors were all PD. (bottom) Receiver-operator curve for our ipilimumab-naive predictive model applied to pre-ipilimumab tumors (n = 15), with an AUC of 0.71.CC BY · PubMed Central

Extended Data Fig. 10 Limited validation testing single feature models in validation cohort. (a) Relationship between response and MHC-II HLA score in the ipilimumab-treated subgroup of a validation cohort. Each column is a patient, and patients are sorted by MHC-II HLA score. (b) Validation of Predictive Model for ipilimumab-treated tumors. In a validation cohort (n = 23), a model using the only common available feature (MHC-II HLA score) had an AUC of 0.65 (log likelihood ratio p = 0.09, empiric p = 0.21, Methods). (c) Relationship between response and MHC-II HLA score in the ipilimumab-treated subgroup of a validation cohort. Each column is a patient, and patients are sorted by MHC-II HLA score. (d) Validation of Predictive Model for ipilimumab-naive Tumors. In a validation cohort (n = 20), a model using the only common available feature (heterogeneity) had an AUC of 0.73 (log likelihood ratio p = 0.17, empiric p = 0.066, Methods).CC BY · PubMed Central

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Yu, Q., Zhong, H., Zhu, X., Liu, C., Zhang, X., Wang, J., Li, Z., Shi, S., Zhao, H., Zhou, C.-X., et al. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1521930 · Cited by 5
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Li, Y., Yang, W., Chen, H., Jin, Z., Dong, J., Ma, L., & Ji, Z. (2025) · International Immunopharmacology · doi:10.1016/j.intimp.2025.114381 · Cited by 4
Investigation of sphingolipid-related genes in lung adenocarcinoma
Mao, J., Li, L., Sun, H., Han, J., Li, J., Dong, C.-S., & Zhao, H. (2025) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1548655 · Cited by 4
Breast cancer scoring based on a multiplexed profiling of soluble and cell-associated (immune) markers facilitates the prediction of pembrolizumab therapy
Schweihofer, V., Bruß, C., Seitz, S., Glehr, G., Hetterich, M., Weber, F., Hatzipanagiotou, M. E., Fernandez-Pacheco, M., Ortmann, O., Brockhoff, G., et al. (2025) · Cancer Cell International · doi:10.1186/s12935-025-03729-7 · Cited by 4
Super-high tumor mutational burden predicts complete remission following immunotherapy: from Peto’s paradox to druggable cancer hallmark
Zheng, M. (2025) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2024-010486 · Cited by 5
Comprehensive analysis of single-cell and bulk RNA sequencing data reveals an EGFR signature for predicting immunotherapy response and prognosis in pan-cancer
Ye, C., Chen, X., Chen, Z., Liu, S., Kong, R., Lin, W., Zhu, M., Sun, X., & Xu, Z. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1604394 · Cited by 4
PRECOG update: an augmented resource of clinical outcome associations with gene expression for adult, pediatric, and immunotherapy cohorts
Benard, B. A., Lalgudi, C. K., Ilerten, I., Wang, R. H., & Gentles, A. J. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1215 · Cited by 4
Single-cell mapping of alternative splicing linked to checkpoint immunotherapy response
Xiong, J., Bricard, O., Arijs, I., Demeulemeester, J., Gu, C., Thienpont, B., Daaboul, D., Bassez, A., Bechter, O. E., Poźniak, J., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1171 · Cited by 4
CDK4/6 inhibition reprograms the breast cancer immunopeptidome via Rb-dependent chromatin and transcriptomic remodeling
Minati, R., Cahuzac, M., Perrault, J., Lullier, V., Apavaloaei, A., Bonneil, É., Courcelles, M., Lanoix, J., Kina, E., Perreault, C., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116676 · Cited by 4
Reference-guided computational framework identifies microenvironment metabolic subtypes and targets using pan-cancer single-cell datasets
Tang, K., Han, Y., Sun, D., Dong, X., Han, T., Wei, H., Shao, W., Hu, J., Liu, Z., Zhang, L., et al. (2025) · Genome Medicine · doi:10.1186/s13073-025-01572-z · Cited by 4
MHC class I on target cells regulates CD4+ T cell-mediated immunity
Lauder, E., Gondal, M. N., Wu, M.-C., Yamamoto, A., Maneix, L., Zhao, D., Sun, Y., Cieslik, M., Chinnaiyan, A. M., & Reddy, P. R. (2026) · Nature Immunology · doi:10.1038/s41590-026-02480-z · Cited by 4
Somatic mutations in DCC are associated with genomic instability and favourable outcomes in melanoma patients treated with immune checkpoint inhibitors
Li, Y., Wang, Q., Chen, Y., & Zhao, L. (2022) · British Journal of Cancer · doi:10.1038/s41416-022-01921-4 · Cited by 4
Identification of comutation in signaling pathways to predict the clinical outcomes of immunotherapy
Qiu, J., Li, X., He, Y., Wang, Q., Li, J., Wu, J., Jiang, Y., & Han, J. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03836-3 · Cited by 4
Identification of ATM Mutation as a Potential Prognostic Biomarker for Immune Checkpoint Inhibitors Therapy
Cui, S., Chen, T., Zhao, Y., Xiao, Z., Liu, M., Huang, X., Cao, S., Zhou, R., Li, Y., Huo, X., et al. (2023) · Current Cancer Drug Targets · doi:10.2174/0115680096250376231025062652 · Cited by 4
Distinct patterns of proteostasis network gene expression are associated with different prognoses in melanoma patients
Wellman, R., Jacobson, D. H., Secrier, M., & Labbadia, J. P. (2024) · Scientific Reports · doi:10.1038/s41598-023-50640-0 · Cited by 4
Discovery of a novel ROS-based signature for predicting prognosis and immunosuppressive tumor microenvironment in lung adenocarcinoma
Guo, Y., Chang, G., Wan, R., Zhang, X., Ma, Z., Bai, H., & Wang, J. (2024) · Journal of Cancer · doi:10.7150/jca.93975 · Cited by 4
Transcriptome Deconvolution Reveals Absence of Cancer Cell Expression Signature in Immune Checkpoint Blockade Response
Guo, Y. A., Kulshrestha, T., Chang, M. M., Kassam, I., Revkov, E., Rizzetto, S., Tan, A. C., Tan, D. S. W., Tan, I. B., & Skanderup, A. J. (2024) · Cancer Research Communications · doi:10.1158/2767-9764.crc-23-0442 · Cited by 4
KRT81 and HNF1A expression in pancreatic ductal adenocarcinoma: investigation of predictive and prognostic value of immunohistochemistry‐based subtyping
Rao, J., Sinn, M., Pelzer, U., Riess, H. B., Oettle, H., Demir, İ. E., Friess, H., Jäger, C., Steiger, K., & Muckenhuber, A. (2024) · The Journal of Pathology Clinical Research · doi:10.1002/2056-4538.12377 · Cited by 4
ITGB4 is a prognostic biomarker and correlated with lung adenocarcinoma brain metastasis
Zhang, J., Li, L., Luo, W., Ma, S., & Zhao, Y. (2024) · Clinical & Translational Oncology · doi:10.1007/s12094-024-03527-z · Cited by 4
Differential Infiltration of Key Immune T-Cell Populations Across Malignancies Varying by Immunogenic Potential and the Likelihood of Response to Immunotherapy
Eljilany, I., Coleman, S. W., Tan, A. C., McCarter, M. D., Carpten, J. D., Colman, H., Naqash, A. R., Puzanov, I. J., Arnold, S. M., Churchman, M. L., et al. (2024) · Cells · doi:10.3390/cells13231993 · Cited by 4
Adaptive individualized gene pair signatures distinguishing melanoma and predicting response to immune checkpoint blockade
Du, Z., Chen, Q., Huang, W., Zhou, Y., Wen, H., Wang, D., Chen, Y., Cheng, L., & Zheng, X. (2025) · iScience · doi:10.1016/j.isci.2025.113329 · Cited by 4
Prediction performance of twelve tumor mutation burden panels in melanoma and non-small cell lung cancer
Xu, D., Li, J., Wang, D., Zhou, L. Q., Jin, J., & Wang, Y. (2021) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2021.103573 · Cited by 5
TIRSF: a web server for screening gene signatures to predict Tumor immunotherapy response
Chen, L., Chen, T., Zhang, Y., Lin, H., Wang, R., Wang, Y., Li, H., Zuo, Z., Ren, J. M., & Xie, Y. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac374 · Cited by 4
Comprehensive characterization of pyroptosis phenotypes with distinct tumor immune profiles in gastric cancer to aid immunotherapy
Huang, K., Lin, Y., Qiu, G., Wang, S.-Y., Feng, L., Zheng, Z., Gao, Y., Fan, X., Zheng, W., Zhuang, J., et al. (2023) · Aging · doi:10.18632/aging.204958 · Cited by 4
Genomic and tumour microenvironmental biomarkers of immune checkpoint inhibitor response in advanced Taiwanese melanoma
Chang, J. W., Huang, C., Huang, W., Wang, Y., Hsieh, J., Chang, Y., Huang, Y., Wu, C., Wang, Y., Chen, S., et al. (2023) · Clinical & Translational Immunology · doi:10.1002/cti2.1465 · Cited by 4
RPTOR mutation: a novel predictor of efficacious immunotherapy in melanoma
Jiang, Y., Hu, X., Wang, Z., Zhang, Q., Chen, D., & Zhao, P. (2023) · Investigational New Drugs · doi:10.1007/s10637-023-01413-z · Cited by 4
GRIN2A mutation is a novel indicator of stratifying beneficiaries of immune checkpoint inhibitors in multiple cancers
Li, G., Chang, R.-Z., Liu, T., Jin, G.-N., Lu, K., Yong, T., Li, Z., Liu, J., Zhang, B., Zhang, W., et al. (2024) · Cancer Gene Therapy · doi:10.1038/s41417-024-00730-6 · Cited by 4
Different lactate metabolism subtypes reveal heterogeneity in clinical outcomes and immunotherapy in lung adenocarcinoma patients
Zhang, J., Bao, Y., Yang, L., Shi, X., Su, X., & He, X. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e30781 · Cited by 4
Evaluation of the Interplay between the ADAR Editome and Immunotherapy in Melanoma
Tusup, M., Cheng, P. F., Picardi, E., Raziunaite, A., Dummer, R., Levesque, M. P., French, L. E., Guenova, E., Kündig, T. M., & Pascolo, S. (2021) · Non-Coding RNA · doi:10.3390/ncrna7010005 · Cited by 4
Loss of Ubiquitin Ligase STUB1 Amplifies IFNγ-R1/JAK1 Signaling and Sensitizes Tumors to IFNγPreprint
Apriamashvili, G., Vredevoogd, D. W., Krijgsman, O., Bleijerveld, O. B., Ligtenberg, M. A., de Bruijn, B., Boshuizen, J., Altimari, D. M. D., Visser, N. L., Londino, J. D., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.07.191650 · Cited by 4
Improving T-cell mediated immunogenic epitope identification via machine learning: the neoIM modelPreprint
Pfitzer, L., Lybaert, L., Bogaert, C., & Fant, B. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.03.494687 · Cited by 4
Clinical, histological, and molecular differences in melanoma due to different TERT promoter mutations subtypes. A retrospective cross‐sectional study in 684 melanoma patients
Manrique‐Silva, E., David, M., Aguerralde‐Martin, M., García‐Casado, Z., Moro, R., Requena, C., Traves, V., Virós, A., Kumar, R., & Nagore, E. (2023) · Pigment Cell & Melanoma Research · doi:10.1111/pcmr.13155 · Cited by 3
Harnessing the gut microbiome to enhance cancer immunotherapy: Current advances and future directions in microbiota‐based therapeutic strategies
Zhao, B., Zhou, B., Li, Q., Su, C., Ma, J., & Yang, L. (2024) · MedComm – Future Medicine · doi:10.1002/mef2.70006 · Cited by 3
Metabolites and metabolic pathway reactions links to sensitization of immunotherapy in pan-cancer
Yu, S., Gao, Y., Zhao, F., Zhou, J., & Zhang, J. (2025) · Molecular Therapy Oncology · doi:10.1016/j.omton.2025.200933 · Cited by 3
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Li, Y., Chen, C., Ji, X., Jiang, N., Wang, F., Gao, X., Chen, W., Tang, Q., Yan, L., Zhang, S., et al. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1549656 · Cited by 3
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Dumas, O., Rozza, N., Cheishvili, D., Luk, S., Sin, S. K., Khassenova, A., Kremer, R. B., Moshe Szyf, A., Mihalcioiu, C. L. D., & Rabbani, S. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-13952-x · Cited by 3
An attention-based mRNA transformer network for accurate prediction of melanoma response to immune checkpoint inhibitors
Yin, Y., Zhang, T., & Wang, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-15830-y · Cited by 3
Evolution of a melanoma that escapes allogeneic rejection
Rokan, A., Diniz, M. O., Heras, J. I. D. L., Hall, C., Noorsaeed, S., Bennett, C. L., Secrier, M., Kassiotis, G., & Fassati, A. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116288 · Cited by 3
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Hu, Z., Chen, Y., Yang, H., Pan, Q., Li, H., Li, S., Wang, J., Huang, Y., Huang, G., & Qu, S. (2025) · Smart Medicine · doi:10.1002/smmd.70023 · Cited by 3
S-nitrosylation of IRF7 induced by NOS1 expression in melanoma suppresses anti-tumor immunity
Dai, Y., Zeng, S., Li, K., Huang, J., Yang, M., Gao, W., Chen, X., Huang, M., Ye, S., Wang, Q., et al. (2026) · Cell Death and Disease · doi:10.1038/s41419-025-08201-y · Cited by 4
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Ali, A. M., Kausar, M. A., Anwar, S., Khan, F. H., Elagib, H. M., Shahien, M. M., & Alshabrmi, F. M. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1795879 · Cited by 3
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Wu, Y., Yu, H., Zhang, Z., Xiong, W., Zeng, Z., Liu, W., Tu, H., & Lin, X. (2026) · iScience · doi:10.1016/j.isci.2026.115724 · Cited by 3
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Yang, Y. S., Wang, L., Zhang, Y., Lyu, S., Ruan, Q., Wang, C., Dou, F., Liang, G., Yan, G., Wang, M., et al. (2026) · The Journal of Pathology · doi:10.1002/path.70063 · Cited by 3
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Liu, G. (2026) · Applied Artificial Intelligence Research · doi:10.65455/q72yq328 · Cited by 3
Statescope: an integrative deconvolution framework for discovering cell states in tumors
Janssen, J., Steketee, M., Bose, A., van Asten, S. D., Eijk, P. P., Dijk, F., Sarasqueta, A. F., van Maldegem, F., Noske, D. P., Bahce, I., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-74997-8 · Cited by 3
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Vo, D. H. T., McGleave, G., & Overton, I. M. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12060958 · Cited by 3
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Zefferino, R., & Conese, M. (2023) · Vaccines · doi:10.3390/vaccines11111701 · Cited by 3
Identification and validation of the association of Janus kinase 2 mutations with the response to immune checkpoint inhibitor therapy
Chen, P., Long, J., Zhang, J., Xie, F., Wu, W., Tian, Z., Zhang, S., & Yu, K. (2024) · Inflammation Research · doi:10.1007/s00011-023-01833-w · Cited by 3
Decoupling the correlation between cytotoxic and exhausted T lymphocyte states enhances melanoma immunotherapy response prediction
Wang, B., Wang, K., Wu, D., Sahni, S., Jiang, P., & Ruppin, E. (2024) · iScience · doi:10.1016/j.isci.2024.109926 · Cited by 3
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Lin, J., Zhu, L., Chen, Y., Li, Q., Ke, Z., Zhang, H., Huang, Y.-F., Lu, J., & Chen, Y. (2024) · Acta Dermato Venereologica · doi:10.2340/actadv.v104.22146 · Cited by 3
Single‑cell RNA‑seq necroptosis‑related genes predict the prognosis of breast cancer and affect the differentiation of CD4+ T cells in tumor immune microenvironment
Guo, L., Ma, X., Li, H., Yan, S., Zhang, K., & Li, J. (2024) · Molecular and Clinical Oncology · doi:10.3892/mco.2024.2747 · Cited by 3
Gender and melanoma subtype‐based prognostic implications of MUC16 and TTN co‐occurrent mutations in melanoma: A retrospective multi‐study analysis
Kodali, N., Alomary, S., Bhattaru, A., Eldaboush, A., Schwartz, R. A., & Lipner, S. R. (2024) · Cancer Medicine · doi:10.1002/cam4.70199 · Cited by 3
Contactin-4 suppresses antitumor T cell responses by engaging amyloid precursor protein
Jeon, B., Kim, S., Kim, Y., Yu, H., Park, C.-H., Kim, G., Ha, Y., Kim, G.-Y., Kim, H., Palucka, K., et al. (2024) · Science Immunology · doi:10.1126/sciimmunol.adk7237 · Cited by 3
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Wang, Y., Wang, Z., Han, M., Miao, Q., Shao, B., Wang, N., Wang, J., Li, X., Yan, F., & Feng, S. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202417707 · Cited by 4
Single cell transcriptomic analysis reveals tumor immune infiltration by macrophage cells gene signature in lung adenocarcinoma
Guo, X., Deng, Y., Jiang, W., Li, H., Luo, Y., Zhang, H., & Wu, H. (2025) · Discover Oncology · doi:10.1007/s12672-025-01834-7 · Cited by 3
Classification of lung adenocarcinoma based on senescence-related genes identifies a cluster with immunotherapy resistance and poor prognosis
Gao, X., Shen, X., Huang, S., & Huang, S. (2025) · Discover Oncology · doi:10.1007/s12672-025-02127-9 · Cited by 3
Immune Checkpoint Inhibitors in Melanoma Treatment: Advances and Obstacles
Султанбаев, А. В. (2025) · Creative Surgery and Oncology · doi:10.24060/2076-3093-2025-15-1-57-65 · Cited by 3
Reframing Cancer as an Immunological Disorder Through Personalized Nutrition and AI Integration
Ghosh, J., & Roy, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9725-1.ch012 · Cited by 3
Distant metastases of melanoma exhibit varying extent of intrapatient proteogenomic heterogeneity
Szeitz, B., Hagemeijer, Y. P., Páhi, Z. G., Újfaludi, Z., Kuras, M., Murillo, J. R., Doma, V., Mohácsi, R., Herold, M., Herold, Z., et al. (2025) · Clinical and Translational Medicine · doi:10.1002/ctm2.70477 · Cited by 3
Programmed Cell Death Protein Ligand 2 Is a Potential Biomarker That Predicts the Efficacy of Immunotherapy
Wang, A., Chu, H., Zheng, J., Jia, Q., & Zhu, B. (2021) · Disease Markers · doi:10.1155/2021/9453692 · Cited by 3
Assessing proteolytic events in bioinformatic reanalysis of public secretome data from melanoma cell lines
Salardani, M. D. S., Barcick, U., & Zelanis, A. (2022) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2022.101259 · Cited by 3
Clustering by antigen-presenting genes reveals immune landscapes and predicts response to checkpoint immunotherapy
Gong, X., & Karchin, R. (2023) · Scientific Reports · doi:10.1038/s41598-023-28167-1 · Cited by 3
A mutational signature and significantly mutated driver genes associated with immune checkpoint inhibitor response across multiple cancers
Wang, Q., Zhang, W., Guo, Y., Shi, F., Li, Y., Kong, Y., Lyu, J., & Wang, S. (2023) · International Immunopharmacology · doi:10.1016/j.intimp.2023.109821 · Cited by 3
CD3ζ as a novel predictive biomarker of PD-1 inhibitor resistance in melanoma
Zhang, Z., Zhang, D., Wang, F., Liu, J., Jiang, X. L., Anuchapreeda, S., Tima, S., Xiao, Z., & Duangmano, S. (2023) · Molecular and Cellular Probes · doi:10.1016/j.mcp.2023.101925 · Cited by 3
Inflammatory response-based prognostication and personalized therapy decisions in clear cell renal cell cancer to aid precision oncology
Zhong, W., Chen, H., Yang, J., Huang, C., Lin, Y., & Huang, J. (2023) · BMC Medical Genomics · doi:10.1186/s12920-023-01687-5 · Cited by 3
Immunomodulatory Function of Pien Tze Huang in T Cell-Mediated Anti-tumor Activity against B16–F10, MC38 and Hep1-6 Tumor Models
Fu, Y., Liu, C., Wang, J., Ji, X., Tang, R., Liao, K., Chen, L.-Y., Hong, Y., Fan, B.-B., Wang, S.-C., et al. (2024) · Chinese Journal of Integrative Medicine · doi:10.1007/s11655-023-3749-2 · Cited by 3
N6-Methyladenosine Regulator-Mediated Methylation Modification Patterns with Distinct Prognosis, Oxidative Stress, and Tumor Microenvironment in Renal Cell Carcinoma
Li, C., Zhu, M., Gao, C., Lu, F., Chen, H., Liu, J., & Zhong, W. (2024) · Frontiers in Bioscience-Landmark · doi:10.31083/j.fbl2901033 · Cited by 3
Role of artificial intelligence in cancer diagnostics and therapeutics
Singh, R. S., Masih, G. D., Joshi, R., Sharma, S., Singh, A. K., & Medhi, B. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95116-6.00015-3 · Cited by 3
Predicting Resistance to Immunotherapy in Melanoma, Glioblastoma, Renal, Stomach and Bladder Cancers by Machine Learning on Immune Profiles
Mestrallet, G. (2024) · Onco · doi:10.3390/onco4030014 · Cited by 3
Gender Disparities in Melanoma: Advances in Diagnosis, Treatment, and the Role of Artificial Intelligence
Kakish, D. R. K., AlSamhori, J. F., Qaqish, L. N., Aburumman, L., Sarsur, R., Salkhadi, A. A., Nassif, Z. B., Akmal, M. A., & Nashwan, A. J. (2025) · Dermatological Reviews · doi:10.1002/der2.70022 · Cited by 3
ADCYAP1 as a pan-solid cancer biomarker: predictor of immunotherapy efficacy in bladder cancer and prognostic potential across solid tumors
Jing, X., & Deng, Y. (2025) · Discover Oncology · doi:10.1007/s12672-025-02408-3 · Cited by 3
Bioinformatics mining for gene expression data
Guo, A. (2020) · Scientia Sinica Vitae · doi:10.1360/ssv-2020-0260 · Cited by 3
Immune checkpoints related-LncRNAs can identify different subtypes of lung cancer and predict immunotherapy and prognosis
Zhang, H., Liu, M., Du, G., Yu, B., Ma, X., Gui, Y., Cao, L., Li, X., & Tan, B. (2022) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-022-03940-3 · Cited by 3
Tumor immunology
Sharif, P. M., Khoshbin, A. P., Nasrollahzadeh, E., Keshavarz‐Fathi, M., & Rezaei, N. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-818006-8.00003-7 · Cited by 3
Nischarin expression may have differing roles in male and female melanoma patients
Ostojić, M., Jevric, M., Ajtić, O. M., Živić, K., Tanić, M., Cavic, M. R., Srdić‐Rajić, T., & Grahovac, J. (2023) · Journal of Molecular Medicine · doi:10.1007/s00109-023-02339-y · Cited by 3
Artificial intelligence in cancer research and precision medicine
Corti, C., Cobanaj, M., Criscitiello, C., & Curigliano, G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13671-9.00005-3 · Cited by 3
Development of an immune-related signature for predicting survival outcome and immunotherapy response in osteosarcoma
Wang, Q., Zhang, W., Guo, Y., Li, Y., & Fu, K. (2021) · Aging · doi:10.18632/aging.203671 · Cited by 3
Die Numeruskongruenz von Subjekt und finitem Verb im Deutschen
Wegerer, M. (2012) · doi:10.25365/thesis.18319 · Cited by 3
Enhancer Reprogramming in Melanoma Immune Checkpoint Therapy ResistancePreprint
Maitituoheti, M., Shi, A., Tang, M., Ho, L., Terranova, C. J., Galani, K., Keung, E. Z.-Y., Creasy, C. A., Wu, M., Chen, J., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.31.506051 · Cited by 3
Self-citation
Stratified analysis identifies HIF-2 α as a therapeutic target for highly immune-infiltrated melanomasPreprint
Huang, A. Y., Burke, K. P., Porter, R. M., Meiger, L., Fatouros, P. R., Yang, J., Robitschek, E. J., Vokes, N. I., Ricker, C. A., Rosado, V., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.29.620300 · Cited by 3
Self-citation
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He, B., Zhang, L., Jiang, L., Yu, L., Li, Q., Tian, X., He, J.-Q., Zeng, L., Yang, Y., Wang, C., et al. (2021) · doi:10.21203/rs.3.rs-620390/v1 · Cited by 82
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Wang, K., Shi, J., Tong, X., Qu, N., Kong, X., Ni, S., Xing, J., Li, X., & Zheng, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae017 · Cited by 2
Pan-cancer analysis of MET mutation and its association with the efficacy of immune checkpoint blockade
Chen, L., Li, Y., Zhao, H., Huang, J., Yan, H., Lin, X., & Zhao, B. (2024) · Genes & Diseases · doi:10.1016/j.gendis.2024.101450 · Cited by 2
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Badwy, B., Ghanem, N. M., Banawan, K., & Ismail, N. E. (2024) · IEEE Access · doi:10.1109/access.2024.3514799 · Cited by 2
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Egea‐Rodriguez, S., Váraljai, R., Nordmann, T. M., Lubis, R., Philip, M., Rambow, F., Roesch, A., Flaig, M. J., Horn, S., Stoll, R., et al. (2025) · Clinical and Translational Medicine · doi:10.1002/ctm2.70094 · Cited by 2
Self-citation
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Lin, W., Huang, C.-J., & Wang, Y. (2025) · Discover Oncology · doi:10.1007/s12672-025-03184-w · Cited by 2
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Authors: Liu, D., Schilling, B., Liu, D., Sucker, A., Livingstone, E., Jerby-Arnon, L., Zimmer, L., Gutzmer, R., Satzger, I., Loquai, C., Grabbe, S., Vokes, N., Margolis, C. A., Conway, J., He, M. X., Elmarakeby, H., Dietlein, F., Miao, D., Tracy, A., Gogas, H., Goldinger, S. M., Utikal, J., Blank, C. U., Rauschenberg, R., von Bubnoff, D., Krackhardt, A., Weide, B., Haferkamp, S., Kiecker, F., Izar, B., Garraway, L., Regev, A., Flaherty, K., Paschen, A., Van Allen, E. M., & Schadendorf, D.
Affiliations: Broad Institute (US)
German Cancer Research Center (DE)
Medizinische Hochschule Hannover (DE)
University Medical Center of the Johannes Gutenberg University Mainz (DE)
National and Kapodistrian University of Athens (GR)
University Hospital Zurich (CH)
The Netherlands Cancer Institute (NL)
Universitätsklinikum Tübingen (DE)
University Hospital Regensburg (DE)
Charité – Universitätsmedizin Berlin (DE)
Eli Lilly (United States) (US)
Massachusetts General Hospital (US)
Corresponding author: Schadendorf, Dirk
ORCID: Liu, David
Schilling, Bastian
Liu, Derek
Sucker, Antje
Livingstone, Elisabeth
Zimmer, Lisa
Gutzmer, Ralf
Satzger, Imke
Loquai, Carmen
Grabbe, Stephan
Vokes, Natalie
Margolis, Claire A.
Conway, Jake
He, Meng Xiao
Elmarakeby, Haitham
Dietlein, Felix
Miao, Diana
Tracy, Adam
Gogas, Helen
Goldinger, Simone M.
Utikal, Jochen
Blank, Christian U.
Rauschenberg, Ricarda
Krackhardt, Angela
Weide, Benjamin
Haferkamp, Sebastian
Garraway, Levi
Regev, Aviv
Flaherty, Keith
Paschen, Annette
Van Allen, Eliezer M.
Schadendorf, Dirk
Venue: Nature Medicine
Publisher: Springer Science and Business Media LLC
Published: 2019-12
Volume: 25
Issue: 12
Pages: 1916-1927
ISSN: 1078-8956 (ISSN-L)
1546-170X
DOI: 10.1038/s41591-019-0654-5
PMC: PMC6898788
OpenAlex: W2991341799
Cited by: 1,039
References: 80
Citations by year: 2026: 118 · 2025: 156 · 2024: 203 · 2023: 210 · 2022: 157 · 2021: 129 · 2020: 64
FWCI: 37.04
Type: journal article
Language: en
Update notices: correction (2020-06-19)
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor, 1 d)
Locations: Nature Medicine (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by)
University of Regensburg Publication Server (University of Regensburg) (submittedVersion)
mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
Nature
Funders: National Science Foundation (1144152)
Conquer Cancer Foundation (T32 GM008313)
Damon Runyon Cancer Research Foundation
Dr. Miriam and Sheldon G. Adelson Medical Research Foundation
Deutsche Forschungsgemeinschaft (SCHA422/17-1, SCHA 422/17, PA 2376/1‐, HO 6389/2-1, KFO337, 2376/1-1, 6389/2-1, PA 2376/1‐1)
Universität des Saarlandes
Nationales Centrum für Tumorerkrankungen Heidelberg
National Institutes of Health (DGE1144152)
National Human Genome Research Institute (T32HG002295)
National Cancer Institute (R01CA227388, K08CA222663, K08CA234458, U01CA233100)
National Institute of General Medical Sciences (T32GM008313)
Topics: Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
Keywords: metastatic melanoma, predictive biomarkers, tumor mutational burden, MHC class I antigen presentation, MHC class II antigen presentation, immune checkpoint inhibitor resistance, transcriptomic features, clinical outcome prediction
MeSH terms: Exome Sequencing, Nivolumab, Female, Humans, Male, Melanoma, Mutation, Neoplasm Metastasis, Antigen Presentation, Drug Resistance, Neoplasm, Transcriptome, CTLA-4 Antigen, Programmed Cell Death 1 Receptor, Antibodies, Monoclonal, Humanized

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Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma

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