Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma
Open accessAbstract
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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Differential pathway representation analysis in the Liu cohort identified 78 pathways significantly dysregulated between responders and nonresponders (two-sided t test P < 0.05).
“Our analysis identified distinct pathway signatures differentiating responders from nonresponders, with 78 pathways showing significant dysregulation (two-sided t test, P < 0.05; Fig. 6A, Supplementary Text, and fig. S4, A to E).”
From PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction (2026) · cites this paper as [34]
In Results, “Genomic and transcriptomic features associated with response” (page unavailable), Liu et al. report pathways differing between responders and progressors. They report 24 pathways enriched in responders by GSEA at FDR q < 0.1, not 78 pathways by a two-sided t test at P < 0.05. The citing sentence calls the 78-pathway result “Our analysis,” so that result should not be treated as a finding reported by Liu et al. (LLM, conf=0.88)
Experimental — accuracy not yet measuredIn the Liu cohort, model-predicted responders had significantly improved overall survival compared with nonresponders (log-rank test P < 0.0001).
“Analysis demonstrated statistically significant improvements in both overall survival (OS) and progression-free survival (PFS) among model-predicted responders compared to nonresponders (log-rank test, P < 0.0001; Fig. 2, E and F).”
From PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction (2026) · cites this paper as [34]
The Results section, “Integrative predictive modeling of primary resistance” (page unavailable), reports better overall survival for patients with high versus low model scores, with log-rank P < 0.0001 in the ipilimumab-naive subgroup. The claim applies that P value to the Liu cohort generally, but the ipilimumab-treated subgroup had an overall-survival P value of 0.001. The source compares model-score groups rather than explicitly labeling them model-predicted responders and nonresponders. (LLM, conf=0.88)
Experimental — accuracy not yet measuredIn univariate Cox regression in the Liu cohort, PathTIGR was significantly associated with patient outcomes and showed the lowest hazard ratio (HR = 0.189, P < 0.001).
“univariate Cox proportional hazard regression analysis further revealed that bone metastasis, brain metastasis, and the PathTIGR model were significantly associated with patient outcomes, with PathTIGR showing the lowest hazard ratio (HR = 0.189, P < 0.001; Fig. 2H)”
From PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction (2026) · cites this paper as [34]
The Results section’s cohort-characteristics table identifies Liu’s melanoma cohort, and Methods > Patient cohort and clinical end points defines its survival outcomes (page numbers unavailable). Our copy does not report PathTIGR or the stated univariate Cox result, HR = 0.189 and P < 0.001. It supports identifying the cohort, but not attributing that finding to Liu et al. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIn the Liu cohort, model-predicted responders had significantly improved progression-free survival compared with nonresponders (log-rank test P < 0.0001).
“Analysis demonstrated statistically significant improvements in both overall survival (OS) and progression-free survival (PFS) among model-predicted responders compared to nonresponders (log-rank test, P < 0.0001; Fig. 2, E and F).”
From PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction (2026) · cites this paper as [34]
In Results, ‘Integrative predictive modeling of primary resistance’ (page unavailable), Liu et al. report longer PFS for patients with high versus low model scores in both ipilimumab subgroups. Their PFS log-rank P values are 0.0001 and 0.005, respectively—not P < 0.0001. The passage also concerns Liu et al.’s own model, so it does not verify the citing paper’s model-predicted groups. (LLM, conf=0.88)
Experimental — accuracy not yet measuredPathTIGR achieved an AUC of 0.975 (95% CI 0.962 to 0.997) for immune checkpoint inhibitor response prediction in the Liu melanoma cohort of 144 patients treated with anti-PD-1 therapy.
“Receiver operating characteristic (ROC) curve analysis demonstrated exceptional discriminatory performance for immune checkpoint inhibitor (ICI) response prediction, yielding an area under the ROC curve (AUC) of 0.975 [95% confidence interval (CI): 0.962 to 0.997; Fig. 2A].”
From PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction (2026) · cites this paper as [34]
The Results section on cohort characteristics reports 144 patients with advanced melanoma treated with anti-PD-1 therapy. The Results section on predictive modeling reports AUCs of 0.90 and 0.77 for Liu’s subgroup models; our copy does not report PathTIGR or an AUC of 0.975 with the stated confidence interval. (LLM, conf=0.88)
Experimental — accuracy not yet measured
In the Liu cohort, PathTIGR-stratified predicted responders had significantly higher objective response rates than predicted nonresponders (Fisher's exact test P < 0.0001).
“The clinical validation of PathTIGR predictions was established through the comparative analysis of objective response rates (ORRs) between PathTIGR-stratified responder and nonresponder subgroups, revealing significantly elevated response rates among predicted responders relative to nonresponders (Fisher’s exact test, P < 0.0001; Fig. 2D)”
From PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction (2026) · cites this paper as [34]
PathTIGR achieved an AUPRC of 0.969 in the Liu melanoma cohort.
“precision-recall curve analysis was used, revealing an area under the precision-recall curve (AUPRC) of 0.969 (Fig. 2B).”
From PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction (2026) · cites this paper as [34]
The Methods section on predictive model generation reports cross-validated AUCs, and the Results section on integrative predictive modeling reports ROC AUCs for the paper’s own models (page numbers unavailable). Searches for PathTIGR, precision-recall analysis, and 0.969 found no passage reporting the claimed AUPRC. The cited paper identifies the melanoma cohort but does not verify that performance result. (LLM, conf=0.78)
Experimental — accuracy not yet measuredPathTIGR achieved an AUC of 0.85 for 4-year survival prognostication in the Liu cohort.
“with PathTIGR achieving an AUC of 0.85 for 4-year survival prognostication (Fig. 2G)”
From PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction (2026) · cites this paper as [34]
The Results section on integrative predictive modeling reports AUCs for the paper’s own models of primary resistance, including 0.77 in the ipilimumab-naive group, but does not evaluate PathTIGR. Searches for PathTIGR, AUC 0.85, and four-year survival found no passage reporting the claimed prognostic performance in this cohort; page numbers were unavailable in our copy. (LLM, conf=0.77)
Experimental — accuracy not yet measuredIn multivariate Cox regression, PathTIGR remained an independent prognostic factor in the training cohort (HR = 0.141, P < 0.001).
“multivariate Cox regression analysis confirmed that PathTIGR remained an independent prognostic factor in training cohort (HR = 0.141, P < 0.001; Fig. 2H and Table 1)”
From PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction (2026) · cites this paper as [34]
The Results section on integrative predictive modeling reports independent predictors of anti-PD1 response, but not PathTIGR or the claimed hazard ratio. The Methods sections describe logistic regression for the predictive model and log-rank tests for survival curves. Searches for PathTIGR, multivariate Cox regression, and HR = 0.141 found no passage addressing the claim; page numbers were unavailable in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredPathTIGR maintained AUC values consistently exceeding 0.8 at 2-year and 3-year intervals in the Liu cohort.
“maintaining AUC values consistently exceeding 0.8 at 2-year and 3-year intervals”
From PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction (2026) · cites this paper as [34]
The Results section on integrative predictive modeling reports AUCs for Liu et al.’s own models, including 0.77 in the ipilimumab-naive group, but does not report PathTIGR performance. Searches for PathTIGR and for 2-year and 3-year time-dependent AUCs found no passage addressing the claimed values; page numbers were unavailable in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredPathTIGR consistently outperformed individual clinical variables (gender, brain metastasis, lung metastasis, bone metastasis, and M stage) in predicting patient prognosis in the Liu cohort.
“The PathTIGR model consistently outperformed all these individual clinical variables in predicting patient prognosis (fig. S1).”
From PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction (2026) · cites this paper as [34]
The Methods section on predictive model generation lists sex, M stage, and metastatic sites among the clinical features evaluated. The Results section on integrative predictive modeling reports Liu et al.'s own models, but our copy does not mention PathTIGR or compare it with those individual variables. Searched for PathTIGR and the specified clinical-variable comparison; page numbers were not available. (LLM, conf=0.76)
Experimental — accuracy not yet measuredSimilar results were observed in a larger validation cohort of advanced melanoma patients treated with ICB.
“We observed similar results in a larger validation cohort of advanced melanoma patients treated with ICB (Liu et al., 2019) (Figures 6C, S6E, and S6F).”
From Microbiota triggers STING-type I IFN-dependent monocyte reprogramming of the tumor microenvironment (2021) · cites this paper as Liu et al., 2019
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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
- Published
- 2019-12
- Volume
- 25
- Issue
- 12
- Pages
- 1916-1927
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- 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 · 2015: 1 · 2012: 1
- FWCI
- 37.04
- Type
- journal article
- Language
- en
- Update notices
- correction (2020-06-19)
- Open access
- hybrid
- doi.org
- 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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