Pan-tumor genomic biomarkers for PD-1 checkpoint blockade–based immunotherapy
Open accessAbstract
Mining immunotherapy clinical trials: Clinical trial data can provide a wealth of information about how drugs work. Yet such information often belongs to pharmaceutical companies and is rarely accessible to the scientific community at large. Cristescu et al. provide exploratory analysis of a cancer genomics dataset, collected from four separate clinical trials of Merck's PD-1 immunotherapy drug, pembrolizumab. This informative public resource examines more than 300 patient samples representing 22 different tumor types. Two widely used signatures that currently predict immunotherapy response are tumor mutational burden and a “hot” T cell–inflamed microenvironment. The study analyzed these two proposed biomarkers in combination to see what predictive clinical utility they may hold. Science , this issue p. eaar3593
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Existing predictive biomarkers (PD-L1 expression, TMB, microsatellite instability, and mismatch repair deficiency) have clinical utility in specific contexts, but their predictive accuracy varies considerably across tumor histologies and patient populations.
“While these markers have clinical utility in specific contexts, their predictive accuracy varies considerably across tumor histologies and patient populations.”
From PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction (2026) · cites this paper as [16]
Split into 2 atoms — 0 confirmed, 1 partially supported, 1 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
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Experimental — accuracy not yet measuredThe unsupervised model effectively identifies two main clusters resembling the Immune-Inflamed and Immune-Desert phenotypes.
“This model effectively identifies two main clusters resembling the Immune-Inflamed and Immune-Desert phenotypes42–44”
From Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images (2024) · cites this paper as [44]
The ‘Stratification of additional genomic features by TMB and T cell–inflamed GEP’ section (page unavailable) describes groups defined by TMB and GEP status and K-means clustering of gene coexpression modules. It does not report an unsupervised model identifying two tumor clusters resembling Immune-Inflamed and Immune-Desert phenotypes (searched: ‘unsupervised’ and the phenotype names). (LLM, conf=0.73)
Experimental — accuracy not yet measuredIn this model, Lkb1 loss changes the cytokine environment, raising IL-1β, IL-6, CXCL7, and G-CSF, which promote myeloid cell recruitment.
“Lkb1 loss in this model results in altered tumor cytokine milieu with increased expression of interleukin 1β (IL-1β), IL-6, CXCL7 and G-CSF that foster myeloid cell recruitment135.”
From Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy (2019) · cites this paper as [135]
In the KrasLSL-G12D/+; Lkb1Fl/Fl GEMM, Lkb1 ablation by Cre causes tumor-associated neutrophils to accumulate; these neutrophils suppress T cells and express more ARG1 and IL-10.
“These findings are recapitulated in the KrasLSL-G12D/+; Lkb1Fl/Fl GEMM, where Cre-mediated Lkb1 ablation triggers marked influx of tumor-associated neutrophils with T cell suppressive properties including increased expression of Arginase 1 (ARG1) and Interleukin 10 (IL-10)135.”
From Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy (2019) · cites this paper as [135]
Low tumor mutational burden does not prevent effective antigen responses, and high tumor mutational burden does not guarantee response to immune checkpoint therapy.
“In addition, low TMB does not preclude generation of effective antigen responses, nor does high TMB guarantee response to ICT (130-132).”
From The Next Decade of Immune Checkpoint Therapy (2021) · cites this paper as [130]
TMB plus a T cell-inflamed gene expression profile or TMB plus PD-L1 expression showed improved prediction performance compared with single biomarkers.
“For example, TMB plus a T cell-inflamed geneexpression profile or TMB plus PD-L1 expression showed an improved prediction performance compared with single biomarkers (130).”
From The Next Decade of Immune Checkpoint Therapy (2021) · cites this paper as [130]
Combining existing biomarkers gives better predictive capacity than using single biomarkers.
“Recent studies demonstrated that the combination of existing biomarkers has better predictive capacity than single biomarkers.”
From The Next Decade of Immune Checkpoint Therapy (2021) · cites this paper as [130]
Higher tumor mutational loads are strongly associated with response to anti-CTLA-4 and anti-PD-1 across multiple cancer indications.
“Another well-recognized biomarker that predicts response or nonresponse to ICIs is the mutational burden of the tumor, with higher tumor mutational loads strongly associated with response to both anti-CTLA-4 and anti-PD-1 across multiple cancer indications (83)(84)(85)(86).”
From Immune Checkpoint Inhibitors for the Treatment of Cancer: Clinical Impact and Mechanisms of Response and Resistance (2021) · cites this paper as [84]
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- Authors
- Cristescu, R., Mogg, R., Ayers, M., Albright, A., Murphy, E., Yearley, J., Sher, X., Liu, X. Q., Lu, H., Nebozhyn, M., Zhang, C., Lunceford, J. K., Joe, A., Cheng, J., Webber, A. L., Ibrahim, N., Plimack, E. R., Ott, P. A., Seiwert, T. Y., Ribas, A., McClanahan, T. K., Tomassini, J. E., Loboda, A., & Kaufman, D.
- Affiliations
- Merck & Co., Kenilworth, NJ 07033, USA.
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- Fox Chase Cancer Center (US)
- Dana-Farber Cancer Institute (US)
- University of Chicago (US)
- University of California, Los Angeles (US)
- ORCID
- Venue
- Science
- Published
- 2018-10-12
- Volume
- 362
- Issue
- 6411
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6718162
- OpenAlex
- W2897815318
- Cited by
- 2,301
- References
- 60
- Citations by year
- 2026: 178 · 2025: 219 · 2024: 282 · 2023: 351 · 2022: 414 · 2021: 370 · 2020: 292 · 2019: 176 · 2018: 14 · 2017: 1 · 2012: 1
- FWCI
- 80.54
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Merck and Co., Inc.
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Ferroptosis and cancer prognosis
- Keywords
- anti-PD-1 therapy, pembrolizumab, tumor mutational burden, T-cell-inflamed gene expression profile, predictive biomarkers, immunotherapy response prediction, PD-L1, T cell activation, anti-PD-1 resistance, combination immunotherapy, pan-cancer biomarker
- MeSH terms
- Antineoplastic Agents, Immunological, Genetic Markers, Humans, Immunotherapy, Inflammation, Mutation, Neoplasms, T-Lymphocytes, Biomarkers, Tumor, Tumor Burden, Molecular Targeted Therapy, Cell Cycle Checkpoints, Transcriptome, Programmed Cell Death 1 Receptor, Antibodies, Monoclonal, Humanized
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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