Evidence map›Paper›PMID 34965960›Full record

ArticleMolecular cancer therapeutics2022

Reverse Translating Molecular Determinants of Anti-Programmed Death 1 Immunotherapy Response in Mouse Syngeneic Tumor Models.

Peter Georgiev, Eric S Muise, Douglas E Linn, Marlene C Hinton, Yun Wang, Mingmei Cai, Louise Cadzow, Douglas C Wilson, Selvakumar Sukumar, Michael Caniga and 19 more

Abstract read
In one paragraph

Article in Molecular cancer therapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Article
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  6. Lenvatinib enhances antitumor immunity of anti-PD-1 antibody.International journal of clinical oncology · 2025
    Review
  7. Article
  8. Article
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  19. In vivo (Athens, Greece)
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

29 authors.

Peter Georgiev *Department of Discovery Oncology, Merck & Co., Inc., Kenilworth, New Jersey.ORCID 0000-0002-4613-1255
Eric S Muise *Department of Genome and Biomarker Sciences, Merck & Co., Inc., Kenilworth, New Jersey.ORCID 0000-0002-1238-5118
Douglas E Linn *Department of Quantitative Biosciences, Merck & Co., Inc., Kenilworth, New Jersey.
Marlene C HintonDepartment of Quantitative Biosciences, Merck & Co., Inc., Kenilworth, New Jersey.
Yun WangDepartment of Discovery Oncology, Merck & Co., Inc., Kenilworth, New Jersey.
Mingmei CaiDepartment of Quantitative Biosciences, Merck & Co., Inc., Kenilworth, New Jersey.
Louise CadzowDepartment of Quantitative Biosciences, Merck & Co., Inc., Kenilworth, New Jersey.
Douglas C WilsonDepartment of Genome and Biomarker Sciences, Merck & Co., Inc., Kenilworth, New Jersey.
Selvakumar SukumarDepartment of Discovery Oncology, Merck & Co., Inc., Kenilworth, New Jersey.
Michael CanigaDepartment of Quantitative Biosciences, Merck & Co., Inc., Kenilworth, New Jersey.
Lan ChenDepartment of Informatics IT, Merck & Co., Inc., Kenilworth, New Jersey.
Hui XiaoDepartment of Informatics IT, Merck & Co., Inc., Kenilworth, New Jersey.
Jennifer H YearleyDepartment of Genome and Biomarker Sciences, Merck & Co., Inc., Kenilworth, New Jersey.
Venkataraman SriramDepartment of Discovery Oncology, Merck & Co., Inc., Kenilworth, New Jersey.ORCID 0000-0003-3433-5258
Michael NebozhynDepartment of Genome and Biomarker Sciences, Merck & Co., Inc., Kenilworth, New Jersey.
Manjiri SatheDepartment of Genome and Biomarker Sciences, Merck & Co., Inc., Kenilworth, New Jersey.
Wendy M BlumenscheinDepartment of Genome and Biomarker Sciences, Merck & Co., Inc., Kenilworth, New Jersey.
Kimberly S KerrDepartment of Genome and Biomarker Sciences, Merck & Co., Inc., Kenilworth, New Jersey.
Heather A HirschDepartment of Genome and Biomarker Sciences, Merck & Co., Inc., Kenilworth, New Jersey.
Sarah JavaidDepartment of Genome and Biomarker Sciences, Merck & Co., Inc., Kenilworth, New Jersey.
Aleksandra K OlowDepartment of Genome and Biomarker Sciences, Merck & Co., Inc., Kenilworth, New Jersey.
Lily Y MoyDepartment of Quantitative Biosciences, Merck & Co., Inc., Kenilworth, New Jersey.
Derek Y ChiangDepartment of Genome and Biomarker Sciences, Merck & Co., Inc., Kenilworth, New Jersey.ORCID 0000-0002-1131-6065
Andrey LobodaDepartment of Genome and Biomarker Sciences, Merck & Co., Inc., Kenilworth, New Jersey.ORCID 0000-0003-4239-184X
Razvan CristescuDepartment of Genome and Biomarker Sciences, Merck & Co., Inc., Kenilworth, New Jersey.ORCID 0000-0002-9978-0339
Svetlana SadekovaDepartment of Discovery Oncology, Merck & Co., Inc., Kenilworth, New Jersey.
Brian J LongDepartment of Quantitative Biosciences, Merck & Co., Inc., Kenilworth, New Jersey.
Terrill K McClanahan *Department of Genome and Biomarker Sciences, Merck & Co., Inc., Kenilworth, New Jersey.
Elaine M Pinheiro *Department of Discovery Oncology, Merck & Co., Inc., Kenilworth, New Jersey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Targeting the programmed death 1/programmed death ligand 1 (PD-1/PD-L1) pathway with immunotherapy has revolutionized the treatment of many cancers. Somatic tumor mutational burden (TMB) and T-cell-inflamed gene expression profile (GEP) are clinically validated pan-tumor genomic biomarkers that can predict responsiveness to anti-PD-1/PD-L1 monotherapy in many tumor types. We analyzed the association between these biomarkers and the efficacy of PD-1 inhibitor in 11 commonly used preclinical syngeneic tumor mouse models using murinized rat anti-mouse PD-1 DX400 antibody muDX400, a surrogate for pembrolizumab. Response to muDX400 treatment was broadly classified into three categories: highly responsive, partially responsive, and intrinsically resistant to therapy. Molecular and cellular profiling validated differences in immune cell infiltration and activation in the tumor microenvironment of muDX400-responsive tumors. Baseline and on-treatment genomic analysis showed an association between TMB, murine T-cell-inflamed gene expression profile (murine-GEP), and response to muDX400 treatment. We extended our analysis to investigate a canonical set of cancer and immune biology-related gene signatures, including signatures of angiogenesis, myeloid-derived suppressor cells, and stromal/epithelial-to-mesenchymal transition/TGFβ biology previously shown to be inversely associated with the clinical efficacy of immune checkpoint blockade. Finally, we evaluated the association between murine-GEP and preclinical efficacy with standard-of-care chemotherapy or antiangiogenic agents that previously demonstrated promising clinical activity, in combination with muDX400. Our profiling studies begin to elucidate the underlying biological mechanisms of response and resistance to PD-1/PD-L1 blockade represented by these models, thereby providing insight into which models are most appropriate for the evaluation of orthogonal combination strategies.

Indexed as

B7-H1 AntigenImmunotherapyNeoplasmsProgrammed Cell Death 1 ReceptorAnimalsBiomarkers, TumorCell Line, TumorDisease Models, AnimalHumansImmune Checkpoint InhibitorsMiceTumor MicroenvironmentB7-H1 AntigenBiomarkers, TumorImmune Checkpoint InhibitorsProgrammed Cell Death 1 Receptor

Identifiers

PMID34965960
PMCPMC9377732

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.