---
title: "Mapping Differences Between Mouse and Human Tumor Immunity: Implications for Preclinical Models"
id: "pubmed-42302174"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42302174"
content_type: "clinical_feed_article"
specialty: "Pharmacology"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42302174/"
doi: "10.1158/0008-5472.CAN-26-2561"
published_at: "2026-09-02T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Mapping Differences Between Mouse and Human Tumor Immunity: Implications for Preclinical Models
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42302174
- **Specialty:** [Pharmacology](https://medichelpline.com/clinical-feed/pharmacology.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42302174/)
- **DOI:** [10.1158/0008-5472.CAN-26-2561](https://doi.org/10.1158%2F0008-5472.CAN-26-2561)
- **Published At:** 2026-09-02T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- A cross-species immune profiling study compared 15 widely used **mouse models** with corresponding human tumor cohorts to evaluate how well mouse tumors recapitulate human tumor immunity. - Commonly used mouse tumors tend to represent **macrophage-rich, T cell–poor** microenvironments and therefore capture only part of the diversity seen in human disease. - The analysis found that mouse models largely miss **immune-rich, CXCL13-organized** human tumor types; those human tumors are more likely to respond to **immune checkpoint blockade** clinically. - Beyond cell composition, investigators identified **species-specific chemokine networks** and distinct cell–cell interaction patterns that may explain divergent immune landscapes between mouse and human tumors. - Despite interspecies differences, consensus gene expression profiling revealed **conserved transcriptional modules**, indicating areas of biological convergence. - A prominent conserved transcriptional program links **interferon-responsive myeloid cells** with **T-cell cytotoxicity**; this program correlates with patient survival and represents a translationally relevant axis. - The work serves both as a caution about limitations of current preclinical mouse models and as a resource: a **queryable atlas** to align specific preclinical models with the human tumor contexts they more credibly model. - Overall, the study emphasizes the need to match preclinical models to the human tumor immune phenotypes under study, and to account for species-specific signaling and interactions when interpreting translational immunotherapy research.
## Clinical Analysis & Structured Key Points
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Affiliations Expand ### Affiliations * 1 The Jackson Laboratory , Bar Harbor, Maine. * 2 Tufts University School of Medicine, Boston, Massachusetts. * 3 Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, Maine. * PMID: **42302174** * DOI: [ 10.1158/0008-5472.CAN-26-2561 ](https://doi.org/10.1158/0008-5472.can-26-2561) Item in Clipboard # Mapping the Divergence and Convergence of Mouse and Human Tumor Immunity Chaojia Chen et al. Cancer Res. 2026. Show details Display options Display options Format Abstract PubMed PMID Cancer Res Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Cancer+Res%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Cancer+Res%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42302174/) . 2026 Sep 2;86(17):4197-4198. doi: 10.1158/0008-5472.CAN-26-2561. ### Authors [Chaojia Chen](https://pubmed.ncbi.nlm.nih.gov/?term=Chen+C&cauthor_id=42302174)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42302174/#short-view-affiliation-1 "The Jackson Laboratory , Bar Harbor, Maine."), [Guangwen Ren](https://pubmed.ncbi.nlm.nih.gov/?term=Ren+G&cauthor_id=42302174)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42302174/#short-view-affiliation-1 "The Jackson Laboratory , Bar Harbor, Maine.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42302174/#short-view-affiliation-2 "Tufts University School of Medicine, Boston, Massachusetts.")[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42302174/#short-view-affiliation-3 "Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, Maine.") ### Affiliations * 1 The Jackson Laboratory , Bar Harbor, Maine. * 2 Tufts University School of Medicine, Boston, Massachusetts. * 3 Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, Maine. * PMID: **42302174** * DOI: [ 10.1158/0008-5472.CAN-26-2561 ](https://doi.org/10.1158/0008-5472.can-26-2561) Item in Clipboard Full text links Cite Display options Display options Format Abstract PubMed PMID ## Abstract Mouse models remain the premier preclinical model organisms for cancer immunotherapy, yet how they faithfully recapitulate the human tumor microenvironment has remained poorly defined. In a recent Nature Immunology study, Courau and colleagues addressed this gap through a systematic, cross-species immune profiling spanning 15 widely used mouse models and corresponding human cohorts. Their analysis revealed that commonly used mouse tumors only capture a portion of human disease-predominantly macrophage-rich, T cell-poor microenvironments-but largely miss immune-rich, CXCL13-organized human tumor types, which are more responsive to immune checkpoint blockade in the clinic. Beyond cellular composition, the authors also uncovered species-specific chemokine networks and cell-cell interactions that may account for the unique immune profiles in mouse and human tumors. Despite the differences, conserved transcriptional modules were detected by consensus gene expression profile analysis. Notably, a strong association between interferon-responsive myeloid cells and T-cell cytotoxicity marks a key transcriptional program in which mouse biology and human biology converge, which is able to predict patient survival. Taken together, this work serves as a cautionary guide to the limitations of mouse models; more importantly, it offers a queryable atlas for precisely aligning preclinical models with the human conditions they credibly emulate. ©2026 American Association for Cancer Research. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Similar articles * [ [Machine learning-based programmed cell death signature model for precise prediction of prognosis and treatment response in melanoma]. ](https://pubmed.ncbi.nlm.nih.gov/41656832/) Wei B, Liu H.Wei B, et al.Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2025 Nov 28;50(11):1961-1978. doi: 10.11817/j.issn.1672-7347.2025.250286.Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2025.PMID: 41656832Free PMC article.Chinese. * [ Tumor-immune profiling of murine syngeneic tumor models as a framework to guide mechanistic studies and predict therapy response in distinct tumor microenvironments. ](https://pubmed.ncbi.nlm.nih.gov/30388137/) Yu JW, Bhattacharya S, Yanamandra N, Kilian D, Shi H, Yadavilli S, Katlinskaya Y, Kaczynski H, Conner M, Benson W, Hahn A, Seestaller-Wehr L, Bi M, Vitali NJ, Tsvetkov L, Halsey W, Hughes A, Traini C, Zhou H, Jing J, Lee T, Figueroa DJ, Brett S, Hopson CB, Smothers JF, Hoos A, Srinivasan R.Yu JW, et al.PLoS One. 2018 Nov 2;13(11):e0206223. doi: 10.1371/journal.pone.0206223. eCollection 2018.PLoS One. 2018.PMID: 30388137Free PMC article. * [ Reverse Translating Molecular Determinants of Anti-Programmed Death 1 Immunotherapy Response in Mouse Syngeneic Tumor Models. ](https://pubmed.ncbi.nlm.nih.gov/34965960/) Georgiev P, Muise ES, Linn DE, Hinton MC, Wang Y, Cai M, Cadzow L, Wilson DC, Sukumar S, Caniga M, Chen L, Xiao H, Yearley JH, Sriram V, Nebozhyn M, Sathe M, Blumenschein WM, Kerr KS, Hirsch HA, Javaid S, Olow AK, Moy LY, Chiang DY, Loboda A, Cristescu R, Sadekova S, Long BJ, McClanahan TK, Pinheiro EM.Georgiev P, et al.Mol Cancer Ther. 2022 Mar 1;21(3):427-439. doi: 10.1158/1535-7163.MCT-21-0561.Mol Cancer Ther. 2022.PMID: 34965960Free PMC article. * [ Chemokine-chemokine receptor networks in conventional type I dendritic cells: an opportunity to prime and boost anticancer immunity. ](https://pubmed.ncbi.nlm.nih.gov/41135412/) Kuo N, Shinn CK, Schokrpur S, Idoyaga J, Handel T, Gutkind JS.Kuo N, et al.J Pharmacol Exp Ther. 2025 Nov;392(11):103725. doi: 10.1016/j.jpet.2025.103725. 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