---
title: "Spatially informed computational model predicts which breast-to-lung metastases respond to dual ch"
id: "biorxiv-4-computational-modeling-of-breast-cancer-metastases-predicts-response-to-dual"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-4-computational-modeling-of-breast-cancer-metastases-predicts-response-to-dual"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.16.759478v1?rss=1"
published_at: "2026-10-22T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Spatially informed computational model predicts which breast-to-lung metastases respond to dual ch
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-4-computational-modeling-of-breast-cancer-metastases-predicts-response-to-dual
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.16.759478v1?rss=1)
- **Published At:** 2026-10-22T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- Investigators developed an **agent-based model** of the tumor microenvironment tuned to in vivo murine **breast-to-lung metastases** data; the model represents nine functional cell types including **CD8+ T cells**, NK cells, M1/M2-like macrophages, Tregs, and MDSC-like cells. - The team incorporated a novel continuum description of effector-cell exhaustion, where **CD8+ T cell** and NK-cell exhaustion results from direct and indirect cell-cell interactions within the TME. - The model simulated spatiotemporal trajectories for 19 metastases under two conditions: no treatment and treatment with dual immune checkpoint inhibitors (anti-PD-1 plus anti-CTLA-4, termed **dual ICIs**). - Data indicated that dual ICIs produced statistically significant reductions in cancer cell proliferation in seven metastases (reported as 37% of lesions). - Spatial analyses identified structural commonalities among responding metastases across multiple scales of tumor organization; high colocalization of **CD8+ T cells** with **Tregs** was associated with poor predicted response. - Simulation of combining Treg blockade with **dual ICIs** increased the number of metastases with a statistically significant treatment effect to 15 of 19 lesions (reported as 80% total). - The preprint notes the model offers a high-resolution spatiotemporal platform to explore cell-cell interactions that can drive resistance to ICIs and to test spatially targeted combination strategies. - The authors declared no competing interests and made code/data available via a public repository, and the work is reported as a preprint (not peer reviewed). - The source did not report specific in vivo dosing schedules, pharmacokinetics, or detailed experimental treatment protocols for the simulated interventions; those methodological details were not provided in the article.
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Tyler King University of Southern California * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Tyler%2BKing%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=King%20T&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ATyler%2BKing%2B) Edgar Gonzalez University of Southern California * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Edgar%2BGonzalez%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Gonzalez%20E&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AEdgar%2BGonzalez%2B) * [ORCID record for Edgar Gonzalez](http://orcid.org/0000-0003-1569-9320 "Open in new tab") Evanthia T Roussos Torres University of Southern California * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Evanthia%2BT%2BRoussos%2BTorres%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Roussos%20Torres%20ET&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AEvanthia%2BT%2BRoussos%2BTorres%2B) * [ORCID record for Evanthia T Roussos Torres](http://orcid.org/0000-0002-0740-5102 "Open in new tab") Stacey D Finley University of Southern California * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Stacey%2BD%2BFinley%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Finley%20SD&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AStacey%2BD%2BFinley%2B) * [ORCID record for Stacey D Finley](http://orcid.org/0000-0001-6901-3692 "Open in new tab") * For correspondence: sfinley@usc.edu * [Abstract](https://www.biorxiv.org/content/10.64898/2026.10.16.759478v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5894170/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.10.16.759478v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5894170/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.10.16.759478v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5894170/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.10.16.759478v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5894170/1) * [Data/Code](https://www.biorxiv.org/content/10.64898/2026.10.16.759478v1.external-links)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_data_code/node:5894170/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.10.16.759478v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5894170/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract In advanced breast cancers, the highly suppressive tumor microenvironment contributes to poor responses to immune checkpoint inhibition. The degree of suppression varies by site of disease, which can affect metastatic progression. To investigate this phenomenon, we have developed an agent-based model of cell-cell interactions within the tumor microenvironment, specifically tuned to in vivo data from murine breast-to-lung metastases. The model includes nine functional cell types (cancer cells, M0-, M1-, and M2-like macrophages, CD4+ T cells, T regulatory cells [Tregs], CD8+ T cells, NK cells, and MDSC-like cells). We incorporated a novel continuum model to describe exhaustion of CD8+ T and NK cells, where their exhaustion is impacted by cell-cell interactions in the TME via direct and indirect interactions. We also modeled the effect of the immune checkpoint inhibitors (ICI) anti-PD-1 and anti-CTLA-4, termed "dual ICIs". We used this model to simulate the spatiotemporal trajectories of 19 metastases without treatment and with dual ICIs. The model predicted that dual ICIs lead to statistically significant reductions in cancer cell proliferation in seven of the metastases (37%), and spatial analyses of these metastases indicate they have structural similarities at multiple scales of tumor organization. Additionally, spatial analysis suggests that high colocalization of CD8+ T cells with Tregs could prevent dual ICIs from being effective, and simulation of Treg blockade combined with dual ICIs led to 15 total metastases having a statistically significant treatment effect (80% total). Thus, our model provides a highly resolved spatiotemporal platform for studying the cell-cell interactions that can drive resistance to ICIs in diverse TME settings and identifies potential therapeutic avenues to enhance the efficacy of emerging ICI therapies. ### Competing Interest Statement The authors have declared no competing interest. ## Footnotes * ## Funder Information Declared National Institutes of Health, https://ror.org/01cwqze88, CA232137 USC Center for Comptuational Modeling of Cancer Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-NC-ND 4.0 International license](http://creativecommons.org/licenses/by-nc-nd/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. [Donate to openRxiv ](https://www.zeffy.com/en-US/donation-form/donate-to-make-a-difference-10981) [ Back to top](https://www.biorxiv.org/content/10.64898/2026.10.16.759478v1?rss=1#page) [ Previous](https://www.biorxiv.org/content/10.64898/2026.10.16.759704v1 "Microscale Characterization of Undecalcified Bone Tissue Using Correlative Atomic Force Microscopy and Histology") Posted October 22, 2026. [ Download PDF](https://www.biorxiv.org/content/10.64898/2026.10.16.759478v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/10/22/2026.10.16.759478.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [Supplementary Material ](https://www.biorxiv.org/content/10.64898/2026.10.16.759478v1.supplementary-material) [ Data/Code](https://www.biorxiv.org/content/early/2026/10/22/2026.10.16.759478.external-links) [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) Computational modeling of breast cancer metastases predicts response to dual checkpoint inhibition and reveals spatially-informed treatments Tyler King, Edgar Gonzalez, Evanthia T Roussos Torres, Stacey D Finley bioRxiv 2026.10.16.759478; doi: https://doi.org/10.64898/2026.10.16.759478 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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