---
title: "Mechanical crowding controls resistance and informs adaptive therapy in a 2D melanoma model"
id: "biorxiv-3-crowding-governs-resistance-confinement-and-guides-the-improvement-of-adaptive"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-3-crowding-governs-resistance-confinement-and-guides-the-improvement-of-adaptive"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.06.757145v1?rss=1"
published_at: "2026-10-09T09:30:27.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Mechanical crowding controls resistance and informs adaptive therapy in a 2D melanoma model
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-3-crowding-governs-resistance-confinement-and-guides-the-improvement-of-adaptive
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.06.757145v1?rss=1)
- **Published At:** 2026-10-09T09:30:27.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The study investigates how **mechanical crowding** in densely packed solid-tumor tissue influences the emergence and spatial expansion of treatment-resistant cells, using a 2D melanoma model system. - Authors combined genetically tailored, therapy-mimicking experiments in spatially structured **2D melanoma colonies** with mechanistic, particle-based tissue simulations acting as **digital twins**. - The real-to-sim-to-real framework revealed that treatment-induced changes in local crowding alter competitive interactions between sensitive and resistant cells, producing a cascade: changes in crowding → resistance expansion → spatial competitive escape → therapy failure. - Mechanical interactions between individual cells were shown to underpin population-level treatment responses, indicating that tissue mechanics can determine resistance confinement or escape beyond cell-intrinsic factors. - The team abstracted the identified physical mechanisms into a mathematical model used for **reinforcement-learning-based** therapy optimization and derived a distilled treatment-decision heuristic. - The treatment-decision heuristic was tested experimentally, linking mechanistic understanding to actionable population-level observables. - Overall, the results propose **mechanical crowding and escape** as governing principles of resistance-mediated therapy failure in solid tumors and provide a foundation for integrating tissue mechanics into evolution-based adaptive treatment strategies. - The article is a preprint and has not been peer reviewed; specific experimental parameters, quantitative results, and detailed algorithmic descriptions were not reported in the abstract and would require consultation of the full manuscript or supplementary materials.
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Jona Kayser 1 Max-Planck-Zentrum fuer Physik und Medizin; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jona%2BKayser%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Kayser%20J&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AJona%2BKayser%2B) * For correspondence: jona.kayser@mpzpm.mpg.de Maximilian Eiche 2 Max-Planck Zentrum fuer Physik und Medizin; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Maximilian%2BEiche%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Eiche%20M&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AMaximilian%2BEiche%2B) Elias Ferdinand Fischer 2 Max-Planck Zentrum fuer Physik und Medizin; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Elias%2BFerdinand%2BFischer%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Fischer%20EF&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AElias%2BFerdinand%2BFischer%2B) Ana-Suncana Smith 3 Ruder Boskovic Institute; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Ana-Suncana%2BSmith%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Smith%20A&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AAna-Suncana%2BSmith%2B) Maja Milas 3 Ruder Boskovic Institute; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Maja%2BMilas%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Milas%20M&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AMaja%2BMilas%2B) Timon Citak 1 Max-Planck-Zentrum fuer Physik und Medizin; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Timon%2BCitak%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Citak%20T&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ATimon%2BCitak%2B) Serhii Aif 4 Max Planck Zentrum fuer Physik und Medizin * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Serhii%2BAif%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Aif%20S&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ASerhii%2BAif%2B) * [Abstract](https://www.biorxiv.org/content/10.64898/2026.10.06.757145v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5852800/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.10.06.757145v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5852800/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.10.06.757145v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5852800/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.10.06.757145v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5852800/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.10.06.757145v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5852800/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract The emergence of treatment resistant mutants is one of the primary contributors to therapy failure in cancer. Evolution-based adaptive therapies aim to exploit the competition between sensitive and resistant cells to prolong therapeutic control. However, in the densely packed tissues of solid tumors, competition is not only determined by cell-intrinsic factors but also a result of local physical interactions between neighboring cells. The successful development of physics-informed adaptive therapies therefore hinges upon the bottom-up integration of tissue mechanics and experimentally grounded models of tumor evolution. Here, we show how population-level treatment response emerges from mechanical interactions between individual cells. Combining genetically tailored therapy-mimicry experiments in spatially structured 2D melanoma colonies with mechanistic, particle-based tissue simulations as digital twins in a real-to-sim-to-real framework, we reveal how treatment-induced changes in mechanical crowding shape a cascade of resistance expansion, spatial competitive escape, and consequent therapy failure. We then link these underlying physical mechanisms to population-level observables in an abstracted mathematical model for reinforcement-learning-based therapy optimization and a distilled treatment-decision-heuristic, which we test experimentally. Together, our results identify mechanical crowding and escape as governing principles of resistance-mediated therapy failure in solid tumours and provide a basis for mechanistically informed, evolution-based treatment strategies. ### Competing Interest Statement The authors have declared no competing interest. 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. 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