Mechanical compression reprograms cell states associated with patient survival and therapy response

Mechanical compression reprograms cell states associated with patient survival and therapy response | Research Square Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mechanical compression reprograms cell states associated with patient survival and therapy response Styliana Georgiou, Chrystalla Stylianou, Ekaterini Himonas, Sotiris Kyriakou, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9276682/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Precision oncology frameworks primarily rely on tumor-intrinsic molecular features to guide therapeutic decision-making, yet the contribution of mechanical forces within the tumor microenvironment remains poorly defined. Here, we investigate how compressive stress, a fundamental mechanical force arising during tumor growth, reshapes tumor cell states and modulates clinically relevant therapeutic pathways. Using a controlled compression system, we comprehensively profiled transcriptional responses in pancreatic cancer cells and integrated these with datasets from mechanically compressed breast and liver cancer models. Compression induced extensive, tumor type–specific transcriptional reprogramming, characterized by activation of stress-adaptive pathways and suppression of proliferative programs. Gene-level overlap was limited between tumor types, though partial convergence emerged at the pathway level. Compression-associated transcriptional signatures were independent of pre-existing genomic alterations, yet detectable in patient tumors across TCGA cohorts, underscoring their clinical relevance. Higher compression signature scores were associated with reduced survival in pancreatic and liver cancers, but not breast cancer, revealing tumor type–specific prognostic associations. Integration with pharmacogenomic databases showed that compression-responsive genes intersect with multiple drug-target networks in pancreatic cancer, including inflammatory, angiogenic, and kinase signaling pathways. Complementary metabolomic profiling identified coordinated alterations in purine metabolism, sphingolipid signaling, and redox pathways, further linking mechanical stress to drug-relevant cellular processes. Together, these findings identify tumor compression as a regulator of molecular states that interface with therapeutic targets and patient outcomes. Incorporating compression-associated molecular signatures into precision oncology frameworks may improve the identification of therapeutic vulnerabilities and prediction of treatment response in solid tumors. Cancer Biology Bioinformatics Biotechnology and Bioengineering Systems Biology Biophysics compressive force solid stress drug resistance pancreatic cancer liver cancer breast cancer transcriptomics metabolomics precision oncology Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information

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