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

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.