Statistical models to characterize colon tumor stiffness heterogeneity through representative atomic force microscopy maps
Abstract
The study of the impact of physical forces and on cells has emerged as a fertile field of investigation. Applications in oncology are especially transformative since tumor stiffness was found associated with fundamental processes such as tumorigenicity, disease progression, and resistance to therapy. We present an integrated atomic force microscopy-statistical modeling and machine learning approach in colon cancer to link clinical and phenotypical parameters with local maps of tissue stiffness. Statistical modeling found both known associations, such as with age or tumor stage, but also novel associations, such as with RAS mutations, tumor left-/right-colon localization, and DNA repair deficiencies (microsatellite instabilities). Machine learning was able to infer clinical parameters from stiffness data. This work establishes a computational framework to build global models integrating all the available clinical parameters and assess their relevance with respect to stiffness, while they are usually explored separately. Similarly, we established spatial statistics techniques to interpolate and model the topographical information embedded in local stiffness maps.
Related
- High frame rate in vivo two-photon microscopy to quantify murine cerebrospinal fluid flow heterogeneity
- Intravital microscopy and statistical image analysis of early engraftment of haematopoietic stem cells reveal dynamic and evolving cellular behaviours linked to progressive differentiation.
- Complementary axes of functional system integrity and integration with divergent clinical correlates characterize focal epilepsy
- A multi-electrode array and immunofluorescence workflow to characterize impacts of HHV-6 infection in induced pluripotent stems cells differentiated to mixed neuronal endpoints
- Intratumoral microbiota-host gene associations characterize stage-associated microecological patterns in colorectal cancer
BibTeX
@article{gadouas2026statistical,
title = {{Statistical models to characterize colon tumor stiffness heterogeneity through representative atomic force microscopy maps}},
author = {Gauthier Gadouas and Guillaume Tosato and Luca Costa and Alain R Thierry and Patrice Ravel and Thibault Mazard and Pierre-Emmanuel Milhiet and Christine Bénistant and Evelyne Lopez-Crapez and Jacques Colinge},
journal = {Scientific Reports},
year = {2026},
month = sep,
doi = {10.1038/s41598-026-43396-w},
url = {https://hal.science/hal-05763689v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Statistical models to characterize colon tumor stiffness heterogeneity through representative atomic force microscopy maps
AU - Gauthier Gadouas
AU - Guillaume Tosato
AU - Luca Costa
AU - Alain R Thierry
AU - Patrice Ravel
AU - Thibault Mazard
AU - Pierre-Emmanuel Milhiet
AU - Christine Bénistant
AU - Evelyne Lopez-Crapez
AU - Jacques Colinge
PY - 2026
DA - 2026/09/25
JO - Scientific Reports
DO - 10.1038/s41598-026-43396-w
UR - https://hal.science/hal-05763689v1
AB - The study of the impact of physical forces and on cells has emerged as a fertile field of investigation. Applications in oncology are especially transformative since tumor stiffness was found associated with fundamental processes such as tumorigenicity, disease progression, and resistance to therapy. We present an integrated atomic force microscopy-statistical modeling and machine learning approach in colon cancer to link clinical and phenotypical parameters with local maps of tissue stiffness. Statistical modeling found both known associations, such as with age or tumor stage, but also novel associations, such as with RAS mutations, tumor left-/right-colon localization, and DNA repair deficiencies (microsatellite instabilities). Machine learning was able to infer clinical parameters from stiffness data. This work establishes a computational framework to build global models integrating all the available clinical parameters and assess their relevance with respect to stiffness, while they are usually explored separately. Similarly, we established spatial statistics techniques to interpolate and model the topographical information embedded in local stiffness maps.
SN - 2045-2322
ER -