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This research investigates osteosarcoma, a complex malignant bone tumor predominantly affecting adolescents and young adults. It is characterized by anarchic bone matrix production by tumor cells. Its high spatial and temporal heterogeneity across multiple scales presents significant challenges for identifying therapeutic targets.
This study examines chemotherapy resistance and subsequent metastasis development at the tissue level by modeling the tumor as a spatially heterogeneous porous medium comprising three phases: the bone extracellular matrix (solid phase), the interstitial space (fluid phase), and the cellular phase. Employing machine learning K-nearest neighbor methods combined with non-linear filters, we developed an approach for segmenting large immunohistology images (~10⁹ pixels) to explore relationships between extracellular matrix and cell density distributions within the tumor microenvironment [1].
Since bone is a mechano-sensitive organ, osteosarcoma tissue components experience both structural and fluid mechanical effects, adding complexity to understanding the tumor microenvironment. By applying porous media theory, and sequential [2] grid block techniques [3] to patient-specific images, we characterize the mechanical properties of tumor tissue by homogenization methods. These methods have been optimized and adapted for parallel computing to process extensive image datasets from a large patient cohort recruited at Toulouse Hospital and provide spatially heterogeneous maps of tumor tissue equivalent properties. The numerical framework utilizes the GMSH® mesh generator and FEniCS® finite element Python environment.
Our analyses reveal that elevated lymphocyte density in the tumor microenvironment correlates with metastasis-free survival. Furthermore, an ongoing investigation based on numerical homogenization methods enables determination of equivalent elastic properties and examination of their correlation with treatment response and metastasis-free survival. These innovative computational approaches in biological porous media, bridging immune and mechanical phenomena, offer promising avenues for refined patient stratification and tailored therapeutic strategies in osteosarcoma.
| References | [1] Gomez-Mascard, Anne, Nathalie Van Acker, Guillaume Cases, et al. « Intratumoral Heterogeneity Assessment of the Extracellular Bone Matrix and Immune Microenvironment in Osteosarcoma Using Digital Imaging to Predict Therapeutic Response ». Laboratory Investigation 104, no 9 (2024). https://doi.org/10.1016/j.labinv.2024.102122. [2] Kfoury, Moussa and Ababou , Rachid and Noetinger, Benoît and Quintard, Michel Upscaling Fractured Heterogeneous Media: Permeability and Mass Exchange Coefficient. (2005) Journal of Applied Mechanics, vol. 73 (n° 1). pp. 41-46. ISSN 0021-8936 [3] Durlofsky, L. J. (1991), Numerical calculation of equivalent grid block permeability tensors for heterogeneous porous media, Water Resour. Res., 27(5), 699–708, doi:10.1029/91WR00107. |
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| Country | France |
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