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Abstract Colorectal cancer, a significant cause of cancer-related mortality, often exhibits drug resistance, highlighting the need for improved tumor models to advance personalized drug testing and precision therapy. We generated organoids from primary colorectal cancer cells cultured through the conditional reprogramming technique, establishing a framework to perform short-term drug testing studies on patient-derived cells. To model interactions with stromal cells in the tumor microenvironment, we combined cancer cell organoids with carcinoma-associated fibroblasts, a cell type implicated in disease progression and drug resistance. Our organotypic models revealed that carcinoma-associated fibroblasts promote cancer cell proliferation and stemness primarily through hepatocyte growth factor–MET paracrine signaling and activation of cyclin-dependent kinases. Disrupting these tumor–stromal interactions reduced organoid size while limiting oncogenic signals and cancer stemness. Leveraging this tumor model, we identified effective drug combinations targeting colorectal cancer cells and their tumorigenic activities. Our study highlights a path to incorporate patient-derived cells and tumor–stromal interactions into a drug testing workflow that could identify effective therapies for individual patients.
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This content will become publicly available on December 1, 2026
Mathematical modeling of multicellular tumor spheroids quantifies inter-patient and intra-tumor heterogeneity
Abstract In the study of brain tumors, patient-derived three-dimensional sphere cultures provide an important tool for studying emerging treatments. The growth of such spheroids depends on the combined effects of proliferation and migration of cells, but it is challenging to make accurate distinctions between increase in cell number versus the radial movement of cells. To address this, we formulate a novel model in the form of a system of two partial differential equations (PDEs) incorporating both migration and growth terms, and show that it more accurately fits our data compared to simpler PDE models. We show that traveling-wave speeds are strongly associated with population heterogeneity. Having fitted the model to our dataset we show that a subset of the cell lines are best described by a “Go-or-Grow”-type model, which constitutes a special case of our model. Finally, we investigate whether our fitted model parameters are correlated with patient age and survival.
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- Award ID(s):
- 2320244
- PAR ID:
- 10616603
- Publisher / Repository:
- Nature Partner Journals
- Date Published:
- Journal Name:
- npj Systems Biology and Applications
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2056-7189
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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