A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma.
Abstract
Mechanistic models, when combined with pertinent data, can improve our knowledge regarding important molecular and cellular mechanisms found in cancer. These models make the prediction of tissue-level response to drug treatment possible, which can lead to new therapies and improved patient outcomes. Here we present a data-driven multiscale modeling framework to study molecular interactions between cancer, stromal, and immune cells found in the tumor microenvironment. We also develop methods to use molecular data available in The Cancer Genome Atlas to generate sample-specific models of cancer. By combining published models of different cells relevant to pancreatic ductal adenocarcinoma (PDAC), we built an agent-based model of the multicellular pancreatic tumor microenvironment, formally describing cell type-specific molecular interactions and cytokine-mediated cell-cell communications. We used an ensemble-based modeling approach to systematically explore how variations in the tumor microenvironment affect the viability of cancer cells. The results suggest that the autocrine loop involving EGF signaling is a key interaction modulator between pancreatic cancer and stellate cells. EGF is also found to be associated with previously...Continue Reading
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Peer review of "A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma"
Peer review of "A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma"
Peer review of "A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma"
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