Open AccessA generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma
Author(s) -
Boris Aguilar,
David L. Gibbs,
David J. Reiss,
Mark McConnell,
Samuel A. Danziger,
Andrew Dervan,
Matthew Trotter,
Douglas E. Bassett,
Robert M. Hershberg,
Alexander V. Ratushny,
Ilya Shmulevich
Publication year - 2020
Publication title -
gigascience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.947
H-Index - 54
ISSN - 2047-217X
DOI - 10.1093/gigascience/giaa075
Subject(s) - stromal cell , pancreatic cancer , tumor microenvironment , autocrine signalling , multicellular organism , computational biology , cancer cell , paracrine signalling , hepatic stellate cell , cancer , biology , cancer research , immune system , pancreatic ductal adenocarcinoma , bioinformatics , computer science , cell , medicine , tumor cells , immunology , pathology , cell culture , receptor , genetics
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.
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