BACKGROUND: 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. RESULTS: 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 described subtypes of PDAC. Moreover, the model allows a systematic exploration of the effect of possible therapeutic perturbations; our simulations suggest that reducing bFGF secretion by stellate cells will have, on average, a positive impact on cancer apoptosis. CONCLUSIONS: The developed framework allows model-driven hypotheses to be generated regarding therapeutically relevant PDAC states with potential molecular and cellular drivers indicating specific intervention strategies.
BACKGROUND: 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. RESULTS: 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 described subtypes of PDAC. Moreover, the model allows a systematic exploration of the effect of possible therapeutic perturbations; our simulations suggest that reducing bFGF secretion by stellate cells will have, on average, a positive impact on cancer apoptosis. CONCLUSIONS: The developed framework allows model-driven hypotheses to be generated regarding therapeutically relevant PDAC states with potential molecular and cellular drivers indicating specific intervention strategies.
Authors: Peter Bailey; David K Chang; Katia Nones; Amber L Johns; Ann-Marie Patch; Marie-Claude Gingras; David K Miller; Angelika N Christ; Tim J C Bruxner; Michael C Quinn; Craig Nourse; L Charles Murtaugh; Ivon Harliwong; Senel Idrisoglu; Suzanne Manning; Ehsan Nourbakhsh; Shivangi Wani; Lynn Fink; Oliver Holmes; Venessa Chin; Matthew J Anderson; Stephen Kazakoff; Conrad Leonard; Felicity Newell; Nick Waddell; Scott Wood; Qinying Xu; Peter J Wilson; Nicole Cloonan; Karin S Kassahn; Darrin Taylor; Kelly Quek; Alan Robertson; Lorena Pantano; Laura Mincarelli; Luis N Sanchez; Lisa Evers; Jianmin Wu; Mark Pinese; Mark J Cowley; Marc D Jones; Emily K Colvin; Adnan M Nagrial; Emily S Humphrey; Lorraine A Chantrill; Amanda Mawson; Jeremy Humphris; Angela Chou; Marina Pajic; Christopher J Scarlett; Andreia V Pinho; Marc Giry-Laterriere; Ilse Rooman; Jaswinder S Samra; James G Kench; Jessica A Lovell; Neil D Merrett; Christopher W Toon; Krishna Epari; Nam Q Nguyen; Andrew Barbour; Nikolajs Zeps; Kim Moran-Jones; Nigel B Jamieson; Janet S Graham; Fraser Duthie; Karin Oien; Jane Hair; Robert Grützmann; Anirban Maitra; Christine A Iacobuzio-Donahue; Christopher L Wolfgang; Richard A Morgan; Rita T Lawlor; Vincenzo Corbo; Claudio Bassi; Borislav Rusev; Paola Capelli; Roberto Salvia; Giampaolo Tortora; Debabrata Mukhopadhyay; Gloria M Petersen; Donna M Munzy; William E Fisher; Saadia A Karim; James R Eshleman; Ralph H Hruban; Christian Pilarsky; Jennifer P Morton; Owen J Sansom; Aldo Scarpa; Elizabeth A Musgrove; Ulla-Maja Hagbo Bailey; Oliver Hofmann; Robert L Sutherland; David A Wheeler; Anthony J Gill; Richard A Gibbs; John V Pearson; Nicola Waddell; Andrew V Biankin; Sean M Grimmond Journal: Nature Date: 2016-02-24 Impact factor: 49.962
Authors: Lola Rahib; Benjamin D Smith; Rhonda Aizenberg; Allison B Rosenzweig; Julie M Fleshman; Lynn M Matrisian Journal: Cancer Res Date: 2014-06-01 Impact factor: 12.701
Authors: Daniel K Wells; Yishan Chuang; Louis M Knapp; Dirk Brockmann; William L Kath; Joshua N Leonard Journal: PLoS Comput Biol Date: 2015-04-23 Impact factor: 4.475
Authors: Jonathan Ozik; Nicholson Collier; Justin M Wozniak; Charles Macal; Chase Cockrell; Samuel H Friedman; Ahmadreza Ghaffarizadeh; Randy Heiland; Gary An; Paul Macklin Journal: BMC Bioinformatics Date: 2018-12-21 Impact factor: 3.169
Authors: Hamid Bolouri; Mary Young; Joshua Beilke; Rebecca Johnson; Brian Fox; Lu Huang; Cristina Costa Santini; Christopher Mark Hill; Anne-Renee van der Vuurst de Vries; Paul T Shannon; Andrew Dervan; Pallavur Sivakumar; Matthew Trotter; Douglas Bassett; Alexander Ratushny Journal: Sci Rep Date: 2020-02-05 Impact factor: 4.379