Modeling the pancreatic cancer microenvironment in search of control targets. (English) Zbl 1475.92052

Plaugher, Daniel; Murrugarra, David

Bull. Math. Biol. 83, No. 11, Paper No. 115, 26 p. (2021).

Summary: Pancreatic ductal adenocarcinoma is among the leading causes of cancer-related deaths globally due to its extreme difficulty to detect and treat. Recently, research focus has shifted to analyzing the microenvironment of pancreatic cancer to better understand its key molecular mechanisms. This microenvironment can be represented with a multi-scale model consisting of pancreatic cancer cells (PCCs) and pancreatic stellate cells (PSCs), as well as cytokines and growth factors which are responsible for intercellular communication between the PCCs and PSCs. We have built a stochastic Boolean network (BN) model, validated by literature and clinical data, in which we probed for intervention strategies that force this gene regulatory network (GRN) from a diseased state to a healthy state. To do so, we implemented methods from phenotype control theory to determine a procedure for regulating specific genes within the microenvironment. We identified target genes and molecules, such that the application of their control drives the GRN to the desired state by suppression (or expression) and disruption of specific signaling pathways that may eventually lead to the eradication of the cancer cells. After applying well-studied control methods such as stable motifs, feedback vertex sets, and computational algebra, we discovered that each produces a different set of control targets that are not necessarily minimal nor unique. Yet, we were able to gain more insight about the performance of each process and the overlap of targets discovered. Nearly every control set contains cytokines, KRas, and HER2/neu, which suggests they are key players in the system’s dynamics. To that end, this model can be used to produce further insight into the complex biological system of pancreatic cancer with hopes of finding new potential targets.

MSC:

92C32	Pathology, pathophysiology
92C42	Systems biology, networks
94C11	Switching theory, applications of Boolean algebras to circuits and networks

Keywords:

math modeling; phenotype control; pancreatic cancer; Boolean networks

Software:

Macaulay2

References:

[1]	Aguilar, B.; Gibbs, DL; Reiss, DJ; McConnell, M.; Danziger, SA; Dervan, A.; Trotter, M.; Bassett, D.; Hershberg, R.; Ratushny, AV; Shmulevich, I., A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma, Gigascience, 9, 7, 07 (2020)
[2]	Blausen (2014) Medical gallery of blausen medical 2014. WikiJ Med 8
[3]	Bray F, Ferlay J, Soerjomataram I, Siegel R, Torre L, Jemal A (2018) Global cancer statistics 2018: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries: Global cancer statistics 2018. CA: A Cancer Journal for Clinicians, 68, 09
[4]	Deisboeck, TS; Wang, Z.; Macklin, P.; Cristini, V., Multiscale cancer modeling, Annu Rev Biomed Eng, 13, 1, 127-155 (2011)
[5]	Erkan, M.; Reiser-Erkan, C.; Michalski, C.; Kleeff, J., Tumor microenvironment and progression of pancreatic cancer, Exp Oncol, 32, 128-31 (2010)
[6]	Farrow, B.; Albo, D.; Berger, DH, The role of the tumor microenvironment in the progression of pancreatic cancer, J Surg Res, 149, 2, 319-328 (2008)
[7]	Feig, C.; Gopinathan, A.; Neesse, A.; Chan, DS; Cook, N.; Tuveson, DA, The pancreas cancer microenvironment, Clin Cancer Res, 18, 16, 4266-4276 (2012)
[8]	Festa, P.; Pardalos, P.; Resende, M., Feedback set problems, Encyclopedia Optim, 2, 06 (1999) · Zbl 1253.90193
[9]	Fiedler, B.; Mochizuki, A.; Kurosawa, G.; Saito, D., Dynamics and control at feedback vertex sets. I: Informative and determining nodes in regulatory networks, J Dyn Differ Equ, 25, 3, 563-604 (2013) · Zbl 1337.92074
[10]	Galinier, P.; Lemamou, E.; Bouzidi, M., Applying local search to the feedback vertex set problem, J Heurist, 19, 10 (2013)
[11]	Gore, J.; Korc, M., Pancreatic cancer stroma: friend or foe?, Cancer Cell, 25, 711-712 (2014)
[12]	Grayson DR, Stillman ME (2010) Macaulay2, a software system for research in algebraic geometry. http://www.math.uiuc.edu/Macaulay2/
[13]	Karp RM (1972) Reducibility among combinatorial problems. In: Proceedings of the symposium on complexity of computer computations, pp 85-103 · Zbl 1467.68065
[14]	Kleeff, J.; Beckhove, P.; Esposito, I.; Herzig, S.; Huber Peter, E.; Löhr, JM; Friess, H., Pancreatic cancer microenvironment, Int J Cancer, 121, 4, 699-705 (2007)
[15]	Mochizuki, A.; Fiedler, B.; Kurosawa, G.; Saito, D., Dynamics and control at feedback vertex sets. II: A faithful monitor to determine the diversity of molecular activities in regulatory networks, J Theor Biol, 335, 130-146 (2013) · Zbl 1397.92256
[16]	Murrugarra, D.; Aguilar, B., Algebraic and combinatorial computational biology, chapter 5, 149-150 (2018), New York: Academic Press, New York
[17]	Murrugarra, D.; Veliz-Cuba, A.; Aguilar, B.; Arat, S.; Laubenbacher, R., Modeling stochasticity and variability in gene regulatory networks, EURASIP J Bioinf Syst Biol, 2012, 1, 5 (2012)
[18]	Murrugarra, D.; Veliz-Cuba, A.; Aguilar, B.; Laubenbacher, R., Identification of contrfol targets in boolean molecular network models via computational algebra, BMC Syst Biol, 10, 1, 94 (2016)
[19]	Padoan, A.; Plebani, M.; Basso, D., Inflammation and pancreatic cancer: focus on metabolism, cytokines, and immunity, Int J Mol Sci, 20, 676 (2019)
[20]	Pancreatic cancer symptoms, diagnosis, and treatment: Saint john’s cancer institute (2021)
[21]	Rahib, Lola; Smith, Benjamin; Aizenberg, Rhonda; Rosenzweig, Allison; Fleshman, Julie; Matrisian, Lynn, Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States, Cancer Res, 74, 05 (2014)
[22]	Saadatpour, A.; Albert, R.; Reluga, T., A reduction method for boolean network models proven to conserve attractors, SIAM J Appl Dyn Syst, 12, 1997-2011 (2013) · Zbl 1308.92040
[23]	Veliz-Cuba, A., Reduction of Boolean network models, J Theor Biol, 289, 167-172 (2011) · Zbl 1397.92265
[24]	Veliz-Cuba, A.; Aguilar, B.; Hinkelmann, F.; Laubenbacher, R., Steady state analysis of boolean molecular network models via model reduction and computational algebra, BMC Bioinform, 15, 221 (2014)
[25]	Wang Q, Miskov-Zivanov N, Liu B, Faeder J, Lotze M Clarke E (2016) Formal modeling and analysis of pancreatic cancer microenvironment. 9859: 289-305, 09
[26]	Williamson E.A.B.S.G (2010) Lists, Decisions and Graphs. S. Gill Williamson
[27]	Yang G, Zañudo Jorge GT, Albert R (2018) Target control in logical models using the domain of influence of nodes. Front Physiol 9
[28]	Zañudo Jorge, GT; Albert, R., Cell fate reprogramming by control of intracellular network dynamics, PLoS Comput Biol, 11, 4 (2015)
[29]	Zañudo Jorge, GT; Yang, G.; Albert, R., Structure-based control of complex networks with nonlinear dynamics, Proc Natl Acad Sci USA, 114, 28, 7234-7239 (2017)
[30]	Zañudo, J.; Albert, R., An effective network reduction approach to find the dynamical repertoire of discrete dynamic networks, Chaos (Woodbury, NY), 23 (2013) · Zbl 1331.92055