Uncovering potential interventions for pancreatic cancer patients via mathematical modeling. (English) Zbl 1493.92028

Plaugher, Daniel; Aguilar, Boris; Murrugarra, David

J. Theor. Biol. 548, Article ID 111197, 11 p. (2022).

Summary: Pancreatic ductal adenocarcinoma (PDAC) is widely known for its poor prognosis because it is often diagnosed when the cancer is in a later stage. We built a Boolean model to analyze the microenvironment of pancreatic cancer in order to better understand the interplay between pancreatic cancer, stellate cells, and their signaling cytokines. Specifically, we have used our model to study the impact of inducing four common mutations: KRAS, TP53, SMAD4, and CDKN2A. After implementing the various mutation combinations, we used our stochastic simulator to derive aggressiveness scores based on simulated attractor probabilities and long-term trajectory approximations. These aggression scores were then corroborated with clinical data. Moreover, we found sets of control targets that are effective among common mutations. These control sets contain nodes within both the pancreatic cancer cell and the pancreatic stellate cell, including PIP3, RAF, PIK3 and BAX in pancreatic cancer cell as well as ERK and PIK3 in the pancreatic stellate cell. Many of these nodes were found to be differentially expressed among pancreatic cancer patients in the TCGA database. Furthermore, literature suggests that many of these nodes can be targeted by drugs currently in circulation. The results herein help provide a proof of concept in the path towards personalized medicine through a means of mathematical systems biology. All data and code used for running simulations, statistical analysis, and plotting is available on a GitHub repository at https://github.com/drplaugher/PCC_Mutations.

MSC:

92C50	Medical applications (general)
92C42	Systems biology, networks

Keywords:

pancreatic cancer; phenotype control; cytokines; pancreatic stellate cells; Boolean networks

Software:

PCC_Mutations; Macaulay2

References:

[1]	Aguilar, Boris; Fang, Pan; Laubenbacher, Reinhard; Murrugarra, David, A near-optimal control method for stochastic boolean networks, Lett. Biomath., 7, 1, 67-80 (2020)
[2]	Aguilar, Boris; Gibbs, David L.; Reiss, David J.; McConnell, Mark; Danziger, Samuel A.; Dervan, Andrew; Trotter, Matthew; Bassett, Douglas; Hershberg, Robert; Ratushny, Alexander V.; Shmulevich, Ilya, A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma, Gigascience, 9, 7 (2020)
[3]	Sunjida Ahmed, Azore-Dee Bradshaw, Shweta Gera, M. Dewan, and Ruliang Xu. The tgfβsmad4 signaling pathway in pancreatic carcinogenesis and its clinical significance. Journal of Clinical Medicine, 6:5, 01 2017.
[4]	Bolouri, Hamid; Young, Mary; Beilke, Joshua; Johnson, Rebecca; Fox, Brian; Huang, Lu; Santini, Cristina Costa; Hill, Christopher Mark; Vries, Anne-Renee van der Vuurst de; Shannon, Paul T., Integrative network modeling reveals mechanisms underlying t cell exhaustion, Scientific Rep., 10, 1, 1-15 (2020)
[5]	Bray, Freddie, Ferlay, Jacques, Soerjomataram, Isabelle, Siegel, Rebecca, Torre, Lindsey, Jemal, Ahmedin, 2018. Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries: Global cancer statistics 2018. CA: Cancer J. Clin. 68.
[6]	Bryant, Kirsten L.; Mancias, Joseph D.; Kimmelman, Alec C.; Der, Channing J., Kras: feeding pancreatic cancer proliferation, Trends Biochem. Sci., 39, 2, 91-100 (2014)
[7]	Castiglione, Filippo; Tieri, Paolo; Palma, Alessandro; Jarrah, Abdul Salam, Statistical ensemble of gene regulatory networks of macrophage differentiation, BMC Bioinf., 17, 19, 119-128 (2016)
[8]	Choi, Minsoo; Shi, Jue; Jung, Sung Hoon; Chen, Xi; Cho, Kwang-Hyun, Attractor landscape analysis reveals feedback loops in the p53 network that control the cellular response to dna damage, Sci. Signal., 5, 251 (2012), ra83-ra83
[9]	Chu, Wu.; Yang, Ping-Lian; Liu, Bingxue; Tang, Yunlian, Is there a cdkn2a-centric network in pancreatic ductal adenocarcinoma?, OncoTargets Ther., 13, 2551-2562 (2020)
[10]	Cicenas, J., Kvederaviciute, Kotryna, Meskinyte, Ingrida, Meskinyte-Kausiliene, Edita, Skeberdyte, Aiste, 2017. Kras, tp53, cdkn2a, smad4, brca1, and brca2 mutations in pancreatic cancer. Cancers 9.
[11]	Davidson-Pilon, Cameron, Kalderstam, Jonas, Jacobson, Noah, Reed, Sean, Kuhn, Ben, Zivich, Paul, Williamson, Mike,Abdeali, J.K., Datta, Deepyaman, Fiore-Gartland, Andrew, Parij, Alex, WIlson, Daniel, Gabriel, Moneda, Luis, Moncada-Torres, Arturo, Stark, Kyle, Gadgil, Harsh, JoseLlanes, Jona, Singaravelan, Karthikeyan, Besson, Lilian, Sancho Peña, Miguel, Anton, Steven, Klintberg, Andreas, Jeff, Growth, Noorbakhsh, Javad, Begun, Matthew, Kumar, Ravin, Hussey, Sean, Seabold, Skipper, 2021. Camdavidsonpilon/lifelines: 0.26.0.
[12]	Erkan, Mert; Reiser-Erkan, Carolin; Michalski, Christoph; Kleeff, Jörg, Tumor microenvironment and progression of pancreatic cancer, Exp. Oncol., 32, 128-31, 09 (2010)
[13]	Farrow, Buckminster; Albo, Daniel; Berger, David H., The role of the tumor microenvironment in the progression of pancreatic cancer, J. Surg. Res., 149, 2, 319-328 (2008)
[14]	Feig, Christine; Gopinathan, Aarthi; Neesse, Albrecht; Chan, Derek S.; Cook, Natalie; Tuveson, David A., The pancreas cancer microenvironment, Clin. Cancer Res., 18, 16, 4266-4276 (2012)
[15]	Gore, Jesse; Korc, Murray, Pancreatic cancer stroma: Friend or foe?, Cancer Cell, 25, 711-712 (2014)
[16]	Grayson, Daniel R., Stillman, Michael E. Macaulay2, a software system for research in algebraic geometry. Available at URL:http://www.math.uiuc.edu/Macaulay2/.
[17]	Hoffman, J.I.E., 2015. Biostatistics for medical and biomedical practitioners. pp. 1-744.
[18]	Kleeff, Jörg; Beckhove, Philipp; Esposito, Irene; Herzig, Stephan; Huber, Peter E.; Matthias Löhr, J.; Friess, Helmut, Pancreatic cancer microenvironment, Int. J. Cancer, 121, 4, 699-705 (2007)
[19]	Liu, Zhiqing; Ding, Ye; Ye, Na; Wild, Christopher; Chen, Haiying; Zhou, Jia, Direct activation of bax protein for cancer therapy, Med. Res. Rev., 36, 2, 313-341 (2016)
[20]	Mendoza, Luis; Xenarios, Ioannis, A method for the generation of standardized qualitative dynamical systems of regulatory networks, Theor. Biol. Med. Model., 3, 1, 1-18 (2006)
[21]	Murrugarra, David, Aguilar, Boris, 2018. Algebraic and Combinatorial Computational Biology, chapter 5. Academic Press. pp. 149-150.
[22]	Murrugarra, David; Veliz-Cuba, Alan; Aguilar, Boris; Arat, Seda; Laubenbacher, Reinhard, Modeling stochasticity and variability in gene regulatory networks, EURASIP J. Bioinf. Syst. Biol., 2012, 1, 5 (2012)
[23]	Murrugarra, David; Veliz-Cuba, Alan; Aguilar, Boris; Laubenbacher, Reinhard, Identification of control targets in boolean molecular network models via computational algebra, BMC Syst. Biol., 10, 1, 94 (2016)
[24]	Padoan, Andrea; Plebani, Mario; Basso, Daniela, Inflammation and pancreatic cancer: Focus on metabolism, cytokines, and immunity, Int. J. Mol. Sci., 20, 676 (2018)
[25]	Palma, Alessandro; Jarrah, Abdul Salam; Tieri, Paolo; Cesareni, Gianni; Castiglione, Filippo, Gene regulatory network modeling of macrophage differentiation corroborates the continuum hypothesis of polarization states, Front. Physiol., 9, 1659 (2018)
[26]	Plaugher, Daniel; Murrugarra, D., Modeling the pancreatic cancer microenvironment in search of control targets, Bull. Math. Biol., 83, 115, 10 (2021) · Zbl 1475.92052
[27]	Rex, Julia; Albrecht, Ute; Ehlting, Christian; Thomas, Maria; Zanger, Ulrich M.; Sawodny, Oliver; Häussinger, Dieter; Ederer, Michael; Feuer, Ronny; Bode, Johannes G., Model-based characterization of inflammatory gene expression patterns of activated macrophages, PLoS Comput. Biol., 12, 7 (2016), e1005018
[28]	Rich, Jason T.; Gail Neely, J.; Paniello, Randal C.; Voelker, Courtney C. J.; Nussenbaum, Brian; Wang, Eric W., A practical guide to understanding kaplan-meier curves, Otolaryngol.-Head Neck Surg., 143, 3, 331-336, 201 (2010), PMID: 20723767
[29]	Rosenzweig, Allison, Npr focuses on one of the biggest challenges in pancreatic cancer, Kras (2018)
[30]	Tieri, Paolo, Prana, Vinca, Colombo, Teresa, Santoni, Daniele, Castiglione, Filippo, 2014. Multi-scale simulation of t helper lymphocyte differentiation. In: Brazilian Symposium on Bioinformatics, Springer. pp. 123-134.
[31]	Vallat, Raphael, Pingouin: statistics in python, J. Open Source Software, 3, 31, 1026 (2018)
[32]	Vieira, Luis Sordo; Laubenbacher, Reinhard C.; Murrugarra, David, Control of intracellular molecular networks using algebraic methods, Bull. Math. Biol., 82, 1, 1-22 (2020) · Zbl 1432.92040
[33]	Vundavilli, Haswanth; Datta, Aniruddha; Sima, Chao; Hua, Jianping; Lopes, Rosana; Bittner, Michael, In silico design and experimental validation of combination therapy for pancreatic cancer, IEEE/ACM Trans. Comput. Biol. Bioinf., 17, 3, 1010-1018 (2018)
[34]	Vundavilli, Haswanth; Datta, Aniruddha; Sima, Chao; Hua, Jianping; Lopes, Rosana; Bittner, Michael, In silico design and experimental validation of combination therapy for pancreatic cancer, IEEE/ACM Trans. Comput. Biol. Bioinf., 17, 3, 1010-1018 (2020)
[35]	Wang, Qinsi, Miskov-Zivanov, Natasa, Liu, Bing, Faeder, James R., Lotze, Michael, Clarke, Edmund M., 2016. Formal modeling and analysis of pancreatic cancer microenvironment. In: Ezio Bartocci, Pietro Lio, and Nicola Paoletti, editors, Computational Methods in Systems Biology. Cham. Springer International Publishing. pp. 289-305.
[36]	LaMorte, Wayne W., 2016. Estimating the survival function.
[37]	Wooten, David J.; Zañudo, Jorge Gómez Tejeda; Murrugarra, David; Perry, Austin M.; Dongari-Bagtzoglou, Anna; Laubenbacher, Reinhard; Nobile, Clarissa J.; Albert, Réka, Mathematical modeling of the candida albicans yeast to hyphal transition reveals novel control strategies, PLoS Comput. Biol., 17, 3 (2021), e1008690
[38]	Xia, Yinglin, Correlation and association analyses in microbiome study integrating multiomics in health and disease, 171, 05 (2020)
[39]	Yang, Shenghong; Wang, Xiaoxu; Contino, Gianmarco; Liesa, Marc; Sahin, Ergun; Ying, Haoqiang; Bause, Alexandra; Li, Yinghua; Stommel, Jayne M.; Dell’Antonio, Giacomo; Mautner, Josef; Tonon, Giovanni; Haigis, Marcia; Shirihai, Orian S.; Doglioni, Claudio; Bardeesy, Nabeel; Kimmelman, Alec C., Pancreatic cancers require autophagy for tumor growth, Genes Develop., 25, 7, 717-729 (2011)
[40]	Yun, Chul Won; Lee, Sang Hun, The roles of autophagy in cancer, Int. J. Mol. Sci., 19, 11 (2018)
[41]	Zañudo, Jorge G. T.; Albert, Réka, Cell fate reprogramming by control of intracellular network dynamics, PLoS Comput. Biol., 11, 4 (2015), e1004193
[42]	Zañudo, Jorge Gomez Tejeda; Yang, Gang; Albert, Réka, Structure-based control of complex networks with nonlinear dynamics, Proc. Natl. Acad. Sci. U.S.A., 114, 28, 7234-7239 (2017)