×

Model prediction and validation of an order mechanism controlling the spatiotemporal phenotype of early hepatocellular carcinoma. (English) Zbl 1395.92078

Summary: Recently, hepatocyte-sinusoid alignment (HSA) has been identified as a mechanism that supports the coordination of hepatocytes during liver regeneration to reestablish a functional micro-architecture [S. Hoehme et al. “Prediction and validation of cell alignment along microvessels as order principle to restore tissue architecture in liver regeneration”, Proc. Natl. Acad. Sci. USA 107, No. 23, 10371–10376 (2010; doi:10.1073/pnas.0909374107)]. HSA means that hepatocytes preferentially align along the closest micro-vessels. Here, we studied whether this mechanism is still active in early hepatocellular tumors. The same agent-based spatiotemporal model that previously correctly predicted HSA in liver regeneration was further developed to simulate scenarios in early tumor development, when individual initiated hepatocytes gain increased proliferation capacity. The model simulations were performed under conditions of realistic liver micro-architectures obtained from 3D reconstructions of confocal laser scanning micrographs. Interestingly, the established model predicted that initiated hepatocytes at first arrange in elongated patterns. Only when the tumor progresses to cell numbers of approximately 4000, does it adopt spherical structures. This prediction may have relevant consequences, since elongated tumors may reach critical structures faster, such as larger vessels, compared to a spherical tumor of similar cell number. Interestingly, this model prediction was confirmed by analysis of the spatial organization of initiated hepatocytes in a rat liver tumor initiation study using single doses of 250 mg/kg of the genotoxic carcinogen \(N\)-nitrosomorpholine (NNM). Indeed, small clusters of GST-P positive cells induced by NNM were elongated, almost columnar, while larger GDT-P positive foci of approximately the size of liver lobuli adopted spherical shapes. From simulations testing numerous possible mechanisms, only HSA could explain the experimentally observed initial deviation from spherical shape. The present study demonstrates that the architecture of small cell clusters of hepatocytes early after initiation is still controlled by physiological mechanisms. However, this coordinating influence is lost when the tumor grows to approximately 4000 cells, leading to further growth in spherical shape. Our findings stress the potential importance of organ micro-architecture in understanding tumor phenotypes.

MSC:

92C50 Medical applications (general)
92C37 Cell biology

Software:

CellSys; SuperLU; TiQuant
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] Alcaraz, J; Buscemi, L; Grabulosa, M; Trepat, X; Fabry, B; Farre, R; Navajas, D, Microrheology of human lung epithelial cells measured by atomic force, Biophys J , 84, 2071-2079, (2003)
[2] Anderson, AR; Weaver, AM; Cummings, PT; Quaranta, V, Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment, Cell, 127, 905-915, (2006)
[3] Anderson AR, Chaplain MAJ, Rejniak KA (2007) Single-cell-based models in biology and medicine. Birkhäuser, Basel · Zbl 1228.92018
[4] Beysens, D; Forgacs, G; Glazier, JA, Cell sorting is analogous to phase ordering in fluids, Proc Natl Acad Sci USA, 97, 9467-9471, (2000)
[5] Braeuning, A; Gavrilov, A; Geissler, M; Wenz, C; Colnot, S; Templin, MF; Metzger, U; Römer, M; Zell, A; Schwarz, M, Tumor promotion and inhibition by phenobarbital in livers of conditional apc-deficient mice, Arch Toxicol, 90, 1481-1494, (2016)
[6] Casciari, JJ; Sotirchos, SV; Sutherland, RM, Glucose diffusivity in multicellular tumor spheroids, Cancer Res, 48, 3905-3909, (1988)
[7] Chesla, SE; Selvaraj, P; Zhu, C, Measuring two-dimensional receptor-ligand binding kinetics by micropipette, Biophys J, 75, 1553-1557, (1998)
[8] Chu, Y-S; Dufour, S; Paul Thiery, J; Perez, E; Pincet, F, Johnson-Kendall-Roberts theory applied to living cells, Phys Rev Lett, 94, 028102, (2005)
[9] D’Alessandro L, Höhme S, Drasdo* D, Klingmüller* U (2015) Unraveling liver complexity from molecular to organ level: challenges and perspectives. Prog Biophys Mol Biol 117(1):78-86 · Zbl 1136.65312
[10] Davidson, LA; Koehl, MAR; Keller, R; Oster, GF, How do sea urchins invaginate? using bio-mechanics to distinguish between mechanisms of primary invagination, Development, 121, 2005-2018, (1995)
[11] Drasdo, D, Buckling instabilities in one-layered growing tissues, Phys Rev Lett, 84, 4244-4247, (2000)
[12] Drasdo, D, Coarse graining in simulated cell populations, Adv Complex Syst, 8, 319-363, (2005) · Zbl 1077.92014
[13] Drasdo, D; Hoehme, S, A single-cell-based model of tumor growth in vitro: monolayers and spheroids, Phys Biol, 2, 133-147, (2005)
[14] Drasdo D, Loeffler M (2001) Individual-based models to growth and folding in one-layered tissues: intestinal crypts and early development. Nonlinear Anal Theory Methods Appl 47(1):245-256 · Zbl 1042.92504
[15] Drasdo D, Hoehme S (2005) A single-cell-based model of tumor growth in vitro: monolayers and spheroids. Phys Biol 2:133-147
[16] Drasdo, D; Hoehme, S; Block, M, On the role of physics in the growth and pattern formation of multi-cellular systems: what can we learn from individual-cell based models?, J Stat Phys, 128, 287-345, (2007) · Zbl 1118.82033
[17] Drasdo, D; Hoehme, S; Hengstler, JG, How predictive quantitative modeling of tissue organization can inform liver disease pathogenesis, J Hepatol, 61, 951-956, (2014)
[18] Drasdo, D; Bode, J; Dahmen, U; Dirsch, O; Dooley, S; etal., The virtual liver: state of the art and future perspectives, Arch Toxicol, 88, 2071-2075, (2014)
[19] EASL (2012) EASL-EORTC clinical practice guidelines: management of hepatocellular carcinoma. J Hepat 56:908-943
[20] Friebel A, Neitsch J, Johann T, Hammad S, Hengstler JG, Drasdo* D, Hoehme* S (2015) TiQuant: Software for tissue analysis, quantification and surface reconstruction. Bioinformatics. https://doi.org/10.1093/bioinformatics/btv346
[21] Galle, J; Loeffler, M; Drasdo, D, Modelling the effect of deregulated proliferation and apoptosis on the growth dynamics of epithelial cell populations in vitro, Biophys J , 88, 62-75, (2005)
[22] Ghallab A, Cellière G, Henkel SG, Driesch D, Hoehme S, Hofmann U, Zellmer S, Godoy P, Sachinidis A, Blaszkewicz M, Reif R, Marchan R, Kuepfer L, Häussinger D, Drasdo* D, Gebhardt* R, Hengstler* JG (2016) Model guided identification and therapeutic implications of an ammonia sink mechanism. J Hepat 64:860-871
[23] Godoy, P; Hewitt, NJ; Albrecht, U; Andersen, ME; Ansari, N; etal., Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME, Arch Toxicol, 87, 1315-1530, (2013)
[24] Grasl-Kraupp, B; Luebeck, G; Wagner, A; Löw-Baselli, A; Gunst, M; Waldhör, T; Moolgavkar, S; Schulte-Hermann, R, Quantitative analysis of tumor initiation in rat liver: role of cell replication and cell death (apoptosis), Carcinogenesis, 21, 1411-1421, (2000)
[25] Hammad, S; Hoehme, S; Friebel, A; Recklinghausen, I; Othman, A; Begher-Tibbe, B; Reif, R; Godoy, P; Johann, T; Vartak, A; Golka, K; Bucur, PO; Vibert, E; Marchan, R; Christ, B; Dooley, S; Meyer, C; Ilkavets, I; Dahmen, U; Dirsch, O; Böttger, J; Gebhardt, R; Drasdo, D; Hengstler, JG, Protocols for staining of bile canalicular and sinusoidal networks of human, mouse and pig livers, three-dimensional reconstruction and quantification of tissue microarchitecture by image processing and analysis, Arch Toxicol, 88, 1161-1183, (2014)
[26] Hoehme, S; Drasdo, D, A cell-based simulation software for multi-cellular systems, Bioinformatics, 26, 2641-2642, (2010) · Zbl 1195.92016
[27] Hoehme, S; Brulport, M; Bauer, A; Bedawy, E; Schormann, W; Gebhardt, R; Zellmer, S; Schwarz, M; Bockamp, E; Timmel, TG; Hengstler, JG; Drasdo, D, Prediction and validation of cell alignment along microvessels as order principle to restore tissue architecture in liver regeneration, Proc Natl Acad Sci, 107, 10371-10376, (2010)
[28] Holzhuetter, H-G; Drasdo, D; Preusser, T; Lippert, J; Henney, AM, The virtual liver: a multidisciplinary, multi-level challenge for systems biology, Wiley Interdiscipl Rev Syst Biol Med, 4, 221-235, (2012)
[29] Hutchinson, LG; Gaffney, EA; Maini, PK; Wagg, J; Phipps, A; Byrne, HM, Vascular phenotype identification and anti-angiogenic treatment recommendation: a pseudo-multiscale mathematical model of angiogenesis, J Theor Biol, 398, 162-180, (2016) · Zbl 1343.92226
[30] Jagiella N, Müller B, Müller M, Vignon-Clementel IE, Drasdo D (2016) Inferring growth control mechanisms in growing multi-cellular spheroids of NSCLC cells from spatial-temporal image data. PLoS Comput Biol 12(2):e1004412
[31] Klaassen CD, Casarett LJ, Doull J (2013) Casarett and Doull’s toxicology: the basic science of Poisons, 8th edn. McGraw-Hill Education/Medical, New York
[32] Kowalik, MA; Perra, A; Ledda-Columbano, GM; Ippolito, G; Piacentini, M; Columbano, A; Falasca, L, Induction of autophagy promotes the growth of early preneoplastic rat liver nodules, Oncotarget, 7, 5788-5799, (2016)
[33] Landau DP, Binder K (2000) Monte Carlo simulations in statistical physics. Cambridge University Press, Cambridge · Zbl 0998.82504
[34] Lekka, M; Laidler, P; Gil, D; Lekki, J; Stachura, Z; Hrynkiewicz, AZ, Elasticity of normal and cancerous human bladder cells studied by scanning force microscopy, Eur Biophys J, 28, 312-316, (1999)
[35] Li XS (2005) An overview of SuperLU: algorithms, implementation, and user interface. ACM Trans Math Softw 31(3):302-325 · Zbl 1136.65312
[36] Liedekerke, P; Palm, M; Jagiella, N; Drasdo, D, Simulating tissue mechanics with agent-based models: concepts, perspectives and some novel results, Comput Particle Mech, 26, 401-444, (2015)
[37] Macklin, P; Edgerton, ME; Thompson, AM; Cristini, V, Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): from microscopic measurements to macroscopic predictions of clinical progression, J Theor Biol, 301, 122-140, (2012) · Zbl 1397.92346
[38] Mahaffy, RE; Shih, CK; MacKintosh, FC; Käs, J, Scanning probe-based frequency-dependent microrheology of polymer gels and biological cells, Phys Rev Lett, 85, 880-883, (2000)
[39] Malmgren, RA, Observations on a liver mitotic stimulant present in tumor tissue, Cancer Res, 16, 232-236, (1956)
[40] Perra, A; Kowalik, MA; Ghiso, E; Ledda-Columbano, GM; Di Tommaso, L; Angioni, MM; Raschioni, C; Testore, E; Roncalli, M; Giordano, S; Columbano, A, YAP activation is an early event and a potential therapeutic target in liver cancer development, J Hepatol, 61, 1088-1096, (2014)
[41] Petrelli, A; Perra, A; Cora, D; Sulas, P; Menegon, S; Manca, C; Migliore, C; Kowalik, MA; Ledda-Columbano, GM; Giordano, S; Columbano, A, Microrna/gene profiling unveils early molecular changes and nuclear factor erythroid related factor 2 (NRF2) activation in a rat model recapitulating human hepatocellular carcinoma (HCC), Hepatology, 59, 228-241, (2014)
[42] Piper, JW; Swerlick, RA; Zhu, C, Determining force dependence of two-dimensional receptor-ligand binding affinity by centrifugation, Biophys J, 74, 492-513, (1998)
[43] Ramis-Conde* I, Drasdo D*, Anderson ARA, Chaplain MA J (2008) Modeling the influence of the E-cadherin-beta-catenin pathway in cancer cell invasion: a multiscale approach. Biophys J 95: 155-165
[44] Ramis-Conde, I; Drasdo, D, From genotypes to phenotypes: classification of the multi-cellular spatial-temporal tumour profiles for different variants of the cadherin adhesion pathway, Phys Biol, 9, 11, (2012)
[45] Ricken T, Dahmen U, Dirsch O (2010) A biphasic model for sinusoidal liver perfusion remodeling after outflow obstruction. Biomech Model Mechanobiol 9:435-450
[46] Ricken T, Werner D, Holzhütter HG, König M, Dahmen U, Dirsch O (2014) Modeling function-perfusion behavior in liver lobules including tissue, blood, glucose, lactate and glycogen by use of a coupled two-scale PDE-ODE approach. Biomech Model Mechanobiol 14(3):515-536
[47] Riegler, T; Nejabat, M; Eichner, J; Stiebellehner, M; Subosits, S; Bilban, M; Zell, A; Huber, WW; Schulte-Hermann, R; Grasl-Kraupp, B, Proinflammatory mesenchymal effects of the non-genotoxic hepatocarcinogen phenobarbital: a novel mechanism of antiapoptosis and tumor promotion, Carcinogenesis, 36, 1521-1530, (2015)
[48] Robertson-Tessi, M; Gillies, RJ; Gatenby, RA; Anderson, AR, Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes, Cancer Res, 75, 1567-1579, (2015)
[49] Rohr-Udilova, N; Sieghart, W; Eferl, R; Stoiber, D; Björkhem-Bergman, L; Eriksson, LC; Stolze, K; Hayden, H; Keppler, B; Sagmeister, S; Grasl-Kraupp, B; Schulte-Hermann, R; Peck-Radosavljevic, M, Antagonistic effects of selenium and lipid peroxides on growth control in early hepatocellular carcinoma, Hepatology, 55, 1112-1121, (2012)
[50] Satoh, K; Yamakawa, D; Kasai, K; Hayakari, M; Uchida, K; Miura, T, Nonclonal growth of preneoplastic cells positive for glutathione S-transferase P-form in the rat liver, Cancer Sci, 103, 1445-1450, (2012)
[51] Schienbein, M; Franke, M; Gruler, H, Random walk and directed movement: comparison between inert particles and self-organized molecular machines, Phys Rev E, 49, 5462-5471, (1994)
[52] Schliess F, Hoehme S, Henkel S, Ghallab A, Driesch D, Böttger J, Guthke R, Pfaff M, Hengstler JG, Gebhardt R, Häussinger D, Drasdo* D, Zellmer* S (2014) Integrated metabolic spatial-temporal model for the prediction of ammonia detoxification during liver damage and regeneration. Hepatology 60(6):2040-2051
[53] Tang, J; Enderling, H; Becker-Weimann, S; Pham, C; Polyzos, A; Chen, CY; Costes, SV, Phenotypic transition maps of 3D breast acini obtained by imaging-guided agent-based modeling, Integr Biol, 3, 408-421, (2011)
[54] Vartak, N; Damle-Vartak, A; Richter, B; Dirsch, O; Dahmen, U; Hammad, S; Hengstler, JG, Cholestasis-induced adaptive remodeling of interlobular bile ducts, Hepatology, 63, 951-964, (2016)
[55] Vintermyr OK, Døskeland SO (1987) Cell cycle parameters of adult rat hepatocytes in a defined medium. A note on the timing of nucleolar DNA replication. J Cell Physiol 132:12-21
[56] Warth A, Muley T, Meister M, Stenzinger A, Thomas M, Schirmacher P, Schnabel PA, Budczies J, Hoffmann H, Weichert W (2012) The novel histologic International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification system of lung adenocarcinoma is a stage-independent predictor of survival. J Clin Oncol 30(13):1438-1446
[57] Xiaoye, SL, An overview of superlu: algorithms, implementation, and user interface, ACM Trans Math Softw (TOMS), 31, 302-325, (2005) · Zbl 1136.65312
[58] Zou, Y; Bao, Q; Kumar, S; Hu, M; Wang, G-Y; etal., Four waves of hepatocyte proliferation linked with three waves of hepatic fat accumulation during partial hepatectomy-induced liver regeneration, PLoS ONE, 7, e30675, (2012)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.