Mathematical Modeling to Address Challenges in Pancreatic Cancer

Author(s): Prashant Dogra, Javier R. Ramírez, María J. Peláez, Zhihui Wang, Vittorio Cristini, Gulshan Parasher, Manmeet Rawat*.

Journal Name: Current Topics in Medicinal Chemistry

Volume 20 , Issue 5 , 2020

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Pancreatic Ductal Adenocarcinoma (PDAC) is regarded as one of the most lethal cancer types for its challenges associated with early diagnosis and resistance to standard chemotherapeutic agents, thereby leading to a poor five-year survival rate. The complexity of the disease calls for a multidisciplinary approach to better manage the disease and improve the status quo in PDAC diagnosis, prognosis, and treatment. To this end, the application of quantitative tools can help improve the understanding of disease mechanisms, develop biomarkers for early diagnosis, and design patient-specific treatment strategies to improve therapeutic outcomes. However, such approaches have only been minimally applied towards the investigation of PDAC, and we review the current status of mathematical modeling works in this field.

Keywords: Mathematical modeling, Numerical simulation, Desmoplasia, Pancreatic ductal adenocarcinoma, Cancer, Carcinoembryonic Antigen (CEA).

[1]
Ferlay, J.; Soerjomataram, I.; Dikshit, R.; Eser, S.; Mathers, C.; Rebelo, M.; Parkin, D.M.; Forman, D.; Bray, F. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer, 2015, 136(5), E359-E386.
[http://dx.doi.org/10.1002/ijc.29210] [PMID: 25220842]
[2]
Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics, 2019. CA Cancer J. Clin., 2019, 69(1), 7-34.
[http://dx.doi.org/10.3322/caac.21551] [PMID: 30620402]
[3]
Orth, M.; Metzger, P.; Gerum, S.; Mayerle, J.; Schneider, G.; Belka, C.; Schnurr, M.; Lauber, K. Pancreatic ductal adenocarcinoma: biological hallmarks, current status, and future perspectives of combined modality treatment approaches. Radiat. Oncol., 2019, 14(1), 141.
[http://dx.doi.org/10.1186/s13014-019-1345-6] [PMID: 31395068]
[4]
Ryan, D.P.; Hong, T.S.; Bardeesy, N. Pancreatic adenocarcinoma. N. Engl. J. Med., 2014, 371(11), 1039-1049.
[http://dx.doi.org/10.1056/NEJMra1404198] [PMID: 25207767]
[5]
McGuigan, A.; Kelly, P.; Turkington, R.C.; Jones, C.; Coleman, H.G.; McCain, R.S. Pancreatic cancer: A review of clinical diagnosis, epidemiology, treatment and outcomes. World J. Gastroenterol., 2018, 24(43), 4846-4861.
[http://dx.doi.org/10.3748/wjg.v24.i43.4846] [PMID: 30487695]
[6]
Riva, G.; Pea, A.; Pilati, C.; Fiadone, G.; Lawlor, R.T.; Scarpa, A.; Luchini, C. Histo-molecular oncogenesis of pancreatic cancer: From precancerous lesions to invasive ductal adenocarcinoma. World J. Gastrointest. Oncol., 2018, 10(10), 317-327.
[http://dx.doi.org/10.4251/wjgo.v10.i10.317] [PMID: 30364837]
[7]
Waters, A.M.; Der, C.J. KRAS: the critical driver and therapeutic target for pancreatic cancer. Cold Spring Harb. Perspect. Med., 2018, 8(9)a031435
[http://dx.doi.org/10.1101/cshperspect.a031435] [PMID: 29229669]
[8]
Cannon, A.; Thompson, C.; Hall, B.R.; Jain, M.; Kumar, S.; Batra, S.K. Desmoplasia in pancreatic ductal adenocarcinoma: insight into pathological function and therapeutic potential. Genes Cancer, 2018, 9(3-4), 78-86.
[PMID: 30108679]
[9]
Le Large, T. Bijlsma, M.; Kazemier, G.; van Laarhoven, H.; Giovannetti, E.; Jimenez, C. Seminars in cancer biology. Elsevier:Amsterdam, , 2017; 44, pp. 153-169.
[10]
Poruk, K.E.; Gay, D.Z.; Brown, K.; Mulvihill, J.D.; Boucher, K.M.; Scaife, C.L.; Firpo, M.A.; Mulvihill, S.J. The clinical utility of CA 19-9 in pancreatic adenocarcinoma: diagnostic and prognostic updates. Curr. Mol. Med., 2013, 13(3), 340-351.
[PMID: 23331006]
[11]
Rawat, M.; Kadian, K.; Gupta, Y.; Kumar, A.; Chain, P.S.G.; Kovbasnjuk, O.; Kumar, S.; Parasher, G. MicroRNA in pancreatic cancer: from biology to therapeutic potential. Genes (Basel), 2019, 10(10), 752.
[http://dx.doi.org/10.3390/genes10100752] [PMID: 31557962]
[12]
Moore, M.J.; Goldstein, D.; Hamm, J.; Figer, A.; Hecht, J.R.; Gallinger, S.; Au, H.J.; Murawa, P.; Walde, D.; Wolff, R.A.; Campos, D.; Lim, R.; Ding, K.; Clark, G.; Voskoglou-Nomikos, T.; Ptasynski, M.; Parulekar, W. Erlotinib plus gemcitabine compared with gemcitabine alone in patients with advanced pancreatic cancer: a phase III trial of the National Cancer Institute of Canada Clinical Trials Group. J. Clin. Oncol., 2007, 25(15), 1960-1966.
[http://dx.doi.org/10.1200/JCO.2006.07.9525] [PMID: 17452677]
[13]
Conroy, T.; Desseigne, F.; Ychou, M.; Ducreux, M.; Bouche, O.; Guimbaud, R.; Becouarn, Y.; Montoto-Grillot, C.; Gourgou-Bourgade, S.; Adenis, A. Randomized phase III trial comparing FOLFIRINOX (F: 5FU/leucovorin [LV], irinotecan [I], and oxaliplatin [O]) versus gemcitabine (G) as first-line treatment for metastatic pancreatic adenocarcinoma (MPA): preplanned interim analysis results of the PRODIGE 4/ACCORD 11 trial. J. Clin. Oncol., 2010, 28(15)(Suppl.), 4010-4010.
[http://dx.doi.org/10.1200/jco.2010.28.15_suppl.4010]
[14]
Dogra, P.; Adolphi, N.L.; Wang, Z.; Lin, Y-S.; Butler, K.S.; Durfee, P.N.; Croissant, J.G.; Noureddine, A.; Coker, E.N.; Bearer, E.L.; Cristini, V.; Brinker, C.J. Establishing the effects of mesoporous silica nanoparticle properties on in vivo disposition using imaging-based pharmacokinetics. Nat. Commun., 2018, 9(1), 4551.
[http://dx.doi.org/10.1038/s41467-018-06730-z] [PMID: 30382084]
[15]
Tsoi, K.M.; MacParland, S.A.; Ma, X-Z.; Spetzler, V.N.; Echeverri, J.; Ouyang, B.; Fadel, S.M.; Sykes, E.A.; Goldaracena, N.; Kaths, J.M.; Conneely, J.B.; Alman, B.A.; Selzner, M.; Ostrowski, M.A.; Adeyi, O.A.; Zilman, A.; McGilvray, I.D.; Chan, W.C. Mechanism of hard-nanomaterial clearance by the liver. Nat. Mater., 2016, 15(11), 1212-1221.
[http://dx.doi.org/10.1038/nmat4718] [PMID: 27525571]
[16]
Brocato, T.A.; Coker, E.N.; Durfee, P.N.; Lin, Y-S.; Townson, J.; Wyckoff, E.F.; Cristini, V.; Brinker, C.J.; Wang, Z. Understanding the connection between nanoparticle uptake and cancer treatment efficacy using mathematical modeling. Sci. Rep., 2018, 8(1), 7538.
[http://dx.doi.org/10.1038/s41598-018-25878-8] [PMID: 29795392]
[17]
Goel, S.; Ferreira, C.A.; Dogra, P.; Yu, B.; Kutyreff, C.J.; Siamof, C.M.; Engle, J.W.; Barnhart, T.E.; Cristini, V.; Wang, Z.; Cai, W. Size-optimized ultrasmall porous silica nanoparticles depict vasculature-based differential targeting in triple negative breast Cancer. Small, 2019, 15(46)e1903747
[http://dx.doi.org/10.1002/smll.201903747] [PMID: 31565854]
[18]
Hosoya, H.; Dobroff, A.S.; Driessen, W.H.P.; Cristini, V.; Brinker, L.M.; Staquicini, F.I.; Cardó-Vila, M.; D’Angelo, S.; Ferrara, F.; Proneth, B.; Lin, Y-S.; Dunphy, D.R.; Dogra, P.; Melancon, M.P.; Stafford, R.J.; Miyazono, K.; Gelovani, J.G.; Kataoka, K.; Brinker, C.J.; Sidman, R.L.; Arap, W.; Pasqualini, R. Integrated nanotechnology platform for tumor-targeted multimodal imaging and therapeutic cargo release. Proc. Natl. Acad. Sci. USA, 2016, 113(7), 1877-1882.
[http://dx.doi.org/10.1073/pnas.1525796113] [PMID: 26839407]
[19]
Liu, X.; Situ, A.; Kang, Y.; Villabroza, K.R.; Liao, Y.; Chang, C.H.; Donahue, T.; Nel, A.E.; Meng, H. Irinotecan delivery by lipid-coated mesoporous silica nanoparticles shows improved efficacy and safety over liposomes for pancreatic cancer. ACS Nano, 2016, 10(2), 2702-2715.
[http://dx.doi.org/10.1021/acsnano.5b07781] [PMID: 26835979]
[20]
Meng, H.; Wang, M.; Liu, H.; Liu, X.; Situ, A.; Wu, B.; Ji, Z.; Chang, C.H.; Nel, A.E. Use of a lipid-coated mesoporous silica nanoparticle platform for synergistic gemcitabine and paclitaxel delivery to human pancreatic cancer in mice. ACS Nano, 2015, 9(4), 3540-3557.
[http://dx.doi.org/10.1021/acsnano.5b00510] [PMID: 25776964]
[21]
Meng, H.; Zhao, Y.; Dong, J.; Xue, M.; Lin, Y-S.; Ji, Z.; Mai, W.X.; Zhang, H.; Chang, C.H.; Brinker, C.J.; Zink, J.I.; Nel, A.E. Two-wave nanotherapy to target the stroma and optimize gemcitabine delivery to a human pancreatic cancer model in mice. ACS Nano, 2013, 7(11), 10048-10065.
[http://dx.doi.org/10.1021/nn404083m] [PMID: 24143858]
[22]
Wilhelm, S.; Tavares, A.J.; Dai, Q.; Ohta, S.; Audet, J.; Dvorak, H.F.; Chan, W.C.W. Analysis of nanoparticle delivery to tumours. Nat. Rev. Mater., 2016, 1, 16014.
[http://dx.doi.org/10.1038/natrevmats.2016.14]
[23]
Dogra, P.; Butner, J.D.; Chuang, Y.L.; Caserta, S.; Goel, S.; Brinker, C.J.; Cristini, V.; Wang, Z. Mathematical modeling in cancer nanomedicine: a review. Biomed. Microdevices, 2019, 21(2), 40.
[http://dx.doi.org/10.1007/s10544-019-0380-2] [PMID: 30949850]
[24]
Hilmi, M.; Bartholin, L.; Neuzillet, C. Immune therapies in pancreatic ductal adenocarcinoma: Where are we now? World J. Gastroenterol., 2018, 24(20), 2137-2151.
[http://dx.doi.org/10.3748/wjg.v24.i20.2137] [PMID: 29853732]
[25]
Chauviere, A.H.; Hatzikirou, H.; Lowengrub, J.S.; Frieboes, H.B.; Thompson, A.M.; Cristini, V. Mathematical oncology: how are the mathematical and physical sciences contributing to the war on breast cancer? Curr. Breast Cancer Rep., 2010, 2(3), 121-129.
[http://dx.doi.org/10.1007/s12609-010-0020-6] [PMID: 21151486]
[26]
Cristini, V.; Frieboes, H.B.; Gatenby, R.; Caserta, S.; Ferrari, M.; Sinek, J. Morphologic instability and cancer invasion. Clin. Cancer Res., 2005, 11(19 Pt 1), 6772-6779.
[http://dx.doi.org/10.1158/1078-0432.CCR-05-0852] [PMID: 16203763]
[27]
Cristini, V.; Lowengrub, J. Multiscale modeling of cancer: an integrated experimental and mathematical modeling approach; Cambridge University Press: Cambridge, 2010.
[http://dx.doi.org/10.1017/CBO9780511781452]
[28]
Das, H.; Wang, Z.; Niazi, M.K.K.; Aggarwal, R.; Lu, J.; Kanji, S.; Das, M.; Joseph, M.; Gurcan, M.; Cristini, V. Impact of diffusion barriers to small cytotoxic molecules on the efficacy of immunotherapy in breast cancer. PLoS One, 2013, 8(4)e61398
[http://dx.doi.org/10.1371/journal.pone.0061398] [PMID: 23620747]
[29]
Deisboeck, T.S.; Wang, Z.; Macklin, P.; Cristini, V. Multiscale cancer modeling. Annu. Rev. Biomed. Eng., 2011, 13(1), 127-155.
[http://dx.doi.org/10.1146/annurev-bioeng-071910-124729] [PMID: 21529163]
[30]
Edgerton, M.E.; Chuang, Y-L.; Macklin, P.; Yang, W.; Bearer, E.L.; Cristini, V. A novel, patient-specific mathematical pathology approach for assessment of surgical volume: application to ductal carcinoma in situ of the breast. Anal. Cell. Pathol. (Amst.), 2011, 34(5), 247-263.
[http://dx.doi.org/10.1155/2011/803816] [PMID: 21988888]
[31]
Lowengrub, J.S.; Frieboes, H.B.; Jin, F.; Chuang, Y.L.; Li, X.; Macklin, P.; Wise, S.M.; Cristini, V. Nonlinear modelling of cancer: bridging the gap between cells and tumours. Nonlinearity, 2010, 23(1), R1-R9.
[http://dx.doi.org/10.1088/0951-7715/23/1/R01] [PMID: 20808719]
[32]
Macklin, P.; McDougall, S.; Anderson, A.R.A.; Chaplain, M.A.J.; Cristini, V.; Lowengrub, J. Multiscale modelling and nonlinear simulation of vascular tumour growth. J. Math. Biol., 2009, 58(4-5), 765-798.
[http://dx.doi.org/10.1007/s00285-008-0216-9] [PMID: 18781303]
[33]
Sanga, S.; Sinek, J.P.; Frieboes, H.B.; Ferrari, M.; Fruehauf, J.P.; Cristini, V. Mathematical modeling of cancer progression and response to chemotherapy. Expert Rev. Anticancer Ther., 2006, 6(10), 1361-1376.
[http://dx.doi.org/10.1586/14737140.6.10.1361]
[34]
Wang, Z. Butner, J.D.; Kerketta, R.; Cristini, V.; Deisboeck, T.S. In: Seminars in cancer biology; Elsevier: Amsterdam,. , 2015; 30, pp. 70-78.
[35]
Wang, Z.; Kerketta, R.; Chuang, Y-L.; Dogra, P.; Butner, J.D.; Brocato, T.A.; Day, A.; Xu, R.; Shen, H.; Simbawa, E.; Al-Fhaid, A.S.; Mahmoud, S.R.; Curley, S.A.; Ferrari, M.; Koay, E.J.; Cristini, V. Theory and experimental validation of a spatio-temporal model of chemotherapy transport to enhance tumor cell kill. PLOS Comput. Biol., 2016, 12(6)e1004969
[http://dx.doi.org/10.1371/journal.pcbi.1004969] [PMID: 27286441]
[36]
Frieboes, H.B.; Smith, B.R.; Wang, Z.; Kotsuma, M.; Ito, K.; Day, A.; Cahill, B.; Flinders, C.; Mumenthaler, S.M.; Mallick, P.; Simbawa, E.; Al-Fhaid, A.S.; Mahmoud, S.R.; Gambhir, S.S.; Cristini, V. Predictive modeling of drug response in non-hodgkin’s lymphoma. PLoS One, 2015, 10(6)e0129433
[http://dx.doi.org/10.1371/journal.pone.0129433] [PMID: 26061425]
[37]
Wang, Z.; Deisboeck, T.S. Mathematical modeling in cancer drug discovery. Expert Opin. Drug Discov., 2013, 12(8), 785-799.
[PMID: 23831857]
[38]
Pascal, J.; Ashley, C.E.; Wang, Z.; Brocato, T.A.; Butner, J.D.; Carnes, E.C.; Koay, E.J.; Brinker, C.J.; Cristini, V. Mechanistic modeling identifies drug-uptake history as predictor of tumor drug resistance and nano-carrier-mediated response. ACS Nano, 2013, 7(12), 11174-11182.
[http://dx.doi.org/10.1021/nn4048974] [PMID: 24187963]
[39]
Wang, Z.; Butner, J.D.; Cristini, V.; Deisboeck, T.S. Integrated PK-PD and agent-based modeling in oncology. J. Pharmacokinet. Pharmacodyn., 2015, 42(2), 179-189.
[http://dx.doi.org/10.1007/s10928-015-9403-7] [PMID: 25588379]
[40]
Wang, Z.; Deisboeck, T.S. Dynamic targeting in cancer treatment. Front. Physiol., 2019, 10, 96-96.
[http://dx.doi.org/10.3389/fphys.2019.00096] [PMID: 30890944]
[41]
Brocato, T.A.; Brown-Glaberman, U.; Wang, Z.; Selwyn, R.G.; Wilson, C.M.; Wyckoff, E.F.; Lomo, L.C.; Saline, J.L.; Hooda-Nehra, A.; Pasqualini, R.; Arap, W.; Brinker, C.J.; Cristini, V. Predicting breast cancer response to neoadjuvant chemotherapy based on tumor vascular features in needle biopsies. JCI Insight, 2019, 5pii: 126518
[http://dx.doi.org/10.1172/jci.insight.126518] [PMID: 30835256]
[42]
Brocato, T.; Dogra, P.; Koay, E.J.; Day, A.; Chuang, Y-L.; Wang, Z.; Cristini, V. Understanding drug resistance in breast cancer with mathematical oncology. Curr. Breast Cancer Rep., 2014, 6(2), 110-120.
[http://dx.doi.org/10.1007/s12609-014-0143-2] [PMID: 24891927]
[43]
Pascal, J.; Bearer, E.L.; Wang, Z.; Koay, E.J.; Curley, S.A.; Cristini, V. Mechanistic patient-specific predictive correlation of tumor drug response with microenvironment and perfusion measurements. Proc. Natl. Acad. Sci. USA, 2013, 110(35), 14266-14271.
[http://dx.doi.org/10.1073/pnas.1300619110] [PMID: 23940372]
[44]
Cristini, V.; Koay, E.; Wang, Z. An Introduction to Physical Oncology: How Mechanistic Mathematical Modeling Can Improve Cancer Therapy Outcomes; CRC Press: Boca Raton, 2017.
[http://dx.doi.org/10.4324/9781315374499]
[45]
Lee, J.J.; Huang, J.; England, C.G.; McNally, L.R.; Frieboes, H.B. Predictive modeling of in vivo response to gemcitabine in pancreatic cancer. PLOS Comput. Biol., 2013, 9(9)e1003231
[http://dx.doi.org/10.1371/journal.pcbi.1003231] [PMID: 24068909]
[46]
Cristini, V.; Lowengrub, J.; Nie, Q. Nonlinear simulation of tumor growth. J. Math. Biol., 2003, 46(3), 191-224.
[http://dx.doi.org/10.1007/s00285-002-0174-6] [PMID: 12728333]
[47]
Frieboes, H.B.; Jin, F.; Chuang, Y-L.; Wise, S.M.; Lowengrub, J.S.; Cristini, V. Three-dimensional multispecies nonlinear tumor growth-II: Tumor invasion and angiogenesis. J. Theor. Biol., 2010, 264(4), 1254-1278.
[http://dx.doi.org/10.1016/j.jtbi.2010.02.036] [PMID: 20303982]
[48]
Li, X.; Cristini, V.; Nie, Q.; Lowengrub, J.S. Nonlinear three-dimensional simulation of solid tumor growth. Discrete Continuous Dyn. Syst. Ser. B, 2007, 7(3), 581.
[http://dx.doi.org/10.3934/dcdsb.2007.7.581]
[49]
Haeno, H.; Gonen, M.; Davis, M.B.; Herman, J.M.; Iacobuzio-Donahue, C.A.; Michor, F. Computational modeling of pancreatic cancer reveals kinetics of metastasis suggesting optimum treatment strategies. Cell, 2012, 148(1-2), 362-375.
[http://dx.doi.org/10.1016/j.cell.2011.11.060] [PMID: 22265421]
[50]
Haeno, H.; Iwasa, Y.; Michor, F. The evolution of two mutations during clonal expansion. Genetics, 2007, 177(4), 2209-2221.
[http://dx.doi.org/10.1534/genetics.107.078915] [PMID: 18073428]
[51]
Yamamoto, K.N.; Nakamura, A.; Haeno, H. The evolution of tumor metastasis during clonal expansion with alterations in metastasis driver genes. Sci. Rep., 2015, 5, 15886-15886.
[http://dx.doi.org/10.1038/srep15886] [PMID: 26515895]
[52]
Yamamoto, K.N.; Yachida, S.; Nakamura, A.; Niida, A.; Oshima, M.; De, S.; Rosati, L.M.; Herman, J.M.; Iacobuzio-Donahue, C.A.; Haeno, H. Personalized management of pancreatic ductal adenocarcinoma patients through computational modeling. Cancer Res., 2017, 77(12), 3325-3335.
[http://dx.doi.org/10.1158/0008-5472.CAN-16-1208] [PMID: 28381541]
[53]
Yamamoto, K.N.; Nakamura, A.; Liu, L.L.; Stein, S.; Tramontano, A.C.; Kartoun, U.; Shimizu, T.; Inoue, Y.; Asakuma, M.; Haeno, H.; Kong, C.Y.; Uchiyama, K.; Gonen, M.; Hur, C.; Michor, F. Computational modeling of pancreatic cancer patients receiving FOLFIRINOX and gemcitabine-based therapies identifies optimum intervention strategies. PLoS One, 2019, 14(4)e0215409
[http://dx.doi.org/10.1371/journal.pone.0215409] [PMID: 31026288]
[54]
Yachida, S.; Jones, S.; Bozic, I.; Antal, T.; Leary, R.; Fu, B.; Kamiyama, M.; Hruban, R.H.; Eshleman, J.R.; Nowak, M.A.; Velculescu, V.E.; Kinzler, K.W.; Vogelstein, B.; Iacobuzio-Donahue, C.A. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature, 2010, 467(7319), 1114-1117.
[http://dx.doi.org/10.1038/nature09515] [PMID: 20981102]
[55]
Tuckwell, H.C. Poisson process in biology. In: Stochastic non linear systems in physics, chemistry and biology; Springer: Berlin, 1981; pp. 162-171.
[56]
Makohon-Moore, A.P.; Matsukuma, K.; Zhang, M.; Reiter, J.G.; Gerold, J.M.; Jiao, Y.; Sikkema, L.; Attiyeh, M.A.; Yachida, S.; Sandone, C.; Hruban, R.H.; Klimstra, D.S.; Papadopoulos, N.; Nowak, M.A.; Kinzler, K.W.; Vogelstein, B.; Iacobuzio-Donahue, C.A. Precancerous neoplastic cells can move through the pancreatic ductal system. Nature, 2018, 561(7722), 201-205.
[http://dx.doi.org/10.1038/s41586-018-0481-8] [PMID: 30177826]
[57]
Lange, F.; Rateitschak, K.; Fitzner, B.; Pöhland, R.; Wolkenhauer, O.; Jaster, R. Studies on mechanisms of interferon-gamma action in pancreatic cancer using a data-driven and model-based approach. Mol. Cancer, 2011, 10(1), 13.
[http://dx.doi.org/10.1186/1476-4598-10-13] [PMID: 21310022]
[58]
Lange, F.; Rateitschak, K.; Kossow, C.; Wolkenhauer, O.; Jaster, R. Insights into erlotinib action in pancreatic cancer cells using a combined experimental and mathematical approach. World J. Gastroenterol., 2012, 18(43), 6226-6234.
[http://dx.doi.org/10.3748/wjg.v18.i43.6226] [PMID: 23180942]
[59]
Louzoun, Y.; Xue, C.; Lesinski, G.B.; Friedman, A. A mathematical model for pancreatic cancer growth and treatments. J. Theor. Biol., 2014, 351, 74-82.
[http://dx.doi.org/10.1016/j.jtbi.2014.02.028] [PMID: 24594371]
[60]
Chen, J.; Weihs, D.; Vermolen, F.J. Computational modeling of therapy on pancreatic cancer in its early stages. Biomech. Model. Mechanobiol., 2019. Epub ahead of Print
[http://dx.doi.org/10.1007/s10237-019-01219-0] [PMID: 31501963]
[61]
Roy, M.; Finley, S.D. Computational model predicts the effects of targeting cellular metabolism in pancreatic cancer. Front. Physiol., 2017, 8, 217.
[http://dx.doi.org/10.3389/fphys.2017.00217] [PMID: 28446878]
[62]
Gong, H.; Zuliani, P.; Wang, Q.; Clarke, E.M. 50th IEEE conference on decision and control and european control conference, 2011, pp. 4855-4860.
[http://dx.doi.org/10.1109/CDC.2011.6161540]
[63]
Koay, E.J.; Lee, Y.; Cristini, V.; Lowengrub, J.S.; Kang, Y.; Lucas, F.A.S.; Hobbs, B.P.; Ye, R.; Elganainy, D.; Almahariq, M.; Amer, A.M.; Chatterjee, D.; Yan, H.; Park, P.C.; Rios Perez, M.V.; Li, D.; Garg, N.; Reiss, K.A.; Yu, S.; Chauhan, A.; Zaid, M.; Nikzad, N.; Wolff, R.A.; Javle, M.; Varadhachary, G.R.; Shroff, R.T.; Das, P.; Lee, J.E.; Ferrari, M.; Maitra, A.; Taniguchi, C.M.; Kim, M.P.; Crane, C.H.; Katz, M.H.; Wang, H.; Bhosale, P.; Tamm, E.P.; Fleming, J.B. A visually apparent and quantifiable CT imaging feature identifies biophysical subtypes of pancreatic ductal adenocarcinoma. Clin. Cancer Res., 2018, 24(23), 5883-5894.
[64]
Bali, M.A.; Metens, T.; Denolin, V.; Delhaye, M.; Demetter, P.; Closset, J.; Matos, C. Tumoral and nontumoral pancreas: correlation between quantitative dynamic contrast-enhanced MR imaging and histopathologic parameters. Radiology, 2011, 261(2), 456-466.
[http://dx.doi.org/10.1148/radiol.11103515] [PMID: 21852570]
[65]
Liu, K.; Xie, P.; Peng, W.; Zhou, Z. Dynamic contrast-enhanced magnetic resonance imaging for pancreatic ductal adenocarcinoma at 3.0-T magnetic resonance: correlation with histopathology. J. Comput. Assist. Tomogr., 2015, 39(1), 13-18.
[http://dx.doi.org/10.1097/RCT.0000000000000171] [PMID: 25340589]
[66]
Root, A. Mathematical modeling of the challenge to detect pancreatic adenocarcinoma early with biomarkers. Challenges, 2019, 10(1), 26.
[http://dx.doi.org/10.3390/challe10010026]
[67]
Koay, E.J.; Truty, M.J.; Cristini, V.; Thomas, R.M.; Chen, R.; Chatterjee, D.; Kang, Y.; Bhosale, P.R.; Tamm, E.P.; Crane, C.H.; Javle, M.; Katz, M.H.; Gottumukkala, V.N.; Rozner, M.A.; Shen, H.; Lee, J.E.; Wang, H.; Chen, Y.; Plunkett, W.; Abbruzzese, J.L.; Wolff, R.A.; Varadhachary, G.R.; Ferrari, M.; Fleming, J.B. Transport properties of pancreatic cancer describe gemcitabine delivery and response. J. Clin. Invest., 2014, 124(4), 1525-1536.
[http://dx.doi.org/10.1172/JCI73455] [PMID: 24614108]
[68]
Koay, E.J.; Baio, F.E.; Ondari, A.; Truty, M.J.; Cristini, V.; Thomas, R.M.; Chen, R.; Chatterjee, D.; Kang, Y.; Zhang, J.; Court, L.; Bhosale, P.R.; Tamm, E.P.; Qayyum, A.; Crane, C.H.; Javle, M.; Katz, M.H.; Gottumukkala, V.N.; Rozner, M.A.; Shen, H.; Lee, J.E.; Wang, H.; Chen, Y.; Plunkett, W.; Abbruzzese, J.L.; Wolff, R.A.; Maitra, A.; Ferrari, M.; Varadhachary, G.R.; Fleming, J.B. Intra-tumoral heterogeneity of gemcitabine delivery and mass transport in human pancreatic cancer. Phys. Biol., 2014, 11(6)065002
[http://dx.doi.org/10.1088/1478-3975/11/6/065002] [PMID: 25427073]


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 20
ISSUE: 5
Year: 2020
Page: [367 - 376]
Pages: 10
DOI: 10.2174/1568026620666200101095641
Price: $65

Article Metrics

PDF: 14
HTML: 2