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Research Paper

A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin

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Article: 2321769 | Received 11 May 2023, Accepted 18 Feb 2024, Published online: 27 Feb 2024

References

  • Holohan C, Van Schaeybroeck S, Longley D, Johnston P. Cancer drug resistance: an evolving paradigm. Nat Rev Cancer. 2013;13(10):714–16. doi:10.1038/nrc3599.
  • Zheng HC. The molecular mechanisms of chemoresistance in cancers. Oncotarget. 2017;8(35):59950–59964. PMID: 28938696; PMCID: PMC5601792. doi:10.18632/oncotarget.19048.
  • Dagogo-Jack I, Shaw A. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018;15(2):81–94. London. doi:10.1038/nrclinonc.2017.166.
  • Easwaran H, Tsai H, Baylin S. Cancer epigenetics: Tumor heterogeneity, plasticity of stem-like states, and drug resistance. Mol Cell. 2014;54(5):716–727. doi:10.1016/j.molcel.2014.05.015.
  • Dong H, Wang W, Mo S, Chen R, Zou K, Han J, Zhang F, Hu J. RETRACTED ARTICLE: SP1-induced lncRNA AGAP2-AS1 expression promotes chemoresistance of breast cancer by epigenetic regulation of MyD88. J Exp Clin Cancer Res. 2018;37(1):202–215. doi:10.1186/s13046-018-0875-3.
  • Ji X, Lu Y, Tian H, Meng X, Wei M, Cho W. Chemoresistance mechanisms of breast cancer and their countermeasures. Biomed Pharmacother. 2019. 114:108800. doi:10.1016/j.biopha.2019.108800.
  • Polyak K. Heterogeneity in breast cancer. J Clin Invest. 2011;121(10):3786–3788. doi:10.1172/JCI60534.
  • Alizadeh A, Aranda V, Bardelli A, Blanpain C, Bock C, Borowski C, Caldas C, Califano A, Doherty M, Elsner M, et al. Toward understanding and exploiting tumor heterogeneity. Nat Med. 2015;21(8):846–853. doi:10.1038/nm.3915.
  • Sun XX, Yu Q. Intra-tumor heterogeneity of cancer cells and its implications for cancer treatment. Acta Pharmacol Sin. 2015;36(10):1219–1227. doi:10.1038/aps.2015.92.
  • Gatenby RA, Grove O, Gillies RJ. Quantitative imaging in cancer evolution and ecology. Radiology. 2013;269(1):8–14. doi:10.1148/radiol.13122697.
  • Syed A, Whisenant J, Barnes S, Sorace A, Yankeelov T. Multiparametric analysis of longitudinal quantitative mri data to identify distinct tumor habitats in preclinical models of breast cancer. Cancers (Basel). 2020;12(6):1–20. doi:10.3390/cancers12061682.
  • Kazerouni A, Hormuth D 2nd, Davis T, Bloom M, Mounho S, Rahman G, Virostko J, Yankeelov T, Sorace A Quantifying tumor heterogeneity via MRI habitats to characterize microenvironmental alterations in HER2+ breast cancer. Cancers (Basel). 2022; 14, 1837.
  • Hanahan D. Hallmarks of cancer: new dimensions. Cancer Discov. 2022;12(1):31–46. doi:10.1158/2159-8290.CD-21-1059.
  • Jarrett A, Faghihi D, Hormuth D 2nd, Lima E, Virostko J, Biros G, Patt D, Yankeelov T. Optimal control theory for personalized therapeutic regimens in oncology: background, history, challenges, and opportunities. J Clin Med. 2020;9(5):1314. PMID: 32370195; PMCID: PMC7290915. doi:10.3390/jcm9051314.
  • Yankeelov T, Atuegwu N, Hormuth D 2nd, Weis J, Barnes S, Miga M, Rericha E, Quaranta V. Clinically relevant modeling of tumor growth and treatment response. Sci Transl Med. 2013;5(187):187ps9. doi:10.1126/scitranslmed.3005686.
  • Anderson A, Quaranta V. Integrative mathematical oncology. Nat Rev Cancer. 2008;8(3):227–234. doi:10.1038/nrc2329.
  • Ghaffari Laleh N, Loeffler C, Grajek J, Stankova K, Pearson A, Muti H, Trautwein C, Enderling H, Poleszczuk J, Kather J, et al. Classical mathematical models for prediction of response to chemotherapy and immunotherapy. PLoS Comput Biol. 2022;18(2):e1009822. doi:10.1371/journal.pcbi.1009822.
  • Yang E, Howard G, Brock A, Yankeelov T, Lorenzo G. Mathematical characterization of population dynamics in breast cancer cells treated with doxorubicin. Front Mol Biosci. 2022. 9:972146. doi:10.3389/fmolb.2022.972146.
  • Liu J, Hormuth D 2nd, Davis T, Yang J, McKenna M, Jarrett A, Enderling H, Brock A, Yankeelov T. A time-resolved experimental–mathematical model for predicting the response of glioma cells to single-dose radiation therapy. Integr Biol. 2021;202113(7):167–183. (Camb) PMID: 34060613; PMCID: PMC8271006. doi:10.1093/intbio/zyab010.
  • Yang J, Virostko J, Hormuth D 2nd, Liu J, Brock A, Kowalski J, Yankeelov T, Carlier A. An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines. PloS One. 2021;16(7):e0240765. PMID: 34255770; PMCID: PMC8277046. doi:10.1371/journal.pone.0240765.
  • Pozzi G, Grammatica B, Chaabane L, Catucci M, Mondino A, Zunino P, Ciarletta P. T cell therapy against cancer: a predictive diffuse-interface mathematical model informed by pre-clinical studies. J Theor Biol. 2022;547:111172. ISSN 0022-5193. doi:10.1016/j.jtbi.2022.111172.
  • Luo M, Nikolopoulou E, Gevertz J. 2022. From fitting the average to fitting the individual: a cautionary tale for mathematical modelers. Front Oncol. 12. doi: 10.3389/fonc.2022.793908.
  • Hormuth D 2nd, Weis J, Barnes S, Miga M, Rericha E, Quaranta V, Yankeelov T. A mechanically coupled reaction–diffusion model that incorporates intra-tumoural heterogeneity to predict in vivo glioma growth. J R Soc Interface. 2017;14(128):142016101020161010. doi:10.1098/rsif.2016.1010.
  • Wu C, Jarrett A, Zhou Z, Elshafeey N, Adrada B, Candelaria R, Mohamed R, Boge M, Huo L, White J, et al. MRI-Based digital models forecast patient-specific treatment responses to neoadjuvant chemotherapy in triple-negative breast cancer. Cancer Res. 2022; 82(18):3394–3404. PMID: 35914239; PMCID: PMC9481712. doi:10.1158/0008-5472.CAN-22-1329.
  • Hormuth D 2nd, Al Feghali K, Elliott A, Yankeelov T, Chung C. Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation. Sci Rep. 2021;11(1):8520. PMID: 33875739; PMCID: PMC8055874. doi:10.1038/s41598-021-87887-4.
  • Rockne R, Trister A, Jacobs J, Hawkins-Daarud A, Neal M, Hendrickson K, Mrugala M, Rockhill J, Kinahan P, Krohn K, et al. A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using (18)F-FMISO-PET. J R Soc Interface. 2015;12(103):20141174. doi:10.1098/rsif.2014.1174.
  • Hogea C, Davatzikos C, Biros G. An image-driven parameter estimation problem for a reaction-diffusion glioma growth model with mass effects. J Math Biol. 2008;56(6):793–825. doi:10.1007/s00285-007-0139-x.
  • Clatz O, Sermesant M, Bondiau P-Y, Delingette H, Warfield S, Malandain G, Ayache N. Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation. IEEE Trans Med Imaging. 2005;1(10):1334–1346. doi:10.1109/TMI.2005.857217.
  • O’Connor J, Rose C, Waterton J, Carano R, Parker G, Jackson A. Imaging intratumor heterogeneity: Role in therapy response, resistance, and clinical outcome. Clin Cancer Res. 2015;21:249–257. doi:10.1158/1078-0432.CCR-14-0990.
  • Hu L, Hawkins-Daarud A, Wang L, Li J, Swanson K. Imaging of intratumoral heterogeneity in high-grade glioma. Cancer Lett.2020;477:97–106. PMID: 32112907; PMCID: PMC7108976. doi:10.1016/j.canlet.2020.02.025.
  • Slavkova K, Patel S, Cacini Z, Kazerouni A, Gardner A, Yankeelov T, Hormuth DA. Hormuth 2nd D. Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma. Sci Rep. 2023;13(1):2916. doi:10.1038/s41598-023-30010-6.
  • Zhang J, Cunningham J, Brown J, Gatenby R Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat Commun. 2017; 8, 1816. 10.1038/s41467-017-01968-5.
  • Brady-Nicholls R, Enderling H. Range-bounded adaptive therapy in metastatic prostate cancer. Cancers. 2022;14(21):5319. doi:10.3390/cancers14215319.
  • Brady-Nicholls R, Zhang J, Zhang T, Wanh A, Butler R, Gatenby R, Enderling H. Predicting patient-specific response to adaptive therapy in metastatic castration-resistant prostate cancer using prostate-specific antigen dynamics. Neoplasia. 2021;23(9):851–858. doi:10.1016/j.neo.2021.06.013.
  • Kazerouni A, Gadde M, Gardner A, Hormuth D 2nd, Jarrett A, Johnsn K, Lina E, Lorenzo G, Philips C, Brock A, et al. Integrating quantitative assays with biologically based mathematical modeling for predictive oncology. iScience. 2020;23(12):101807. doi:10.1016/j.isci.2020.101807.
  • McKenna M, Weis J, Quaranta V, Yankeelov T. Variable cell line pharmacokinetics contribute to non-linear treatment response in heterogeneous cell populations. Ann Biomed Eng. 2018;46(6):899–911. doi:10.1007/s10439-018-2001-2.
  • Tyson D, Garbett S, Frick P, Quaranta V. Fractional proliferation: a method to deconvolve cell population dynamics from single-cell data. Nat Methods. 2012;9(9):923. doi:10.1038/nmeth.2138.
  • Lima E, Ghousifam N, Ozkan A, Oden J, Shahmoradi A, Rylander M, Wohlmuth B, Yankeelov T. Calibration of multi-parameter models of avascular tumor growth using time resolved microscopy data. Sci Rep. 2018;8(1):14558. doi:10.1038/s41598-018-32347-9.
  • Howard G, Jost T, Yankeelov T, Brock A, Finley S. Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules. PLoS Comput Biol. 2022;18(3):e1009104. doi:10.1371/journal.pcbi.1009104.
  • Liu J, Hormuth D 2nd, Yang J, Yankeelov T. A data assimilation framework to predict the response of glioma cells to radiation. Math Biosci Eng. 2023;20(1):318–336. doi:10.3934/mbe.2023015.
  • Wu C, Hormuth D 2nd, Lorenzo G, Jarrett A, Pineda F, Howard F, Karczmar G, Yankeelov T. Towards patient-specific optimization of neoadjuvant treatment protocols for breast cancer based on image-guided fluid dynamics. IEEE Trans Biomed Eng. 2022;69(11):3334–3344. doi:10.1109/TBME.2022.3168402.
  • Jarrett A, Hormuth D 2nd, Wu C, Kazerouni A, Ekrut D, Virostko J, Sorace A, DiCarlo J, Kowalski J, Patt D, et al. Evaluating patient-specific neoadjuvant regimens for breast cancer via a mathematical model constrained by quantitative magnetic resonance imaging data. Neoplasia. 2020;12(12):820–830. doi:10.1016/j.neo.2020.10.011.
  • Lima E, Wyde R, Sorace A, Yankeelov T. Optimizing combination therapy in a murine model of HER2+ breast cancer. Comput Methods Appl Mech Eng. 2022. 402:115484. doi:10.1016/j.cma.2022.115484.
  • Kowarz E, Löscher D, Marschalek R. Optimized sleeping beauty transposons rapidly generate stable transgenic cell lines. Biotechnol J. 2015;10(4):647–653. doi:10.1002/biot.201400821.
  • Mátés L, Chuah M, Belay E, Jerchow B, Manoj N, Acosta-Sanchez A, Grzela D, Schmitt A, Becker K, Matrai J, et al. Molecular evolution of a novel hyperactive sleeping beauty transposase enables robust stable gene transfer in vertebrates. Nat Genet. 2009;41(6):753–761. doi:10.1038/ng.343.
  • Jarrett A, Lima E, Hormuth D 2nd, McKenna M, Feng X, Ekrut D, Resende A, Brock A, Yankeelov T. Mathematical models of tumor cell proliferation: a review of the literature. Expert Rev Anticancer Ther. 2018;18(12):1271–1286. doi:10.1080/14737140.2018.1527689.
  • Gewirtz D. A critical evaluation of the mechanisms of action proposed for the antitumor effects of the anthracycline antibiotics adriamycin and daunorubicin. Biochem Pharmacol. 1999;57(7):727–741. doi:10.1016/s0006-2952(98)00307-4.
  • Carvalho C, Santos R, Cardoso S, Correia S, Oliveira P, Santos M, Moreira PD. Doxorubicin: the good, the bad and the ugly effect. Curr Med Chem. 2009;16(25):3267–3285. doi:10.2174/092986709788803312.
  • McKenna M, Weis J, Barnes S, Tyson D, Miga M, Quaranta V, Yankeelov T. A predictive mathematical modeling approach for the study of doxorubicin treatment in triple negative breast cancer. Sci Rep. 2017;7(1):5725. doi:10.1038/s41598-017-05902-z.
  • Lin L. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989;45(1):255–268. JSTOR. doi:10.2307/2532051.
  • Kostelich E, Kuang Y, McDaniel J, Moore N, Martirosyan N, Preul M. Accurate state estimation from uncertain data and models: an application of data assimilation to mathematical models of human brain tumors. Biol Direct. 2011;6(1):64. doi:10.1186/1745-6150-6-64.
  • Johnson K, Howard G, Morgan D, Brenner E, Gardner A, Durrett R, Mo W, Al-Khafaji A, Sontag E, Jarret A, et al. Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer. Phys Biol. 2020;18(1):016001. doi:10.1088/1478-3975/abb09c.
  • Hormuth D 2nd, Philips C, Wu C, Lima E, Lorenzo G, Jha P, Jarrett A, Oden J, Yankeelov T. Biologically-based mathematical modeling of tumor vasculature and angiogenesis via time-resolved imaging data. Cancers. 2021;13(12, Art. no. 12):3008. doi:10.3390/cancers13123008.
  • Navon I. Data assimilation for numerical weather prediction: a review. In: Park SK Xu L, editors. Data assimilation for atmospheric, oceanic and hydrologic applications. Berlin, Heidelberg: Springer; 2009. pp. 21–65. 10.1007/978-3-540-71056-1_2
  • Zahid M, Mohsin N, Mohamed A, Caudell J, Harrison L, Fuller C, Moros E, Enderling H. Forecasting individual patient response to radiation therapy in head and neck cancer with a dynamic carrying capacity model. Int J Radiat Oncol Bio Phys. 2021;111(3):693–704. doi:10.1016/j.ijrobp.2021.05.132.
  • Zahid M, Mohamed A, Caudell J, Harrison L, Fuller C, Moros E, Enderling H. Dynamics-Adapted Radiotherapy Dose (DARD) for head and neck cancer radiotherapy dose personalization. J Pers Med. 2021;11(11):1124. doi:10.3390/jpm11111124.
  • Hu C, He S, Lee Y, HE Y, Kong E, Li H, Anastasio M, Popesco G. Live-dead assay on unlabeled cells using phase imaging with computational specificity. Nat Commun. 2022;13(1):713. doi:10.1038/s41467-022-28214-x.
  • Gutierrez C, Al’khafaji A, Brenner E, Johnson K, Gohil S, Lin Z, Knisbacher B, Durrett R, Li S, Parvin S, et al. Multifunctional barcoding with ClonMapper enables high-resolution study of clonal dynamics during tumor evolution and treatment. Nat Cancer. 2021;2(7):758–772. doi:10.1038/s43018-021-00222-8.
  • Hormuth D 2nd, Farhat M, Bronk J, Langshaw H, Yankeelov T. Effect of chemoradiation on high-grade gliomas can be forecasted by mid-treatment images via image-driven mathematical modeling. Proc Intl Soc Mag Reson Med. 2023;31:0136.
  • Moffat B, Chenevert T, Meyer C, Mckeever P, Hall D, Hoff B, Johnson T, Rehemtulla A, Ross B. The functional diffusion map: an imaging biomarker for the early prediction of cancer treatment outcome. Neoplasia. 2006;8(4):259–267. doi:10.1593/neo.05844.