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| Title: | 在Goldie-Coldman模型下野生型癌症細胞對癌症治療影響之探討
An investigation on the Effect of Wild Type Cancer Cells to Cancer Treatment under Goldie-Coldman Model |
| Authors: | 施銘威
Shih, Ming-Wei |
| Contributors: | 陳政輝
Chen, Jeng-Huei
施銘威
Shih, Ming-Wei |
| Keywords: | 癌症
抗藥性
Goldie-Coleman模型
Cancer
Drug resistance
Goldie and Coldman’s model |
| Date: | 2022 |
| Issue Date: | 2023-03-09 18:12:46 (UTC+8) |
| Abstract: | 化療被用於治療多種癌症，在治療的過程中，因癌細胞突變產抗藥性，嚴重影響治療成效。為探討抗藥性對治療的影響，Goldie和Coldman在1979年提出了第一個癌症治療的抗藥性數學模型。Goldie和Coldman的模型考慮以兩種藥物治療含有野生型癌細胞及兩種分別對採用的其中一種藥物具抗藥性的突變癌細胞。治療的目標是如何在治療後使雙重抗藥性癌細胞產生的機率最低。Goldie和Coldman隨後與Guaduskas合作，分別以數值與解析方法證明，當兩種藥物效力與模型參數具對稱性時，交替使用兩種藥物療效最佳。Chen等學者推廣Goldie等人的工作，提出若兩種藥物對野生型癌細胞有相同效力時，最佳治療方式可以數學解析方式求得。
本論文中，我們考慮兩種藥物對野生型癌細胞效力不同對最佳用藥策略的影響。根據我們對突變過程的觀察，野生型癌細胞個數相較於抗藥性細胞個數必須很大，才可能對最佳用藥策略造成影響，因此，我們猜想當野生型癌細胞數量不是太大時，Chen等學者所提產生用藥策略的方法，仍是最佳用藥策略。數值結果與我們猜想的預期相符合。此外，當野生型癌細胞個數相較於抗藥性細胞很大時，我們提出了兩種演算法來考慮野生型癌細胞影響，產生用藥策略。數值結果顯示此兩種演算法可改進Chen等學者提出之方法。藉由適當合併這些已提出的演算法，可以產生有潛力的近似型演算法來找到好的治療用藥策略。因此，如何以數學方法嚴謹的證明我們所提關於野生型癌細胞個數對用藥策略影響之猜想，將是重要的工作，也是未來繼續研究的方向。
Chemotherapy is applied to treat many different cancers. However, due to mutation, drug resistance might occur and might seriously affect the efficacy of treatment. To study the effect of drug resistance, Goldie and Coldman proposed the first mathematical model of drug resistance in cancer treatment in 1979. In their model, two drugs are applied to treat cancers consisting of sensitive wild-type cancer cells and two mutant cancer cells without cross-resistance. The treatment goal is to minimize the risk of developing double resistant cancer cells after treatment. In cooperation with Guaduskas, Goldie and Coldman later demonstrated that, both numerically and analytically, if two drugs have symmetric potencies and the model possesses symmetric structure in its parameter setting, alternating usage of two drugs is the optimal treatment policy. In 2013, Chen et al. further showed that the optimal treatment policy can be obtained analytically under the assumption that two drugs have the same efficacy on wild-type cancer cells.
In this thesis, we consider the optimal treatment policy when two drugs have different efficacies on wild-type cancer cells. Based on our observation to the mutation process, the number of wild-type cancer cells must be large compared to that of drugresistant cells in order to determine the optimal policy. We therefore conjecture that when the number of wild-type cancer cells is not too large, the optimal treatment policy can still be determined by Chen’s method. Numerical results are in line with the prediction of our conjecture. For the case that the number of wild-type cancer cells is sufficiently large compared to that of drug-resistant cells, we also propose two algorithms, which take the influence of wild-type cancer cells to treatment into account. Numerical results show that both algorithms have good performance. Through combining Chen’s and our proposed algorithms, it is of great potential to create approximate algorithms for finding good treatment policies. Therefore, how to rigorously prove our conjecture regarding the influence of the number of wild-type cancer cells is important and will be our future work. |
| Reference: | [1] K.Bao, An elementary mathematical modeling of drug resistance in cancer,
Mathematical Biosciences and Engineering, vol.18, no.1, p.339-341, 2020.
[2] V.T. Devita, Principles of chemotherapy, in: V.T. Devita, S. Hellman, S.A.
Rosenberg, V. Devita (Eds.), Principles and Practice of Oncology, Lippincott,
Philedelphia, PA, no.3, p.278, 1989.
[3] A.J. Coldman, J.M. Murray, Optimal control for a stochastic model of cancer
chemotherapy, Mathematical Biosciences, vol.168, 2000.
[4] R.S. Day, Treatment sequencing, asymmetry, and uncertainty: protocol strategies for
combination chemotherapy, Cancer Research, vol.46, p.3876-3885, 1986.
[5] A.A. Katouli, N.L. Komarova, The worst drug rule revisited: mathematical
modeling of cyclic cancer treatments, Bull. Math. Biol. , vol.73, p.549–584, 2011.
[6] J.H. Goldie, A.J. Coldman, A mathematical model for relating the drug sensitivity of
tumors to their spontaneous mutation rate, Cancer Treatment Reports, vol.63, no.11-
12, p.1727-1733, 1979.
[7] F.Michor, M.A. Nowak, Y. Iwasa, Evolution of resistance to cancer therapy,
Current Pharmaceutical Design, vol.12, no.3, p.261–271, 2006.
[8] M. Kimmel, A. Swierniak, A. Polanski, Infinite-dimensional model of evolution of
drug resistance of cancer cells, Journal of Mathematical Systems, Estimation, and
Control, vol.8, p.1–16, 1998.
[9] N. Komarova, Stochastic modeling of drug resistance in cancer, Theoretical
Population Biology, vol.239, no.3, p.351–366, 2006.
[10] N.L. Komarova, D. Wodarz, Drug resistance in cancer: principle of emergence and
prevention, Proceedings of the National Academy of Sciences USA, vol.102,
p.9714-9719, 2005.
[11] N.L. Komarova, Stochastic modeling of drug resistance in cancer, Journal of
Theoretical Biology, vol.239, no.3, p.351-366, 2006.
[12] E.C. de Bruin, T.B. Taylor, S C. wanton, Intra-tumor heterogeneity: lessons from
microbial evolution and clinical implications, Genome Medicine, vol.5, p.1–11,
2013.
[13] M. Gerlinger, A.J. Rowan, S. Horswell, J. Larkin, D. Endesfelder, E. Gronroos, P.
Martinez, Intratumor heterogeneity and branched evolution revealed by multiregion
sequencing, The New England Journal of Medicine, vol.366, p.883–892, 2012.
[14] J.H. Goldie, A.J. Coldman, G.A. Gudauskas, Rationale for the use of alternating
non-cross-resistant chemotherapy, Cancer Treat. Rep. , vol.66, p.39, 1982.
[15] A.J. Coldman, J.H. Goldie, A model for the resistance of tumor cells to cancer
chemotherapeutic agents, Math. Biosci. , vol.65, p.291, 1983.
[16] H.E. Skipper, Reasons for success and failure in treatment of murine leukemias with
the drugs now employed in treating human leukemias, Cancer Chemotherapy,
University Microfilms International, Ann Arbor, MI, vol.1, p.1-166, 1978.
[17] H.E. Skipper, On reducing yreatment failures due to overgrowth of specifically and
permanently drug resistant neoplastic cells, Cancer Chemotherapy, University
Microfilms International, Ann Arbor, MI, vol.2, 1979.
[18] R.W. Brockman, Circumvention of resistance, in the Pharmacological Basis of
Cancer Chemotherapy, Baltimore, MD, The Williams and Wilkins Co, Ltd, pp 691-
710, 1975.
[19] F.M. Schabel, Jr, H.E. Skipper, M.W. Trader, et al, Concepts for controlling drugresistant tumor cells. In Breast Cancer — Experimental and Clinical Aspects
(Mouridsen HT,and Palshof T, eds ), Oxford , England, Pergamon Press, pp 199-
212, 1980.
[20] J.H. Chen, Y.H. Kuo, H. Luh, Optimal policies of non-cross-resistant chemotherapy
on Goldie and Coldman’s cancer model, Mathematical Biosciences, vol.245, p.282–
298, 2013.
[21] J. Ferlay, M. Ervik, F. Lam, M. Colombet, L. Mery, M. Piñeros, et al, Global cancer
observatory: cancer today, International Agency for Research on Cancer, Lyon,
2020.
[22] 世界衛生組織https://www.who.int/en/news-room/fact-sheets/detail/cancer.
[23] Defining cancer. National Cancer Institute. [2014-06-10].
[24] H.N. Weerasinghe, P.M. Burrage, K. Burrage, D.V. Nicolau, Mathematical models
of cancer cell plasticity, Journal of Oncology, 2019.
[25] A. Yin, D.J.A.R. Moes, J.G.C.V. Hasselt, J.J. Swen, H.J. Guchelaar, A review of
mathematical models for tumor dynamics and treatment resistance evolution of solid
tumors, National Library of Medicine, vol.8, no.10, p.720-737, 2019.
[26] P.M. Altrock, L.L. Liu, F. Michor, The mathematics of cancer: integrating
quantitative models, Nature Reviews Cancer, vol.15, p.730–745, 2015.
[27] Y. Watanabe, E.L. Dahlman, K.Z. Leder, S.K. Hui, A mathematical model of tumor growth and its response to single irradiation, Theoretical Biology and Medical
Modelling, vol.13, no.6, 2016.
[28] R. Brady, H. Enderling, Mathematical models of cancer: when to predict novel
therapies, and when not to, Bulletin of Mathematical Biology, vol.81, p.3722-3731,
2019.
[29] D. Dingli, M.D. Cascino, K. Josić, S.J. Russell, Ž. Bajzer, Mathematical modeling
of cancer radiovirotherapy, Mathematical Biosciences, vol.199, p.55-78, 2006.
[30] K.M. Turner, S.K. Yeo, T.M. Holm, E. Shaughnessy, J.L. Guan, Heterogeneity
within molecular subtypes of breast cancer, American Journal of Physiology-Cell
Physiology, vol.321, no.2, 2021.
[31] D. Mathur, E. Barnett, H.I. Scher, J.B. Xavier, Optimizing the future: how
mathematical models inform treatment schedules for cancer, Trends in Cancer,
vol.8, no.6, p.506-516, 2022.
[32] C. Grassberger, D. McClatchy III, C. Geng, etc., Patient-specific tumor growth
trajectories determine persistent and resistant cancer cell populations during
treatment with targeted therapies, Cancer Research, vol.79, no.14, p.3776-3788,
2019.
[33] J.W.T. Yates, H. Mistry, Clone wars: quantitatively understanding cancer drug
resistance, JCO Clinical Cancer Informatics, vol.4, p.938-946, 2020.
[34] N. Carels, A.J. Conforte, C.R. Lima, etc., Challenges for the optimization of drug
therapy in the treatment of cancer, Networks in Systems Biology, vol.32, p.163-198,
2020.
[35] J.Wijaya, T.Gose, J.D. Schuetz, etc., Using pharmacology to squeeze the life out of
childhood leukemia, and potential strategies to achieve breakthroughs in
medulloblastoma treatment, Pharmacological Reviews, vol.72, no.3, p.668-691,
2020.
[36] A. Major, S.M. Smith, DA-R-EPOCH vs R-CHOP in DLBCL: How do we choose?,
Clinical Advances in Hematology & Oncology, vol.19, no.11, p.698-709, 2021.
[37] N. Tokutomi, C. Moyret-Lalle, A. Puisieux, etc., Quantifying local malignant
adaptation in tissue-specific evolutionary trajectories by harnessing cancer’s
repeatability at the genetic level, Evolutionary Applications, vol.12, no.5, p.1062-
1075, 2019.
[38] TaLa, W. Sun, X. Zhao, J. Zhang , etc., A mathematical model to study the effect of drug kinetics on the drug-induced resistance in tumor growth dynamics, IOP
Conference Series: Earth and Environmental Science, vol.332, no.3, 2019.
[39] X. Wang, H. Zhang, X. Chen, Drug resistance and combating drug resistance in
cancer, Cancer Drug Resist., vol.2, no.1, p.141-160, 2019.
[40] N. Vasan, J. Baselga , D.M. Hyman, A view on drug resistance in cancer, Nature,
vol.575, p.299-309, 2019.
[41] W. Löscher, H. Potschka, S.M. Sisodiya, etc., Drug resistance in epilepsy: clinical
impact, Potential Mechanisms, and New Innovative Treatment Options, vol.72,
no.3, p.606-638, 2020.
[42] J. Berman, D.J. Krysan, Drug resistance and tolerance in fungi, Nature Reviews
Microbiology, vol.11, no.18, p.319-331, 2020.
[43] Y. Yao, Y. Zhou, L. Liu, etc., Nanoparticle-based drug delivery in cancer therapy
and its role in overcoming drug resistance, Frontiers in Molecular Biosciences,
vol.20, 2020.
[44] Y. Lee, E. Puumala, N. Robbins, etc., Antifungal drug resistance: molecular
mechanisms in candida albicans and beyond, vol.121, no.6, p.3390-3411, 2021.
[45] W. Si, J. Shen, H. Zheng, etc., The role and mechanisms of action of microRNAs in
cancer drug resistance, Clinical Epigenetics, vol.11, no.25, 2019.
[46] S. Lee, J. Rauch 1, W. Kolch, Targeting MAPK signaling in cancer: mechanisms of
drug resistance and sensitivity, Int. J. Mol. Sci., vol.21, no.3, 2020.
[47] Z.F. Lim, P.C. Ma, Emerging insights of tumor heterogeneity and drug resistance
mechanisms in lung cancer targeted therapy, Journal of Hematology & Oncology,
vol.12, no.134, 2019.
[48] L. Wei, J. Sun, N. Zhang, etc., Noncoding RNAs in gastric cancer: implications for
drug resistance, Molecular Cancer, vol.19, no.62, 2020.
[49] L. Mashouri, H. Yousefi, A.R. Aref, Exosomes: composition, biogenesis, and
mechanisms in cancer metastasis and drug resistance, Molecular Cancer, vol.18,
no.75, 2019.
[50] C. Cerrone, R. Cerulli, B. Golden, Carousel greedy: a generalized greedy algorithm
with applications in optimization, Computers & Operations Research, vol.85, 2017.
[51] S.A. Curtis, The classification of greedy algorithms, Science of Computer
Programming, vol.49, p.125-157, 2003. |
| Description: | 碩士
國立政治大學
應用數學系
107751001 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107751001 |
| Data Type: | thesis |
| Appears in Collections: | [應用數學系] 學位論文
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