中山大学数学学院,广东 广州 510275
刘泽铭(1998年生),男;研究方向:数学肿瘤学;E-mail:liuzm7@mail2.sysu.edu.cn
孙小强(1988年生),男;研究方向:计算系统生物学;E-mail:sunxq6@mail.sysu.edu.cn
纸质出版日期:2024-11-25,
网络出版日期:2024-09-11,
收稿日期:2024-04-08,
录用日期:2024-05-02
移动端阅览
刘泽铭,孙小强.细胞微环境介导肿瘤耐药性的动力学建模研究进展[J].中山大学学报(自然科学版)(中英文),2024,63(06):236-253.
LIU Zeming,SUN Xiaoqiang.A survey on dynamic modeling of microenvironment-mediated cancer drug resistance[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(06):236-253.
刘泽铭,孙小强.细胞微环境介导肿瘤耐药性的动力学建模研究进展[J].中山大学学报(自然科学版)(中英文),2024,63(06):236-253. DOI: 10.13471/j.cnki.acta.snus.ZR20240106.
LIU Zeming,SUN Xiaoqiang.A survey on dynamic modeling of microenvironment-mediated cancer drug resistance[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(06):236-253. DOI: 10.13471/j.cnki.acta.snus.ZR20240106.
肿瘤耐药性是限制癌症疗效的最大障碍之一,肿瘤微环境在耐药性的发生发展过程中发挥着重要的作用. 为了深入探究微环境介导肿瘤耐药性的作用和机制,需要全面系统地研究药物治疗过程中肿瘤生态系统的动态演化. 数学模型可用于描述肿瘤微环境中的各种组分的相互作用和变化,进而揭示微环境介导肿瘤耐药性的机理和演化规律,并为设计更有效的治疗策略提供参考和依据. 本文首先介绍了微环境介导肿瘤耐药性的生物学知识和相关概念;随后分类介绍了几类典型数学模型的最新研究进展;之后以脑胶质瘤微环境为例,介绍了使用不同方法建立耐药性的演化动力学模型的流程;最后展望了进一步的研究方向.
Tumor drug resistance is one of the biggest obstacles limiting cancer treatment, and the tumor microenvironment plays an important role in the occurrence and development of drug resistance. In order to further explore the role and mechanism of microenvironment-mediated tumor drug resistance, it is necessary to comprehensively and systematically study the dynamic evolution of the tumor ecosystem during drug treatment. Mathematical models can be used to describe the interactions and changes of various components in the tumor microenvironment, thereby revealing the mechanism and evolution of tumor drug resistance mediated by the microenvironment, and providing a reference and theoretical basis for designing more effective treatment strategies. In this paper, we first introduce some concepts of microenvironment-mediated evolutionary dynamics modeling of tumor drug resistance, then classify and introduce the latest research progress of various mathematical models in this field. Furthermore, we introduce the process of developing mathematical models of drug resistance using various methods by taking glioma microenvironment as an example. At last, we look forward to further research directions.
数学肿瘤学演化动力学建模耐药性肿瘤微环境
mathematical oncologyevolutionary dynamics modelingdrug resistancetumor microenvironment
ALBANO G, GIORNO V, ROMÁN-ROMÁN P, et al, 2015. Estimating and determining the effect of a therapy on tumor dynamics by means of a modified Gompertz diffusion process[J]. J Theor Biol, 364: 206-219.
ALON U, 2007. An introduction to systems biology: Design principles of biological circuits[M]. Boca Raton: Chapman & Hall/CRC.
ALTROCK P M, LIU L L, MICHOR F, 2015. The mathematics of cancer: Integrating quantitative models[J]. Nat Rev Cancer, 15(12): 730-745.
ANGARONI F, GUIDI A, ASCOLANI G, et al, 2022. J-SPACE: A Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments[J]. BMC Bioinformatics, 23(1): 269.
ARVANITIS C D, ASKOXYLAKIS V, GUO Y, et al, 2018. Mechanisms of enhanced drug delivery in brain metastases with focused ultrasound-induced blood-tumor barrier disruption[J]. Proc Natl Acad Sci, 115(37): E8717-E8726.
BAAR M, COQUILLE L, MAYER H, et al, 2016. A stochastic model for immunotherapy of cancer[J]. Sci Rep, 6: 24169.
BACEVIC K, NOBLE R, SOFFAR A, et al, 2017. Spatial competition constrains resistance to targeted cancer therapy[J]. Nat Commun, 8(1): 1995.
BARKER H E, PAGET J T E, KHAN A A, et al, 2015. The tumour microenvironment after radiotherapy: Mechanisms of resistance and recurrence[J]. Nat Rev Cancer, 15(7): 409-425.
BASANTA D, GATENBY R A, ANDERSON A R A, 2012. Exploiting evolution to treat drug resistance: Combination therapy and the double bind[J]. Mol Pharm, 9(4): 914-921.
BAUER B, SIEBERT R, TRAULSEN A, 2014. Cancer initiation with epistatic interactions between driver and passenger mutations[J]. J Theor Biol, 358: 52-60.
BERGMAN D, MARAZZI L, CHOWKWALE M, et al, 2022. PhysiPKPD: A pharmacokinetics and pharmacodynamics module for PhysiCell[J]. GigaByte, 2022: gigabyte72.
BOZIC I, ANTAL T, OHTSUKI H, et al, 2010. Accumulation of driver and passenger mutations during tumor progression[J]. Proc Natl Acad Sci, 107(43): 18545-18550.
BOZIC I, NOWAK M A, 2013. Unwanted evolution[J]. Science, 342(6161): 938-939.
BOZIC I, WU C J, 2020. Delineating the evolutionary dynamics of cancer from theory to reality[J]. Nat Cancer, 1(6): 580-588.
BRESSLOFF P C, 2014. Stochastic processes in cell biology[M]. 2nd ed. Cham:Springer:1-96.
BROWN R, CURRY E, MAGNANI L, et al, 2014. Poised epigenetic states and acquired drug resistance in cancer[J]. Nat Rev Cancer, 14(11): 747-753.
CESS C G, FINLEY S D, 2020a. Data-driven analysis of a mechanistic model of CAR T cell signaling predicts effects of cell-to-cell heterogeneity[J]. J Theor Biol, 489: 110125.
CESS C G, FINLEY S D, 2020b. Multi-scale modeling of macrophage-T cell interactions within the tumor microenvironment[J]. PLoS Comput Biol, 16(12): e1008519.
CHAKRABARTI S, MICHOR F, 2017. Pharmacokinetics and drug interactions determine optimum combination strategies in computational models of cancer evolution[J]. Cancer Res, 77(14): 3908-3921.
CHEN L, YANG J, TAN Y, et al, 2021. Threshold dynamics of a stochastic model of intermittent androgen deprivation therapy for prostate cancer[J]. Commun Nonlinear Sci Numer Simul, 100: 105856.
CHENG J, ZHANG J, WU Z, et al, 2021. Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19[J]. Brief Bioinform, 22(2): 988-1005.
CHKHAIDZE K, HEIDE T, WERNER B, et al, 2019. Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data[J]. PLoS Comput Biol, 15(7): e1007243.
COJOC M, MÄBERT K, MUDERS M H, et al, 2015. A role for cancer stem cells in therapy resistance: Cellular and molecular mechanisms[J]. Semin Cancer Biol, 31: 16-27.
CONDEELIS J, POLLARD J W, 2006. Macrophages: Obligate partners for tumor cell migration, invasion, and metastasis[J]. Cell, 124(2): 263-266.
da SILVA-DIZ V, LORENZO-SANZ L, BERNAT-PEGUERA A, et al, 2018. Cancer cell plasticity: Impact on tumor progression and therapy response[J]. Semin Cancer Biol, 53: 48-58.
de PALMA M, BIZIATO D, PETROVA T V, 2017. Microenvironmental regulation of tumour angiogenesis[J]. Nat Rev Cancer, 17(8): 457-474.
DEAN M, FOJO T, BATES S, 2005. Tumour stem cells and drug resistance[J]. Nat Rev Cancer, 5(4): 275-284.
EWENS W J, 2004. Mathematical population genetics: I theoretical introduction[M]. New York: Springer.
FISCHER A, VÁZQUEZ-GARCÍA I, MUSTONEN V, 2015. The value of monitoring to control evolving populations[J]. Proc Natl Acad Sci, 112(4): 1007-1012.
GALLAHER J A, ENRIQUEZ-NAVAS P M, LUDDY K A, et al, 2018. Spatial heterogeneity and evolutionary dynamics modulate time to recurrence in continuous and adaptive cancer therapies[J]. Cancer Res, 78(8): 2127-2139.
GATENBY R A, SILVA A S, GILLIES R J, et al, 2009. Adaptive therapy[J]. Cancer Res, 69(11): 4894-4903.
GILLIES R J, VERDUZCO D, GATENBY R A, 2012. Evolutionary dynamics of carcinogenesis and why targeted therapy does not work[J]. Nat Rev Cancer, 12(7): 487-493.
GLUZMAN M, SCOTT J G, VLADIMIRSKY A, 2020. Optimizing adaptive cancer therapy: Dynamic programming and evolutionary game theory[J]. Proc Biol Sci, 287(1925): 20192454.
GREENE J M, GEVERTZ J L, SONTAG E D, 2019. Mathematical approach to differentiate spontaneous and induced evolution to drug resistance during cancer treatment[J]. JCO Clin Cancer Inform, 3: 1-20.
GREENE J M, SANCHEZ-TAPIA C, SONTAG E D, 2020. Mathematical details on a cancer resistance model[J]. Front Bioeng Biotechnol, 8: 501.
GUO Y, NIE Q, MacLEAN A L, et al, 2017. Multiscale modeling of inflammation-induced tumorigenesis reveals competing oncogenic and oncoprotective roles for inflammation[J]. Cancer Res, 77(22): 6429-6441.
HAMIS S, NITHIARASU P, POWATHIL G G, 2018. What does not kill a tumour may make it stronger: In silico insights into chemotherapeutic drug resistance[J]. J Theor Biol, 454: 253-267.
HANAHAN D, COUSSENS L M, 2012. Accessories to the crime: Functions of cells recruited to the tumor microenvironment[J]. Cancer Cell, 21(3): 309-322.
HANNON B, RUTH M, 2014. Modeling dynamic biological systems[M]. Cham: Springer.
HINOHARA K, WU H J, VIGNEAU S, et al, 2018. KDM5 histone demethylase activity links cellular transcriptomic heterogeneity to therapeutic resistance[J]. Cancer Cell, 34(6): 939-953.
HOLOHAN C, van SCHAEYBROECK S, LONGLEY D B, et al, 2013. Cancer drug resistance: An evolving paradigm[J]. Nat Rev Cancer, 13(10): 714-726.
HU Z, DING J, MA Z, et al, 2019. Quantitative evidence for early metastatic seeding in colorectal cancer[J]. Nat Genet, 51(7): 1113-1122.
JACKSON T L, BYRNE H M, 2000. A mathematical model to study the effects of drug resistance and vasculature on the response of solid tumors to chemotherapy[J]. Math Biosci, 164(1): 17-38.
JAIN R K, TONG R T, MUNN L L, 2007. Effect of vascular normalization by antiangiogenic therapy on interstitial hypertension, peritumor edema, and lymphatic metastasis: Insights from a mathematical model[J]. Cancer Res, 67(6): 2729-2735.
JAYATHILAKE P G, VICTORI P, PAVILLET C E, et al, 2024. Metabolic symbiosis between oxygenated and hypoxic tumour cells: An agent-based modelling study[J]. PLoS Comput Biol, 20(3): e1011944.
JOHNSON K A, GOODY R S, 2011. The original Michaelis constant: Translation of the 1913 Michaelis-Menten paper[J]. Biochemistry, 50(39): 8264-8269.
KERKAR S P, RESTIFO N P, 2012. Cellular constituents of immune escape within the tumor microenvironment[J]. Cancer Res, 72(13): 3125-3130.
KHAN K H, CUNNINGHAM D, WERNER B, et al, 2018. Longitudinal liquid biopsy and mathematical modeling of clonal evolution forecast time to treatment failure in the PROSPECT-C phase II colorectal cancer clinical trial[J]. Cancer Discov, 8(10): 1270-1285.
KIMMEL M, AXELROD D E, 2015. Branching processes in biology [M]. 2nd ed. New York: Springer.
KLEMM F, JOYCE J A, 2015. Microenvironmental regulation of therapeutic response in cancer[J]. Trends Cell Biol, 25(4): 198-213.
LAI X L, STIFF A, DUGGAN M, et al, 2018. Modeling combination therapy for breast cancer with BET and immune checkpoint inhibitors[J]. Proc Natl Acad Sci, 115(21): 5534-5539.
LIANG W S, ZHENG Y J, ZHANG J, et al, 2019. Multiscale modeling reveals angiogenesis-induced drug resistance in brain tumors and predicts a synergistic drug combination targeting EGFR and VEGFR pathways[J]. BMC Bioinformatics, 20(Suppl 7): 203.
LIGGETT T M, 2005. Interacting particle systems [M]. Berlin: Springer.
LIMA E A B F, FAGHIHI D, PHILLEY R, et al, 2021. Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth[J]. PLoS Comput Biol, 17(11): e1008845.
LIU R Y, WANG S, TAN X W, et al, 2022. Identifying optimal adaptive therapeutic schedules for prostate cancer through combining mathematical modeling and dynamic optimization[J]. Appl Math Model, 107: 688-700.
LUGANO R, RAMACHANDRAN M, DIMBERG A, 2020. Tumor angiogenesis: Causes, consequences, challenges and opportunities[J]. Cell Mol Life Sci, 77(9): 1745-1770.
LUO J X, DENG M H, ZHANG X G, et al, 2023. ESICCC as a systematic computational framework for evaluation, selection, and integration of cell-cell communication inference methods[J]. Genome Res, 33(10): 1788-1805.
MA Y F, TANG N, THOMPSON R C, et al, 2016. InsR/IGF1R pathway mediates resistance to EGFR inhibitors in glioblastoma[J]. Clin Cancer Res, 22(7): 1767-1776.
McDONALD T O, CHENG Y C, GRASER C, et al, 2023. Computational approaches to modelling and optimizing cancer treatment[J]. Nat Rev Bioeng, 1: 695-711.
MEADS M B, GATENBY R A, DALTON W S, 2009. Environment-mediated drug resistance: A major contributor to minimal residual disease[J]. Nat Rev Cancer, 9(9): 665-674.
MIRAMS G R, ARTHURS C J, BERNABEU M O, et al, 2013. Chaste: An open source C++ library for computational physiology and biology[J]. PLoS Comput Biol, 9(3): e1002970.
MORAN P A P, 1962. The statistical processes of evolutionary theory[M]. Oxford: Clarendon Press.
NI X R, WU W C, SUN X Q, et al, 2022. Interrogating glioma-M2 macrophage interactions identifies Gal-9/Tim-3 as a viable target against PTEN-null glioblastoma[J]. Sci Adv, 8(27): eabl5165.
NICHOLSON D J, 2019. Is the cell really a machine?[J]. J Theor Biol, 477: 108-126.
NICOL P B, BARABÁSI D L, COOMBES K R, et al, 2022. SITH: An R package for visualizing and analyzing a spatial model of intratumor heterogeneity[J]. Comput Syst Oncol, 2(2): e1033.
NOBLE R, BURRI D, Le SUEUR C, et al, 2022. Spatial structure governs the mode of tumour evolution[J]. Nat Ecol Evol, 6(2): 207-217.
OBENAUF A C, ZOU Y L, JI A L, et al, 2015. Therapy-induced tumour secretomes promote resistance and tumour progression[J]. Nature, 520(7547): 368-372.
OLSEN M M, SIEGELMANN H T, 2013. Multiscale agent-based model of tumor angiogenesis[J]. Procedia Comput Sci, 18: 1016-1025.
OPAŠIĆ L, SCOTT J G, TRAULSEN A, et al, 2020. CancerSim: A cancer simulation package for python 3[J]. J Open Source Softw, 5(53): 2436.
ORLANDO P A, GATENBY R A, BROWN J S, 2012. Cancer treatment as a game: Integrating evolutionary game theory into the optimal control of chemotherapy[J]. Phys Biol, 9(6): 065007.
PARYAD-ZANJANI S, SAINT-ANTOINE M M, SINGH A, 2021. Optimal scheduling of therapy to delay cancer drug resistance[J]. IFAC-PapersOnLine, 54(15): 239-244.
PIVONKA P, ZIMAK J, SMITH D W, et al, 2008. Model structure and control of bone remodeling: A theoretical study[J]. Bone, 43(2): 249-263.
PYONTECK S M, AKKARI L, SCHUHMACHER A J, et al, 2013. CSF-1R inhibition alters macrophage polarization and blocks glioma progression[J]. Nat Med, 19(10): 1264-1272.
QUAIL D F, BOWMAN R L, AKKARI L, et al, 2016. The tumor microenvironment underlies acquired resistance to CSF-1R inhibition in gliomas[J]. Science, 352(6288): aad3018.
QUAIL D F, JOYCE J A, 2017. The microenvironmental landscape of brain tumors[J]. Cancer Cell, 31(3): 326-341.
REJNIAK K A, ANDERSON A R A, 2011. Hybrid models of tumor growth[J]. WIREs Mechanisms Disease, 3(1): 115-125.
SCHUSS Z, 2010. Theory and applications of stochastic processes: An analytical approach[M]. New York: Springer.
SEFIDGAR M, SOLTANI M, RAAHEMIFAR K, et al, 2015. Numerical modeling of drug delivery in a dynamic solid tumor microvasculature[J]. Microvasc Res, 99: 43-56.
SFAKIANAKIS N, MADZVAMUSE A, CHAPLAIN M A J, 2020. A hybrid multiscale model for cancer invasion of the extracellular matrix[J]. Multiscale Model Simul, 18(2): 824-850.
SMITH J M, PRICE G R, 1973. The logic of animal conflict[J]. Nature, 246: 15-18.
SOMASUNDARAM R, ZHANG G, FUKUNAGA-KALABIS M, et al, 2017. Tumor-associated B-cells induce tumor heterogeneity and therapy resistance[J]. Nat Commun, 8(1): 607.
STANKOVÁ K, BROWN J S, DALTON W S, et al, 2019. Optimizing cancer treatment using game theory: A review[J]. JAMA Oncol, 5(1): 96-103.
STEIN S, ZHAO R, HAENO H, et al, 2018. Mathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients[J]. PLoS Comput Biol, 14(1): e1005924.
STRAUSSMAN R, MORIKAWA T, SHEE K, et al, 2012. Tumour micro-environment elicits innate resistance to RAF inhibitors through HGF secretion[J]. Nature, 487(7408): 500-504.
STROBL M A R, WEST J, VIOSSAT Y, et al, 2021. Turnover modulates the need for a cost of resistance in adaptive therapy[J]. Cancer Res, 81(4): 1135-1147.
SUN X Q, BAO J G, NELSON K C, et al, 2013. Systems modeling of anti-apoptotic pathways in prostate cancer: Psychological stress triggers a synergism pattern switch in drug combination therapy[J]. PLoS Comput Biol, 9(12): e1003358.
SUN X Q, BAO J G, SHAO Y Z, 2016a. Mathematical modeling of therapy-induced cancer drug resistance: Connecting cancer mechanisms to population survival rates[J]. Sci Rep, 6: 22498.
SUN X Q, HU B, 2018. Mathematical modeling and computational prediction of cancer drug resistance[J]. Brief Bioinform, 19(6): 1382-1399.
SUN X Q, SU J, BAO J G, et al, 2012a. Cytokine combination therapy prediction for bone remodeling in tissue engineering based on the intracellular signaling pathway[J]. Biomaterials, 33(33): 8265-8276.
SUN X Q, ZHANG J, NIE Q, 2021. Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples[J]. PLoS Comput Biol, 17(3): e1008379.
SUN X Q, ZHANG J, ZHAO Q, et al, 2016b. Stochastic modeling suggests that noise reduces differentiation efficiency by inducing a heterogeneous drug response in glioma differentiation therapy[J]. BMC Syst Biol, 10(1): 73.
SUN X Q, ZHANG L, TAN H, et al, 2012b. Multi-scale agent-based brain cancer modeling and prediction of TKI treatment response: Incorporating EGFR signaling pathway and angiogenesis[J]. BMC Bioinformatics, 13: 218.
SUN X Q, ZHENG X K, ZHANG J J, et al, 2015. Mathematical modeling reveals a critical role for cyclin D1 dynamics in phenotype switching during glioma differentiation[J]. FEBS Lett, 589(18): 2304-2311.
SUN X Q, BAO J G, 2023. Multiscale mathematical models for biological systems[J]. Front Math China, 18(2): 75-94.
TANAKA G, HIRATA Y, GOLDENBERG S L, et al, 2010. Mathematical modelling of prostate cancer growth and its application to hormone therapy[J]. Philos Trans A Math Phys Eng Sci, 368(1930): 5029-5044.
TANG S Y, LI S, TANG B, et al, 2023. Hormetic and synergistic effects of cancer treatments revealed by modelling combinations of radio- or chemotherapy with immunotherapy[J]. BMC Cancer, 23(1): 1040.
van LIEDEKERKE P, PALM M M, JAGIELLA N, et al, 2015. Simulating tissue mechanics with agent-based models: Concepts, perspectives and some novel results[J]. Comput Part Mech, 2(4): 401-444.
VOUTOURI C, KIRKPATRICK N D, CHUNG E, et al, 2019. Experimental and computational analyses reveal dynamics of tumor vessel cooption and optimal treatment strategies[J]. Proc Natl Acad Sci, 116(7): 2662-2671.
WACLAW B, BOZIC I, PITTMAN M E, et al, 2015. A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity[J]. Nature, 525(7568): 261-264.
WEST J, YOU L, ZHANG J S, et al, 2020. Towards multidrug adaptive therapy[J]. Cancer Res, 80(7): 1578-1589.
WEST J B, DINH M N, BROWN J S, et al, 2019. Multidrug cancer therapy in metastatic castrate-resistant prostate cancer: An evolution-based strategy[J]. Clin Cancer Res, 25(14): 4413-4421.
YAN L L, SUN X Q, 2023. Benchmarking and integration of methods for deconvoluting spatial transcriptomic data[J]. Bioinformatics, 39(1):btac805.
YANG H, LIN H F, SUN X Q, 2023. Multiscale modeling of drug resistance in glioblastoma with gene mutations and angiogenesis[J]. Comput Struct Biotechnol J, 21: 5285-5295.
ZHANG J, GUAN M G, WANG Q L, et al, 2020. Single-cell transcriptome-based multilayer network biomarker for predicting prognosis and therapeutic response of gliomas[J]. Brief Bioinform, 21(3): 1080-1097.
ZHANG J J, ZHOU T S, 2019a. Markovian approaches to modeling intracellular reaction processes with molecular memory[J]. Proc Natl Acad Sci, 116(47): 23542-23550.
ZHANG J J, ZHU W B, WANG Q L, et al, 2019b. Differential regulatory network-based quantification and prioritization of key genes underlying cancer drug resistance based on time-course RNA-seq data[J]. PLoS Comput Biol, 15(11): e1007435.
ZHANG X N, FANG Y L, ZHAO Y D, et al, 2014. Mathematical modeling the pathway of human breast cancer[J]. Math Biosci, 253: 25-29.
ZHENG X M, KOH G Y, JACKSON T, 2013. A continuous model of angiogenesis: Initiation, extension, and maturation of new blood vessels modulated by vascular endothelial growth factor, angiopoietins, platelet-derived growth factor-B, and pericytes[J].. Discrete Contin Dyn Syst B, 18(4): 1109-1154.
ZHENG Y J, BAO J G, ZHAO Q Y, et al, 2018. A spatio-temporal model of macrophage-mediated drug resistance in glioma immunotherapy[J]. Mol Cancer Ther, 17(4): 814-824.
ZHOU D, LUO Y, DINGLI D, et al, 2019. The invasion of de-differentiating cancer cells into hierarchical tissues[J]. PLoS Comput Biol, 15(7): e1007167.
0
浏览量
144
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构