基于达尔文演化动力学的适应性治疗策略在肿瘤治疗中的研究进展

doi:10.19401/j.cnki.1007-3639.2023.04.010

摘要/Abstract

摘要：

辅助化疗药物和靶向治疗药物的应用极大地改善了晚期癌症患者的预后，但很快出现的耐药问题以及随之而来的病情迅速进展一直是癌症治疗领域的痛点。揉合了达尔文演化动力学的肿瘤适应性治疗（adaptive therapy，AT）是有别于传统最大耐受剂量（maximum tolerated dose，MTD）的新颖治疗理念，有望颠覆晚期癌症的治疗范式，实现患者长期的低负荷带瘤生存。本文对适应性治疗理论研究进展以及数学模型在优化适应性治疗策略中的重要地位进行综述，并对基于达尔文演化动力学的其他肿瘤治疗策略予以简述，以期为临床医师提供新的思路。

关键词: 肿瘤演化, 适应性治疗, 数学模型, 达尔文, 个体化治疗

Abstract:

The applications of adjuvant chemotherapy and targeted drugs have greatly improved the prognosis of patients with advanced cancer, however the rapid emergence of drug resistance and the subsequent rapid progress of the disease have always been the unmet challenges in the field of cancer treatment. Adaptive therapy (AT) combined with Darwinian evolutionary dynamics is a new treatment concept different from the traditional maximum tolerated dose (MTD), which is expected to overturn the treatment paradigm of advanced cancer and achieve long-term low load tumor bearing survival. This paper reviewed the theory of AT and the important role of mathematical models in optimizing adaptive treatment strategies, then briefly described other tumor treatment strategies based on Darwinian dynamics, in order to provide new ideas for clinicians in the game with advanced cancer.

Key words: Tumor evolution, Adaptive therapy, Mathematical model, Darwin, Individualized treatment

中图分类号:

R730.5

刘家霖, 张显玉, 庞达. 基于达尔文演化动力学的适应性治疗策略在肿瘤治疗中的研究进展[J]. 中国癌症杂志, 2023, 33(4): 397-402.

LIU Jialin, ZHANG Xianyu, PANG Da. Research progress of adaptive therapy strategy based on Darwinian dynamics in tumor therapy[J]. China Oncology, 2023, 33(4): 397-402.

图/表 1

参考文献 44

[1]	CHEN Y L, CHANG M C, CHENG W F. Metronomic chemotherapy and immunotherapy in cancer treatment[J]. Cancer Lett, 2017, 400: 282-292. doi: 10.1016/j.canlet.2017.01.040
[2]	GATENBY R A, BROWN J S. Integrating evolutionary dynamics into cancer therapy[J]. Nat Rev Clin Oncol, 2020, 17(11): 675-686. doi: 10.1038/s41571-020-0411-1
[3]	MCGRANAHAN N, SWANTON C. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution[J]. Cancer Cell, 2015, 27(1): 15-26. doi: 10.1016/j.ccell.2014.12.001 pmid: 25584892
[4]	JOHNSON D B, MENZIES A M, ZIMMER L, et al. Acquired BRAF inhibitor resistance: a multicenter meta-analysis of the spectrum and frequencies, clinical behaviour, and phenotypic associations of resistance mechanisms[J]. Eur J Cancer, 2015, 51(18): 2792-2799. doi: 10.1016/j.ejca.2015.08.022 pmid: 26608120
[5]	ENRIQUEZ-NAVAS P M, WOJTKOWIAK J W, GATENBY R A. Application of evolutionary principles to cancer therapy[J]. Cancer Res, 2015, 75(22): 4675-4680.
[6]	SHAFFER S M, DUNAGIN M C, TORBORG S R, et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance[J]. Nature, 2017, 546(7658): 431-435. doi: 10.1038/nature22794
[7]	SAUNDERS N A, SIMPSON F, THOMPSON E W, et al. Role of intratumoural heterogeneity in cancer drug resistance: molecular and clinical perspectives[J]. EMBO Mol Med, 2012, 4(8): 675-684. doi: 10.1002/emmm.201101131 pmid: 22733553
[8]	GATENBY R A, SILVA A S, GILLIES R J, et al. Adaptive therapy[J]. Cancer Res, 2009, 69(11): 4894-4903. doi: 10.1158/0008-5472.CAN-08-3658 pmid: 19487300
[9]	SIRAVEGNA G, MUSSOLIN B, BUSCARINO M, et al. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients[J]. Nat Med, 2015, 21(7): 827. doi: 10.1038/nm0715-827b pmid: 26151329
[10]	SCHREUER M, JANSEN Y, PLANKEN S, et al. Combination of dabrafenib plus trametinib for BRAF and MEK inhibitor pretreated patients with advanced BRAF^V600-mutant melanoma: an open-label, single arm, dual-centre, phase 2 clinical trial[J]. Lancet Oncol, 2017, 18(4): 464-472. doi: 10.1016/S1470-2045(17)30171-7
[11]	SMALLEY I, KIM E, LI J N, et al. Leveraging transcriptional dynamics to improve BRAF inhibitor responses in melanoma[J]. EBioMedicine, 2019, 48: 178-190. doi: S2352-3964(19)30627-9 pmid: 31594749
[12]	ZHANG J S, CUNNINGHAM J J, BROWN J S, et al. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer[J]. Nat Commun, 2017, 8(1): 1816. doi: 10.1038/s41467-017-01968-5 pmid: 29180633
[13]	ENRIQUEZ-NAVAS P M, KAM Y, DAS T, et al. Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer[J]. Sci Transl Med, 2016, 8(327): 327ra24.
[14]	SILVA A S, KAM Y, KHIN Z P, et al. Evolutionary approaches to prolong progression-free survival in breast cancer[J]. Cancer Res, 2012, 72(24): 6362-6370. doi: 10.1158/0008-5472.CAN-12-2235 pmid: 23066036
[15]	BACEVIC K, NOBLE R, SOFFAR A, et al. Spatial competition constrains resistance to targeted cancer therapy[J]. Nat Commun, 2017, 8(1): 1995. doi: 10.1038/s41467-017-01516-1 pmid: 29222471
[16]	STROBL M A R, WEST J, VIOSSAT Y, et al. Turnover modulates the need for a cost of resistance in adaptive therapy[J]. Cancer Res, 2021, 81(4): 1135-1147. doi: 10.1158/0008-5472.CAN-20-0806 pmid: 33172930
[17]	VIOSSAT Y, NOBLE R. A theoretical analysis of tumour containment[J]. Nat Ecol Evol, 2021, 5(6): 826-835. doi: 10.1038/s41559-021-01428-w pmid: 33846605
[18]	CUNNINGHAM J, THUIJSMAN F, PEETERS R, et al. Optimal control to reach eco-evolutionary stability in metastatic castrate-resistant prostate cancer[J]. PLoS One, 2020, 15(12): e0243386. doi: 10.1371/journal.pone.0243386
[19]	BELKHIR S, THOMAS F, ROCHE B. Darwinian approaches for cancer treatment: benefits of mathematical modeling[J]. Cancers, 2021, 13(17): 4448. doi: 10.3390/cancers13174448
[20]	KIMKO H, PINHEIRO J. Model-based clinical drug development in the past, present and future: a commentary[J]. Br J Clin Pharmacol, 2015, 79(1): 108-116. doi: 10.1111/bcp.12341 pmid: 24527997
[21]	YIN A Y, VAN HASSELT J G C, GUCHELAAR H J, et al. Anti-cancer treatment schedule optimization based on tumor dynamics modelling incorporating evolving resistance[J]. Sci Rep, 2022, 12(1): 4206. doi: 10.1038/s41598-022-08012-7 pmid: 35273301
[22]	JI J T, WU H, FENG X B, et al. Dynamics of acquired resistance to nivolumab therapies varies from administration strategies[J]. Clin Ther, 2021, 43(12): 2088-2103. doi: 10.1016/j.clinthera.2021.10.004 pmid: 34782163
[23]	WEST J B, DINH M N, BROWN J S, et al. Multidrug cancer therapy in metastatic castrate-resistant prostate cancer: an evolution-based strategy[J]. Clin Cancer Res, 2019, 25(14): 4413-4421. doi: 10.1158/1078-0432.CCR-19-0006 pmid: 30992299
[24]	MA Y, NEWTON P K. Role of synergy and antagonism in designing multidrug adaptive chemotherapy schedules[J]. Phys Rev E, 2021, 103(): 032408. doi: 10.1103/PhysRevE.103.032408
[25]	KIM E, BROWN J S, EROGLU Z, et al. Adaptive therapy for metastatic melanoma: predictions from patient calibrated mathematical models[J]. Cancers (Basel), 2021, 13(4): 823. doi: 10.3390/cancers13040823
[26]	WANG Z H, BUTNER J D, KERKETTA R, et al. Simulating cancer growth with multiscale agent-based modeling[J]. Semin Cancer Biol, 2015, 30: 70-78. doi: 10.1016/j.semcancer.2014.04.001 pmid: 24793698
[27]	DEISBOECK T S, WANG Z, MACKLIN P, et al. Multiscale cancer modeling[J]. Annu Rev Biomed Eng, 2011, 13: 127-155. doi: 10.1146/annurev-bioeng-071910-124729 pmid: 21529163
[28]	JOHNSON D, MCKEEVER S, STAMATAKOS G, et al. Dealing with diversity in computational cancer modeling[J]. Cancer Inform, 2013, 12: 115-124.
[29]	REJNIAK K A, ANDERSON A R A. Hybrid models of tumor growth[J]. Wiley Interdiscip Rev Syst Biol Med, 2011, 3(1): 115-125. doi: 10.1002/wsbm.v3.1
[30]	CHISHOLM R H, LORENZI T, CLAIRAMBAULT J. Cell population heterogeneity and evolution towards drug resistance in cancer: biological and mathematical assessment, theoretical treatment optimisation[J]. Biochim Biophys Acta, 2016, 1860(11 Pt B): 2627-2645. doi: 10.1016/j.bbagen.2016.06.009 pmid: 27339473
[31]	YOU L, BROWN J S, THUIJSMAN F, et al. Spatial vs non-spatial eco-evolutionary dynamics in a tumor growth model[J]. J Theor Biol, 2017, 435: 78-97. doi: 10.1016/j.jtbi.2017.08.022
[32]	STROBL M A R, GALLAHER J, WEST J, et al. Spatial structure impacts adaptive therapy by shaping intra-tumoral competition[J]. Commun Med (Lond), 2022, 2: 46.
[33]	FU X, ZHAO Y, LOPEZ J I, et al. Spatial patterns of tumour growth impact clonal diversification in a computational model and the TRACERx renal study[J]. Nat Ecol Evol, 2022, 6(1): 88-102. doi: 10.1038/s41559-021-01586-x
[34]	GALLAHER J A, ENRIQUEZ-NAVAS P M, LUDDY K A, et al. Spatial heterogeneity and evolutionary dynamics modulate time to recurrence in continuous and adaptive cancer therapies[J]. Cancer Res, 2018, 78(8): 2127-2139. doi: 10.1158/0008-5472.CAN-17-2649 pmid: 29382708
[35]	STOREY K M, JACKSON T L. An agent-based model of combination oncolytic viral therapy and anti-PD-1 immunotherapy reveals the importance of spatial location when treating glioblastoma[J]. Cancers (Basel), 2021, 13(21): 5314. doi: 10.3390/cancers13215314
[36]	PIN C, COLLINS T, GIBBS M, et al. Systems modeling to quantify safety risks in early drug development: using bifurcation analysis and agent-based modeling as examples[J]. AAPS J, 2021, 23(4): 77. doi: 10.1208/s12248-021-00580-2 pmid: 34018069
[37]	ROJAS-DOMÍNGUEZ A, ARROYO-DUARTE R, RINCÓN-VIEYRA F, et al. Modeling cancer immunoediting in tumor microenvironment with system characterization through the Ising-model Hamiltonian[J]. BMC Bioinformatics, 2022, 23(1): 200. doi: 10.1186/s12859-022-04731-w
[38]	GATENBY R A, BROWN J S. The evolution and ecology of resistance in cancer therapy[J]. Cold Spring Harb Perspect Med, 2020, 10(11): a040972. doi: 10.1101/cshperspect.a040972
[39]	MALEY C C, REID B J, FORREST S. Cancer prevention strategies that address the evolutionary dynamics of neoplastic cells: simulating benign cell boosters and selection for chemosensitivity[J]. Cancer Epidemiol Biomarkers Prev, 2004, 13(8): 1375-1384. doi: 10.1158/1055-9965.1375.13.8
[40]	SCHWEIZER M T, ANTONARAKIS E S, WANG H, et al. Effect of bipolar androgen therapy for asymptomatic men with castration-resistant prostate cancer: results from a pilot clinical study[J]. Sci Transl Med, 2015, 7(269): 269ra2.
[41]	SENA L A, WANG H, LIM SCM S J, et al. Bipolar androgen therapy sensitizes castration-resistant prostate cancer to subsequent androgen receptor ablative therapy[J]. Eur J Cancer, 2021, 144: 302-309. doi: 10.1016/j.ejca.2020.11.043 pmid: 33383350
[42]	GATENBY R A, BROWN J, VINCENT T. Lessons from applied ecology: cancer control using an evolutionary double bind[J]. Cancer Res, 2009, 69(19): 7499-7502. doi: 10.1158/0008-5472.CAN-09-1354 pmid: 19752088
[43]	ANTONIA S J, MIRZA N, FRICKE I, et al. Combination of p53 cancer vaccine with chemotherapy in patients with extensive stage small cell lung cancer[J]. Clin Cancer Res, 2006, 12(3): 878-887. doi: 10.1158/1078-0432.CCR-05-2013
[44]	STANKOVÁ K, BROWN J S, DALTON W S, et al. Optimizing cancer treatment using game theory: a review[J]. JAMA Oncol, 2019, 5(1): 96-103. doi: 10.1001/jamaoncol.2018.3395 pmid: 30098166