Mathematical Modelling and Bioinformatics Analyses of Drug Resistance
for Cancer Treatment

Lingling      Li; Ting      Zhao; Yulu      Hu; Shanjing      Ren; Tianhai      Tian
doi:10.2174/1574893618666230512141427
Abstract

Cancer is a leading cause of human death worldwide. Drug resistance, mainly caused by gene mutation, is a key obstacle to tumour treatment. Therefore, studying the mechanisms of drug resistance in cancer is extremely valuable for clinical applications.
This paper aims to review bioinformatics approaches and mathematical models for determining the evolutionary mechanisms of drug resistance and investigating their functions in designing therapy schemes for cancer diseases. We focus on the models with drug resistance based on genetic mutations for cancer therapy and bioinformatics approaches to study drug resistance involving gene co-expression networks and machine learning algorithms.
We first review mathematical models with single-drug resistance and multidrug resistance. The resistance probability of a drug is different from the order of drug administration in a multidrug resistance model. Then, we discuss bioinformatics methods and machine learning algorithms that are designed to develop gene co-expression networks and explore the functions of gene mutations in drug resistance using multi-omics datasets of cancer cells, which can be used to predict individual drug response and prognostic biomarkers.
It was found that the resistance probability and expected number of drug-resistant tumour cells increase with the increase in the net reproductive rate of resistant tumour cells. Constrained models, such as logistical growth resistance models, can be used to identify more clinically realistic treatment strategies for cancer therapy. In addition, bioinformatics methods and machine learning algorithms can also lead to the development of effective therapy schemes.
« Previous Next »
[1]
Komarova NL, Wodarz D. Drug resistance in cancer: Principles of emergence and prevention. Proc Natl Acad Sci  2005; 102(27): 9714-9.
 [http://dx.doi.org/10.1073/pnas.0501870102] [PMID:  15980154]

[2]
Gottesman MM. Mechanisms of cancer drug resistance. Annu Rev Med  2002; 53(1): 615-27.
 [http://dx.doi.org/10.1146/annurev.med.53.082901.103929] [PMID:  11818492]

[3]
James CE, Hudson AL, Davey MW. Drug resistance mechanisms in helminths: Is it survival of the fittest? Trends Parasitol  2009; 25(7): 328-35.
 [http://dx.doi.org/10.1016/j.pt.2009.04.004] [PMID:  19541539]

[4]
Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell  2017; 168(4): 707-23.
 [http://dx.doi.org/10.1016/j.cell.2017.01.017] [PMID:  28187290]

[5]
Goldie JH, Coldman AJ. The genetic origin of drug resistance in neoplasms: Implications for systemic therapy. Cancer Res  1984; 44(9): 3643-53.
 [PMID:  6744284]

[6]
Norton L, Simon R, Brereton HD, Bogden A. Predicting the course of Gompertzian growth. Nature  1976; 264(5586): 542-5.
 [http://dx.doi.org/10.1038/264542a0] [PMID:  1004590]

[7]
Shinoda T, Hayase F, Kato H. Suppression of Cell-cycle Progression during the S Phase of Rat Fibroblasts by 3-Deoxyglucosone, a Maillard Reaction Intermediate. Biosci Biotechnol Biochem  1994; 58(12): 2215-9.
 [http://dx.doi.org/10.1271/bbb.58.2215]

[8]
Zankari E, Allesøe R, Joensen KG, Cavaco LM, Lund O, Aarestrup FM. PointFinder: A novel web tool for WGS-based detection of antimicrobial resistance associated with chromosomal point mutations in bacterial pathogens. J Antimicrob Chemother  2017; 72(10): 2764-8.
 [http://dx.doi.org/10.1093/jac/dkx217] [PMID:  29091202]

[9]
Zehir A, Benayed R, Shah RH, et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med  2017; 23(6): 703-13.
 [http://dx.doi.org/10.1038/nm.4333] [PMID:  28481359]

[10]
Luria SE, Delbrück M. Mutations of bacteria from virus sensitivity to virus resistance. Genetics  1943; 28(6): 491-511.
 [http://dx.doi.org/10.1093/genetics/28.6.491] [PMID:  17247100]

[11]
Crump KS, Hoel DG. Mathematical models for estimating mutation rates in cell populations. Biometrika  1974; 61(2): 237-52.
 [http://dx.doi.org/10.1093/biomet/61.2.237]

[12]
Kimmel M, Axelrod DE. Mathematical models of gene amplification with applications to cellular drug resistance and tumorigenicity. Genetics  1990; 125(3): 633-44.
 [http://dx.doi.org/10.1093/genetics/125.3.633] [PMID:  2379824]

[13]
Goldie JH. Modelling the process of drug resistance. Lung Cancer  1994; 10 (Suppl. 1): S91-6.
 [http://dx.doi.org/10.1016/0169-5002(94)91671-3] [PMID:  7916254]

[14]
Goldie JH, Coldman AJ. A mathematic model for relating the drug sensitivity of tumors to their spontaneous mutation rate. Cancer Treat Rep  1979; 63(11-12): 1727-33.
 [PMID:  526911]

[15]
Maeda M, Yamashita H. A numerical approach for a discrete Markov model for progressing drug resistance of cancer. PLOS Comput Biol  2019; 15(2): e1006770.
 [http://dx.doi.org/10.1371/journal.pcbi.1006770] [PMID:  30779730]

[16]
Iwasa Y, Nowak MA, Michor F. Evolution of resistance during clonal expansion. Genetics  2006; 172(4): 2557-66.
 [http://dx.doi.org/10.1534/genetics.105.049791] [PMID:  16636113]

[17]
Haeno H, Iwasa Y, Michor F. The evolution of two mutations during clonal expansion. Genetics  2007; 177(4): 2209-21.
 [http://dx.doi.org/10.1534/genetics.107.078915] [PMID:  18073428]

[18]
Bozic I, Reiter JG, Allen B, et al. Evolutionary dynamics of cancer in response to targeted combination therapy. eLife  2013; 2: e00747.

[19]
Zhang J, Cunningham JJ, Brown JS, Gatenby RA. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat Commun  2017; 8(1): 1816.
 [http://dx.doi.org/10.1038/s41467-017-01968-5] [PMID:  29180633]

[20]
Cunningham JJ, Brown JS, Gatenby RA. Staňková K. Optimal control to develop therapeutic strategies for metastatic castrate resistant prostate cancer. J Theor Biol  2018; 459: 67-78.
 [http://dx.doi.org/10.1016/j.jtbi.2018.09.022] [PMID:  30243754]

[21]
Gatenby RA, Brown JS. Integrating evolutionary dynamics into cancer therapy. Nat Rev Clin Oncol  2020; 17(11): 675-86.
 [http://dx.doi.org/10.1038/s41571-020-0411-1] [PMID:  32699310]

[22]
Gu S, Lheureux S, Sayad A, et al. Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening. Proc Natl Acad Sci USA  2021; 118(25): e2026663118.
 [http://dx.doi.org/10.1073/pnas.2026663118] [PMID:  34161278]

[23]
Nicholson MD, Antal T. Competing evolutionary paths in growing populations with applications to multidrug resistance. PLOS Comput Biol  2019; 15(4): e1006866.
 [http://dx.doi.org/10.1371/journal.pcbi.1006866] [PMID:  30986219]

[24]
Hori SS, Tong L, Swaminathan S, et al. A mathematical model of tumor regression and recurrence after therapeutic oncogene inactivation. Sci Rep  2021; 11(1): 1341.
 [http://dx.doi.org/10.1038/s41598-020-78947-2]

[25]
Saini A, Gallo JM. Epigenetic instability may alter cell state transitions and anticancer drug resistance. PLOS Comput Biol  2021; 17(8): e1009307.
 [http://dx.doi.org/10.1371/journal.pcbi.1009307] [PMID:  34424912]

[26]
Yin A, Hasselt JG, Guchelaar HJ, et al. Anti-cancer treatment schedule optimization based on tumour dynamics modelling incorporating evolving resistance. Sci Rep  2022; 12: 1-14.
 [http://dx.doi.org/10.1038/s41598-022-09014-1]

[27]
Tutsoy O. Pharmacological, non-pharmacological policies and mutation: An artificial intelligence based multi-dimensional policy making algorithm for controlling the casualties of the pandemic diseases. IEEE Trans Pattern Anal Mach Intell  2022; 44(12): 9477-88.
 [http://dx.doi.org/10.1109/TPAMI.2021.3127674] [PMID:  34767503]

[28]
Tutsoy O, Polat A. Linear and non-linear dynamics of the epidemics: System identification based parametric prediction models for the pandemic outbreaks. ISA Trans  2022; 124: 90-102.
 [http://dx.doi.org/10.1016/j.isatra.2021.08.008] [PMID:  34412892]

[29]
William P, Wang TY, Riely GJ, et al. Kras mutations and primary resistance of lung adenocarcinomas to gefitinib or erlotinib. PLoS Med  2018; 2: e17-45.

[30]
Shamieh SE, Saleh F, Assaad S, et al. Next-generation sequencing reveals mutations in rb1, cdk4 and tp53 that may promote chemo-resistance to palbociclib in ovarian cancer.  Drug Metabol Therapy 2019; p. 34.

[31]
Färkkilä A, Rodríguez A, Oikkonen J, et al. Heterogeneity and clonal evolution of acquired parp inhibitor resistance in tp53-and brca1-deficient cells. Cancer Res  2021; 81(10): 2774-87.
 [http://dx.doi.org/10.1158/0008-5472.CAN-20-2912] [PMID:  33514515]

[32]
Kontomanolis EN, Koutras A, Syllaios A, et al. Role of oncogenes and tumour-suppressor genes in carcinogenesis: A review. Anticancer Res  2020; 40(11): 6009-15.
 [http://dx.doi.org/10.21873/anticanres.14622] [PMID:  33109539]

[33]
King MC, Wilson AC. Evolution at two levels in humans and chimpanzees. Science  1975; 188(4184): 107-16.
 [http://dx.doi.org/10.1126/science.1090005] [PMID:  1090005]

[34]
Welch JS, Ley TJ, Link DC, et al. The origin and evolution of mutations in acute myeloid leukemia. Cell  2012; 150(2): 264-78.
 [http://dx.doi.org/10.1016/j.cell.2012.06.023] [PMID:  22817890]

[35]
Tsimberidou AM. Targeted therapy in cancer. Cancer Chemother Pharmacol  2015; 76(6): 1113-32.
 [http://dx.doi.org/10.1007/s00280-015-2861-1] [PMID:  26391154]

[36]
Ellis LM, Hicklin DJ. VEGF-targeted therapy: Mechanisms of anti-tumour activity. Nat Rev Cancer  2008; 8(8): 579-91.
 [http://dx.doi.org/10.1038/nrc2403] [PMID:  18596824]

[37]
Crystal AS, Shaw AT, Sequist LV, et al. Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science  2014; 346(6216): 1480-6.
 [http://dx.doi.org/10.1126/science.1254721] [PMID:  25394791]

[38]
Coldman A, Goldie J. A stochastic model for the origin and treatment of tumors containing drug-resistant cells. Bull Math Biol  1986; 48(3-4): 279-92.
 [http://dx.doi.org/10.1016/S0092-8240(86)90028-5] [PMID:  3828558]

[39]
Goldie JH, Coldman AJ. Drug resistance in cancer: Mechanisms and models. J Biol Chem  1998; 938: 142-53.

[40]
Coldman AJ, Goldie JH. A model for the resistance of tumor cells to cancer chemotherapeutic agents. Math Biosci  1983; 65(2): 291-307.
 [http://dx.doi.org/10.1016/0025-5564(83)90066-4]

[41]
Komarova N. Stochastic modeling of drug resistance in cancer. J Theor Biol  2006; 239(3): 351-66.
 [http://dx.doi.org/10.1016/j.jtbi.2005.08.003] [PMID:  16194548]

[42]
Komarova NL, Wodarz D. Combination therapies against chronic myeloid leukemia: short-term versus long-term strategies. Cancer Res  2009; 69(11): 4904-10.
 [http://dx.doi.org/10.1158/0008-5472.CAN-08-1959] [PMID:  19458080]

[43]
Michor F, Nowak M, Iwasa Y. Evolution of resistance to cancer therapy. Curr Pharm Des  2006; 12(3): 261-71.
 [http://dx.doi.org/10.2174/138161206775201956] [PMID:  16454743]

[44]
Foo J, Michor F. Evolution of acquired resistance to anti-cancer therapy. J Theor Biol  2014; 355: 10-20.
 [http://dx.doi.org/10.1016/j.jtbi.2014.02.025] [PMID:  24681298]

[45]
Foo J, Michor F. Evolution of resistance to anti-cancer therapy during general dosing schedules. J Theor Biol  2010; 263(2): 179-88.
 [http://dx.doi.org/10.1016/j.jtbi.2009.11.022] [PMID:  20004211]

[46]
Foo J, Michor F. Evolution of resistance to targeted anti-cancer therapies during continuous and pulsed administration strategies. PLOS Comput Biol  2009; 5(11): e1000557.
 [http://dx.doi.org/10.1371/journal.pcbi.1000557] [PMID:  19893626]

[47]
Harnevo LE, Agur Z. The dynamics of gene amplification described as a multitype compartmental model and as a branching process. Math Biosci  1991; 103(1): 115-38.
 [http://dx.doi.org/10.1016/0025-5564(91)90094-Y] [PMID:  1804437]

[48]
Sun X, Bao J, Shao Y. Mathematical modeling of therapy induced cancer drug resistance: connecting cancer mechanisms to population survival rates. Sci Rep  2016; 6(1): 22498.
 [http://dx.doi.org/10.1038/srep22498] [PMID:  26928089]

[49]
Ghosh M, Chandra P, Sinha P, Shukla JB. Modelling the spread of bacterial infectious disease with environmental effect in a logistically growing human population. Nonlinear Anal Real World Appl  2006; 7(3): 341-63.
 [http://dx.doi.org/10.1016/j.nonrwa.2005.03.005]

[50]
Nakasu S, Nakasu Y, Fukami T, Jito J, Nozaki K. Growth curve analysis of asymptomatic and symptomatic meningiomas. J Neurooncol  2011; 102(2): 303-10.
 [http://dx.doi.org/10.1007/s11060-010-0319-1] [PMID:  20686821]

[51]
Anderson ARA, Hassanein M, Branch KM, et al. Microenvironmental independence associated with tumor progression. Cancer Res  2009; 69(22): 8797-806.
 [http://dx.doi.org/10.1158/0008-5472.CAN-09-0437] [PMID:  19887618]

[52]
Tomasetti C, Levy D. Role of symmetric and asymmetric division of stem cells in developing drug resistance. Proc Natl Acad Sci  2010; 107(39): 16766-71.
 [http://dx.doi.org/10.1073/pnas.1007726107] [PMID:  20826440]

[53]
Komarova NL, Wodarz D. Effect of cellular quiescence on the success of targeted CML therapy. PLoS One  2007; 2(10): e990.
 [http://dx.doi.org/10.1371/journal.pone.0000990] [PMID:  17912367]

[54]
Tian T, Olson S, Whitacre JM, Harding A. The origins of cancer robustness and evolvability. Integr Biol  2011; 3(1): 17-30.
 [http://dx.doi.org/10.1039/C0IB00046A] [PMID:  20944865]

[55]
Fu F, Nowak MA, Bonhoeffer S. Spatial heterogeneity in drug concentrations can facilitate the emergence of resistance to cancer therapy. PLOS Comput Biol  2015; 11(3): e1004142.
 [http://dx.doi.org/10.1371/journal.pcbi.1004142] [PMID:  25789469]

[56]
Regales L, Gong Y, Shen R, et al. Dual targeting of EGFR can overcome a major drug resistance mutation in mouse models of EGFR mutant lung cancer. J Clin Invest  2009; 119(10): 3000-10.
 [http://dx.doi.org/10.1172/JCI38746] [PMID:  19759520]

[57]
Kars MD. Işeri ÖD, Gündüz U, Ural AU, Arpaci F, Molnár J. Development of rational in vitro models for drug resistance in breast cancer and modulation of MDR by selected compounds. Anticancer Res  2006; 26(6B): 4559-68.
 [PMID:  17201178]

[58]
Picco N, Sahai E, Maini PK, Anderson ARA. Integrating models to quantify environment-mediated drug resistance. Cancer Res  2017; 77(19): 5409-18.
 [http://dx.doi.org/10.1158/0008-5472.CAN-17-0835] [PMID:  28754669]

[59]
Tian T, Chen A, Zhou T. Integrated pinelines for inferring gene regulatory networks from single cell data. Curr Bioinform  2022; 17(7): 559-64.
 [http://dx.doi.org/10.2174/1574893617666220511234247]

[60]
Shoemaker RH. The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer  2006; 6(10): 813-23.
 [http://dx.doi.org/10.1038/nrc1951] [PMID:  16990858]

[61]
Yang W, Soares J, Greninger P, et al. Genomics of drug sensitivity in cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res  2013; 41: D955-61.
 [PMID:  23180760]

[62]
Weinstein JN, Collisson EA, Mills GB, et al. The cancer genome atlas pan-cancer analysis project. Nat Genet  2013; 45(10): 1113-20.
 [http://dx.doi.org/10.1038/ng.2764] [PMID:  24071849]

[63]
Seashore-Ludlow B, Rees MG, Cheah JH, et al. Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov  2015; 5(11): 1210-23.
 [http://dx.doi.org/10.1158/2159-8290.CD-15-0235] [PMID:  26482930]

[64]
Ghandi M, Huang FW, Jané-Valbuena J, et al. Next-generation characterization of the cancer cell line encyclopedia. Nature  2019; 569(7757): 503-8.
 [http://dx.doi.org/10.1038/s41586-019-1186-3] [PMID:  31068700]

[65]
Tate JG, Bamford S, Jubb HC, et al. COSMIC: The catalogue of somatic mutations in cancer. Nucleic Acids Res  2019; 47(D1): D941-7.
 [http://dx.doi.org/10.1093/nar/gky1015] [PMID:  30371878]

[66]
Cui H, Kong H, Peng F, et al. Inferences of individual drug response-related long non-coding RNAs based on integrating multi-omics data in breast cancer. Mol Ther Nucleic Acids  2020; 20: 128-39.
 [http://dx.doi.org/10.1016/j.omtn.2020.01.038] [PMID:  32163894]

[67]
Zhang Z, Li D, Zhang H, et al. Inferences from dysregulated long non-coding RNA-mediated competing endogenous RNAs in various chemotherapy drugs and evaluation of drug response in breast cancer. Mol Ther Nucleic Acids  2020; 20: 128-39.

[68]
Oh M, Park S, Lee S, et al. DRIM: A web-based system for investigating drug response at the molecular level by condition-specific multi-omics data integration. Front Genet  2020; 11: 564792.
 [http://dx.doi.org/10.3389/fgene.2020.564792] [PMID:  33281870]

[69]
Deng Y, Zhang F, Liu J, et al. Development and validation of a prognostic signature based on autophagy-related long non-coding RNA analysis in hepatocellular carcinoma. Front Med  2021; 8: 762570.
 [http://dx.doi.org/10.21203/rs.3.rs-378004/v1]

[70]
Zhao T, Xu J, Liu L, et al. Computational identification of epigenetically regulated lncRNAs and their associated genes based on integrating genomic data. FEBS Lett  2015; 589(4): 521-31.
 [http://dx.doi.org/10.1016/j.febslet.2015.01.013] [PMID:  25616131]

[71]
Xu Y, Dong Q, Li F, et al. Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data. J Transl Med  2019; 17(1): 255.
 [http://dx.doi.org/10.1186/s12967-019-2010-4] [PMID:  31387579]

[72]
Chen BJ, Causton HC, Mancenido D, Goddard NL, Perlstein EO, Pe’er D. Harnessing gene expression to identify the genetic basis of drug resistance. Mol Syst Biol  2009; 5(1): 310.
 [http://dx.doi.org/10.1038/msb.2009.69] [PMID:  19888205]

[73]
Qi W, Zhang Q. Gene’s co-expression network and experimental validation of molecular markers associated with the drug resistance of gastric cancer. Biomarkers Med  2020; 14(9): 761-73.
 [http://dx.doi.org/10.2217/bmm-2019-0504] [PMID:  32715733]

[74]
Wang Y, Chen L, Ju L, et al. Novel biomarkers associated with progression and prognosis of bladder cancer identified by co-expression analysis. Front Oncol  2019; 9: 1030.
 [http://dx.doi.org/10.3389/fonc.2019.01030] [PMID:  31681575]

[75]
Liu S, Wu J, Feng Y. The Prediction of Anti-cancer Drug Response by Integrating Multi-omics Data In International Conference on Intelligent Automation and Soft Computing. Cham: Springer Cham.   2021; 5: pp. 1149-56.

[76]
Li YK, Hsu HM, Lin MC, et al. Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer. Sci rep-UK  2021; 11: 1-13.

[77]
Neog Bora P, Baruah VJ, Borkotokey S, et al. Identifying the salient genes in microarray data: A novel game theoretic model for the co-expression network. Diagnostics  2020; 10(8): 586-93.
 [http://dx.doi.org/10.3390/diagnostics10080586] [PMID:  32823765]

[78]
Li Z, Cai S, Li H, et al. Developing a lncRNA signature to predict the radiotherapy response of lower-grade gliomas using co-expression and ceRNA network analysis. Front Oncol  2021; 11: 622880.
 [http://dx.doi.org/10.3389/fonc.2021.622880] [PMID:  33767991]

[79]
Yao C, Chen BH, Joehanes R, et al. Integromic analysis of genetic variation and gene expression identifies networks for cardiovascular disease phenotypes. Circulation  2015; 131(6): 536-49.
 [http://dx.doi.org/10.1161/CIRCULATIONAHA.114.010696] [PMID:  25533967]

[80]
Zhang J, Zhu W, Wang Q, Gu J, Huang LF, Sun X. Differential regulatory network-based quantification and prioritization of key genes underlying cancer drug resistance based on time-course RNA-seq data. PLOS Comput Biol  2019; 15(11): e1007435.
 [http://dx.doi.org/10.1371/journal.pcbi.1007435] [PMID:  31682596]

[81]
Zhao Y, Chen J, Freudenberg JM, Meng Q, Rajpal DK, Yang X. Network-based identification and prioritization of key regulators of coronary artery disease loci. Arterioscler Thromb Vasc Biol  2016; 36(5): 928-41.
 [http://dx.doi.org/10.1161/ATVBAHA.115.306725] [PMID:  26966275]

[82]
Gonçalves JP, Francisco AP, Mira NP, et al. TFRank: network-based prioritization of regulatory associations underlying transcriptional responses. Bioinformatics  2011; 27(22): 3149-57.
 [http://dx.doi.org/10.1093/bioinformatics/btr546] [PMID:  21965816]

[83]
Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol  2022; 23(1): 40-55.
 [http://dx.doi.org/10.1038/s41580-021-00407-0] [PMID:  34518686]

[84]
You Y, Lai X, Pan Y, et al. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther  2022; 7(1): 156.
 [http://dx.doi.org/10.1038/s41392-022-00994-0] [PMID:  35538061]

[85]
Frejno M, Zenezini Chiozzi R, Wilhelm M, et al. Pharmacoproteomic characterisation of human colon and rectal cancer. Mol Syst Biol  2017; 13(11): 951.
 [http://dx.doi.org/10.15252/msb.20177701] [PMID:  29101300]

[86]
Frejno M, Meng C, Ruprecht B, et al. Proteome activity landscapes of tumor cell lines determine drug responses. Nat Commun  2020; 11(1): 3639.
 [http://dx.doi.org/10.1038/s41467-020-17336-9] [PMID:  32686665]

[87]
Kong J, Lee H, Kim D, et al. Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients. Nat Commun  2020; 11(1): 5485.
 [http://dx.doi.org/10.1038/s41467-020-19313-8] [PMID:  33127883]

[88]
Liu R, Zhang G, Yang Z. Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning. Chem Commun (Camb)  2019; 55(5): 616-9.
 [http://dx.doi.org/10.1039/C8CC08296K] [PMID:  30525135]

[89]
Yu L, Zhou D, Gao L, Zha Y. Prediction of drug response in multilayer networks based on fusion of multiomics data. Methods  2021; 192: 85-92.
 [http://dx.doi.org/10.1016/j.ymeth.2020.08.006] [PMID:  32798653]

[90]
Choi J, Park S, Ahn J. RefDNN: A reference drug based neural network for more accurate prediction of anticancer drug resistance. Sci rep-UK  2020; 10: 1-11.
 [http://dx.doi.org/10.1038/s41598-020-58821-x]

[91]
Zhu Y, Brettin T, Evrard YA, et al. Ensemble transfer learning for the prediction of anti-cancer drug response. Sci rep-UK  2020; 10: 1-11.
 [http://dx.doi.org/10.1038/s41598-020-74921-0]

[92]
Wang H, Zhao Y, Wu Y, et al. Enhanced anti-tumor efficacy by co-delivery of doxorubicin and paclitaxel with amphiphilic methoxy PEG-PLGA copolymer nanoparticles. Biomaterials  2011; 32(32): 8281-90.
 [http://dx.doi.org/10.1016/j.biomaterials.2011.07.032] [PMID:  21807411]

[93]
Yu C, Liu X, Yang J, et al. Combination of immunotherapy with targeted therapy: Theory and practice in metastatic melanoma. Front Immunol  2019; 10: 990.
 [http://dx.doi.org/10.3389/fimmu.2019.00990] [PMID:  31134073]

[94]
Wu XY, Ma W, Gurung K, Guo CH. Mechanisms of tumor resistance to small-molecule vascular disrupting agents: Treatment and rationale of combination therapy. J Formos Med Assoc  2013; 112(3): 115-24.
 [http://dx.doi.org/10.1016/j.jfma.2012.09.017] [PMID:  23473523]

[95]
Xiao Y, Yin C, Wang Y, et al. FBXW 7 deletion contributes to lung tumor development and confers resistance to gefitinib therapy. Mol Oncol  2018; 12(6): 883-95.
 [http://dx.doi.org/10.1002/1878-0261.12200] [PMID:  29633504]

[96]
Garber K. Melanoma combination therapies ward off tumor resistance. Nat Biotechnol  2013; 31(8): 666-7.
 [http://dx.doi.org/10.1038/nbt0813-666b] [PMID:  23929325]

[97]
Hu CMJ, Zhang L. Nanoparticle-based combination therapy toward overcoming drug resistance in cancer. Biochem Pharmacol  2012; 83(8): 1104-11.
 [http://dx.doi.org/10.1016/j.bcp.2012.01.008] [PMID:  22285912]

[98]
Gevertz JL, Aminzare Z, Norton KA, et al. Emergence of anticancer drug resistance: Exploring the importance of the microenvironmental niche via a spatial model. Springer   2015; 158: 1-34.
 [http://dx.doi.org/10.1007/978-1-4939-2782-1_1]

[99]
Lefebvre G, Cornelis F, Cumsille P, Colin T, Poignard C, Saut O. Spatial modelling of tumour drug resistance: The case of GIST liver metastases. Math Med Biol  2017; 34(2): 151-76.
 [PMID:  27034422]

[100]
Florence D, Thomas L, Sylvain G. Evolutionary epidemiology of drug resistance, a spatial model. PLOS Comput Biol  2014; 5: e1000337.

[101]
Jackson TL, Byrne HM. A mathematical model to study the effects of drug resistance and vasculature on the response of solid tumours to chemotherapy. Math Biosci  2000; 164: 0-38.

[102]
Vasan N, Baselga J, Hyman DM. A view on drug resistance in cancer. Nature  2019; 575(7782): 299-309.
 [http://dx.doi.org/10.1038/s41586-019-1730-1] [PMID:  31723286]

[103]
Hsu PP, Sabatini DM. Cancer cell metabolism: Warburg and beyond. Cell  2008; 134(5): 703-7.
 [http://dx.doi.org/10.1016/j.cell.2008.08.021] [PMID:  18775299]

[104]
Trédan O, Galmarini CM, Patel K, Tannock IF. Drug resistance and the solid tumor microenvironment. J Natl Cancer Inst  2007; 99(19): 1441-54.
 [http://dx.doi.org/10.1093/jnci/djm135] [PMID:  17895480]

[105]
Wu A, Loutherback K, Lambert G, et al. Cell motility and drug gradients in the emergence of resistance to chemotherapy. Proc Natl Acad Sci  2013; 110(40): 16103-8.
 [http://dx.doi.org/10.1073/pnas.1314385110] [PMID:  24046372]

[106]
Feinerman O, Veiga J, Dorfman JR, Germain RN, Altan-Bonnet G. Variability and robustness in T cell activation from regulated heterogeneity in protein levels. Science  2008; 321(5892): 1081-4.
 [http://dx.doi.org/10.1126/science.1158013] [PMID:  18719282]
Rights & Permissions Print Cite
Journal Information
For Authors
For Editors
For Reviewers
Explore Articles
Open Access
Open Access Articles
For Visitors
DOI https://dx.doi.org/10.2174/1574893618666230512141427	Print ISSN 1574-8936
Publisher Name Bentham Science Publisher	Online ISSN 2212-392X
Current Bioinformatics

Mathematical Modelling and Bioinformatics Analyses of Drug Resistance for Cancer Treatment

Abstract Play Pause

Related Journals

Related Books

Abstract
