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Reviews on Recent Clinical Trials

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ISSN (Print): 1574-8871
ISSN (Online): 1876-1038

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General Review Article

Drug Sensitivity Testing for Cancer Therapy, Key Areas

Author(s): Da-Yong Lu*, Ting-Ren Lu, Nagendra Sastry Yarla and Bin Xu

Volume 17, Issue 4, 2022

Published on: 09 September, 2022

Page: [291 - 299] Pages: 9

DOI: 10.2174/1574887117666220819094528

Price: $65

Abstract

Aims: Cancer is a high-mortality disease (9.6 million deaths in 2018 worldwide). Given various anticancer drugs, drug selection plays a key role in patient survival in clinical trials.

Methods: Drug Sensitivity Testing (DST), one of the leading drug selective systems, was widely practiced for therapeutic promotion in the clinic. Notably, DSTs assist in drug selection that benefits drug responses against cancer from 20-22% to 30-35% over the past two decades. The relationship between drug resistance in vitro and drug treatment benefits was associated with different tumor origins and subtypes. Medical theory and underlying DST mechanisms remain poorly understood until now. The study of the clinical scenario, sustainability and financial support for mechanism and technical promotions is indispensable.

Results: Despite the great technical advance, therapeutic prediction and drug selection by DST needs to be miniature, versatility and cost-effective in the clinic. Multi-parameters and automation of DST should be a future trend. Advanced biomedical knowledge and clinical approaches to translating oncologic profiles into drug selection were the main focuses of DST developments. With a great technical stride, the clinical architecture of the DST platform was entering higher levels (drug response testing at any stage of cancer patients and miniaturization of tumor samples).

Discussion: The cancer biology and pharmacology for drug selection mutually benefit the clinic. New proposals to reveal more therapeutic information and drug response prediction at genetic, molecular and omics levels should be estimated overall.

Conclusion: By upholding this goal of non-invasive, versatility and automation, DST could save the life of several thousand annually worldwide. In this article, new insights into DST novelty and development are highlighted.

Keywords: Drug sensitivity testing, cancer pathology, microfluidics, diagnostic platform, anti-cancer drug, drug selection, clinical pharmacology.

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[1]
Hanahan D, Weinberg RA. Hallmarks of cancer: The next generation. Cell 2011; 144(5): 646-74.
[http://dx.doi.org/10.1016/j.cell.2011.02.013] [PMID: 21376230]
[2]
Mina LA, Sledge GW Jr. Rethinking the metastatic cascade as a therapeutic target. Nat Rev Clin Oncol 2011; 8(6): 325-32.
[http://dx.doi.org/10.1038/nrclinonc.2011.59] [PMID: 21502993]
[3]
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin 2017; 67(1): 7-30.
[http://dx.doi.org/10.3322/caac.21387] [PMID: 28055103]
[4]
Ahmad AS, Ormiston-Smith N, Sasieni PD. Trends in the lifetime risk of developing cancer in great Britain: Comparison of risk for those born from 1930 to 1960. Br J Cancer 2015; 112(5): 943-7.
[http://dx.doi.org/10.1038/bjc.2014.606] [PMID: 25647015]
[5]
Fojo T. The high cost of ignorance in oncology. Semin Oncol 2016; 43(6): 623-4.
[http://dx.doi.org/10.1053/j.seminoncol.2016.11.010] [PMID: 28061979]
[6]
Ahuja V. New drug approvals by FDA from 2013-2017. EC Pharmacology Toxicology 2018; 6(9): 772-4.
[7]
Meyer UA. Pharmacogenetics - five decades of therapeutic lessons from genetic diversity. Nat Rev Genet 2004; 5(9): 669-76.
[http://dx.doi.org/10.1038/nrg1428] [PMID: 15372089]
[8]
Lu DY, Chen XL, Ding J. Individualized cancer chemotherapy integrating drug sensitivity tests, pathological profile analysis and computa-tional coordination - An effective strategy to improve clinical treatment. Med Hypotheses 2006; 66(1): 45-51.
[http://dx.doi.org/10.1016/j.mehy.2005.07.023] [PMID: 16168568]
[9]
Lu DY, Lu TR, Chen XL, Ding J. Individualized cancer chemotherapy. In: Shoja MM, Agutter PS, Tubbs RS, Eds. Hypotheses in Clinical MedicineUS. Nova Science Publisher 2012; pp. 199-216.
[10]
Lu DY. Personalized cancer chemotherapy, an effective way for enhancing outcomes in clinics. in UK: Woodhead Publishing, Amster-dam. Netherlands Elsevier 2014; pp. 13-20.
[11]
Volm M, Efferth T. Prediction of cancer drug resistance and implications for personalized medicine. Front Oncol 2015; 5: 282.
[http://dx.doi.org/10.3389/fonc.2015.00282] [PMID: 26734568]
[12]
Lu DY, Lu TR, Ding J, Xu B, Che JY, Wu HY. Anticancer drug sensitivity testing, a historical review and future perspectives. Curr Drug Ther 2015; 10(1): 44-55.
[http://dx.doi.org/10.2174/157488551001150825100450]
[13]
Lu DY, Lu TR. Drug sensitivity testing, a unique drug selection strategy. Adv Biomarker Sci Technol 2020; 2: 59-66.
[http://dx.doi.org/10.1016/j.abst.2020.11.001]
[14]
Popova AA, Levkin PA. Precision medicine in oncology: In vitro Drug Sensitivity And Resistance Test (DSRT) for selection of personal-ized anticancer therapy. Adv Ther 2020; 1900100.
[http://dx.doi.org/10.1002/adtp.201900100]
[15]
Hyman DM, Taylor BS, Baselga J. Implementing genome-driven oncology. Cell 2017; 168(4): 584-99.
[http://dx.doi.org/10.1016/j.cell.2016.12.015] [PMID: 28187282]
[16]
Lu DY, Lu TR, Cao S. Individualized cancer chemotherapy by detecting cancer biomarkers? Metabolomics 2012; 2(5): e121.
[17]
Lu DY, Lu TR, Chen XL, Chen EH, Ding J, Xu B. Cancer bioinformatics, its impacts on cancer therapy. Metabolomics 2015; 5(2): e133.
[18]
Ocaña A, Pandiella A. Personalized therapies in the cancer “omics” era. Mol Cancer 2010; 9: 202.
[http://dx.doi.org/10.1186/1476-4598-9-202] [PMID: 20670437]
[19]
Stransky B, Galante P. Application of bioinformatics in cancer research. In:An IMICS Perspective on Cancer Research. Netherlands Springer 2010; pp. 211-33.
[20]
Huang YH, Vakoc CR. A biomarker harvest from one thousand cancer cell lines. Cell 2016; 166(3): 536-7.
[http://dx.doi.org/10.1016/j.cell.2016.07.010] [PMID: 27471963]
[21]
Lu DY, Qu RX, Lu TR, Wu HY. Cancer bioinformatics for update anticancer drug developments and personalized therapeutics. Rev Recent Clin Trials 2017; 12(2): 101-10.
[http://dx.doi.org/10.2174/1574887112666170209161444] [PMID: 28190390]
[22]
Huang RS, Ratain MJ. Pharmacogenetics and pharmacogenomics of anticancer agents. CA Cancer J Clin 2009; 59(1): 42-55.
[http://dx.doi.org/10.3322/caac.20002] [PMID: 19147868]
[23]
Lu DY, Lu TR, Xu B, Ding J. Pharmacogenetics of cancer therapy: Breakthroughs from beyond? Future Sci OA 2015; 1(4): 80.
[http://dx.doi.org/10.4155/fso.15.80]
[24]
Montero J, Sarosiek KA, DeAngelo JD, et al. Drug-induced death signaling strategy rapidly predicts cancer response to chemotherapy. Cell 2015; 160(5): 977-89.
[http://dx.doi.org/10.1016/j.cell.2015.01.042] [PMID: 25723171]
[25]
Lu DY, Lu TR, Che JY, Yarla NS. Individualized cancer therapy, what is the next generation? EC Cancer 2018; 2(6): 286-97.
[26]
Lu DY, Lu TR, Che JY, Shen Y, Yarla NS. Individualized cancer therapy, future approaches. Curr Pharmacogenomics Person Med 2018; 16(2): 156-63.
[http://dx.doi.org/10.2174/1875692116666180821095434]
[27]
Damyanov C, Pavlov V, Maslev I. Personalized treatment application in integrative oncology. Indian J Res 2018; 7(1): 222-5.
[28]
Lu DY, Lu TR. Drug sensitivity testing for cancer therapy, technique analysis and trend. Curr Rev Clin Exp Pharmacol 2022.
[29]
Eduati F, Utharala R, Madhavan D, et al. A microfluidics platform for combinatorial drug screening on cancer biopsies. Nat Commun 2018; 9(1): 2434.
[http://dx.doi.org/10.1038/s41467-018-04919-w] [PMID: 29934552]
[30]
Xu Z, Gao Y, Hao Y, et al. Application of a microfluidic chip-based 3D co-culture to test drug sensitivity for individualized treatment of lung cancer. Biomaterials 2013; 34(16): 4109-17.
[http://dx.doi.org/10.1016/j.biomaterials.2013.02.045] [PMID: 23473962]
[31]
Sveen A, Bruun J, Eide PW, et al. Colorectal cancer consensus molecular subtypes translated to preclinical models uncover potentially targetable cancer cell dependencies. Clin Cancer Res 2018; 24(4): 794-806.
[http://dx.doi.org/10.1158/1078-0432.CCR-17-1234] [PMID: 29242316]
[32]
Van de Wetering M, Francies HE, Francis JM, et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 2015; 161(4): 933-45.
[http://dx.doi.org/10.1016/j.cell.2015.03.053] [PMID: 25957691]
[33]
Praharaj PP, Bhutia SK, Nagrath S, Bitting RL, Deep G. Circulating tumor cell-derived organoids: Current challenges and promises in medi-cal research and precision medicine. Biochim Biophys Acta Rev Cancer 2018; 1869(2): 117-27.
[http://dx.doi.org/10.1016/j.bbcan.2017.12.005] [PMID: 29360544]
[34]
Cristofanilli M. The biological information obtainable from circulating tumor cells. Breast 2009; 18(3): S38-40.
[http://dx.doi.org/10.1016/S0960-9776(09)70270-X] [PMID: 19914540]
[35]
Chakraborty S, Gourain V, Benz M, Scheiger JM, Levkin PA, Popova AA. Droplet microarrays for cell culture: Effect of surface proper-ties and nanoliter culture volume on global transcriptomic landscape. Mater Today Bio 2021; 11: 100112.
[http://dx.doi.org/10.1016/j.mtbio.2021.100112] [PMID: 34124640]
[36]
Lambert AW, Pattabiraman DR, Weinberg RA. Emerging biological principles of metastasis. Cell 2017; 168(4): 670-91.
[http://dx.doi.org/10.1016/j.cell.2016.11.037] [PMID: 28187288]
[37]
Chaffer CL, Weinberg RA. A perspective on cancer cell metastasis. Science 2011; 331(6024): 1559-64.
[http://dx.doi.org/10.1126/science.1203543] [PMID: 21436443]
[38]
Lu DY, Lu TR, Wu HY, Cao S. Cancer metastasis treatments. Curr Drug Ther 2013; 8(1): 24-9.
[http://dx.doi.org/10.2174/1574885511308010003]
[39]
Guadagni S, Clementi M, Masedu F, et al. A pilot study of the predictive potential of themosensitivity and gene expression assays using circulating tumour cells from patients with recurrent ovarian cancer. Int J Med Sci 2020; 21: 4813.
[40]
Van Denderen BJW, Thompson EW. Cancer: The to and fro of tumour spread. Nature 2013; 493(7433): 487-8.
[http://dx.doi.org/10.1038/493487a] [PMID: 23344357]
[41]
Gupta GP, Massagué J. Cancer metastasis: Building a framework. Cell 2006; 127(4): 679-95.
[http://dx.doi.org/10.1016/j.cell.2006.11.001] [PMID: 17110329]
[42]
Mehlen P, Puisieux A. Metastasis: A question of life or death. Nat Rev Cancer 2006; 6(6): 449-58.
[http://dx.doi.org/10.1038/nrc1886] [PMID: 16723991]
[43]
Ali I, Haque A, Wani WA, Saleem K, Al Za’abi M. Analyses of anticancer drugs by capillary electrophoresis: A review. Biomed Chromatogr 2013; 27(10): 1296-311.
[http://dx.doi.org/10.1002/bmc.2953] [PMID: 23843248]
[44]
Lu DY, Chen EH, Wu HY, Lu TR, Xu B, Ding J. Anticancer drug combination, how far we can go through? Anticancer Agents Med Chem 2017; 17(1): 21-8.
[http://dx.doi.org/10.2174/1871520616666160404112028] [PMID: 27039923]
[45]
Lu DY, Lu TR, Yarla NS, et al. Drug combination in clinical cancer treatment. Rev Recent Clin Trials 2017; 12(3): 202-11.
[http://dx.doi.org/10.2174/1574887112666170803145955] [PMID: 28782482]
[46]
Reig-Vano B, Tylkowski B, Montané X, Giamberini M. Alginate-based hydrogels for cancer therapy and research. Int J Biol Macromol 2021; 170: 424-36.
[http://dx.doi.org/10.1016/j.ijbiomac.2020.12.161] [PMID: 33383080]
[47]
Sharifi-Rad J, Quispe C, Butnariu M, et al. Chitosan nanoparticles as a promising tool in nanomedicine with particular emphasis on onco-logical treatment. Cancer Cell Int 2021; 21(1): 318.
[http://dx.doi.org/10.1186/s12935-021-02025-4] [PMID: 34167552]
[48]
Jain V, Kumar H, Anod HV, et al. A review of nanotechnology-based approaches for breast cancer and triple-negative breast cancer. J Control Release 2020; 326: 628-47.
[http://dx.doi.org/10.1016/j.jconrel.2020.07.003] [PMID: 32653502]
[49]
Peng M, Cheng X, Xiong W, Lu Y, Wang YH. Integrated analysis of a competing endogenomic RNA network reveals a prognostic Inc RNA signature in bladder cancer. Fron Onc 2021; p. 684242.
[50]
Lu DY, Lu TR. Mathematics or physics-majored students on the biomedical fields, insiders or outsiders? Metabolomics 2015; 5(4): e142.
[51]
Lu DY, Wu HY, Lu TR, Che JY, Lu Y. Updating biomedical studies by recruiting more mathematics or physics-majored talents. Metabolomics 2016; 6(2): e148.
[52]
Lu DY, Lu TR, Xu B, et al. Cancer metastasis, a clinical dilemma for therapeutics. Curr Drug Ther 2016; 11(2): 163-9.
[http://dx.doi.org/10.2174/1574885511666160810143216]
[53]
Franssen LC, Chaplain MAJ. A mathematical multi-organ model for bidirectional epithelial-mesenchymal transitions in the metastatic spread of cancer. IMA J Appl Math 2020; 85(5): 724-61.
[http://dx.doi.org/10.1093/imamat/hxaa022]
[54]
Weidenfeld K, Barkan D. EMT and stemness in tumor dormacy and outgrowth: Are they intertwined processes? Front Oncol 2018; 8: 381.
[http://dx.doi.org/10.3389/fonc.2018.00381] [PMID: 30258818]
[55]
Zhang Y, Xu J, Yu Y, Shang W, Ye A. Anti-cancer drug sensitivity assay with quantitative heterogeneity testing using single-cell raman spectroscopy. Molecules 2018; 23(11): 2903.
[http://dx.doi.org/10.3390/molecules23112903] [PMID: 30405051]
[56]
Farge T, Saland E, de Toni F, Aroua N, Hosseini M, Perry R, et al. Acute myeloid leukemia cells are not enriched for leukemia stem cells but require oxidative metabolism. Cancer Discov 2017; 7(7): 716-35.
[http://dx.doi.org/10.1158/2159-8290.CD-16-0441] [PMID: 28416471]
[57]
Dvorak HF. Tumor stroma, tumor blood vessels, and anti-angiogenesis therapy. Cancer J 2015; 21(4): 237-43.
[http://dx.doi.org/10.1097/PPO.0000000000000124] [PMID: 26222073]
[58]
Dvorak HF, Weaver VM, Tlsty TD, Bergers G. Tumor microenvironment and progression. J Surg Oncol 2011; 103(6): 468-74.
[http://dx.doi.org/10.1002/jso.21709] [PMID: 21480238]
[59]
Lu DY, Chen XL, Ding J. Treatment of solid tumors and metastases by fibrinogen-targeted anticancer drug therapy. Med Hypotheses 2007; 68(1): 188-93.
[http://dx.doi.org/10.1016/j.mehy.2006.06.045] [PMID: 16956730]
[60]
Bobek V. Anticoagulant and fibrinolytic drugs - possible agents in treatment of lung cancer? Anticancer Agents Med Chem 2012; 12(6): 580-8.
[http://dx.doi.org/10.2174/187152012800617687] [PMID: 22292773]
[61]
Lu DY, Lu TR. Antimetastatic activities and mechanisms of bisdioxopiperazine compounds. Anticancer Agents Med Chem 2010; 10(7): 564-70.
[http://dx.doi.org/10.2174/187152010793498654] [PMID: 20950258]
[62]
Lu DY, Lu TR. Anticancer activities and mechanisms of bisdioxopiperazine compounds probimane and MST-16. Anticancer Agents Med Chem 2010; 10(1): 78-91.
[http://dx.doi.org/10.2174/1871520611009010078] [PMID: 19845502]
[63]
Zhu H, Liao SD, Shi JJ, et al. DJ-1 mediates the resistance of cancer cells to dihydroarteminisinin through cancer cells through reactive oxygen species removal. Free Radic Biol Med 2014; 71: 121-32.
[http://dx.doi.org/10.1016/j.freeradbiomed.2014.03.026] [PMID: 24681255]
[64]
Lu DY, Lu TR, Wu HY. Development of antimetastatic drugs by targeting tumor sialic acids. Sci Pharm 2012; 80(3): 497-508.
[http://dx.doi.org/10.3797/scipharm.1205-01] [PMID: 23008802]
[65]
Lu DY, Lu TR, Xu B, et al. Anti-metastatic drug development, work out towards new direction. Med Chem 2018; 8(7): 192-6.
[66]
Lu DY, Lu TR, Ding J, et al. Anti-metastatic therapy at aberrant sialylation in cancer cells, a potential hotspot. Clin Proteom Bioinform 2017; 2(1): 118.
[http://dx.doi.org/10.15761/CPB.1000118]
[67]
Herter-Sprie GS, Kung AL, Wong KK. New cast for a new era: Preclinical cancer drug development revisited. J Clin Invest 2013; 123(9): 3639-45.
[http://dx.doi.org/10.1172/JCI68340] [PMID: 23999436]
[68]
Suggitt M, Bibby MC. 50 years of preclinical anticancer drug screening: Empirical to target-driven approaches. Clin Cancer Res 2005; 11(3): 971-81.
[PMID: 15709162]
[69]
Ali I, Lone MN, Alothman ZA, Badjah AY, Alanazi AG. Spectroscopic and in silico DNA binding studies on the interaction of some new N-substituted rhodanines with calf-thymus DNA: In vitro anticancer activities. Anticancer Agents Med Chem 2019; 19(3): 425-33.
[http://dx.doi.org/10.2174/1871520618666181002131125] [PMID: 30277166]
[70]
Ali I. Nano drugs: Novel agents for cancer chemotherapy. Curr Cancer Drug Targets 2011; 11(2): 131-4.
[http://dx.doi.org/10.2174/156800911794328457] [PMID: 21062238]
[71]
Ali I. Nano anticancer drug drugs: Pros and cons and future perspectives. Curr Cancer Drug Targets 2011; 11(2): 130.
[http://dx.doi.org/10.2174/156800911794328466] [PMID: 21247391]
[72]
Makhtar M, Bilal M, Rahdar A, et al. Nanomaterials for diagnosis and treatment of brain cancer: Recent update. Chemosensors (Basel) 2020; 8: 117.
[http://dx.doi.org/10.3390/chemosensors8040117]
[73]
Zou Y, Henry WS, Ricq EL, et al. Plasticity of ether lipids promotes ferroptosis susceptibility and evasion. Nature 2020; 585(7826): 603-8.
[http://dx.doi.org/10.1038/s41586-020-2732-8] [PMID: 32939090]
[74]
Suares A, Medina MV, Coso O. Autophagy in viral development and progression of cancer. Front Oncol 2021; 11: 603224.
[http://dx.doi.org/10.3389/fonc.2021.603224] [PMID: 33763351]
[75]
Di Sotto A, Mancinelli R, Gullì M, et al. Chemopreventive potential of caryophyllane sesquiterpenes—An overview preliminary evidence. Cancers (Basel) 2020; 12(10): 3034.
[http://dx.doi.org/10.3390/cancers12103034] [PMID: 33081075]
[76]
Pantano F, Croset M, Driouch K, et al. Integrin alpha5 in human breast cancer is a mediator of bone metastasis and a therapeutic target for the treatment of osteolytic lesions. Oncogene 2021; 40(7): 1284-99.
[http://dx.doi.org/10.1038/s41388-020-01603-6] [PMID: 33420367]
[77]
Hernández-Balmaseda I, Guerra IR, Declerck K, et al. Marine seagrass extract of Thalassia testudinum suppresses colorectal tumor growth, motility and angiogenesis by autophagic stress and immunogenic cell death pathways. Mar Drugs 2021; 19(2): 52.
[http://dx.doi.org/10.3390/md19020052] [PMID: 33499163]
[78]
Ali I, Saleem K, Wesselinova D, Haque A. Synthesis, DNA binding, hemolytic, and anticancer assays of curcumin I-based ligands and their ruthenium complex (potential treatment of (III) cervical cancer. Med Chem Res 2013; 22(3): 1386-98.
[http://dx.doi.org/10.1007/s00044-012-0133-8]
[79]
Yang J, Antin P, Berx G, et al. Guidelines and definitions for research on epitherlial-mesenchymal transition. Nat Rev Mol Biol 2021.
[80]
Ali I, Lone MN, Al-Othman ZA, Al-Warthan A, Sanagi MM. Heterocyclic scaffolds: Centrality in anticancer drug development. Curr Drug Targets 2015; 16(7): 711-34.
[http://dx.doi.org/10.2174/1389450116666150309115922] [PMID: 25751009]
[81]
Ali I, Wani WA, Saleem K, Haque A. Platinum compounds: A hope for future cancer chemotherapy. Anticancer Agents Med Chem 2013; 13(2): 296-306.
[http://dx.doi.org/10.2174/1871520611313020016] [PMID: 22583420]
[82]
Lu DY, Lu TR. Herbal medicine in new era. Hospice Palliative Med Int J 2019; 3(4): 125-30.
[http://dx.doi.org/10.15406/hpmij.2019.03.00165]
[83]
Lu DY, Lu TR. Drug discoveries from natural resources. J Primary Health Care & General Practice 2019; 3(1): 28.
[84]
Lu DY, Lu TR, Yarla NS, et al. Natural drug cancer treatment strategies from herbal medicine to chemical or biological drug. Studies in Nat Products Chem 2020; 66: 91-115.
[http://dx.doi.org/10.1016/B978-0-12-817907-9.00004-0]
[85]
Ali I, Wani WA, Haque A, Saleem K. Glutamic acid and its derivatives: Candidates for rational design of anticancer drugs. Future Med Chem 2013; 5(8): 961-78.
[http://dx.doi.org/10.4155/fmc.13.62] [PMID: 23682571]
[86]
Ali I, Wani WA, Saleem K, Wesselinova D. Syntheses, DNA binding and anticancer profiles of L-glutamic acid ligand and its copper(II) and ruthenium(III) complexes. Med Chem 2013; 9(1): 11-21.
[http://dx.doi.org/10.2174/157340613804488297] [PMID: 22741786]
[87]
Cui HJ, Wang XX, Wesslowski J, et al. Assembly of multi-sphoroid cellular architectures by programmable droplet merging. Adv Mater 2020; 2006434.
[88]
Rosenfeld A, Göckler T, Kuzina M, Reischl M, Schepers U, Levkin PA. Designing inherently photodegradable cell-adhesive hydrogels for 3D cell culture. Adv Healthc Mater 2021; 10(16): e2100632.
[http://dx.doi.org/10.1002/adhm.202100632] [PMID: 34111332]
[89]
Lu DY, Lu TR, Wu HY. Cost-effectiveness considerations of individualized cancer chemotherapy. Adv Pharmacoepidemiol Drug Saf 2013; 2(5): e121.
[90]
Franssen LC, Lorenzi T, Burgess AEF, Chaplain MAJ. A mathematical framework for modeling the metastatic spread of cancer. Bull Math Biol 2019; 81(6): 1965-2010.
[http://dx.doi.org/10.1007/s11538-019-00597-x] [PMID: 30903592]
[91]
Gerlee P, Johansson M. Inferring rates of metastatic dissemination using stochastic network models. PLOS Comput Biol 2019; 15(4): e1006868.
[http://dx.doi.org/10.1371/journal.pcbi.1006868] [PMID: 30933969]
[92]
Lu DY, Shen Y, Xu B, Lu TR. Anatomic approaches for cancer metastatic study. EC Clin Exper Anatomy 2020; 3(9): 32-4.

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