Cancer Chemotherapy and Pharmacology

, Volume 79, Issue 5, pp 889–898 | Cite as

Effective chemotherapy of heterogeneous and drug-resistant early colon cancers by intermittent dose schedules: a computer simulation study

  • David E. Axelrod
  • Sudeepti Vedula
  • James Obaniyi
Original Article



The effectiveness of cancer chemotherapy is limited by intra-tumor heterogeneity, the emergence of spontaneous and induced drug-resistant mutant subclones, and the maximum dose to which normal tissues can be exposed without adverse side effects. The goal of this project was to determine if intermittent schedules of the maximum dose that allows colon crypt maintenance could overcome these limitations, specifically by eliminating mixtures of drug-resistant mutants from heterogeneous early colon adenomas while maintaining colon crypt function.


A computer model of cell dynamics in human colon crypts was calibrated with measurements of human biopsy specimens. The model allowed simulation of continuous and intermittent dose schedules of a cytotoxic chemotherapeutic drug, as well as the drug’s effect on the elimination of mutant cells and the maintenance of crypt function.


Colon crypts can tolerate a tenfold greater intermittent dose than constant dose. This allows elimination of a mixture of relatively drug-sensitive and drug-resistant mutant subclones from heterogeneous colon crypts. Mutants can be eliminated whether they arise spontaneously or are induced by the cytotoxic drug.


An intermittent dose, at the maximum that allows colon crypt maintenance, can be effective in eliminating a heterogeneous mixture of mutant subclones before they fill the crypt and form an adenoma.


Drug resistance Heterogeneity Dose schedules Intermittent Colon cancer Adenoma Crypt 



We thank Rafael Bravo for writing the code for the original colon crypt model, Dr. Steven Schiff for providing human biopsy specimens, members of the Division of Life Sciences IT Support Group for computer services, RUCore staff for file archive services, and Uri Wilensky for making available the NetLogo open-source application.

Authors’ contributions

DEA. conceived the project, analyzed and interpreted the simulation results, and wrote the manuscript. SV and DEA. carried out the simulations. JO and DEA wrote the revised the code. All authors approved the manuscript.

Compliance with ethical standards


The Human Genetics Institute of New Jersey, the New Jersey Breast Cancer Research Fund, and the Rutgers Cancer Institute of New Jersey (PA30CA072720).

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

D.E.A. obtained coded de-identified slides containing sections from biopsies of the sigmoid colon of normal patients enrolled in a clinical research study of Dr. Steven Shiff, Cancer Institute of New Jersey, Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey. Individualized information about the identity of the subjects and specific clinical information were not made available to D.E.A. Dr. Shiff received approval Reference Number 4611 from the Institutional Review Board for Research Study Involving Human Subjects. The approved protocol included informed consent of participants.

Supplementary material

280_2017_3272_MOESM1_ESM.docx (44 kb)
Fig 1 Supplement (DOCX 66 KB)
280_2017_3272_MOESM2_ESM.docx (41 kb)
Fig 2 Supplement (DOCX 80 KB)
280_2017_3272_MOESM3_ESM.docx (46 kb)
Colon Crypt Model 110514 G. nlogo The model program is available to download at The model program runs on the open-source multi-platform NetLogo application, version 4.1.3 or 5.1.3, available to download at (DOCX 46 KB)


  1. 1.
    Leslie A, Carey FA, Pratt NR, Steele RJC (2002) The colorectal adenoma-carcinoma sequence. Br J Surg 89:845–860CrossRefPubMedGoogle Scholar
  2. 2.
    Humphries A, Wright NA (2008) Colonic crypt organization and tumorigenesis. Nat Rev Cancer 8:415–425. doi: 10.1038/nrc2392 CrossRefPubMedGoogle Scholar
  3. 3.
    Strum WB (2016) Colorectal adenomas. N Engl J Med 371:1065–1075. doi: 10.1056/NEJMc1604867 Google Scholar
  4. 4.
    Benedict WF, Baker MS, Haroun L, Choi E, Ames BN (1977) Mutagenicity of cancer chemotherapeutic agents in the Salmonella/microsome test. Cancer Res 37:2209–2213PubMedGoogle Scholar
  5. 5.
    Marusyk A, Polyak K (2010) Tumor heterogeneity: causes and consequences. Biochim Biophys Acta 1805:105–117. doi: 10.1016/j.bbcan.2009.11.002 PubMedGoogle Scholar
  6. 6.
    Pasquier E, Kavallaris M, Andre N (2010) Metronomic chemotherapy: new rationale for new directions. Nat Rev Clin Oncol 7:455–465. doi: 10.1038/nrclinonc.2010.82 CrossRefPubMedGoogle Scholar
  7. 7.
    Gatenby RA, Silva AS, Gillies RJ, Frieden BR (2009) Adaptive therapy. Cancer Res 69:4894–4903. doi: 10.1158/0008-5472.CAN-08-3658 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Enriquez-Navas PM, Kam Y, Das T, Hassen S, Silva A, Foroutan P, Rulz E, Martinez G, Minton S, Gilles RJ, Gatenby RA (2016) Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer. Sci Trans Med 8:325ra24. doi: 10.1126/scitranslmed.aad7842
  9. 9.
    Leder K, Pitter K, Laplant Q, Hambardzumyan D, Ross BD, Chan TA, Holland EC, Michor F (2014) Mathematical modeling of PDGF-driven glioblastoma reveals optimized radiation dosing schedules. Cell 156:603–616. doi: 10.1016/j.cell.2013.12.029 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Kim JJ, Tannock IF (2005) Repopulation of cancer cells during therapy: an important cause of treatment failure. Nat Rev Cancer 5:516–525CrossRefPubMedGoogle Scholar
  11. 11.
    Marcu L, Bezak E (2010) Modeling of tumour repopulation after chemotherapy. Australas Phys Eng Sci Med 33:265–270CrossRefPubMedGoogle Scholar
  12. 12.
    Venkatakrishnan K, Friberg LE, Ouellet D, Mettetal JT, Stein A, Trocóniz IF, Bruno R, Mehrotra N, Gobburu J, Mould DR (2015) Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities. Clin Pharmacol Ther 97:37–54. doi: 10.1002/cpt.7 CrossRefPubMedGoogle Scholar
  13. 13.
    Shi J, Alagoz O, Erenay FS, Su Q (2014) A survey of optimization models on cancer chemotherapy treatment planning. Ann Oper Res 221:331–356. doi: 10.1007/s10479-011-0869-4 CrossRefGoogle Scholar
  14. 14.
    Bravo R, Axelrod DE (2013) A calibrated agent-based computer model of stochastic cell dynamics in normal human colon crypts useful for in silico experiments. Theor Biol Med Model 10:66. doi: 10.1186/1742-4682-10-66 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Wright NA (2006) Review article: is there a common principle in the development of gastrointestinal cancers? Stem cells in the origin of cancer. Aliment Pharmacol Ther 24(Suppl 4):31–40CrossRefGoogle Scholar
  16. 16.
    Potten CS (1990) A comprehensive study of the radiobiological response of the murine (BDF1) small intestine. Int J Radiat Biol 58:925–973CrossRefPubMedGoogle Scholar
  17. 17.
    Shimada Y, Yoshino M, Wakui A, Nakao I, Futatsuki K, Sakata Y, Kambe M, Taguchi T, Ogawa N (1993) Phase II study of CPT-11, a new camptothecin derivative, in metastatic colorectal cancer. CPT-11 Gastrointestinal Cancer Study Group. J Clin Oncol 11:909–913CrossRefPubMedGoogle Scholar
  18. 18.
    Borras E, San Lucas FA, Chang K, Zhou R, Masand G, Fowler J, Mor ME, You YN, Taggart MW, McAllister F, Jones DA, Davies GE, Edelmann EA, Ehli EA, Lynch PM, Hwak ET, Capella G, Scheet PI, Vlar E (2016) Genomic landscape of colorectal mucosa and adenomas. Cancer Prev Res 9:417–427CrossRefGoogle Scholar
  19. 19.
    Sottoriva A, Kang H, Ma Z, Graham TA, Salomon MP, Zhao J, Marjoram P, Siegmund K, Press MF, Shibata D, Curtis C (2015) A Big Bang model of human colorectal tumor growth. Nat Genet 47:209–216. doi: 10.1038/ng.3214 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Charames GS, Bapat B (2003) Genomic instability and cancer. Curr Mol Med 3:589–596CrossRefPubMedGoogle Scholar
  21. 21.
    Oddo D, Sennott EM, Barault L, Valtorta E, Arena S, Cassingena A et al (2016) Molecular landscape of acquired resistance to targeted therapy combinations in BRAF-mutant colorectal cancer. Cancer Res 76:4504–4515. doi: 10.1158/0008-5472.CAN-16-0396 CrossRefPubMedGoogle Scholar
  22. 22.
    Shih IM, Zhou W, Goodman SN, Lengauer C, Kinzler KW, Vogelstein B (2001) Evidence that genetic instability occurs at an early stage of colorectal tumorigenesis. Cancer Res 61:818–822PubMedGoogle Scholar
  23. 23.
    Tomasetti C, Vogelstein B, Parmigiani G (2013) Half or more of the somatic mutations in cancers of self-renewing tissues originate prior to tumor initiation. Proc Natl Acad Sci USA 110:1999–2004. doi: 10.1073/pnas.1221068110 CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Panczyk M (2014) Pharmacogenetics research on chemotherapy resistance in colorectal cancer over the last 20 years. World J Gastroenterol 20:9775–9827. doi: 10.1038/nrc3599 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Foo J, Michor F (2014) Evolution of acquired resistance to anti-cancer therapy. J Theor Biol 355:10–20. doi: 10.1016/j.jtbi.2014.02.025 CrossRefPubMedGoogle Scholar
  26. 26.
    Komarova N (2006) Stochastic modeling of drug resistance in cancer. J Theor Biol 239:351–366CrossRefPubMedGoogle Scholar
  27. 27.
    Cox JD (1988) Time, dose, and fractionation in radiation therapy: An historical perspective. In: Vaeth JM, Meyer J (eds) Time, dose and fractionation in the radiation therapy of cancer: a frontier revisited. Karger, Basel, pp 14–18Google Scholar
  28. 28.
    Ahmed KA, Correa CR, Dilling J, Rao NG, Shridhar R, Trotti AM, Wilder RB, Caudell JJ (2014) Altered fractionation schedules in radiation treatment: a review. Semin Oncol 41:430–750. doi: 10.1053/j.seminoncol.2014.09.012 CrossRefGoogle Scholar
  29. 29.
    DeVita V, Chu E (2008) A history of cancer chemotherapy. Cancer Res 68:8643–8653. doi: 10.1158/0008-5472.CAN-07-6611 CrossRefPubMedGoogle Scholar
  30. 30.
    Beex L, Rose C, Mouridsen H, Jassem J, Nooij M, Estape J, Paridaens R, Piccart M, Gorlia T, Lardenoije S, Baila L (2006) Continuous versus intermittent tamoxifen versus intermittent/alternated tamoxifen and medroxyprogesterone acetate as first line endocrine treatment in advanced breast cancer: an EORTC phase III study (10863). Eur J Cancer 42:3178–3185CrossRefPubMedGoogle Scholar
  31. 31.
    Vázquez S, León L, Fernandez O, Lázaro M, Grande E, Aparicio L (2012) Sunitinib: the first to arrive at first-line metastatic renal cell carcinoma. Adv Ther 29:202–217. doi: 10.1007/s12325-011-0099-9 CrossRefPubMedGoogle Scholar
  32. 32.
    Gruca D, Bacher P, Tunn U (2012) Safety and tolerability of intermittent androgen deprivation therapy: a literature review. Int J Urol 19:614–625. doi: 10.1111/j.1442-2042.2012.03001.x CrossRefPubMedGoogle Scholar
  33. 33.
    Maughan TS, James RD, Kerr DJ, Ledermann JA, Seymour MT, Topham C, McArdle C, Cain D, Stephens RJ, Medical Research Council Colorectal Cancer Group (2003) Comparison of intermittent and continuous palliative chemotherapy for advanced colorectal cancer: a multicentre randomised trial. Lancet 361:457–464CrossRefPubMedGoogle Scholar
  34. 34.
    Van Cutsem E, Findlay M, Osterwalder B, Kocha W, Dalley D, Pazdur R, Cassidy J, Dirix L, Twelves C, Allman D, Seitz JF, Schölmerich J, Burger HU, Verweij J (2000) Capecitabine, an oral fluoropyrimidine carbamate with substantial activity in advanced colorectal cancer: results of a randomized phase II study. J Clin Oncol 18:1337–1345CrossRefPubMedGoogle Scholar
  35. 35.
    Tournigand C, Cervantes A, Figer A, Lledo G, Flesch M, Buyse M, Mineur L, Carola E, Etienne P, Rivera F, Chirivella I, Perez-Staub N, Louvet C, André T, Tabah-Fisch I, de Gramont A (2006) OPTIMOX1: a randomized study of FOLFOX4 or FOLFOX7 with oxaliplatin in a stop-and-go fashion in advanced colorectal cancer—a GERCOR study. J Clin Oncol 24:294–440CrossRefGoogle Scholar
  36. 36.
    Adams RA, Meade AM, Seymour MT, Wilson RH, Madi A, Fisher D, Kenny SL, Kay E, Hodgkinson E, Pope M, Rogers P, Wasan H, Falk S, Gollins S, Hickish T, Bessell EM, Propper D, Kennedy MJ, Kaplan R, Maughan TS, MRC COIN Trial Investigators (2011) Intermittent versus continuous oxaliplatin and fluoropyrimidine combination chemotherapy for first-line treatment of advanced colorectal cancer: results of the randomised phase 3 MRC COIN trial. Lancet Oncol 12:642–653. doi: 10.1016/S1470-2045(11)70102-4 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Berry SR, Cosby R, Asmis T, Chan K, Hammad N, Krzyzanowska MK (2015) Continuous versus intermittent chemotherapy strategies in metastatic colorectal cancer: a systematic review and meta-analysis. Ann Oncol 26:477–485CrossRefPubMedGoogle Scholar
  38. 38.
    Blackburn EH (2011) Cancer interception. Cancer Prev Res 4:787–792. doi:10.1158/1940-6207.CAPR-11-0195CrossRefGoogle Scholar
  39. 39.
    Corley DA, Levin TR, Doubeni CA (2014) Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med 370:1298–1306. doi: 10.1056/NEJMc1405329 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Cruz-Correa M, Giardiello FM (2002) Diagnosis and management of hereditary colon cancer. Gastroenterol Clin North Am 31:537–549CrossRefPubMedGoogle Scholar
  41. 41.
    Roncucci L, Stamp D, Medline A, Cullen JB, Bruce WR (1991) Identification and quantification of aberrant crypt foci and microadenomas in the human colon. Hum Pathol 22:287–294CrossRefPubMedGoogle Scholar
  42. 42.
    Kuno T, Yamada Y, Hirose Y, Katayama M, Sakata K, Hara A, Saji S, Mori H (2002) Induction of apoptosis by sulindac in azoxymethane-induced possible colonic premalignant lesions in rats. Jpn J Cancer Res 93:242–246CrossRefPubMedGoogle Scholar
  43. 43.
    Krishn SR, Kaur S, Smith LM, Johansson SL, Jain M, Patel A, Gautam SK, Hollingsworth MA, Mandel U, Clausen H, Lo WC, Fan WT, Manne U, Batra SK (2016) Mucins and associated glycan signatures in colon adenoma-carcinoma sequence: prospective pathological implications(s) for early diagnosis of colon cancer. Cancer Lett 374:304–341. doi: 10.1016/j.canlet.2016.02.016 CrossRefPubMedGoogle Scholar
  44. 44.
    Rossez Y, Burtea C, Laurent S, Gossset P, Léonard R, Gonzalez W, Ballet S, Raynal I, Rousseaux O, Dugué T, vander Elst L, Jichalski J-C, Muller RN Robbe-Masselot C (2016) Early detection of colonic dysplasia by magnetic resonance molecular imaging with a contrast agent raised against colon cancer marker MUC5AC. Contrast Media Mol Imaging 11:211–221. doi: 10.1002/cmmi.1682 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Genetics and Cancer Institute of New JerseyRutgers UniversityPiscatawayUSA
  2. 2.Department of Biomedical EngineeringRutgers UniversityPiscatawayUSA
  3. 3.Department of Molecular Biology and BiochemistryRutgers UniversityPiscatawayUSA

Personalised recommendations