Prevention of Colon Cancer Recurrence from Minimal Residual Disease: Computer Optimized Dose Schedules of Intermittent Apoptotic Adjuvant Therapy

Chase Cockrell, Joseph Teague, David E. Axelrod

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

PURPOSE Adjuvant chemotherapy is used after surgery for stages II and III colorectal cancer to reduce recurrence. Nevertheless, recurrence may occur years later with the emergence of initially undetected minimal residual disease (MRD). Attempts to reduce recurrence by increasing the dose intensity and increasing the time of adjuvant therapy have been limited by the adverse effects of the recommended cytotoxic agents. The goals of this study were to suggest an alternative to the recommended cytotoxic agents and to determine optimal adjuvant therapy dose schedules that would reduce the percentage of recurrence at 5 years while retaining colon crypt function. METHODS A total of 84,400 dose schedules with different duration, interval between doses, and intensity of treatment were simulated with a high-performance computer. Simulated treatments used the drug sulindac, which had previously been used in primary prevention. With appropriate dose schedules, it can induce apoptosis at the crypt lumen surface while retaining crypt function. We used a computer model of cell dynamics in colon crypts that had been calibrated with measurements of human biopsy specimens. Proliferating mutant cells were assumed to emerge from MRD within crypts. Simulated outcomes included the recurrence percentage at 5 years and the retention of crypt function.RESULTS Optimal dose schedules were determined for adjuvant treatment of MRD that reduced the percentage of recurrence at 5 years of stages I, II, and III colon cancer to zero. CONCLUSION A new adjuvant therapy for colon cancer based upon optimum dose schedules of intermittent apoptotic treatment may prevent the recurrence of colon cancer from MRD and avoid the adverse effects of cytotoxic treatments.

Original languageEnglish (US)
Pages (from-to)514-520
Number of pages7
JournalJCO Clinical Cancer Informatics
Volume4
DOIs
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • Oncology
  • Health Informatics
  • Cancer Research

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