Stochastic Simulations of Tumor Progression Under Targeted Therapy Suggest a Tradeoff Between Evolutionary Trajectories
Open Access
Author:
Liu, Mengde
Area of Honors:
Biomedical Engineering
Degree:
Bachelor of Science
Document Type:
Thesis
Thesis Supervisors:
Justin R Pritchard, Thesis Supervisor Jian Yang, Thesis Honors Advisor
Keywords:
Cancer Biomedical Engineering Computational Modeling
Abstract:
Targeted therapies are anticancer drugs that are designed rationally to target cancer-causing genes. They offer dramatic improvements in progression-free survival (PFS) for some patients. However, most patients eventually fail targeted therapy due to the emergence of drug resistance. Metanalysis of clinical data on resistance mechanisms of front-line and second-line targeted therapies show that single nucleotide variations confer a significantly higher degree of drug resistance to targeted therapies than amplification of oncogenes. However, it is generally accepted that an SNV mutation is extremely rare compared to a CNV of the wildtype oncogene or that of a potent off-target oncogene. We hypothesize that multiple evolutionary scenarios can favor drug resistance arising through gene amplification on and off-target, point mutations, or a combination of mechanisms. An inexact Monte Carlo simulation model is employed to explore these possible evolutionary trajectories and their effects on relapse under targeted therapy. Our results show that heterogeneity of tumor tissues in terms of their composition profiles prior to treatment due to the stochasticity in mutations is responsible for the diversity in resistance mechanisms to targeted therapy as well as the individual differences in progression-free survival.
