title: Mathematical Modeling of Therapy-induced Cancer Drug Resistance: Connecting Cancer Mechanisms to Population Survival Rates.
abstract: Drug resistance significantly limits the long-term effectiveness of targeted therapeutics for cancer patients. Recent experimental studies have demonstrated that cancer cell heterogeneity and microenvironment adaptations to targeted therapy play important roles in promoting the rapid acquisition of drug resistance and in increasing cancer metastasis. The systematic development of effective therapeutics to overcome drug resistance mechanisms poses a major challenge. In this study, we used a modeling approach to connect cellular mechanisms underlying cancer drug resistance to population-level patient survival. To predict progression-free survival in cancer patients with metastatic melanoma, we developed a set of stochastic differential equations to describe the dynamics of heterogeneous cell populations while taking into account micro-environment adaptations. Clinical data on survival and circulating tumor cell DNA (ctDNA) concentrations were used to confirm the effectiveness of our model. Moreover, our model predicted distinct patterns of dose-dependent synergy when evaluating a combination of BRAF and MEK inhibitors versus a combination of BRAF and PI3K inhibitors. These predictions were consistent with the findings in previously reported studies. The impact of the drug metabolism rate on patient survival was also discussed. The proposed model might facilitate the quantitative evaluation and optimization of combination therapeutics and cancer clinical trial design.
authors.affiliation: Zhong-shan School of Medicine, Sun Yat-Sen University, Guangzhou 510089, China.|School of Mathematical and Computational Science, Sun Yat-Sen University, Guangzhou 510000, China.
authors.forename: Xiaoqiang
authors.initials: X
authors.lastname: Sun
authors.affiliation: School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.
authors.forename: Jiguang
authors.initials: J
authors.lastname: Bao
authors.affiliation: Department of Population Health, NYU School of Medicine, New York University, New York, NY 10016, USA.|Interdisciplinary Melanoma Collaborative Group, NYU Cancer Institute, New York, NY 10016, USA.
authors.forename: Yongzhao
authors.initials: Y
authors.lastname: Shao
mesh_terms: D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use*; D004273:DNA, Neoplasm / Q000097:blood; D018572:Disease-Free Survival; D019008:Drug Resistance, Neoplasm / Q000502:physiology*; D006801:Humans; D008545:Melanoma / Q000188:drug therapy* / Q000473:pathology; D008962:Models, Theoretical; D058990:Molecular Targeted Therapy; D000081082:Phosphoinositide-3 Kinase Inhibitors; D047428:Protein Kinase Inhibitors / Q000627:therapeutic use*; D048493:Proto-Oncogene Proteins B-raf / Q000037:antagonists & inhibitors*; D012878:Skin Neoplasms / Q000188:drug therapy* / Q000473:pathology; D015996:Survival Rate
chemical_list: D004273:DNA, Neoplasm; D000081082:Phosphoinositide-3 Kinase Inhibitors; D047428:Protein Kinase Inhibitors; C482119:BRAF protein, human; D048493:Proto-Oncogene Proteins B-raf