Evolutionary Modeling of Combination Treatment Strategies To Overcome Resistance to Tyrosine Kinase Inhibitors in Non-Small Cell Lung Cancer

Abstract

Many initially successful anticancer therapies lose effectiveness over time, and eventually, cancer cells acquire resistance to the therapy. Acquired resistance remains a major obstacle to improving remission rates and achieving prolonged disease-free survival. Consequently, novel approaches to overcome or prevent resistance are of significant clinical importance. There has been considerable interest in treating non-small cell lung cancer (NSCLC) with combinations of EGFR-targeted therapeutics (e.g., erlotinib) and cytotoxic therapeutics (e.g., paclitaxel); however, acquired resistance to erlotinib, driven by a variety of mechanisms, remains an obstacle to treatment success. In about 50% of cases, resistance is due to a T790M point mutation in EGFR, and T790M-containing cells ultimately dominate the tumor composition and lead to tumor regrowth. We employed a combined experimental and mathematical modeling-based approach to identify treatment strategies that impede the outgrowth of primary T790M-mediated resistance in NSCLC populations. Our mathematical model predicts the population dynamics of mixtures of sensitive and resistant cells, thereby describing how the tumor composition, initial fraction of resistant cells, and degree of selective pressure influence the time until progression of disease. Model development relied upon quantitative experimental measurements of cell proliferation and death using a novel microscopy approach. Using this approach, we systematically explored the space of combination treatment strategies and demonstrated that optimally timed sequential strategies yielded large improvements in survival outcome relative to monotherapies at the same concentrations. Our investigations revealed regions of the treatment space in which low-dose sequential combination strategies, after preclinical validation, may lead to a tumor reduction and improved survival outcome for patients with T790M-mediated resistance.

A mathematical approach to differentiate spontaneous and induced evolution to drug resistance during cancer treatment

“…Mathematical modeling studies in particular have been used to explore both broad and detailed aspects of cancer drug resistance, as reviewed in [43,7,25]. The fundamental question of how the presence of drug resistant cells influences tumor dynamics and treatment outcomes has been thoroughly explored in mathematical models under a wide variety of assumptions [14,37,39,47,58,40,23,24,70,16,45,60,63,4,22,33,44,52,65,3,32,80,13,26,29,46,49,51,57,59,77,18,68,1,9,10,20]. Cancer models have also been utilized to assess how various underlying mechanisms contribute to the resistant phenotype [80,13,59,18,20], and to calculate the probability that drug resistance emerges within a specified time frame, be it before or during cancer treatment …”

“…The question of how frequently, and at what dose, a single cancer drug should be administered given the presence of (or risk of developing) resistant cells has also been explored through mathematical modeling [37,23,24,33,46,49,68,1,9]. When multiple drugs are available, mathematical models have been used to determine the drug schedule (number of drugs to use, dose, sequence, timing) that best controls tumor progression in spite of drug resistance [39,47,70,16,60,63,4,51,10].Resistance to cancer drugs can be classified as either pre-existing or acquired [34]. Preexisting (intrinsic) drug resistance describes the case in which a tumor contains a subpopulation of drug resistant cells at the initiation of treatment, making the therapy (eventually) ineffective due to resistant cell selection [34].…”

“…As examples, pre-existing BCR-ABL kinase domain mutations confer resistance to the tyrosine kinase inhibitor imatinib in chronic myeloid leukemia patients [66,36], and pre-existing MEK1 mutations confer resistance to BRAF inhibitors in melanoma patients [8]. Many mathematical models have considered how the presence of preexisting resistant cells impact cancer progression and treatment [37,39,58,40,23,24,70,16,60,63,4,22,44,3,32,80,29,49,57,59,77,68,9,10].On the other hand, acquired drug resistance broadly describes the case in which drug resistance develops during the course of therapy from a population of cells that were initially drug sensitive [34]. The term "acquired resistance" is really an umbrella term for two distinct phenomena, which complicates the study of acquired resistance.…”

Diverse array-designed modes of combination therapies in Fangjiomics

“…This information was used to sensitize otherwise resistant triple-negative breast cancer cells to conventional DNA-damaging chemotherapy by administering doxorubicin (Adriamycin, Doxil) and erlotinib (Tarceva) in an order-and time-dependent fashion [74] . Moreover, the application of evolutionary models in drug-resistant non-small cell lung cancer (NSCLC), along with cell-based studies, has revealed that sequential therapy using cytotoxic agents with either erlotinib (Tarceva) or gefitinib (Iressa) was more effective than monotherapy or concurrent combinatorial dosing [75] .…”

Hotspot oncomutations: implications for personalized cancer treatment

“…Furthermore, the idea that polyclonal tumors develop through loss of normal cell communication and tissue architecture suggests that novel strategies combining immunotherapy and/or therapies that target tumor stroma [119] may be combined effectively with therapies directed against activated signaling pathways. A recent study aimed at modeling population dynamics within mixtures of sensitive and resistant tumor cells concluded that low-dose sequential combination strategies may lead to the best survival outcomes [120].…”