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. 2017 Mar 13;7:44206. doi: 10.1038/srep44206

Figure 6. Engineering optimal treatment strategies for concurrent, clonal genetic alterations in EGFR-mutant lung adenocarcinoma and predicting their therapeutic impact.

Figure 6

(A) Simulations of the optimal treatment strategy predicted by algorithm 1 (SI, Section 2.2) consisting of 1.5 μM erlotinib + 0.5 μM crizotinib for days (0–5) followed by 0.5 μM afatinib + 0.5 μM trametinib for days (5–30); the same strategy but with the switch occurring at day 10 and, constant strategies of 0.5 μM afatinib + 0.5 μM trametinib or 1.5 μM erlotinib + 0.5 μM crizotinib for 30 days, for an initial tumor cell population of 89% EGFRL858R, 10% EGFRL858RBRAFV600E, 1% EGFRL858R, T790M, HGF treated. (B) Evolution experiment shows that the predicted strategy for an initial tumor cell population of 89% EGFRL858R, 10% EGFRL858RBRAFV600E, 1% EGFRL858R, T790M, treated with 50 ng/ml HGF, is optimal. Overlaid numbers indicate the relative cell density of each well at day 30 compared to the erlotinib + crizotinib well (magenta). Computational simulations in (A) show that the predicted optimal strategy has the greatest reduction in tumor cells in vitro (B, red) compared to the same strategy with a 10 day switch (yellow). A simulation of the model predicts that a constant treatment of afatinib + trametinib produces little change in number of tumor cells (B, blue) and that a constant treatment of erlotinib + crizotinib predicts the exponential outgrowth of the initial EGFRL858R, T790M MET amplified subpopulation, experimentally validated in (B, magenta).