Collateral sensitivity networks reveal evolutionary instability and novel treatment strategies in ALK mutated non-small cell lung cancer

Abstract

Drug resistance remains an elusive problem in cancer therapy, particularly for novel targeted therapies. Much work is focused upon the development of an arsenal of targeted therapies, towards oncogenic driver genes such as ALK-EML4, to overcome the inevitable resistance that develops over time. Currently, after failure of first line ALK TKI therapy, another ALK TKI is administered, though collateral sensitivity is not considered. To address this, we evolved resistance in an ALK rearranged non-small cell lung cancer line (H3122) to a panel of 4 ALK TKIs, and performed a collateral sensitivity analysis. All ALK inhibitor resistant cell lines displayed significant cross-resistance to all other ALK inhibitors. We then evaluated ALK-inhibitor sensitivities after drug holidays of varying length (1-21 days), and observed dynamic patterns of resistance. This unpredictability led us to an expanded search for treatment options, where we tested 6 further anti-cancer agents for collateral sensitivity among resistant cells, uncovering possibilities for further treatment, including cross-sensitivity to standard cytotoxic therapies, as well as Hsp90 inhibitors. Taken together, these results imply that resistance to targeted therapy in non-small cell lung cancer is highly dynamic, and also one where there are many opportunities to re-establish sensitivities where there was once resistance. Drug resistance in cancer inevitably emerges during treatment; particularly with novel targeted therapies, designed to inhibit specific molecules. A clinically-relevant example of this phenomenon occurs in ALK-positive non-small cell lung cancer, where targeted therapies are used to inhibit the ALK-EML4 fusion protein. A potential solution to this may lie in finding drug sensitivities in the resistant population, termed collateral sensitivities, and then using these as second-line agents. This study shows how the evolution of resistance in ALK-positive lung cancer is a dynamic process through time, one in which patterns of drug resistance and collateral sensitivity change substantially, and therefore one where temporal regimens, such as drug cycling and drug holidays may have great benefit.

Figure 1

Left: Collateral sensitivity matrix of fold change of EC50 for resistant cell lines (columns) as treated by the panel of ALK TKIs (rows). All sequences of therapy resulted in cross-resistance except alectinib followed by lorlatinib, which was neutral. Right: Pop-out figure shows example of EC50 comparison in case of collateral resistance of lorlatinib resistant cells treated with ceritinib, as compared to treatment-naive (WT). Experimental data (markers) and model fit (solid lines) are shown. The asterisk (*) refers to a significant change in EC50 from treatment-naive, with non-overlapping 95% confidence intervals, and the caret (^) refers to a case in which the confidence intervals for the EC50 in one of the treated or treatment-naive cases was indeterminate. Non-significant changes are those not demarcated by either symbol.

Figure 2

Upper panels: Collateral sensitivity heatmaps depicting fold change of EC50 for resistant cell lines, during the therapy holiday lasting for 1 day, 3 days, 7 days, 14 days, and 21 days, as treated with the panel of ALK TKIs (rows). The asterisk (*) refers to a significant change in EC50 from treatment-naive, with non-overlapping 95% confidence intervals. Non-significant changes are those not demarcated by either symbol. Below these are collateral sensitivity networks depicted as directed graphs for cells during therapy holiday. Each named node represents the resistant population to that drug; an edge exists from node i to node j if cells resistant to drug i are sensitive to drug j. The colour of each node represents the number of drugs to which the cell line represented by that node is sensitive (i.e. the number of out edges). Dotted arrows indicate those in which 95% confidence intervals for treatment-naive EC50 overlapped with that of cell line tested. Lower panels: Time-series representations of EC50 data, depicting comparison between relaxing EC50 and treatment-naive EC50, during the course of experiment. The average treatment-naive EC50 (green confidence interval) is derived from the five treatment-naive measurements. Significance in the upper panel is determined by non-overlapping confidence intervals, exemplified by the marked data (orange stars), for the case of lorlR cells treated with alectinib on day 7 of relaxation.

Figure 3

(A) Collateral sensitivity matrix of fold change of EC50 for resistant cell lines (columns) as treated by the panel of anti-cancer therapies (rows). Grey boxes are indeterminate due to significant resistance to drug. The asterisk (*) refers to a significant change in EC50 from treatment-naive, with non-overlapping 95% confidence intervals, and the caret (^) refers to a case in which the confidence intervals for the EC50 in one of the treated or treatment-naive cases was indeterminate. Non-significant changes are those not demarcated by either symbol. (B) Bar graphs depicting the number of collaterally sensitive or resistant cell lines to each drug. (C) Ranked probabilities of collateral sensitivity of each drug to the resistant cell lines, when the drug is used as second-line therapy. (D) Ranked probabilities of cross-resistance of each drug to resistant cell lines, when the drug is used as second-line therapy.

Figure 4

(A) Collateral sensitivity network depicted as a directed graph, with edges pointing from nodes at which resistance to a particular drug has developed to a node for which sensitivity of the drug has increased. Arrows in red indicate an example of a cycling regimen of 4 drugs. (B) Graph of number of drug cycles of given length in the graph presented in (A).

Figure 5

Experimental design diagram. Panel (A) depicts the evolution of resistance by exposure for 16 weeks to drug to create a drug-resistant population and in (B) the population is removed from drug (‘relaxed’).