Colorectal tumours that are wild type for KRAS are often sensitive to EGFR blockade, but almost always develop resistance within several months of initiating therapy1,2. The mechanisms underlying this acquired resistance to anti-EGFR antibodies are largely unknown. This situation is in marked contrast to that of small-molecule targeted agents, such as inhibitors of ABL, EGFR, BRAF and MEK, in which mutations in the genes encoding the protein targets render the tumours resistant to the effects of the drugs3,4,5,6. The simplest hypothesis to account for the development of resistance to EGFR blockade is that rare cells with KRAS mutations pre-exist at low levels in tumours with ostensibly wild-type KRAS genes. Although this hypothesis would seem readily testable, there is no evidence in pre-clinical models to support it, nor is there data from patients. To test this hypothesis, we determined whether mutant KRAS DNA could be detected in the circulation of 28 patients receiving monotherapy with panitumumab, a therapeutic anti-EGFR antibody. We found that 9 out of 24 (38%) patients whose tumours were initially KRAS wild type developed detectable mutations in KRAS in their sera, three of which developed multiple different KRAS mutations. The appearance of these mutations was very consistent, generally occurring between 5 and 6 months following treatment. Mathematical modelling indicated that the mutations were present in expanded subclones before the initiation of panitumumab treatment. These results suggest that the emergence of KRAS mutations is a mediator of acquired resistance to EGFR blockade and that these mutations can be detected in a non-invasive manner. They explain why solid tumours develop resistance to targeted therapies in a highly reproducible fashion.
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The authors thank J. Schaeffer, J. Ptak, N. Silliman and L. Dobbyn for technical assistance and M. Ekdahl for operational assistance. This work was supported by The Virginia and D. K. Ludwig Fund for Cancer Research, the National Colorectal Cancer Research Alliance, NIH grants CA129825, CA43460, CA57345, CA62924, CA095103, and R01GM078986, NCI contract N01-CN-43309, ERC Start grant (279307: Graph Games), FWF NFN Grant No S11407-N23 (Rise), and the John Templeton Foundation. Simulations were performed on the Orchestra cluster supported by the Harvard Medical School Research Information Technology Group.
This file contains Supplementary Table 1-6, Supplementary Figures 1-3 and a Supplementary Appendix, which contains Supplementary Text and Data 1-4, Supplementary Figure 1 and additional references.