Abstract
Personalized cancer medicine aims to use tumor genomics to match patients with the most effective treatment option. For example, the presence of a KRAS mutation has been determined to be a contraindication to the treatment of colorectal cancer patients with anti-EGFR targeted therapy. Interestingly, the original Phase 3 clinical trials of anti-EGFR treatment for colorectal cancer included KRAS mutant patients, and analysis of that data has previously suggested that patients with the KRAS G13D mutation benefit from EGFR inhibition. However, no mechanism had been presented to explain why this specific mutation would be an exceptional responder. Clinical guidelines therefore consider KRAS G13D equivalent to the other oncogenic KRAS mutations. We have now combined computational systems biology and experimental cancer cell biology to identify a mechanism that explains why KRAS G13D, but not the other common KRAS mutations, would respond to EGFR inhibition. Oncogenic KRAS mutations are known to be insensitive to inactivation by tumor suppressor and RAS negative regulator NF1. KRAS G13D is comparatively unable to bind to NF1 while the other common KRAS mutants can bind to NF1. The model reveals that this difference in the ability to bind NF1 is critical. By binding non-productively to NF1, most KRAS mutants competitively inhibit NF1 and promote wild-type RAS activation in an EGFR independent manner. In contrast, KRAS G13D cannot promote wild-type RAS activation through the competitive inhibition of NF1, so wild-type RAS activation remains EGFR dependent. Our experiments confirm that wild-type RAS activation decreases in KRAS G13D colorectal cancer cells treated with EGFR inhibitors, but not in colorectal cancer cells that include a different KRAS mutant. Overall, this work reveals how biophysically-based mathematical models combined with experimental cell biology can elucidate biophysically based mechanisms with direct clinical impact.
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