Novel Clinical Tool to Estimate Risk of False-Negative KRAS Mutations in Circulating Tumor DNA Testing.

Abstract

In metastatic colorectal cancer, the detection of <i>RAS</i> mutations by circulating tumor DNA (ctDNA) has emerged as a valid and noninvasive alternative approach to determining <i>RAS</i> status. However, some <i>RAS</i> mutations may be missed, that is, false negatives can occur, possibly compromising important treatment decisions. We propose a statistical model to assess the probability of false negatives when performing ctDNA testing for <i>RAS.</i> Cohorts of 172 subjects with tissue and multipanel ctDNA testing from MD Anderson Cancer Center and 146 subjects from Massachusetts General Hospital were collected. We developed a Bayesian model that uses observed frequencies of reference mutations (the maximum of <i>APC</i> and <i>TP53</i>) to provide information about the probability of <i>KRAS</i> false negatives. The model was alternatively trained on one cohort and tested on the other. All data were collected on Guardant assays. The model suggests that negative <i>KRAS</i> findings are believable when the maximum of APC and TP53 frequencies is at least 8% (corresponding posterior probability of false negative <5%). Validation studies demonstrated the ability of our tool to discriminate between false-negative and true-negative subjects. Simulations further confirmed the utility of the proposed approach. We suggest clinicians use the tool to more precisely quantify <i>KRAS</i> false-negative ctDNA results when at least one of the reference mutations (<i>APC</i>, <i>TP53</i>) is observed; usage may be especially important for subjects with a maximum reference frequency of <8%. Extension of the methodology to predict false negatives of other genes is possible. Additional reference genes can also be considered. Use of personal training data sets is supported. An open-source R Shiny application is available for public use.