Abstract P6-09-22: Detecting high mutational load ER+ breast cancer patients through Foundation One cancer gene panel mutations
Authors: P Raska, J Abraham, T Budd
Journal: Cancer Research (2017)

Abstract Background: Cancer gene panels such as Foundation One are widely used clinically for aiding with cancer treatment decision-making since single point mutations in important genes and gene pathways in the tumor can point to tumor susceptibility that can be targeted with specific drugs. Tumor mutational load has more recently been proposed as a useful prognostic factor and indicator of clinical benefit in treatment with PD-1 and CTLA-4 blockade therapy. Breast cancer estrogen receptor positive patients (ER+) in particular appear to have a subset of high mutational load patients that could benefit if identified. However whole exome sequencing needed to directly measure mutational load is not yet widely available as a clinical tool. In this study we evaluate the predictive value of Foundation One cancer gene panel mutations for estimating tumor genome-wide mutational load and its use in identifying a clinically meaningful subset of breast cancer patients. Methods: The Cancer Genome Atlas breast cancer sequencing data on 569 ER+ patients was used to establish mutational load distribution. ER+ patients were divided into low mutational load and high mutational load groups according to 3 criteria: mean mutational load, the point of inflection in the mutational load distribution and the mutational load that optimally separates groups in terms of survival. Foundation One (FO) mutational load was then calculated as the number of mutations present within the 314 genes queried by the panel. FO mutational load was used to predict whether patients fell into the low or high mutational load groups found through analysis of the full exome data. Receiver Operating Characteristic (ROC) curves were constructed and optimal values for specificity and sensitivity of the FO mutational load classification were found. Results: Mean mutational load for ER+ patients was found to be 57 mutations, the point of inflection of the mutational load distribution was established at 100 mutations, and the number of mutations that best separated groups in terms of survival was 160, (HR = 6.6, p-value=0.004). FO mutational load was found to be a good predictor for the low and high classifications established by all three criteria, with areas under the curve of 0.74, 0.91 and 0.945 respectively. The optimal predictive value of the FO mutational load classification was found at 5 mutations as the cut-off, with 94.2% specificity and 88% sensitivity for predicting groups defined by survival and 95% specificity and 71% sensitivity for those defined by the mean. Conclusion: The Foundation One cancer gene panel can be used to effectively identify a clinically meaningful subgroup of ER+ patients with high mutational load. These patients may benefit from targeted treatments such as PD-1 inhibitors being offered through clinical trials. The compromise in sensitivity that results from the reduction in number of genes queried by a panel means an important proportion of patients with high mutational load will be missed but this still translates to a large improvement in the identification of these patients given the wide availability of gene panels in the clinic. Basic and clinical follow-up studies need to take place to clinically validate the high mutational load ER+ patient subgroup. Citation Format: Raska P, Abraham J, Budd T. Detecting high mutational load ER+ breast cancer patients through Foundation One cancer gene panel mutations [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-09-22.