Abstract 4545: A liquid biopsy for breast cancer diagnosis

Abstract

Abstract Purpose: Although ductal carcinoma in situ (DCIS) is considered a precursor to invasive breast cancer, only 30-50% of DCIS lesions progress to invasive cancers. There has been considerable debate over the treatment of DCIS, with some groups recommending frequent monitoring of this population for progression instead of aggressive treatment. This approach would require either recurrent biopsy, an unattractive option for most women, or a useful diagnostic test capable of discriminating DCIS and invasive breast cancer. Here, we report a blood diagnostic that is capable of detecting and discriminating epigenetic biomarkers in breast lesions that are more likely to become either invasive or those that are more benign. Our approach is based on the premise that circulating lymphocytes undergo epigenetic changes upon exposure to the tumor microenvironment and these changes can be followed to track tumor progression from a non-invasive to an invasive disease state. To test the utility of these epigenetic biomarkers, a novel diagnostic model utilizing a combined machine learning approach integrated with the recombinatorial strategy of a genetic algorithm, was used to screen a large response space of 1,000's of CpG sites to winnow down to a small set of 40 CpG sites with the highest combined predictive power. Methods: Blood was obtained from women with histologically confirmed DCIS (n=14), histologically confirmed invasive ductal carcinoma (n=10) or women with no evidence of breast lesions on mammography (n=10). Lymphocytes were isolated using a modified Ficoll-Paque gradient, and DNA was extracted using standard commercial kits. Epigenetic profiling of DNA was performed using a highly sensitive and quantitative analytics platform, which utilizes methylation sensitive restriction endonucleases to detect changes in methylation of CpG sites from standard NGS data.Results: Non-metric multidimensional scaling ordination analysis of the CpG sites revealed highly distinct methylation patterns between Normal, DCIS and invasive samples. Using a Likelihood Ratio Test with defined ANOVA contrasts, over 14,000 significantly different methylated CpG sites were identified (p<0.05 after false discovery rate correction). A proprietary machine learning diagnostic model was employed to reduce this high-dimensional variable space to the most effective set of CpG sites. DCIS and invasive samples were discriminated in blinded tests (n=17) with 69% accuracy. Conclusions: Preliminary studies show strong ability of the identified DNA methylation metrics to detect and discriminate invasive and non-invasive breast lesions. The quantitative sensitivity and selectivity of this new diagnostic/prognostic blood test will be determined in larger patient cohorts. Citation Format: Jennifer Sims-Mourtada, Kimberly M. Arnold, Adam Marsh. A liquid biopsy for breast cancer diagnosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4545.