Abstract A053: Liquid biopsy-based detection of triple negative breast cancer using DNA methylation biomarkers

Abstract Triple-negative breast cancer (TNBC) presents significant clinical challenges due to its aggressive phenotype, rapid progression, and limited targeted treatment options. TNBC often mimics benign lesions on ultrasound and metastatic features can be missed in routine mammography screening leading to misdiagnosis and delayed treatment. These limitations led us to investigate whether a blood-based liquid biopsy could provide a non-invasive, sensitive method for early TNBC detection, potentially overcoming the constraints of conventional imaging techniques. To discover biomarkers for TNBC, we performed targeted bisulfite sequencing on a cohort of 46 breast cancer solid tissue biopsy samples, comprising 19 TNBC and 27 non-TNBC cases. We identified 150 differentially methylated regions (mean length 703bp) separating TNBC and non-TNBC samples. Upon linking each region to its nearest gene, we observed several genes previously associated with prognosis and survival (HOXB3, PAX9, SOX9) substantiating the clinical relevance of these biomarkers. Using public TCGA data (369 breast cancer samples), we integrated expression and methylation data and observed directionally consistent changes. For example, HOXB3 showed increased methylation at its associated differential regions and decreased expression in TNBC cases. To ascertain whether these biomarkers could be useful in a liquid biopsy approach, we performed targeted bisulfite- sequencing on cell-free DNA samples derived from prospectively collected patients with breast cancer. All samples had independent tissue-based assessment of ER, PR and HER2 using immunohistochemistry or in situ hybridization. We then built a feed-forward neural network classifier with classes TNBC and non-TNBC that used transfer-learning and the identified biomarkers to learn methylation signatures in the biopsy samples and map them to cell-free DNA. To emulate clinical workflow, where subtyping follows cancer diagnosis and tissue of origin identification, we evaluated cfDNA samples correctly identified as breast cancer using two independent machine learning models trained using 2,393 prospectively collected multi- indication cancer and non-cancer samples (NCT05435066). Subsequent TNBC subtyping of breast cancer-positive cases (N=56) accurately identified 77% (10/13) of TNBC samples and 91% (39/43) of non-TNBC cases, with overall accuracy of 84%. The estimated tumor content range for correctly predicted TNBC samples was 0.5-33% with the three mis-classified samples all showing very low tumor content (0.22%, 0.28%, 0.59%). Non-TNBC cases showed a tumor content range of 0.07-36% for correct and 2.3-14% for misclassified samples. In conclusion, we identified novel methylation biomarkers that distinguish TNBC in the cell-free DNA of patients without needing invasive tissue biopsies. The high sensitivity of our assay at low tumor fractions has the potential to improve outcomes through earlier intervention and may enable earlier screening in high-risk groups, monitoring of minimal residual disease and longitudinal monitoring during treatment. Citation Format: Jocelyn Charlton, Shiva Farashahi, Kade Pettie, Elie Massaad, Josh Hubbell, Feras Hantash, Kieran I Chacko. Liquid biopsy-based detection of triple negative breast cancer using DNA methylation biomarkers [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A053.