Abstract B005: Investigating distinct methylation signatures characteristic of breast cancer subtypes in residual disease via cell free DNA methylation

Abstract Breast cancer is the most common type of cancer in females and recurrence increases over time, unlike many other cancers. Treatment for recurrent cancer is often the same as the primary with no additional biopsies taken. Current research suggests that subtype switching and tumor character changes frequently occur between primary and recurrent breast cancers. Therefore, it may be beneficial to patients to switch treatment based on these changes. Since physical biopsies are cumbersome and not always feasible, liquid biopsies open a way to monitor tumor changes less invasively and more comprehensively. It has been previously established that cell free DNA (cfDNA) shed into the bloodstream from dying cells can reflect cell type of origin via methylation pattern and rate of death via concentration of cfDNA. In this pilot study, we seek to look at the cfDNA of fifteen late stage pre-/ post-surgical breast cancer patients who also received radiation treatment. We will perform cfDNA extraction on the serum of these patients and both whole genome bisulfite sequencing (WGBS), which chemically converts unmethylated cytosine to uracil/thymine in the DNA and is the current gold-standard of methylation sequencing, and a newer method, enzymatic methylation sequencing (EM-seq), which enzymatically converts (TET2/APOBEC) unmethylated cytosine to uracil/thymine and potentially preserves more of the cfDNA. To validate any signatures found in the cfDNA of the breast cancer patients, we have begun the genomic DNA (gDNA) extraction and WGBS/ EM-seq protocols on a variety of breast cancer cell lines including: MCF10A, MCFDCIS, MCF7, T47D, BT474 MDA MB453, MDA MB436 and MDA MB231 (including in-lab brain, bone, and lung metastatic clones). Bioanalyzer traces are produced from the extracted cfDNA/gDNA and also for the final sequencing libraries. The success of the methylation conversion is evaluated after sequencing data is returned and conversion rates of the cytosine to uracil/thymine are compared to unmethylated DNA control (lambda) and methylated DNA control (pUC19). Once this sequencing data is obtained, we use an in-lab deconvolution algorithm to detect cell types of origin and intend to make the algorithm more robust for cancer cell types as well. We have currently produced breast cancer cell line methylation sequencing libraries and are in the process of producing the libraries for the patient samples. Our current data suggests that there are changes in the cfDNA general fragmentation patterns and cfDNA concentrations between pre-/post- surgery samples. Once our sequencing data is obtained for the patient samples, we will run our deconvolution algorithm. The potential of characterizing breast cancer subtype and progression signatures in cfDNA of late stage pre-/ post-surgical breast cancer patients can have significant impact on patient treatment options. Identifying these breast cancer signatures less invasively and, therefore, more frequently may allow for early and more targeted intervention to improve breast cancer patient outcomes. Citation Format: Amber Alley, Megan McNamara, Sidharth Jain, Anton Wellstein. Investigating distinct methylation signatures characteristic of breast cancer subtypes in residual disease via cell free DNA methylation [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 B005.