Abstract PO1-07-02: Sensitive tumor detection, accurate quantification, and ER subtyping using low-pass methylome of liquid biopsy samples from patients (pts) with metastatic breast cancer (mBC)

Abstract Background The presence and amount of blood circulating tumor DNA (Tumor Content, TC) is emerging as a clinically relevant factor in many cancers, including BC. In the metastatic setting, a commonly used tumor tissue agnostic approach involves analyzing Copy Number Alterations (CNA) from low-pass whole-genome sequencing (lpWGS) by ichorCNA. However, with a lower limit of detection of 3%, it is unsuitable for low TC applications, such as monitoring disease in response to treatment. Here we report on the use of low-pass whole genome bisulfite sequencing (lpWGBS) coupled with a novel tool, METER (METhylation analyzER), to overcome current limitations of lpWGS, enabling sensitive TC detection, accurate quantification, and ER subtyping from mBC liquid biopsies. Methods METER is a computational tool to analyse TC exploiting BC-specific Differentially Methylated Sites (DMS) and Regions (DMR) in lpWGBS data. To define BC DMSs and DMRs, Rocker-meth was applied to the WGBS profiles of 30 BC tissue (BASIS dataset) and 23 healthy donor cell free DNA (cfDNA) (Fox-Fisher 2021). METER consists of three modules: 1) METER-quant, a DMS-based quantifier of TC; 2) METER-detect, a DMR-based z-score method to classify samples as TC+ (METER+) or TC- (METER-); 3) METER-subtype, to infer ER status via Robust Partial Correlation applied to ER+ vs. ER- BC DMR. We generated lpWGBS data (coverage 0.5-1X) of 135 cfDNA samples from 58 pts (45 ER+, 13 ER-, 55 (95%) up to 3 previous lines of treatment; before starting treatment (T0, n=58), after the first cycle (T1, n=25), and at progression (T2, n=22)) and 30 from healthy individuals as controls. ichorCNA using sensitive parameters (allowing normal fraction up to 0.99) was used to estimate reference TC. A leave-one-out strategy applied to the control samples was used to estimate the false discovery rate (FDR) of METER-detect classification. METER+/- pts were tested for association with OS and PFS using the log-rank test and compared with ichorCNA detection (TC by ichorCNA above or below 3%, ichorCNA+/-). Results TC of 105 cfDNA BC samples by METER-quant was concordant with estimates from the state-of-the-art tool ichorCNA (R 0.90, p 1e-10). In 22 pts with complete longitudinal data, METER-quant median TC of 0.05 at T0 (IQR 0.02-0.12), 0.02 at T1 (0.01-0.07), and 0.06 at T2 (0.03-0.11) were obtained, with significant difference observed for T0 vs T1 and T2 vs T1 (paired Wilcoxon p 0.05). Across all time points, METER-detect classified 43% of the n=42 ichorCNA- samples as METER+. The reliability and prognostic performance of METER and ichorCNA detection classification were then compared using clinical outcome data. Based on T0 data, METER+ pts had significantly worse OS than METER- (HR=4.2 CI=2.0-8.9, p 0.001). This effect was stronger than using ichorCNA (HR=2.3 CI=1.3-4.0, p=0.006). Worse PFS at T0 was observed for METER+ compared with METER- pts (HR=3.7 CI=1.8-7.9, p 0.001), while no significant association was observed for ichorCNA+ pts (HR=1.4 CI=0.8-2.5, p=0.2). Of note, ichorCNA-/METER+ pts (N=11) at T0 had worse PFS compared with ichorCNA-/METER- (N=11) (HR=3.7 CI 1.4-9.7, p=0.005). Considering the 73 METER+ and 53 samples with METER-quant 5%, METER-subtype showed an accuracy of 0.84 (CI=0.73-0.92) and 0.94 (CI=0.83-0.99) respectively in classifying ER status based on IHC. Discussion lpWGBS enables tumor tissue agnostic analysis and concurrent investigation of complementary molecular features, including CNA and DNA methylation patterns. In a small and heterogeneous cohort, METER showed comparable performance to state-of-the-art tools in terms of TC quantification (ichorCNA) and ER subtyping (IHC), while offering enhanced sensitivity and ensuring a low FDR. METER showed promising prognostic stratification capabilities, particularly for patients with low TC. Validation in an extended prospective cohort is currently ongoing. Citation Format: Marta Paoli, Agostina Nardone, Francesca Galardi, Chiara Biagioni, Dario Romagnoli, Francesca De Luca, Gian Marco Franceschini, Ilenia Migliaccio, Marta Pestrin, Giuseppina Sanna, Emanuela Risi, Luca Livraghi, Erica Moretti, Laura Biganzoli, Luca Malorni, Francesca Demichelis, Matteo Benelli. Sensitive tumor detection, accurate quantification, and ER subtyping using low-pass methylome of liquid biopsy samples from patients (pts) with metastatic breast cancer (mBC) abstract. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-02.