Abstract 2343: CancerSpot: a multi-cancer early detection test using targeted methylation sequencing

Abstract Background: Liquid biopsy-based cancer detection is a noninvasive method to identify cell-free DNA (cfDNA) fragment based signatures in the bloodstream characterized by distinct methylation patterns. Tumor-derived fragments (ctDNA) form a tiny fraction of all cfDNA fragments in the bloodstream. ctDNA originating from some specific genomic regions can have altered methylation patterns. Reliable and cost-effective detection of altered methylation pattern in these regions requires a broad targeted sequencing approach. Methods: We developed CancerSpot, a multi-cancer early detection (MCED) test based on cfDNA methylation signatures in a cohort of 688 cancer patients and 326 healthy controls representing 10 cancer types. A custom genomics workflow prioritizes high-quality reads by rejecting poorly aligned or imperfectly converted data while rescuing reliable reads to calculate methylation scores across genomic regions at the read level accounting for detection of a weak signal from tumor derived DNA. Machine learning (ML) models were developed using methylation signatures from ∼7000 genomic markers. A gradient-boosted trees approach with repeated cross-validation incorporated data perturbations to account for variability in lab protocols and sequencing platforms ensuring robust prediction accuracy and error tolerance. Results: CancerSpot achieved 79% sensitivity and 97% specificity for detecting Stage I-III tumors across 10 cancer types. It also demonstrated a 77% accuracy in identifying the tumor tissue of origin within the top two predictions. The robust workflow enhances detection of tumor-derived cfDNA signals even in samples with low conversion efficiency while mitigating background noise from normal cfDNA. The regions used by the test show strong differential methylation in tumor-normal data from a diverse population in TCGA indicating its wide applicability. Conclusions: CancerSpot provides a cost-effective and accurate MCED solution tailored to diverse cohorts. Citation Format: Urvashi Bahadur, Divya Vishwanath, Prakash Hiremath, Nihesh Rathod, Swaraj Basu, Sampuran Chakraborty, Aastha Tripathi, Preetha R T, Aditi Chatterjee, Arunima Ghosh, Sameer Phalke, Subuhi Yete, Ankita Khan, Sweta Sanguri, Mohammed Rahman, Sewanti Limaye, Charles Cantor, Vamsi Veeramachaneni, Ramesh Hariharan. CancerSpot: a multi-cancer early detection test using targeted methylation sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2343.