Abstract Background: Multi-cancer early detection (MCED) from plasma liquid biopsy has advanced rapidly, demonstrating that diverse cfDNA features can reveal early tumor signals. Most current assays rely on whole-genome or methylation sequencing, which are costly and require deep coverage. EarlySeek is a novel, highly multiplexed cfDNA amplicon-based alternative that targets ∼800,000 SINE-enriched loci using long and short amplicons, generating a bimodal insert-size distribution and requiring as little as 0.25 ng of DNA input. This design captures complementary signals such as fragmentation, aneuploidy, genomic abundance shifts, and sequence motif patterns. We evaluated a multi-signal framework integrating these features using artificial intelligence and machine learning. Methods: Three datasets were analyzed: a training set (237 cancers, 463 normals), a calibration set (72 cancers, 140 normals), and an independent test set with tissues present in training (78 cancers, 192 normals). EarlySeek output yields six biological scores: fragment length, two aneuploidy scores, two coverage/abundance scores, and a 6-mer motif score. Scores were generated using representation and deep learning architectures, including autoencoders that compress amplicon-level and binned genomic information into informative latent features, combined with machine learning classifiers such as support vector machines and gradient-boosted trees. Each score was calibrated via quantile-to-quantile regression, and a predefined multi-signal rule was applied to generate final scores for the test cohort. Results: Across 270 independent test samples, the combined six-score framework achieved 51% sensitivity at 99% specificity (95% CI: 40%-62%). Sensitivity by stage showed meaningful early detection: 45% for stage I (CI 26%-66%), 58% for stage II ( CI 39%-74%), 57% for stage III ( CI 37%-67%), and 100% for stage IV (2/2; CI 34%-100%). Performance varied by cancer type, with strongest detection in colorectal (61%), gastric (71%), liver (80%), ovarian (75%), and pancreatic cancer (64%). Lower sensitivities in breast, and prostate cancers reflected known low cfDNA shedding. Conclusions: EarlySeek’s bimodal amplicon design enables extraction of diverse cfDNA signals from a single sequencing assay. Integrating these orthogonal features through artificial intelligence and machine learning supports high-specificity MCED detection and yields meaningful early-stage performance. This approach offers a scalable, cost-effective MCED test, a desirable feature for population-level cancer screening. Citation Format: Kamel Lahouel, Kameron Bates, Victoria Zismann, Candice Wike, Kunjur Manasa Upadhyaya, Matteo Munini, Mete Mulazimoglu, Gracyn Benck, Kianna Martos Rupp, Payton Smith, Chaney Jambor, Sophie Pénisson, Stephanie Pond, Jeffrey Trent, Cristian Tomasetti. Liquid biopsy MCED enabled by a novel 800K-locus bimodal amplicon sequencing technology abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7624.
Lahouel et al. (Fri,) studied this question.