Abstract A045: Unlocking deep learning for cell-free DNA-based early colorectal cancer detection

Abstract Introduction: Colorectal cancer (CRC) is the second most common cause of cancer-related death in the US. Screening reduces cancer mortality through early detection, but only 59% of eligible individuals are up to date with recommended CRC screening. Non-invasive and more convenient tests can increase adherence to screening guidelines, and blood tests using next generation sequencing to detect cancer-associated methylation patterns in cell-free DNA (cfDNA) have recently shown great promise and are on track to or have recently achieved FDA approval. Such tests produce data for millions of cfDNA fragments (and billions of bases) per sample and require sophisticated featurization and classification algorithms. Deep learning (DL) outperforms traditional machine learning (ML) given enough training data, but application to blood-based cancer screening remains challenging due to the immense input space and low training case counts. Here, we present an interpretable fragment-level DL model that outperforms a state-of-the-art ML approach. Methods: Each sample yields millions of fragments represented as a multimodal feature based on nucleotide sequence, CpG methylation pattern at single-base resolution, and other biologically relevant characteristics. Our DL model first learns a fragment embedding; then, a specialized attention mechanism uses cancer-indicative fragments to learn a sample embedding. Finally, the sample embedding is used to predict CRC status. To assess classification accuracy, we used two independent test sets: challenging contrived positive material (plasma from an advanced-CRC subject diluted into plasma from healthy controls to a level just above the detection limit, n = 148); and a research cohort of patients with CRC (n = 211) or harder-to-detect advanced precancerous lesions (APLs, n = 388). Results: We trained DL models using 70% (DL1) or 100% (DL2) of available training data (925 cases; 3,469 controls). For each model, we set a classification threshold to yield 90% specificity in test-set controls (n = 331). DL2 was more sensitive than the ML model in contrived positives (82% vs 70%), CRCs (90% vs 88%), and APLs (30% vs 27%). Further, DL2 improved on DL1 in each positive sample type (82% vs 72%, 90% vs 89%, and 30% vs 28%, respectively), showing that DL model performance increases with volume of training data. Conclusion: A DL model that operates on millions of fragments per subject outperformed a state-of-the-art ML method when applied to an independent test cohort and exhibited improved performance as training data volume increased. For interpretability, the model can be analyzed via attention values and contribution analysis at the fragment level, providing insight into previously unrecognized cancer-associated fragment characteristics, and the sample embedding can be used to visualize sample distributions and assess model generalizability. Together, these results pave the way for effective DL in blood-based early cancer detection. Citation Format: Michael Widrich, Anooj Patel, Peter Ulz, Kaitlyn Coil, Thomas Royce, Jimmy Lin, Richard Bourgon, Anindita Dutta. Unlocking deep learning for cell-free DNA-based early colorectal cancer detection [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A045.

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