Abstract LB241: FateAI: Analysis of circulating DNA fragmentomics with an artificial intelligence model for cancer screening

Abstract Cell-free DNA (cf-DNA) circulates in the blood due to cell death, which can result from normal cellular processes or specific diseases such as cancer and others. The set of epigenetic footprints of cf-DNA, such as nucleosome organization, end-motifs distribution, fragmentation pattern, and DNA methylation signatures, constitute a key factor in non-tumor-informed liquid biopsies because the epigenetic status is highly tissue-specific and can distinguish cancer types and cancer subtypes. We developed Fragment Analysis for Tumor Evaluation with Artificial Intelligence (Fate-AI). It integrates multiple epigenetic footprints extracted from Low Pass Whole Genome Sequencing to train an encoder-decoder machine learning model performing a multidimensional embedding of the feature along the genome for tumor detection and tissue of origin classification. Peripheral blood plasma samples, 10 mL, from 167 treatment-naive patients with Prostate Cancer (n=35), Lung Cancer (n=33), and Colon Cancer (n=99) were collected in multiple Italian cancer centers. Blood samples from 62 healthy donors were used as controls. In a leave-one-out cross-validation evaluation, Fate-AI outperforms state-of-the-art liquid biopsy approaches based on fragmentomics such as DELFI (Cristiano et al. , Nature 2019) and GRIFFIN (Doeble et al. Nat. Comm. 2020) (Table 1). The model trained with the fragmentomics features of our cohort was applied to an external colon cancer encompassing 43 tumor samples and 61 healthy cases (Hallermayr et al. J. Hematol. Oncol. 2022), Fate-AI obtained an AUC of 0. 923 vs. an AUC of 0. 722 for DELFI and 0. 695 for GRIFFIN. In conclusion, Fate-AI is a novel, highly accurate AI-based tool that outperforms current genomewide non-tumor-informed cancer detection approaches. Table 1. AUC, sensitivity and specificity of Fate-AI in cancer detection, and its comparison with state-of-the-art methods. Cancer AUC Sensitivity Specificity Fate-AI DELFI GRIFFIN Fate-AI DELFI GRIFFIN Fate-AI DELFI GRIFFIN Colon 0. 973 0. 852 0. 82 0. 961 0. 706 0. 902 0. 891 0. 873 0. 582 Lung 0. 905 0. 905 0. 884 0. 939 0. 788 0. 97 0. 982 0. 855 0. 661 Prostate 0. 992 0. 787 0. 94 0. 943 0. 943 0. 829 0. 98 0. 639 0. 608 Citation Format: Antonio De Falco, Piera Grisolia, Rossella Tufano, Cinzia Graziano, Marianna Scrima, Clara Iannarone, Marco Bocchetti, Michele Falco, Francesca Carlino, Teresa Noviello, Anna Ceccarelli, Gabriella Misso, Chiara Tammaro, Maria Carminia Della Cort, Floriana Morgillo, Fortunato Ciardiello, Maria Rosaria Rizzo, Alfonso Fiorelli, Noemi Maria Giorgiano, Pasquale Vitale, Raffaele Addeo, Michele Orditura, Stefano Forte, Raffaella Giuffrida, Michele Caraglia, Michele Ceccarelli. FateAI: Analysis of circulating DNA fragmentomics with an artificial intelligence model for cancer screening abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (7Suppl): Abstract nr LB241.