Abstract 5162: TuFEst: a sensitive and cost-effective pan-cancer detection approach with accurate tumor fraction estimation

Abstract Detecting cancer at early stages or upon recurrence is critical to decreasing cancer morbidity and mortality. We developed TuFEst (Tumor Fraction Estimator), a cost-effective computational approach for pan-cancer detection and tumor burden estimation from ultra-low coverage whole genome sequencing (~0.1x, ULP-WGS) of minimally invasive cell-free DNA (cfDNA). Current state-of-the-art methods estimate tumor fraction (TF) from ULP-WGS depending exclusively on total copy number variation, which loses tumor signal in either copy number-quiet tumors or tumors with copy-neutral loss-of-heterozygosity. Additionally, it is difficult in many cases to distinguish clonal from sub-clonal copy-number events, therefore complicating the ability to estimate tumor fraction. On the other hand, fragments shed into the blood from cancer cells, i.e., circulating tumor DNA (ctDNA), of various cancer types show significantly different length distribution than that from normal cells in healthy donors. By synergistically integrating both (i) copy number variation and (ii) altered fragment length signals, TuFEst successfully achieved higher sensitivity and more accurate TF estimation than current methods in >200 cfDNA samples across different cancer types, even in low tumor-fraction cases (TF < 0.1%). Application of TuFEst to serial cfDNA samples from blood biopsies demonstrate its utility in accurately estimating TF in ~100 cfDNAs, suggesting that TuFEst can be used to detect early cancer recurrence during different treatments. In one breast cancer patient receiving CDK4/6 therapy, TuFEst indicated disease progression 262 days earlier than routine imaging. Altogether, our work suggests that accurate TF estimation from cfDNA can not only aid in detecting cancer at early stages but also provide evidence of disease progression during treatment. We believe that such a non-invasive, cost-effective, pan-cancer detection method will benefit both initial cancer screening and monitoring of resistance to therapy in clinical applications. Citation Format: Ziao Lin, Chip Stewart, Elizabeth E. Martin, Brian P. Danysh, Raquel A. Jacobs, Kara Slowik, Lee Lawton, Elizabeth Lightbody, Kahn Rhrissorrakrai, Filippo Utro, Chaya Levovitz, Carrie Cibulskis, Irene M. Ghobrial, Margaret Shipp, Ryan B. Corcoran, Dejan Juric, Laxmi Parida, Heather A. Parsons, Gad Getz. TuFEst: a sensitive and cost-effective pan-cancer detection approach with accurate tumor fraction estimation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5162.