Abstract IA02: Multicancer detection of early-stage cancers with simultaneous tissue localization using a plasma cfDNA-based targeted methylation assay
Authors: Hartman Anne-Renee , Oxnard Geoffrey , Klein Eric , Seiden Michael , Hubbell Earl , Venn Oliver , Jamshidi Arash , Zhang Nan , Beausang John , Gross Samuel , Kurtzman Kathryn , Fung Eric , Allen Brian , Fields Alexander , Liu Hai , Sekeres Mikkael , Richards Donald , Yu Peter , Aravanis Alexander , Liu Minetta 
Published: June 2020

Abstract
Background: Earlier cancer detection could identify tumors when they are more treatable and thus may improve clinical outcomes. A blood-based multicancer early detection test using circulating tumor cell-free DNA (cfDNA) offers the potential to address this unmet need. Our previous discovery work identified whole-genome bisulfite sequencing as outperforming whole-genome and targeted sequencing approaches for multicancer detection across stages. We thus developed a targeted methylation (TM) assay for multicancer detection and tissue-of-origin (TOO) localization.
Methods: Participants were from the Circulating Cell-free Genome Atlas (CCGA; NCT02889978) and STRIVE (NCT03085888) studies, both prospective, multicenter, observational studies with longitudinal follow-up. cfDNA samples spanning &gt;20 cancer types of all stages were divided into a cross-validated training set and an independent validation set; there were 3,583 evaluable samples in training (1,530 cancer; 2,053 noncancer) and 1,803 in validation (678 cancer; 1,125 noncancer). Performance in a prespecified set of 12 high-signal cancers was also reported (anus, bladder, colon/rectum, esophagus, head and neck, liver/bile-duct, lung, lymphoma, ovary, pancreas, plasma cell neoplasm, stomach). This multicancer early detection test evaluated cfDNA for the presence of cancer and localization of TOO; the TM classifier was trained to target &gt;99% specificity. Precision was defined as the fraction of correct calls.
Results: Participants with and without cancer were similar in age. Specificity across all cancer types in the training and validation sets was 99.8% (95% CI, 99.2-99.9%) and 99.3% (98.3-99.8%), respectively, which reflects a consistent (P=0.292) false positive rate of &lt;1%. Across all cancer types, aggregate sensitivity was also consistent between the training and validation sets (55% [53-58%] and 55% [51-59], respectively; P=0.897). In the validation set, aggregate sensitivity in the prespecified group was 76% (73-78%); detection was 39% (27–52%) in stage I (n=62), 69% (56–80%) in stage II (n=62), 83% (75–90%) in stage III (n=102), and 92% (86–96%) in stage IV (n=130). Overall, the classifier assigned a TOO across &gt;20 cancer types in 96% of samples (344/359); of these, the TOO was correct in 93% (321/344) of cases, which was consistent with training set analyses.
Conclusions: This multicancer early detection test detected cancer signal across &gt;20 cancer types with a single, fixed, low false positive rate and highly accurate TOO localization. Importantly, results in the independent validation set were consistent with the training set, demonstrating the robustness of machine learning classifier training, and confirms that data were not overfitted. These data support the feasibility of a single blood-based test that can detect multiple cancers, supporting further clinical development in preparation for the return of results.
Citation Format: Anne-Renee Hartman, Geoffrey Oxnard, Eric Klein, Michael Seiden, Earl Hubbell, Oliver Venn, Arash Jamshidi, Nan Zhang, John Beausang, Samuel Gross, Kathryn Kurtzman, Eric Fung, Brian Allen, Alexander Fields, Hai Liu, Mikkael Sekeres, Donald Richards, Peter Yu, Alexander Aravanis, Minetta Liu. Multicancer detection of early-stage cancers with simultaneous tissue localization using a plasma cfDNA-based targeted methylation assay [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr IA02.