Clinical validation of a multicancer detection blood test by circulating cell-free DNA (cfDNA) methylation sequencing: The THUNDER study.

Abstract

10544 Background: The development of ultra-sensitive genomic and epigenomic assays enables the early detection of multiple cancers in parallel. However, large-scale prospective clinical validation data are rare. Here, we report clinical validation data from the THUNDER (The Unintrusive Detection of EaRly-stage cancers, NCT04820868) study, which evaluates the performance of ELSA-seq among 6 cancer types in lung, colorectum, liver, esophagus, pancreas and ovary, which account for 50% of cancer morbidity and 62% of cancer mortality. Methods: This prospective case-control study consists of four stages: marker discovery, model training, validation, and independent validation. A customized panel covering 161,984 CpG sites was established using public data and in-house data. Cancer patients were pre-specified into the training and validation sets, and healthy controls (HC) were age-matched. Two multi-cancer detection blood test (MCDBT-1 and MCDBT-2) models with different cut-offs were established from the retrospectively collected training set and tested in the validation set. An independent validation set was enrolled prospectively, matched by age, and tested with the locked MCDBT-1/2 models. An interception model was then applied based on the model performance and China cancer incidence data to infer potential positive predictive value (PPV) and clinical utility in real-world practice. Results: In total, the training set consisted of 399 cases and 626 HC; the validation set consisted of 301 cases and 123 HC; and the independent validation set consisted of 505 cases and 505 HC. In the training set, the specificities were 98.9% (95% confidence interval [CI], 97.7%‒99.5%) and 99.7% (98.8%‒100.0%) for MCDBT-1/2 models, respectively. In the independent validation set ( n = 856, the rest to be sequenced and reported), MCDBT-1 and MCDBT-2 yielded sensitivity of 76.2% (72.0%‒80.0%) and 70.2% (65.8%‒74.4%) in 6 cancers with specificity of 96.3% (93.9%‒97.9%) and 99.3% (97.8%‒99.8%), respectively. Stage I‒III sensitivity was 68.5% (63.1%‒73.5%) and 60.8% (55.3%‒66.2%) for MCDBT-1/2 models, respectively. The prediction accuracy of top predicted origin was 79.1% (74.5%‒83.2%) and 83.0% (78.4%‒87.0%) for MCDBT-1/2, respectively. The interception model projected an estimated PPV of 3.1% and 12.4% for MCDBT-1/2 models, respectively. MCDBT-1 could reduce 5-year cancer mortality by 20.3%‒24.6% and reduce late-stage incidence by 51.7%‒61.7%, and MCDBT-2 could reduce 5-year cancer mortality by 15.9%‒19.9% and reduce late-stage incidence by 40.9%‒49.2%. Conclusions: cfDNA methylation-based MCDBT-1/2 models can effectively identify multiple cancers simultaneously at early stages with promising sensitivity, specificity, and accuracy of predicted origin. Their performances are to be further validated in a prospective interventional study (NCT05227534).