In Annals of oncology : official journal of the European Society for Medical Oncology
BACKGROUND : Early detection of cancer offers the opportunity to identify candidates when curative treatments are achievable. The THUNDER study (THe UNintrusive Detection of EaRly-stage cancers, NCT04820868) aimed to evaluate the performance of ELSA-seq, a previously described cfDNA methylation-based technology, in the early detection and localization of six types of cancers in the colorectum, esophagus, liver, lung, ovary and pancreas.
PATIENTS AND METHODS : A customized panel of 161,984 CpG sites was constructed and validated by public and in-house (cancer: n=249; non-cancer: n=288) methylome data, respectively. The cfDNA samples from 1,693 participants (cancer: n=735; non-cancer: n=958) were retrospectively collected to train and validate two multi-cancer detection blood test models (MCDBT-1/2) for different clinical scenarios. The models were validated on a prospective and independent cohort of age-matched 1,010 participants (cancer: n=505; non-cancer: n=505). Simulation using the cancer incidence in China was applied to infer stage-shift and survival benefits to demonstrate the potential utility of the models in the real world.
RESULTS : MCDBT-1 yielded a sensitivity of 69.1% (64.8%‒73.3%), a specificity of 98.9% (97.6%‒99.7%) and tissue origin accuracy of 83.2% (78.7%‒87.1%) in the independent validation set. For early stage (I‒III) patients, the sensitivity of MCDBT-1 was 59.8% (54.4%‒65.0%). In the real-world simulation, MCDBT-1 achieved the sensitivity of 70.6% in detecting the six cancers, thus decreasing late-stage incidence by 38.7%‒46.4%, and increasing 5-year survival rate by 33.1%‒40.4%, respectively. In parallel, MCDBT-2 was generated at a slightly low specificity of 95.1% (92.8%-96.9%) but a higher sensitivity of 75.1% (71.9%-79.8%) than MCDBT-1 for populations at relatively high risk of cancers, and also had ideal performance.
CONCLUSION : In this large-scale clinical validation study, MCDBT-1/2 models showed a high sensitivity, specificity, and accuracy of predicted origin in detecting six types of cancers.
Gao Q, Lin Y P, Li B S, Wang G Q, Dong L Q, Shen B Y, Lou W H, Wu W C, Ge D, Zhu Q L, Xu Y, Xu J M, Chang W J, Lan P, Zhou P H, He M J, Qiao G B, Chuai S K, Zang R Y, Shi T Y, Tan L J, Yin J, Zeng Q, Su X F, Wang Z D, Zhao X Q, Nian W Q, Zhang S, Zhou J, Cai S L, Zhang Z H, Fan J
2023-Feb-25
Machine learning, Methylation, Multi-cancer early detection, cell-free DNA (cfDNA)
