Letter to the Editor: An ultra-sensitive assay using cell-free DNA fragmentomics for multi-cancer early detection

Abstract

Early detection can benefit cancer patients with more effective treatments and better prognosis, but existing early screening tests are limited, especially for multi-cancer detection. This study investigated the most prevalent and lethal cancer types, including primary liver cancer (PLC), colorectal adenocarcinoma (CRC), and lung adenocarcinoma (LUAD). Leveraging the emerging cell-free DNA (cfDNA) fragmentomics, we developed a robust machine learning model for multi-cancer early detection. 1,214 participants, including 381 PLC, 298 CRC, 292 LUAD patients, and 243 healthy volunteers, were enrolled. The majority of patients (N = 971) were at early stages (stage 0, N = 34; stage I, N = 799). The participants were randomly divided into a training cohort and a test cohort in a 1:1 ratio while maintaining the ratio for the major histology subtypes. An ensemble stacked machine learning approach was developed using multiple plasma cfDNA fragmentomic features. The model was trained solely in the training cohort and then evaluated in the test cohort. Our model showed an Area Under the Curve (AUC) of 0.983 for differentiating cancer patients from healthy individuals. At 95.0% specificity, the sensitivity of detecting all cancer reached 95.5%, while 100%, 94.6%, and 90.4% for PLC, CRC, and LUAD, individually. The cancer origin model demonstrated an overall 93.1% accuracy for predicting cancer origin in the test cohort (97.4%, 94.3%, and 85.6% for PLC, CRC, and LUAD, respectively). Our model sensitivity is consistently high for early-stage and small-size tumors. Furthermore, its detection and origin classification power remained superior when reducing sequencing depth to 1× (cancer detection: ≥ 91.5% sensitivity at 95.0% specificity; cancer origin: ≥ 91.6% accuracy). In conclusion, we have incorporated plasma cfDNA fragmentomics into the ensemble stacked model and established an ultrasensitive assay for multi-cancer early detection, shedding light on developing cancer early screening in clinical practice.

Early Detection of Cancers in the Era of Precision Oncology

“…The study of cfDNA fragmentation (Fragmentomics) is especially promising for the early detection of liver cancer [35 ▪▪ ], lung adenocarcinoma [36 ▪ ], advanced colorectal adenoma, and adenocarcinoma [37 ▪▪ ,38 ▪ ]. The circulating tumor DNA (ctDNA) fragments from cancer patients were shorter than the wild-type fragments from the healthy donors [39,40 ▪▪ ]. cfDNA fragmentation patterns reflect in vivo gene regulation across multiple molecular layers, such as nucleosome positioning and gene expression.…”

Cerebrospinal fluid: A unique source of circulating tumor DNA with broad clinical applications

“…circular versus linear DNA) [ 80 ]. These techniques have been useful in cancer detection as well as determining tissues of origin in cfDNA [ 81 , 82 ]. This could be integrated into the genomic analyses in CSF as another layer of information and assist in tumor detection, classification and monitoring of minimal residual disease because of its high sensitivity.…”

Detection and characterization of pancreatic and biliary tract cancers using cell-free DNA fragmentomics

“…Plasma cfDNA fragmentomics is an emerging field that covers various features like fragment size, end point and nucleosome footprint, where many studies have demonstrated that significant difference could be observed between cancer patients and healthy individuals [ 9 – 12 ], even at early stages [ 13 – 16 ]. Cristiano et al introduced an approach focusing on the fragmentation size ratio in a multi-cancer cohort, in which the machine learning model had sensitivities of detection ranging from 57% to > 99% among seven cancer types at 98% specificity, with an overall AUC of 0.94 [ 9 ].…”