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Circulating DNA fragmentomics and cancer screening.

ABSTRACT: The high fragmentation of nuclear circulating DNA (cirDNA) relies on chromatin organization and protection or packaging within mononucleosomes, the smallest and the most stabilized structure in the bloodstream. The detection of differing size patterns, termed fragmentomics, exploits information about the nucleosomal packing of DNA. Fragmentomics not only implies size pattern characterization but also considers the positioning and occupancy of nucleosomes, which result in cirDNA fragments being protected and persisting in the circulation. Fragmentomics can determine tissue of origin and distinguish cancer-derived cirDNA. The screening power of fragmentomics has been considerably strengthened in the omics era, as shown in the ongoing development of sophisticated technologies assisted by machine learning. Fragmentomics can thus be regarded as a strategy for characterizing cancer within individuals and offers an alternative or a synergistic supplement to mutation searches, methylation, or nucleosome positioning. As such, it offers potential for improving diagnostics and cancer screening.

SUBMITTER: Thierry AR

PROVIDER: S-EPMC9903826 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Circulating DNA fragmentomics and cancer screening.

Thierry A R AR

Cell genomics 20230111 1

The high fragmentation of nuclear circulating DNA (cirDNA) relies on chromatin organization and protection or packaging within mononucleosomes, the smallest and the most stabilized structure in the bloodstream. The detection of differing size patterns, termed fragmentomics, exploits information about the nucleosomal packing of DNA. Fragmentomics not only implies size pattern characterization but also considers the positioning and occupancy of nucleosomes, which result in cirDNA fragments being p ...[more]

PMID: 36777187

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Project description:Currently, approximately 30%-55% of the patients with non-small cell lung cancer (NSCLC) develop recurrence due to minimal residual disease (MRD) after receiving surgical resection of the tumor. This study aims to develop an ultrasensitive and affordable fragmentomic assay for MRD detection in patients with NSCLC. A total of 87 patients with NSCLC, who received curative surgical resections (23 patients relapsed during follow-up), enrolled in this study. A total of 163 plasma samples, collected at 7 days and 6 months postsurgical, were used for both whole-genome sequencing (WGS) and targeted sequencing. WGS-based cell-free DNA (cfDNA) fragment profile was used to fit regularized Cox regression models, and leave-one-out cross-validation was further used to evaluate models' performance. The models showed excellent performances in detecting patients with a high risk of recurrence. At 7 days postsurgical, the high-risk patients detected by our model showed an increased risk of 4.6 times, while the risk increased to 8.3 times at 6 months postsurgical. These fragmentomics determined higher risk compared with the targeted sequencing-based circulating mutations both at 7 days and 6 months postsurgical. The overall sensitivity for detecting patients with recurrence reached 78.3% while using both fragmentomics and mutation results from 7 days and 6 months postsurgical, which increased from the 43.5% sensitivity by using only the circulating mutations. The fragmentomics showed great sensitivity in predicting patient recurrence compared with the traditional circulating mutation, especially after the surgery for early-stage NSCLC, therefore exhibiting great potential to guide adjuvant therapeutics.SignificanceThe circulating tumor DNA mutation-based approach shows limited performance in MRD detection, especially for landmark MRD detection at an early-stage cancer after surgery. Here, we describe a cfDNA fragmentomics-based method in MRD detection of resectable NSCLC using WGS, and the cfDNA fragmentomics showed a great sensitivity in predicting prognosis.

Project description: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.

Dataset Information

Circulating DNA fragmentomics and cancer screening.

Publications

Circulating DNA fragmentomics and cancer screening.

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