Genome-wide analysis of aberrant position and sequence of plasma DNA fragment ends in patients with cancer

Genome-wide fragmentation patterns in cell-free DNA (cfDNA) in plasma are strongly influenced by cellular origin due to variation in chromatin accessibility across cell types. Such differences between healthy and cancer cells provide the opportunity for development of novel cancer diagnostics. Here, we investigated whether analysis of cfDNA fragment end positions and their surrounding DNA sequences reveals the presence of tumor-derived DNA in blood. We performed genome-wide analysis of cfDNA from 521 samples and analyzed sequencing data from an additional 2147 samples, including healthy individuals and patients with 11 different cancer types. We developed a metric based on genome-wide differences in fragment positioning, weighted by fragment length and GC content [information-weighted fraction of aberrant fragments (iwFAF)]. We observed that iwFAF strongly correlated with tumor fraction, was higher for DNA fragments carrying somatic mutations, and was higher within genomic regions affected by copy number amplifications. We also calculated sample -level means of nucleotide frequencies observed at genomic positions spanning fragment ends. Using a combi-nation of iwFAF and nine nucleotide frequencies from three positions surrounding fragment ends, we devel-oped a machine learning model to differentiate healthy individuals from patients with cancer. We observed an area under the receiver operative characteristic curve (AUC) of 0.91 for detection of cancer at any stage and an AUC of 0.87 for detection of stage I cancer. Our findings remained robust with as few as 1 million frag-ments analyzed per sample, demonstrating that analysis of fragment ends can become a cost-effective and ac-cessible approach for cancer detection and monitoring.

Automated summary

The fragmentation patterns of cell-free DNA (cfDNA) in plasma are influenced by cellular origin and can be used to detect tumor-derived DNA. Researchers developed a metric called iwFAF, which is based on fragment positioning and correlates with tumor fraction. They also used machine learning to differentiate healthy individuals from cancer patients based on iwFAF and nucleotide frequencies.