Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment.

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole-genome sequencing (WGS). Here we now introduce MRD-EDGE, a machine-learning-guided WGS ctDNA single-nucleotide variant (SNV) and copy-number variant (CNV) detection platform designed to increase signal enrichment.

Genomic landscape of virus-associated cancers.

It has been estimated that 15%-20% of human cancers are attributable to infections, mostly by carcinogenic viruses. The incidence varies worldwide, with a majority affecting developing countries. Here, we present a comparative analysis of virus-positive and virus-negative tumors in nine cancers linked to five viruses.

Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring.

In many areas of oncology, we lack sensitive tools to track low-burden disease. Although cell-free DNA (cfDNA) shows promise in detecting cancer mutations, we found that the combination of low tumor fraction (TF) and limited number of DNA fragments restricts low-disease-burden monitoring through the prevailing deep targeted sequencing paradigm. We reasoned that breadth may supplant depth of sequencing to overcome the barrier of cfDNA abundance.