Enhanced detection of minimal residual disease by targeted sequencing of phased variants in circulating tumor DNA.

Circulating tumor-derived DNA (ctDNA) is an emerging biomarker for many cancers, but the limited sensitivity of current detection methods reduces its utility for diagnosing minimal residual disease. Here we describe phased variant enrichment and detection sequencing (PhasED-seq), a method that uses multiple somatic mutations in individual DNA fragments to improve the sensitivity of ctDNA detection. Leveraging whole-genome sequences from 2,538 tumors, we identify phased variants and their associations with mutational signatures. We show that even without molecular barcodes, the limits of detection of PhasED-seq outperform prior methods, including duplex barcoding, allowing ctDNA detection in the ppm range in participant samples. We profiled 678 specimens from 213 participants with B cell lymphomas, including serial cell-free DNA samples before and during therapy for diffuse large B cell lymphoma. In participants with undetectable ctDNA after two cycles of therapy using a next-generation sequencing-based approach termed cancer personalized profiling by deep sequencing, an additional 25% have ctDNA detectable by PhasED-seq and have worse outcomes. Finally, we demonstrate the application of PhasED-seq to solid tumors.

Integrating genomic features for non-invasive early lung cancer detection.

Radiologic screening of high-risk adults reduces lung-cancer-related mortality1,2; however, a small minority of eligible individuals undergo such screening in the United States3,4. The availability of blood-based tests could increase screening uptake. Here we introduce improvements to cancer personalized profiling by deep sequencing (CAPP-Seq)5, a method for the analysis of circulating tumour DNA (ctDNA), to better facilitate screening applications. We show that, although levels are very low in early-stage lung cancers, ctDNA is present prior to treatment in most patients and its presence is strongly prognostic. We also find that the majority of somatic mutations in the cell-free DNA (cfDNA) of patients with lung cancer and of risk-matched controls reflect clonal haematopoiesis and are non-recurrent. Compared with tumour-derived mutations, clonal haematopoiesis mutations occur on longer cfDNA fragments and lack mutational signatures that are associated with tobacco smoking. Integrating these findings with other molecular features, we develop and prospectively validate a machine-learning method termed 'lung cancer likelihood in plasma' (Lung-CLiP), which can robustly discriminate early-stage lung cancer patients from risk-matched controls. This approach achieves performance similar to that of tumour-informed ctDNA detection and enables tuning of assay specificity in order to facilitate distinct clinical applications. Our findings establish the potential of cfDNA for lung cancer screening and highlight the importance of risk-matching cases and controls in cfDNA-based screening studies.