Longitudinal ultrasensitive ctDNA monitoring for high-resolution lung cancer risk prediction.

Abstract

Biomarkers accurately informing prognostic assessment and therapeutic strategy are critical for improving patient outcome in oncology. Here, we apply a whole-genome, tumor-informed circulating tumor DNA (ctDNA) detection approach to address this challenge, leveraging 1,800 variants across 2,994 plasma samples from 431 patients with non-small cell lung cancer (NSCLC) from the TRACERx study. We show that ultrasensitive ctDNA detection below 80 parts per million both pre- and postoperatively is highly prognostic, and combinatorial analysis of the pre- and postoperative ctDNA status identifies an intermediate risk group, improving disease stratification. ctDNA kinetics demonstrate clinical utility during adjuvant therapy, where patients that "clear" ctDNA during adjuvant therapy experience improved outcomes. Moreover, characterization of patterns in postoperative ctDNA kinetics reveals insights into the timing, risk, and anatomical pattern of relapses. By incorporating longitudinal ultrasensitive ctDNA detection, we propose a refined schema for guiding the stratification and treatment recommendations in early stage NSCLC.