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

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.

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Publication Details

Journal
Cell
Published
2025-11-07
DOI
https://doi.org/10.1016/j.cell.2025.10.020
Citations
5
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
4.68
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article

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

Steven Dea, Charles W. Abbott, Gábor Bartha, Charles Swanton et al.
5 citations
Cell
Cancer Genomics and Diagnostics
4.68
article

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

Steven Dea, Charles W. Abbott, Gábor Bartha, Charles Swanton, Takahiro Karasaki, Allan Hackshaw, John Lyle, James R. Black, Maise Al Bakir, Fábio C. P. Navarro, Alexander M. Frankell, Neeraja Ravi, Christopher Abbosh, Charlotte Grieco, Mariam Jamal‐Hanjani, Fahd Derkaoui Hassani, Selvaraju Veeriah, Oliver Shutkever, Nicholas McGranahan, Daniele Marinelli, Rachel Marty Pyke, Sean M. Boyle, Carlos Martínez‐Ruiz, Jacqui Shaw, Richard Chen, Jason Harris, David Allan Moore, Karen Grimes, Ariana Huebner, Cian Murphy, Wing Kin Liu, Kathleen C. Keough, Fiona Helen Blackhall, Lydia Liu, Bailiang Li, Piotr Pawlik, Katherine D. Brown, Dean A. Fennell
article en
5 citations

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.

CellVol. 188(25)
University Hospitals of Leicester NHS Trust (GB), London Cancer (GB), Personalis (United States) (US), University College London (GB), Sapienza University of Rome (IT)
Openalex Percentile: Top 5%
Cancer Genomics and Diagnostics
4.68
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