Evidence map›Paper›PMID 42639369›Full record

ReviewFrontiers in oncology2026

Liquid biopsies for detection, characterization, and interception of therapy resistance in thoracic malignancies.

Ezgi Oner, Volga M Saini, Christina Cahill, Hannah O'Toole, Jennifer W Li, Kathy Gately, Valsamo Anagnostou

Abstract readReview
In one paragraph

Review in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ezgi OnerThe Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Volga M SainiThoracic Oncology Research Group, Trinity Translational Medicine Institute, St James's Hospital, Dublin, Ireland.
Christina CahillThoracic Oncology Research Group, Trinity Translational Medicine Institute, St James's Hospital, Dublin, Ireland.
Hannah O'TooleThoracic Oncology Research Group, Trinity Translational Medicine Institute, St James's Hospital, Dublin, Ireland.
Jennifer W LiThe Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Kathy GatelyThoracic Oncology Research Group, Trinity Translational Medicine Institute, St James's Hospital, Dublin, Ireland.
Valsamo AnagnostouThe Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States.

Funding

Translational Research Central ServicesP30CA006973 · NCI · JOHNS HOPKINS UNIVERSITY · PI Rosalie Wollett · 1985 to 2026
$208.6M
ECOG-ACRIN Thoracic Malignancies Integrated Translational Science CenterUG1CA233259 · NCI · EMORY UNIVERSITY · PI CARBONE, DAVID P., LEAL, TICIANA · 2019 to 2025
$5.1M
Large-Scale Genetic Analyses of Human CancerR01CA121113 · NCI · JOHNS HOPKINS UNIVERSITY · PI ANAGNOSTOU, VALSAMO, VELCULESCU, VICTOR E. · 2006 to 2022
$4.3M
Matching genotypes with personalized therapies: Development of a decision support infrastructure to augment the value of precision medicineU01CA274631 · NCI · JOHNS HOPKINS UNIVERSITY · PI ANAGNOSTOU, VALSAMO, BOTSIS, TAXIARCHIS · 2023 to 2025
$1.2M
FDA HHS U01 FD005942NCI NIH HHS P30 CA006973NCI NIH HHS R01 CA121113NCI NIH HHS U01 CA274631NCI NIH HHS UG1 CA233259
6 · The paper itself

Abstract

Therapy resistance remains a leading cause of treatment failure and mortality in thoracic malignancies, despite major advances in targeted therapies and immunotherapies. Resistance evolves through heterogeneous, patient-specific mechanisms, including genetic alterations, epigenetic reprogramming, lineage plasticity, and tumor-microenvironment interactions, often emerging before radiographic progression or clinical relapse. Conventional tissue biopsies and imaging, while essential for diagnosis and treatment selection, are limited in their ability to capture spatial and temporal tumor heterogeneity or to support dynamic monitoring of tumor evolution. Liquid biopsy has therefore emerged as a minimally invasive approach for real-time assessment of tumor-derived biomarkers in circulation, enabling longitudinal tracking of resistance biology across the cancer care continuum. In this review, we highlight recent advances in liquid biopsy applications for thoracic malignancies, focusing on circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs) as complementary analytes for baseline molecular profiling, detection of primary and acquired resistance to therapy, and identification of minimal residual disease and molecular relapse. Beyond mutation-based approaches, we highlight emerging non-genomic ctDNA features, including epigenomic and fragmentomic signatures, that capture treatment-induced adaptation and lineage plasticity not detectable by conventional plasma genomic profiling, and discuss advances in CTC technologies that preserve cellular and phenotypic context relevant to resistance and metastatic potential. Finally, we examine multimodal liquid biopsy strategies that integrate multiple circulating analytes with artificial intelligence-assisted methods to enhance sensitivity, provide a more comprehensive view of tumor biology, and inform adaptive therapy strategies. We also outline key analytical and clinical challenges that must be addressed through standardized, prospective trials to translate liquid biopsy-guided surveillance and early interception of resistance into improved patient outcomes.

Indexed as

circulating tumor cells (CTCs)circulating tumor DNA (ctDNA)liquid biopsieslung cancertherapy resistancethoracic malignancies

Identifiers

PMID42639369
PMCPMC13501364

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.