J Liq Biopsy. 2026 Sep 18;14:100501. doi: 10.1016/j.jlb.2026.100501. eCollection 2026 Dec.

ABSTRACT

INTRODUCTION: Circulating tumor DNA (ctDNA) is a valuable tool for assessing minimal residual disease (MRD) and predicting recurrence in resected non-small cell lung cancer (NSCLC) patients. Combining ctDNA-detection with radiological tumor volume may improve risk stratification.

METHODS: Patients with stage I-III resectable NSCLC were prospectively enrolled in the RESIDUAL study. Plasma samples were collected before surgery (T0), at landmark (10 days after surgery), during surveillance (T2, 20 days, T3, 1 months after surgery and every 3 months for the first year and then at the end of the second year after surgery), and at relapse. Samples were analyzed using Guardant Reveal, a tissue-free methylation-based ctDNA assay. Receiver operating characteristic analysis associated T1 ctDNA status with tumor volume; volume thresholds were calculated via Youden's J, and Cox regression analysis was performed.

RESULTS: Forty-eight patients were enrolled (median age 72 years; 64.6% male). Most had stage I disease (54.2%) and adenocarcinoma histology (79.2%). Median follow-up was 41.8 months, and 19 patients (39.6%) relapsed. Overall ctDNA detection rate was 15.2% across all timepoints. Pre-surgical ctDNA detection was higher in squamous histology and stage II-III disease and was associated with worse disease-free survival (DFS; p = 0.022). Landmark MRD detection was also associated with worse DFS (p = 0.024). Serial surveillance sampling anticipated radiologic recurrence by a median of 2.6 months (range 2.0-7.5). Patients with tumor volume >26,378 mm³ had a significantly higher relapse risk (p < 0.001).

CONCLUSIONS: MRD detection in NSCLC resected patients predicts relapse and poor outcome; integrating ctDNA with tumor volume enhances identification of high-risk patients.

PMID:42828040 | PMC:PMC13631347 | DOI:10.1016/j.jlb.2026.100501

Front Oncol. 2026 Aug 27;16:1919279. doi: 10.3389/fonc.2026.1919279. eCollection 2026.

ABSTRACT

Lung Cancer (LC) continues to be the biggest cause of cancer-related deaths around the world, mostly because of delayed diagnosis. Even if tissue biopsies and circulating tumor DNA (ctDNA) tests have revolutionized clinical management of LC patients, their effectiveness is restricted in settings with lower tumor burden, molecular heterogeneity, and bias in sampling approaches. In this scenario, the epigenetic profiling of cell-free DNA (cfDNA) stands out as a promising, less invasive approach, accurately detect cancer traces. Evidence from stage I-II disease and CT-detected pulmonary nodules supports the diagnostic potential of cfDNA methylation, although further validation in prospective screening cohorts remains necessary. Beyond genomic alterations, cfDNA epigenetic changes, including DNA methylation, chromatin organization, nucleosome positioning, and fragmentation patterns, reflect multi-dimensional complexity of tumor biology. These properties convey both the functional status and the origin of the circulating DNA fragments, accelerating for tumor integrating genomic analysis. Within this group, DNA methylation is the biologically robust and clinically well-established epigenetic marker, as alterations in methylation linked to cancer often occur in the early stages of tumorigenesis and are commonly found across different cancer cell types. Here, we explored the biological and clinical relevance of the epigenetic landscape of cfDNA in LC patients, particularly focusing on DNA methylation-based biomarkers and their evolving applications towards early diagnosis and post-surgical monitoring of minimal residual disease (MRD). We aimed to comprehensively overview analytical approaches for cfDNA methylation analysis, including targeted and genome-wide profiling strategies, and discuss their integration with machine learning (ML) and multi-omics frameworks in order to improve diagnostic performance and clinical applicability in LC management.

PMID:42724581 | PMC:PMC13559918 | DOI:10.3389/fonc.2026.1919279

Front Oncol. 2026 Aug 27;16:1919279. doi: 10.3389/fonc.2026.1919279. eCollection 2026.

ABSTRACT

Lung Cancer (LC) continues to be the biggest cause of cancer-related deaths around the world, mostly because of delayed diagnosis. Even if tissue biopsies and circulating tumor DNA (ctDNA) tests have revolutionized clinical management of LC patients, their effectiveness is restricted in settings with lower tumor burden, molecular heterogeneity, and bias in sampling approaches. In this scenario, the epigenetic profiling of cell-free DNA (cfDNA) stands out as a promising, less invasive approach, accurately detect cancer traces. Evidence from stage I-II disease and CT-detected pulmonary nodules supports the diagnostic potential of cfDNA methylation, although further validation in prospective screening cohorts remains necessary. Beyond genomic alterations, cfDNA epigenetic changes, including DNA methylation, chromatin organization, nucleosome positioning, and fragmentation patterns, reflect multi-dimensional complexity of tumor biology. These properties convey both the functional status and the origin of the circulating DNA fragments, accelerating for tumor integrating genomic analysis. Within this group, DNA methylation is the biologically robust and clinically well-established epigenetic marker, as alterations in methylation linked to cancer often occur in the early stages of tumorigenesis and are commonly found across different cancer cell types. Here, we explored the biological and clinical relevance of the epigenetic landscape of cfDNA in LC patients, particularly focusing on DNA methylation-based biomarkers and their evolving applications towards early diagnosis and post-surgical monitoring of minimal residual disease (MRD). We aimed to comprehensively overview analytical approaches for cfDNA methylation analysis, including targeted and genome-wide profiling strategies, and discuss their integration with machine learning (ML) and multi-omics frameworks in order to improve diagnostic performance and clinical applicability in LC management.

PMID:42724581 | PMC:PMC13559918 | DOI:10.3389/fonc.2026.1919279

Front Oncol. 2026 Aug 27;16:1919279. doi: 10.3389/fonc.2026.1919279. eCollection 2026.

ABSTRACT

Lung Cancer (LC) continues to be the biggest cause of cancer-related deaths around the world, mostly because of delayed diagnosis. Even if tissue biopsies and circulating tumor DNA (ctDNA) tests have revolutionized clinical management of LC patients, their effectiveness is restricted in settings with lower tumor burden, molecular heterogeneity, and bias in sampling approaches. In this scenario, the epigenetic profiling of cell-free DNA (cfDNA) stands out as a promising, less invasive approach, accurately detect cancer traces. Evidence from stage I-II disease and CT-detected pulmonary nodules supports the diagnostic potential of cfDNA methylation, although further validation in prospective screening cohorts remains necessary. Beyond genomic alterations, cfDNA epigenetic changes, including DNA methylation, chromatin organization, nucleosome positioning, and fragmentation patterns, reflect multi-dimensional complexity of tumor biology. These properties convey both the functional status and the origin of the circulating DNA fragments, accelerating for tumor integrating genomic analysis. Within this group, DNA methylation is the biologically robust and clinically well-established epigenetic marker, as alterations in methylation linked to cancer often occur in the early stages of tumorigenesis and are commonly found across different cancer cell types. Here, we explored the biological and clinical relevance of the epigenetic landscape of cfDNA in LC patients, particularly focusing on DNA methylation-based biomarkers and their evolving applications towards early diagnosis and post-surgical monitoring of minimal residual disease (MRD). We aimed to comprehensively overview analytical approaches for cfDNA methylation analysis, including targeted and genome-wide profiling strategies, and discuss their integration with machine learning (ML) and multi-omics frameworks in order to improve diagnostic performance and clinical applicability in LC management.

PMID:42724581 | PMC:PMC13559918 | DOI:10.3389/fonc.2026.1919279

Front Oncol. 2026 Aug 27;16:1919279. doi: 10.3389/fonc.2026.1919279. eCollection 2026.

ABSTRACT

Lung Cancer (LC) continues to be the biggest cause of cancer-related deaths around the world, mostly because of delayed diagnosis. Even if tissue biopsies and circulating tumor DNA (ctDNA) tests have revolutionized clinical management of LC patients, their effectiveness is restricted in settings with lower tumor burden, molecular heterogeneity, and bias in sampling approaches. In this scenario, the epigenetic profiling of cell-free DNA (cfDNA) stands out as a promising, less invasive approach, accurately detect cancer traces. Evidence from stage I-II disease and CT-detected pulmonary nodules supports the diagnostic potential of cfDNA methylation, although further validation in prospective screening cohorts remains necessary. Beyond genomic alterations, cfDNA epigenetic changes, including DNA methylation, chromatin organization, nucleosome positioning, and fragmentation patterns, reflect multi-dimensional complexity of tumor biology. These properties convey both the functional status and the origin of the circulating DNA fragments, accelerating for tumor integrating genomic analysis. Within this group, DNA methylation is the biologically robust and clinically well-established epigenetic marker, as alterations in methylation linked to cancer often occur in the early stages of tumorigenesis and are commonly found across different cancer cell types. Here, we explored the biological and clinical relevance of the epigenetic landscape of cfDNA in LC patients, particularly focusing on DNA methylation-based biomarkers and their evolving applications towards early diagnosis and post-surgical monitoring of minimal residual disease (MRD). We aimed to comprehensively overview analytical approaches for cfDNA methylation analysis, including targeted and genome-wide profiling strategies, and discuss their integration with machine learning (ML) and multi-omics frameworks in order to improve diagnostic performance and clinical applicability in LC management.

PMID:42724581 | PMC:PMC13559918 | DOI:10.3389/fonc.2026.1919279