Int J Clin Oncol. 2026 Mar 6. doi: 10.1007/s10147-026-03001-6. Online ahead of print.

ABSTRACT

BACKGROUND: Early detection of cancer and precise recurrence monitoring remain major unmet needs in oncology. Conventional screening is limited to a few cancer types, leaving nearly half of cancers without established programs. Multi-cancer early detection (MCED) tests based on circulating tumor biomarkers have shown promise, but sensitivity for early-stage remains a challenge. In parallel, detection of molecular residual disease (MRD) using circulating tumor DNA (ctDNA) has emerged as a powerful prognostic and predictive tool, though current assays remain limited in sensitivity and specificity. This study aims to integrate multi-omics data to develop more refined and highly sensitive MCED and MRD assays.

METHODS: This study leverages clinical information and biospecimens from patients with cancer and cancer-naïve individuals. Samples from patients with cancers will be derived from the MONSTAR-SCREEN-3 study, while those from cancer-naïve individuals will be obtained from the Tohoku Medical Megabank Project. Comprehensive analyses will include whole-genome sequencing (WGS), whole-exome sequencing (WES), whole-transcriptome sequencing (WTS), proteomics, metabolomics, and microbiome profiling using stool and saliva. Artificial intelligence (AI)-based multi-omics integration will be performed to develop novel MCED and MRD assays and to evaluate their clinical performance. The primary endpoints are the sensitivity and specificity of MCED and MRD assays.

DISCUSSION: This is the first large-scale study to integrate comprehensive multi-omics profiling with AI for MCED and MRD assay development. The findings are expected to advance precision oncology by improving early diagnosis and recurrence monitoring.

TRIAL REGISTRATION: UMIN000053815, approved by the Institutional Review Board of the National Cancer Center Hospital East.

PMID:41790338 | DOI:10.1007/s10147-026-03001-6

Transl Lung Cancer Res. 2025 Nov 30;14(11):4746-4755. doi: 10.21037/tlcr-2025-940. Epub 2025 Nov 27.

ABSTRACT

BACKGROUND: Liquid biopsy based on cell-free DNA (cfDNA) in oncology has emerged as a promising technique for tracking cancer dynamics, especially for detecting minimal residual disease. To date, most studies have used cfDNA for static evaluations of tumor burden. In this study, we propose a novel approach integrating serial cfDNA and computed tomography (CT) tumor volume to fully reflect the dynamic nature of tumor response after treatment.

METHODS: This prospective study involved 25 patients treated with curative-intent radiotherapy for localized non-small cell lung cancer (NSCLC) between June 2019 and November 2020, with 17 subsequently included in final analysis. Longitudinal blood samples were divided into two phases relative to day 3 after treatment initiation, and kinetic parameters, such as velocity and acceleration of cfDNA levels, were calculated. To complement sparse samplings in later days, volume data from routine CT scans were incorporated. K-means clustering using two different variable sets (cfDNA only and cfDNA with volume parameters) and conventional assessment using Response Evaluation Criteria in Solid Tumors (RECIST) v1.1 were applied to stratify patients, and their performance was compared.

RESULTS: The model incorporating both cfDNA and volume parameters effectively separated responders (mean progression-free survival, 44.2 months) from non-responders [16.6 months, P=0.02; area under the receiver operating characteristic curve (AUC) =0.955], outperforming cfDNA only model (36.0 vs. 14.5 months, P=0.04; AUC =0.848). In contrast, RECIST v1.1-based conventional assessment showed no significant difference (P=0.62, AUC =0.70).

CONCLUSIONS: Therefore, our study demonstrates that integration of longitudinal cfDNA and tumor volume dynamics yielded improved assessment of treatment response and prognosis in NSCLC.

PMID:41367558 | PMC:PMC12683446 | DOI:10.21037/tlcr-2025-940

JHEP Rep. 2025 Jul 10;7(11):101518. doi: 10.1016/j.jhepr.2025.101518. eCollection 2025 Nov.

ABSTRACT

BACKGROUND & AIMS: To enhance our understanding of the tumor immune microenvironment (TIME) in hepatocellular carcinoma (HCC), we investigated the heterogeneity of T-cell exhaustion and its association with HBV integrations and direct oncogenic potential in HCC.

METHODS: We conducted a multi-omics analysis, including single-cell RNA sequencing, whole-exome sequencing, whole-transcriptome sequencing, and next-generation sequencing (NGS)-based HBV integration analysis, in eight patients with virus-related HCC. For validation, bulk RNA sequencing and NGS-based HBV integration analysis were performed in an independent cohort (n = 106).

RESULTS: Based on the expression scores of exhaustion markers in effector CD8⁺ T cells, patients were classified into high (n = 2) and low (n = 6) exhaustion groups (p <0.001). The high-exhaustion group exhibited higher clonal expansion (Gini index: 0.83 vs. 0.48, p = 0.006) and sharing of CD8⁺ T effector memory and cycling T cells with elevated exhaustion markers. This group also showed increased clonal expansion of CD4⁺ regulatory T cells and follicular helper T cells (p <0.001) with higher PDCD1 expression. In addition, the high-exhaustion group had higher TP53 mutation rates and signature scores for proliferation subtypes compared with the low-exhaustion group, who predominantly harbored TERT mutations. Moreover, the high-exhaustion group demonstrated more pronounced HBV integrations with elevated intrahepatic covalently closed circular DNA (cccDNA) and pregenomic (pg)RNA levels. Similarly, in the validation cohort, the high-exhaustion group (n = 28) demonstrated stronger proliferation subtype signatures (p <0.001), along with higher HBV integrations, S-fusion transcripts, and an increased intrahepatic viral reservoir (cccDNA/pgRNA) (p <0.05) compared with the low-exhaustion group (n = 78).

CONCLUSIONS: Our study revealed the heterogeneity in T-cell exhaustion in the TIME of HCC, along with differences in HBV integrations and molecular subtypes. These findings provide insight into the intricate relationship between high exhaustion, proliferation subtype, increased HBV integrations, and enhanced HBV-induced oncogenic potential in virus-related HCC.

IMPACT AND IMPLICATIONS: This study provides a comprehensive immune landscape of T-cell exhaustion using multi-omics analysis, offering critical insights into T cell heterogeneity in virus-related HCC. It establishes a strong association between higher HBV integration, enhanced oncogenic potential, T-cell exhaustion, and proliferation subtypes in HCC. Our results also establish a basis for personalized therapies tailored to the immune-exhaustion status within the TIME of each patient with HCC.

PMID:41113120 | PMC:PMC12529496 | DOI:10.1016/j.jhepr.2025.101518