J Liq Biopsy. 2026 Sep 19;14:100496. doi: 10.1016/j.jlb.2026.100496. eCollection 2026 Dec.

ABSTRACT

BACKGROUND: Circulating tumor DNA (ctDNA) analysis has revolutionized minimal residual disease (MRD) monitoring, but conventional tumor-informed amplicon-based sequencing (AMP) is limited by the narrow variant capacity and diversity. Hybrid capture-based sequencing (HYB) is more versatile and enables both tumor-informed and tumor-naïve liquid biopsy profiling.

METHODS: We analytically validated the performance of our novel HYB workflow and VarSURE variant calling pipeline, using reference standards (n = 6), plasma samples of cancer patients (n = 75) and healthy donors (n = 90). Genome-wide (GW) non-mutation features including copy number alterations, fragmentomics, and end-motif signatures were also evaluated to enhance ctDNA-MRD detection. Clinical performance was directly compared against our legacy AMP method (K-TRACK, Gene Solutions), using pre-treatment blood samples across multiple cancers (n = 290) and longitudinal cohorts of colorectal cancer (CRC, n = 64), and hepatocellular carcinoma (HCC, n = 47).

RESULTS: Optimal parameters to maximize assay performance included single-stranded DNA ligation technology, cfDNA input ≥ 15 ng, post-UMI sequencing depth ≥ 2500X, and high number of tracked mutations. In the tumor-informed setting, the HYB workflow was modestly better than the AMP method in detection of pre-treatment ctDNA; addition of GW features was marginally beneficial except in lung cancer. Surveillance ctDNA determined by the HYB workflow had superior sensitivity to predict recurrence in both CRC (AMP: 90.0%, HYB: 100%) and HCC (AMP: 80.0%, HYB: 96.0%). In the tumor-naïve setting, the performance gap widened significantly, and the combined HYB and GW workflow showed the highest performance in baseline ctDNA detection across all cancers, and achieved sensitivity of 90.0% and 92.0% to detect recurrence in CRC and HCC respectively.

CONCLUSIONS: The new methodology offers a streamlined and scalable solution for both comprehensive liquid biopsy profiling and longitudinal MRD tracking in routine clinical practice.

PMID:42830887 | PMC:PMC13634064 | DOI:10.1016/j.jlb.2026.100496

J Liq Biopsy. 2026 Sep 19;14:100496. doi: 10.1016/j.jlb.2026.100496. eCollection 2026 Dec.

ABSTRACT

BACKGROUND: Circulating tumor DNA (ctDNA) analysis has revolutionized minimal residual disease (MRD) monitoring, but conventional tumor-informed amplicon-based sequencing (AMP) is limited by the narrow variant capacity and diversity. Hybrid capture-based sequencing (HYB) is more versatile and enables both tumor-informed and tumor-naïve liquid biopsy profiling.

METHODS: We analytically validated the performance of our novel HYB workflow and VarSURE variant calling pipeline, using reference standards (n = 6), plasma samples of cancer patients (n = 75) and healthy donors (n = 90). Genome-wide (GW) non-mutation features including copy number alterations, fragmentomics, and end-motif signatures were also evaluated to enhance ctDNA-MRD detection. Clinical performance was directly compared against our legacy AMP method (K-TRACK, Gene Solutions), using pre-treatment blood samples across multiple cancers (n = 290) and longitudinal cohorts of colorectal cancer (CRC, n = 64), and hepatocellular carcinoma (HCC, n = 47).

RESULTS: Optimal parameters to maximize assay performance included single-stranded DNA ligation technology, cfDNA input ≥ 15 ng, post-UMI sequencing depth ≥ 2500X, and high number of tracked mutations. In the tumor-informed setting, the HYB workflow was modestly better than the AMP method in detection of pre-treatment ctDNA; addition of GW features was marginally beneficial except in lung cancer. Surveillance ctDNA determined by the HYB workflow had superior sensitivity to predict recurrence in both CRC (AMP: 90.0%, HYB: 100%) and HCC (AMP: 80.0%, HYB: 96.0%). In the tumor-naïve setting, the performance gap widened significantly, and the combined HYB and GW workflow showed the highest performance in baseline ctDNA detection across all cancers, and achieved sensitivity of 90.0% and 92.0% to detect recurrence in CRC and HCC respectively.

CONCLUSIONS: The new methodology offers a streamlined and scalable solution for both comprehensive liquid biopsy profiling and longitudinal MRD tracking in routine clinical practice.

PMID:42830887 | PMC:PMC13634064 | DOI:10.1016/j.jlb.2026.100496

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

Nat Med. 2026 Oct 2. doi: 10.1038/s41591-026-04673-3. Online ahead of print.

ABSTRACT

Urine biomarkers promise to improve noninvasive detection and molecular characterization of genitourinary malignancies. Here we describe urine random priming and affinity capture of cell-free RNA (cfRNA) fragments for enrichment analysis by sequencing (uRARE-seq), a liquid biopsy method for urine cfRNA profiling, and apply it to 683 urine samples from patients with cancer and controls. Urine cfRNA contained transcripts from genitourinary tissues and, in patients with prostate, kidney or bladder cancer, tumor-derived transcripts. uRARE-seq demonstrated 95% sensitivity at 90% specificity for detecting localized bladder cancer. The method outperformed urine tumor DNA analysis and was unaffected by the presence of field-effect mutations. Urine cfRNA analysis also sensitively detected minimal residual disease and distinguished complete molecular responses after surgery from those after intravesical Bacillus Calmette-Guérin (BCG). Pretreatment urine from complete responders to BCG was enriched for T cell and other immune signatures, suggesting a preexisting antitumor immune response, whereas nonresponders showed higher expression of proliferation-related genes. In pretreatment urine from 114 patients, this biological difference enabled development of a biomarker predicting likelihood of response to BCG versus chemotherapy (area under the curve 0.93) that was strongly associated with risk of recurrence. Urine cfRNA analysis is therefore a promising biomarker approach for bladder cancer and potentially other urologic malignancies, although prospective studies are needed to assess its clinical utility.

PMID:42827132 | DOI:10.1038/s41591-026-04673-3

J Bone Oncol. 2026 Sep 16;61:100803. doi: 10.1016/j.jbo.2026.100803. eCollection 2026 Dec.

ABSTRACT

Osteosarcoma relapse surveillance remains dominated by scheduled imaging because salvage treatment depends on anatomical confirmation of pulmonary, local or extrapulmonary recurrence. However, radiological recurrence may occur after a biologically active phase in which residual viable disease or micrometastatic progression is already present but not yet localizable. This clinical-translational review reframes postoperative osteosarcoma surveillance as an action-linked decision workflow rather than a comparison of isolated biomarker technologies. Current evidence suggests that tumor-informed circulating tumor DNA (ctDNA) sequencing provides the strongest osteosarcoma-specific minimal residual disease (MRD) signal, with postoperative positivity associated with inferior event-free survival and, in selected patients, molecular detection preceding imaging-confirmed relapse or progression. Cell-free DNA methylation may offer a mutation-independent adjunct, whereas circulating tumor cells, extracellular vesicles and circulating microRNAs remain exploratory signals without validated postoperative surveillance actions. Chest computed tomography (CT) and local magnetic resonance imaging (MRI) remain indispensable for disease localization and treatment planning, while diffusion-weighted imaging, dynamic contrast-enhanced MRI and radiomics currently provide mainly local viability or risk-enrichment information rather than proven surveillance-intervention evidence. The near-term role of integrated biomarkers is therefore not to replace guideline-based imaging, but to define protocolized pathways for molecular-positive/imaging-negative, imaging-positive/molecular-negative, concordant high-risk and concordant low-risk states. Future studies should test whether biomarker-triggered reassessment improves clinically meaningful outcomes, including resectability, second complete remission, clinical trial access, patient burden and survival, rather than simply documenting recurrence earlier.

PMID:42824543 | PMC:PMC13628598 | DOI:10.1016/j.jbo.2026.100803

Int J Biol Sci. 2026 Sep 10;22(14):8090-8118. doi: 10.7150/ijbs.137262. eCollection 2026.

ABSTRACT

The tumor leading edge (TLE) is a critical region where tumor cells interact with the microenvironment to drive invasion and metastasis; however, its cellular architecture in early hepatocellular carcinoma (HCC) remains poorly understood. Here, we integrated single-nucleus RNA-seq (snRNA-seq), spatial transcriptomics, and computational pathology to investigate TLE in early HCC. We annotated 35 cell subpopulations and identified STMN1-high tumor cells as a key malignant subset enriched at the invasive front, interacting with Treg, plasma B, LAMP3⁺ dendritic cells and SPP1⁺ macrophages. Spatial analysis revealed three co-localized cell pairs-(SPP1⁺ macrophages co-localized with Tip-like and inflammatory endothelial cells), (LAMP3⁺ DCs co-localized with naive T cells), and (plasma B cells co-localized with cancer-associated fibroblasts)-forming a leading-edge tumor microenvironment (L-TME) niche associated with early relapse. We developed an L-TME-related machine-learning benchmark framework incorporating 71 imaging features (65 deep-learning + 6 pathological) based on the snRNA-seq, spatial transcriptomics and pathomics. The pathology model achieved robust performance (mean C-index=0.77) and successfully predicted the recurrence of early HCC (log-rank p < 0.05) in TCGA (n=147) and an independent in-house cohort (n=123). This study delineates the TLE cellular ecosystem of early HCC, defines a spatially coordinated immunosuppressive L-TME niche, and provides a clinically applicable predictive tool for postoperative recurrence. Integrating multi-omics with computational pathology deepens our understanding of early HCC metastasis and offers insights into improved prognostication and therapeutic strategies.

PMID:42807944 | PMC:PMC13618224 | DOI:10.7150/ijbs.137262

Cell. 2026 Sep 29:S0092-8674(26)01081-0. doi: 10.1016/j.cell.2026.09.012. Online ahead of print.

ABSTRACT

Cachexia is a major cause of morbidity in pancreatic cancer, but the cellular circuitry linking tumor progression to systemic wasting remains incompletely understood. Integrating single-cell RNA sequencing, Xenium spatial transcriptomics, multiplex immunohistochemistry, bulk transcriptomics, and functional studies across human non-cachexia, pre-cachexia, and cachexia samples, together with mouse models, we define a cachexia-associated microenvironmental niche composed of SEMA4A⁺ tumor cells, AQP9⁺ macrophages, and LOXL2⁺ cancer-associated fibroblasts. Mechanistically, SEMA4A-associated signaling promotes bone morphogenetic protein-2 (BMP2)-dependent acquisition of an AQP9-associated macrophage phenotype, and macrophage-derived CXCL8 activates LOXL2⁺ fibroblasts. LOXL2⁺ fibroblasts reciprocally enhance tumor cell FOSL1/SEMA4A signaling through exosomal N-glycosylated LOXL2. Spatial analyses demonstrate progressive enrichment of this niche with cachexia severity and association with postoperative development of cachexia in previously non-cachectic patients. These findings provide a framework linking local tumor ecosystem dynamics to cachexia progression.

PMID:42810340 | DOI:10.1016/j.cell.2026.09.012

Nat Commun. 2026 Aug 29;17(1):10311. doi: 10.1038/s41467-026-77187-8.

ABSTRACT

Type 2 diabetes (T2D) subgroups defined by clinical variables differ in disease progression and treatment response. To uncover potential molecular drivers of this heterogeneity, we performed a multi-omics analysis of 826 drug-naïve T2D patients from two phase 3 trials of the insulin sensitizer chiglitazar. Here we show that severe insulin-resistant diabetes (SIRD) is characterized by distinct miRNA profiles (e.g., miR-122-5p) correlated with liver injury, and metabolic shifts in amino acids and primary bile acids. Mild obesity-related diabetes (MOD) showed the lowest level of phenylacetylglutamine, a metabolite known to promote cardiovascular disease. Severe insulin-deficient diabetes (SIDD) exhibited high pancreas-specific miR-7-5p, while mild age-related diabetes (MARD) presented the mildest abnormalities. Finally, integrating these multi-omics signatures into machine learning models enhanced prediction of insulin sensitizer efficacy over clinical data alone. Our findings define the distinct molecular signatures of T2D subgroups, facilitating the prediction of heterogeneous treatment responses and supporting personalized clinical management.

PMID:42805981 | PMC:PMC13620142 | DOI:10.1038/s41467-026-77187-8

Signal Transduct Target Ther. 2026 Sep 28;11(1):418. doi: 10.1038/s41392-026-02875-2.

ABSTRACT

Hepatocellular carcinoma (HCC) exhibits a suboptimal response to immune checkpoint blockade (ICB) therapy; to overcome this resistance, we aimed to delineate key immune resistance factors via multi-omics analysis, develop strategies to block their immunosuppressive axes, and engineer a targeted nanosystem to enhance immunotherapy efficacy against PD-1 resistance in HCC. Using transcriptomic and proteomic data from anti-PD-1-treated HCC patients, along with functional validation in murine models and mechanistic molecular and cell biology studies, we identified transketolase-like 1 (TKTL1) as a dual-nature biomarker where overexpression predicted poor baseline prognosis yet enhanced response to ICB. Mechanistically, TKTL1 diverts glucose flux into glycolysis rather than pentose phosphate pathway (PPP), recruiting USP9X to deubiquitinate and stabilize HIF-1α, which upregulates HK2 to amplify glycolytic output and lactate accumulation. This metabolic rewiring orchestrates dual immunosuppressive circuits through HIF-1α-driven CCL4 secretion recruiting PD-L1^high dendritic cells (DCs), coupled with lactate-induced TRIM28^K408 lactylation that stabilizes PD-L1 by blocking ubiquitin-mediated degradation. We engineered a hepatoma-membrane-coated MnO₂ nanosystem (CQLH) co-delivering a TKTL1 inhibitor and lactate oxidase, which disrupted the TKTL1-HIF-1α-HK2 axis, depleted lactate, and reprogrammed the tumor microenvironment, thereby enhanced anti-PD-1 therapy to suppress tumor growth, especially in TKTL1^high tumors. These findings define a critical "TKTL1-glycolysis-lactate-DC" axis driving anti-PD-1 sensitivity in HCC, position TKTL1 as both a potential biomarker for ICB response and a tractable therapeutic target, and demonstrate that the targeted CQLH nanosystem overcomes resistance and enhances anti-PD-1 efficacy, offering a precision immunotherapeutic strategy for TKTL1^high HCC.

PMID:42802226 | PMC:PMC13616917 | DOI:10.1038/s41392-026-02875-2

Biomedicines. 2026 Aug 25;14(9):1898. doi: 10.3390/biomedicines14091898.

ABSTRACT

Background/Objectives: Hepatocellular carcinoma (HCC) is the most prevalent primary liver tumor and is often diagnosed at advanced stages with very poor therapeutic response, leading to high mortality. Thus, new therapeutic strategies and biomarkers are urgently needed. We previously showed that the combination of loratadine, raloxifene, and sorafenib exerts synergistic cytotoxicity on HCC cells. Here, we explored potential molecular mechanisms underlying the anticancer effects of this combination using multiomics analyses. Methods: We performed proteomic analyses based on mass spectrometry, transcriptomic analyses using the Clariom D Plus human microarray (Affymetrix), and metabolomic analyses based on nuclear magnetic resonance to investigate the profile changes induced by the drug combination in HuH7 cells. Bioinformatic analyses were applied to associate the omics changes with biological functions, molecular interactions, and clinical relevance in terms of patient survival. Results: We identified several molecules whose expression changed in response to treatment across the three omics profiles analyzed. Some of them were found to be involved in hallmarks of cancer, including sustained proliferation, evasion of growth suppressors, and resistance to cell death. Integrated multi-omics analyses revealed that the drug combination suppresses critical oncogenic drivers (C7orf50, NUP188, and HS2ST1) and that the mitotic cell cycle process, DNA synthesis and cholesterol biosynthesis are the primary pathways affected. Protein-protein interaction analysis revealed five key hubs (KIF2C, PCNA, TRIP13, NDC80, and RPA3), whose expression in HCC is associated with poor clinical prognosis. Conclusions: The combined treatment rewired molecular networks involved in HCC progression. These findings identify clinically relevant molecular targets associated with poor prognosis and provide mechanistic insights into the synergistic anticancer activity of this drug combination.

PMID:42792641 | PMC:PMC13604568 | DOI:10.3390/biomedicines14091898

Arch Bronconeumol. 2026 Sep 25:S0300-2896(26)00318-2. doi: 10.1016/j.arbres.2026.08.007. Online ahead of print.

ABSTRACT

BACKGROUND: Highly effective CFTR modulator therapy with elexacaftor-tezacaftor-ivacaftor (ETI) has revolutionized clinical outcomes in cystic fibrosis (CF), yet its effects on gut and lung microbiota, especially at the functional level, are poorly understood.

METHODS: In a 12-month prospective study, we enrolled 35 clinically stable CF patients initiating ETI. Paired fecal and sputum samples, collected at baseline and after 12 months, were analyzed using shotgun metagenomics, metaproteomics, and short-chain fatty acid (SCFA) quantification. Multi-omics data were integrated with clinical parameters assessing lung, hepatic, pancreatic, and intestinal function.

RESULTS: ETI drove significant clinical improvements, including increased ppFEV₁, higher fecal elastase, and better nutritional status, despite persistent major lung pathogens and minimal changes in liver or intestinal inflammation markers. Microbiota composition showed limited shifts: alpha diversity was stable, and beta diversity changes accounted for only small variance in both compartments. However, butyrate-producing genera enriched in feces, while oropharyngeal taxa increased in sputum. Metaproteomics revealed broad downregulation of host neutrophil-driven inflammatory proteins; sputum additionally showed increased abundance of extracellular matrix-related proteins. Microbial proteins linked to carbohydrate/lipid metabolism, particularly butanoate pathways, increased in feces alongside a trend for higher butyrate. In sputum, formaldehyde dehydrogenase enzymes rose, indicating enhanced oxidative microbial metabolism.

CONCLUSIONS: ETI is associated with minimal compositional but substantial functional reprogramming in CF microbiota. These changes are accompanied by an increase in butyrate-producing taxa, attenuation of host pro-inflammatory pathways, and a shift in lung metabolism toward oxidation. Despite ongoing pathogenic colonization, these changes suggest CFTR modulation is associated with a less inflammatory, more stable host-microbiota ecosystem.

PMID:42791132 | DOI:10.1016/j.arbres.2026.08.007

Metabolites. 2026 Aug 25;16(9):608. doi: 10.3390/metabo16090608.

ABSTRACT

With the advent of the new definition, fatty liver disorders have been reframed into metabolic dysfunction-associated steatotic liver disease (MASLD), alcohol-related liver disease (ALD), and the mixed phenotype referred to as MetALD (MASLD and increased alcohol intake). This change reflects the real-world clinical practice, where metabolic dysfunction and alcohol frequently coexist and synergize to increase risks of steatohepatitis, fibrosis, and hepatocellular carcinoma (HCC). While conventional non-invasive tests (NITs) remain the backbone of risk stratification, lipidomics and metabolomics can capture biological information on disease mechanisms and may improve early detection and prognosis. Here, we summarize the current evidence on circulating and tissue lipidomic and metabolomic signatures across MASLD, ALD and MetALD, discuss how the new definitions affect clinical risk assessment, and highlight recent studies which partially distinguish molecular fingerprints for mixed etiology disease.

PMID:42783733 | PMC:PMC13609168 | DOI:10.3390/metabo16090608