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PubMed2026

The future of blood-based biomarkers in liver cancer.

Liquid biomarkers hold substantial promise in liver cancer, with potential applications in risk stratification, surveillance and early detection, therapeutic decision-making, and treatment-response monitoring. In parallel with oncologic advances, liquid biopsy has gained increasing attention. However, despite expanded research efforts, prospective clinical validation remains limited. While cell-free DNA-based detection of actionable alterations has entered clinical practice in select contexts, most candidate liquid biomarkers still require rigorous evaluation through translational research embedded in clinical trials and prospective cohort studies. In this review, we summarise the current landscape of blood-based biomarkers across the cancer care continuum for individuals at risk, or diagnosed with hepatocellular carcinoma and biliary tract cancers, and discuss the key challenges and opportunities that lie ahead.

Humans↗

PubMed2026

Multiparametric flow cytometry immune profiling of pulmonary and extra-pulmonary tuberculosis reveals distinct blood-based biomarker signatures.

This study investigated immune cell distributions, cell-specific immune markers, and selected biomarker targets in pulmonary tuberculosis (PTB) and extrapulmonary tuberculosis (EPTB) using multiparametric flow cytometry (MFC). Whole blood was collected from 45 individuals, including healthy controls (HC), EPTB, and PTB patients (n = 15/group). Peripheral blood leukocytes were analysed by MFC to characterize CD4+ and CD8+ T cells, natural killer (NK), invariant NKT (iNKT) and NKT cells, classical (CM), intermediate (IM) and non-classical monocytes (NCM), and activated monocytes (AM). Expression of GBP1, CALCOCO2, IFIT3, SNX10, ARG1, PD-1, and PD-L1 was assessed across these immune subsets. Increased frequencies of NK, NKT, and monocytes were observed in PTB and EPTB compared with HC, while CD4+, CD8+, iNKT, and AM were reduced. Monocyte-to-lymphocyte ratios were incrementally elevated in EPTB and PTB compared with HC. Despite variability of expression within groups, median biomarker fold-change expression changes were found between HC, EPTB and PTB groups; (i) (>2.0FC) for ARG1 in CD4, CD8, CM and AM, for CALCOCO2 in AM, GBP1 in CD8 and NCM, PD-1 in CD4, CD8, NK, IM and AM, PD-L1 in CD4, CD8, iNKT and NKT, NK, IM and AM and SNX10 in CD4, CD8, NCM, IM and AM (ii) (<2.0FC) in TB vs HC for CALCOCO2 in iNKT and NKT, IFIT3 in NCM, PD-1 in NK and NCM, PD-L1 in NCM, IM and AM and SNX10 in AM. Statistical significance was achieved for ARG1 (P = 0.017) in CD4 cells. Our findings highlight distinct immune cell and biomarker signatures in PTB and EPTB.

Humans↗

PubMed2026

Blood-based biomarkers of Alzheimer's disease and neurodegeneration in an indigenous African cohort using both Simoa and NULISA platforms.

In low- and middle-income countries, Alzheimer's disease (AD) constitutes a growing public health burden. However, AD biomarkers research remains underrepresented in African populations. This study assesses core biomarkers of AD and their relevance in the African context as potential aid in clinical diagnosis. Nigerian older adults from VALIANT cohort (n = 967) underwent biomarker quantification in plasma (p-tau217, GFAP, NfL, Aβ42 and Aβ40) employing both the Single Molecule Assay (Simoa, Quanterix) and Nucleic acid-Linked Immuno-Sandwich Assay (NULISA, Alamar). Biomarkers were associated with disease severity in clinical-diagnostic and clinical-biological groups, with stepwise increases of p-tau217, NfL and GFAP from cognitively unimpaired to dementia (p < 0.05). Results were consistent across platforms. Comparison between sexes showed higher biomarker levels in male participants across diagnostic groups. A significant effect of apoE-E4 proteotype on p-tau217 levels, after adjusting for age and sex was identified. These findings support the application of plasma AD biomarkers in the African context and the relevance of further AD biomarker research in diverse populations.

Biomarkers↗

PubMed2026

Prognostic Value of Blood-Based P-Tau217 Levels for Progression to Cognitive Impairment.

IMPORTANCE: Blood-based biomarkers for Alzheimer disease, particularly plasma phosphorylated tau 217 (p-tau217), accurately reflect early Alzheimer disease brain pathology in cognitively unimpaired individuals, but estimates of absolute risk of progression to cognitive impairment across multiple cohorts are needed. OBJECTIVE: To estimate absolute risk of progression to cognitive impairment and rates of cognitive decline based on plasma p-tau217 across cognitively unimpaired older adults. DESIGN, SETTING, AND PARTICIPANTS: Longitudinal cohort study using harmonized data from 2684 cognitively unimpaired older adults (defined within cohort) across 6 observational and clinical trial cohorts based in North America, Japan, and Australia. The earliest enrollment was in 2004, with most recent follow-up in 2025. EXPOSURE: Baseline plasma p-tau217. MAIN OUTCOMES AND MEASURES: The primary outcome was time to progression to cognitive impairment (mild cognitive impairment, dementia, or 2 consecutive global Clinical Dementia Rating scores ≥0.5). The secondary outcome was longitudinal change on the latent Preclinical Alzheimer Cognitive Composite (PACC; higher values indicate better performance). RESULTS: Among the 2684 participants (median [IQR] age, 69.6 [66.2-74.2] years; 1697 [63%] female), there were 478 events of progression to cognitive impairment over a median follow-up of 5.4 years (maximum follow-up of 13.5 years). Each 1-SD increase in baseline p-tau217 level was associated with an increased risk of progression to cognitive impairment (hazard ratio, 1.38 [95% CI, 1.30-1.46]), and the association remained significant after adjustment, including β-amyloid positron emission tomography scan Centiloids (hazard ratio, 1.32 [95% CI, 1.24-1.41]). Participants with high (1.1-2.4 SD) and very high (>2.5 SD) baseline p-tau217 had 24% (95% CI, 20%-28%) and 38% (95% CI, 33%-43%) absolute risk of progression over 5 years, respectively, and risk was markedly higher over 10 years, although longer-term estimates were constrained by limited data. Elevated p-tau217 was also associated with faster cognitive decline based on change in latent PACC score. Among the overall sample, baseline latent PACC scores ranged from -0.8 to 2.7. The 5-year annualized decline for the very high p-tau217 group was -0.07 latent PACC units/y (95% CI, -0.10 to -0.05), relative to 0.03 units/y (95% CI, 0.02-0.04) in the low p-tau217 group. CONCLUSIONS AND RELEVANCE: In a pooled sample of multiple selected cohorts of cognitively unimpaired older adults, higher plasma p-tau217 levels were consistently associated with increased risk of clinical progression and accelerated cognitive decline. By providing time-specific absolute risk estimates, these findings support the potential of p-tau217 for prognostic model development, with direct implications for future trial design. Further validation in unselected populations is needed to inform individual prognosis and clinical decision-making in cognitively unimpaired individuals.

Aged↗

PubMed2026

Liquid biopsies reveal dual compartments of cancer risk from tumor and host-derived mutations.

MOTIVATION: Circulating tumor DNA (ctDNA) and clonal hematopoiesis of indeterminate potential (CHIP) are two biologically distinct sources of somatic mutations detectable in blood. While ctDNA captures tumor-intrinsic alterations, CHIP arises from age-related hematopoietic clones and is often considered background noise. Here, we conduct a large-scale, tumor-type-resolved analysis of over 9000 patients with CHIP data and 1500 patients with ctDNA data across solid tumors profiled at Memorial Sloan Kettering Cancer Center. RESULTS: Our results reveal that CHIP and ctDNA mutations exhibit non-overlapping, clinically meaningful signals. CHIP mutations, particularly in DNA damage response and epigenetic regulators (e.g. PPM1D, CHEK2, ATM, TP53, ASXL1), are associated with worse overall survival, increased metastatic potential, and site-specific dissemination. ctDNA mutations in canonical oncogenic drivers (e.g. TP53, EGFR, KRAS, STK11) reflect tumor aggressiveness and correlate with poor prognosis and metastasis across multiple cancer types. Joint modeling in lung adenocarcinoma confirms the independent prognostic contributions of both compartments. Additionally, longitudinal clonal analysis links specific CHIP mutations to the emergence of hematologic malignancies under therapeutic pressure. These findings support a dual-compartment model of liquid biopsy, in which tumor- and host-derived mutations jointly inform on cancer risk, progression, and metastatic behavior. Integrating both compartments may enhance the clinical utility of blood-based biomarkers in oncology. AVAILABILITY: All genomic and clinical data used in this study are available through cBioPortal. Summarized outputs and processed results tables are provided in Supplementary Data.

Humans↗

PubMed2022

Evaluation of cross-platform compatibility of a DNA methylation-based glucocorticoid response biomarker.

BACKGROUND: Identifying blood-based DNA methylation patterns is a minimally invasive way to detect biomarkers in predicting age, characteristics of certain diseases and conditions, as well as responses to immunotherapies. As microarray platforms continue to evolve and increase the scope of CpGs measured, new discoveries based on the most recent platform version and how they compare to available data from the previous versions of the platform are unknown. The neutrophil dexamethasone methylation index (NDMI 850) is a blood-based DNA methylation biomarker built on the Illumina MethylationEPIC (850K) array that measures epigenetic responses to dexamethasone (DEX), a synthetic glucocorticoid often administered for inflammation. Here, we compare the NDMI 850 to one we built using data from the Illumina Methylation 450K (NDMI 450). RESULTS: The NDMI 450 consisted of 22 loci, 15 of which were present on the NDMI 850. In adult whole blood samples, the linear composite scores from NDMI 450 and NDMI 850 were highly correlated and had equivalent predictive accuracy for detecting DEX exposure among adult glioma patients and non-glioma adult controls. However, the NDMI 450 scores of newborn cord blood were significantly lower than NDMI 850 in samples measured with both assays. CONCLUSIONS: We developed an algorithm that reproduces the DNA methylation glucocorticoid response score using 450K data, increasing the accessibility for researchers to assess this biomarker in archived or publicly available datasets that use the 450K version of the Illumina BeadChip array. However, the NDMI850 and NDMI450 do not give similar results in cord blood, and due to data availability limitations, results from sample types of newborn cord blood should be interpreted with care.

Adult↗

PubMed2026

Proteomic Analysis of Extracellular Vesicles Reveals Vitronectin and Laminin Subunit Alpha-3 as Candidate Biomarkers for Gastric Cancer.

BACKGROUND/AIMS: Clinically useful noninvasive biomarkers for gastric cancer remain limited. Extracellular vesicles (EVs) carry a molecular cargo reflective of their cells of origin and have emerged as promising candidates for blood-based cancer biomarkers. We aimed to identify EV-associated protein biomarkers for gastric cancer via a proteomic approach. METHODS: Proteomic profiling of EVs was performed using one normal gastric cell line (Hs738st/int) and two gastric cancer cell lines (AGS and NCI-N87). Selected proteins were validated in blood-derived EVs isolated from plasma samples of 10 healthy controls and 36 patients with gastric cancer. RESULTS: Proteomic analysis identified 224 differentially expressed proteins whose expression was consistently altered in gastric cancer cell line-derived EVs. Among these, vitronectin (VTN) and laminin subunit alpha-3 (LAMA3) were selected based on their consistent upregulation. EV-associated LAMA3 levels were significantly higher in patients with gastric cancer than in healthy controls (p=0.003), with significant elevations observed from stage II onward (p=0.041, p=0.017, and p=0.004 for stages II, III, and IV, respectively). EV-associated VTN levels were not significantly different overall (p=0.089); however, stage-specific analysis demonstrated significant increases in VTN levels in patients with stage III (p=0.036) and stage IV (p=0.005) gastric cancer. Both EV-associated VTN and LAMA3 levels showed significant positive correlations with the cancer stage (ρ=0.564 and ρ=0.611, respectively; both p<0.001). CONCLUSIONS: The levels of EV-associated VTN and LAMA3 appear to be more closely associated with disease progression than with early-stage detection of gastric cancer. These findings suggest that EV-based proteomic biomarkers may have clinical utility for monitoring tumor progression in patients with clinically advanced gastric cancer.

Humans↗

PubMed2026

Tau proteoforms as plasma biomarkers in Alzheimer's disease: mechanisms, measurement, and medicine.

INTRODUCTION: Blood-based tau proteoforms have emerged as specific, scalable biomarkers of Alzheimer's pathology, addressing the limitations of symptom-based diagnosis, neuroimaging, and invasive cerebrospinal fluid (CSF) testing. This review synthesizes advances in tau phosphorylation and truncation biology, evaluates translation from CSF to plasma with state-of-the-art proteomics, and outlines the analytical standards and cross-matrix calibration needed for clinical adoption. AREAS COVERED: We conducted a literature search in PubMed and Google Scholar. We reviewed studies published between January 2005 and September 2025 investigating tau proteoforms in Alzheimer's disease. EXPERT OPINION: Blood-based tau proteoforms are poised to move Alzheimer's diagnostics from specialized imaging to accessible frontline testing, with plasma p-tau217 approaching positron emission tomography (PET) and CSF performance and multi-analyte panels with glial fibrillary acidic protein (GFAP) or neurofilament light (NfL) improving differential diagnosis while reducing invasiveness and cost. Building on the first FDA-cleared plasma assay (Lumipulse G p-tau217/Aβ1-42 Ratio) in May 2025, we anticipate a dual pathway over the next decade in which referral centers use high-plex mass spectrometry (MS) panels for phosphoforms and truncations, while primary care adopts automated high-throughput immunoassays (e.g. chemiluminescent enzyme immunoassay (CLEIA)) for triage, supported by harmonized standard operating procedures (SOPs), cross-matrix calibration, and robust reference materials.

Humans↗

PubMed2026

Predicted brain-regional gene expression patterns in individuals living with Alzheimer's disease.

Studying brain gene expression in Alzheimer's Disease (AD) remains difficult as postmortem brain is difficult to access, cannot be used to guide donor treatment, may be confounded by environmental factors before and after death, and is difficult to link to early AD states or disease progression. To circumvent these limitations, several studies have tested blood transcriptome biomarkers for AD. However, gene-expression levels in the blood have limited correlation with those in the brain. To evaluate the potential of monitoring Alzheimer's progression with peripheral data, we used transcriptome-imputation to identify brain-region-specific AD-associated gene-expression differences in cohorts with blood-based transcriptome data. This approach provides a high-resolution image of AD-associated molecular differences in the brains of individuals actively living with disease. We analyzed eight AD studies (777 AD cases, 779 cognitively unimpaired controls), imputing transcriptomes in 10 brain regions via the Brain Gene Expression and Network Imputation Engine (BrainGENIE). Hundreds of differentially expressed genes (DEGs) associated with AD were identified in nine brain regions, with anterior cingulate cortex and amygdala showing the most differential expression. AD-associated genes were enriched in pathways such as proteostasis, mitochondrial dysfunction, and immune activation. We observed significant yet moderate concordance between imputed AD-associated changes and those directly measured in the dorsolateral prefrontal cortex and cerebellum. These transcriptomic changes can guide future in vitro studies focused on pathogenesis or be targets of novel therapeutic development. In conclusion, we demonstrated the scope and utility of brain expression imputation from the peripheral transcriptome, laying the groundwork for biomarker discovery and prospective AD studies.

Alzheimer Disease↗

PubMed2026

Beyond genes: EpiSwitch® and Orion platform-powered 3D genome architecture biomarkers reveal shared biology across ME/CFS, long COVID, PTSD, rheumatoid arthritis, and multiple sclerosis.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Long COVID (LC19), post-traumatic stress disorder (PTSD), rheumatoid arthritis (RA), and multiple sclerosis (MS) are clinically distinct disorders that share substantial symptom overlap, including persistent fatigue, cognitive impairment, autonomic dysfunction, and immune dysregulation. Although these conditions differ in diagnosis and clinical presentation, their underlying biological mechanisms remain poorly understood and may involve convergent regulatory pathways. METHODS: The EpiSwitch® 3D genomics platform and Orion knowledgebase were used to integrate chromosome conformation signatures with genome-wide association study (GWAS)-derived datasets across ME/CFS, LC19, PTSD, RA, and MS. Three-dimensional genomic anchors were mapped to coding genes and analysed using STRING protein-protein interaction networks and Cytoscape-based systems biology approaches. Disease-specific anchor datasets were generated and compared at both gene and network levels to identify shared biological processes and regulatory mechanisms. RESULTS: Analysis of the ME/CFS dataset identified 552 unique 3D genomic anchors mapped to 567 genes, with analogous disease-specific anchor sets generated for LC19, PTSD, RA, and MS. Direct overlap between disease-associated genes was limited; however, higher-order network analyses revealed substantial interconnectivity and convergence across conditions. Shared biological pathways included immune and cytokine signalling, interferon responses, mitochondrial function, metabolic regulation, and neuroendocrine processes. Highly connected hub genes included immune regulatory nodes such as LAG3 and components of the mTOR signalling pathway, implicating T-cell exhaustion, chronic immune activation, and immunometabolic dysregulation as common mechanisms underlying these disorders. CONCLUSIONS: These findings support a systems-level model in which clinically overlapping fatigue-associated syndromes arise from perturbations of interconnected regulatory networks rather than discrete disease-specific pathways. Despite limited genetic overlap, substantial convergence at the network level suggests shared biological architecture across ME/CFS, LC19, PTSD, RA, and MS. The identification of common regulatory pathways provides a mechanistic framework for the development of cross-disease diagnostic and therapeutic strategies. By capturing dynamic regulatory states, 3D genomic biomarkers offer significant potential for objective blood-based diagnostics, patient stratification, and the identification of shared therapeutic targets across complex chronic disorders. These findings support the application of precision medicine approaches and may accelerate the development of novel interventions for fatigue-associated multisystem diseases.

Humans↗

PubMed2026

Unlocking the Circulating Proteome: Toward Clinical Translation.

Blood-based proteomics is approaching a translational inflection point. Driven by advances in measurement technologies, rapid expansion of analytical capabilities, and growing adoption across research and medical communities, there is increasing demand for clinically actionable biomarkers. As the field transitions away from purely large-scale discovery-oriented studies toward more informed, targeted, application-driven analyses, the generation of proteomic data is no longer the bottleneck. Instead, the central challenge is to translate these measurements into robust, reproducible, and clinically meaningful insights. In this Review, we assess recent technological and methodological developments, evaluate persistent preanalytical and interpretative limitations, and outline the key steps required for clinical translation. We focus on three deeply interconnected dimensions: the capabilities and constraints of current measurement platforms, the role of computational and machine learning approaches in extracting biological and clinical signals, and the emergence of large-scale population studies that create new opportunities for validation and generalization. Finally, we discuss a forward-looking vision in which proteomics plays a central role in dynamic, multilayered omics frameworks, where integration with genomics, temporal profiling, and imaging can deepen our understanding of health, disease, and therapeutic response.

Humans↗

PubMed2025

Blood-based DNA methylation markers for autism spectrum disorder identification using machine learning.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder lacking objective biomarkers for early diagnosis. DNA methylation is a promising epigenetic marker, and machine learning offers a data-driven classification approach. However, few studies have examined whole-blood, genome-wide DNA methylation profiles for ASD diagnosis in school-aged children. METHODS: We analyzed genome-wide DNA methylation data from GEO dataset GSE113967, including 52 children with ASD and 48 typically developing (TD) controls. Differentially methylated positions (DMPs) were identified, and feature selection was performed using support vector machine-recursive feature elimination with cross-validation (SVM-RFECV). Classification models were developed using random forest (RF), extreme gradient boosting (XGBoost), and decision tree (DT) classifiers. A nomogram visualized feature contributions. RESULTS: A total of 138 DMPs differentiated ASD from TD children. Eleven CpG sites selected by SVM-RFECV formed the basis for model construction. RF and XGBoost achieved the highest accuracy (75%), with DT reaching 70%. Functional annotation indicated enrichment in cell adhesion and immune-related pathways. CONCLUSIONS: This exploratory study demonstrates the feasibility of integrating peripheral blood DNA methylation data with machine learning to distinguish children with ASD. While limited by sample size and moderate accuracy, this study provides methodological insights into the feasibility of integrating epigenetic and computational approaches for ASD-related biomarker exploration.

Humans↗

PubMed2023

International Liver Cancer Association (ILCA) white paper on hepatocellular carcinoma risk stratification and surveillance.

Major research efforts in liver cancer have been devoted to increasing the efficacy and effectiveness of surveillance for hepatocellular carcinoma (HCC). As with other cancers, surveillance programmes aim to detect tumours at an early stage, facilitate curative-intent treatment, and reduce cancer-related mortality. HCC surveillance is supported by a large randomised-controlled trial in patients with chronic HBV infection and several cohort studies in cirrhosis; however, effectiveness in clinical practice is limited by several barriers, including inadequate risk stratification, underuse of surveillance, and suboptimal accuracy of screening tests. There are several proposed strategies to address these limitations, including risk stratification algorithms and biomarkers to better identity at-risk individuals, interventions to increase surveillance, and emerging imaging- and blood-based surveillance tests with improved sensitivity and specificity for early HCC detection. Beyond clinical validation, data are needed to establish clinical utility, i.e. increased early tumour detection and reduced HCC-related mortality. If successful, these data could facilitate a precision screening paradigm in which surveillance strategies are tailored to individual HCC risk to maximise overall surveillance value. However, practical and logistical considerations must be considered when designing and implementing these validation efforts. To address these issues, ILCA (the International Liver Cancer Association) adjourned a single topic workshop on HCC risk stratification and surveillance in June 2022. Herein, we present a white paper on these topics, including the status of the field, ongoing research efforts, and barriers to the translation of emerging strategies.

Humans↗

PubMed2026

Integrative genetic and transcriptomic analyses prioritize CDC16 as a candidate marker for gastric cancer.

BackgroundGastric cancer (GC) remains a major cause of cancer-related mortality, and biomarkers for early detection are needed.MethodsStomach and blood expression quantitative trait loci were integrated with two GC genome-wide association studies using Mendelian randomization (MR), Bayesian colocalization, and summary-data-based MR/heterogeneity in dependent instruments (SMR/HEIDI) testing. Bulk and single-cell transcriptomic analyses characterized candidate expression and lesion-associated patterns. CDC16 protein expression was evaluated by immunohistochemistry in 53 paired GC and non-neoplastic tissues, followed by paired and exploratory receiver operating characteristic analyses.ResultsMR prioritized PILRB, CDC16, and GABPB1-AS1; SMR/HEIDI provided complementary support, while colocalization for CDC16 and GABPB1-AS1 was suggestive and model-dependent. Bulk-tissue CDC16 abundance was higher in GC, but the modest TCGA-STAD tumor-normal difference (log2FC = 0.210, FDR = 0.019) was attenuated after proliferation adjustment (log2FC = -0.002, FDR = 0.987), indicating close coupling with proliferative activity. Single-cell analysis localized CDC16 predominantly to epithelial populations, and the proportion of CDC16-detectable epithelial cells increased across lesion categories (ρ = 0.735; permutation P = 0.031). CDC16 H-scores were higher in GC than in paired non-neoplastic tissues (161.15 ± 45.11 vs 102.15 ± 54.50; P < 0.001), with higher cancer-tissue scores in 41 of 53 cases. Exploratory AUC was 0.794 (95% CI, 0.704-0.874; sensitivity, 66.0%; specificity, 79.2%).ConclusionsConvergent genetic, transcriptomic, and protein-level evidence prioritizes CDC16 as a GC-associated candidate tissue marker whose expression is closely linked to proliferative activity. Prospective validation in independent cohorts, including appropriate disease controls and blood-based evaluation, is warranted.

Stomach Neoplasms↗

PubMed2026

Development and preliminary validation of plasma cell-free DNA methylation-based diagnostic prediction model for colorectal cancer detection.

BACKGROUND: Colorectal cancer (CRC) is a common malignancy associated with genetic and epigenetic alterations. Several methylation biomarkers have been investigated for non-invasive CRC detection; however, their reported performance varies across clinical settings, and the detection of early-stage or precancerous disease and discrimination from non-malignant colorectal conditions remain challenging. This exploratory study aimed to identify reproducible CRC-associated plasma cell-free DNA (cfDNA) methylation regions and to develop and preliminarily evaluate diagnostic prediction model for distinguishing CRC from healthy controls and benign samples. METHODS: Public CRC tissue methylation datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify reproducible CRC-associated methylation alterations. Plasma cfDNA methylation was profiled using methyl-CpG-binding-domain enrichment followed by paired-end sequencing in patients with CRC, patients with colorectal polyps, and healthy controls. After quality-control filtering, 30 CRC and healthy-control samples were randomly allocated at the participant level in a 7:3 ratio to a development set comprising 10 patients with CRC and 11 healthy controls and a held-out test set comprising 4 patients with CRC and 5 healthy controls. Hypermethylated regions were selected using least absolute shrinkage and selection operator (LASSO) logistic regression. The 12-region model was evaluated in the held-out test set and subsequently applied to 10 colorectal polyp samples without refitting or recalibration. RESULTS: Tissue methylation analysis identified reproducible CRC-associated alterations across independent datasets. In the plasma development set, 707 differentially methylated regions (DMRs) were identified between CRC and healthy-control samples, including 324 hypermethylated and 383 hypomethylated regions. LASSO regression selected a 12-region hypermethylation signature. In the held-out test set, the model achieved an area under the curve (AUC) of 0.85 [95% confidence interval (CI): 0.579-1.000]. At the development-set-derived threshold, sensitivity was 75.0% (3/4), specificity was 60.0% (3/5), and accuracy was 66.7% (6/9). When the original model was applied to colorectal polyp samples, model scores were significantly higher in both CRC and polyp samples than in healthy controls, while CRC samples showed a tendency toward higher scores than polyp samples. CONCLUSIONS: This exploratory study identified a 12-region plasma cfDNA hypermethylation signature associated with CRC and developed a LASSO-based diagnostic prediction model that showed preliminary discrimination between CRC and healthy controls in a small held-out test set. By integrating tissue methylation evidence with plasma cfDNA profiling, this study expands the repertoire of candidate region-level methylation markers for blood-based CRC detection.

Colorectal cancer (CRC)↗

PubMed2026

Liquid biopsy for early detection of pancreatic ductal adenocarcinoma.

There is no clinically relevant blood-based assay for the detection of early-stage pancreatic ductal adenocarcinoma (PDAC), a solid malignancy characterized by poor outcomes. Here we developed, validated and tested a blood-based microRNA (miRNA) assay (which included hsa-miR-142-3p, hsa-miR-30c-5p, hsa-miR-335-5p, hsa-miR-340-5p, hsa-miR-200b-3p, hsa-miR-1260b, hsa-miR-145-3p, hsa-miR-145-5p, hsa-miR-429 and hsa-miR-200a-3p) and a composite score, PANXEON (PANcreatic cancer eXosome Early detectiON), that integrates the miRNA signature with carbohydrate antigen 19-9 for the detection of early-stage PDAC. We conducted an international, multicenter, observational, prospective biomarker study that involved 1,785 individuals with and without PDAC from four countries. The miRNA signature achieved an area under the receiver operating characteristic curve of 88.6% in the testing cohort, with a sensitivity of 83.8% for early-stage PDAC, while showing minimal cross-reactivity with other gastrointestinal cancers. In a cohort of 19 individuals, the miRNA signature levels decreased during neoadjuvant chemotherapy and after surgery and increased before disease recurrence. When combined with carbohydrate antigen 19-9 levels, this blood assay demonstrated a sensitivity of 86.8% for stage I-II PDAC, false-positive rates of 3.2% in low-risk controls and 15.6% in high-risk controls in the testing cohort. PANXEON demonstrates potential for detecting high-grade dysplasia in individuals with high-risk pancreatic cysts (64.3%). Collectively, we present a composite biomarker that may complement existing strategies for the detection of early-stage PDAC and warrants further large-scale prospective studies. ClinicalTrials.gov registration: NCT06388967 .

Journal Article↗

PubMed2025

In-depth assessment of BRAF, NRAS, KRAS, EGFR, and PIK3CA mutations on cell-free DNA in the blood of melanoma patients receiving immune checkpoint inhibition.

INTRODUCTION: Circulating tumor DNA (ctDNA) holds promise for guiding immune checkpoint inhibitor (ICI) therapy and stratifying responders from non-responders. While tumor-informed ctDNA detection approaches are sensitive and mutation-inclusive, they require tumor tissue, which limits applicability in real-world settings. Conversely, tumor-agnostic methods often have limited genomic coverage. In this study, we evaluated a tumor-agnostic, broad-panel ctDNA assay in patients with advanced melanoma treated with ICI. METHODS: We conducted a prospective analysis of 241 longitudinal samples from 39 patients with unresectable stage III/IV melanoma using a SYSMEX targeted NGS panel covering 1,114 COSMIC mutations. Plasma samples were collected at baseline and during ICI therapy. The assay's sensitivity reached seven mutant molecules, corresponding to a 0.07% mutation allele frequency (MAF). ctDNA profiles were compared with matched tumor tissue and correlated with clinical features and survival. RESULTS: At baseline, ctDNA was detected in 64.5% of patients. Common mutations included BRAFV600E (43.8%) and NRASG12D (36.4%), followed by KRAS, EGFR, and PIK3CA variants. Overall tissue-plasma concordance was 51.6%, with more extended biopsy-plasma intervals associated with discordance (p = 0.0105). Notably, 12.2% of cases exhibited partial concordance, characterized by shared mutations and additional plasma-only alterations, underscoring the complementary value of blood-based profiling. Persistent or re-emerging ctDNA positivity post-therapy correlated with shorter progression-free survival (PFS, p = 0.003), while ctDNA-negative patients showed significantly improved outcomes. Patients that remained ctDNA-negative had significantly longer progression-free survival (median not reached) compared to those with persistent ctDNA positivity (median 3 months) or those converting to positive (median 7.5 months; p = 0.0073). Early NRAS and KRAS ctDNA levels strongly predicted poor response (p = 0.0069 and p = 0.028). The prognostic impact extended beyond canonical drivers, as non-hotspot variants also correlated with the outcome. Notably, even low-level ctDNA persistence (5-10 MM/mL) carried adverse prognostic implications (p = 0.0054). Concerning a shorter PFS, ctDNA positivity was also associated with elevated S100 levels (p = 0.047). Organ-specific mutation enrichment (e.g., KRASG12D in brain, EGFRG719A in lymph nodes) suggested possible metastatic tropism. CONCLUSION: Broad tumor-agnostic ctDNA analysis effectively identified clinically relevant mutations and predicted outcomes in ICI-treated melanoma patients. This approach enables tissue-independent and real-time ctDNA monitoring and may inform patient selection and therapeutic strategies in future interventional trials.

Humans↗

PubMed2026

Multimodal risk assessment for oral potentially malignant disorders: Integrating patient-centered and specimen-derived data.

BACKGROUND: Oral potentially malignant disorders exhibit heterogeneous malignant transformation risk that clinical approaches fail to adequately predict. Histopathologic dysplasia grading, the reference standard of risk assessment, is associated with poor interobserver reliability and limited prognostic discrimination. It is necessary to define other potential patient- and tissue-associated risk modifiers to improve patient-specific disease prediction. TYPES OF STUDIES REVIEWED: PubMed was queried for patient- and specimen-derived factors as they relate to oral cancer and oral potentially malignant disorders, with preference for systematic review and meta-analysis articles published within the past 5 years. When not available, guidelines from the American Cancer Society, National Cancer Institute, or other national organizations or the most recent best articles were referenced to support the data presented. RESULTS: Within patient-associated factors, validated measures of tobacco and alcohol exposure, clinical lesion characteristics, systemic health factors including metabolic syndrome components, comorbidity risk, and dental health indexes were found. Within specimen-derived data, tissue-based analyses encompassing histopathology and advanced molecular profiling (genomic, epigenomic, transcriptomic, spatial approaches), blood-based germline and somatic mutation analysis, and saliva-based microbiome characterization and inflammatory biomarker assessment were addressed. PRACTICAL IMPLICATIONS: Malignant transformation reflects intersecting patient and specimen risk pathways that affect each patient differently; no single modality captures this complexity. Realizing precision prognostication in oral precancer will require coordinated expansion and standardization of data collection across research groups. This review is intended to guide covariate selection for prospective study design, improve reproducibility, and ultimately enable the development of validated multimodal risk prediction tools for clinical deployment.

Humans↗