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PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗

PubMed2026

Predictive Biomarkers for Immune Checkpoint Inhibitor Efficacy: Challenges, Innovations, and a Pathway to Precision Medicine in the Era of Cancer Immunotherapy.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed oncology practice. However, treatment response remains heterogeneous, rendering predictive biomarkers critical for optimal patient care. The 3 established biomarkers, programmed death-ligand 1, tumor mutational burden (TMB), and microsatellite instability-high/deficient mismatch repair, are approved and clinically validated but are modest predictors of benefit. As a result, multiple novel predictive biomarkers remain under investigation. CONTENT: This review highlights established and investigational predictive ICI efficacy biomarkers. For established biomarkers, we describe biology, assay modalities, approved companion diagnostics, landmark studies, and notable limitations. Due to the multisystem nature of antitumor immune effects, investigational biomarkers span multiple domains, including tumor genomic biomarkers (e.g., mutational signatures, TMB, neoantigen clonality), tumor microenvironment (e.g., tumor-infiltrating lymphocytes [TILs], tertiary lymphoid structures), systemic immune biomarkers (e.g., cytokines, autoantibodies, glycoproteins, peripheral blood mononuclear cells), and the microbiome (e.g., gastrointestinal microbial diversity, responder-enriched taxa). SUMMARY: The established biomarkers PD-L1, TMB, and microsatellite instability-high/deficient mismatch repair inform ICI use in clinical practice but have important limitations. Multiple investigational biomarkers show promise in refining patient selection and optimizing therapy. Moving forward, increased assay harmonization, prospective validation, and standardized parameters may improve performance. Composite models integrating complementary signals across domains may further individualize treatment and lead to an era of personalized cancer immunotherapy.

Humans↗

PubMed2026

Systemic Proteomic Alterations and Predictive Biomarkers of Paroxetine Response in Refractory Rosacea: A Secondary Analysis of a Randomized Clinical Trial.

IMPORTANCE: Rosacea is a chronic inflammatory cutaneous disorder characterized by persistent erythema and vascular dysregulation. While paroxetine has shown clinical efficacy in reducing these symptoms, the systemic molecular mechanisms underlying its therapeutic response remain poorly characterized. OBJECTIVE: To investigate systemic proteomic alterations and identify potential predictive biomarkers in patients with refractory erythematous rosacea following paroxetine treatment. DESIGN, SETTING, AND PARTICIPANTS: This prospective plasma proteomic analysis was nested within a multicenter, randomized, double-blind, placebo-controlled clinical trial (Prospective Rosacea Refractory Erythema Randomized Clinical Trial [PRRERCT]). Participants included patients aged 18 to 65 years with refractory rosacea (Clinician's Erythema Assessment [CEA] score ≥3). Plasma samples were collected at baseline and after 12 weeks of treatment. The data for this study were analyzed between September 2025 and November 2025. INTERVENTIONS: Participants received oral paroxetine, 25 mg per day, for a 12-week treatment period. MAIN OUTCOMES AND MEASURES: Systemic protein expression profiles were analyzed using data-independent acquisition liquid chromatography-tandem mass spectrometry. Clinical response was evaluated using CEA and the Flushing Assessment Tool. Correlations between proteomic changes and clinical improvements were assessed, and predictive biomarkers were identified using receiver operating characteristic curve analysis. RESULTS: Among 24 participants (mean [SD] age, 35 [11] years; 24 [100%] female), paroxetine treatment significantly reduced mean (SD) CEA scores from 3.1 (0.3) to 2.3 (0.7) and Flushing Assessment Tool scores from 3.1 (0.6) to 2.0 (0.9) (P < .001). Exploratory proteomic analysis revealed 497 candidate differentially expressed proteins after treatment. Downregulated proteins showed preliminary enrichment in pathways related to immune response activation, insulin receptor signaling, and neuronal remodeling. A subset of 98 reversed-response proteins was observed, primarily linked to synaptic vesicle cycles and vascular smooth muscle contraction. Proteomic alterations were associated with clinical improvement (65 proteins for erythema; 73 for flushing). Candidate biomarkers, notably OLFML3 (area under the receiver operating characteristic curve [AUC], 0.87 [95% CI, 0.70-1.00]) and IGFBP2 (AUC, 0.80 [95% CI 0.55-1.00]), demonstrated high predictive value for clinical response. CONCLUSIONS AND RELEVANCE: In this secondary analysis of a randomized clinical trial, paroxetine treatment was associated with modulation of systemic neuro-vascular-immune networks in patients with rosacea. These exploratory findings provide preliminary mechanistic clues regarding the possible disease-modifying potential of paroxetine and point to circulating protein signatures that may facilitate personalized therapeutic strategies for rosacea management. TRIAL REGISTRATION: Chinese Clinical Trial Registry Identifier: ChiCTR2000031479.

Humans↗

PubMed2026

Predictive biomarkers in cancer immunotherapy for genitourinary malignancies.

Immunotherapy has transformed the management of genitourinary cancers, offering durable responses in selected patient groups. However, the clinical benefit of immune checkpoint inhibitors varies significantly across renal cell carcinoma, urothelial carcinoma, and prostate cancer, underscoring the need for reliable predictive biomarkers. This review summarizes current knowledge on established and emerging biomarkers, including PD L1 expression, tumor mutational burden, molecular subtypes, genomic alterations, tumor microenvironment characteristics, circulating biomarkers, microbiome influences, and multi omic integrative approaches. We discuss their potential clinical relevance, limitations, and applicability across different tumor types. Future directions emphasize the development of composite biomarkers, standardization of testing platforms, real time monitoring strategies, and the integration of advanced technologies such as artificial intelligence and spatial profiling. Understanding and validating these biomarkers will be essential for optimizing personalized immunotherapy in genitourinary cancers.

Circulating tumor DNA↗

PubMed2025

Proposal of real-world solutions for the implementation of predictive biomarker testing in patients with operable non-small cell lung cancer.

The implementation of biomarker testing for targeted therapies and immune checkpoint inhibitors is a cornerstone in the management of metastatic and locally advanced non-small cell lung cancer (NSCLC), playing a pivotal role in guiding treatment decisions and patient care. The emergence of precision medicine in the realm of operable NSCLC has been marked by the recent approvals of osimertinib, atezolizumab, nivolumab, pembrolizumab and alectinib for early-stage disease, signifying a shift towards more tailored therapeutic strategies. Concurrently, the landscape of this disease is rapidly evolving, with several further pending approvals and numerous clinical trials in progress. To harness the benefits of these innovative neo-adjuvant and adjuvant therapies, the integration of predictive biomarker testing into standard clinical protocols is imperative for patients with operable NSCLC. A multidisciplinary international consortium has identified three primary obstacles impeding the effective testing of patients with operable NSCLC. These challenges encompass the limited number of test requests by physicians, the inadequacy of tissue samples for comprehensive testing, and the prevalence of cost-reduction measures leading to suboptimal testing practices. This review delineates the aforementioned challenges and proposed solutions, and strategic recommendations aimed at enhancing the testing process. By addressing these issues, we strive to optimize patient outcomes in operable NSCLC, ensuring that individuals receive the most appropriate and effective care based on their unique disease profile.

Humans↗

PubMed2026

Sex-specific biomarkers predict bone mineral density loss at the contralateral hip after hip fracture.

OBJECTIVE: To identify inflammatory and hormonal biomarkers that predict bone loss at the contralateral (non-fractured) hip following hip fracture in males and females. METHODS: White participants who were not receiving pre-fracture glucocorticoids, sex-hormone therapy, or bone-active medications (100 males, 76 females) with hip fractures. Data were collected within 22 days of hip fracture and at 2, 6, and 12 months follow-up. Biomarkers were categorized into tertiles: estradiol, 25-hydroxyvitamin D3/D2, intact parathyroid hormone (iPTH), interleukin-1 receptor antagonist (IL-1RA), interleukin-6 (IL-6), insulin-like growth factor-1 (IGF-1), soluble tumor necrosis factor-α receptor 1, sex hormone-binding globulin, and testosterone. Femoral neck bone mineral density (BMD) at the contralateral hip was assessed, and losses exceeding the mean decline were classified as greater than average. Logistic regression models, stratified by sex, were adjusted for confounders and evaluated selected biomarker associations. RESULTS: Among males, the 2nd (OR = 4.79, P = 0.012) and 3rd (OR = 6.36, P = 0.005) IGF-1 tertiles were associated with greater odds of BMD loss than the 1st tertile. The 3rd iPTH tertile (OR = 3.79, P = 0.037) was similarly associated with increased odds. Among females, the 3rd (OR = 0.20, P = 0.031) IL-1RA tertile was associated with lower odds of BMD loss compared to the 1st tertile, while the 2nd IL-6 tertile (OR = 5.99, P = 0.036) was associated with higher odds. CONCLUSION: These findings suggest that inflammatory and hormonal biomarkers may be sex-specific predictors of accelerated BMD loss following hip fracture.

Biomarkers↗

PubMed2024

Pan-cancer analysis of biallelic inactivation in tumor suppressor genes identifies KEAP1 zygosity as a predictive biomarker in lung cancer.

The canonical model of tumor suppressor gene (TSG)-mediated oncogenesis posits that loss of both alleles is necessary for inactivation. Here, through allele-specific analysis of sequencing data from 48,179 cancer patients, we define the prevalence, selective pressure for, and functional consequences of biallelic inactivation across TSGs. TSGs largely assort into distinct classes associated with either pan-cancer (Class 1) or lineage-specific (Class 2) patterns of selection for biallelic loss, although some TSGs are predominantly monoallelically inactivated (Class 3/4). We demonstrate that selection for biallelic inactivation can be utilized to identify driver genes in non-canonical contexts, including among variants of unknown significance (VUSs) of several TSGs such as KEAP1. Genomic, functional, and clinical data collectively indicate that KEAP1 VUSs phenocopy established KEAP1 oncogenic alleles and that zygosity, rather than variant classification, is predictive of therapeutic response. TSG zygosity is therefore a fundamental determinant of disease etiology and therapeutic sensitivity.

Kelch-Like ECH-Associated Protein 1↗

PubMed2026

REG3α is a Predictive Biomarker of Complicated Disease from Preclinical through Established Crohn's Disease.

BACKGROUND: Regenerating islet-derived 3-alpha (REG3α) is a serum biomarker in patients with graft-versus-host disease (GVHD) linked to 6-month mortality. REG3α is produced by intestinal Paneth cells, which are implicated in Crohn's disease (CD) pathophysiology. OBJECTIVE: To assess associations between serum REG3α and progressive CD DESIGN: Serum REG3α was measured in two cross-sectional (M: Mount Sinai, L: Leuven) and a pre-diagnostic cohort (P: PREDICTS) with serial samples up to 10 years before CD diagnosis. Tissue REG3α expression was assessed via bulk RNA sequencing from paired ileal and colonic biopsies. Serum REG3α and tissue REG3α were associated with CD progression (hospitalization, surgery, steroid course, or new advanced therapy). Single-cell RNA sequencing data explored associations between REG3α expression, Paneth cell phenotypes, and CD. RESULTS: In 394 patients, high serum REG3α associated with CD progression, independent of C-reactive protein and endoscopic activity (M: HR 1.9 (95%CI 1.3-2.8); L: HR 2.9 (95%CI 1.9-4.6), both p<0.001). The association persisted in patients with mild or inactive CD. In the pre-diagnostic cohort, high serum REG3α predicted the development of CD, particularly complicated (B2/3) and surgical presentations, up to 10 years before diagnosis (P). Analysis of REG3α expression and Paneth cell transcriptomes suggested that CD is associated with loss of regenerative Paneth cell populations and enrichment in REG3α-expressing populations, suggesting a mechanism through which changes in serum REG3α associate with complicated CD. CONCLUSION: Serum REG3α holds potential as a non-invasive, prognostic biomarker in CD, independent of disease activity. High serum REG3α, even years before diagnosis, is linked to a complicated disease course.

Crohn’s Disease↗

PubMed2026

Integrated multi-omics profiling of amniotic fluid identifies predictive biomarkers for fetal growth restriction trajectories.

BACKGROUND: Fetal growth restriction (FGR) is a complex condition with highly heterogeneous clinical outcomes, making prenatal distinction between transient and persistent growth failure challenging. This study aims to identify amniotic fluid (AF) biomarkers capable of differentiating distinct FGR trajectories and characterizing persistent growth failure mechanisms. METHODS: Integrated proteomic and metabolomic profiling was performed on AF samples from transient FGR (n = 11), persistent FGR (n = 9), and healthy controls (n = 13). Diagnostic and prognostic models were developed using multivariate analysis. Selected protein candidates were validated via ELISA in an independent cohort (n = 69). RESULTS: Multi-omics analysis revealed distinct molecular signatures for FGR stratification. A two-protein diagnostic panel (PDGFA and phospho-STAT5A) achieved an AUC of 1.000 in the discovery stage and 0.780 in the external validation cohort. For prognostic assessment, a molecular signature including IREB2, HLA-C, and PLXNB2 accurately predicted persistent growth failure from transient recovery (AUC = 0.966). Cross-platform integration highlighted the mass spectrometry-derived WASHC2C as a central hub protein with a significant progressive increase across the control, transient, and persistent groups (p < 0.001). CONCLUSIONS: This study establishes a multi-omics framework for prenatal FGR stratification. Our findings identify distinct molecular signatures reflecting the intrauterine environment and provide high-performance molecular tools for predicting divergent fetal growth trajectories to guide personalized clinical decision-making.

Humans↗

PubMed2026

Multi-omics dynamic profiling reveals predictive biomarkers for first-line immunochemotherapy in extensive-stage small-cell lung cancer.

BACKGROUND: Extensive-stage small-cell lung cancer (ES-SCLC) is associated with a poor prognosis. Although first-line immunochemotherapy improves clinical outcomes, robust prognostic biomarkers for this treatment modality remain unavailable. The aim of this study was to identify non-invasive, easily accessible, and dynamically monitored biomarkers of ES-SCLC by machine learning integrating serum metabolomics, lipidomics, and proteomics at multiple time points. METHODS: A total of 816 serum samples were collected from ES-SCLC patients receiving first-line immunotherapy combined with chemotherapy or first-line chemotherapy for metabolomics, lipidomics, and proteomics analysis. The immunochemotherapy cohort was randomly divided into training and validation subsets at a 6:4 ratio. Biomarkers were identified using machine learning algorithms, and their prognostic significance was evaluated through receiver operating characteristic (ROC) analysis, Kaplan–Meier survival analysis, and multivariate Cox regression. Potential metabolic pathways and mechanisms were further explored via integrated multi-omic analysis. RESULTS: The immunochemotherapy exhibited a prolonged median progression-free survival (PFS) and higher objective response rate (ORR) compared to the chemotherapy group. A total of 5 serum metabolites (uric acid, L-aspartate-semialdehyde, dimethisterone, xanthine, L-cysteine), 6 lipids (Cer d18:1/26:0, Cer d18:2/25:0, SM d18:1/20:1, SM d17:1/25:1, DG O-18:1_16:0, PS 18:0_24:0), and 3 proteins (ACIN1, ACSL4, PHGDH) were identified and constructed into independent prognostic models. Among patients receiving immunochemotherapy, those categorized as low-risk based on the model demonstrated significantly longer PFS compared with those in the high-risk group. These prognostic signatures also retained predictive value in patients who underwent second-line treatment with anlotinib plus immunochemotherapy. Integrated analysis revealed that glycine, serine, and threonine metabolism was the commonly enriched pathway across all three omics layers. Notably, PHGDH (protein), L-aspartate-semialdehyde and L-cysteine (metabolites), and PS (18:0_24:0) (lipid), key elements in this pathway, were all incorporated in the predictive model. In addition, models of the composition of these substances after one cycle of treatment can still predict the prognosis of patients. CONCLUSION: In this study, we constructed and validated a set of non-invasive, dynamically monitorable prognostic models (containing 5 metabolites, 6 lipids, and 3 proteins) using machine learning by integrating multiple time point data from the serum metabolome, lipid panel, and proteome to accurately distinguish the prognostic risk of patients with ES-SCLC receiving immunochemotherapy. PFS was significantly prolonged in patients in the low-risk group, and this model remains predictive in the subsequent second-line treatment with anlotinib in combination with immunochemotherapy. Glycine-serine-threonine metabolic pathway may be the key mechanism, of which PHGDH, L-aspartate semialdehyde, L-cysteine and PS (18:0_24:0) are the core predictors. This study provides the first multi-omics dynamic prognostic tool for ES-SCLC immunochemotherapy and reveals potential therapeutic targets.

Humans↗

PubMed2026

HPV circulating tumor DNA as a potential prognostic and predictive biomarker in head and neck squamous cell carcinoma: a systematic review.

PURPOSE: Human papillomavirus circulating tumor DNA (HPVctDNA) has emerged as a promising prognostic biomarker in HPV-related head and neck squamous cell carcinoma (HNSCC). This systematic review aimed to synthesize current evidence on the diagnostic accuracy and prognostic value of HPVctDNA in HNSCC management. MATERIAL/METHODS: We systematically reviewed a PubMed-indexed database of studies published between January 2012 and September 2025. Eligible studies were assessed for design, primary tumor site and stage, treatment modality, HPVctDNA detection method, diagnostic accuracy (sensitivity and specificity), and reported clinical endpoints. Descriptive syntheses were performed; sensitivity and specificity were standardized to proportions and summarized as median values per group. RESULTS: A total of 60 studies, including 8,234 patients were analyzed, of which 41 (68.3%) focused exclusively on oropharyngeal squamous cell carcinoma (OPSCC) and 17 (28.3%) included mixed HPV-related HNSCC subsites and HPV-positive cancers of unknown primary. The median follow-up across the included studies was 23 months. Among the included studies, 19 were retrospective (31.7%) and 33 were prospective (55.0%), with a small proportion of cross-sectional and randomized clinical trials. Overall, 40 (66.7%) evaluated the role of HPVctDNA in a curative setting. Plasma was the most common sample type, analyzed in 55 studies (91.7%), while 5 studies also included saliva. Detection methods varied: 40 employed droplet digital PCR (ddPCR), 16 used quantitative PCR (qPCR) and 4 applied NGS-based assays. Most of these studies (38, 63.3%) evaluated the prognostic utility of HPVctDNA, while only 4 (6.7%) assessed HPVctDNA in a screening or diagnostic setting. Regarding diagnostic accuracy, the median sensitivity across evaluable studies was 91.1%, while the median specificity was 99.4%. In OPSCC-only cohorts, the median sensitivity and specificity were 89.4% and 99.4%, respectively. Dynamic changes in HPVctDNA levels during or after treatment were consistently associated with outcomes: clearance or sustained negativity correlated with higher response rates, improved progression-free survival and overall survival, while persistent positivity or increasing levels predicted disease progression and recurrence. CONCLUSIONS: HPVctDNA demonstrates high diagnostic and prognostic accuracy in HPV-related HNSCC, especially OPSCC, supporting its use for prognosis, treatment monitoring and early detection of recurrence. However, prospective interventional studies are still required to demonstrate that HPVctDNA-guided treatment decisions improve clinical outcomes before routine implementation.

Humans↗

PubMed2026

Using cancer profiles to identify synthetic lethal therapeutic targets and predictive biomarkers in cancer gene dependency data.

MOTIVATION: Large scale loss-of-function screens utilising CRISPR or siRNA can provide profound insights into the importance of individual genes for the survival of a cancer cell and can drive the identification of therapeutic targets and biomarkers, and the development of targeted drugs. However, the analysis of these data and the substantial bodies of metadata that relate to them, is technically challenging and typically requires substantial expertise in data science and computer coding. RESULTS: To facilitate the analysis of cancer gene dependency data by cancer biologists and clinical scientists, we have developed DepMine-a computational toolkit providing a powerful system for framing complex queries relating cancer gene dependency to the underlying genetic changes that occur in cancer cells. DepMine identifies synthetic lethal relationships between putative target genes and complex 'cancer profiles' built from user-specified combinations of mutations, copy-number variation, and expression levels, and can refine these to optimal biomarker definitions for target dependency. AVAILABILITY: The Python implementation of DepMine and associated data files can be obtained at https://github.com/UOSbioinformaticslab/depmine and is free to academics and Not-For-Profit organisations. The DepMine release referenced in this paper is archived as DOI: 10.5281/zenodo.19570601.

Humans↗

PubMed2026

MicroRNAs in Oral Bio-Fluids as Predictive Biomarkers of Orthodontic Tooth Movement: A Systematic Review.

This systematic review was designed to assess scientific evidence of the association of microRNA expression during orthodontic tooth movement through various time points. A systematic review was performed in accordance with the PRISMA checklist. A search strategy was developed in electronic databases including Med Line, Scopus, EBSCO Host and ProQuest Dissertations & Theses Global until June 2025. Eligibility criteria included studies that investigated microRNA expression in saliva/GCF during orthodontic treatment. The risk of bias of the included studies was analysed using the QUADAS-2 and RoB-2 tools. The search retrieved 2800 records, of which nine studies were selected. Minor variations in GCF collection were noted, while stimulated saliva was collected in one study. RT-PCR and the Fluro meter accounted for the majority of miRNA estimation. Thirteen miRNAs were identified as target biomarkers for OTM regulation. Despite the high risk of bias, the evidence from the current systematic review indicates that microRNAs can be considered as potential biomarkers of orthodontic tooth movement in oral biofluids. Trial Registration: Prospero ID-CRD420251153064.

Humans↗

PubMed2026

Identification of NR4A2 as a Potential Predictive Biomarker for Atherosclerosis.

INTRODUCTION/OBJECTIVE: Atherosclerosis, a leading cause of death globally, is characterized by the buildup of immune cells and lipids in medium to large-sized arteries. However, its precise mechanism remains unclear. The purpose of this study is to explore innovative and reliable biomarkers as a viable approach for the identification and management of atherosclerosis. METHODS: The atherosclerosis-related datasets GSE100927 and GSE66360 were retrieved from the Gene Expression Omnibus (GEO) database. The Limma package in the R programming language was utilized, applying the criteria of |logFC| > 1 and P < 0.05. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the 127 identified DEGs using R. Machine learning techniques were then applied to these data to explore and pinpoint potential biomarkers. The diagnostic potential of these markers was assessed via Receiver Operating Characteristic (ROC) curve analysis. Finally, western blot, real-time quantitative PCR (qRT-PCR), and immunohistochemistry (IHC) were employed to confirm the key biomarkers. RESULTS: Our research indicated that a total of 127 DEGs linked to atherosclerosis were successfully identified. Through the application of machine learning methods, eight critical genes were highlighted. Among these, Nuclear Receptor Subfamily 4 Group A Member-2 (NR4A2) emerged as the most promising marker for further investigation. CIBERSORT analysis revealed that NR4A2 expression levels were significantly correlated with multiple immune cell types, including B cells, plasma cells, and macrophages. Additional validation experiments confirmed that NR4A2 expression was indeed elevated in atherosclerotic plaques, supporting its potential as a biomarker for atherosclerosis. CONCLUSION: Our study identified NR4A2 as a potential immune-related biomarker for the diagnosis and treatment of atherosclerosis.

Atherosclerosis↗

PubMed2026

Multiomics Integration Identifies a Molecular Subtype of Intrahepatic Cholangiocarcinoma With Enhanced Benefit From Adjuvant Therapy.

Intrahepatic cholangiocarcinoma (iCCA) is a molecularly heterogeneous liver cancer with a poor prognosis. Improved stratification is needed to guide postoperative therapy. In this study, we applied integrative multiomics analysis to classify iCCA and identify biomarkers predictive of adjuvant treatment benefit. Using publicly available datasets (including whole exome sequencing, RNA sequencing, proteomics, and phosphoproteomics from FU-iCCA cohort and a transcriptomic cohort GSE244807), we defined 3 robust molecular subtypes of iCCA. These subtypes exhibited distinct genomic alterations, pathway activation, and immune microenvironments, with significant differences in overall survival (OS). Through protein-protein interaction network analysis and consensus feature selection using 10 clustering algorithms, we prioritized 8 marker genes distinguishing the subtypes. A Cox proportional-hazards model constructed from these markers stratified patients into high- and low-risk groups. High-risk iCCA, characterized by elevated expression of markers such as CLDN18, MUC1, and MUC5AC, had significantly worse OS in the absence of adjuvant therapy. Notably, in an independent validation of 174 patients with iCCA who underwent resection (single-center cohort), high expression of any of these 3 markers were associated with markedly prolonged OS in patients who received adjuvant chemotherapy or chemoembolization, compared with those who did not. In contrast, marker-negative patients showed no clear benefit from adjuvant therapy. In conclusion, our multiomics approach identified a high-risk, mucin-enriched subtype of iCCA. CLDN18, MUC1, and MUC5AC emerge as candidate predictive biomarkers for adjuvant chemotherapy benefit in iCCA, warranting prospective validation to improve personalized postoperative management.

Humans↗

PubMed2026

Spindle Assembly Checkpoint Competency Determines Sensitivity to KIF18A Inhibition in Small-Cell Lung Cancer.

BACKGROUND: Small-cell lung cancer (SCLC) is characterized by pervasive chromosomal instability (CIN) and remains largely refractory to targeted therapies. KIF18A, a motor protein that regulates chromosome alignment during mitosis, has emerged as a selective dependency in CIN-high tumors. Whether this dependency extends to SCLC, a prototypical CIN-high cancer, has not been established, and biomarkers predicting response to KIF18A inhibition, currently in clinical trials, are lacking. METHODS: We integrated analyses of patient tumor datasets, neuroendocrine (NE) and non- NE SCLC cell lines, and functional perturbation models to define the determinants of response to KIF18A inhibition. Chromosomal instability metrics, transcriptional programs, mitotic dynamics, and spindle assembly checkpoint (SAC) function were assessed using genomic profiling, live-cell imaging, genetic perturbation, and pharmacologic inhibition. RESULTS: KIF18A expression was elevated in SCLC tumors and correlated with CIN-associated transcriptional programs, proliferative markers, and NE status; however, these features did not predict sensitivity to KIF18A inhibition. Instead, response was determined by the functional integrity of the SAC. SAC-proficient SCLC cells underwent sustained mitotic arrest followed by apoptotic cell death upon KIF18A inhibition, whereas SAC-defective cells failed to maintain checkpoint activation and survived. Mechanistically, resistant cells exhibited impaired kinetochore recruitment of core SAC components, including MAD1 and BUBR1. Importantly, transient induction of acute CIN through MPS1 inhibition partially restored sensitivity to KIF18A inhibition in resistant models. CONCLUSIONS: This study provides the first mechanistic characterization of KIF18A dependency in SCLC, identifying SAC competency as the primary determinant of response. These findings establish a biologically informed framework for patient stratification and rational combination strategies. TRANSLATIONAL RELEVANCE: Small-cell lung cancer (SCLC) is an aggressive malignancy with few effective targeted therapies and marked chromosomal instability. KIF18A has emerged as a potential therapeutic target in genomically unstable cancers, but biomarkers predicting response to KIF18A inhibition are lacking. We demonstrate that sensitivity to KIF18A inhibition in SCLC is determined not by KIF18A expression, neuroendocrine subtype, or baseline chromosomal instability, but by the functional integrity of the spindle assembly checkpoint (SAC). SCLC cells with intact SAC signaling undergo sustained mitotic arrest and apoptosis upon KIF18A inhibition, whereas SAC-defective cells bypass checkpoint activation and survive aberrant mitosis. Notably, transient induction of acute chromosomal instability through MPS1 inhibition partially restores sensitivity in resistant models. Together, these findings identify mitotic checkpoint competency as a mechanistic determinant and candidate predictive biomarker for KIF18A-targeted therapies, providing a biologically informed framework for patient stratification and rational combination strategies relevant to ongoing KIF18A inhibitor clinical trials.

Journal Article↗

PubMed2026

Protein Profiling Identifies Biomarkers for Predicting Disease Severity in Anti-NMDAR Encephalitis.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a severe autoimmune neurological disorder characterized by pathogenic antibodies against the NMDAR. A systematic protein profiling approach is warranted to identify biomarkers capable of predicting disease status. An Olink proximity extension assay (PEA) profiled 91 inflammation-related proteins from anti-NMDAR encephalitis patients. Disease severity or prognosis were assessed by CASE score or mRS score at 6-month follow-up. Patients were stratified into distinct molecular clusters using unsupervised clustering. Logistic regression models incorporating selected biomarkers were developed to predict disease severity and prognosis, followed by absolute quantification using ELISA. Patients were classified into four consensus clusters. Clusters 1 and 2 corresponded to the mild group, while Cluster 3 represented the severe group, consistent with CASE score above 6. Cluster 4 showed heterogeneous clinical features. Elevated serum levels of IL-10, IL-6, and SIRT2, as well as increased CSF levels of CXCL10, CXCL11, and MMP10, were positively associated with severe disease. Conversely, several proteins including LTA and CCL11, CCL8, TGFB1, CXCL6 were associated with severe disease or unfavorable 6-month outcomes. A logistic regression model combining serum CXCL6 and CCL11 with CSF MMP10 achieved an area under the curve (AUC) of 0.95 for predicting disease severity. Serum CCL11 alone showed predictive value for 6-month prognosis, with an AUC of 0.79. These findings delineate distinct protein signatures associated with clinical heterogeneity of anti-NMDAR encephalitis. Prediction models incorporating multiple biomarkers may provide an approach for disease severity stratification and prognosis forecast.

Humans↗

PubMed2026

Inclusion of Multi-Omic Biomarkers Improves Prediction Accuracy of Response, Relapse, and Overall Survival in Acute Myeloid Leukemia Patients Receiving High-Intensity Induction Chemotherapy.

BACKGROUND: Despite advancements in genetic markers for acute myeloid leukemia (AML) risk stratification, outcome prediction remains challenging due to disease heterogeneity and dynamic genetic changes, highlighting the need for reliable biomarkers to improve AML treatment strategies and patient outcomes. To refine outcome predictions, we investigated the use of microbial-derived biomarkers to predict composite complete remission (CRc), relapse, and survival for patients on high- and low-intensity regimens, and to integrate those variables into the widely clinically utilized European Leukemia Network (ELN-2022) genetic risk classification model for high-intensity-treated patients. METHODS: We first developed machine learning models that integrate baseline fecal metabolomics, 16S rRNA-based stool microbiome features, and clinical metadata (sex, antibiotic administration, AML somatic mutations, and cytogenetics) from two cohorts of AML patients (n = 83) undergoing remission induction chemotherapy. Univariate tests and sparse canonical correlation analysis were employed for variable selection and to explore fecal metabolite-microbe relationships. A robust machine learning approach using XGBoost was employed, with 100 stratified data splits (80% training, 20% testing) and coarse-to-fine hyperparameter optimization. Variable importance was aggregated across all models to select key predictors. RESULTS: For high-intensity-treated patients, XGBoost models achieved aggregated AUROC scores of 0.719, 0.729, and 0.65 for CRc, relapse, and overall survival, respectively. For low-intensity-treated patients, these models achieved aggregate AUROC scores of 0.945, 0.724, and 0.768 for these same outcomes, respectively. Integrating the biomarkers identified in the high-intensity machine-learning models with the current ELN-2022 AML risk stratification system effectively stratified patients into risk categories, which obtained higher concordance indices and likelihood ratios, demonstrating improved prognostic accuracy for each outcome compared to ELN-2022 alone. CONCLUSIONS: The inclusion of microbial-derived biomarkers serves as a robust prognostic tool to improve outcome prediction in AML patients, highlighting the potential of its integration into AML risk assessment and paving the way for personalized treatment strategies and improved patient outcomes.

Humans↗