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PubMed2026

Identification and characterization of ectopic chromosomal amplifications in acute myeloid leukemia cell limes using high-throughput chromosome conformation capture screening.

Despite advanced molecular diagnostics, improving outcomes for refractory acute myeloid leukemia (AML) remains challenging. Although many cancer-related genes are identified, their molecular mechanisms are not fully elucidated. Amplification is a mechanism of cancer-associated gene activation, and ectopic gene amplification may have particularly high pathological significance. However, research on ectopically amplified cancer-associated genes in leukemia remains limited. Here, we evaluated the usefulness of high-throughput chromosomal conformation capture (Hi-C) as a screening method for ectopic gene amplification and assessed whether ectopic amplification of cancer-associated genes may represent a general phenomenon in AML. We screened the U-937 and NB-4 cell lines using in situ Hi-C. Regions appearing as "high-intensity bands" in Hi-C contact maps were identified and validated using fluorescence in situ hybridization (FISH). Additionally, copy number variation analysis was performed using whole-genome sequencing (WGS) to extract cancer-associated genes with ectopic amplification. In the U-937, three genomic regions showing "high-intensity bands" were identified and confirmed as ectopic amplifications-including PDCD1LG2 (PD-L2), CD274 (PD-L1), and JAK2; that is, four copies were detected by WGS, and amplification signals were observed by FISH. In the NB-4, four such regions were detected, including MYC and KRAS, with expression level of 498 transcripts per million (TPM) and 34 TPM, respectively. Copy number variation analysis further identified multiple cancer-associated genes with ectopic amplification. Overall, these findings demonstrate the presence of ectopic amplification of cancer-associated genes in AML cell lines and support the usefulness of Hi-C as a screening method for detecting such genomic alterations.

Acute myeloid leukemia↗

PubMed2024

Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer.

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Humans↗

PubMed2026

Low-Grade Myofibroblastic Sarcoma Represents an Epigenetically Distinct Myofibroblastic Tumor With USP6 Upregulation and Stable Genome.

Low-grade myofibroblastic sarcoma (LGMS) is a rare, indolent mesenchymal neoplasm exhibiting myofibroblastic differentiation, with a propensity for local recurrence. The molecular basis of LGMS and its precise relationship with other histological mimics have remained largely undefined. To address this gap, we conducted the first comprehensive multiomics analysis of 6 LGMS cases, integrating whole-exome sequencing, RNA sequencing, and Illumina Methylation EPICv2 array profiling with comparative analysis against public sarcoma methylation cohorts and related fibroblastic tumors. Clinically, patients (median age 35.5 years) presented with small tumors (median size 1.45 cm), predominantly located in the head and neck, displaying classic histological features of diffusely infiltrative spindle cell fascicles with patchy mononuclear inflammation. Two of the 5 patients with follow-up developed local recurrence, and none metastasized (median follow-up duration 92.5 months). Genomically, all LGMS exhibited a low tumor mutational burden (median 2.31 mut/Mb) and a minimal fraction of genome altered, with TP53 and TSC2 deletions and NTRK1 and ERBB3 amplifications found in a subset of cases. No pathogenic fusions were detected. Transcriptomic profiling revealed a distinct signature featuring prominent USP6 overexpression and upregulation of inflammatory and immune-related genes, including CD274 (PD-L1), and enrichment of inflammatory and interferon-gamma response signatures. Epigenetically, LGMS formed a unique methylation cluster closest to inflammatory myofibroblastic tumor, with numerous differentially methylated regions and higher immune infiltration, particularly monocytes, compared with other fibroblastic tumors. These findings establish LGMS as a genomically stable, epigenetically distinct myofibroblastic sarcoma driven by USP6 overexpression and an inflammation-enriched transcriptome. They support its recognition as a standalone entity, facilitate integration into methylation-based sarcoma classifiers for improved diagnostic precision, and nominate USP6-associated pathways and immune checkpoint blockade as promising therapeutic strategies for recurrent or unresectable disease.

Humans↗

PubMed2026

Decoding tumor immune microenvironment heterogeneity by single-cell and spatial multi-omics: From immunotherapy resistance to translational biomarkers.

Immune checkpoint blockade has transformed cancer therapy, yet primary and acquired resistance remain major clinical challenges. Increasing evidence indicates that immunotherapy resistance cannot be fully explained by tumor-intrinsic alterations or conventional biomarkers such as PD-L1 expression, tumor mutational burden, or microsatellite instability. Instead, therapeutic response is shaped by the tumor immune microenvironment (TIME) as a heterogeneous, spatially organized, and dynamically evolving ecosystem. Single-cell omics has revealed diverse immune and stromal cell states, including progenitor and terminally exhausted T cells, suppressive myeloid programs, B-cell/TLS-associated immune-reactive states, and CAF-mediated exclusion phenotypes. Spatial transcriptomics, spatial proteomics, and imaging-based approaches further demonstrate that these cell states assemble into distinct immune niches, including immune-inflamed, T-cell-excluded, myeloid-suppressive, metabolic/hypoxic, and TLS-associated niches. These spatial ecosystems determine whether antitumor immune cells can access malignant cells, receive antigen-presenting support, or become restrained by stromal, vascular, metabolic, and myeloid barriers. In this review, we summarize how single-cell and spatial multi-omics redefine TIME heterogeneity in immunotherapy resistance, highlight ligand-receptor communication networks linking cell states to spatial immune dysfunction, and discuss emerging translational biomarkers for patient stratification. We further propose that future immunotherapy biomarkers should evolve from static single-marker assays toward longitudinal, spatially resolved, and interpretable multi-omics models that guide precision combination immunotherapy.

Humans↗

PubMed2026

Radiomics as a spatial context for treatment decision-making in head and neck cancer.

Radiomics has been widely explored as a non-invasive biomarker in head and neck squamous cell carcinoma (HNSCC), yet its clinical role remains unclear. Tissue-based biomarkers differ in their susceptibility to spatial sampling. Biomarkers such as PD-L1 expression, immune-cell infiltration, necrosis, and immune exclusion may exhibit substantial spatial heterogeneity, whereas HPV/p16 status and some genomic alterations are generally more stable across the tumor. Nevertheless, localized sampling may incompletely capture heterogeneity in selected clinical contexts. This mismatch becomes clinically relevant when treatment decisions, particularly for chemoradiotherapy, immunotherapy, or de-escalation, are based on potentially non-representative biopsy findings. In this narrative review, we argue that the role of radiomics is not to outperform established biomarkers, but to contextualize them by capturing spatial heterogeneity related to hypoxia, necrosis, stromal architecture, and immune exclusion. We synthesize current evidence linking radiomic features to these biological processes and map them to specific clinical decision points, including larynx preservation, immunotherapy stratification, and recurrence assessment. Rather than serving as a standalone predictor, radiomics may provide complementary spatial information that helps identify situations in which biopsy-derived biomarkers should be interpreted with caution. Although current evidence is largely retrospective, radiomics offers a pragmatic framework for integrating spatial information into biomarker-guided clinical workflows.

Journal Article↗

PubMed2026

Clinical translation of senescence-related pan-cancer multi-omics: tools for assessment and immunotherapy prediction.

Cellular senescence (CS) exerts dual roles in tumorigenesis, yet its pan-cancer molecular characteristics and clinical value remain unclear, hindering its translation to oncology and personalized therapy. To address the lack of specific and universal tools for senescence assessment and immunotherapy response prediction, this study systematically analyzed 1259 CS-related genes from the CellAge database across 31 cancer types by integrating multi-omics data, including bulk RNA-seq, single-cell/spatial transcriptomics, and CRISPR screening. We developed a rank-based algorithm SenScoreR (publicly available at https://gxhub.shinyapps.io/SenScoreR/ ) for senescence quantification, validated with 10 independent datasets, and constructed a machine learning-based predictive model CS.Sig for immunotherapy response. Results showed that tumors had significantly lower Rank-based Senescence Score (RSS) than normal tissues across 31 cancers (average diagnostic AUC = 0.895), with low RSS linked to poor survival; high RSS correlated with reduced genomic instability, enriched CD8⁺ T/NK cell/macrophage infiltration, upregulated PD-L1 expression, and elevated immune cytolytic activity. CS.Sig demonstrated robust performance in predicting ICI response (AUC = 0.716 across 10 cohorts), outperforming 13 existing signatures, while CRISPR screening identified 17 senescence-related targets (e.g., CEP55, PPP1CC) whose knockout enhanced anti-tumor immunity. Our findings clarify CS's role in maintaining tumor genomic stability and shaping immune microenvironments, and the developed SenScoreR, CS.Sig, and identified targets bridge basic CS research with clinical oncology, providing a translational resource and hypothesis basis for future experimental and clinical validation.

Journal Article↗

PubMed2024

ALDH1A1 promotes immune escape of tumor cells through ZBTB7B-glycolysis pathway.

The primary impediment to the success of immunotherapy lies in the immune evasion orchestrated by tumors, contributing to the suboptimal overall response rates observed. Despite this recognition, the intricacies of the underlying mechanisms remain incompletely understood. Through preliminary detection of clinical patient tissues, we have found that ALDH1A1 was a key gene for the prognosis of cancer patients and tumor glycolysis. In vitro experiments and tumor formation in nude mice suggested that targeting ALDH1A1 could inhibit tumor growth. Through further analysis of xenograft tumor models in immune-normal mice and flow cytometry, we found that deficiency in ALDH1A1 could promote immune system suppression of tumors in vivo. Specifically, RNA-seq analysis, combined with qPCR and western blot, identified the transcription factor ZBTB7B as downstream of ALDH1A1. The binding sites of the transcription factor ZBTB7B on the LDHA promoter region, which is responsible for regulating the rate-limiting enzyme gene LDHA in glycolysis, were determined using luciferase reporter gene detection and Chip-qPCR, respectively. In addition, the increased SUMOylation of ZBTB7B stabilized its transcriptional activity. Further in vivo and in vitro experiments confirmed that the combination of targeting ALDH1A1 and ZBTB7B with immune checkpoint inhibitors could synergistically inhibit tumors in vivo. Finally, after conducting additional verification of patient tissue and clinical data, we have confirmed the potential translational value of targeting ALDH1A1 and ZBTB7B for tumor immunotherapy. These results emphasize the potential translational significance of targeting ALDH1A1 and ZBTB7B in the realm of tumor immunotherapy. The convergence of ALDH1A1 inhibition and immune checkpoint blockade, particularly with PD-L1/PD-1 mAb, presents a compelling avenue for curtailing tumor immune escape.

Animals↗

PubMed2026

TFAP2B and FOXC1 are associated with biologically and clinically distinct differentiation states in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) comprises biologically distinct subtypes, including luminal androgen receptor (LAR) tumors, but routine immunohistochemical markers incompletely capture lineage-associated differentiation states. We investigated TFAP2B, an AP-2 family transcription factor linked to epithelial differentiation, and FOXC1, a basal-like-associated transcription factor, as complementary markers of luminal and basal differentiation in TNBC. In a tissue microarray of 105 TNBCs, tumors were stratified as TFAP2B-dominant, FOXC1-dominant, or double-negative according to relative marker predominance and characterized using lineage, proliferation, molecular, immune, and chemotherapy-response parameters. TFAP2B-dominant tumors were associated with a coherent luminal phenotype, including apocrine/lobular enrichment, strong MUCL1 and AR expression, high CK18, low SOX10, reduced proliferation, frequent wild-type p53 patterns and retained RB1 expression, and low PD-L1 expression. FOXC1-dominant tumors showed contrasting basal-like features. These associations were independently supported at the transcriptional and genomic levels in the METABRIC TNBC cohort. FOXC1-dominant tumors also showed more frequent chemotherapy-induced downstaging than TFAP2B-dominant tumors. Together, we identify TFAP2B as a marker associated with luminal differentiation in TNBC and support combined TFAP2B/FOXC1 assessment as a practical framework for identifying clinically relevant TNBC differentiation states.

Humans↗

PubMed2026

Deep visual multi-omics profiling links morphology and molecular programs in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma exhibits striking intra-tumoral heterogeneity at morphological and genetic levels, complicating treatment and contributing to disease progression. CcRCCs with rhabdoid differentiation are highly aggressive tumors characterized by distinct histopathologies. However, the relationship between morphology, underlying molecular alterations, and tumor behavior remains largely unclear. Here, we present Deep Visual Multi-Omics, an approach integrating digital pathology, morphology-guided single-cell isolation, and ultra-sensitive multi-omics profiling to link cell morphologies to their molecular underpinnings. Across five tumors, we profiled ~40,000 AI-classified and expert-curated cells. We identified progressive molecular dysregulation across cells with increasing histopathological grade coexisting within heterogeneous tumors as well as distinct molecular alterations associated with aggressive rhabdoid ccRCC cells, including signatures consistent with enhanced FOXM1-driven proliferation, altered cell-matrix interactions, and a putative immunomodulatory phenotype. Notably, rhabdoid cells exhibited elevated expression of IFN-beta, PD-L1, CD38, ITGB2, and integrin signaling, suggesting that they themselves may act as a source of signals influencing the local immune microenvironment. Besides providing new insights into the biology of ccRCC and highlighting avenues for future translational studies, this illustrates the potential of Deep Visual Multi-omics to dissect cancer heterogeneity and characterize high-risk cell populations.

Humans↗

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

Matched targeted therapy use after broad genomic profiling in advanced Non-Small cell lung cancer.

INTRODUCTION: While broad genomic profiling is increasingly used in advanced NSCLC (aNSCLC), the impact of test results on subsequent guideline-concordant targeted therapy selection remains incompletely understood. METHODS: Using a merged dataset of two large, nationwide, patient-level databases, we identified patients who were diagnosed with aNSCLC 2017-2023, had potentially actionable genomic profiling findings, and initiated systemic therapy. Patients were categorized into actionability subgroups based on contemporaneous regulatory approvals and NCCN guideline recommendations. Within each subgroup, we assessed receipt of guideline-concordant targeted therapy within 24 months, including potential underuse (non-receipt of recommended treatment) and overuse (receipt of non-recommended treatment). RESULTS: Among 6620 patients (67.4% ≥65 years, 54.6% female, 68.9% White), guideline-concordant targeted therapy use varied substantially by actionability category: 2313 (89.6%) of 2582 patients with available 1st-line on-label options received them (10.4% underuse), while 212 (67.3%) of 315 patients with available later-line on-label options received them after 1st-line (32.7% underuse). Among 441 patients with available guideline-concordant off-label options, only 122 (27.7%) received them (72.3% underuse). Conversely, 238 (8.6%) of 3282 patients received matched but guideline-discordant off-label options, representing overuse of ineffective or unestablished therapies. Smoking history, squamous histology, and high PD-L1 expression were associated with lower targeted therapy receipt. CONCLUSIONS: In this cohort study of aNSCLC care, the guideline concordance of targeted therapy use varied by clinical actionability of molecular testing results. Underuse was more common in patients with later-line and off-label targeted therapy options. Patients with classical smoking-related risk profiles were substantially less likely to receive targeted therapy even when actionable alterations were identified.

Journal Article↗

PubMed2026

DEK::AFF2 Fusion-Associated Sinonasal Carcinoma With Intracranial and Orbital Involvement.

Sinonasal carcinomas encompass a molecularly heterogeneous group of malignancies. DEK::AFF2 fusion-associated carcinoma is a recently recognized subtype of nonkeratinizing squamous cell carcinoma characterized by deceptively bland morphology yet clinically aggressive behavior. We report the case of a 67-year-old woman who presented with a rapidly enlarging sinonasal mass with extensive local invasion involving both the intracranial compartment and orbit. Histologically, the tumor was composed of cytologically bland cells with monotonous nuclei and exhibited a combination of exophytic and endophytic growth patterns that closely mimicked a sinonasal papilloma. Although the tumor shows predominantly squamous differentiation, focal extracellular mucin was also present. The brain-invasive foci demonstrated tumor cells in sheets and nests floating within pools of mucin, a pattern focally reminiscent of mucinous adenocarcinoma. RNA sequencing identified a DEK::AFF2 fusion with breakpoints at DEK exon 7 and AFF2 exon 6, confirming the diagnosis. The tumor demonstrated positive PD-L1 expression with a combined positive score (CPS) of 40 and a low tumor mutational burden (TMB) of 0.5 mutations/Mb. This case highlights the diagnostic challenges posed by DEK::AFF2 fusion-associated carcinoma, underscores the importance of molecular profiling for accurate classification, and demonstrates the aggressive clinical behavior of this rare entity.

Journal Article↗

PubMed2026

A Randomized Phase II Study of Combination Atezolizumab and Varlilumab (CDX-1127) with or without Cobimetinib in Previously Treated Unresectable Biliary Tract Cancer.

PURPOSE: The addition of MEK inhibition (MEKi) to programmed cell death ligand 1 (PD-L1) blockade improves progression-free survival (PFS) in patients with advanced biliary tract cancer. Although MEK inhibitors may increase tumor cell immunogenicity, they can impair T-cell priming/effector function, limiting combination efficacy. We hypothesized that the addition of a CD27 agonist could restore T-cell function and enhance antitumor immunity in this combination. PATIENTS AND METHODS: We conducted a randomized, phase II trial evaluating atezolizumab (840 mg, intravenously, days 1 and 15) in combination with the CD27 costimulatory monoclonal antibody [CDX-1127/varlilumab (3 mg/kg, intravenously, days 1 and 15)], with/without the addition of an MEK inhibitor [cobimetinib (60 mg, orally, daily, days 1-21, off days 22-28)] in unresectable biliary tract cancer following at least one metastatic therapy. Overall response rate (ORR) and PFS were coprimary endpoints. Treatment-related changes in CD8+ tumor-infiltrating lymphocytes (TIL) were the primary correlative outcomes. RESULTS: The trial was closed early following interim preplanned ORR analysis. At closure, 57 patients had been enrolled [n = 29 in the cobimetinib + atezolizumab + varlilumab (CAV) arm; n = 28 in the atezolizumab + varlilumab (AV) arm]. A majority (67%) had intrahepatic cholangiocarcinoma, and 32% were immunotherapy experienced. Both regimens were well tolerated without new safety signals. Objective responses were rare [0% (CAV); 3.8% (AV)]. The median PFS (mPFS) was 2.40 (CAV) and 1.84 (AV) months [hazard ratio (HR), 0.67; 95% confidence interval (CI), 0.38-1.18]. Among immunotherapy-experienced patients, the mPFS was 3.62 (CAV) and 1.84 (AV) months (HR, 0.54; 95% CI, 0.18-1.62). Treatment with CAV increased intratumoral CD8+ T-cell density compared with treatment with AV. CONCLUSIONS: The combinations of atezolizumab and varlilumab with/without cobimetinib were safe, but neither meaningfully improved outcomes in biliary tract cancer treated in the later lines. Correlative tissue studies validated preclinical work that MEKi increases CD8+ TILs.

Humans↗

PubMed2025

m6A regulator-based molecular classification and hub genes associated with immune infiltration characteristics and clinical outcomes in diffuse gliomas.

BACKGROUND: m6A methylation modification is a new regulatory mechanism involved in tumorigenesis and tumor-immunity interaction. However, its impact on glioma immune microenvironment and clinical outcomes remains unclear. METHODS: Comprehensive expression profiles of 18 m6A regulators were used to identify molecular subtypes exhibiting distinct m6A modification patterns in 1673 glioma samples sourced from public datasets. A multi-genes signature was constructed for predicting clinical outcomes and response to immunotherapy in glioma patients. Immunohistochemistry and cellular experiments were performed for validation. RESULTS: Two m6A subtypes of gliomas were identified. The m6A-low-risk subtype was characterized by paucity of immune infiltrates; While the m6A-high-risk subtype had higher abundances of multiple immune cells including lymphocyte and macrophage as well as increased expression of PD-L1, corresponding to an immunosuppressive phenotype. The m6A-high-risk subtype had poorer survival than the m6A-low-risk subtype in both the glioblastoma and lower grade gliomas cohorts. Eight m6A-related hub genes of high prognostic significances were identified and selected for developing a scoring signature termed as m6Ascore. Elevated m6Ascore indicated worse survival for glioma patients under standard care, but showed enhanced response to immunotherapy. Moreover, we demonstrated that overexpression of FTO, a m6A demethylase, inhibited the expressions of m6A-related hub genes (PTX3, SPAG4), impaired glioma cell viability and reduced macrophage chemotaxis. CONCLUSION: This work develops an immune- and clinical-relevant m6A subtyping and a scoring model, which enhances our understanding of the role of m6A modification in regulating immune infiltration microenvironment in gliomas and helps to identify patients who are more likely to benefit from immunotherapy.

Humans↗

PubMed2026

Secreted protein circuits in the gastrointestinal tumour microenvironment: determinants of immunotherapy response and resistance.

Immune checkpoint blockade has transformed treatment in selected gastrointestinal (GI) cancers, yet primary resistance, incomplete responses and acquired resistance remain common. This heterogeneity is not explained by tumour-cell genomics alone; extracellular signalling programmes within the tumour microenvironment can determine immune recruitment, access and adaptation to therapy. The tumour secretome-including cytokines, chemokines, growth factors, complement components, matricellular proteins, soluble checkpoint molecules and extracellular-vesicle-associated cargo-regulates immune-cell recruitment, exclusion, suppression, tertiary lymphoid structure formation and exhaustion across anatomical and molecular contexts. Across gastric and esophageal cancers, colorectal cancer, pancreatic ductal adenocarcinoma, hepatocellular carcinoma and biliary tract cancers, recurrent suppressive circuits include TGF-β, VEGF, CXCL12-CXCR4, CXCL8/IL-8-CXCR1/2, CCL2-CCR2, CSF1-CSF1R, IL-6-family cytokines, SPP1/osteopontin, periostin, galectins, DKK1, MIF, complement and soluble or vesicular PD-L1. Conversely, CXCL9/10/11-CXCR3 signalling and CXCL13-associated tertiary lymphoid structures characterise immune-permissive states that can support checkpoint responsiveness. We organise these circuits into four overlapping functional modules-myeloid-enriched, fibroblast-driven exclusion, angiogenic-immunosuppressive and immune-permissive-and apply a four-level evidence hierarchy that separates clinical validation from mechanistic inference. Clinically useful secretome biomarkers will therefore need to integrate cellular source, spatial localisation, receptor context, temporal dynamics and linkage to actionable immune-state transitions.

Humans↗

PubMed2025

Malaria: Factors affecting disease severity, immune evasion mechanisms, and reversal of immune inhibition to enhance vaccine efficacy.

Malaria is a complex parasitic disease caused by species of Plasmodium parasites. Infection with the parasites can lead to a spectrum of symptoms and disease severity, influenced by various parasite, host, and environmental factors. There have been some successes in developing vaccines against the disease recently, but the vaccine efficacies require improvement. Some issues associated with the difficulties in developing a sterile vaccine include high antigenic diversity, switching expression of the immune targets, and inhibition of immune pathways. Current vaccine research focuses on identifying conserved and protective epitopes, developing multivalent vaccines (including the whole parasite), and using more powerful adjuvants. However, overcoming the systematic immune inhibition and immune cell dysfunction/exhaustion may be required before high titers of protective antibodies can be achieved. Increased expression of surface molecules such as CD86 and MHC II on antigen-presenting cells and blocking immune checkpoint pathways (interactions of PD-1 and PD-L1; CTLA-4 and CD80) using small molecules could be a promising approach for enhancing vaccine efficacy. This assay reviews the factors affecting the disease severity, the genetics of host-parasite interaction, immune evasion mechanisms, and approaches potentially to improve host immune response for vaccine development.

Humans↗

PubMed2026

Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.

BACKGROUND: Predicting enterocutaneous fistula (ECF)-associated sepsis and mortality poses significant challenges in digital health care due to the disease's complexity and heterogeneous clinical manifestations. Current approaches that rely on single-modal data or traditional scoring systems often fail to capture the intricate immune-inflammatory dynamics and multisystem involvement in patients with ECF. OBJECTIVE: This study aims to develop an artificial intelligence (AI)-driven multimodal fusion model integrating clinical, imaging, and transcriptomic data for early prediction of ECF-associated sepsis and 28-day mortality, addressing the limitations of conventional single-dimensional models. METHODS: This study leveraged publicly available datasets (Medical Information Mart for Intensive Care III [MIMIC-III], electronic Intensive Care Unit [eICU], and The Cancer Genome Atlas) to construct a multimodal framework. Clinical parameters were processed using Extreme Gradient Boosting, abdominal imaging features were extracted via convolutional neural networks, and transcriptomic profiles were analyzed with variational autoencoders. A Transformer-based fusion network was employed for joint prediction and validated through cross-validation and external testing. Key features were identified using Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretability algorithms, while immune regulatory mechanisms were explored via weighted gene co-expression network analysis. RESULTS: The multimodal model achieved an area under the curve (AUC) of 0.89 for predicting sepsis and 28-day mortality, outperforming unimodal models (clinical-only model, AUC 0.72, and imaging-only model, AUC 0.78). Critical predictors included Sequential Organ Failure Assessment score, lactate levels, intra-abdominal free fluid on imaging, and immunoregulatory genes (programmed death-ligand 1 [PD-L1] and indoleamine 2,3-dioxygenase 1 [IDO1]). Mechanistic analysis revealed distinct immune reprogramming in patients with sepsis, characterized by increased regulatory T cells and M2 macrophages, along with downregulated cluster of differentiation 8+ (CD8+) T cells. CONCLUSIONS: This multimodal AI model offers an innovative digital solution in medical informatics, enabling precise early risk stratification for ECF-associated sepsis. By integrating multisource data and providing interpretable insights into immune-inflammatory pathways, the model enhances health care quality for patients with ECF and paves the way for personalized intervention strategies.

Humans↗

PubMed2026

RPN1 at the crossroads of glycosylation, tumor immunity, and disulfidptosis.

Ribophorin I (RPN1), a core component of the oligosaccharyltransferase complex, is traditionally known for its role in endoplasmic reticulum-associated N-glycosylation. Recent studies have identified RPN1 as an emerging regulator of tumor progression and immunity. Aberrant RPN1 overexpression has been reported in multiple malignancies, including glioma, hepatocellular carcinoma, sarcoma, and triple-negative breast cancer, where it is frequently associated with aggressive clinicopathological features and poor prognosis. RPN1 promotes tumor immune evasion by promoting N-glycosylation and stabilization of programmed death-ligand 1 (PD-L1), thereby enhancing immune checkpoint signaling and directly inhibiting anti-tumor T-cell responses. Consequently, elevated RPN1 expression is consistently associated with an immunosuppressive tumor microenvironment rich in M2 macrophages and poor in CD8+ T cells. More importantly, multiple omics signature analyses indicate RPN1 is integrated into several disulfidptosis-related risk models; however, direct experimental evidence confirming the causal linkage between RPN1 and disulfidptosis remains limited. Correlative database data also show potential associations between RPN1 upregulation and genomic instability and treatment resistance. Based on tiered classification of existing evidence (biochemical functional validation vs. multi-omics correlation), this review systematically summarizes the biological roles of RPN1 in cancer, its functions in tumor immunity and disulfidptosis-associated pathways and finally evaluates its potential as a therapeutic target in precision oncology.

PDL1↗