IntroductionLung cancer remains the leading cause of cancer mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for the majority of cases. Although immune checkpoint inhibitors (ICIs) have transformed the therapeutic landscape of NSCLC, clinical responses remain highly variable. Emerging evidence implicates the gut microbiome in modulating the outcomes of ICI treatment; however, most studies to date have focused on taxonomic composition rather than microbial functional capacity. This study aimed to systematically compare the predictive value of taxonomic versus functional gut microbiome features across multiple ICI-related outcomes.MethodsPretreatment fecal samples from 77 Japanese patients with NSCLC receiving ICIs were profiled using 16S rRNA sequencing. Six feature sets, comprising three taxonomic (family, genus, and species) and three functional (KEGG Orthology, Enzyme Commission, and MetaCyc pathways), were assessed using permutational multivariate analysis of variance for their association with clinical outcomes, including treatment response, irAEs, progression-free survival, and overall survival. Machine-learning models were subsequently developed based on MetaCyc pathway features to predict treatment response, with nested internal and external validation to ensure robustness and SHapley Additive exPlanations (SHAP) analysis for model interpretability.ResultsOf all the feature sets tested, the functional profiles derived from the MetaCyc pathways exhibited the strongest association with the RECIST-defined response. A four-pathway signature, comprising PWY-4984 (urea cycle), SALVADEHYPOX-PWY (adenosine nucleotide degradation), OANTIGEN-PWY (O-antigen biosynthesis in E. coli), and PWY-5088 (L-glutamate degradation VIII to propanoate), achieved robust predictive performance, substantially outperforming any single feature. SHAP analysis confirmed that the primary drivers of responder classification were pathways involved in nitrogen metabolism and short-chain fatty acid biosynthesis.ConclusionsIn this study, gut microbial functional profiles consistently outperformed taxonomic features in predicting ICI response in patients with NSCLC. These findings suggest that metabolic pathway-based signatures may capture functional microbiome-host interactions more effectively and hold greater promise as translatable, safer targets for precision intervention, particularly through metabolite-oriented strategies.

Clear cell renal cell carcinoma (ccRCC) presents a unique immunological paradox: abundant CD8+ tumor-infiltrating lymphocytes (TILs) correlate with poor prognosis. To clarify their functional status and therapeutic potential, we performed single-cell transcriptomic profiling of TILs from 15 patients with ccRCC and functionally validated dominant T cell receptor (TCR) clonotypes using autologous tumor-derived organoids. Single-cell RNA sequencing revealed dynamic shifts in T cell composition, with effector and progenitor-exhausted CD8+ T cells declining and terminally exhausted CD8+ and regulatory CD4+ T cells enriched in advanced tumors. Despite this exhausted phenotype, in an exploratory analysis with five patients, approximately half of the top 20 TCR clonotypes retained anti-tumor reactivity when re-expressed in non-exhausted T cells, as evidenced by TCR-T cell-mediated cytotoxicity and IFN-γ production against autologous organoids. Transcriptomic signatures enabled the development of a penalized logistic regression classifier that distinguished tumor-reactive from bystander T cells with high accuracy, with AUCs of 0.903 (training) and 0.913 (test). Cross-cancer comparison with pancreatic ductal adenocarcinoma (PDAC) datasets revealed limited generalizability, highlighting the need for cancer type-specific models. Notably, ccRCC-specific TILs exhibited mature, functionally differentiated profiles with limited proliferation, consistent with chronic antigen exposure, whereas PDAC-reactive TILs showed highly proliferative and activated phenotypes indicative of ongoing clonal expansion. Collectively, these findings suggest key features of the immune landscape in ccRCC and provide a preliminary, proof-of-concept transcriptomic framework for prioritizing candidate tumor-reactive TCRs. These insights suggest the feasibility of identifying candidate TCRs for future development of TCR-based adoptive T cell therapies in ccRCC and emphasize the importance of integrating single-cell profiling with functional analyses to refine immunotherapeutic strategies. Given the limited sample size, our results should be considered exploratory and hypothesis-generating, and future studies will be required to validate these findings in larger, independent ccRCC cohorts.

Neoadjuvant chemotherapy (NAC) with anti-HER2 agents is standard for HER2-positive breast cancer, achieving pathological complete response (pCR) in 40–50

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality. It is often diagnosed at advanced stages and characterized by high recurrence rates. Although chronic liver inflammation and metabolic dysfunction are established contributors to tumorigenesis, the molecular mechanisms that link microenvironmental stress to malignant transformation remain poorly understood. MYCN, a proto-oncogenic transcription factor, has emerged as a potential biomarker of cancer stemness. However, its role in hepatocarcinogenesis remains unclear. In this study, we elucidated the oncogenic role of MYCN and its dynamic transcriptional regulation during liver tumorigenesis. Using a hydrodynamic tail vein injection-based transposon system in mice, we demonstrated that MYCN overexpression synergizes with AKT activation to promote liver tumorigenesis. Transcriptomic profiling revealed that MYCN-driven tumors exhibited features of human HCC subtypes enriched in stress-adaptive transcriptional programs. Time-resolved spatial transcriptomics further uncovered a MYCN-enriched niche characterized by epithelial-mesenchymal transition (EMT) and Wnt/β-catenin signaling, which expanded during tumor progression and was spatially proximate to transformed malignant cells. To translate these findings to human HCC, we developed a machine learning-based MYCN niche score and validated its clinical relevance across multiple human HCC cohorts. This score reliably predicted recurrence risk and identified EMT-prone microenvironments, with stronger predictive performance in nontumor tissues, suggesting its potential in detecting precancerous niches predisposed to de novo tumorigenesis. Collectively, our findings establish MYCN as a functional driver and spatial marker of tumor-promoting microenvironments in liver tumorigenesis; additionally, we propose a clinically actionable strategy to identify high-risk patients through transcriptomic profiling of nontumor liver tissue.

The advancements in technology and computation are progressively enhancing the sensitivity and accuracy of mass spectrometry-based immunopeptidomics. In our previous study, we developed a machine learning filter by incorporating retention time as well as predicted physicochemical properties of peptides to eliminate false positives in identifications by Mascot-based traditional shotgun proteomic workflow. The present study provides a step-by-step guide on emphasizing how to prepare and organize the data, as well as applying the machine learning model to analyze the users' own experimental data.

BACKGROUND:Transcription factor (TF)-dependent DNA demethylation is associated with generation of specific DNA methylation profiles in normal cellular development and disease, although only a small fraction of TFs are known to promote DNA demethylation. RESULTS:Here, we systematically predicted which TFs have DNA-demethylation-promoting activity. Experiments with deletion mutants of the TFs RUNX1 and SPI1 revealed that this activity is associated with a relatively long intrinsically disordered region (IDR). Examination of the IDRs from eight TFs previously confirmed to have such activity revealed that at least one IDR was active in each TF. We constructed a Random Forest classifier based on 25 numeric physicochemical features extracted from length-controlled 26 positive (active) and 32 negative (inactive) IDRs. Four key features- aromaticity, aliphatic index, fractional charge ratio, and side chain hydrophobic density-were identified as the most informative contributors to prediction of positive IDRs. A model based on these features achieved an area under the receiver operating characteristic curve of 0.84, with an optimized decision threshold of 0.303. Applying this model to all TFs, we predicted 959 of 2364 IDRs to be positive, corresponding to 825 of 1308 TFs. The model correctly identified all of 14 previously validated positive TFs. The predicted positive TFs showed significant enrichment of Gene Ontology terms related to morphogenesis and development and may be clinically relevant to certain cancer types. CONCLUSION:The developed model with high predictive performance and the predicted TFs with DNA-demethylation-promoting activity will be useful for further analysis of TFs involved in generation of DNA methylation profiles in normal cell development and disease.

Introduction:Immune checkpoint inhibitors (ICIs) targeting the programmed death-1/ligand-1 (PD-1/PD-L1) axis have significantly improved treatment outcomes in non-small cell lung cancer (NSCLC); however, challenges remain owing to the limited durability of therapeutic responses and the occurrence of immune-related adverse events (irAEs). This study aimed to characterize dynamic changes in the circulating autoantibody (CAAB) profile during ICI treatment and explore their association with treatment outcomes in patients with NSCLC. Methods:A panel of 59 CAABs showing substantial treatment-related changes was initially identified using AlphaScreen assays in a primary screening of five patients who developed ir-pneumonitis. These CAABs were subsequently profiled in paired pre-and post-treatment plasma samples obtained from 179 patients with NSCLC treated with anti-PD-1/PD-L1 therapy at two Japanese centers. Associations between CAAB dynamics and clinical parameters-including baseline characteristics, treatment regimens, and treatment outcomes (irAEs, ir-pneumonitis, response, progression-free survival [PFS], and overall survival [OS])-were evaluated using permutational multivariate analysis of variance and univariate binary logistic and Cox regression, elastic net regularization regression, and random forest regression. Results:Using permutational multivariate analysis of variance and univariate binary logistic/Cox regression, we comprehensively assessed the global associations between CAAB dynamics and eight clinical parameters, including background factors (PD-L1 expression and treatment line), treatment regimens (chemotherapy exposure), and treatment outcomes (irAE occurrence, ir-pneumonitis development, RECIST-assessed response, PFS, and OS), indicating that chemotherapy exposure was the only significant and strong factor influencing CAAB dynamics. In patients receiving ICI monotherapy, univariate logistic or Cox regression analyses were performed to identify individual CAABs significantly associated with each outcome, highlighting both shared and distinct immunological features underlying different clinical endpoints. Through machine learning-based evaluation of the predictive potential of CAAB dynamics for five treatment outcomes across the overall cohort and six subgroups defined by three stratification variables, four optimized CAAB signatures with robust predictive performance for ICI treatment outcomes were established. Conclusions:These findings suggest the involvement of distinct immune pathways in therapeutic benefits and toxicity. Collectively, our results provide mechanistic insights into ICI-induced humoral immune regulation, highlight the potential utility of CAABs as biomarkers to enhance benefit-to-risk assessment, and guide the development of personalized immunotherapy strategies for NSCLC.

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by a dismal prognosis. Treatment outcomes exhibit substantial variability across patients, underscoring the urgent need for robust predictive models to effectively estimate survival probabilities and therapeutic responses in PDAC.MethodsMetabolic and immune-related genes exhibiting differential expression were identified using the TCGA-PDAC and GTEx datasets. A genetic prognostic model was developed via univariable Cox regression analysis on a training cohort. Predictive accuracy was assessed using Kaplan-Meier (K-M) curves, calibration plots, and ROC curves. Additional analyses, including GSAE and immune cell infiltration studies, were conducted to explore relevant biological mechanisms and predict therapeutic efficacy.ResultsAn 8-gene prognostic model (AK2, CXCL11, TYK2, ANGPT4, IL20RA, MET, ENPP6, and CA12) was established. Three genes (AK2, ENPP6, and CA12) were associated with metabolism, while the others were immune-related. Most genes correlated with poor prognosis. Validation in TCGA-PDAC and GSE57495 datasets demonstrated robust performance, with AUC values for 1-, 3-, and 5-year OS exceeding 0.7. The model also effectively predicted responses to adjuvant therapy.ConclusionThis 8-gene signature enhances prognostic accuracy and therapeutic decision-making in PDAC, offering valuable insights for clinical applications and personalized treatment strategies.

The omentum is a common site of peritoneal metastasis in various cancers, including gastric cancer. It contains immune cell aggregates known as milky spots, which provide a microenvironment for peritoneal immunity by regulating innate and adaptive immune responses. In this study, we investigated gene expression profiles in cells from omental milky spots of patients with gastric cancer (n = 37) by RNA sequencing analysis and classified the patients into four groups (G1-4). Notably, significant differences were observed between the groups in terms of macroscopic type, lymphatic invasion, venous invasion, and pathological stage (pStage). G3, which was enriched in genes related to acquired immunity, showed earlier tumor stages (macroscopic type 0, Ly0, V0, and pStage I) and a better prognosis. In contrast, G4 showed enrichment of genes related to neutrophils and innate immunity; G1 and G2 showed no enrichment of innate or adaptive immune-related genes, suggesting an immune desert microenvironment. Cytometric analysis revealed significantly more T and B cells and fewer neutrophils in G3. Accordingly, the immune microenvironment in omental milky spots may vary depending on the stage of gastric cancer progression. When univariate Cox proportional hazards regression models were used to search for prognostically relevant genes specific to G3, 23 potential prognostic genes were identified as common genes associated with relapse-free survival and overall survival. In addition, the multivariate Cox proportional hazards model using these prognostic genes and clinicopathological information showed that combining the B cell marker CD19 and Ly had a high predictive accuracy for prognosis. Based on this study’s results, it is possible that tumor progression, such as lymphatic and/or venous infiltration of tumor cells, may affect the immune cell composition and proportions in omental milky spots of patients with gastric cancer and analysis of gene expression in omental milky spots may help to predict gastric cancer prognosis.

Neutrophil extracellular traps (NETs) released from neutrophils are related to cancer progression. However, the relationship between the therapeutic effects of immune checkpoint inhibitors (ICIs) such as anti-PD-1 and anti-PD-L1 antibodies and plasma NET concentration in patients with non-small cell lung cancer (NSCLC) is poorly understood. In this study, concentrations of citrullinated histone H3 (CitH3), a surrogate marker of NETs, in plasma before/after treatment were examined in patients with advanced or recurrent NSCLC undergoing ICI treatment (n = 185). The clinical significances of NET levels before/after treatment and posttreatment changes were statistically evaluated. As a result, multivariate Cox analysis showed that high NET levels before treatment were statistically significant predictors of unfavorable overall survival (OS; p < 0.001, HR 1.702, 95% CI 1.356-2.137) and progression-free survival (PFS; p < 0.001, HR 1.566, 95% CI 1.323-1.855). The Kaplan-Meier curves showed significant separation between the high- and low-NET groups in OS (p = 0.002) and PFS (p < 0.001). Additionally, high NET levels after treatment were also significantly associated with worse OS (p < 0.001) and PFS (p < 0.001) by multivariate Cox analysis. Notably, the pretreatment NET levels were significantly correlated with the plasma levels of NET-related inflammatory cytokines, such as IL-6 and IL-8, and with NET-related gene expression and immune-suppressive profile in peripheral blood mononuclear cells. Our findings suggest that NETs released from activated neutrophils might reduce the clinical efficacy of ICIs in patients with NSCLC.

The proto-oncogene MYCN expression marked a cancer stem-like cell population in hepatocellular carcinoma (HCC) and served as a therapeutic target of acyclic retinoid (ACR), an orally administered vitamin A derivative that has demonstrated promising efficacy and safety in reducing HCC recurrence. This study investigated the role of MYCN as a predictive biomarker for therapeutic response to ACR and prognosis of HCC. MYCN gene expression in HCC was analyzed in the Cancer Genome Atlas and a Taiwanese cohort (N = 118). Serum MYCN protein levels were assessed in healthy controls (N = 15), patients with HCC (N = 116), pre- and post-surgical patients with HCC (N = 20), and a subset of patients from a phase 3 clinical trial of ACR (N = 68, NCT01640808). The results showed increased MYCN gene expression in HCC tumors, which positively correlated with HCC recurrence in non-cirrhotic or single-tumor patients. Serum MYCN protein levels were higher in patients with HCC, decreased after surgical resection of HCC, and were associated with liver functional reserve and fibrosis markers, as well as long-term HCC prognosis (>4 years). Subgroup analysis of a phase 3 clinical trial of ACR identified serum MYCN as the risk factor most strongly associated with HCC recurrence. Patients with HCC with higher serum MYCN levels after a 4-week treatment of ACR exhibited a significantly higher risk of recurrence (hazard ratio 3.27; p = .022). In conclusion, serum MYCN holds promise for biomarker-based precision medicine for the prevention of HCC, long-term prognosis of early-stage HCC, and identification of high-response subgroups for ACR-based treatment.

Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.