OBJECTIVES:To describe the utilization trend of radiation-based and surgery-based treatment in patients with resectable IIIC1 cervical adenocarcinoma and explore the optimal treatment strategy for these patients. METHODS:Patients with resectable IIIC1 cervical adenocarcinoma in 2005-2022 from Surveillance, Epidemiology, and End Results program (SEER) were analyzed. Trends over time in the utilization of radiation- and surgery-based treatment were plotted and estimated using Mantel-Haenszel test. Logistic regression analysis was performed to identify factors associated with the utilization of treatment. Survival outcomes were assessed and compared using Kaplan-Meire method and log-rank test, respectively. Inverse probability of treatment weighting (IPTW) was performed for adjustment of baseline characteristics. Sensitivity analysis was conducted using a cohort of cervical adenocarcinoma patients from our institution. RESULTS:The utilization of radiation-based treatment has grown steadily from 2005 to 2022 while the trend for surgery-based treatment showed opposite way (P = 0.002). Age, year of diagnosed, tumor size and T stage impacted the utilization of radiation-based treatment (All P < 0.05). Surgery-based treatment demonstrated superior overall survival (HR = 0.55, 95%CI:0.44-0.69; P < 0.001) and cancer specific survival (HR = 0.58, 95%CI:0.45-0.75; P < 0.001) to radiation-based treatment before adjustment of IPTW. However, no significant differences were observed in overall survival (HR = 0.77, 95%CI:0.56-1.05; P = 0.1) and cancer specific survival (HR = 0.86, 95%CI:0.60-1.23; P = 0.4) after baseline characteristics were balanced. Besides, the cohort from our institution further verified that similar survival outcomes were observed between two treatment strategies. CONCLUSIONS:The utilization of radiation-based treatment has increased over time and showed non-inferior efficacy for patients with resectable IIIC1 cervical adenocarcinoma when compared to surgery-based treatment.

PIK3R1G376R as a Recurrent Shared Neoantigen Candidate in Glioblastoma.

Clinical and molecular characteristics of the 21 patients enrolled in this study.

HLA class I typing of the matched healthy PBMC donors used for ELISpot assays.

All candidate neoantigen peptides selected for experimental validation in this study.

ELISpot validation of low-ranking neoantigen peptides predicted by TCRscore.

Low-ranking neoantigen candidates that elicited positive immune responses in functional assays.

PIK3R1G376R mutant peptide induces apoptosis in GBM organoids.

Comparison of neoantigen-associated mutations between glioblastoma organoids and matched parental tumors in RNA level.

High-ranking neoantigen candidates predicted by TCRscore and validated as immunogenic in functional assays.

Glioblastoma (GBM) is the most common malignant intracranial tumor in adults, with a median survival of only 16 to 20 months. Neoantigen therapy has shown advantages in the treatment of GBM, as it improves the immunosuppressive microenvironment within the tumor. However, the identification of truly immunogenic neoantigens remains a major challenge. Current computational prediction tools primarily focus on antigen presentation, whereas algorithms that incorporate T-cell immunogenicity features remain limited. Furthermore, standard validation methods, such as enzyme-linked immunospot (ELISpot) assays, lack physiologic relevance and do not fully recapitulate the tumor microenvironment. In this study, we developed a neoantigen prediction algorithm, TCRscore, based on publicly available datasets by integrating human leukocyte antigen binding and T-cell receptor (TCR) recognition features. Twenty-one patient-derived GBM organoid models were established from isocitrate dehydrogenase wild-type tumors to validate the performance of the algorithm. Predicted neoantigens were evaluated using ELISpot assays, flow cytometry, and in vitro killing assays based on organoid-T cell coculture systems. TCRscore outperformed six existing tools in predicting immunogenic neoepitopes. The organoid models retained the key histologic and transcriptomic features of parental tumors and provided an effective platform for functional validation. Coculture assays confirmed that neoantigen-specific T cells could induce targeted killing in GBM organoids. In particular, the analysis identified that the recurrent PIK3R1G376R mutation contributed to a potential shared neoantigen in GBM. Overall, by integrating TCRscore with organoid-based validation, this study provides a high-fidelity, high-quality GBM neoantigen database with significantly enhanced prediction accuracy. SIGNIFICANCE:A clinically impactful framework that integrates a TCR-aware AI algorithm with glioblastoma organoids enables accurate neoantigen prediction and validation, advancing both personalized and population-level immunotherapy strategies.

Sepsis-associated cognitive impairment involves disrupted sleep and prefrontal dysfunction. Dexmedetomidine (DEX) induces NREM-like sedation and cognitive protection, but the circuit mechanism remains unclear. We tested whether DEX acts via a VLPO→mPFC pathway to restore NREM sleep and prefrontal plasticity. Sepsis was induced by CLP in male mice. We performed behavioral tests, EEG/EMG, c-Fos mapping, anatomical/functional tracing, chemogenetic inhibition, and mPFC synaptic plasticity assays. CLP mice showed cognitive deficits, anxiety-like behavior, reduced NREM sleep, increased fragmentation, and lower SWA. DEX reversed these abnormalities and activated VLPO neurons. VLPO neurons directly projected to and inhibited mPFC glutamatergic activity. Chemogenetic inhibition of this pathway blocked DEX’s benefits on behavior, sleep, SWA, and mPFC plasticity. These findings indicate that DEX restores NREM sleep and prefrontal plasticity via the VLPO→mPFC pathway, alleviating cognitive deficits. This circuit represents a potential translational target for ICU sleep management and cognitive protection in septic patients.

Validation of HLA-restricted neoantigen-specific T cell responses using ELISpot assays with HLA-blocking antibodies and HLA-mismatched donors’ PBMC.

HLA class I typing of the 21 glioblastoma patients.

Peptide length distribution among IEDB database.

Pituitary neuroendocrine tumors (PitNETs) are common sellar neoplasms and represent a major component of routine pituitary pathology. In the 2022 World Health Organization (WHO) Classification, transcription factor-defined lineage assignment is central to diagnosis. However, lineage-related morphologic information on routine hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) has not been systematically characterized. We developed an attention-guided graph neural network to predict PitNET lineage directly from H&E-stained WSIs and to identify regions prioritized for classification. Consecutive patients who underwent surgical resection for PitNETs at Beijing Tiantan Hospital between 2021 and 2025 were included. In the internal hold-out set, the five fold-specific models achieved a mean F1-score of 92.78% and a mean balanced accuracy of 94.84%. In a temporally independent validation cohort, the final model achieved an F1-score of 87.64% and a balanced accuracy of 89.48%. Performance varied across lineages and histomorphologic subtypes, and the most common error pattern was misassignment of PIT-1 and T-PIT tumors to SF-1. Attention maps predominantly highlighted tumor-rich regions. Quantitative cell-level morphometry supported lineage-associated patterns, including larger cell and nuclear sizes in PIT-1 tumors, more elongated nuclei in SF-1 tumors, and higher cellular density with reduced intercellular spacing in T-PIT tumors. In six cases with multiple synchronous PitNETs of distinct lineages, patch-level prediction maps corresponded closely to transcription factor immunohistochemistry. In a small exploratory subset with available DNA methylation data, methylation classifier results were concordant with model predictions in 11 of 15 cases and with routine histopathologic diagnoses in 8 of 15 cases. These findings indicate that routine H&E-stained WSIs contain learnable morphologic information related to PitNET lineage. Attention-guided spatial modeling provides an interpretable framework for characterizing lineage-associated patterns in PitNETs.

HLA-I Epitope Data in our GBM cohort for Evaluating the Prediction Accuracy of Various Algorithms in Identifying Immunogenic Neoepitopes.

Mutant neoantigen peptides induce apoptosis in patient-derived GBM organoids.

Validation of the autoencoder performance on TCR CDR3 sequence reconstruction.