Genomic and transcriptomic analyses of residual invasive triple-negative breast cancer after neoadjuvant chemotherapy in the prospective MIRINAE trial (a randomized phase II trial of adjuvant atezolizumab plus capecitabine compared to capecitabine; KCSG-BR18-21)

The relationship between molecular features and clinical courses of medulloblastoma at onset has been recently reported; however, data in a recurrent setting are scarce. We collected medical records, analyzed molecular genetic data of eight patients with medulloblastoma that recurred between 2014 and 2023 in our institution, and retrospectively analyzed the clinical course. Molecular classification and copy number data were obtained using the Illumina Methylation EPIC array®. The pathological findings were classic and large cell/anaplastic in six and two cases, respectively. The molecular classifications were sonic hedgehog, Group 3, and Group 4 in two, two, and four cases, respectively. Among Group 3/4 cases, two were subclassified as subclass II and III, which were regarded as poor prognostic. A copy number abnormality of MYC/MYCN amplification was observed in three cases. The cases with poor prognostic molecular features showed worse prognosis than that of others (the median durations from the first diagnosis to recurrence 8.5 months vs. 60 months; p = 0.02857, and the median survival time from recurrent diagnosis 3.5 months vs. 42 months; p = 0.00673). Regarding treatment response, no cases with poor prognostic molecular features showed tumor shrinkage with chemotherapy and/or radiation therapy at relapse, except for one case. In contrast, three cases with non-poor prognostic molecular features have been able to control the disease status for more than 2 years. The clinical course of recurrent medulloblastoma depends on molecular features. Salvage treatment effectiveness should be evaluated on the basis of molecular genetic background information.

Molecular subphenotypes, identified through biomarker profiling independent of clinical diagnosis, have the potential to guide targeted therapeutics in critical care. We have previously published that intensive insulin management has subphenotype-specific beneficial effects among children with hyperglycemia accompanying cardiorespiratory failure. However, due to the operational aspects of biomarker assays, prospective real-time application of subphenotype-based strategies remains daunting. This study compared biomarker values measured via a rapid immunoassay requiring minimal handling to a conventional laboratory-based multiplex assay, assessed its ability to classify subphenotypes with a parsimonious classifier, and compared clinical outcomes between rapid immunoassay-based subphenotypes. This retrospective multicenter study included re-assaying plasma samples from 269 children with acute cardiorespiratory failure and hyperglycemia. Latent class analysis (LCA) was previously used to derive hyper-inflammatory and hypo-inflammatory classes. A parsimonious classifier was fit to LCA-derived subphenotypes and produced a model consisting of IL-6, IL-8, and sTNFR-1. We found that, despite the rapid immunoassay systematically overestimating biomarker values relative to the conventional assay, biomarkers were strongly correlated between platforms (Pearson r = 0.87–0.93). Using the parsimonious classifier, subphenotype classifications matched between platforms in 95% of patients (n = 256/269). When compared to previously derived LCA-derived subphenotypes, rapid immunoassay-based subphenotypes demonstrated an AUC of 0.90 (95% CI 0.85–0.95). The rapid immunoassay-based hyper-inflammatory class was associated with higher mortality (26% vs. 11%; P = 0.01) and heterogeneity of treatment effect to intensive insulin management (interaction P = 0.01). Our findings suggest that subphenotyping using a rapid immunoassay is feasible and accurate, laying the foundation for future precision medicine strategies in pediatric critical care.

Multiple hematological indicators have been shown to be associated with the prediction and prognosis of gliomas. The present study aimed to explore the value of 22 preoperative peripheral blood routine indicators in the diagnosis and prognosis prediction of glioma. Patients with glioma were grouped according to the 2021 World Health Organization (WHO) guidelines for glioma. The correlations among preoperative peripheral blood routine indicators in all patients with glioma were assessed using Spearman's rank correlation analysis. The relationships between these indicators and glioma molecular subtypes were evaluated using ANOVA, independent samples t-test and logistic regression analysis. Analysis of prognosis prediction was conducted using multivariate Cox regression. Based on the WHO 2021 guidelines, patients with glioma were classified into 92 cases of astrocytoma, 77 cases of oligodendroglioma and 402 cases of glioblastoma. Among 22 routine blood indicators, 10 low-correlation indicators were identified, 9 of which demonstrated significant value for the diagnosis of glioma. Among them, the neutrophil percentage (NEUT%) showed a notable predictive power in glioma molecular subtypes classification. In the study of overall survival (OS) prediction in patients with glioma, 6 biomarkers were found to be significantly associated with OS. Through statistical analysis, 4 independent biomarkers were selected to construct a multivariable Cox regression model. However, in the regression model, NEUT% was identified as the sole statistically significant biomarker. Despite its statistical significance, the results indicated that NEUT% had limited effectiveness in predicting OS in patients with glioma. Certain preoperative blood indicators had significant predictive value for glioma diagnosis, with NEUT% emerging as a key biomarker for predicting the presence of glioma. For prognosis, although NEUT% showed a modest effect size, it was significantly associated with poor outcomes in patients with glioma. Monitoring preoperative NEUT% levels is therefore clinically important for glioma diagnosis and prognosis.

Signal transducer and activator of transcription 3 (STAT3)is acritical factor involved in various physiological and oncogenic signalingpathways. Machine learning models are valuable tools for predictingor screening STAT3 inhibitors. However, the predictive performanceand interpretability of existing models still require improvement.In this study, we introduce a fingerprint-enhanced graph (FPG) attentionnetwork model, which integrates sequence-based fingerprints and structure-basedgraph representations to predict STAT3 inhibitors. During the featurelearning process, the FPG model converts sequence information intoa fingerprint vector, while structural information is encoded intoa separate vector using a graph attention network module. These twovectors are then concatenated and passed through a multilayer perceptronfor molecular activity classification. Among 49 models with variousrepresentations and algorithm combinations, the FPG-based model achievedthe best predictive performance, with an average area under the curveof 0.897 on the test set. Furthermore, the model outperformed existingprediction models for identifying STAT3 inhibitors. Additionally,fingerprint analysis and attention heatmaps, combined with SHAP algorithms,provided valuable insights into the structure–activity relationshipof STAT3 inhibitors, enhancing model interpretability. To facilitaterelated research and applications, we developed a web service (STAT3Pro: https://gzliang.cqu.edu.cn/software/Stat3Pro.html) for STAT3inhibitor prediction.

Endometrial Carcinoma Molecular Classification and Barriers to Implementation, Possible Solutions, and the Implications for Ongoing Clinical Trials.

ABSTRACTBackgroundHeterozygous TP63 mutations cause a spectrum of disorders including split‐hand/foot malformation 4 (SHFM4) and ectrodactyly‐ectodermal dysplasia‐cleft lip/palate syndrome 3 (EEC3). While some SHFM4 mutations concurrently induce EEC3‐like phenotypes (designated SHFM4/EEC3 mutations), their prevalence and distribution—particularly those near the p63 C‐terminus—remain poorly characterized.MethodA multigenerational Chinese family with an isolated form of SHFM was investigated. Genetic analysis included real‐time quantitative PCR and Sanger sequencing. Disease mutation databases and literature were systematically reviewed to identify all reported TP63 mutations causing isolated SHFM4 and to classify these mutations by clinical phenotypes.ResultsWe identified a novel likely pathogenic variant (NM_003722.5: c.2032G>C, p.E678Q) within a sumoylation motif near the C‐terminus of p63. Analysis of 72 families (182 carriers) revealed 28 SHFM4‐causing TP63 mutations, comprising 12 dual‐phenotype SHFM4/EEC3 mutations and 16 isolated SHFM4‐only mutations. Certain clinical traits of SHFM4 mutations and distribution characteristics for SHFM4‐only mutations were observed.ConclusionsThis study expands the SHFM4 mutation spectrum, demonstrating significant overlap between SHFM4 mutations and EEC3 mutations. The p.E678Q represents the most reliable SHFM4‐only mutation near the protein C‐terminus. These findings will improve molecular classification and genetic counseling for TP63‐related disorders.

Breast cancer can be categorised into a number of histological types, based on microscopic appearances. There is some evidence that the different breast cancer histological types are associated with different patient and tumour characteristics, but few previous studies have been large enough to investigate this systematically, especially for rare histological types. National cancer registration data were used to describe trends in the incidence of specific histological types of invasive breast cancer in women diagnosed when aged 18–89 years in England from January 1988 to December 2016, and to investigate associations between breast cancer histological types and patient and tumour characteristics. There were 838,776 women diagnosed with a first primary invasive breast cancer in this 29‐year period, including 614,698 (73%) cases of ductal carcinoma NST [no special type (NST)], 90,028 (11%) cases of lobular carcinoma, and more than 16,000 (2%) cases each of tubular and mucinous carcinomas. Rarer histological types included medullary, papillary, metaplastic, and cribriform carcinomas, with >1000 cases of each type. Data quality and completeness improved substantially during the study period. The different histological types of breast cancer showed different patterns in incidence by calendar period of diagnosis, age at diagnosis, and screen‐detection status, as well as different associations with tumour characteristics such as grade, stage at diagnosis, and molecular subtype. This large nationwide study provides an overview of the changing incidence of the different histological types of invasive breast cancer in England over almost 30 years. It also gives an opportunity to investigate the characteristics of rare histological types, which smaller studies have been unable to explore. In addition, the results demonstrate the continuing value of histological types defined by microscopic morphology, alongside newer molecular classifications.

Proteomic analysis uncovers biological diversity in molecularly defined endometrial carcinomas

BackgroundThe management of low- and intermediate-risk endometrial carcinoma (EC) at the Salah Azaiez Institute (SAI) has evolved with international recommendations and the advent of the molecular era. We lack access to molecular profiling; consequently, both undertreatment and overtreatment are observed. This study explores how adherence to international recommendations affects clinical outcomes.MethodsWe performed a retrospective analytic study including 180 women with stage I–II low-, low-intermediate-, and high-intermediate-risk EC treated at SAI between 2015 and 2020, with at least 3 years of follow-up. Ethical approval and patient consent were obtained; for telephone interviews, verbal consent was secured. We selected 60 women per risk group from a larger pool to ensure comparability. Eleven histopathologic slides exhibiting lymphovascular space invasion (LVSI) were reviewed independently by 2 experienced pathologists to distinguish focal from extensive LVSI; no slides without LVSI were reexamined. Risk stratification followed the 2020 ESGO–ESTRO–ESP guidelines, and adjuvant treatments were assessed according to the 2021 ESGO–ESTRO recommendations. Pelvic lymphadenectomy was the standard nodal assessment; sentinel lymph node (SLN) mapping was not available during the study period.ResultsMedian age was 60 years (range 35–69). After histopathologic review, 51.1% of tumors were grade 2, 54.5% had under 50% myometrial invasion, 8.3% had extensive LVSI, and 3.9% had focal LVSI. Forty-seven per cent of patients received guideline-concordant therapy, 47% were over-treated, and 6% were under-treated. Over-treated women had a 7.9-fold higher risk of death and a 6.6-fold higher risk of recurrence than appropriately treated women. Under-treatment was not a significant prognostic factor. Overtreatment and guideline-concordant therapy were both associated with higher rates of gastrointestinal, genitourinary, and sexual toxicities.ConclusionLimited access to molecular profiling constrains personalized care in Tunisia. Strict adherence to current guidelines is essential to avoid unnecessary toxicity, and the integration of molecular classification and SLN mapping should be prioritized.

Clip-domain serine proteinases and their homologs: Role in crustacean immunity.

Immune evasion in endometrial cancer: Unraveling the latest mechanisms and future directions.

Endometrial carcinoma (EC) is the most common gynecologic malignancy in the USA, with surgical staging as the cornerstone of treatment. Although most patients are diagnosed at an early stage, adjuvant therapies, including external beam radiation therapy (EBRT), vaginal brachytherapy (VBT), and systemic treatments, are employed in select cases to reduce recurrence risk. This review highlights data from randomized controlled trials assessing the efficacy of adjuvant radiotherapy in early-stage EC. For low-risk EC, studies suggest that postoperative brachytherapy has minimal impact on locoregional recurrence and is not recommended without significant uterine risk factors. In intermediate-risk EC, trials such as GOG 99 and PORTEC-1 demonstrated reduced recurrence with pelvic RT, particularly in high-intermediate risk subsets, though overall survival benefits were not observed. For high-intermediate risk EC, PORTEC-2 showed that VBT effectively controls vaginal recurrence with less morbidity compared to EBRT, recommending VBT as the preferred modality. Medium-risk cases benefit similarly from VBT alone, as shown in Swedish trials. In high-risk EC, RCTs such as GOG 249 and PORTEC-3 examined the addition of chemotherapy to radiotherapy, finding comparable recurrence and survival outcomes between VBT with chemotherapy and EBRT, though acute toxicity was higher with combined therapy. Across these trials, the degree of lymphovascular space invasion (LVSI), patient age, tumor grade, and histology were key prognostic factors influencing treatment recommendations. Despite advancements in molecular classification and modern radiotherapy techniques, most data derive from earlier studies, emphasizing the need for updated research to refine treatment paradigms.

Fibrinolysis plays a crucial role in maintaining coagulation homeostasis, but its functions in hepatocellular carcinoma (HCC) remain poorly understood. This study aimed to develop a fibrinolysis-based molecular classification and prognostic signature for HCC and to identify a key regulatory gene. Using non-negative matrix factorization (NMF), we identified distinct fibrinolysis-related HCC subtypes with specific clinical outcomes and tumor microenvironment characteristics. A six-gene prognostic signature comprising ACAT1, GRHPR, HPX, PCK2, IYD, and PON1 was established through weighted gene co-expression network analysis (WGCNA) and LASSO-Cox regression, which effectively stratified patients into different risk groups across multiple cohorts. Hemopexin (HPX) was identified as the top candidate and functionally validated: HPX overexpression suppressed clonogenicity and migration, promoted apoptosis, and inhibited xenograft tumor growth. RNA sequencing analysis suggested associations between HPX and apoptosis as well as TNF-α signaling pathways, which were confirmed through flow cytometry apoptosis assays, mitochondrial membrane potential measurements, and TUNEL staining. Western blot and immunohistochemical analyses further demonstrated that HPX upregulates the Bax/Bcl-2 ratio via the TNF-α signaling pathway. This study defines novel molecular subtypes of HCC and reveals that HPX exerts anti-tumor effects through TNF-α-mediated mitochondrial apoptosis, characterized by an increased Bax/Bcl-2 ratio.

Endometrial carcinoma (EC) represents a significant clinical challenge due to its pronounced molecular heterogeneity, directly influencing prognosis and therapeutic responses. Accurate classification of molecular subtypes (CNV-high, CNV-low, MSI-H, POLE) and precise tumor mutational burden (TMB) assessment is crucial for guiding personalized therapeutic interventions. Integrating proteomics data with advanced machine learning (ML) techniques offers a promising strategy for achieving precise, clinically actionable classification and biomarker discovery in EC. Using proteomic data from 95 EC patients (83 endometrioid, 12 serous), sourced from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), we developed an ML pipeline integrating proteomic feature selection (Lasso-penalized logistic regression), classification modeling, and interpretability analysis. The dataset was divided into training (70%) and test (30%) sets, with synthetic minority oversampling (SMOTE) applied to address the class imbalance. Logistic regression models were trained for molecular subtypes classification, and the TMB prediction model performance was evaluated using accuracy, AUC, precision, recall, and F1-score. Model interpretability was enhanced using explainable AI (XAI) techniques: SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). Feature selection reduced the proteomic dataset from 11,000 to eight key proteins. The proteomics-based ML model demonstrated robust predictive performance, accurately classifying EC molecular subtypes (accuracy: 82.8%; AUC: 0.990) and distinguishing high (≥10 mutations/Mb) versus low TMB (<10 mutations/Mb) cases (accuracy: 89.7%; AUC: 0.984). SHAP analysis highlighted clinically recognized biomarkers (MLH1, PMS2, STAT1) and identified novel protein candidates (MTHFD2, MAST4, RPL22L1, MX2, SEC16A). LIME analysis provided individualized prediction interpretations, clarifying each protein biomarker's influence on model decisions. Our proteomics-driven ML approach demonstrates high accuracy and interpretability in EC subtype classification and TMB prediction. By identifying validated and novel biomarkers, this strategy provides essential biological insights and a strong foundation for the future development of non-invasive diagnostics, personalized treatments, and precision medicine in EC.

Skin cutaneous melanoma (SKCM) is the third most common type of cutaneous malignant tumor with a poor prognosis. This research aimed to recognize molecular clusters and develop a novel prognostic signature based on natural killer (NK) cell-related genes (NKCRGs) in SKCM. The data were obtained from public databases, including ImmPort, TCGA, GEO, GTEx and GEPIA2. The crucial NKCRGs in SKCM were determined by using a Venn diagram to intersect NKCRGs, differentially expressed genes and prognosis-related genes. The "clusterProfiler" software was employed to perform KEGG and GO analyses of crucial NKCRGs. The molecular subtypes were recognized based on crucial NKCRGs by consensus cluster analysis, and Kaplan-Meier survival curves of samples in different subtypes were performed by the "survival" package. Tumor microenvironment, drug sensitivity and somatic mutation analyses were conducted among different subtypes. A prognostic signature was constructed based on crucial NKCRGs by multiple machine learning algorithms. The core NKCRGs were identified by uni- and multi-variate Cox analyses, quantitative real-time PCR experiment, overall survival, immune cell infiltration, single-cell RNA sequencing and pan-cancer analyses. 32 crucial NKCRGs were identified in SKCM, and KEGG and GO analyses exhibited that these crucial NKCRGs were primarily related to NK cell-mediated cytotoxicity and immune system process. Two distinct clusters (C1 and C2) in TCGA-SKCM were recognized based on 32 crucial NKCRGs. Compared with C1, C2 presented higher expression levels of 32 crucial NKCRGs and higher overall survival (Log-rank, p < 0.0001). There were significant disparities between two clusters in both drug sensitivity and tumor microenvironment. TTN (78.7%) and MUC16 (72.7%) genes exhibited the highest mutation frequency and the RTK-RAS pathway had the highest proportion of affected samples in C1 and C2. A 12-NKCRG optimal prognostic signature was constructed by 13 combinations of 7 machine learning algorithms utilizing 32 crucial NKCRGs. Two core NKCRGs, CD247 and KIR2DL4, were identified in SKCM. This research demonstrated a novel molecular classification and prognostic signature based on NKCRGs in SKCM, which might be used to forecast the prognosis of SKCM and assist clinicians in making therapeutic strategies, and our results suggested that CD247 and KIR2DL4 might be valuable prognostic biomarkers and potential therapeutic targets for SKCM patients.

Improved Prognostic Stratification With 2023 International Federation of Gynecology and Obstetrics Staging in Endometrial Cancer Reflecting Poor Prognosis of Aggressive Histological Types and p53 Abnormality.

Impact of surgery and molecular classification in stage IV endometrial cancer.

Real World Multi-centre UK Review of Nivolumab Monotherapy in Metastatic Endometrial Cancer With Mismatch Repair Deficiency During COVID-19.

Molecular findings in endometrial mucinous carcinoma of the gastric [Gastrointestinal] type: A report of 5 additional cases and a systematic review of the literature.