Ehlers-Danlos syndromes (EDS) are rare heritable connective tissue disorders, most commonly linked to collagen abnormalities. In dogs, reported cases are infrequent and typically involve skin fragility and joint laxity, with limited understanding of underlying genetic causes. This report describes an unusual, aggressive, and fatal case of an Ehlers-Danlos-like syndrome (EDlS) in a Maltese dog, with several uncommon features. Unlike most canine EDS cases, which show collagen defects, this case revealed minimal collagen alterations; instead, elastic fibers were primarily affected. A one-and-a-half-year-old male Maltese dog presented with progressive abdominal masses, skin fragility, joint deformities, and frequent bruising since early life. Clinical examination revealed hyperextensible, fragile skin, hematomas, and contractures of the hind limbs. Imaging confirmed a hernia lacking supportive connective structures. Histopathological analysis showed elastic fiber hypertrophy and fragmentation, with minimal collagen changes. Despite palliative wound management, the patient died within ten days of the initial consultation due to spontaneous evisceration and vascular rupture. The histological features are consistent with a possible Tenascin-X deficiency. Definitive molecular classification was beyond the scope of this case. This report expands the spectrum of EDlS in dogs.

Background This study aims to define a novel molecular subtype of LUAD by integrating multiple omics data. Additionally, we develop and validate an Artificial Intelligence Derived Prognostic Index (AIDPI) that predicts the prognosis of LUAD patients, identifies potential therapeutic targets. Methods This study employed ten clustering algorithms from the R package “MOVICS” to integrate multi-omics data of LUAD sourced from TCGA database for molecular typing. Subsequently, an Artificial Intelligence Derived Prognostic Index (AIDPI) was constructed as the most effective indicator for predicting the overall survival rate of LUAD patients. The biological functions and mechanisms of NPC2 in lung adenocarcinoma were elucidated through both in vitro and in vivo experiments, which included CCK-8 assays, colony formation assays, flow cytometry, Transwell assays, and xenograft tumor models. Additionally, the impact of NPC2 on Ribociclib sensitivity was investigated through drug correlation analysis and molecular docking, while the predictive value of NPC2 regarding immunotherapy benefits was validated using the immune cell infiltration analysis. Results Through multi-omics clustering, we identified two subtypes of lung adenocarcinoma associated with prognosis, with the CS1 subtype exhibiting the most favorable prognostic outcomes. The low AIDPI group exhibited a more positive prognosis, accompanied by increased immune cell infiltration and activation of immune pathways. Meanwhile, NPC2 was recognized as a standalone risk factor for LUAD, with its high expression significantly improving the overall survival of LUAD patients. Functionally, the overexpression of NPC2 promotes tumorigenesis in LUAD both in vitro and in vivo . Mechanistically, the upregulation of NPC2 expression inhibits the progression of LUAD by suppressing the PI3K/AKT signaling pathway. Our study also demonstrated that high NPC2 expression is positively correlated with Ribociclib sensitivity, as confirmed by in vitro experiments. Furthermore, NPC2 expression is positively correlated with ImmuneScore, and may serve as a predictive indicator for the efficacy of immune checkpoint inhibitor (ICI) therapy. Conclusion The comprehensive analysis of multiple omics data significantly enhances the molecular classification of lung adenocarcinoma. Furthermore, AIDPI is a potential biomarker that predicts the prognosis of LUAD patients. NPC2 inhibits the progression of LUAD by suppressing the PI3K/AKT signaling pathway and enhancing the chemotherapy sensitivity to Ribociclib.

Abstract Introduction: Molecular testing in lung and other solid tumors has led to the identification of driver mutations that can be effectively targeted by new therapeutics. In addition to single point mutations that activate signaling proteins such as EGFR and KRAS, the presence of various fusion proteins that lead to protein overexpression and activation of key pathways have become prominent drug targets. Detection of these fusion proteins by FISH or sequencing is possible but may be limited due to changes in breakpoint location, the panel may not target the specific exons or fusion partner, or RNA quality may be affected by sample fixation or processing. This study aims to improve detection rate of targetable fusions and rearrangements in solid tumors using a new method called Hi-C sequencing. Methods: Hi-C sequencing is a novel whole genome DNA-sequencing assay for detection of structural variation based on unique Hi-C chemistry which leverages sequencing of linked pairs of reads which occur nearby one another in 3-dimensional and linear space, from FFPE samples. Linking reads amplifies the rearrangement signal giving it much higher sensitivity and overcoming non-unique sequences masking fusions. In previous studies, Hi-C has been shown to detect gene fusions and rearrangements in many different tumor types missed by other clinical testing modalities such as FISH and RNA sequencing. Results: In a set of 110 NSCLC samples, Hi-C sequencing demonstrated 100% concordance with FISH and/or RNA sequencing results (10/10, including 4 ALK, 2 MET, 2 ROS, 1 NTRK, and 1 RET fusions). The remainder of samples were previously determined by standard DNA and RNA sequencing to be negative for drivers such as EGFR and KRAS mutations and fusions of ALK, MET, NTRK, RET, and ROS. In this cohort, we have detected biomarkers related to drug sensitivity in 15 cases. These include targetable fusions such as NTRK2 (1), NRG1 (1), PRKCA (1), loss of function variants indicating sensitivity to checkpoint inhibitors (2 cases), or PARP inhibitors (6), and others (4). In addition, noncanonical fusions were detected in NRG1 (1), and ALK (1), both retaining their functional domains and potentially indicating sensitivity to inhibitors. Finally, we detected additional rearrangements proximal to targetable genes which led to increased and exogenous expression of their gene products, revealing additional potential targetable biomarkers. Conclusions: Encouraging results from this study suggest that Hi-C sequencing may be a valuable tool in the molecular classification of solid tumors and could lead to improvement in patient care. Hi-C can detect gene fusions and rearrangements that are known to drive cancer and may be used for therapy selection, including in cases which were negative by standard genetic testing. Citation Format: Alex Hastie. Hi-C DNA Sequencing of Solid Tumors for Rearrangements and Fusions Detects Targetable Biomarkers Missed by RNA Sequencing [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Fusion-Positive Cancer: From Discovery to Therapy; 2026 Jan 13-15; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(1_Suppl):Abstract nr A013.

Breast cancer (BC) is a heterogeneous disease classified into 4 molecular subtypes, each with distinct molecular characteristics that influence treatment strategies, clinical outcomes and prognosis. These subtypes are associated with specific changes in cellular metabolism, which may play a crucial role in tumor development and progression. To identify distinctive serum metabolic biomarkers for each molecular BC subtype and to evaluate their associations with estrogen receptor (ER) and human epidermal growth factor 2 (HER2) receptor status, thereby refining molecular classification and informing personalized treatment strategies. The study utilized the proton nuclear magnetic resonance (1H NMR) metabolomics method to collect serum metabolic profiles from BC patients. Pattern recognition analysis was employed to analyze the metabolic data. Metabolic markers specific to each molecular subtype were selected, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was employed to explore serum metabolic pathway heterogeneity. Distinct metabolic markers were identified for each molecular subtype, demonstrating strong discriminatory power. Additionally, we identified specific serum metabolites whose levels correlate with ER and HER2 expression profiles. The KEGG pathway analysis revealed significant heterogeneity in serum metabolic pathways across different subtypes. This study demonstrates pronounced metabolic differences across BC subtypes that mirror their distinct molecular profiles and may underlie variations in therapeutic response. These metabolomic insights hold promise for refining tumor classification, improving diagnostic accuracy and guiding more personalized treatment strategies.

Accurate prediction of Collision Cross-Section (CCS) values is essential for identifying molecular structures in complex environmental mixtures. This study integrates supervised machine learning and deep learning to predict CCS values for a diverse array of dissolved organic molecules, including carbohydrates, hydrocarbons, lignins, lipids, proteins, tannins, and unassigned molecules. We evaluated eight regression models─Gradient Boosted Regression, K-Nearest Neighbors, LASSO, Linear Regression, Partial Least Squares, Random Forest, Support Vector Regression, and a Voting Regressor─alongside a Graph Neural Network (GNN) trained on molecular fingerprints (SMILES) and structural descriptors (m/z, O/C, H/C, AImod, DBE). Model performance varied by molecular class and the characteristics of the data set. The best-performing models were as follows: Voting Regressor for carbohydrates and unknowns, Random Forest for hydrocarbons and proteins, SVR for lignins and lipids, and LASSO for tannins. The GNN consistently delivered competitive accuracy across all classes. Validation using High-Resolution Mass Spectrometry (HRMS) data from the Arctic Ocean confirmed the predictive power of these models, enabling more precise selection of correct molecular structures from candidate lists generated by conventional workflows. This work presents a robust, data-driven framework for CCS prediction that enhances molecular classification and improves contaminant detection in environmental samples.

Introduction Gliomas are the most frequent type of primary malignant central nervous system (CNS) tumors, representing a group of heterogeneous neoplasms with variable clinical behavior that require adequate diagnostic accuracy. The identification of molecular biomarkers has recently gained significance for the diagnosis, prognosis, and treatment of CNS tumors; the application of current clinical guidelines is necessary. Our study performed a molecular characterization of gliomas in a cohort of Colombian patients using the recommendations of the 2021 World Health Organization (WHO) CNS 5 classification. Materials and methods We analyzed 22 Colombian patients with CNS tumors. Molecular techniques including Sanger sequencing, multiplex ligation-dependent probe amplification (MLPA) and methylation-specific MLPA (MS-MLPA) were used to identify mutations in IDH1 , IDH2 , TERT , and EGFR , as well as 1p/19q co-deletion and MGMT promoter methylation status. Results Our results demonstrated a 23% discordance rate between histopathologic and molecular classifications, with most of the discrepancies due to an initial histopathologic classification of glioblastomas, which were molecularly reclassified as astrocytomas. In addition, molecular profiling allowed us to identify non-canonical mutations, including IDH1 p.R132S, which has shown an impact on patient prognosis. Discussion We highlight the importance of incorporating molecular methods to improve diagnostic accuracy and achieve personalized treatments for gliomas, as proposed by the current 2021 WHO CNS 5 tumor classification guidelines. Performing new studies with larger patient cohorts integrating clinical data is necessary to determine the behavior, epidemiology, and therapeutic outcomes of this type of tumor more comprehensively.

Gliomas, the most prevalent primary malignant brain tumors, pose a significant clinical challenge, particularly glioblastoma (GBM), the most aggressive subtype with a median survival of just 12–15 months post-diagnosis. Molecular profiling has revolutionized GBM classification, revealing distinct subtypes—Proneural, Neural, Classical, and Mesenchymal—each associated with unique genetic and epigenetic signatures. Despite these advances, the heterogeneity within GBM demands more comprehensive approaches for effective stratification and treatment. In this study, we integrated multi-omics data, including mRNA, lncRNA, somatic mutations, and DNA methylation profiles, using consensus clustering across ten algorithms. This approach led to the identification of novel GBM subtypes and prognostic biomarkers, enhancing the precision of molecular classification. Our findings underscore the critical role of multi-omics integration in refining GBM subtypes, offering new avenues for personalized therapeutic strategies in combating this lethal malignancy.

Thyroid Nodule Molecular Classifiers—Reclaiming Deliberation in an Era of Automation

Unveiling oral cancer's molecular blueprint: A novel classification to guide precision therapy.

Surgical stage in the era of molecular profiling of endometrial cancer.

Corrigendum to “Leveraging foundation models and transfer learning for peptide transport prediction, molecular taste classification, and visual texture analysis” [Innovative Food Science & Emerging Technologies 105, 104247

Sweetpotato genotypes, often known by regional names, are easily propagated via cuttings, which can lead to mixing and misidentification of cultivars. This complicates traceability and commercialization. Accurate characterization of common genotypes would support their formal registration and strengthen the sweetpotato value chain. Sweetpotato is a staple crop in Brazil, and in this study, four states, representing different geographic regions in Brazil, were selected. A total of 37 samples were collected in these states, and the samples were evaluated by SSR molecular markers and morphological traits. The samples were cleaned of virus and compared to the global sweetpotato collection held at the International Potato Center under the International Treaty on Plant Genetic Resources for Food and Agriculture. SSR markers effectively distinguished among accessions. The genotype locally known as "Canadense" matched closely both genetically and morphologically to the CIP accession 'Blesbok'. This alignment paves the way for formalizing cuttings and root production of "Canadense"/'Blesbok' for commercial use. In contrast, several accessions marketed in Sergipe as "white skin sweetpotato" did not correspond to any known CIP accession, suggesting that they may be unique regional genotypes or acquired from other sources, since sweetpotato is an exotic crop in Brazil. Overall, the research identified key genotypes, supporting their official registration with Brazil's Ministry of Agriculture, Livestock, and Supply, thereby enhancing the legal commercialization of cuttings and roots. Additionally, the clear molecular and trait-based classification will assist sweetpotato crop improvement programs in selecting appropriate parent lines for future crosses.

MGScreener: A multi-view mammography-based model optimized with active learning for breast cancer diagnosis.

On the value of uncertainty quantification in deep learning based breast cancer molecular subtype classification

The incidence of endometrial cancer (EC) is unfortunately increasing. Often diagnosis is made at an early stage and surgery is the curative treatment. Nevertheless, adjuvant treatment is proposed to reduce the risk of relapse. This treatment is tailored based on the extent of the disease, such as the presence of distant metastasis, the extent of involvement of adjacent organs or lymph nodes. However, histological parameters such as myometrial invasion, substantial lymphovascular space invasion, invasion of the cervical stroma or tumor grade are also key to selecting adjuvant treatment. The Cancer Genome Atlas (TCGA) project has demonstrated the superiority of molecular classification over histological evaluation in EC to determine the prognosis. The International Federation of Gynecology and Obstetrics 2023 staging for EC is the first staging system that has incorporated molecular biomarkers on top of morpho-histological classification. Currently, all patients should have a molecular profile of their tumor and a lymph node assessment. The landmark treatment of stage I-III ECs is surgery followed by radiotherapy. The European guidelines updated in 2025 has divided EC in 4 risk categories with specific adjuvant treatment. For clinicians, it is seen as a complex landscape from surveillance to chemo-radiotherapy. Therefore, we propose here a practical pocket guideline for adjuvant treatment of early-stage EC patients based on a review of the different clinical trials in the adjuvant setting and on existing guidelines. This pocket guideline may also serve as a base for incorporation of new clinical trials under the RAINBO umbrella research program.

Clinicopathological features and mutational landscape of colorectal cancer subgroups defined by MSI and EMAST status.

HER2 immunoreactivity in advanced non-p53abn endometrial carcinoma: Association with clinical features, prognosis, and molecular characteristics.

Redefining Endometrial Cancer Phenotypes in the Era of Molecular Classification and Sentinel Lymph Node Mapping: Results from a Multicenter Italian Study

Background/Objectives: Combined chromosome 7 gain and chromosome 10 loss (+7/−10) is the most frequent cytogenetic alteration and a defining diagnostic criterion for isocitrate dehydrogenase wild-type (IDHwt) glioblastoma. Despite the association with poor prognosis, its clinical and therapeutic significance remains unclear. We aim to systematically review its clinical significance, focusing on prevalence, prognostic value, and potential association with therapeutic resistance in adult patients. Methods: PubMed, Embase, CENTRAL, Scopus, EBSCOhost, and Web of Science were searched from inception to April 2025, using controlled vocabulary and free-text terms. Eligible studies included adult glioblastoma with molecular confirmation of combined chromosome 7 gain and chromosome 10 loss and reported survival or treatment response. Quality was assessed qualitatively, and findings were synthesized descriptively. Results: Of 3249 records, 5 observational studies (523 patients) were included. The signature was present in 60% to 70% of glioblastoma cases and frequently co-occurred with epidermal growth factor receptor amplification and telomerase reverse transcriptase promoter mutations. This alteration was consistently associated with shorter survival (mean, 8–70 weeks) compared with tumors lacking the alteration (19–170 weeks). In one study, the signature was more common in radioresistant tumors (9/20 vs. 1/10). Molecular evidence suggests that this alteration arises early in tumorigenesis. Conclusions: The +7/−10 cytogenetic alteration, common in glioblastoma, is frequently associated with aggressive clinical behavior. While exploratory data suggest a possible association with radiotherapy response, current evidence is insufficient to establish a predictive or therapeutic role. Its principal clinical value lies in diagnosis, molecular classification, and risk stratification. Incorporating cytogenetic testing for this alteration into routine glioblastoma workup may improve risk stratification and guide individualized management.

Breast cancer (BRCA)'s molecular heterogeneity complicates prognosis and treatment. Tumor Doubling Time (TDT), a critical growth rate metric with clinical and prognostic significance, offers untapped potential as a biomarker to decode heterogeneity and improve therapeutic strategies. Based on transcriptomic and clinical data from TCGA and GEO, this study analyzed BRCA. Through differential expression and survival analyses, differentially expressed tumor doubling time-related genes (TDTRGs) with prognostic significance were identified. Consensus clustering using these genes defined two molecular subtypes. A prognostic risk model was constructed and validated through LASSO and multivariate Cox regression. Comprehensive evaluation was performed on these molecular subtypes and risk groups, encompassing immune infiltration (ssGSEA, CIBERSORT, ESTIMATE), mutational burden, response to immunotherapy (IMvigor210), and drug sensitivity (CellMiner, pRRophetic). This study constructed and validated an 8 gene prognostic risk model demonstrating robust predictive performance in both training (AUCs: 1-year=0.703, 3-year=0.693, 5-year=0.671) and validation cohorts. The low-risk group showed significantly enhanced immune cell infiltration, elevated immune checkpoint expression, and improved response to immunotherapy. Conversely, the high-risk group displayed increased tumor purity, metabolic reprogramming (e.g., respiratory electron transport), genomic instability, higher tumor mutational burden, and differential drug sensitivity (e.g., resistance to Gemcitabine/Tamoxifen). This study establishes a novel TDTRGs framework for BRCA molecular classification and validated prognostic stratification. It reveals key disparities in immune microenvironment and genomic stability, enhancing understanding and guiding personalized therapeutic strategies.