Molecular Classification Research Articles

Radiotherapy remains crucial to the management of gynecologic cancers. This review highlights recent advances in radiation delivery, integration with systemic therapies, and the evolving role of radiotherapy across definitive, adjuvant, recurrent, and palliative settings. Trials in cervical cancer have established survival gains with novel systemic combinations, while adaptive and standardized radiation protocols continue to improve precision and outcomes. In endometrial cancer, molecular classification is informing adjuvant therapy selection and driving subtype-specific clinical trials. Expanding use of stereotactic body radiotherapy and proton therapy in ovarian and recurrent disease demonstrates feasibility and durable control. Efforts in reirradiation, palliative care, and survivorship underscore the need for safe dose escalation, symptom management, and long-term quality of life research. Persistent disparities and rising costs emphasize the importance of value-based and equitable care delivery. Emerging imaging and adaptive techniques are making radiation for gynecologic cancers more precise and individualized. Advances in brachytherapy, stereotactic approaches, and proton therapy are refining delivery, while integration with systemic and molecularly guided strategies is broadening therapeutic impact. Ongoing priorities include reducing disparities, improving survivorship, and translating technological progress into accessible, patient-centered care.

Molecular classification of nonurothelial histologic subtypes of bladder cancer.

Prevalence of Atypical and Subclonal p53 Immunohistochemistry Expression in Mismatch Repair Deficient and/or POLE-Mutant Endometrial Carcinomas with TP53 Mutation.

Immature granulocytes as novel blood-based biomarkers forbrain tumours: Aprospective case-control study.

Small nucleic acid therapeutics: delivery breakthroughs, clinical translation, and future paradigms in precision medicine.

Preoperative molecular classification of endometrial cancer: Validation through biopsy and matched hysterectomy specimens.

Epidermal Growth Factor Receptor (EGFR) plays a critical role in the development of several cancers. Thus, modulation/inhibition of EGFR activity is an appealing target of developing novel cancer therapeutics. With the advent of modern machine learning technologies, it is now possible to simulate interactions with high precision between EGFR and small molecules to predict inhibitory/ modulatory activity at an unprecedented scale. In this work, we propose a novel machine-learning method to fast and precise classification of small compounds that are active, intermediate or inactive in inhibiting/modulating EGFR activity. We developed DeepEGFR, a novel multi-class graph neural network (GNN) model, to classify compounds into Active, Inactive, and Intermediate functional categories. DeepEGFR leverages complementary molecular representations, combining SMILES strings and molecular fingerprint matrices (Klekota-Roth and PubChem) to capture both structural and property-based features of compounds. The model constructs an advanced molecular graph representing atom type, formal charge, bond type, and bond order, through nodes and edges. DeepEGFR achieved superior performance compared to baseline machine learning algorithms (e.g., SVM, Random Forest, ANN), with approximately 94% F1-scores across training and test datasets for all activity classes. To ensure interpretability, the top 20 features identified by DeepEGFR were validated against the five key characteristics of FDA-approved EGFR inhibitors (Afatinib, Gefitinib, Osimertinib, Dacomitinib, Erlotinib), confirming the biological relevance of the features. Moreover, DeepEGFR successfully identified 300 underexplored EGFR-targeting compounds, demonstrating its potential to accelerate the discovery of therapeutic agents. These results highlight the effectiveness of graph neural networks in advancing molecular activity classification, setting a potential new benchmark for EGFR inhibitor prediction. These findings demonstrate the DeepEGFR’s ability to highlight the promising EGFR inhibitors, that have received limited prior investigation, thereby supporting its role in facilitating the rational development of targeted therapies for precision oncology.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-22126-8.

Hepatocellular carcinoma (HCC) is characterized by distinct molecular and pathological subtypes, each with unique prognostic implications. This review aims to synthesize the imaging features associated with these HCC subtypes and discuss their potential to guide therapeutic decision-making. We searched PubMed and Embase for articles published from September 2004 to December 2024. The search strategy combined terms for imaging modalities ("CT," "MRI"), the primary disease ("hepatocellular carcinoma"), and various molecular and pathological subtypes (e.g., "macrotrabecular-massive," "steatohepatitic," "CK19," and "CTNNB1"). HCC is a biologically heterogeneous malignancy with varied prognosis and sensitivity to treatment. Assessment of its molecular and pathological subtypes relies on invasive histopathological examination, which is subject to sampling errors and often unavailable prior to treatment selection. A growing body of evidence suggests that radiologic features aid in the non-invasive classification of HCC subtypes, thereby informing individualized therapy. Given the substantial overlap between molecular, pathological, and imaging features, this review hypothesize that a comprehensive phenotyping system integrating all these information could significantly enhance personalized prognostication and treatment strategies. Radiologic imaging features not only provide valuable information for identifying molecular and pathological subtypes of HCC but also serve as practical tools to guide individualized therapeutic decision-making. Question Can CT and MRI reliably infer the molecular classification and pathological subtypes that drive prognosis in HCC? Findings Several imaging features have been found to reflect underlying molecular and pathological subtypes, but they do not demonstrate a one-to-one correlation. Clinical relevance An integrated classification system incorporating clinical, imaging, pathological, and molecular data may help mitigate the limitations of histologic and molecular analyses and facilitate individualized prognostication.

Preliminary investigations into the feasibility of Carbamoyl-phosphate synthetase 2, Aspartate transcarbamoylase, and Dihydroorotase (CAD)-targeted therapies have been conducted in a limited range of cancer types in pre-clinical studies. A comprehensive exploration of the diagnostic and prognostic capabilities of CAD, along with an understanding of its underlying biological mechanisms, is needed. A range of bioinformatics tools was employed to produce an extensive pan-cancer analysis of CAD expression. Experimental validation of the role of CAD in enzalutamide resistance in prostate cells was conducted. The molecular classification and drug patents of CAD were reviewed using the Worldwide Espacenet ®. Our study revealed that CAD was upregulated in tumor tissues in most cancer types. The expression of CAD was significantly different in clinical stages, pathological grades, and clinical prognosis. The highest frequency of CAD mutation was shown, but CAD mutations did not affect the clinical outcome of cancer patients. Comprehensive data across different cancer types illustrate the relationship between the expression of CAD and tumor mutation burden (TMB), microsatellite instability (MSI), and homologous recombination deficiency (HRD). Immune infiltration algorithms showed a positive link between CAD level and the prevalence of tumor-associated fibroblasts, MDSC, mast cells, and CD4+T cells. CAD level was positively linked to the immune checkpoint, suggesting a potential synergistic effect between CAD and immunotherapy. The GSEA analysis revealed that CAD expression is significantly associated with angiogenesis and epithelial-mesenchymal transition (EMT) pathways. Finally, we demonstrated that knockdown of CAD inhibits the growth of prostate cancer (PCa) cells and resistance to enzalutamide. This study revealed the diagnostic and prognostic potential of CAD. Notably, CAD exhibits essential functions in PCa cell proliferation and enzalutamide resistance.

Objectives: Endometrial carcinoma is the most common gynaecological cancer in developed countries, with both incidence and mortality rates continuing to rise globally. For women of reproductive age diagnosed with early-stage disease or endometrial intraepithelial neoplasia, fertility-preserving treatment should be considered to maintain the possibility of future childbearing. Effective fertility-sparing management requires a multidisciplinary approach that includes patient education, reduction in risk factors, accurate molecular and histological classification to guide targeted therapies, assisted reproductive technologies to improve early conception rates, and attention to the psycho-sexual well-being of patients to support treatment adherence. Methods: This retrospective cohort study analysed the clinicopathological features and treatment outcomes of thirteen patients who received fertility-preserving therapy between 2018 and 2023. Results: The mean age of the patients (n = 13) was 34.4 years, with a range of 20 to 41 years. The overall treatment response rate was 76.9%, including 69.2% complete and 7.7% partial responses. Stable disease was observed in 15.4% of cases, while progression occurred in 7.7%. Among those who achieved complete remission, in vitro fertilisation (IVF) was initiated in four cases, with two ongoing as of the time of data analysis. In one of the cases, after two unsuccessful assisted reproductive attempts, spontaneous conception occurred, resulting in the birth of a child. Conclusions: Our findings support the feasibility and success of fertility-preserving treatment in carefully selected patients, allowing the preservation of reproductive potential alongside oncological care.

Burkitt lymphoma (BL) remains a prevalent pediatric cancer in sub-Saharan Africa and was the first human cancer identified with a virus when Epstein-Barr virus (EBV) was discovered in a Ugandan BL tumor in 1964. The impact of EBV in BL is highlighted by a new molecular tumor classification of EBV positivity versus negativity which is starting to supersede longstanding epidemiologic classifications. The high incidence of EBV-positive BL in Africa and Papua New Guinea has been linked to Plasmodium falciparum (Pf) malaria coinfections in young children. Epidemiologic studies have yielded insight into early-age EBV infections and have demonstrated direct impacts of Pf malaria infections on EBV reactivation and disruptions in EBV persistence. Moreover, when children residing in malaria holoendemic regions are contending with chronic Pf malaria infections, they undergo immune adaptations to mitigate life-threatening immunopathology. We postulate that this malaria-induced immune conditioning leads to diminished EBV-specific cellular immune surveillance, when combined with higher B cell proliferation, and EBV load creates a permissive environment for BL tumorigenesis.

Advancing Lung Cancer Staging: Integrating IASLC Recommendations and Bioinformatics to Delineate Tumor Origins.

Molecular subtype classification of heterogeneous breast cancer is crucial for personalized therapies yet is limited by the low specificity of conventional single-target diagnosis systems. Herein, we developed a compact and versatile catalytic DNA computing (CDC) circuit as a programmable cancer evaluator for efficient dual-microRNA (miRNA) detection, enabling precise breast cancer subtype identification in clinical samples through a sequentially amplified multiplexed molecular imaging technique. Using an innovative and exquisite probe-concatenating and grafting strategy, the compact CDC system was engineered with minimal strand complexity, incorporating only two tandem-caged probes to form two distinct catalytic hairpin assembly (CHA) circuitry modules: pre-CDC and post-CDC modules. These CHA-based modules were sequentially activated by multiple miRNAs, enabling localized cascade signal amplification for the cancer subtype evaluation. Through systematic experimental validation and complementary theoretical simulations, we elucidated the sequential reaction mechanism and discovered the reaction kinetic confinement of the upstream pre-CDC module on the downstream post-CDC module activation. These findings provided valuable insights into the molecular reaction processes and offered critical guidance for designing efficient CDC probes. With its comprehensive multianalyte recognition and synergistic cascade amplification capabilities, the compact CDC circuit enabled the magnified detection of multiple miRNAs within cancer cells. The CDC platform demonstrated exceptional specificity in identifying clinical cancer tissues, making it a robust cancer cell subtype evaluator for breast cancer. Due to its high accuracy and reliability, this molecular evaluator serves as a promising diagnostic tool with potential applications in clinical diagnosis and disease-related molecular mechanism studies.

In ABO-incompatible (ABOi) kidney transplantation, C4d deposition is associated with accommodation rather than rejection. Isoagglutinins targeting blood group antigens A/B are also classified as donor-specific antibodies (DSA). Therefore, the diagnosis of antibody-mediated rejection (AMR) relies primarily on microvascular inflammation (MVI). We analyzed 66 ABOi and 251 ABO-compatible (ABOc) KTRs concerning anti-HLA DSA development. 46 protocol biopsies from ABOi KTRs were classified according to Banff 2022. In addition, 25 ABOi protocol biopsies were assessed by the Molecular Microscope Diagnostics System (MMDx) and compared to ABOc biopsies: (1) 35 DSA-negative, C4d-negative cases with MVI<2, (2) 16 C4d-positive cases with MVI<2, (3) 35 DSA-positive, C4d-negative cases with MVI=1 (probable AMR), and (4) 87 C4d-negative/positive cases with MVI≥2. ABOi KTRs showed lower rates of de novo anti-HLA DSA (p=0.001) and clinical AMR (p=0.018) than ABOc KTRs. Among 25 ABOi protocol biopsies analyzed with MMDx, 56% met AMR criteria due to anti-A/B DSA: 20% active AMR, 20% probable AMR, 16% chronic AMR. However, molecular AMR was confirmed in only 14% by MMDx (p<0.001). ABOi and DSA-negative, C4d-negative ABOc biopsies with MVI below threshold did not differ in molecular rejection, archetype, and lesion scores (p>0.05) and showed stable graft function. Molecular AMR classifier scores were significantly lower in ABOi and DSA-negative, C4d-negative ABOc cases with MVI below threshold compared to C4d-positive ABOc and ABOc cases with probable AMR (p=0.007). Notably, C4d drives molecular AMR activity in ABOc biopsies already at C4d1 levels by immunofluorescence (p=0.011) and even in the absence of a histological Banff AMR diagnosis (p=0.003). ABOi transplantation reduces the risk of developing de novo anti-HLA DSA. Banff 2022 criteria may over-diagnose AMR. Biopsy-based transcript diagnostics differentiate anti-HLA- and anti-A/B-mediated alloimmune injury from C4d deposition due to accommodation. Interestingly, C4d deposition drives molecular AMR activity in ABOc biopsies.

Abstract Castration-resistant prostate cancer (CRPC) is a considerable clinical challenge, driven by complex molecular mechanisms that enable tumors to evade androgen deprivation therapy. This review explores the molecular mechanisms driving CRPC progression, focusing on androgen receptor (AR) signaling, cancer stem cells (CSCs), and neuroendocrine differentiation (NED). In AR-dependent CRPC, AR signaling remains pivotal in disease progression. Mutations, splice variants, alternative pathways, and transcriptional regulation facilitate sustained AR activation despite androgen deprivation therapy. In addition, CSCs promote tumor recurrence and treatment resistance by maintaining cellular heterogeneity and evading conventional therapies. Furthermore, castration-resistant neuroendocrine prostate cancer, an aggressive subtype of CRPC, is characterized by AR independence and NED, making treatment challenging. These findings underscore the need for therapeutic strategies targeting AR-, CSC-, and NED-specific mechanisms. Crucially, the molecular classification of CRPC into AR-dependent CRPC, stem cell–like CRPC, and castration-resistant neuroendocrine prostate cancer subtypes—based on the interplay between AR signaling, CSCs, and neuroendocrine features—is essential for advancing precision medicine. Tailoring treatments to the molecular subtype and characteristics of each patient offers the potential to substantially improve prognosis and survival in CRPC.

To develop a deep learning model for predicting molecular subgroups of medulloblastoma (MB) using preoperative brain MRI. This study included a cohort of 350 patients with MB for model development. Preoperative multiparametric brain MRIs were acquired, and molecular classification data for tumor samples were analyzed. A dual-task deep learning model, composed of a 3D Swin Transformer backbone and a Transformer-based mask decoder, was developed for the prediction of MB molecular subgroups. The model was jointly optimized with a parallel task of tumor and cerebellum segmentation. Ablation analysis was conducted to verify the effectiveness of the dual-task model design. An independent test cohort of 126 patients with MB was established to validate the predictive performance of the dual-task model. Our dual-task deep learning model demonstrated superior performance for MB molecular subgroup prediction, achieving an AUC of 0.877, accuracy of 88.9%, sensitivity of 71.6%, and specificity of 91.9%. The performance remained robust across both adult and pediatric age populations, with AUCs of 0.915 and 0.871, respectively. Furthermore, our approach exhibited effective generalization to the independent test cohort, yielding an AUC of 0.853, accuracy of 89.7%, sensitivity of 73.5%, and specificity of 92.1%. Ablation analysis demonstrated a significant improvement in AUC of 0.169 (95% CI 0.097-0.244) when using the dual-task model design. In comparison with the radiomics-based model, our deep learning model achieved a higher AUC by 0.156 (95% CI 0.079-0.233). Our proposed dual-task deep learning model enables automated and accurate prediction of MB molecular subgroups.

Hepatocellular carcinoma (HCC) is the most common variant of primary liver cancer, characterized by high mortality and unfavorable prognosis. Global incidence and mortality from HCC continue to rise despite progress in the treatment of viral hepatitis due to the increasing prevalence of metabolic dysfunction-associa­ted steatotic liver disease and obesity. Based on the analysis of lite­rature sources from the Pubmed, MedLine, The Cochrane Library, Embase databases, the review summarizes current data on the epidemiology, key risk factors, pathogenesis and molecular classification of HCC. Special attention is paid to modern approaches to diagnosis, in particular imaging methods, the Liver Imaging Reporting and Data System, the role of non-invasive biomarkers and morphological verification. The importance of screening programs in high-risk groups and timely interdisciplinary interaction is emphasized, which allows optimizing the strategy of patient management in accordance with international clinical guidelines.

Prognostic value of positive peritoneal cytology in FIGO 2009 stage IA grade 1 endometrioid endometrial cancer.

BackgroundEarly identification of molecular subtypes and WHO grades in adult-type diffuse gliomas (ADGs) provides critical evidence for prognostic evaluation and personalized therapeutic decision-making. This study aims to develop and validate radiopathomics models for the prediction of molecular subtypes and WHO grades in ADGs, addressing the limitations of unimodal approaches.MethodsIn this retrospective multicenter study, 499 consecutive ADG patients from three centers (training set: n = 306, testing set: n = 132, external validation set: n = 61) were included. Radiomics features were extracted from preoperative MRI sequences (T2-FLAIR and CE-T1WI), while pathomics features were derived from whole-slide images (WSIs). Feature selection methods and Multilayer Perceptron (MLP) classifier were performed to construct radiomics, pathomics, and radiopathomics models for molecular subtype classification and ADG grading. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score. Decision curve analysis (DCA) was performed to assess clinical efficacy. The Shapley Additive Explanation (SHAP) analysis was employed to explore the interpretability of models.ResultsFor discriminating molecular subtypes, the radiopathomics model demonstrated superior performance compared to standalone radiomics or pathomics models, achieving AUCs (macro/micro) of 0.847/0.864 in the testing set, and AUCs (macro/micro) of 0.858/0.867 in the external validation set. For differentiating WHO grades, the radiopathomics model achieved superior performance compared to models based solely on radiomics or pathomics features. The AUCs for the radiopathomics model were 0.849 (95% CI 0.775–0.915) in the testing set and 0.855 (95% CI 0.748–0.945) in the external validation set. DCA confirmed superior net clinical benefit across wider risk thresholds compared to unimodal alternatives. SHAP analysis provided interpretable insights into the predictive significance and contributions of individual features.ConclusionThe proposed radiopathomics models demonstrate robust diagnostic performance by synergizing cross-scale features, offering a clinically actionable tool for ADG stratification.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12967-025-07073-2.

Precision oncology in gastric cancer: Shaping the future of personalized treatment.