BackgroundDeep generative models can improve the generalization of deep learning in medical imaging by enriching limited training data with diverse, realistic synthetic images.PurposeTo assess whether Denoising Diffusion Probabilistic Models (DDPM) generated synthetic MRI, with and without mutual information (MI) regularization, enhances brain tumor classification across heterogeneous datasets.Study TypeRetrospective.PopulationA total of 559 patients with low and high grade brain tumors (LGG, HGG) were included from two datasets: public dataset (BraTS, n = 335) and clinical dataset (TASMC, n = 224), used exclusively to evaluate model generalization.Field Strength/Sequence1.5 T/3.0T-MR / T1WI, T1WI + C, T2WI, and FLAIR images.AssessmentDDPM models were trained to generate synthetic MR images of low grade glioma (LGG) and high grade glioma (HGG), with a variant incorporating MI. Image quality was assessed using Pearson-correlation, Frechet-Inception-Distance (FID) and Inception-Score (IS). For classification purposes. For classification, a 2D ResNet-152 was trained under four setups: (1) real images (baseline), (2) +augmentation, (3) +DDPM, and (4) +DDPM + MI. Performance was assessed by accuracy and F1-score. Robustness was tested through cross-dataset evaluation using a 5-fold ensemble.ResultsThe DDPM models, with and without MI, generated high-quality synthetic images, achieving FID = 31.47, 45.00, and IS = 1.50, 1.25, respectively. Lower FID and higher IS indicate enhanced realism and diversity, suggesting that MI improved both the quality and variability of the generated images. Cross-dataset evaluation demonstrated that DDPMs with MI achieved superior generalization performance in brain tumor classification task, with accuracies of 0.89 and 0.85 for BraTS-to-TAMSC and TAMSC-to-BraTS evaluations, respectively. These results outperform the baseline model (0.87, 0.80), traditional data augmentation (0.85, 0.78), and the standard DDPM without MI (0.82, 0.83).Data ConclusionDDPM + MI with ensemble learning significantly improves brain tumor generalization across diverse datasets, consistently outperforming baseline, traditional augmentation, and standard DDPM. This combination offers a robust solution for cross-institutional clinical applications.

From October 12-16, 2025, the 47th annual International Society of Oncology and Biomarkers (ISOBM) conference was held in Murnau, Bavaria, under the subtitle "New Era of Biomarkers in Oncology". The focus of the conference was the integration of modern technologies in oncology, including biomarker research, molecular diagnostics, and artificial intelligence applications in cancer care. The event welcomed more than 300 participants, 38 exhibitors, and 7 corporate partners from a total of 27 countries. The rich program featured presentations across 17 sessions, panel discussions, 12 workshops, and over 70 posters. The conference thus provided a multidisciplinary platform bringing together laboratory medicine, pathology, molecular biology, clinical oncology, and specialists in data processing and artificial intelligence.

Natural killer (NK) cells may be engineered with chimeric antigen receptors (CARs) to recognize tumor-associated antigens which bolsters their antitumor activity. More so than CAR-T cells, CAR-NK cell responses result from an integration of signals from a wider range of innate activating cytotoxic receptors, inhibitory receptors, and adhesion receptors in addition to the engineered CAR, making computational modeling of CAR-NK cell cytotoxicity more difficult than CAR-T cells. Uncovering mechanisms and predicting tumor cell responses to CAR-NK cytotoxicity is essential for improving therapeutic efficacy. The complexity of these effector-target interactions and the donor-to-donor variations in NK cell receptor (NKR) repertoire preclude the use of predictive models based on a single receptor, requiring function to be determined experimentally for each donor, CAR, and target combination. Computational modeling generates frameworks that allow the relationships of these factors to biologic outcomes to be explored without resource-consuming experiments. Here, we developed a computational mechanistic multiscale model which considers heterogenous expression of CARs, NKRs, adhesion receptors, and their cognate ligands, signal transduction, and NK cell-target cell population kinetics. The model is trained with quantitative flow cytometry and in-vitro cytotoxicity data and accurately predicts the short-term, long-term, and in-vivo cytotoxicity of CAR-NK cells. Furthermore, using Pareto optimization we explored the effect of CAR proportion and NK cell signaling on the differential cytotoxicity of CD33CAR-NK cells to cancer and healthy cells. This model can be extended to predict CAR-NK cytotoxicity across many antigens and tumor targets and serves as a tool to mechanistically explore CAR-NK signaling and biology.

Thymic epithelial tumors (TETs), rare yet clinically significant malignancies, face diagnostic challenges due to their occult presentation and lack of noninvasive risk-stratification tools, leading to systemic overtreatment and poor prognoses for high-risk subtypes. To address this unmet need, we developed a Fe₃O₄@Fe metal-organic framework heterojunction-enhanced laser desorption ionization mass spectrometry (LDI MS) platform for the efficient analysis of serum metabolic fingerprints (SMFs). Engineered through gradient pyrolysis, this nanomaterial synergizes ultraviolet absorption and photothermal conversion from its two constituent components with enhanced charge separation, achieving 1,000-fold improvement in sensitivity and thus enabling direct SMF acquisition from 1 μL of serum. Coupled with machine learning, the platform demonstrates robust diagnostic performance, yielding area under the curve (AUC) of 0.960 for distinguishing TETs from benign control and AUC of 0.856 for hierarchical risk stratification, outperforming clinical workflows. Beyond advancing material design for LDI MS, this work establishes a clinically translatable framework for rapid, large-scale screening, addressing critical gaps in TET management through metabolic-driven stratification.

Transforming growth factor beta (TGF-β) is a pleiotropic cytokine and participates in multiple cellular processes, such as cell development, proliferation, epithelial mesenchymal transition (EMT), and immune responses through SMAD-dependent or SMAD-independent signaling pathways. Notably, TGF-β signaling plays a dual role in tumors, acting as a potent tumor suppressor during early tumorigenesis by inducing apoptosis or cell-cycle arrest while promoting tumor transformation, progression and metastasis in advanced stage through multidimensional mechanisms. Moreover, it is abundant and functions as a master immune checkpoint in the tumor microenvironment (TME), fostering the development of numerous targeted therapies to rectify its aberrant activity in tumors in the past decades. Thus, a comprehensive overview of the pathologic roles, molecular mechanisms and therapeutic potentials of TGF-β signaling in tumors will benefit both the basic and clinical cancer research. Here, we review the complex biology and context-dependent functions of the TGF-β superfamily in regard to tumor, highlighting how it regulates the latter's development, growth, and dissemination by mainly targeting tumor cells, tumor-associated fibroblasts and various immune cells. We also summarize recent advances in the preclinical and clinical development of different types of TGF‑β‑targeting agents, and discuss their therapeutic potentials and challenges as well as approaches to improve the safety and efficacy of TGF-β pathway-targeted therapy in cancers. Through the summary of known knowledge and the latest updates, this review may provide a general picture on the biological functions of TGF-β in tumors, and facilitate the clinical implications of TGF-β-targeted therapy in tumor patients.

Lacrimal sac squamous cell carcinoma (LSSCC) is the most common tumor of the rare disease subsite of the nasolacrimal system. Circulating tumor DNA (ctDNA) has emerged as a biomarker for non-invasive monitoring of tumor burden and treatment response across a variety of solid tumors.

In this case study, we report on two patients with histologically confirmed HPV + (HPV16 and HPV33) LSSCC managed with definitive chemoradiotherapy who underwent serial ctDNA monitoring prior to, during, and following treatment using an ultrasensitive multi-feature HPV whole genome sequencing liquid biopsy.

In this case study, we report on two patients with histologically confirmed HPV + (HPV16 and HPV33) LSSCC managed with definitive chemoradiotherapy who underwent serial ctDNA monitoring prior to, during, and following treatment using an ultrasensitive multi-feature HPV whole genome sequencing liquid biopsy.

This report suggests that ctDNA monitoring is a feasible approach for detection and monitoring of HPV + LSSCC. We advocate for continued p16 immunohistochemistry in all squamous cell carcinomas arising in the lacrimal sac. Confirmation with HPV ISH or PCR should be performed given the potential impact on management and surveillance.

Low-grade fibromyxoid sarcoma (LGFMS) is a deceptively bland spindle cell neoplasm with malignant potential, most commonly arising in the extremities and trunk, and limited to at least110 cases in the head and neck. This report describes the first molecularly confirmed LGFMS of the floor of the mouth demonstrating a FUS::CREB3L2 gene fusion.

An 18-year-old male presented with a painless floor of mouth mass of 1-year duration that was clinically suspected to represent a ranula. The mass was subjected to excisional biopsy. Histological examination was salient for an infiltrating proliferation of bland spindle cells arranged in whorled patterns within a collagenous stroma with focal myxoid change.

Immunohistochemical analysis revealed diffuse MUC4 expression, narrowing the diagnosis. Targeted RNA sequencing identified a FUS::CREB3L2 fusion involving FUS exon 6 and CREB3L2 exon 5, confirming LGFMS. At 1-year follow-up, the patient showed no clinical evidence of disease and no detectable circulating tumor DNA.

This case highlights the critical role of molecular testing in the diagnosis of LGFMS and emphasizes the importance of appropriate management strategies, including aggressive local control and long-term surveillance, given the tumor's risk of late recurrence and distant metastasis.

Efforts to translate advances in immunology into anti-cancer immunotherapies have progressed rapidly in recent years. Six antibodies acting on programmed death ligand 1 or programmed death 1 pathways were approved in 75 cancer indications between 2015 and 2021. Several of these therapies were granted accelerated approval for specific cancer indications based on evidence from single-arm phase II clinical trials. In the absence of randomization, however, patient prognosis for progression-free and overall survival may not have been studied under standard chemotherapies for PD-1 and PD-L1 biomarker subpopulations. In 2021, two immunotherapies were withdrawn from accelerated approval applications for treatment of metastatic urothelial carcinoma after randomized phase III trials failed to demonstrate evidence for survival advantage over standard of care. This re-analysis uses digitized data to quantify PD-L1 heterogeneity in chemotherapy response, extending prior meta-analyses by incorporating digitized data and design simulation. The findings of the IMvigor210 (NCT02108652) and IMvigor211 (NCT02302807) trials of atezolizumab are reviewed to elucidate the statistical implications of PD-L1 subpopulation heterogeneity. To place the findings into the context of external evidence, digitization software is used to combine results from journal articles of eleven trials that assigned metastatic urothelial carcinoma patients to the same chemotherapy agents administered in the IMvigor211 control arm. This article defines the extent to which PD-L1 IC2/3 subpopulations appeared to outperform historical expectations in the IMvigor211 study based on external evidence from digitized data. Given the extent of PD-L1 heterogeneity suggested by this analysis, trial simulation is applied to define the probability that IMvigor211 would have resulted in a positive trial based on its actual design and alternative designs that enrolled more IC2/3 patients or had longer durations.

Head and neck squamous cell carcinoma (HNSCC) exhibits significant survival disparities by ancestry. This review explores how genetic ancestry shapes the clinical and molecular features of HNSCC, providing insights into the molecular mechanisms underlying these disparities. We reveal the variation in gene expression, mutations, copy number alterations, and DNA methylation patterns between ancestral groups. These insights highlight the potential for more personalized, ancestry-informed therapies, advancing equity in cancer treatment.

Colorectal-based brain metastasis formation is a rare and late event in colorectal cancer (CRC) patients and is associated with poor survival. Compared with other metastatic sites, the knowledge about copy number variation (CNV) in brain metastases is still very limited. To get more information about CNVs, we applied SNP array to analyze chromosomal regions with a higher density of SNP markers.

Genome-wide high resolution single nucleotide polymorphism (SNP) array (CytoScan™ HD) analyses were carried out in matched colorectal-based lung and brain metastases of two patients.

Brain metastases harbored more CNVs (77 CNVs) than pulmonary metastases (24 CNVs). Not previously described specific CNVs were: gain of 1p36.33-p36.32, 4p16.3-p16.1, 6q27, 12q24.33, 16p13.3, as well as 16p12.1-p11.2 in lung metastases and gain of 1p36.33-p36.21, 5q11.1-q13.2, 21q22.2-q22.3, 22q11.21-q12.2, as well as 22q12.3-q13.33 in brain metastases. Furthermore, we found 20 copy-neutral loss of heterozygosity (cn-LOH) regions exclusively in brain metastases, of which 11 cn-LOH regions have not been previously described.

Brain metastases of CRC showed more cn-LOH regions than lung metastases. Potentially affected genes within these regions could influence signaling pathways (e.g., PI3K/AKT signaling) as well as transcriptional processes. Perspectively, increased awareness of specific genetic characteristics can potentially increase the chance of early diagnosis of brain metastases, which could contribute to improved treatment options.