European Organisation for Research and Treatment of Cancer

Background Silviculture education requires understanding complex, spatially distributed ecological processes and the long-term effects of forest management decisions. These concepts can be difficult to convey through traditional teaching methods. Virtual Reality (VR) offers an immersive, interactive way to explore complex systems in domains such as silviculture, and design considerations, such as diegesis, may influence learning outcomes. Aim This study introduces SilVR, an immersive VR simulation for teaching silviculture concepts, and investigates the effect on user engagement, usability and learning outcomes of two user interface (UI) paradigms: diegetic (lesson components and controls integrated into the virtual environment, e.g., physical buttons, maps) and non-diegetic (elements presented in a consistent, non-immersive format, e.g., transparent panels, floating text). Method SilVR was developed using a predictive forest growth model, allowing users to explore forest outcomes under different management regimes. A between-participants study was conducted as part of a graduate training workshop, with factors of diegetic and non-diegetic information presentation. Nineteen forestry students participated in the study. Each participant completed a pre- and post-intervention knowledge assessment. The system captured user behaviour, and participants completed self-report measures: Intrinsic Motivation Inventory (IMI), presence, and a general feedback questionnaire. Results Knowledge scores significantly improved following the intervention. While there was no significant interaction effect due to diegesis on knowledge, students in the diegetic condition progressed through scenes more quickly and exhibited stronger preference behaviour. Conclusion Immersive VR effectively supports learning in complex domains such as silviculture. Design considerations, such as diegesis, play a key role in learner engagement and interaction. These findings offer implications for the design of educational VR simulations for forestry and other complex system domains.

Global oncology trials face increasing scrutiny over regional enrollment imbalances, as regulatory agencies such as the US Food and Drug Administration, European Medicines Agency, and Pharmaceuticals and Medical Devices Agency demand data reflective of population diversity. This push is grounded in evidence that genetic polymorphisms (eg, UGT1A1*28, CYP2D6), human leukocyte antigen-related toxicities, and biomarker prevalence (eg, epidermal growth factor receptor mutations in approximately 15% of Western v approximately 50% of Asian patients with lung cancer) can significantly influence treatment outcomes. We reviewed scientific literature, regulatory case studies, and methodological innovations addressing regional heterogeneity in oncology trials. Particular focus was given to statistical tools such as adaptive randomization for real-time enrollment balancing, Bayesian hierarchical models for data borrowing across regions, and Multi-Regional Clinical Trial designs for structured consistency assessments. Control arm variability because of regional differences in standard of care and drug access was also examined. Recent regulatory setbacks, especially involving Asia-centric trials, underscore the consequences of insufficient regional planning. Emerging statistical approaches, including adaptive and Bayesian methods, show promise in managing heterogeneity while preserving trial integrity. Persistent challenges include disparities in trial infrastructure, molecular subtype distributions, and comorbidity patterns. Broader regional inclusion and integration of real-world evidence are increasingly critical to overcoming these limitations. Regional enrollment should be viewed not as a regulatory formality, but as a scientific and ethical priority. The future of global oncology trials hinges on proactive regional planning, innovative methodology, and cross-sector collaboration. Aligning global efficiency with local relevance can enhance scientific robustness, support regulatory alignment, and expand equitable access to novel cancer therapies worldwide.

The "2025 ACC/AHA/HRS/ISACHD/SCAI Guideline for the Management of Adults With Congenital Heart Disease" provides recommendations to guide clinicians on the evaluation and treatment of adult patients with congenital heart disease. It incorporates new evidence to replace the "2018 AHA/ACC Guideline for the Management of Adults With Congenital Heart Disease." A comprehensive literature search was conducted with a focus on literature published from 2017 to 2024; in some instances, older literature was also collected and reviewed. Clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants and published in English were identified from MEDLINE (via PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and CINAHL for selected searches. Recommendations from the "2018 AHA/ACC Guideline for the Management of Adults With Congenital Heart Disease" have been updated with new evidence to guide clinicians.

This extensive review highlights the central role ofclassicalmolecular simulation in advancing hydrogen (H2) technologies.As the transition to a sustainable energy landscape is urgently needed,the optimization of H2 processes, spanning production,purification, transportation, storage, safety, and utilization isessential. To this end, accurate prediction of thermodynamic, transport,structural, and interfacial properties is important for overcomingengineering challenges across the entire H2 value chain.Experimental measurements, despite being the traditional way of obtainingthese properties, can be limited by the distinctive nature of H2, harsh operating conditions, safety constraints, and extensiveparameter spaces. Free from such limitations, classical molecularsimulations, in the general frameworks of Monte Carlo and MolecularDynamics, provide an optimal balance between computational efficiencyand accuracy, bridging the gap between quantum mechanical calculationsand macro-scale modeling. This review also systematically covers molecularsimulation methods and force fields for computing key properties ofH2 systems, such as phase and adsorption equilibria andtransport coefficients. Beyond property prediction, we explore howmolecular simulation reveals fundamental mechanisms governing hydrateformation and dissociation, membrane permeations, and H2 embrittlement. When possible, data from multiple sources are comparedand critically assessed, while effort is put on evaluating the forcefields used and methodological approaches followed in the literature.Finally, this review aims at identifying research gaps and futureopportunities, emphasizing emerging approaches, such as molecularsimulation in the era of artificial intelligence.

Abstract INTRODUCTION In a four-arm study, the CATNON trial investigated the role of concurrent and/or adjuvant temozolomide as addition to radiotherapy in anaplastic astrocytoma patients. The results of the final database lock in October 2024 confirmed benefit of adjuvant temozolomide in IDHmt grade 3 astrocytoma patients, without benefit of concurrent temozolomide. We present the prognostic and predictive molecular analyses of the 444 randomized astrocytoma-IDHmt patients. METHODS Glioma relevant DNA alterations were assessed using dedicated next generation sequencing panels. Genome-wide DNA methylation profiling was performed with the HumanMethylationEPIC beadchip., Tumors were graded by both the Epignostix and the NIH-NCI glioma classifier (astrocytomaIDHmt vs astrocytomaIDHmt high-grade). Two 3-tier methylation-based grading scores were used: Continuous Grading Coefficient (CGC) and Global Methylation Score (GMS). MGMT promoter methylation status was determined using the MGMT-STP27 algorithm. For predictive factor analysis, the outcome of patients in the arm treated with radiotherapy alone was compared to that of patients in the three arms treated with radiotherapy and temozolomide. RESULTS With 10.5 years follow-up, 55% of the IDHmt astrocytoma patients were deceased. PDGFRA gene amplification was associated with worse outcome (HR 3.5 (95% CI 2.2, 5.6) comparable to CDKN2A homozygous deletion (HR 3.6 (95% CI 2.5, 5.1). Grading by the NIH-NCI and the Epignostix classifier were highly correlated, with 10% discrepancy. Both identified a prognostic favorable astrocytoma IDHmt subset (HR 0.37 (95% CI 0.28, 0.49), but CGC and GMS were stronger prognosticators in multivariate analysis. Additional prognostic alterations with lower HR include LOH PTEN locus, total Copy Number Variation and CDK4 amplification. MGMT promoter methylation was not prognostic. None of the prognostic molecular factors was predictive for benefit to temozolomide. DISCUSSION PDGFR amplification is equally prognostic to HD-CDKN2A. Methylation-based grading identifies clinically relevant prognostic subgroups. Assessment of MGMT promoter status in these tumors has neither prognostic nor predictive value.

Abstract Hyperprogressive disease (HPD) is characterized by an acceleration of tumor growth in a subset of patients due to receiving immune-oncology (IO) therapy. Patients, policy makers and treating physicians should be made aware of the potential HPD related to a specific IO-therapy. Multiple methods based on radiological criteria comparing the pre-treatment and post-treatment tumor growth have been proposed in the literature to identify HPD cases. In the absence of a consensus regarding which methods to use, we compared the different methods and proposed two simple combinations of the existing methods to estimate the potential incidence of HPD related to the received IO therapy. Data from 305 patients across multiple centres (Gustave Roussy, Vall d'Hebron Institute of Oncology, START Madrid-CIOCC and Institute of Cancer Research Marsden) were pooled. 163 patients had pre-baseline study visit disease assessment available and progressive disease (PD) as per RECIST version 1.1 at the first post-baseline assessment on target lesions exclusively. The presence of HPD according to the ‘tumor growth rate’ (TGR), ‘tumor growth kinetics’ (TGK) and ‘tumor growth difference’ (TGD) method was analyzed in this subsample of 163 patients. In addition, a conservative method requiring all three methods to declare HPD (“intersection method”) and a sensitive method requiring any of the three methods to declare HPD (“union method”) was also used. The three most common primary malignancies in the pooled data were lung cancer (117 patients, 38.36%), colorectal cancer (32 patients, 10.49%) and melanoma (30 patients, 9.84%). Median age was 59 (IQR=18), 176 patients (57.70%) were male and 129 patients were female (42.30%). The TGR method identified 53 patients (17.38%) with HPD, the TGK method 62 patients (20.33%) and the TGD method 41 patients (13.44%). The pairwise agreement in HPD cases identified across methods was estimated using Cohen’s Kappa. The Kappa statistics were 0.88, 0.71 and 0.70 for the pairwise concordance between TGR-TGK, TGR-TGD, and TGK-TGD, respectively. HPD was declared by all three methods in 37 patients (12.13%), i.e. the intersection method. The union approach identified HPD in 62 patients (20.33%) and was identical to the TGK method, making it the most sensitive method of the three in this analysis. HPD assessment methods comparing tumor growth acceleration before and after receiving IO-therapy provide insight in the dynamics of the target lesions. Our analysis suggests that the TGR, TGK and TGD methods are concordant. Rather than favouring one method over the other, we propose to combine the existing methods into a sensitive and restrictive method. The sensitive method can serve as an upper bound of HPD incidence, and the restrictive as a lower bound. These boundaries can inform patients, policy makers and treating physicians of the potential HPD related to a specific IO-therapy. Citation Format: Luc Boone, Roberto Ferrara, Giuseppe Lo Russo, Penelope Bradbury, Lesley Seymour, Stephane Champiat, Christophe Le Tourneau, Anna Minchom, Ruth Plummer, Larry Schwartz, Bingshu Chen, Elena Garralda Cabanas, Scott Laurie, Saskia Litière, Jan Bogaerts, Emiliano Calvo. Comparing and combining existing radiological criteria for hyperprogressive disease in patients receiving immune-oncology therapy: Building towards a sensitive and conservative method to assess the presence of hyperprogressive disease [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2025 Oct 22-26; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2025;24(10 Suppl):Abstract nr C072.

Abstract Introduction: Glioblastoma multiforme (GBM) is an aggressive malignancy characterized by rapid progression, heterogeneity, and resistance to conventional therapies. Traditional in vitro and animal models often fail to replicate the complex tumor microenvironment and cellular diversity of human GBM, limiting their utility in effective drug testing and translation to clinical application. To address these limitations, we developed a 3D Ex Vivo Patient Tissue (EVPT) assay for GBM which uses patient-derived tumor tissue. This approach maintains the tumor heterogeneity and cytoarchitecture of the tumor, enabling more clinically relevant assessment of therapeutic candidates. In this study, the EVPT model was used to test novel tumor-specific therapies, highlighting potential breakthroughs in the treatment of GBM. Methods: 10 patient tumor tissues from GBM patients were procured from commercial tissue providers and processed within 24 hours of dissection to preserve the TME. Fresh, minimally processed tumor material was embedded in a protein-rich hydrogel and exposed to novel peptide-drug conjugates (PDC): a peptide carrier and a cytotoxic payload from Oncopeptides at various doses in 384-well format for 5-7 days. As controls, we included Staurosporine (maximum killing), Temozolomide (one of the few options and standard of care for GBM), as well as investigational drugs currently in clinical trials: QBS72S (Alkylating Agent) and Panobinostat (Histone Deacetylase (HDAC) Inhibitor). Phenotypic screening was conducted using our proprietary automated high-content imaging (HCI) platform. Additionally, sample characterization was performed on fixed tissue samples by the IHC, validating GBM tumor identity, proliferation and TME presence. Results: All patient tissues responded strongly to Staurosporine, while exhibiting resistance to Temozolomide. QBS72S and Panobinostat showed sample-dependent killing activity but were outperformed by the PDC compounds, demonstrating over a five-fold increase in effectiveness. PDC compounds elicited strong to mild killing responses, reflecting the heterogeneity in the samples' reactions (>10-fold difference). Notably, the lead PDC compound demonstrated strong cytotoxic activity, effectively killing tumor cells at highest dose in 9 out of 10 samples. Conclusion: Our results demonstrate the novel PDCs' great potential over standard of care Temozolomide, highlighting its promise as a more effective therapeutic strategy for GBM. The EVPT platform effectively aided in lead compound selection, with a clear assay window between less and more potent compounds. Moreover, drug response heterogeneity was accurately captured between patients. This represents a major advancement in preclinical drug testing and glioblastoma therapy development, enhancing the likelihood of successful translation to clinical outcomes. Ultimately, this approach promises to improve treatment strategies and patient outcomes in the long term. Citation Format: Ezgi Kaya Aksoy, Ulrica Westermark, Kristin Hammer, Luca Gandini, Jolie Flach, Emma Spanjaard, Stefan Svensson. Gelius, Marrit Putker, Nataliia Beztsinna. A preclinical ex vivo model for glioblastoma captures patient heterogeneity in drug response [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2025 Oct 22-26; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2025;24(10 Suppl):Abstract nr B092.