Refine
Document Type
- Article (4)
Language
- English (4)
Has Fulltext
- yes (4)
Is part of the Bibliography
- no (4)
Keywords
- AML (1)
- Comparative analysis (1)
- Deep neural network (1)
- Gradient boosting (1)
- HDAC (1)
- HDACi (1)
- MYC (1)
- Multi-syndrome classification (1)
- Neurodegenerative syndromes (1)
- Random forest (1)
Objective: Pancreatic ductal adenocarcinoma (PDAC) still carries a dismal prognosis with an overall 5-year survival rate of 9%. Conventional combination chemotherapies are a clear advance in the treatment of PDAC; however, subtypes of the disease exist, which exhibit extensive resistance to such therapies. Genomic MYC amplifications represent a distinct subset of PDAC with an aggressive tumour biology. It is clear that hyperactivation of MYC generates dependencies that can be exploited therapeutically. The aim of the study was to find and to target MYC-associated dependencies.
Design: We analysed human PDAC gene expression datasets. Results were corroborated by the analysis of the small ubiquitin-like modifier (SUMO) pathway in a large PDAC cohort using immunohistochemistry. A SUMO inhibitor was used and characterised using human and murine two-dimensional, organoid and in vivo models of PDAC.
Results: We observed that MYC is connected to the SUMOylation machinery in PDAC. Components of the SUMO pathway characterise a PDAC subtype with a dismal prognosis and we provide evidence that hyperactivation of MYC is connected to an increased sensitivity to pharmacological SUMO inhibition.
Conclusion: SUMO inhibitor-based therapies should be further developed for an aggressive PDAC subtype.
Therapy of acute myeloid leukemia (AML) is unsatisfactory. Histone deacetylase inhibitors (HDACi) are active against leukemic cells in vitro and in vivo. Clinical data suggest further testing of such epigenetic drugs and to identify mechanisms and markers for their efficacy. Primary and permanent AML cells were screened for viability, replication stress/DNA damage, and regrowth capacities after single exposures to the clinically used pan-HDACi panobinostat (LBH589), the class I HDACi entinostat/romidepsin (MS-275/FK228), the HDAC3 inhibitor RGFP966, the HDAC6 inhibitor marbostat-100, the non-steroidal anti-inflammatory drug (NSAID) indomethacin, and the replication stress inducer hydroxyurea (HU). Immunoblotting was used to test if HDACi modulate the leukemia-associated transcription factors β-catenin, Wilms tumor (WT1), and myelocytomatosis oncogene (MYC). RNAi was used to delineate how these factors interact. We show that LBH589, MS-275, FK228, RGFP966, and HU induce apoptosis, replication stress/DNA damage, and apoptotic fragmentation of β-catenin. Indomethacin destabilizes β-catenin and potentiates anti-proliferative effects of HDACi. HDACi attenuate WT1 and MYC caspase-dependently and -independently. Genetic experiments reveal a cross-regulation between MYC and WT1 and a regulation of β-catenin by WT1. In conclusion, reduced levels of β-catenin, MYC, and WT1 are molecular markers for the efficacy of HDACi. HDAC3 inhibition induces apoptosis and disrupts tumor-associated protein expression.
Aim: Left ventricular non-compaction (LVNC) is perceived as a rare high-risk cardiomyopathy characterized by excess left ventricular (LV) trabeculation. However, there is increasing evidence contesting the clinical significance of LV hyper-trabeculation and the existence of LVNC as a distinct cardiomyopathy. The aim of this study is to assess the association of LV trabeculation extent with cardiovascular morbidity and all-cause mortality in patients undergoing clinical cardiac magnetic resonance (CMR) scans across 57 European centers from the EuroCMR registry.
Methods and Results: We studied 822 randomly selected cases from the EuroCMR registry. Image acquisition was according to international guidelines. We manually segmented images for LV chamber quantification and measurement of LV trabeculation (as per Petersen criteria). We report the association between LV trabeculation extent and important cardiovascular morbidities (stroke, atrial fibrillation, heart failure) and all-cause mortality prospectively recorded over 404 ± 82 days of follow-up. Maximal non-compaction to compaction ratio (NC/C) was mean (standard deviation) 1.81 ± 0.67, from these, 17% were above the threshold for hyper-trabeculation (NC/C > 2.3). LV trabeculation extent was not associated with increased risk of the defined outcomes (morbidities, mortality, LV CMR indices) in the whole cohort, or in sub-analyses of individuals without ischaemic heart disease, or those with NC/C > 2.3.
Conclusion: Among 882 patients undergoing clinical CMR, excess LV trabeculation was not associated with a range of important cardiovascular morbidities or all-cause mortality over ~12 months of prospective follow-up. These findings suggest that LV hyper-trabeculation alone is not an indicator for worse cardiovascular prognosis.
Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context.
Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.
Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.
Interventions: N.A.
Main outcomes and measures: Cohen’s kappa, accuracy, and F1-score to assess model performance.
Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy.
Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.