The mitogen-activated protein kinase (MAPK) pathway is constantly activated in Langerhans cell histiocytosis (LCH). Mutations of the downstream kinases BRAF and MAP2K1 mediate this activation in a subset of LCH lesions. In this study, we attempted to identify other mutations which may explain the MAPK activation in nonmutated BRAF and MAP2K1 LCH lesions.We analysed 26 pulmonary and 37 nonpulmonary LCH lesions for the presence of BRAF, MAP2K1, NRAS and KRAS mutations. Grossly normal lung tissue from 10 smoker patients was used as control. Patient spontaneous outcomes were concurrently assessed.BRAF(V600E) mutations were observed in 50% and 38% of the pulmonary and nonpulmonary LCH lesions, respectively. 40% of pulmonary LCH lesions harboured NRAS(Q61K) (/R) mutations, whereas no NRAS mutations were identified in nonpulmonary LCH biopsies or in lung tissue control. In seven out of 11 NRAS(Q61K) (/R)-mutated pulmonary LCH lesions, BRAF(V600) (E) mutations were also present. Separately genotyping each CD1a-positive area from the same pulmonary LCH lesion demonstrated that these concurrent BRAF and NRAS mutations were carried by different cell clones. NRAS(Q61K) (/R) mutations activated both the MAPK and AKT (protein kinase B) pathways. In the univariate analysis, the presence of concurrent BRAF(V600E) and NRAS(Q61K) (/R) mutations was significantly associated with patient outcome.These findings highlight the importance of NRAS genotyping of pulmonary LCH lesions because the use of BRAF inhibitors in this context may lead to paradoxical disease progression. These patients might benefit from MAPK kinase inhibitor-based treatments.
Circulating cell-free DNA (ccfDNA) has great potential for non-invasive diagnostics, and prediction and monitoring of treatment response, but its amount is usually limited. Therefore, the choice of methods to extract and characterize ccfDNA is crucial. In the current study, we performed the most comprehensive comparison of methods for ccfDNA extraction (11 methods), quantification (3 methods), and estimation of the integrity index (2 methods) from small quantities of different kinds of plasma. The QIAamp® Circulating Nucleic Acid Kit and the Norgen Plasma/Serum Circulating DNA Purification Mini Kit showed the best accuracy and reproducibility, but the Norgen kit allowed to extract a higher amount of ccfDNA. This workflow provides a reliable protocol for the multiple applications of ccfDNA in biomedicine.
Recent advances in NGS sequencing, microarrays and mass spectrometry for omics data production have enabled the generation and collection of different modalities of high-dimensional molecular data. The integration of multiple omics datasets is a statistical challenge, due to the limited number of individuals, the high number of variables and the heterogeneity of the datasets to integrate. Recently, a lot of tools have been developed to solve the problem of integrating omics data including canonical correlation analysis, matrix factorization and SM. These commonly used techniques aim to analyze simultaneously two or more types of omics. In this article, we compare a panel of 13 unsupervised methods based on these different approaches to integrate various types of multi-omics datasets: iClusterPlus, regularized generalized canonical correlation analysis, sparse generalized canonical correlation analysis, multiple co-inertia analysis (MCIA), integrative-NMF (intNMF), SNF, MoCluster, mixKernel, CIMLR, LRAcluster, ConsensusClustering, PINSPlus and multi-omics factor analysis (MOFA). We evaluate the ability of the methods to recover the subgroups and the variables that drive the clustering on eight benchmarks of simulation. MOFA does not provide any results on these benchmarks. For clustering, SNF, MoCluster, CIMLR, LRAcluster, ConsensusClustering and intNMF provide the best results. For variable selection, MoCluster outperforms the others. However, the performance of the methods seems to depend on the heterogeneity of the datasets (especially for MCIA, intNMF and iClusterPlus). Finally, we apply the methods on three real studies with heterogeneous data and various phenotypes. We conclude that MoCluster is the best method to analyze these omics data. Availability: An R package named CrIMMix is available on GitHub at https://github.com/CNRGH/crimmix to reproduce all the results of this article.
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