One out of ten patients with pheochromocytoma (PCC) and paraganglioma (PGL) develop malignant disease. Today there are no reliable pathological methods to predict malignancy at the time of diagnosis. Tumors harboring mutations in the succinate dehydrogenase subunit B (SDHB) gene often metastasize but the sequential genetic events resulting in malignant progression are not fully understood. The aim of this study was to identify somatic mutations that contribute to the malignant transformation of PCC/PGL. We performed pair-wise (tumor-normal) whole-exome sequencing to analyze the somatic mutational landscape in five malignant and four benign primary PCC/sympathetic PGL (sPGL), including two biological replicates from each specimen. In total, 225 unique somatic mutations were identified in 215 genes, with an average mutation rate of 0.54 mutations/megabase. Malignant tumors had a significantly higher number of mutations compared to benign tumors (p < 0.001). Three novel genes were identified as recurrently mutated; MYCN, MYO5B and VCL, and mutations in these genes were exclusively found in malignant sPGL tumors. Mutations in the MYO5B gene could be verified in two publicly available data sets. A gene ontology analysis of mutated genes showed enrichment of cellular functions related to cytoskeletal protein binding, myosin complex and motor activity, many of which had functions in Rab and Rac/Rho GTPase pathways. In conclusion, we have identified recurrent mutations in genes related to intracellular transport and cell adhesion, and we have confirmed MYO5B to be recurrently mutated in PCC/PGL cases with malignant potential. Our study suggests that deregulated Rab and Rac/Rho pathways may be important in PCC/PGL tumorigenesis.Pheochromocytomas (PCCs) and paragangliomas (PGLs) are tumors derived from chromaffin cells of the adrenal medulla and extra-adrenal paraganglia. These tumors have a yearly incidence of two to eight per million inhabitants with a peak incidence in the third to fifth decades of life, and occur equally in males and females. 1,2 The proportion of patients that develop malignant disease is estimated to 10% although higher frequencies have been reported, particularly in PGL. 3 The definition of a malignant PCC/PGL is the presence of tumor metastases, 4 and patients with apparently benign
Next-generation sequencing techniques have revealed that leukemic cells in acute myeloid leukemia often are characterized by a limited number of somatic mutations. These mutations can be the basis for the detection of leukemic cells in follow-up samples. The aim of this study was to identify leukemia-specific mutations in cells from patients with acute myeloid leukemia and to use these mutations as markers for minimal residual disease. Leukemic cells and normal lymphocytes were simultaneously isolated at diagnosis from 17 patients with acute myeloid leukemia using fluorescence-activated cell sorting. Exome sequencing of these cells identified 240 leukemia-specific single nucleotide variations and 22 small insertions and deletions. Based on estimated allele frequencies and their accuracies, 191 of these mutations qualified as candidates for minimal residual disease analysis. Targeted deep sequencing with a significance threshold of 0.027% for single nucleotide variations and 0.006% for NPM1 type A mutation was developed for quantification of minimal residual disease. When tested on follow-up samples from a patient with acute myeloid leukemia, targeted deep sequencing of single nucleotide variations as well as NPM1 was more sensitive than minimal residual disease quantification with multiparameter flow cytometry. In conclusion, we here describe how exome sequencing can be used for identification of leukemia-specific mutations in samples already at diagnosis of acute myeloid leukemia. We also show that targeted deep sequencing of such mutations, including single nucleotide variations, can be used for high-sensitivity quantification of minimal residual disease in a patient-tailored manner.
The aim of this study was to define the miRNA profile of small intestinal neuroendocrine tumors and to search for novel molecular subgroups and prognostic biomarkers. miRNA profiling was conducted on 42 tumors from 37 patients who underwent surgery for small intestinal neuroendocrine tumors. Unsupervised hierarchical clustering analysis of miRNA profiles identified two groups of tumor metastases, denoted cluster M1 and M2. The smaller cluster M1 was associated with shorter overall survival and contained tumors with higher grade (WHO grade G2/3) and multiple chromosomal gains including gain of chromosome 14. Tumors of cluster M1 had elevated expression of miR-1246 and miR-663a, and reduced levels of miR-488-3p. Pathway analysis predicted Wnt signaling to be the most significantly altered signaling pathway between clusters M1 and M2. Analysis of miRNA expression in relation to tumor proliferation rate showed significant alterations including downregulation of miR-137 and miR-204-5p in tumors with Ki67 index above 3%. Similarly, tumor progression was associated with significant alterations in miRNA expression, e.g. higher expression of miR-95 and miR-210, and lower expression of miR-378a-3p in metastases. Pathway analysis predicted Wnt signaling to be altered during tumor progression, which was supported by decreased nuclear translocation of β-catenin in metastases. Survival analysis revealed that downregulation of miR-375 was associated with shorter overall survival. We performed in situ hybridization on biopsies from an independent cohort of small intestinal neuroendocrine tumors using tissue microarrays. Expression of miR-375 was found in 578/635 (91%) biopsies and survival analysis confirmed that there was a correlation between downregulation of miR-375 in tumor metastases and shorter patient survival. We conclude that miRNA profiling defines novel molecular subgroups of metastatic small intestinal neuroendocrine tumors and identifies miRNAs associated with tumor proliferation rate and progression. miR-375 is highly expressed in small intestinal neuroendocrine tumors and may be used as a prognostic biomarker.
Biology is increasingly dependent on large-scale analysis, such as proteomics, creating a requirement for efficient bioinformatics. Bioinformatic predictions of biological functions rely upon correctly annotated database sequences, and the presence of inaccurately annotated or otherwise poorly described sequences introduces noise and bias to biological analyses. Accurate annotations are, for example, pivotal for correct identification of polypeptide fragments. However, standards for how sequence databases are organized and presented are currently insufficient. Here, we propose five strategies to address fundamental issues in the annotation of sequence databases: (i) to clearly separate experimentally verified and unverified sequence entries; (ii) to enable a system for tracing the origins of annotations; (iii) to separate entries with high-quality, informative annotation from less useful ones; (iv) to integrate automated quality-control software whenever such tools exist; and (v) to facilitate postsubmission editing of annotations and metadata associated with sequences. We believe that implementation of these strategies, for example as requirements for publication of database papers, would enable biology to better take advantage of large-scale data.
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