NPM1 as a potential therapeutic target for atypical teratoid/rhabdoid tumors.

BACKGROUND: Atypical teratoid/rhabdoid tumors (AT/RTs) are highly malignant brain tumors with inactivation of the SMARCB1 gene, which play a critical role in genomic transcriptional control. In this study, we analyzed the genomic and transcriptomic profiles of human AT/RTs to discover new druggable targets. METHODS: Multiplanar sequencing analyses, including whole exome sequencing (WES), single nucleotide polymorphism (SNP) arrays, array comparative genomic hybridization (aCGH), and whole transcriptome sequencing (RNA-Seq), were performed on 4 AT/RT tissues. Validation of a druggable target was conducted using AT/RT cell lines. RESULTS: WES revealed that the AT/RT genome is extremely stable except for the inactivation of SMARCB1. However, we identified 897 significantly upregulated genes and 523 significantly downregulated genes identified using RNA-Seq, indicating that the transcriptional profiles of the AT/RT tissues changed substantially. Gene set enrichment assays revealed genes related to the canonical pathways of cancers, and nucleophosmin (NPM1) was the most significantly upregulated gene in the AT/RT samples. An NPM1 inhibitor (NSC348884) effectively suppressed the viability of 7 AT/RT cell lines. Network analyses showed that genes associated with NPM1 are mainly involved in cell cycle regulation. Upon treatment with an NPM1 inhibitor, cell cycle arrest at G1 phase was observed in AT/RT cells. CONCLUSIONS: We propose that NPM1 is a novel therapeutic target for AT/RTs.

Genetic Characteristics of Polycythemia Vera and Essential Thrombocythemia in Korean Patients.

BACKGROUND: Despite recent advances in the investigation of myeloproliferative neoplasms (MPN), the impact of genetic heterogeneity on its molecular pathogenesis has not been fully elucidated. Thus, in this study, we aim to characterize the genetic complexity in Korean patients with polycythemia vera (PV) and essential thrombocythemia (ET). METHODS: We conducted association studies using 84 single-nucleotide polymorphisms (SNPs) in 229 patients (96 with PV and 133 with ET) and 170 controls. Further, whole-genome sequencing was performed in six patients (two with JAK2 V617F and four with wild-type JAK2), and putative somatic mutations were validated in a further 69 ET patients. Clinical and laboratory characteristics were also analyzed. RESULTS: Several germline SNPs and the 46 haplotype were significantly associated with PV and ET. Three somatic mutations in MPDZ, IQCH, and CALR genes were selected and validated. The frequency of the CALR mutation was 58.0% (40/69) in ET patients, who did not carry JAK2/MPL mutations. Moreover, compared with JAK2 V617F-positive patients, those with CALR mutations showed lower hemoglobin and hematocrit levels (P = 0.004 and P = 0.002, respectively), higher platelet counts (P =0.008), and a lower frequency of cytoreductive therapy (P = 0.014). CONCLUSION: This study was the first comprehensive investigation of the genetic characteristics of Korean patients with PV and ET. We found that somatic mutations and the 46 haplotype contribute to PV and ET pathogenesis in Korean patients.

A scientific treatment approach for acute mast cell leukemia: using a strategy based on next-generation sequencing data.

BACKGROUND: Mast cell leukemia (MCL) is the most aggressive form of systemic mastocytosis disorders. Owing to its rarity, neither pathogenesis nor standard treatment is established for this orphan disease. Hence, we tried to treat a patient with MCL based on the exome and transcriptome sequencing results of the patient's own DNA and RNA. METHODS: First, tumor DNA and RNA were extracted from bone marrow at the time of diagnosis. Germline DNA was extracted from the patient's saliva 45 days after induction chemotherapy and used as a control. Then, we performed whole-exome sequencing (WES) using the DNA and whole transcriptome sequencing (WTS) using the RNA. Single nucleotide variants (SNVs) were called using MuTect and GATK. Samtools, FusionMap, and Gene Set Enrichment Analysis were utilized to analyze WTS results. RESULTS: WES and WTS results revealed mutation in KIT S476I. Fusion analysis was performed using WTS data, which suggested a possible RARα-B2M fusion. When RNA expression analysis was performed using WTS data, upregulation of PIK3/AKT pathway, downstream of KIT and mTOR, was observed. Based on our WES and WTS results, we first administered all-trans retinoic acid, then dasatinib, and finally, an mTOR inhibitor. CONCLUSION: We present a case of orphan disease where we used a targeted approach using WES and WTS data of the patient. Even though our treatment was not successful, use of our approach warrants further validation.

Integrated genomic analyses identify frequent gene fusion events and VHL inactivation in gastrointestinal stromal tumors.

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract. We sequenced nine exomes and transcriptomes, and two genomes of GISTs for integrated analyses. We detected 306 somatic variants in nine GISTs and recurrent protein-altering mutations in 29 genes. Transcriptome sequencing revealed 328 gene fusions, and the most frequently involved fusion events were associated with IGF2 fused to several partner genes including CCND1, FUS, and LASP1. We additionally identified three recurrent read-through fusion transcripts: POLA2-CDC42EP2, C8orf42-FBXO25, and STX16-NPEPL1. Notably, we found intragenic deletions in one of three exons of the VHL gene and increased mRNAs of VEGF, PDGF-ß, and IGF-1/2 in 56% of GISTs, suggesting a mechanistic link between VHL inactivation and overexpression of hypoxia-inducible factor target genes in the absence of hypoxia. We also identified copy number gain and increased mRNA expression of AMACR, CRIM1, SKP2, and CACNA1E. Mapping of copy number and gene expression results to the KEGG pathways revealed activation of the JAK-STAT pathway in small intestinal GISTs and the MAPK pathway in wild-type GISTs. These observations will allow us to determine the genetic basis of GISTs and will facilitate further investigation to develop new therapeutic options.

Genomic dynamics associated with malignant transformation in IDH1 mutated gliomas.

The genomic mechanism responsible for malignant transformation remains an open question for glioma researchers, where differing conclusions have been drawn based on diverse study conditions. Therefore, it is essential to secure direct evidence using longitudinal samples from the same patient. Moreover, malignant transformation of IDH1-mutated gliomas is of potential interest, as its genomic mechanism under influence of oncometabolite remains unclear, and even higher rate of malignant transformation was reported in IDH1-mutated low grade gliomas than in wild-type IDH1 tumors. We have analyzed genomic data using next-generation sequencing technology for longitudinal samples from 3 patients with IDH1-mutated gliomas whose disease had progressed from a low grade to a high grade phenotype. Comprehensive analysis included chromosomal aberrations as well as whole exome and transcriptome sequencing, and the candidate driver genes for malignant transformation were validated with public database. Integrated analysis of genomic dynamics in clonal evolution during the malignant transformation revealed alterations in the machinery regulating gene expression, including the spliceosome complex (U2AF2), transcription factors (TCF12), and chromatin remodelers (ARID1A). Moreover, consequential expression changes implied the activation of genes associated with the restoration of the stemness of cancer cells. The alterations in genetic regulatory mechanisms may be the key factor for the major phenotypic changes in IDH1 mutated gliomas. Despite being limited to a small number of cases, this analysis provides a direct example of the genomic changes responsible for malignant transformation in gliomas.

Detection of a Distinctive Genomic Signature in Rhabdoid Glioblastoma, A Rare Disease Entity Identified by Whole Exome Sequencing and Whole Transcriptome Sequencing.

We analyzed the genome of a rhabdoid glioblastoma (R-GBM) tumor, a very rare variant of GBM. A surgical specimen of R-GBM from a 20-year-old woman was analyzed using whole exome sequencing (WES), whole transcriptome sequencing (WTS), single nucleotide polymorphism array, and array comparative genomic hybridization. The status of gene expression in R-GBM tissue was compared with that of normal brain tissue and conventional GBM tumor tissue. We identified 23 somatic non-synonymous small nucleotide variants with WES. We identified the BRAF V600E mutation and possible functional changes in the mutated genes, ISL1 and NDRG2. Copy number alteration analysis revealed gains of chromosomes 3, 7, and 9. We found loss of heterozygosity and focal homozygous deletion on 9q21, which includes CDKN2A and CDKN2B. In addition, WTS revealed that CDK6, MET, EZH2, EGFR, and NOTCH1, which are located on chromosomes 7 and 9, were over-expressed, whereas CDKN2A/2B were minimally expressed. Fusion gene analysis showed 14 candidate genes that may be functionally involved in R-GBM, including TWIST2, and UPK3BL. The BRAF V600E mutation, CDKN2A/2B deletion, and EGFR/MET copy number gain were observed. These simultaneous alterations are very rarely found in GBM. Moreover, the NDRG2 mutation was first identified in this study as it has never been reported in GBM. We observed a unique genomic signature in R-GBM compared to conventional GBM, which may provide insight regarding R-GBM as a distinct disease entity among the larger group of GBMs.

Enhanced peptide quantification using spectral count clustering and cluster abundance.

BACKGROUND: Quantification of protein expression by means of mass spectrometry (MS) has been introduced in various proteomics studies. In particular, two label-free quantification methods, such as spectral counting and spectra feature analysis have been extensively investigated in a wide variety of proteomic studies. The cornerstone of both methods is peptide identification based on a proteomic database search and subsequent estimation of peptide retention time. However, they often suffer from restrictive database search and inaccurate estimation of the liquid chromatography (LC) retention time. Furthermore, conventional peptide identification methods based on the spectral library search algorithms such as SEQUEST or SpectraST have been found to provide neither the best match nor high-scored matches. Lastly, these methods are limited in the sense that target peptides cannot be identified unless they have been previously generated and stored into the database or spectral libraries.To overcome these limitations, we propose a novel method, namely Quantification method based on Finding the Identical Spectral set for a Homogenous peptide (Q-FISH) to estimate the peptide's abundance from its tandem mass spectrometry (MS/MS) spectra through the direct comparison of experimental spectra. Intuitively, our Q-FISH method compares all possible pairs of experimental spectra in order to identify both known and novel proteins, significantly enhancing identification accuracy by grouping replicated spectra from the same peptide targets. RESULTS: We applied Q-FISH to Nano-LC-MS/MS data obtained from human hepatocellular carcinoma (HCC) and normal liver tissue samples to identify differentially expressed peptides between the normal and disease samples. For a total of 44,318 spectra obtained through MS/MS analysis, Q-FISH yielded 14,747 clusters. Among these, 5,777 clusters were identified only in the HCC sample, 6,648 clusters only in the normal tissue sample, and 2,323 clusters both in the HCC and normal tissue samples. While it will be interesting to investigate peptide clusters only found from one sample, further examined spectral clusters identified both in the HCC and normal samples since our goal is to identify and assess differentially expressed peptides quantitatively. The next step was to perform a beta-binomial test to isolate differentially expressed peptides between the HCC and normal tissue samples. This test resulted in 84 peptides with significantly differential spectral counts between the HCC and normal tissue samples. We independently identified 50 and 95 peptides by SEQUEST, of which 24 and 56 peptides, respectively, were found to be known biomarkers for the human liver cancer. Comparing Q-FISH and SEQUEST results, we found 22 of the differentially expressed 84 peptides by Q-FISH were also identified by SEQUEST. Remarkably, of these 22 peptides discovered both by Q-FISH and SEQUEST, 13 peptides are known for human liver cancer and the remaining 9 peptides are known to be associated with other cancers. CONCLUSIONS: We proposed a novel statistical method, Q-FISH, for accurately identifying protein species and simultaneously quantifying the expression levels of identified peptides from mass spectrometry data. Q-FISH analysis on human HCC and liver tissue samples identified many protein biomarkers that are highly relevant to HCC. Q-FISH can be a useful tool both for peptide identification and quantification on mass spectrometry data analysis. It may also prove to be more effective in discovering novel protein biomarkers than SEQUEST and other standard methods.

Identification of novel reference genes using multiplatform expression data and their validation for quantitative gene expression analysis.

Normalization of mRNA levels using endogenous reference genes (ERGs) is critical for an accurate comparison of gene expression between different samples. Despite the popularity of traditional ERGs (tERGs) such as GAPDH and ACTB, their expression variability in different tissues or disease status has been reported. Here, we first selected candidate housekeeping genes (HKGs) using human gene expression data from different platforms including EST, SAGE, and microarray, and 13 novel ERGs (nERGs) (ARL8B, CTBP1, CUL1, DIMT1L, FBXW2, GPBP1, LUC7L2, OAZ1, PAPOLA, SPG21, TRIM27, UBQLN1, ZNF207) were further identified from these HKGs. The mean coefficient variation (CV) values of nERGs were significantly lower than those of tERGs and the expression level of most nERGs was relatively lower than high expressing tERGs in all dataset. The higher expression stability and lower expression levels of most nERGs were validated in 108 human samples including formalin-fixed paraffin-embedded (FFPE) tissues, frozen tissues and cell lines, through quantitative real-time RT-PCR (qRT-PCR). Furthermore, the optimal number of nERGs required for accurate normalization was as few as two, while four genes were required when using tERGs in FFPE tissues. Most nERGs identified in this study should be better reference genes than tERGs, based on their higher expression stability and fewer numbers needed for normalization when multiple ERGs are required.

Gene by environment interactions.

This paper summarizes the contributions of group 8 to the Genetic Analysis Workshop 15. Group 8 focused on ways to address the possibility that genetic and environmental effects on phenotype may not be independent, but instead may interact in ways that could play important roles in determining phenotype. Among the eight contributors to this group, all three data sets (expression data, rheumatoid arthritis data, and simulated data) were analyzed. Contributions to this section fell into the two broad categories of refining the data (e.g. stratifying or weighting based on a covariate value) and explicitly modeling the interactions. The contributions also illustrate that there are at least two possible goals for such studies. One goal is simply to identify factors contributing to phenotype in the presence of interactions that might mask the signal to univariate methods. A related but distinct goal is to characterize an interaction (e.g. to determine if the interaction is significant).

Application of structural equation models to construct genetic networks using differentially expressed genes and single-nucleotide polymorphisms.

Understanding the genetic basis of human variation is an important goal of biomedical research. In this study, we used structural equation models (SEMs) to construct genetic networks to model how specific single-nucleotide polymorphisms (SNPs) from two genes known to cause acute myeloid leukemia (AML) by somatic mutation, runt-related transcription factor 1 (RUNX1) and ets variant gene 6 (ETV6), affect expression levels of other genes and how RUNX1 and ETV6 are related to each other. The SEM approach allows us to compare several candidate models from which an explanatory genetic network can be constructed.

Statistical tests for identifying differentially expressed genes in time-course microarray experiments.

MOTIVATION: Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. In time-course experiments in which gene expression is monitored over time, we are interested in testing gene expression profiles for different experimental groups. However, no sophisticated analytic methods have yet been proposed to handle time-course experiment data. RESULTS: We propose a statistical test procedure based on the ANOVA model to identify genes that have different gene expression profiles among experimental groups in time-course experiments. Especially, we propose a permutation test which does not require the normality assumption. For this test, we use residuals from the ANOVA model only with time-effects. Using this test, we detect genes that have different gene expression profiles among experimental groups. The proposed model is illustrated using cDNA microarrays of 3840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells.