Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours.
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ABSTRACT:
SUBMITTER: Darbyshire M
PROVIDER: S-EPMC6748970 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionDespite comparatively favourable prognosis in polycythemia vera (PV) patients (pts), the overall survival is shorter compared to the age-matched general population. The aim of the study was to evaluate the impact of chosen laboratory and genetic factors on the individual disease outcome, i.e. risk of thrombosis, myelofibrosis/blastic transformation and death.Materials and methodsThe study group consisted of 151 pts and 57 healthy donors (HD).ResultsJAK2V617F mutation was found in 96.7% (146/151) of the studied pts. JAK2 exon 12 mutations were identified in 2 individuals. The coexistence of JAK2V617F and JAK2 exon 12 mutation was confirmed in 2 other pts. In one case, neither JAK2V617F nor JAK2 exon 12 mutation was found. The presence of ten different non-driver mutations (ASXL1, SRSF2, U2AF1, IDH2) in eight of the analyzed pts (5.3%) was confirmed. The overall frequency of thrombotic events (TE) in the studied PV group was 23.8% (36/151). In patients with TE, median platelet count was lower than in pts without TE. Thrombotic risk did not depend on JAK2 rs12343867, TERT rs2736100, OBFC1 rs9420907 SNV, however, we found a novel strong tendency towards statistical significance between the CC genotype miR-146a rs2431697 and thrombosis. The disease progression to fibrotic phase was confirmed in 9% of the pts. Fibrotic transformation in PV pts was affected mainly by JAK2V617F variant allele frequency (VAF) and the presence of coexisting non-driver variants. The high JAK2V617F VAF and elevated white blood cell (WBC) count at the time of diagnosis were associated with an increased risk of death.ConclusionTherefore, in our opinion, complex, laboratory and genetic PV pts evaluation at the time of diagnosis should be incorporated into a new prognostic scoring system to more precisely define the PV prognosis and to optimize the therapeutic decision-making process.
| S-EPMC10349754 | biostudies-literature
Project description:MotivationNext-generation sequencing technologies have accelerated the discovery of single nucleotide variants in the human genome, stimulating the development of predictors for classifying which of these variants are likely functional in disease, and which neutral. Recently, we proposed CScape, a method for discriminating between cancer driver mutations and presumed benign variants. For the neutral class, this method relied on benign germline variants found in the 1000 Genomes Project database. Discrimination could, therefore, be influenced by the distinction of germline versus somatic, rather than neutral versus disease driver. This motivates this article in which we consider predictive discrimination between recurrent and rare somatic single point mutations based solely on using cancer data, and the distinction between these two somatic classes and germline single point mutations.ResultsFor somatic point mutations in coding and non-coding regions of the genome, we propose CScape-somatic, an integrative classifier for predictively discriminating between recurrent and rare variants in the human cancer genome. In this study, we use purely cancer genome data and investigate the distinction between minimal occurrence and significantly recurrent somatic single point mutations in the human cancer genome. We show that this type of predictive distinction can give novel insight, and may deliver more meaningful prediction in both coding and non-coding regions of the cancer genome. Tested on somatic mutations, CScape-somatic outperforms alternative methods, reaching 74% balanced accuracy in coding regions and 69% in non-coding regions, whereas even higher accuracy may be achieved using thresholds to isolate high-confidence predictions.Availability and implementationPredictions and software are available at http://CScape-somatic.biocompute.org.uk/.Contactmark.f.rogers.phd@gmail.com or C.Campbell@bristol.ac.uk.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC7320610 | biostudies-literature
Project description:Calculation of pathogen growth rates is important in understanding the natural history of infection and effects of therapy. However, it is often difficult to estimate pathogen growth because patients are treated immediately upon the detection of infection, leaving only one nonzero untreated reading. Previous approaches have relied on the flawed assumption that pathogen loads just prior to detection are at the assay detection threshold. We have developed a novel method for estimating the pathogen growth rate from a single reading and investigated the initial growth of cytomegalovirus (CMV) in allogeneic hematopoietic stem cell transplant (HSCT) patients. We applied this approach to CMV viral loads measured at least weekly in 122 patients in the 3 months posttransplant. Viral growth rates were estimated by using a modeling approach that accounts for the viral load and the time since the last negative reading. Viral growth rates decreased rapidly within the first week, from 0.72/day (doubling time, 0.96 day) at the point of reactivation to 0.22/day (doubling time, 3.1 days) at 1 week. Results from this method correlated closely with a two-point regression analysis of a subset of 58 patients with detectable subthreshold viral loads immediately prior to overt reactivation. Patients with lymphocyte counts of ?0.5 × 10(9)/liter had significantly slower viral growth than patients with low lymphocyte counts (0.612/day versus 0.325/day, P < 0.0001). Thus, our novel method of estimating pathogen growth rates reveals a rapid slowing of CMV growth during reactivation in HSCT patients and a significant impact of the lymphocyte count on CMV growth.
| S-EPMC3592124 | biostudies-literature
Project description:A pathological hallmark of Alzheimer's disease (AD), a late onset neurodegenerative disease, is the development of neuritic amyloid plaques, composed predominantly of aggregates of the β-amyloid (Aβ) peptide. It has been demonstrated that Aβ can aggregate into a variety of polymorphic aggregate structures under different chemical environments, and a potentially important environmental factor in dictating aggregate structure is the presence of surfaces. There are also several mutations clustered around the central hydrophobic core of Aβ (E22G Arctic mutation, E22K Italian mutation, D23N Iowa mutation, and A21G Flemish mutation). These mutations are associated with hereditary diseases ranging from almost pure cerebral amyloid angiopathy (CAA) to typical Alzheimer's disease pathology. The goal of this study was to determine how these mutations influence the morphology of Aβ aggregates under free solution conditions and at an anionic surface/liquid interface. While the rate of formation of specific aggregates was altered by mutations in Aβ under free solution conditions, the respective aggregate morphologies were similar. However, aggregation occurring directly on a negatively charged mica surface resulted in distinct aggregate morphologies formed by different mutant forms of Aβ. These studies provide insight into the potential role anionic surfaces play in dictating the formation of Aβ polymorphic aggregate structures.
| S-EPMC3369758 | biostudies-literature
Project description:Clonal hematopoiesis (CH) arises when a substantial proportion of mature blood cells is derived from a single dominant hematopoietic stem cell lineage. Somatic mutations in candidate driver (CD) genes are thought to be responsible for at least some cases of CH. Using whole-genome sequencing of 11 262 Icelanders, we found 1403 cases of CH by using barcodes of mosaic somatic mutations in peripheral blood, whether or not they have a mutation in a CD gene. We find that CH is very common in the elderly, trending toward inevitability. We show that somatic mutations in TET2, DNMT3A, ASXL1, and PPM1D are associated with CH at high significance. However, known CD mutations were evident in only a fraction of CH cases. Nevertheless, the highly prevalent CH we detect associates with increased mortality rates, risk for hematological malignancy, smoking behavior, telomere length, Y-chromosome loss, and other phenotypic characteristics. Modeling suggests some CH cases could arise in the absence of CD mutations as a result of neutral drift acting on a small population of active hematopoietic stem cells. Finally, we find a germline deletion in intron 3 of the telomerase reverse transcriptase (TERT) gene that predisposes to CH (rs34002450; P = 7.4 × 10-12; odds ratio, 1.37).
| S-EPMC5553576 | biostudies-literature
Project description:Consensus ranking of protein affinity to identify point mutations has not been established. Therefore, analytical techniques that can detect subtle variations without interfering with native biomolecular interactions are required. Here we report a rapid method to identify point mutations by a single nanoparticle sensing system. DNA-directed gold crystallization forms rod-like nanoparticles with bridges based on structural design. The nanoparticles enhance Rayleigh light scattering, achieving high refractive-index sensitivity, and enable the system to monitor even a small number of protein-DNA binding events without interference. Analysis of the binding affinity can compile an atlas to distinguish the potential of various point mutations recognized by MutS protein. We use the atlas to analyze the presence and type of single point mutations in BRCA1 from samples of human breast and ovarian cancer cell lines. The strategy of synthesis-by-design of plasmonic nanoparticles for sensors enables direct identification of subtle biomolecular binding distortions and genetic alterations.
| S-EPMC6381086 | biostudies-literature
Project description:Molecular diagnostics moves more into focus as technology advances. In patients with myeloproliferative neoplasms (MPN), identification and monitoring of the driver mutations have become an integral part of diagnosis and monitoring of the disease. In some patients, none of the known driver mutations (JAK2V617F, CALR, MPL) is found, and they are termed "triple negative" (TN). Also, whole-blood variant allele frequency (VAF) of driver mutations may not adequately reflect the VAF in the stem cells driving the disease. We reasoned that colony forming unit (CFU) assay-derived clonogenic cells may be better suited than next-generation sequencing (NGS) of whole blood to detect driver mutations in TN patients and to provide a VAF of disease-driving cells. We have included 59 patients carrying the most common driver mutations in the establishment or our model. Interestingly, cloning efficiency correlated with whole blood VAF (p = 0.0048), suggesting that the number of disease-driving cells correlated with VAF. Furthermore, the clonogenic VAF correlated significantly with the NGS VAF (p < 0.0001). This correlation was lost in patients with an NGS VAF <15%. Further analysis showed that in patients with a VAF <15% by NGS, clonogenic VAF was higher than NGS VAF (p = 0.003), suggesting an enrichment of low numbers of disease-driving cells in CFU assays. However, our approach did not enhance the identification of driver mutations in 5 TN patients. A significant correlation of lactate dehydrogenase (LDH) serum levels with both CFU- and NGS-derived VAF was found. Our results demonstrate that enrichment for clonogenic cells can improve the detection of MPN driver mutations in patients with low VAF and that LDH levels correlate with VAF.
| S-EPMC9646600 | biostudies-literature
Project description:MOTIVATION:The folding free energy is an important characteristic of proteins stability and is directly related to protein's wild-type function. The changes of protein's stability due to naturally occurring mutations, missense mutations, are typically causing diseases. Single point mutations made in vitro are frequently used to assess the contribution of given amino acid to the stability of the protein. In both cases, it is desirable to predict the change of the folding free energy upon single point mutations in order to either provide insights of the molecular mechanism of the change or to design new experimental studies. RESULTS:We report an approach that predicts the free energy change upon single point mutation by utilizing the 3D structure of the wild-type protein. It is based on variation of the molecular mechanics Generalized Born (MMGB) method, scaled with optimized parameters (sMMGB) and utilizing specific model of unfolded state. The corresponding mutations are built in silico and the predictions are tested against large dataset of 1109 mutations with experimentally measured changes of the folding free energy. Benchmarking resulted in root mean square deviation = 1.78 kcal/mol and slope of the linear regression fit between the experimental data and the calculations was 1.04. The sMMGB is compared with other leading methods of predicting folding free energy changes upon single mutations and results discussed with respect to various parameters. AVAILABILITY:All the pdb files we used in this article can be downloaded from http://compbio.clemson.edu/downloadDir/mentaldisorders/sMMGB_pdb.rar. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC3289912 | biostudies-literature
Project description:High-confidence detection of point mutations is important for disease diagnosis and clinical practice. Hybridization probes are extensively used, but are hindered by their poor single-nucleotide selectivity. Shortening the length of DNA hybridization probes weakens the stability of the probe-target duplex, leading to transient binding between complementary sequences. The kinetics of probe-target binding events are highly dependent on the number of complementary base pairs. Here, we present a single-molecule assay for point mutation detection based on transient DNA binding and use of total internal reflection fluorescence microscopy. Statistical analysis of single-molecule kinetics enabled us to effectively discriminate between wild type DNA sequences and single-nucleotide variants at the single-molecule level. A higher single-nucleotide discrimination is achieved than in our previous work by optimizing the assay conditions, which is guided by statistical modeling of kinetics with a gamma distribution. The KRAS c.34?A mutation can be clearly differentiated from the wild type sequence (KRAS c.34?G) at a relative abundance as low as 0.01% mutant to WT. To demonstrate the feasibility of this method for analysis of clinically relevant biological samples, we used this technology to detect mutations in single-stranded DNA generated from asymmetric RT-PCR of mRNA from two cancer cell lines.
| S-EPMC5338343 | biostudies-other
Project description:Over the past decade, experimental procedures such as metabolic labeling for determining RNA turnover rates at the transcriptome-wide scale have been widely adopted. Several computational methods to estimate RNA processing and degradation rates from such experiments have been suggested, but they all require several RNA sequencing samples. Here we present a method that can estimate RNA processing and degradation rates from a single sample. To this end, we use the Zeisel model and take advantage of its analytical solution, reducing the problem to solving a univariate non-linear equation on a bounded domain. The approach is computationally rapid and enables inference of rates that correlate well with previously published datasets. In addition to saving experimental work and computational time, having a sample-based rate estimation has several advantages. It does not require an error-prone normalization across samples and enables the use of replicates to estimate uncertainty and perform quality control. Finally the method and theoretical results described here are general enough to be useful in other settings such as nucleotide conversion methods.
2020-05-12 | GSE150286 | GEO