ParMap, an Algorithm for the Identification of Complex Genomic Variations in Nextgen Sequencing Data

Next-generation sequencing produces high-throughput data, albeit with greater error and shorter reads than traditional Sanger sequencing methods. This complicates the detection of genomic variations, especially, small insertions and deletions. Here we describe ParMap, a statistical algorithm for the identification of complex genetic variants using partially mapped reads in nextgen sequencing data. We also report ParMap’s successful application to the mutation analysis of chromosome X exome-captured leukemia DNA samples

Effective osimertinib treatment in a patient with discordant T790 M mutation detection between liquid biopsy and tissue biopsy.

BACKGROUND:We report the successful treatment of the patient with osimertinib 80 mg/day following disease progression and a discordance in the detection of a mechanism of resistance epithelial growth factor receptor (EGFR) T790 M between liquid biopsy and tissue biopsy methods. CASE PRESENTATION:A 57-year-old Hispanic male patient initially diagnosed with an EGFR 19 deletion positive lung adenocarcinoma and clinically responded to initial erlotinib treatment. The patient subsequently progressed on erlotinib 150 mg/day and repeat biopsies both tissue and liquid were sent for next-generation sequencing (NGS). A T790 M EGFR mutation was detected in the blood sample using a liquid biopsy technique, but the tissue biopsy failed to show a T790 M mutation in a newly biopsied tissue sample. He was then successfully treated with osimertinib 80 mg/day, has clinically and radiologically responded, and remains on osimertinib treatment after 10 months. CONCLUSIONS:Second-line osimertinib treatment, when administered at 80 mg/day, is both well tolerated and efficacious in a patient with previously erlotinib treated lung adenocarcinoma and a T790 M mutation detected by liquid biopsy

Low Frequency of 185delAG Founder Mutation of BRCA1 Gene in Iranian Breast Cancer Patients

AIM: The mutations in two breast cancer susceptibility genes, BRCA1 and BRCA2, are frequently associated with familial breast cancer. In this study, we aimed to investigate the probable founder mutations of BRCA1 and BRCA2 genes in Iranian breast cancer patients. METHODS: The total 400 patients affected with primary breast cancer were included in this study. Mutation detection was carried out on the basis of a PCR-based amplification, and two founder mutations for BRCA1 (185delAG and 5382insC) and one for BRCA2 (6174delT) were screened and considered by pedigree analysis. RESULTS: The positive family histories of breast cancer and other malignancies were recorded in 27.5% and 52% of patient pedigrees, respectively. The most frequent occurrence of breast cancer across four generations revealed to be 50% in the 1st degree in the 3rd generation, 68.8% in the 2nd degree in the 2nd generation, and 59.5% in the 3rd degree in the 3rd generation. Only 185delAG mutation in the BRCA1 gene was found in 2/400 (0.5%) of investigated pedigrees. There were two sisters of the same family. To our interest both sisters carried 185delAG mutation in the BRCA1 gene, which had a complete penetrance. However, the mutation was observed with two different organ targeting, at almost an early age of onset (proband: 45 yr, her sister: 30 yr). CONCLUSION: Considering the importance of genetic counseling and recording, the adequate information for the pedigrees of cancer patients put forward the principle approaches in cancer clinics to facilitate early detection for preventing challenges

Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing

Deep neural networks (DNN) have been shown to be useful in a wide range of applications. However, they are also known to be vulnerable to adversarial samples. By transforming a normal sample with some carefully crafted human imperceptible perturbations, even highly accurate DNN make wrong decisions. Multiple defense mechanisms have been proposed which aim to hinder the generation of such adversarial samples. However, a recent work show that most of them are ineffective. In this work, we propose an alternative approach to detect adversarial samples at runtime. Our main observation is that adversarial samples are much more sensitive than normal samples if we impose random mutations on the DNN. We thus first propose a measure of `sensitivity' and show empirically that normal samples and adversarial samples have distinguishable sensitivity. We then integrate statistical hypothesis testing and model mutation testing to check whether an input sample is likely to be normal or adversarial at runtime by measuring its sensitivity. We evaluated our approach on the MNIST and CIFAR10 datasets. The results show that our approach detects adversarial samples generated by state-of-the-art attacking methods efficiently and accurately.Comment: Accepted by ICSE 201

Mutations in splicing factor genes are a major cause of autosomal dominant retinitis pigmentosa in Belgian families

Purpose : Autosomal dominant retinitis pigmentosa (adRP) is characterized by an extensive genetic heterogeneity, implicating 27 genes, which account for 50 to 70% of cases. Here 86 Belgian probands with possible adRP underwent genetic testing to unravel the molecular basis and to assess the contribution of the genes underlying their condition. Methods : Mutation detection methods evolved over the past ten years, including mutation specific methods (APEX chip analysis), linkage analysis, gene panel analysis (Sanger sequencing, targeted next-generation sequencing or whole exome sequencing), high-resolution copy number screening (customized microarray-based comparative genomic hybridization). Identified variants were classified following American College of Medical Genetics and Genomics (ACMG) recommendations. Results : Molecular genetic screening revealed mutations in 48/86 cases (56%). In total, 17 novel pathogenic mutations were identified: four missense mutations in RHO, five frameshift mutations in RP1, six mutations in genes encoding spliceosome components (SNRNP200, PRPF8, and PRPF31), one frameshift mutation in PRPH2, and one frameshift mutation in TOPORS. The proportion of RHO mutations in our cohort (14%) is higher than reported in a French adRP population (10.3%), but lower than reported elsewhere (16.5-30%). The prevalence of RP1 mutations (10.5%) is comparable to other populations (3.5%-10%). The mutation frequency in genes encoding splicing factors is unexpectedly high (altogether 19.8%), with PRPF31 the second most prevalent mutated gene (10.5%). PRPH2 mutations were found in 4.7% of the Belgian cohort. Two families (2.3%) have the recurrent NR2E3 mutation p.(Gly56Arg). The prevalence of the recurrent PROM1 mutation p.(Arg373Cys) was higher than anticipated (3.5%). Conclusions : Overall, we identified mutations in 48 of 86 Belgian adRP cases (56%), with the highest prevalence in RHO (14%), RP1 (10.5%) and PRPF31 (10.5%). Finally, we expanded the molecular spectrum of PRPH2, PRPF8, RHO, RP1, SNRNP200, and TOPORS-associated adRP by the identification of 17 novel mutations

Improved detection of Probe Request Attacks : Using Neural Networks and Genetic Algorithm

The Media Access Control (MAC) layer of the wireless protocol, Institute of Electrical and Electronics Engineers (IEEE) 802.11, is based on the exchange of request and response messages. Probe Request Flooding Attacks (PRFA) are devised based on this design flaw to reduce network performance or prevent legitimate users from accessing network resources. The vulnerability is amplified due to clear beacon, probe request and probe response frames. The research is to detect PRFA of Wireless Local Area Networks (WLAN) using a Supervised Feedforward Neural Network (NN). The NN converged outstandingly with train, valid, test sample percentages 70, 15, 15 and hidden neurons 20. The effectiveness of an Intruder Detection System depends on its prediction accuracy. This paper presents optimisation of the NN using Genetic Algorithms (GA). GAs sought to maximise the performance of the model based on Linear Regression (R) and generated R > 0.95. Novelty of this research lies in the fact that the NN accepts user and attacker training data captured separately. Hence, security administrators do not have to perform the painstaking task of manually identifying individual frames for labelling prior training. The GA provides a reliable NN model and recognises the behaviour of the NN for diverse configurations

New approaches in detection and treatment of familial hypercholesterolemia

Familial hypercholesterolemia (FH) is an autosomal dominant genetic disorder that clinically leads to increased low density lipoprotein-cholesterol (LDL-C) levels. As a consequence, FH patients are at high risk for cardiovascular disease (CVD). Mutations are found in genes coding for the LDLR, apoB, and PCSK9, although FH cannot be ruled out in the absence of a mutation in one of these genes. It is pivotal to diagnose FH at an early age, since lipid lowering results in a decreased risk of cardiovascular complications especially if initiated early, but unfortunately FH is largely underdiagnosed. While a number of clinical criteria are available, identification of a pathogenic mutation in any of the three aforementioned genes is seen by many as a way to establish a definitive diagnosis of FH. It should be remembered that clinical treatment is based on LDL-C levels and not solely on presence or absence of genetic mutations as LDL-C is what drives risk. Traditionally, mutation detection has been done by means of dideoxy sequencing. However, novel molecular testing methods are gradually being introduced. These next generation sequencing-based methods are likely to be applied on broader scale once their efficacy and effect on cost are being established. Statins are the first-line therapy of choice for FH patients as they have been proven to reduce CVD risk across a range of conditions including hypercholesterolemia (though not specifically tested in FH). However, in a significant proportion of FH patients LDL-C goals are not met, despite the use of maximal statin doses and additional lipid-lowering therapies. This underlines the need for additional therapies, and inhibition of PCSK9 and CETP is among the most promising new therapeutic options. In this review, we aim to provide an overview of the latest information about the definition, diagnosis, screening, and current and novel therapies for F

Clinical application of high throughput molecular screening techniques for pharmacogenomics.

Genetic analysis is one of the fastest-growing areas of clinical diagnostics. Fortunately, as our knowledge of clinically relevant genetic variants rapidly expands, so does our ability to detect these variants in patient samples. Increasing demand for genetic information may necessitate the use of high throughput diagnostic methods as part of clinically validated testing. Here we provide a general overview of our current and near-future abilities to perform large-scale genetic testing in the clinical laboratory. First we review in detail molecular methods used for high throughput mutation detection, including techniques able to monitor thousands of genetic variants for a single patient or to genotype a single genetic variant for thousands of patients simultaneously. These methods are analyzed in the context of pharmacogenomic testing in the clinical laboratories, with a focus on tests that are currently validated as well as those that hold strong promise for widespread clinical application in the near future. We further discuss the unique economic and clinical challenges posed by pharmacogenomic markers. Our ability to detect genetic variants frequently outstrips our ability to accurately interpret them in a clinical context, carrying implications both for test development and introduction into patient management algorithms. These complexities must be taken into account prior to the introduction of any pharmacogenomic biomarker into routine clinical testing