Implications of genetic testing and informed consent before and after genetic testing in individuals with cancer

Recent advancements in next generation sequencing have allowed for genetic information become more readily available in the clinical setting for those affected by cancer and by treating clinicians.Given the lack of access to geneticists,medical oncologists and other treating physicians have begun ordering and interpreting genetic tests for individuals with cancer through the process of"mainstreaming".While this process has allowed for quicker access to genetic tests,the process of"mainstreaming"has also brought several challenges including the dissemination of variants of unknown significance results,ordering of appropriate tests,and accurate interpretation of genetic results with appropriate followup testing and interventions.In this editorial,we seek to explore the process of informed consent of individuals before obtaining genetic testing and offer potential solutions to optimize the informed consent process including categorization of results as well as a layered consent model.

A dual-RPA based lateral flow strip for sensitive,on-site detection of CP4-EPSPS and Cry1Ab/Ac genes in genetically modified crops

Traditional transgenic detection methods require high test conditions and struggle to be both sensitive and efficient.In this study,a one-tube dual recombinase polymerase amplification(RPA)reaction system for CP4-EPSPS and Cry1Ab/Ac was proposed and combined with a lateral flow immunochromatographic assay,named“Dual-RPA-LFD”,to visualize the dual detection of genetically modified(GM)crops.In which,the herbicide tolerance gene CP4-EPSPS and the insect resistance gene Cry1Ab/Ac were selected as targets taking into account the current status of the most widespread application of insect resistance and herbicide tolerance traits and their stacked traits.Gradient diluted plasmids,transgenic standards,and actual samples were used as templates to conduct sensitivity,specificity,and practicality assays,respectively.The constructed method achieved the visual detection of plasmid at levels as low as 100 copies,demonstrating its high sensitivity.In addition,good applicability to transgenic samples was observed,with no cross-interference between two test lines and no influence from other genes.In conclusion,this strategy achieved the expected purpose of simultaneous detection of the two popular targets in GM crops within 20 min at 37°C in a rapid,equipmentfree field manner,providing a new alternative for rapid screening for transgenic assays in the field.

Genetic mutation of Tas2r104/Tas2r105/Tas2r114 cluster leads to a loss of taste perception to denatonium benzoate and cucurbitacin B

Background:Bitter taste receptors(Tas2rs)are generally considered to sense various bitter compounds to escape the intake of toxic substances.Bitter taste receptors have been found to widely express in extraoral tissues and have important physiological functions outside the gustatory system in vivo.Methods:To investigate the physiological functions of the bitter taste receptor cluster Tas2r106/Tas2r104/Tas2r105/Tas2r114 in lingual and extraoral tissues,multiple Tas2rs mutant mice and Gnat3 were produced using CRISPR/Cas9 gene-editing technique.A mixture containing Cas9 and sgRNA mRNAs for Tas2rs and Gnat3 gene was microinjected into the cytoplasm of the zygotes.Then,T7EN1 assays and sequencing were used to screen genetic mutation at the target sites in founder mice.Quantitative real-time polymerase chain reaction(qRT-PCR)and immunostaining were used to study the expression level of taste signaling cascade and bitter taste receptor in taste buds.Perception to taste substance was also studied using twobottle preference tests.Results:We successfully produced several Tas2rs and Gnat3 mutant mice using the CRISPR/Cas9 technique.Immunostaining results showed that the expression of GNAT3 and PLCB2 was not altered in Tas2rs mutant mice.But qRT-PCR results revealed the changed expression profile of m Tas2rs gene in taste buds of these mutant mice.With two-bottle preference tests,these mutant mice eliminate responses to cycloheximide due to genetic mutation of Tas2r105.In addition,these mutant mice showed a loss of taste perception to quinine dihydrochloride,denatonium benzoate,and cucurbitacin B(CuB).Gnat3-mediated taste receptor and its signal pathway contribute to CuB perception.Conclusions:These findings implied that these mutant mice would be a valuable means to understand the biological functions of TAS2Rs in extraoral tissues and investigate bitter compound-induced responses mediated by these TAS2Rs in many extraoral tissues.

An Optimized Test Case Minimization Technique Using Genetic Algorithm for Regression Testing

Regression testing is a widely used approach to confirm the correct functionality of the software in incremental development.The use of test cases makes it easier to test the ripple effect of changed requirements.Rigorous testingmay help in meeting the quality criteria that is based on the conformance to the requirements as given by the intended stakeholders.However,a minimized and prioritized set of test cases may reduce the efforts and time required for testingwhile focusing on the timely delivery of the software application.In this research,a technique named Test Reduce has been presented to get a minimal set of test cases based on high priority to ensure that the web applicationmeets the required quality criteria.A new technique TestReduce is proposed with a blend of genetic algorithm to find an optimized and minimal set of test cases.The ultimate objective associated with this study is to provide a technique that may solve the minimization problem of regression test cases in the case of linked requirements.In this research,the 100-Dollar prioritization approach is used to define the priority of the new requirements.

Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

Test Case Prioritization(TCP)techniques perform better than other regression test optimization techniques including Test Suite Reduction(TSR)and Test Case Selection(TCS).Many TCP techniques are available,and their performance is usually measured through a metric Average Percentage of Fault Detection(APFD).This metric is value-neutral because it only works well when all test cases have the same cost,and all faults have the same severity.Using APFD for performance evaluation of test case orders where test cases cost or faults severity varies is prone to produce false results.Therefore,using the right metric for performance evaluation of TCP techniques is very important to get reliable and correct results.In this paper,two value-based TCP techniques have been introduced using Genetic Algorithm(GA)including Value-Cognizant Fault Detection-Based TCP(VCFDB-TCP)and Value-Cognizant Requirements Coverage-Based TCP(VCRCB-TCP).Two novel value-based performance evaluation metrics are also introduced for value-based TCP including Average Percentage of Fault Detection per value(APFDv)and Average Percentage of Requirements Coverage per value(APRCv).Two case studies are performed to validate proposed techniques and performance evaluation metrics.The proposed GA-based techniques outperformed the existing state-of-the-art TCP techniques including Original Order(OO),Reverse Order(REV-O),Random Order(RO),and Greedy algorithm.

Generating of Test Data by Harmony Search Against Genetic Algorithms

Many search-based algorithms have been successfully applied in sev-eral software engineering activities.Genetic algorithms(GAs)are the most used in the scientific domains by scholars to solve software testing problems.They imi-tate the theory of natural selection and evolution.The harmony search algorithm(HSA)is one of the most recent search algorithms in the last years.It imitates the behavior of a musician tofind the best harmony.Scholars have estimated the simi-larities and the differences between genetic algorithms and the harmony search algorithm in diverse research domains.The test data generation process represents a critical task in software validation.Unfortunately,there is no work comparing the performance of genetic algorithms and the harmony search algorithm in the test data generation process.This paper studies the similarities and the differences between genetic algorithms and the harmony search algorithm based on the ability and speed offinding the required test data.The current research performs an empirical comparison of the HSA and the GAs,and then the significance of the results is estimated using the t-Test.The study investigates the efficiency of the harmony search algorithm and the genetic algorithms according to(1)the time performance,(2)the significance of the generated test data,and(3)the adequacy of the generated test data to satisfy a given testing criterion.The results showed that the harmony search algorithm is significantly faster than the genetic algo-rithms because the t-Test showed that the p-value of the time values is 0.026<α(αis the significance level=0.05 at 95%confidence level).In contrast,there is no significant difference between the two algorithms in generating the adequate test data because the t-Test showed that the p-value of thefitness values is 0.25>α.

Advances of Genetic Testing Technology in Etiology Diagnosis of Recurrent Spontaneous Abortion

Recurrent spontaneous abortion (RSA) is a complex and heterogeneous disorder with multiple etiologies. Genetic factors are thought to play an important role in the etiology of RSA. With recent advances in genetic testing technologies, there has been an increasing interest in using these tools to diagnose the etiology of RSA. This review discusses the different types of genetic testing methods, such as karyotyping, chromosomal microarray analysis, next-generation sequencing, and their applications in the diagnosis of the etiology RSA. The use of genetic testing in the diagnosis of RSA has the potential to improve the accuracy of diagnosis and the understanding of the underlying mechanisms of the disorder, which could lead to better management and treatment of affected individuals.

Genetic testing in congenital heart disease:A clinical approach

Congenital heart disease(CHD) is the most common type of birth defect. Traditionally, a polygenic model defined by the interaction of multiple genes and environmental factors was hypothesized to account for different forms of CHD. It is now understood that the contribution of genetics to CHD extends beyond a single unified paradigm. For example, monogenic models and chromosomal abnormalities have been associated with various syndromic and non-syndromic forms of CHD. In such instances, genetic investigation and testing may potentially play an important role in clinical care. A family tree with a detailed phenotypic description serves as the initial screening tool to identify potentially inherited defects and to guide further genetic investigation. The selection of a genetic test is contingent upon the particular diagnostic hypothesis generated by clinical examination. Genetic investigation in CHD may carry the potential to improve prognosis by yielding valuable information with regards to personalized medical care, confidence in the clinical diagnosis, and/or targeted patient followup. Moreover, genetic assessment may serve as a tool to predict recurrence risk, define the pattern of inheritance within a family, and evaluate the need for further family screening. In some circumstances, prenatal or preimplantation genetic screening could identify fetuses or embryos at high risk for CHD. Although genetics may appear to constitute a highly specialized sector of cardiology, basic knowledge regarding inheritance patterns, recurrence risks, and available screening and diagnostic tools, including their strengths and limitations, could assist the treating physician in providing sound counsel.

Genetic programming for predictions of effectiveness of rolling dynamic compaction with dynamic cone penetrometer test results

Rolling dynamic compaction (RDC),which employs non-circular module towed behind a tractor,is an innovative soil compaction method that has proven to be successful in many ground improvement applications.RDC involves repeatedly delivering high-energy impact blows onto the ground surface,which improves soil density and thus soil strength and stiffness.However,there exists a lack of methods to predict the effectiveness of RDC in different ground conditions,which has become a major obstacle to its adoption.For this,in this context,a prediction model is developed based on linear genetic programming (LGP),which is one of the common approaches in application of artificial intelligence for nonlinear forecasting.The model is based on in situ density-related data in terms of dynamic cone penetrometer (DCP) results obtained from several projects that have employed the 4-sided,8-t impact roller (BH-1300).It is shown that the model is accurate and reliable over a range of soil types.Furthermore,a series of parametric studies confirms its robustness in generalizing data.In addition,the results of the comparative study indicate that the optimal LGP model has a better predictive performance than the existing artificial neural network (ANN) model developed earlier by the authors.

Forecasting and Evaluating the Efficiency of Test Generation Algorithms by Genetic Algorithm

To generate a test set for a given circuit (including both combinational and sequential circuits), choice of an algorithm within a number of existing test generation algorithms to apply is bound to vary from circuit to circuit. In this paper, the genetic algorithms are used to construct the models of existing test generation algorithms in making such choice more easily. Therefore, we may forecast the testability parameters of a circuit before using the real test generation algorithm. The results also can be used to evaluate the efficiency of the existing test generation algorithms. Experimental results are given to convince the readers of the truth and the usefulness of this approach.

Predictive potential of IL-28B genetic testing for interferon based hepatitis C virus therapy in Pakistan: Current scenario and future perspective

In Pakistan which ranked second in terms of hepatitis C virus(HCV) infection, it is highly needed to have an established diagnostic test for antiviral therapy responseprediction. Interleukin 28B(IL-28B) genetic testing is widely used throughout the world for interferon based therapy prediction for HCV patients and is quite helpful not only for health care workers but also for the patients. There is a strong relationship between single nucleotide polymorphisms at or near the IL-28 B gene and the sustained virological response with pegylated interferon plus ribavirin treatment for chronic hepatitis C. Pakistan is a resource limited country, with very low per capita income and there is no proper social security(health insurance) system. The allocated health budget by the government is very low and is used on other health emergencies like polio virus and dengue virus infection. Therefore it is proposed that there should be a well established diagnostic test on the basis of IL-28 B which can predict the antiviral therapy response to strengthen health care set-up of Pakistan. This test once established will help in better management of HCV infected patients.

Current scenario of the genetic testing for rare neurological disorders exploiting next generation sequencing

Next generation sequencing is currently a cornerstone of genetic testing in routine diagnostics,allowing for the detection of sequence variants with so far unprecedented large scale,mainly in genetically heterogenous diseases,such as neurological disorders.It is a fast-moving field,where new wet enrichment protocols and bioinformatics tools are constantly being developed to overcome initial limitations.Despite the as yet undiscussed advantages,however,there are still some challenges in data analysis and the interpretation of variants.In this review,we address the current state of next generation sequencing diagnostic testing for inherited human disorders,particularly giving an overview of the available high-throughput sequencing approaches;including targeted,whole-exome and whole-genome sequencing;and discussing the main critical aspects of the bioinformatic process,from raw data analysis to molecular diagnosis.

Genetic test in multiple endocrine neoplasia type 1 syndrome: An evolving story

Multiple endocrine neoplasia type 1(MEN1) is an autosomal dominant inherited tumour syndrome expressing various endocrine and non-endocrine lesions and tumours. Since the identification of the causative gene, the oncosuppressor gene MEN1, in 1997, genetic testing has revealed an important approach for the early and differential diagnosis of the disease. The finding of a MEN1 mutation in a patient has important clinical implications for relatives since it allows very early disease diagnosis and identification of carriers, even before biochemical and/or clinical manifestation, permitting their inclusion in a specific program of surveillance and subsequent praecox therapy. Currently, genetic testing for MEN1 consists principally of the sequencing of coding regions and intron-exon junctions of the MEN1 gene. However, the recent acquisition of novel high throughput technologies will allow the design of innovative, accurate, complete and rapid genetic diagnosis. These new tools are able to increase the strength of the analysis and almost completely eliminate the possibility of false negative results. This review aims to give an overview on genetic testing of MEN1 syndrome, reporting the positive aspects of performing the analysis and the future perspectives for improving the performance of the test, as well as its application in clinical practice.

Genetic testing vs microforceps biopsy in pancreatic cysts:Systematic review and meta-analysis

BACKGROUND Carcinoembryonic antigen(CEA)and cytology in pancreatic cystic fluid are suboptimal for evaluation of pancreatic cystic neoplasms.Genetic testing and microforceps biopsy are promising tools for pre-operative diagnostic improvement but comparative performance of both methods is unknown.AIM To compare the accuracy of genetic testing and microforceps biopsy in pancreatic cysts referred for surgery.METHODS We performed a literature search in Medline,Scopus,and Web of Science for studies evaluating genetic testing of cystic fluid and microforceps biopsy of pancreatic cysts,with endoscopic ultrasound with fine-needle aspiration(EUSFNA)prior to surgery and surgical pathology as reference standard for diagnosis.We evaluated the diagnostic accuracy for:1-benign cysts;2-mucinous low-risk cysts;3-high-risk cysts,and the diagnostic yield and rate of correctly identified cysts with microforceps biopsy and molecular analysis.We also assessed publication bias,heterogeneity,and study quality.RESULTS Eight studies,including 1206 patients,of which 203(17%)referred for surgery who met the inclusion criteria were analyzed in the systematic review,and seven studies were included in the meta-analysis.Genetic testing and microforceps biopsies were identical for diagnosis of benign cysts.Molecular analysis was superior for diagnosis of both low and high-risk mucinous cysts,with sensitivities of 0.89(95%CI:0.79-0.95)and 0.57(95%CI:0.42-0.71),specificities of 0.88(95%CI:0.75-0.95)and 0.88(95%CI:0.80-0.93)and AUC of 0.9555 and 0.92,respectively.The diagnostic yield was higher in microforceps biopsies than in genetic analysis(0.73 vs 0.54,respectively)but the rates of correctly identified cysts were identical(0.73 with 95%CI:0.62-0.82 vs 0.71 with 95%CI:0.49-0.86,respectively).CONCLUSION Genetic testing and microforceps biopsies are useful second tests,with identical results in benign pancreatic cysts.Genetic analysis performs better for low-and high-risk cysts but has lower diagnostic yield.

Should multi-gene panel testing replace limited BRCA1/2 testing? A review of genetic testing for hereditary breast and ovarian cancers

Clinical testing of patients for hereditary breast and ovarian cancer syndromes began in the mid-1990s with the identification of the BRCA1 and BRCA2 genes.Since then,mutations in dozens of other genes have been correlated to increased breast,ovarian,and other cancer risk.The following decades of data collection and patient advocacy allowed for improvements in medical,legal,social,and ethical advances in genetic testing.Technological advances have made it possible to sequence multiple genes at once in a panel to give patients a more thorough evaluation of their personal cancer risk.Panel testing increases the detection of mutations that lead to increased risk of breast,ovarian,and other cancers and can better guide individualized screening measures compared to limited BRCA testing alone.At the same time,multi-gene panel testing is more time-and cost-efficient.While the clinical application of panel testing is in its infancy,many problems arise such as lack of guidelines for management of newly identified gene mutations,high rates of variants of uncertain significance,and limited ability to screen for some cancers.Through on-going concerted efforts of pooled data collection and analysis,it is likely that the benefits of multi-gene panel testing will outweigh the risks in the near future.

Test Case Generation from UML-Diagrams Using Genetic Algorithm

Software testing has been attracting a lot of attention for effective software development.In model driven approach,Unified Modelling Language(UML)is a conceptual modelling approach for obligations and other features of the system in a model-driven methodology.Specialized tools interpret these models into other software artifacts such as code,test data and documentation.The generation of test cases permits the appropriate test data to be determined that have the aptitude to ascertain the requirements.This paper focuses on optimizing the test data obtained from UML activity and state chart diagrams by using Basic Genetic Algorithm(BGA).For generating the test cases,both diagrams were converted into their corresponding intermediate graphical forms namely,Activity Diagram Graph(ADG)and State Chart Diagram Graph(SCDG).Then both graphs will be combined to form a single graph called,Activity State Chart Diagram Graph(ASCDG).Both graphs were then joined to create a single graph known as the Activity State Chart Diagram Graph(ASCDG).Next,the ASCDG will be optimized using BGA to generate the test data.A case study involving a withdrawal from the automated teller machine(ATM)of a bank was employed to demonstrate the approach.The approach successfully identified defects in various ATM functions such as messaging and operation.

Automatic Test Data Generation for Java Card Applications Using Genetic Algorithm

The main objective of software testing is to have the highest likelihood of finding the most faults with a minimum amount of time and effort. Genetic Algorithm (GA) has been successfully used by researchers in software testing to automatically generate test data. In this paper, a GA is applied using branch coverage criterion to generate the least possible set of test data to test JSC applications. Results show that applying GA achieves better performance in terms of average number of test data?generations, execution time, and percentage of branch coverage.

Clinicopathological analysis of early-stage breast cancer patients that meet indications for BRCA 1/2 genetic testing

Objective:To investigate the clinicopathological characteristics and prognostic factors of early-stage breast cancer patients with indications for breast cancer susceptibility genes 1/2(BRCA1/2)genetic testing in China.Methods:Based on the indication criteria for BRCA genetic testing specified in the National Comprehensive Cancer Network(NCCN)clinical practice guidelines in oncology,genetic/familial high-risk assessment:Breast and ovarian(Version 2.2019),a retrospective analysis was performed on patients with early-stage invasive breast cancer treated at Breast Disease Center,Peking University First Hospital between January 2008 and December 2016.Clinicopathological characteristics of all patients were analyzed,and prognoses were calculated using the KaplanMeier method and a Cox proportionate hazards model.Results:A total of 906 early-stage breast cancer patients who had indications for BRCA genetic testing and had complete clinicopathological data and follow-up information were included in the study group,accounting for34.7%of all breast cancer patients treated in Breast Disease Center,Peking University First Hospital during the study period.Compared with breast cancer patients without indications for BRCA genetic testing,the overall survival(OS)and disease-free survival(DFS)of patients with indications were not significantly different.In the study group,patients with premenopausal status,high T stage,lymph node positive,estrogen receptor(ER)negative,Ki-67>20%and presence of a vascular tumor thrombus had worse prognosis.There were more family histories of gastrointestinal cancer in patients with related indications than in patients without such indications.Conclusions:Single-center data showed that more than 30%of patients with early-stage breast cancer had indications for BRCA genetic testing.There was no prognostic difference in patients with or without indications for BRCA genetic testing.Premenopausal status,high T stage,lymph node positive,ER negative,Ki-67>20%,and presence of a vascular tumor thrombus were associated with poor prognosis.

A hybrid algorithm based on ILP and genetic algorithm for time-aware test case prioritization

To solve the problem of time-awarc test case prioritization,a hybrid algorithm composed of integer linear programming and the genetic algorithm(ILP-GA)is proposed.First,the test case suite which cm maximize the number of covered program entities a d satisfy time constraints is selected by integer linea progamming.Secondly,the individual is encoded according to the cover matrices of entities,and the coverage rate of program entities is used as the fitness function and the genetic algorithm is used to prioritize the selected test cases.Five typical open source projects are selected as benchmark programs.Branch and method are selected as program entities,and time constraint percentages a e 25%and 75%.The experimental results show that the ILP-GA convergence has faster speed and better stability than ILP-additional and IP-total in most cases,which contributes to the detection of software defects as early as possible and reduces the software testing costs.