Contextual Predictive Mutation Testing

Mutation testing is a powerful technique for assessing and improving test suite quality that artificially introduces bugs and checks whether the test suites catch them. However, it is also computationally expensive and thus does not scale to large systems and projects. One promising recent approach to tackling this scalability problem uses machine learning to predict whether the tests will detect the synthetic bugs, without actually running those tests. However, existing predictive mutation testing approaches still misclassify 33% of detection outcomes on a randomly sampled set of mutant-test suite pairs. We introduce MutationBERT, an approach for predictive mutation testing that simultaneously encodes the source method mutation and test method, capturing key context in the input representation. Thanks to its higher precision, MutationBERT saves 33% of the time spent by a prior approach on checking/verifying live mutants. MutationBERT, also outperforms the state-of-the-art in both same project and cross project settings, with meaningful improvements in precision, recall, and F1 score. We validate our input representation, and aggregation approaches for lifting predictions from the test matrix level to the test suite level, finding similar improvements in performance. MutationBERT not only enhances the state-of-the-art in predictive mutation testing, but also presents practical benefits for real-world applications, both in saving developer time and finding hard to detect mutants

22 years of predictive testing for Huntington's disease: the experience of the UK Huntington's Prediction Consortium

Huntington’s disease (HD) is a progressive neurodegenerative condition. At-risk individuals have accessed predictive testing via direct mutation testing since 1993. The UK Huntington’s Prediction Consortium has collected anonymised data on UK predictive tests, annually, from 1993 to 2014: 9407 predictive tests were performed across 23 UK centres. Where gender was recorded, 4077 participants were male (44.3%) and 5122 were female (55.7%). The median age of participants was 37 years. The most common reason for predictive testing was to reduce uncertainty (70.5%). Of the 8441 predictive tests on individuals at 50% prior risk, 4629 (54.8%) were reported as mutation negative and 3790 (44.9%) were mutation positive, with 22 (0.3%) in the database being uninterpretable. Using a prevalence figure of 12.3 × 10−5, the cumulative uptake of predictive testing in the 50% at-risk UK population from 1994 to 2014 was estimated at 17.4% (95% CI: 16.9–18.0%). We present the largest study conducted on predictive testing in HD. Our findings indicate that the vast majority of individuals at risk of HD (>80%) have not undergone predictive testing. Future therapies in HD will likely target presymptomatic individuals; therefore, identifying the at-risk population whose gene status is unknown is of significant public health value

Predictive Mutation Testing

IEEE Test suites play a key role in ensuring software quality. A good test suite may detect more faults than a poor-quality one. Mutation testing is a powerful methodology for evaluating the fault-detection ability of test suites. In mutation testing, a large number of mutants may be generated and need to be executed against the test suite under evaluation to check how many mutants the test suite is able to detect, as well as the kind of mutants that the current test suite fails to detect. Consequently, although highly effective, mutation testing is widely recognized to be also computationally expensive, inhibiting wider uptake. To alleviate this efficiency concern, we propose Predictive Mutation Testing (PMT): the first approach to predicting mutation testing results without executing mutants. In particular, PMT constructs a classification model, based on a series of features related to mutants and tests, and uses the model to predict whether a mutant would be killed or remain alive without executing it. PMT has been evaluated on 163 real-world projects under two application scenarios (cross-version and cross-project). The experimental results demonstrate that PMT improves the efficiency of mutation testing by up to 151.4X while incurring only a small accuracy loss. It achieves above 0.80 AUC values for the majority of projects, indicating a good tradeoff between the efficiency and effectiveness of predictive mutation testing. Also, PMT is shown to perform well on different tools and tests, be robust in the presence of imbalanced data, and have high predictability (over 60% confidence) when predicting the execution results of the majority of mutants

The Psychosocial Effects of Next Generation Sequence Panels for Predictive Testing of Hereditary Dementias

The current standard of care in offering predictive genetic testing for neurodegenerative diseases is that individuals wishing to have testing must have a known family mutation or well-documented family history of a specific disease. This model denies testing to individuals in families where the phenotype of the disease is less clear. However, NGS panel testing for many genes with overlapping phenotypes helps alleviate both the cost and tedious nature of a genetic “fishing expedition.” Panel testing increases the risk of receiving variants of unknown significance and, therefore, uncertainty. The goal of this research study is to examine the psychological impact of predictive testing of neurodegenerative disease using NGS panels. Methods: This pilot study looked at 15 at-risk participants with a family mutation and 8 without a known family mutation. Participants were evaluated serially: before testing, at 1 month and at 6 months after receiving results. Instruments measuring levels of anxiety, depression, ability to deal with uncertainty, coping strategies, perceived personal control, and rumination were used to evaluate the psychological impact of testing on the 2 groups. Results: No significant differences were found between the two groups. A noted trend was an increase in uncertainty after testing among those with a known mutation and a small decrease among those without a known mutation. Statistical significance was not observed due to small number of participants. Initial data suggest that predictive testing for neurodegenerative disease in individuals with a family history does not result in psychological distress. The study is ongoing

The impact of an interventional counselling procedure in families with a BRCA1/2 gene mutation : efficacy and safety

Background: Predictive genetic testing has high impact on cancer prevention for BRCA carriers and passing this information in BRCA families is important. Mostly, this is proband-mediated but this path is defective and denies relatives lifesaving information. Objective: To assess the efficacy/safety of an intervention, in which relatives are actively informed. Design: Sequential prospective study in new BRCA families. The proband informed relatives about predictive testing (phase I). After 6 months, a letter was sent to adult relatives who had not been reached (phase II). Then a phone call was made to obtain a final notion of their wishes. All subjects received psychometric testing (State-Trait Anxiety Inventory, STAI), an interview and routine counselling. Results: Twenty families were included. Twenty-four of the relatives could not be reached, 59 were 'decliners', 47 participated by the proband and 42 by the letter. Predictive testing was performed in 98 % of the participants of which 30 were mutation carriers. The intervention is psychologically safe: the 95 % CI for the estimated mean difference in STAI DY1 between phase II/I subjects (mean difference -1.07, 95 % CI -4.4 to 2.35, p = 0.53) shows that the mean STAI DY1 score (measured at first consult) for phase II is no more than 2.35 units higher than for phase I, which is not relevant. Conclusions: A protocol directly informing relatives nearly doubles the number of relatives tested and is psychologically safe. This should lead to a change in counselling guidelines in families with a strong germline predisposition for cancer

RE: Universal tumor DNA BRCA1/2 testing of ovarian cancer: prescreening PARPi treatment and genetic predisposition

BRCA1/2 mutations play a predictive role in ovarian cancer risk evaluation. Moreover , patients are today being tested for BRCA1/2 mutations to select a tailored therapy too , because they could benefit from a treatment with PARP inhibitors (PARPi).Therefore in ovarian carcinomas (OCs), BRCA1/2 mutation testing is an important step in planning the correct therapeutic strategy in association with chemotherapy and anti VEGF agents. We read with great interest the paper submitted by Vos et al (1) which investigates the role of universal tumor DNA BRCA1/2 testing of all newly diagnosed OC patients as prescreen for PARPi treatment and cancer predisposition. With the approach described by the authors, both hereditary and somatic aberrations affecting DNA BRCA1/2 could be quickly and correctly detected with tumor BRCA1/2 smMIP-based next generation sequence testing

Fertility intentions following testing for a BRCA1 gene mutation

Journal ArticleObjective: To test whether fertility intentions differed among persons who tested positive, tested negative, or did not know their genetic status for a mutation of the BRCA1 gene. Method: Participants were members of a large Utah-based kindred with an identified mutation at the BRCA1 locus. Participants received genetic counseling prior to testing and were interviewed at baseline before testing and at three points after receiving test results from a genetic counselor. The sample included men and women who completed all interviews, were between ages 18 and 45, and were fertile, resulting in a sample of 101 respondents. The primary dependent variable measured whether a subject indicated that they were moderately or very sure at all three post-testing interviews that they intended to have additional children. Effects of BRCA1 mutation status on fertility intentions were estimated using multivariate logistic regressions where we controlled for gender, age, marital status, and baseline fertility intentions. Results: Female carriers were less likely to want additional children in relation to female noncarriers (odds ratio 0.12, 95% confidence interval 0.01-1.23; P = 0.074). No differences were found among men. There was a significant difference in the effect of mutation status on fertility intentions between males and females (Gender _x0001_ Carrier status interaction; P = 0.009). Persons who did not know their mutation status were less likely to want more children than noncarriers (odds ratio 0.09, 95% confidence interval 0.01-0.75; P = 0.027). Conclusion: Predictive genetic testing for late-onset cancer susceptibility affects family planning decision-making. Persons contemplating predictive testing should be informed about possible effects such testing may have on their plans for future fertility

Nationwide evaluation of mutation-tailored treatment of gastrointestinal stromal tumors in daily clinical practice

Background Molecular analysis of KIT and PDGFRA is critical for tyrosine kinase inhibitor treatment selection of gastrointestinal stromal tumors (GISTs) and hence recommended by international guidelines. We performed a nationwide study into the application of predictive mutation testing in GIST patients and its impact on targeted treatment decisions in clinical practice. Methods Real-world clinical and pathology information was obtained from GIST patients with initial diagnosis in 2017-2018 through database linkage between the Netherlands Cancer Registry and the nationwide Dutch Pathology Registry. Results Predictive mutation analysis was performed in 89% of the patients with high risk or metastatic disease. Molecular testing rates were higher for patients treated in expertise centers (96%) compared to non-expertise centers (75%, P < 0.01). Imatinib therapy was applied in 81% of the patients with high risk or metastatic disease without patient's refusal or adverse characteristics, e.g., comorbidities or resistance mutations. Mutation analysis that was performed in 97% of these imatinib-treated cases, did not guarantee mutation-tailored treatment: 2% of these patients had the PDGFRA p.D842V resistance mutation and 7% initiated imatinib therapy at the normal instead of high dose despite of having a KIT exon 9 mutation. Conclusion In conclusion, nationwide real-world data show that over 81% of the eligible high risk or metastatic disease patients receive targeted therapy, which was tailored to the mutation status as recommended in guidelines in 88% of cases. Therefore, still 27% of these GIST patients misses out on mutation-tailored treatment. The reasons for suboptimal uptake of testing and treatment require further study

Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic 18FFET PET radiomics

PURPOSE To evaluate radiomic features extracted from standard static images (20-40~min p.i.), early summation images (5-15~min p.i.), and dynamic 18FFET PET images for the prediction of TERTp-mutation status in patients with IDH-wildtype high-grade glioma. METHODS A total of 159 patients (median age 60.2~years, range 19-82~years) with newly diagnosed IDH-wildtype diffuse astrocytic glioma (WHO grade III or IV) and dynamic 18FFET PET prior to surgical intervention were enrolled and divided into a training (n = 112) and a testing cohort (n = 47) randomly. First-order, shape, and texture radiomic features were extracted from standard static (20-40~min summation images; TBR20-40), early static (5-15~min summation images; TBR5-15), and dynamic (time-to-peak; TTP) images, respectively. Recursive feature elimination was used for feature selection by 10-fold cross-validation in the training cohort after normalization, and logistic regression models were generated using the radiomic features extracted from each image to differentiate TERTp-mutation status. The areas under the ROC curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive value were calculated to illustrate diagnostic power in both the training and testing cohort. RESULTS The TTP model comprised nine selected features and achieved highest predictability of TERTp-mutation with an AUC of 0.82 (95{\%} confidence interval 0.71-0.92) and sensitivity of 92.1{\%} in the independent testing cohort. Weak predictive capability was obtained in the TBR5-15 model, with an AUC of 0.61 (95{\%} CI 0.42-0.80) in the testing cohort, while no predictive power was observed in the TBR20-40 model. CONCLUSIONS Radiomics based on TTP images extracted from dynamic 18FFET PET can predict the TERTp-mutation status of IDH-wildtype diffuse astrocytic high-grade gliomas with high accuracy preoperatively