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1. Mutation, the source of genetic diversity, is the raw material of evolution; however, the mutation process remains understudied, especially in plants. Using both a simulation and reanalysis framework, we set out to explore and demonstrate the improved performance of variant callers developed for cancer research compared to single nucleotide polymorphism (SNP) callers in detecting de novo somatic mutations. 2. In an in silico experiment, we generated Illumina-like sequence reads spiked with simulated mutations at different allelic fractions to compare the performance of seven commonly-used variant callers to recall them. More empirically, we then reanalyzed two of the largest datasets available for plants, both developed for identifying within-individual variation in long-lived pedunculate oaks. 3. Based on the in silico experiment, variant callers developed for cancer research outperform SNP callers regarding plant mutation recall and precision, especially at low allele frequency. Such variants at low allelic fractions are typically expected for within-individual de novo plant mutations, which initially appear in single cells. Reanalysis of published oak data with Strelka2, the best-performing caller based on our simulations, identified up to 3.4x more candidate somatic mutations than reported in the original studies. 4. Our results advocate the use of cancer research callers to boost de novo mutation research in plants, and to reconcile empirical reports with theoretical expectations.

Abstract Social media have emerged as increasingly popular means and environments for information gathering and propagation. This vigorous growth of social media contributed not only to a pandemic (fast-spreading and far-reaching) of rumors and misinformation, but also to an urgent need for text-based rumor detection strategies. To speed up the detection of misinformation, traditional rumor detection methods based on hand-crafted feature selection need to be replaced by automatic artificial intelligence (AI) approaches. AI decision making systems require to provide explanations in order to assure users of their trustworthiness. Inspired by the thriving development of generative adversarial networks (GANs) on text applications, we propose a GAN-based layered model for rumor detection with explanations. To demonstrate the universality of the proposed approach, we demonstrate its benefits on a gene classification with mutation detection case study. Similarly to the rumor detection, the gene classification can also be formulated as a text-based classification problem. Unlike fake news detection that needs a previously collected verified news database, our model provides explanations in rumor detection based on tweet-level texts only without referring to a verified news database. The layered structure of both generative and discriminative models contributes to the outstanding performance. The layered generators produce rumors by intelligently inserting controversial information in non-rumors, and force the layered discriminators to detect detailed glitches and deduce exactly which parts in the sentence are problematic. On average, in the rumor detection task, our proposed model outperforms state-of-the-art baselines on PHEME dataset by $$26.85\%$$ 26.85 % in terms of macro-f1. The excellent performance of our model for textural sequences is also demonstrated by the gene mutation case study on which it achieves $$72.69\%$$ 72.69 % macro-f1 score.

BackgroundThis article describes a pilot study evaluating a novel liquid biopsy system for non-small cell lung cancer (NSCLC) patients. The electric field-induced release and measurement (EFIRM) method utilizes an electrochemical biosensor for detecting oncogenic mutations in biofluids.MethodsSaliva and plasma of 17 patients were collected from three cancer centers prior to and after surgical resection. The EFIRM method was then applied to the collected samples to assay for exon 19 deletion and p.L858 mutations. EFIRM results were compared with cobas results of exon 19 deletion and p.L858 mutation detection in cancer tissues.ResultsThe EFIRM method was found to detect exon 19 deletion with an area under the curve (AUC) of 1.0 in both saliva and plasma samples in lung cancer patients. For L858R mutation detection, the AUC of saliva was 1.0, while the AUC of plasma was 0.98. Strong correlations were also found between presurgery and post-surgery samples for both saliva (0.86 for exon 19 and 0.98 for L858R) and plasma (0.73 for exon 19 and 0.94 for L858R).ConclusionOur study demonstrates the feasibility of utilizing EFIRM to rapidly, non-invasively, and conveniently detect epidermal growth factor receptor mutations in the saliva of patients with NSCLC, with results corresponding perfectly with the results of cobas tissue genotyping.

Purpose: There is a paucity of consistent data concerning genetic mutations in Brazilian patients with lung cancer. The aim of this study was to retrospectively analyze epidermal growth factor receptor (EGFR) mutations detected in a real-world scenario using a large cohort of Brazilian patients with non-small-cell lung cancer (NSCLC)., Materials and Methods: This was a cross-sectional, observational, descriptive study on the basis of a database of EGFR molecular analysis from tumor samples of patients with a confirmatory histopathological diagnosis of primary lung cancer. Specimens were collected from 2013 to 2017 and were tested using cobas, next-generation sequencing, and Sanger sequencing platforms., Results: A total of 7,413 tumor specimens were tested. The patients were predominantly women with a median age of 67.0 years. Patients with at least one mutation represented 24.2% of the total sample. Among the positive patients, the majority had just one mutation, but two or more simultaneous mutations were observed in 1.52% of patients. Exon 19 deletion was the most prevalent alteration in the sample (12.8%), followed by exon 21 L858R (6.9%) and exon 20 insertion (1.6%). All others were considered uncommon mutations and were observed in 18.5% of all mutated patients and 4.0% of the total sample (2.3%-18.7% depending on the sequencing method)., Conclusion: This study examined the prevalence of EGFR mutations in Brazilian patients with NSCLC using different technologies, suggesting that the type of method used, directed or nondirected against specific mutations, influences the analysis, particularly for uncommon mutations, which will be missed by mutation-specific approaches such as cobas testing. Our estimates are the largest in Latin America and are consistent with previous reports from other parts of the world. Besides the variability in methods described here as technology incorporation advances in a nonhomogeneous manner, it is probably like the real-world clinical setting Brazilian oncologists face in their daily practice.

Abstract Background High resolution melting curve analysis is a cost-effective rapid screening method for detection of somatic gene mutation. The performance characteristics of this technique has been explored previously, however, analytical parameters such as limit of detection of mutant allele fraction and total concentration of DNA, have not been addressed. The current study focuses on comparing the mutation detection efficiency of High-Resolution Melt Analysis (HRM) with Sanger Sequencing in somatic mutations of the EGFR gene in non-small cell lung cancer. Methods The minor allele fraction of somatic mutations was titrated against total DNA concentration using Sanger sequencing and HRM to determine the limit of detection. The mutant and wildtype allele fractions were validated by multiplex allele-specific real-time PCR. Somatic mutation detection efficiency, for exons 19 & 21 of the EGFR gene, was compared in 116 formalin fixed paraffin embedded tumor tissues, after screening 275 tumor tissues by Sanger sequencing. Results The limit of detection of minor allele fraction of exon 19 mutation was 1% with sequencing, and 0.25% with HRM, whereas for exon 21 mutation, 0.25% MAF was detected using both methods. Multiplex allele-specific real-time PCR revealed that the wildtype DNA did not impede the amplification of mutant allele in mixed DNA assays. All mutation positive samples detected by Sanger sequencing, were also detected by HRM. About 28% cases in exon 19 and 40% in exon 21, detected as mutated in HRM, were not detected by sequencing. Overall, sensitivity and specificity of HRM were found to be 100 and 67% respectively, and the negative predictive value was 100%, while positive predictive value was 80%. Conclusion The comparative series study suggests that HRM is a modest initial screening test for somatic mutation detection of EGFR, which must further be confirmed by Sanger sequencing. With the modification of annealing temperature of initial PCR, the limit of detection of Sanger sequencing can be improved.

Low-level tumor somatic DNA mutations in tissue and liquid biopsies obtained from cancer patients can have profound implications for development of metastasis, prognosis, choice of treatment, follow-up, or early cancer detection. Unless detected, such low-frequency DNA alterations can misinform patient management decisions or become missed opportunities for personalized medicine. Next-generation sequencing technologies and digital-PCR can resolve low-level mutations but require access to specialized instrumentation, time, and resources. Enzymatic-based approaches to detection of low-level mutations provide a simple, straightforward, and affordable alternative to enrich and detect such alterations and is broadly available to low-resource laboratory settings. This review summarizes the traditional uses of enzymatic mutation detection and describes the latest exciting developments, potential, and applications with specific reference to the field of liquid biopsy in cancer. [ABSTRACT FROM AUTHOR]

(1) Background: DNA damage response (DDR) pathway gene mutations are detectable in a significant number of patients with metastatic castration-resistant prostate cancer (mCRPC). The study aimed at identification of germline and/or somatic DDR mutations in blood and urine samples from patients with mCRPC for correlation with responses to entire sequence of systemic treatment and survival outcomes. (2) Methods: DDR gene mutations were assessed prospectively in DNA samples from leukocytes and urine sediments from 149 mCRPC patients using five-gene panel targeted sequencing. The impact of DDR status on progression-free survival, as well as treatment-specific and overall survival, was evaluated using Kaplan–Meier curves and Cox regression. (3) Results: DDR mutations were detected in 16.6% of urine and 15.4% of blood samples. BRCA1, BRCA2, CHEK2, ATM and NBN mutations were associated with significantly shorter PFS in response to conventional androgen deprivation therapy and first-line mCRPC therapy with abiraterone acetate. Additionally, BRCA1 and BRCA2 mutation-bearing patients had a significantly worse response to radium-223. However, DDR mutation status was predictive for the favourable effect of second-line abiraterone acetate after previous taxane-based chemotherapy. (4) Conclusions: Our data confirm the benefit of non-invasive urine-based genetic testing for timely identification of high-risk prostate cancer cases for treatment personalization.

As whole-genome sequencing for cancer genome analysis becomes a clinical tool, a full understanding of the variables affecting sequencing analysis output is required. Here using tumour-normal sample pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, we conduct a benchmarking exercise within the context of the International Cancer Genome Consortium. We compare sequencing methods, analysis pipelines and validation methods. We show that using PCR-free methods and increasing sequencing depth to ∼ 100 × shows benefits, as long as the tumour:control coverage ratio remains balanced. We observe widely varying mutation call rates and low concordance among analysis pipelines, reflecting the artefact-prone nature of the raw data and lack of standards for dealing with the artefacts. However, we show that, using the benchmark mutation set we have created, many issues are in fact easy to remedy and have an immediate positive impact on mutation detection accuracy.

Yanhua Song,1 Guirong Wang,2 Qiang Li,1 Rongmei Liu,1 Liping Ma,1 Qi Li,3 Mengqiu Gao1 1Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University, Beijing 101149, People’s Republic of China; 2National Clinical Laboratory on Tuberculosis, Beijing Key Laboratory on Drug-Resistant Tuberculosis Research, Beijing Chest Hospital, Capital Medical University, Beijing 101149, People’s Republic of China; 3Clinical Center on Tuberculosis, Beijing Chest Hospital, Capital Medical University, Beijing 101149, People’s Republic of ChinaCorrespondence: Qi LiClinical Center on Tuberculosis, Beijing Chest Hospital, Capital Medical University, Beijing 101149, People’s Republic of ChinaTel +86 10 89509322Fax +86 1069546790Email liqi88_dr@163.comMengqiu GaoDepartment of Tuberculosis, Beijing Chest Hospital, Capital Medical University, Beijing 101149, People’s Republic of ChinaTel +86 10 89509322Fax +86 1069546790Email gaomengqi55_dr@163.comObjective: This study aims to analyze the correlation between gene inhA mutations by melting curve technology and phenotypic drug susceptibility (DST) results of ethionamide (ETH), and evaluate whether gene inhA mutations detection can serve as a molecular marker in predicting ETH resistance.Methods: A retrospective analysis was conducted on 382 strains of Mycobacterium tuberculosis (MTB) with the anti-tuberculosis drugs isoniazid (INH), rifampicin (RIF), ETH, and others. Phenotypic drug susceptibility and the results of inhA and katG genotypes (mutation and no mutation) were obtained using the melting curve technology MeltPro TB assay.Results: Of the 382 clinical strains of MTB tested, 118 (30.9%) were resistant to INH, and 28 (7.3%) were resistant to ETH. Among the 28 phenotypically ETH-resistant strains, inhA mutations accounted for 42.9% (12/28). These ETH-resistant strains comprise 35.3% (12/34) of the 34 inhA mutant strains. Of 8 single inhA mutation strains (without katG or rpoB mutation), 4(50%) were resistant to INH; however, all of these 8 strains were sensitive to ETH.Conclusion: The inhA mutation test may not be a reliable predictor of ETH resistance. Mutant inhA strains are not necessarily resistant to ETH. The strains with single inhA mutation (without katG or rpoB mutation) may be effective for ETH treatment. The use of ETH in clinical medicine should be guided by gene (other than inhA alone) detection and phenotypic drug susceptibility testing.Keywords: melting curve technique, Mycobacterium tuberculosis, drug resistance, inhA, gene mutation, prothionamide, ethionamide

Background: The concept of liquid biopsy as a surrogate for tumor tissue is not only restricted to blood-derived samples but also includes other malignant bodily fluids such as cerebrospinal fluid, pleural fluid, pericardial fluid, and ascites. The application of liquid biopsy in patients with metastatic lung cancer, with disease progression on epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) treatment, is unexplored in India. Objective: We aimed to determine the presence of EGFR gene mutations in various bodily fluids of patients with EGFR-mutant progressive metastatic lung adenocarcinoma. Materials and Methods: In this retrospective study conducted at the Tata Memorial Hospital in Mumbai, India, data from 11 patients with advanced EGFR-mutant lung adenocarcinoma were analyzed. Between December 2017 and June 2018, the cell-free DNA (cfDNA) obtained from the malignant bodily fluids and plasma of 11 patients were tested for the presence of EGFR-sensitizing mutations using real-time reverse transcription-polymerase chain reaction. The status of EGFR-sensitizing mutations in plasma and other bodily fluids was compared with that of the baseline tissue biopsy. In addition, the status of the acquired T790M resistance mutation in exon 20 at disease progression was determined using plasma and other bodily fluids. Results: Of the 11 patients, at disease progression, EGFR sensitizing mutations (exon 19 deletion and exon 21 L858R insertion) could be detected in the plasma of 7 (63%) and in other bodily fluids of all the 11 (100%) patients. Thus, the concordance for EGFR-sensitizing mutation detection in the plasma and other bodily fluids with the baseline formalin-fixed paraffin-embedded tissue was 63% and 100%, respectively. In addition, the acquired T790M resistance mutation was detected in the plasma of 3 (27.3%) patients and other bodily fluids of 9 (81.8%) patients. Conclusion: The rate of detection of the T790M mutation is higher in the free DNA obtained from malignancy-involved bodily fluid supernatant as compared to the plasma cfDNA. Our results suggest that cfDNA obtained from bodily fluids other than plasma can be used for the detection of EGFR mutations and that the mutation status may be a useful predictor of the response to TKIs.

Abstract This investigation facilitates a better understanding of inhibitor development, the critical treatment morbidity in HA patients. Furthermore, six novel mutations are reported, which would expand the mutation spectrum of the F8 gene.

The impact of variant calling algorithms on the analysis of intra-tumour heterogeneity has not been properly quantified. Here the authors measure the variability of 22 pipelines with different variant callers and clustering algorithms for subclonal reconstruction to inform future analyses.