Mutation Database Research Articles

A framework for automated gene selection in genomic applications

PurposeAn efficient framework to identify disease-associated genes is needed to evaluate genomic data for both individuals with an unknown disease etiology and those undergoing genomic screening. Here, we propose a framework for gene selection used in genomic analyses, including applications limited to genes with strong or established evidence levels and applications including genes with less or emerging evidence of disease association. MethodsWe extracted genes with evidence for gene–disease association from the Human Gene Mutation Database, OMIM, and ClinVar to build a comprehensive gene list of 6,145 genes. Next, we applied stringent filters in conjunction with computationally curated evidence (DisGeNET) to create a restrictive list limited to 3,929 genes with stronger disease associations. ResultsWhen compared to manual gene curation efforts, including the Clinical Genome Resource, genes with strong or definitive disease associations are included in both gene lists at high percentages, while genes with limited evidence are largely removed. We further confirmed the utility of this approach in identifying pathogenic and likely pathogenic variants in 45 genomes. ConclusionOur approach efficiently creates highly sensitive gene lists for genomic applications, while remaining dynamic and updatable, enabling time savings in genomic applications.

Analysis of the HEXA, HEXB, ARSA, and SMPD1 Genes in 68 Iranian Patients.

Lysosomal storage diseases (LSDs) are known as genetic disorders with an overall prevalence of 1 per 7700 live births. Sphingolipidosis, which is a subgroup of LSDs, is resulted from mutations in the coding genes of specific enzymes of sphingolipid hydrolases. The current study aimed to provide additional knowledge on the genotype of sphingolipidoses disease among Iranian patients affected by the disease. In this research, we studied 68 unrelated Iranian patients diagnosed with one kind of sphingolipidoses from 2014 to 2019. Thereafter, genomic DNA was isolated from their peripheral blood leukocytes samples in EDTA in terms of the manufacturer's protocol. All the coding exons and exon-intron boundaries of the related genes were sequenced and then analyzed using the NCBI database. Finally, they were reviewed using some databases such as the Human Gene Mutation Database (HGMD) and ClinVar ( https://www.ncbi.nlm.nih.gov/clinva ). By studying 22 MLD patients, 18 different variations of the ARSA gene were found, one of which was new including, named as c.472T > G p. (Cys158Gly). Out of 15 Sandhoff disease (SD) patients, 11 different variations of the HEXB gene were found. Correspondingly, the c.1083-2delA was not reported earlier. By investigating 21 Iranian patients with Tay-Sachs disease (TSD), one new variant was found as c.622delG. The study of 10 Niemann-Pick disease A/B (NPDA/B (patients has led to the identification of 9 different SMPD1 gene variations, among which 3 variations were novel mutations. The results of the present study can be expanded to the genotypic spectrum of Iranian patients with MLD, SD, TSD, and NPD diseases and also used to innovate more effective methods for the detection of genetic carriers as well as diagnosing and counseling of Iranian patients affected with these disorders.

ViMIC: a database of human disease-related virus mutations, integration sites and cis-effects.

Molecular mechanisms of virus-related diseases involve multiple factors, including viral mutation accumulation and integration of a viral genome into the host DNA. With increasing attention being paid to virus-mediated pathogenesis and the development of many useful technologies to identify virus mutations (VMs) and viral integration sites (VISs), much research on these topics is available in PubMed. However, knowledge of VMs and VISs is widely scattered in numerous published papers which lack standardization, integration and curation. To address these challenges, we built a pilot database of human disease-related Virus Mutations, Integration sites and Cis-effects (ViMIC), which specializes in three features: virus mutation sites, viral integration sites and target genes. In total, the ViMIC provides information on 31 712 VMs entries, 105 624 VISs, 16 310 viral target genes and 1 110 015 virus sequences of eight viruses in 77 human diseases obtained from the public domain. Furthermore, in ViMIC users are allowed to explore the cis-effects of virus-host interactions by surveying 78 histone modifications, binding of 1358 transcription regulators and chromatin accessibility on these VISs. We believe ViMIC will become a valuable resource for the virus research community. The database is available at http://bmtongji.cn/ViMIC/index.php.

Pre-Cancer Diagnosis via TP53 Gene Mutations Applied Ensemble Algorithms

According to current study, individuals with cancer who have a gene mutation have a bad prognosis. Young women with breast cancer had a poorer prognosis than older women, although it is unknown if the p53 gene mutation contributed to this. Due in part to the devastation of cancer, the appropriate technology may help cancer sufferers in regaining their lives. Researchers seek for mutations in cancer-causing gene sequences in order to identify the precancerous stage. While genetic testing may be used to forecast some kinds of cancer, there is presently no effective technique for identifying all cancers caused by TP53 gene mutations. It is one of the most often discovered genetic anomalies in human cancer is a malfunction in the action of the protein P53. As a consequence, the Universal Mutation Database is used to identify gene mutations (UMDCell-line2010).
 The issue is that, although many basic databases (for example, Excel format databases) exist that include datasets of TP53 gene mutations associated with disease (cancer), this huge database is incapable of detecting cancer. Thus, the purpose the objective of this study is to create an approach for data mining that utilizes a neural network to ascertain the pre-cancerous state. To begin, bioinformatics techniques such as BLAST, CLUSTALW, and NCBI were used to determine whether or not there were any malignant mutations; second, the proposed method was carried out in two stages: to begin, bioinformatics techniques such as BLAST, CLUSTALW, and NCBI were used to determine whether or not there were any malignant mutations; and third, the proposed method was carried out in two stages: to begin, bioinformatics techniques such as To begin, bioinformatics tools such as BLAST and CLUSTAL
 Vote Algorithms were utilized to classify pre-cancer by malignant mutations in the disease's early stages. The writers teach and evaluate their subjects using a variety of situations, including cross validation and percentages. This page contains a review of the algorithms discussed before.

Evolutionary and structural constraints influencing apolipoprotein A-I amyloid behavior.

Apolipoprotein A-I (apoA-I) has a key function in the reverse cholesterol transport. However, aggregation of apoA-I single point mutants can lead to hereditary amyloid pathology. Although several studies have tackled the biophysical and structural consequences introduced by these mutations, there is little information addressing the relationship between the evolutionary and structural features that contribute to the amyloid behavior of apoA-I. We combined evolutionary studies, in silico mutagenesis and molecular dynamics (MD) simulations to provide a comprehensive analysis of the conservation and pathogenic role of the aggregation-prone regions (APRs) present in apoA-I. Sequence analysis demonstrated that among the four amyloidogenic regions described for human apoA-I, only two (APR1 and APR4) are evolutionary conserved across different species of Sarcopterygii. Moreover, stability analysis carried out with the FoldX engine showed that APR1 contributes to the marginal stability of apoA-I. Structural properties of full-length apoA-I models suggest that aggregation is avoided by placing APRs into highly packed and rigid portions of its native fold. Compared to silent variants extracted from the gnomAD database, the thermodynamic and pathogenic impact of amyloid mutations showed evidence of a higher destabilizing effect. MD simulations of the amyloid variant G26R evidenced the partial unfolding of the alpha-helix bundle with the concomitant exposure of APR1 to the solvent, suggesting an insight into the early steps involved in its aggregation. Our findings highlight APR1 as a relevant component for apoA-I structural integrity and emphasize a destabilizing effect of amyloid variants that leads to the exposure of this region.

Nabe: an energetic database of amino acid mutations in protein-nucleic acid binding interfaces.

Protein–nucleic acid complexes play essential roles in regulating transcription, translation, DNA replication, repair and recombination, RNA processing and translocation. Site-directed mutagenesis has been extremely useful in understanding the principles of protein–DNA and protein–RNA interactions, and experimentally determined mutagenesis data are prerequisites for designing effective algorithms for predicting the binding affinity change upon mutation. However, a vital challenge in this area is the lack of sufficient public experimentally recognized mutation data, which leads to difficulties in developing computational prediction methods. In this article, we present Nabe, an integrated database of amino acid mutations and their effects on the binding free energy in protein–DNA and protein–RNA interactions for which binding affinities have been experimentally determined. Compared with existing databases and data sets, Nabe is the largest protein–nucleic acid mutation database, containing 2506 mutations in 473 protein–DNA and protein–RNA complexes, and of that 1751 are alanine mutations in 405 protein–nucleic acid complexes. For researchers to conveniently utilize the data, Nabe assembles protein–DNA and protein–RNA benchmark databases by adopting the data-processing procedures in the majority of models. To further facilitate users to query data, Nabe provides a searchable and graphical web page.Database URL: http://nabe.denglab.org

Genetic Analysis and Literature Review of SNCA Variants in Parkinson's Disease.

Parkinson's disease (PD) is the fastest-growing neurodegenerative disorder. Aging, environmental factors, and genetics are considered as risk factors. The alpha-synuclein gene (SNCA), the first pathogenic gene identified in a familial form of PD, was indisputably involved as a heritable component for familial and sporadic PD. In this study, whole-exome sequencing and Sanger sequencing were performed to evaluate the association between the SNCA gene variants and PD. The genetic data of 438 clinically diagnosed patients with PD and 543 matched control populations of the Han Chinese were analyzed. The literature review of SNCA variants for 231 cases reported in 89 articles was extracted from the PubMed and the Movement Disorder Society Genetic mutation database. No potentially causative variant(s) in the SNCA gene, excepting two single-nucleotide nonsynonymous variants c.158C>T (p.A53V, rs542171324) and c.349C>T (p.P117S, rs145138372), were detected. There was no statistically significant difference in the genotypic or allelic frequencies for either variant between the PD group and the control group (all P > 0.05). No copy number variants of the SNCA gene were detected. The results of this study suggest that the variants in the exons of the SNCA gene may have less or no role in the development of PD in the Han Chinese populations. The literature review suggests that psychiatric signs and cognitive decline/dementia were more common among patients with SNCA duplication or triplication (psychiatric signs: χ2 = 7.892, P = 0.005; cognitive decline/dementia: χ2 = 8.991, P = 0.003).

Comparative analysis of the autism‑related variants between different autistic children in a family pedigree.

The present study aimed to provide evidence for the genetic heterogeneity of familial autism spectrum disorder (ASD), which might help to improve our understanding of the complex polygenic basis of this disease. Whole-exome sequencing (WES) was performed on two autistic children in a family pedigree, and reasonable conditions were set for preliminarily screening variant annotations. Sanger sequencing was used to verify the preliminarily screened variants and to determine the possible sources. In addition, autism-related genes were screened according to autism databases, and their variants were compared between two autistic children. The results showed that there were 21 genes respectively for autistic children IV2 and IV4, preliminarily screened from all variants based on the harmfulness (high) and quality (high or medium) of the variants, as well as the association between mutant genes and autism in human gene mutation database. Furthermore, candidate autism-related genes were screened according to the evidence score of >4 in the Autism KnowledgeBase (AutismKB) database or ≥3 in the AutDB database. A total of 11 and 10 candidate autism-related genes were identified in the autistic children IV2 and IV4, respectively. Candidate genes with an evidence score of >16 in AutismKB were credible autism-related genes, which included LAMC3, JMJD1C and CACNA1H in child IV2, as well as SCN1A, SETD5, CHD7 and KCNMA1 in child IV4. Other than the c.G1499A mutation of SCN1A, which is known to be associated with Dravet syndrome, the specific missense variant loci of other six highly credible putative autism-related genes were reported for the first time, to the best of the authors' knowledge, in the present study. These credible autism-related variants were inherited not only from immediate family members but also from extended family members. In summary, the present study established a reasonable and feasible method for screening credible autism-related genes from WES results, which by be worth extending into clinical practice. The different credible autism-related genes between the two autistic children indicated a complex polygenic architecture of ASD, which may assist in the early diagnosis of this disease.

Juvenile form of alexander disease caused by a previously undescribed mutation in the GFAP gene. a case report

Alexander disease is a form of leukoencephalopathy caused by mutations in the GFAP gene. There are three forms of the disease: infant, juvenile and adult. We present the clinical case of a patient born in 2004 (16 years old) with a debut of the disease at the age of 4 years with complex ticks. further neurological symptoms progressed and appeared atactic gait, intention tremor by performing coordination tests, muscle hypotension, decreased tendon reflexes, nasal voices, and behavior changes.Magnetic resonance imaging revealed changes in the white matter of both frontal lobes. An analysis was made of 59 genes of the panel “Leukodystrophy/leukoencephalopathy” by the method of mass parallel sequencing on the Ion S5. A mutation of the GFAP gene (Nm_002055), 4 exon c.758C>A, p.ALA253Asp in a heterozygous state, not described in Human Gene mutation Database, was detected. The patient was confirmed to have a diagnosis of Alexander disease. According to tractography, a decrease in the number of fibers in the frontal lobes was found.The patient is currently receiving symptomatic treatment.

Antimicrobial Resistance Profiling and Phylogenetic Analysis of Neisseria gonorrhoeae Clinical Isolates From Kenya in a Resource-Limited Setting.

BackgroundAfrica has one of the highest incidences of gonorrhea. Neisseria gonorrhoeae is gaining resistance to most of the available antibiotics, compromising treatment across the world. Whole-genome sequencing (WGS) is an efficient way of predicting AMR determinants and their spread in the population. Recent advances in next-generation sequencing technologies like Oxford Nanopore Technology (ONT) have helped in the generation of longer reads of DNA in a shorter duration with lower cost. Increasing accuracy of base-calling algorithms, high throughput, error-correction strategies, and ease of using the mobile sequencer MinION in remote areas lead to its adoption for routine microbial genome sequencing. To investigate whether MinION-only sequencing is sufficient for WGS and downstream analysis in resource-limited settings, we sequenced the genomes of 14 suspected N. gonorrhoeae isolates from Nairobi, Kenya.MethodsUsing WGS, the isolates were confirmed to be cases of N. gonorrhoeae (n = 9), and there were three co-occurrences of N. gonorrhoeae with Moraxella osloensis and N. meningitidis (n = 2). N. meningitidis has been implicated in sexually transmitted infections in recent years. The near-complete N. gonorrhoeae genomes (n = 10) were analyzed further for mutations/factors causing AMR using an in-house database of mutations curated from the literature.ResultsWe observe that ciprofloxacin resistance is associated with multiple mutations in both gyrA and parC. Mutations conferring tetracycline (rpsJ) and sulfonamide (folP) resistance and plasmids encoding beta-lactamase were seen in all the strains, and tet(M)-containing plasmids were identified in nine strains. Phylogenetic analysis clustered the 10 isolates into clades containing previously sequenced genomes from Kenya and countries across the world. Based on homology modeling of AMR targets, we see that the mutations in GyrA and ParC disrupt the hydrogen bonding with quinolone drugs and mutations in FolP may affect interaction with the antibiotic.ConclusionHere, we demonstrate the utility of mobile DNA sequencing technology in producing a consensus genome for sequence typing and detection of genetic determinants of AMR. The workflow followed in the study, including AMR mutation dataset creation and the genome identification, assembly, and analysis, can be used for any clinical isolate. Further studies are required to determine the utility of real-time sequencing in outbreak investigations, diagnosis, and management of infections, especially in resource-limited settings.

IntSplice2: Prediction of the Splicing Effects of Intronic Single-Nucleotide Variants Using LightGBM Modeling.

Prediction of the effect of a single-nucleotide variant (SNV) in an intronic region on aberrant pre-mRNA splicing is challenging except for an SNV affecting the canonical GU/AG splice sites (ss). To predict pathogenicity of SNVs at intronic positions −50 (Int-50) to −3 (Int-3) close to the 3’ ss, we developed light gradient boosting machine (LightGBM)-based IntSplice2 models using pathogenic SNVs in the human gene mutation database (HGMD) and ClinVar and common SNVs in dbSNP with 0.01 ≤ minor allelic frequency (MAF) < 0.50. The LightGBM models were generated using features representing splicing cis-elements. The average recall/sensitivity and specificity of IntSplice2 by fivefold cross-validation (CV) of the training dataset were 0.764 and 0.884, respectively. The recall/sensitivity of IntSplice2 was lower than the average recall/sensitivity of 0.800 of IntSplice that we previously made with support vector machine (SVM) modeling for the same intronic positions. In contrast, the specificity of IntSplice2 was higher than the average specificity of 0.849 of IntSplice. For benchmarking (BM) of IntSplice2 with IntSplice, we made a test dataset that was not used to train IntSplice. After excluding the test dataset from the training dataset, we generated IntSplice2-BM and compared it with IntSplice using the test dataset. IntSplice2-BM was superior to IntSplice in all of the seven statistical measures of accuracy, precision, recall/sensitivity, specificity, F1 score, negative predictive value (NPV), and matthews correlation coefficient (MCC). We made the IntSplice2 web service at https://www.med.nagoya-u.ac.jp/neurogenetics/IntSplice2.

Identification of the TP53 p.R337H Variant in Tumor Genomic Profiling Should Prompt Consideration of Germline Testing for Li-Fraumeni Syndrome.

PURPOSELi-Fraumeni syndrome (LFS) is rare in the worldwide population, but it is highly prevalent in the Brazilian population because of a founder mutation, TP53 p.R337H, accounting for 0.3% of south and southeastern population. Clinical criteria for LFS may not identify all individuals at risk of carrying the Brazilian founder mutation because of its lower penetrance and variable expressivity. This variant is rarely described in databases of somatic mutations. Somatic findings in tumor molecular profiling may give insight to identify individuals who might be carriers of LFS and allow the adoption of risk reduction strategies for cancer.MATERIALS AND METHODSWe determined the frequency of the TP53 p.R337H variant in tumor genomic profiling from 755 consecutive Brazilian patients with pan-cancer. This is a retrospective cohort from January 2013 to March 2020 at a tertiary care center in Brazil.RESULTSThe TP53 p.R337H variant was found in 2% (15 of 755) of the samples. The mutation allele frequency ranged from 30% to 91.7%. A total of seven patients were referred for genetic counseling and germline testing after tumor genomic profiling results were disclosed. All the patients who proceeded with germline testing (6 of 6) confirmed the diagnosis of LFS. Family history was available in 12 cases. Nine patients (9 of 12) did not meet LFS clinical criteria.CONCLUSIONThe identification of the TP53 p.R337H variant in tumor genomic profiling should be a predictive finding of LFS in the Brazilian population and should prompt testing for germline status confirmation.

Prevalence of Wilson disease based on genome databases in Japan.

Wilson disease (WD) is an autosomal recessive disorder caused by mutations in the ATP7B gene. In 1984, Scheinberg and Sternlieb estimated the prevalence of WD to be 1:30000. However, recent epidemiological studies have reported increasing prevalence rates in different populations. The carrier frequency of ATP7B variants and the prevalence of WD in the Japanese population have not been reported using multiple databases. Multiple public databases were used. First, we included mutations in the ATP7B gene that were registered in the Human Gene Mutation Database (HGMD) Professional, where 885 ATP7B variants were identified as pathogenic. Next, we investigated the allele frequencies of these 885 variants in Japanese individuals using the Human Genetic Variation Database (HGVD) and the Japanese Multi Omics Reference Panel (jMorp). Of the 885 variants of ATP7B, 7 and 12 missense and nonsense variants, zero and three splicing variants, and zero and two small deletions were found in the HGVD and in jMorp, respectively. The total allele frequencies of the ATP7B mutations were 0.011 in the HGVD and 0.014 in the jMorp. According to these data, the carrier frequencies were 0.022 (2.2%) and 0.028 (2.8%), respectively, and patient frequencies were 0.000121 (1.21/10000 individuals) and 0.000196 (1.96/10000 individuals), respectively. This is the first study to report the carrier frequency of ATP7B variants and the prevalence of WD in Japan using multiple databases. The calculated prevalence of WD was comparatively higher than that of previous reports, indicating previous underdiagnosis or the existence of less severe phenotypes.

P-PN036. Loss of function mutation in HINT1 gene presenting with neuromyotonia and hereditary neuropathy

Introduction. Histidine Triad Nucleotide binding protein 1 (HINT1) also known as Protein kinase C inhibitor 1 (PRKCNH-1); Protein kinase C interacting protein 1 (PKCI-1), mapped to chromosome 5q31, is a purine phosphoramidase homodimer and is ubiquitously expressed. HINT1 is a tumour suprresor gene and is involved in several apoptotic pathways. Biallelic variants in the HINT1 gene are known to cause Neuromyotonia with Axonal Neuropathy (NMAN). Zimon et al. have identified 8 HINT1 variants in 33 families of NMAN. Results. A 13-year-old male child, born of consanguineous parentage; presented for the first time in 2018 with of bilateral foot drop of 2 years duration. This was followed one and half year later by bilateral forearm and hand muscle weakness in the form of finger and wrist drop with ulner deviation of the wrist. Hyperpigmentation of the knuckles was noted on the examination. Investigations were negative for screening of inborn errors of metabolism by Tandem mass spectrometry. His serum vitamin B12 level was 692 pg/ml (180-914 pg/ml). ENMG demonstrated neurogenic myotonia. NGS of the proband reported a pathogenic start-loss variation c.3G>C_NM005340.7 (p.Met1Ile) in the HINT1 gene aligned to human reference genome (GRCh37/hg19). The variant is not reported in the ClinVar and the Human gemone mutation (HGMD) databases. A variant at c.2T>C has been reported as likely pathogenic for NMAN in the ClinVar database. The variant is reported as damaging by SIFT, possibly damaging by PolyPhen2, and disease causing by MutationTaster2. CADD score is 24. The variant is not reported in gnomAD, 1000 genome and ExAC population frequency databases. Conclusion. Rare genetic causes of CMT are important contributions to the full allelic spectrum of the disease. Here we report an ultra-rare NMAN case of Indian origin; mediated by a novel, pathogenic, biallelic start-loss variant in the HINT1 gene.

Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design.

With the onset of the COVID-19 pandemic, the amount of data on genomic and proteomic sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stored in various databases has exponentially grown. A large volume of these data has led to the production of equally immense sets of immunological data, which require rigorous computational approaches to sort through and make sense of. Immunoinformatics has emerged in the recent decades as a field capable of offering this approach by bridging experimental and theoretical immunology with state-of-the-art computational tools. Here, we discuss how immunoinformatics can assist in the development of high-performance vaccines and drug discovery needed to curb the spread of SARS-CoV-2. Immunoinformatics can provide a set of computational tools to extract meaningful connections from the large sets of COVID-19 patient data, which can be implemented in the design of effective vaccines. With this in mind, we represent a pipeline to identify the role of immunoinformatics in COVID-19 treatment and vaccine development. In this process, a number of free databases of protein sequences, structures and mutations are introduced, along with docking web servers for assessing the interaction between antibodies and the SARS-CoV-2 spike protein segments as most commonly considered antigens in vaccine design.

Abstract 120: Compartmentalized functions of epidermal growth factor receptor (EGFR) in tumorigenesis and malignant phenotypes

Abstract Previous studies demonstrated that nuclear EGFR is associated with malignancy and resistance to tyrosine kinase inhibitor (TKI). To decipher the cellular compartmentalized functions and underlying mechanism of EGFR, we generated EGFR mutants at nuclear translocation signal (NLS) and nuclear export signal (NES). The results showed that EGFR NLS mutant with decreased nuclear translocation sensitized the cancer cells to TKI and impaired invasiveness and stemness of the cancer cells. In contrast, EGFR NES mutant with increased nuclear accumulation promoted aggressiveness of the cancer cells including TKI resistance. Next, we generated genetically engineered mouse model (GEMM) to validate the resulting phenotypes of the EGFR mutants. To this end, CRISPR-Cas9-mediated genome editing approach was used for generating EGFR mutant knock-in mouse, which harbors an internalization-deficient mutation (EGFRLL/AA), pro-nuclear (NES) mutation (EGFRL749P(S)) and the dual mutations. Upon EGF stimulation nuclear EGFR (nEGFR) accumulation in the hepatocytes from EGFRL749P heterozygous mice was increased. Conversely, a significant reduction of nEGFR was observed in the hepatocyte from EGFTRLL/AA homozygous mice. We found that germline expression of EGFRL749P(S) gave rise to deficiency in breeding. However, EGFRLL/AA mutant mice displayed no gross abnormal even in homozygous offspring, suggesting that depletion of nEGFR has no significant phenotype. We failed to generate homozygotes of EGFR NES mutant mice due to embryonic lethality. Of note, the female and male founders and F1 EGFRL798P(S) mice developed tumors with huge spleen. The IHC staining results indicated that the infiltrating lymphocytes in liver, stomach and kidney were B cells. The latency of the B cell lymphomagenesis was from 17 to 23 months, and tumor incidence of F1 offspring of EGFRL749P(S) knock-in mice was 10 of 15 (66.7%). These findings indicated that single allele mutation in EGFR NES domain is capable of driving lymphomagenesis, suggesting that the EGFR NES mutant possesses a strong oncogenic activity. Surprisingly, by searching database of EGFR mutation in human tumors (Cosmic database https://cancer.sanger.ac.uk/cosmic ), we noticed that among the somatic mutations in EGFR gene the gene amplification represents only small part of the gene alternations in human cancers, and EGFR L747P and L747S mutation occur in the lung cancer patients with intrinsic TKI resistance. In addition, the majority of human B lymphocyte originated tumors harboring EGFR NES mutation are also heterozygotes. Cosmic database shows that somatic mutations at the EGFR NES region occur in the patients with plasma cell lymphoma. Taken together, our findings suggest that nEGFR contributes to malignant potential of the tumors and EGFR with NES mutation is a tumor driver. Citation Format: Lei Nie, Junwei Hou, Yu-Yi Chu, Ying-Nai Wnag, Li-Chuan Chan, Longfei Huo, Paul Chiao, Shao-Chun Wang, Mien-Chie Hung, Michael Wang. Compartmentalized functions of epidermal growth factor receptor (EGFR) in tumorigenesis and malignant phenotypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 120.

Abstract 192: MutAnt: Mutation annotation machine learning algorithm for pathogenicity evaluation of single nonsynonymous nucleotide substitutions in cancer cells

Abstract With the wealth of next-generation sequencing (NGS) produced over the last decade, distinguishing between pathogenic and neutral single nucleotide missense mutations is essential to understanding disease pathogenesis and cancer treatment selection and optimization. Current experimental methods, such as overexpression assays, siRNA, knock-out models, and studies in cultured cells or model organisms, can help uncover the phenotype of a set of mutations in genes but are not optimal for the identification of pathogenic mutations in thousands of potential candidates. In this study, we present a new mutation annotation tool, MutAnt, for the prediction of mutation pathogenicity that achieves superior performance by integrating state-of-the-art machine learning techniques with feature selection and hyperparameter tuning. To develop MutAnt, a supervised machine learning algorithm was trained to classify pathogenic and neutral single nonsynonymous nucleotide substitutions. The training dataset was based on ClinVar v20190102, the CancerGenomeInterpreter database of validated pathogenic mutations, and benign mutations from the database of Cancer Passenger Mutations (dbCPM). The Boruta algorithm with Shapley values was used for feature selection, and the Bayesian optimization was applied for the model hyperparameter tuning. Diverse validation methods were utilized in the development of MutAnt, such as cross-validation; the Database of Curated Mutations (DoCM); holdout on new mutations documented in the latest released version of ClinVar database, which contains validated disease-associated single nucleotide polymorphisms (SNPs) not previously annotated in existing algorithms; mutations with high allele frequency; test dataset III from VariBench; and correlation with the mean function score in a saturation genome editing experiment for the BRCA gene. MutAnt exhibited a stronger performance than other methods on all of the tested validation strategies, which was demonstrated by its accuracy (high f1-score, 0.95 to 0.99) and sensitivity-specificity value (ROC-AUC value, 0.97 to 0.99) in classifying mutations in each validation dataset. Furthermore, the present approach allowed for the prediction of novel mutations that are not covered by extensive population studies with higher accuracy than other methods. This was demonstrated by its prediction of the clinically relevant BRCA variants in the saturation genome editing experiment. In conclusion, MutAnt outperformed all compared mutation annotation algorithms in this study for the classification of clinically relevant benign and pathogenic mutations in the ClinVar dataset. Importantly, this approach allows prediction of novel mutations, subsequently allowing for the selection of targeted therapies for a broader set of patients. Citation Format: Aleksandr Sarachakov, Viktor Svekolkin, Zoia Antysheva, Jessica H. Brown, Alexander Bagaev, Nathan Fowler. MutAnt: Mutation annotation machine learning algorithm for pathogenicity evaluation of single nonsynonymous nucleotide substitutions in cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 192.

Abstract 2203: Identifying the transcriptomic signatures of mutational heterogeneity in GBM using single cell genomics

Abstract Glioblastoma (GBM) is the most prevalent and lethal primary brain tumor, which carries a poor prognosis due to a high level of therapeutic resistance and rate of tumor recurrence. Underlying this aggressive behavior is extraordinary genomic and cellular heterogeneity, conferring numerous avenues for treatment escape and refractory growth. This work leverages single cell genomic analyses to link somatic mutations to transcriptomic signatures within the native tumor tissue. We implemented a three-stage pipeline to perform the following tasks: variant calling, machine learning (ML) classification, and biological pathway analysis. Somatic variants were called from smart-seq based scRNA-seq data and filtered based on predicted lethality, reference in cancer mutation databases e.g. COSMIC, and population prevalence. In the second stage, the genotyped cells and normalized gene expression profiles were pre-processed using differential gene expression, and input into ML classifiers trained on the task of predicting mutation status. The utilized ML models included a Support Vector Machine and Ridge Classifier and were selected based on performance and interpretability. The efficacy of the models was evaluated using the AUC-ROC metric across multiple dataset partitions. The output of this stage was a set of “important genes” identified using the learned ML model weights. In the third stage, the sets of identified genes were subsampled and analyzed using the g:Profiler API to identify significant biological pathways. The analysis was performed on 2 datasets containing 677 samples from 5 patients and 1091 samples from 4 patients. The EGFRvIII mutation was used to validate the performance of the methodology due to its prevalence in primary GBM tumors. The mutation was detected in 17 single-cell samples. The ML models both produced a mean AUC-ROC of 0.93 [0.92, 094], and the classifiers identified NPM1, TCEA1, PPIA, SUMO2, among other genes, to be of significance. The individual genes have functions involving cell cycle regulation and protein folding, while the identified biological pathways associated with gene subsets were linked to ribonuclease activity and transcription factor post-transcriptional modifications. The ML component was applied to an additional dataset containing cell samples from 4 patients and correctly identified the 2 patients positive for EGFRvIII. The larger analysis identified point mutations in EGFR, TP53, and ATRX, and the classifiers exhibited AUC-ROC metrics ranging from 0.94 [0.93, 0.94] to 0.99 [0.99, 1.0]. Overall, we developed and employed a pipeline to link somatic variants with their gene expression signatures at the single cell level, identified biological pathways for further examination and established a ML classifier that can be applied to high-throughput scRNA-seq methods that lack the transcript coverage of the variant loci. Citation Format: Christopher Streiffer, Daniel Zhang, Ryan Salinas, Steven Brem, Donald O'Rourke, Zev Binder, Han-Chiao Isaac Chen, Hongjun Song. Identifying the transcriptomic signatures of mutational heterogeneity in GBM using single cell genomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2203.

Mutation of aspartic acid 199 in USP1 disrupts its deubiquitinating activity and impairs DNA repair.

The deubiquitinating enzyme USP1 contains highly conserved motifs forming its catalytic center. Recently, the COSMIC mutation database identified a mutation in USP1 at Asp-199 in endometrial cancer. Here, we investigated the role of Asp-199 for USP1 function. The mutation of aspartic acid to alanine (D199A) resulted in failure of USP1 to undergo autocleavage and form a complex with ubiquitin, indicating D199A Usp1 is catalytically inactive. The D199A mutation did not affect the interaction with Uaf1. Moreover, D199A Usp1 had defects in deubiquitination of FANCD2 and PCNA and displayed reduced FANCD2 foci formation and DNA repair efficiency. Furthermore, mutation of Asp-199 to glutamic acid resulted in phenotypes similar to the D199A mutation. Collectively, our findings demonstrate the importance of Asp-199 for USP1 activity and suggest the implications of USP1 downregulation in cancer.

Non-Driver Mutations and Gene Sequence Variants Identified By Targeted Deep Sequencing in Polycythemia Vera and Essential Thrombocythemia

Background:Driver mutations in myeloproliferative neoplasms involve mutations in the JAK2, MPL, and CALR genes. However, incorporation of next generation sequencing (NGS) testing into routine clinical practice may identify for mutations or variants in additional hematologic malignancy-associated genes, the roles of which have not been extensively elucidated in patients with polycythemia vera (PV) or essential thrombocythemia (ET). The purpose of this study is to characterize the clinical characteristics of ET/PV patients harboring additional non-driver mutations or gene variants.Methods:Eligible cases included patients with PV/ET diagnosed and treated at MD Anderson Cancer Center from January 2012 to January 2017. NGS sequencing of 28 genes known to be mutated in hematologic malignancies was performed on samples from 240 ET/PV patients. Non-driver mutations detected by the NGS platform were classified based on the allele frequency recorded in our database in conjunction with currently available and periodically updated public reference databases of somatic mutations or variants of unclear significance.Results:A total of 110 ET and 130 PV cases had NGS performed on samples collected at the time of initial referral. Among them, 21 (19%) ET cases and 28 (21.5%) PV cases had ≥1 non-driver somatic mutation detected. The distribution of non-driver somatic mutations in ET and PV are shown in Figure 1A. On grouping non-driver genes into functional pathways, the most frequent mutational targets were in genes associated with epigenetic regulation (PV, 22; ET, 25), mainly DNA methylation (PV, 18; ET, 20) followed by epigenetic modifier (PV, 4; ET, 5). In subjects with PV, TET2 (n = 12) was the most frequently mutated gene, followed by DNMT3A (n = 7), IDH1/2 (n = 5), and ASXL1 (n = 4). A similar pattern was noted in ET, with TET2 mutations (n = 8) being most frequent followed by DNMT3A (n = 7), and ASXL1 (n = 5). Older patients were more likely to display non-driver mutations in both ET (p < 0.001) and PV (p = 0.02). The combined prevalence of non-driver somatic mutations was 27% and 32% in PV and ET, respectively. Over a short median follow up of 3.9 (range, 1.0-37.1) months among PV patients with non-driver somatic mutations, no leukemic transformations occurred. Similarly, no leukemic transformations were observed in ET patients with additional non-driver somatic mutations over a median follow up of 3.8 (range, 1.1-36) months. Overall survival (OS) was significantly longer in patients without additional mutations in epigenetic modifiers (Figure 1C). The prognostic relevance of non-driver somatic mutations on OS was not determined due the small sample size and limited follow up duration. Of 13 (12%) triple-negative ET patients without non-driver somatic mutations, nine had non-synonymous gene sequence variants; the most common being in DNA methylation genes namely TET2 &IDH1/2 genes (Figure 1B).Conclusion:The spectrum of non-driver somatic mutations in our ET and PV study cohorts was dominated by mutations in the TET2, DNMT3A, ASXL1 genes, and were correlated with age (Buscarlet Blood, 2017). A larger sample size and longer follow-up is needed to determine the impact of non-driver somatic mutations on transformation-free and overall survival. Germline TET2 variants or their combinations with other variants/non-driver mutations may constitute low penetrance predisposition factors for myeloid neoplasms and their functional relevance warrants further exploration. [Display omitted] DisclosuresBose:Incyte Corporation: Honoraria. Kantarjian:Amgen: Research Funding; Novartis: Research Funding; Bristol-Meyers Squibb: Research Funding; ARIAD: Research Funding; Pfizer: Research Funding; Delta-Fly Pharma: Research Funding. Verstovsek:Astrazeneca: Research Funding; Roche: Research Funding; NS Pharma: Research Funding; Roche: Research Funding; Blueprint Medicines Corp: Research Funding; Galena BioPharma: Research Funding; Genentech: Research Funding; Galena BioPharma: Research Funding; Promedior: Research Funding; Genentech: Research Funding; Blueprint Medicines Corp: Research Funding; Lilly Oncology: Research Funding; Incyte: Research Funding; Pfizer: Research Funding; Bristol Myers Squibb: Research Funding; Lilly Oncology: Research Funding; CTI BioPharma Corp: Research Funding; Pfizer: Research Funding; NS Pharma: Research Funding; Celgene: Research Funding; CTI BioPharma Corp: Research Funding; Bristol Myers Squibb: Research Funding; Astrazeneca: Research Funding; Gilead: Research Funding; Celgene: Research Funding; Promedior: Research Funding; Gilead: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Incyte: Research Funding.