Academic literature on the topic 'Mutations, classification structurale'

A central goal of cancer research is to discover and characterize the functional effects of mutated genes that contribute to tumorigenesis. In this study, we provide a detailed structural classification and analysis of functional dynamics for members of protein kinase families that are known to harbor cancer mutations. We also present a systematic computational analysis that combines sequence and structure-based prediction models to characterize the effect of cancer mutations in protein kinases. We focus on the differential effects of activating point mutations that increase protein kinase activity and kinase-inactivating mutations that decrease activity. Mapping of cancer mutations onto the conformational mobility profiles of known crystal structures demonstrated that activating mutations could reduce a steric barrier for the movement from the basal “low” activity state to the “active” state. According to our analysis, the mechanism of activating mutations reflects a combined effect of partial destabilization of the kinase in its inactive state and a concomitant stabilization of its active-like form, which is likely to drive tumorigenesis at some level. Ultimately, the analysis of the evolutionary and structural features of the major cancer-causing mutational hotspot in kinases can also aid in the correlation of kinase mutation effects with clinical outcomes.

CG Mullighan and T Haferlach: are co-senior authors Introduction: Recent genomic sequencing studies have advanced our understanding of the pathogenesis of myeloid malignancies, including acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS), and improved classification of specific subgroups. Unfortunately, these studies have mostly analyzed specific subtypes and/or used targeted DNA-sequencing, thus limiting discovery of novel mutational patterns and gene expression clusters. Here, we performed an integrated genome-wide mutational/transcriptomic analysis of a large cohort of adult AML and MDS samples to accurately define subtypes of diagnostic, prognostic and therapeutic relevance. Methods: We performed unbiased whole genome (WGS) and transcriptome sequencing (RNA-seq) of 1,304 adult individuals (598 AML and 706 MDS; Fig. 1A), incorporating analysis of somatic and presumed germline sequence mutations, chimeric fusions and structural complex variations. Transcriptomic gene expression data were processed by a rigorous bootstrap procedure to define gene expression subgroups in an unsupervised manner. Associations between genetic variants, gene expression groups and outcome were examined. Results: Genomic/transcriptome sequencing confirmed diagnosis according to WHO 2016 of AML with recurrent genetic abnormalities in 10.9% of cases. These cases had a distinct gene expression profile (Fig. 1A), good prognosis (Fig. 1B) and a combination of mutations in the following genes: KIT, ZBTB7A, ASXL2, RAD21, CSF3R and DNM2 in RUNX1-RUNXT1 leukemia; FLT3, DDX54, WT1 and CALR in PML-RARA promyelocytic leukemia; KIT and BCORL1 in CBFB-rearranged leukemia. In addition, 9% of cases showed rearrangements of KMT2A, with known (e.g. MLLT3) and non-canonical partners (e.g. ACACA, and NCBP1) and poor outcome. Although common targets of mutations have been previously described for myeloid malignancies, the heterogeneity and complexity of mutational patterns, their expression signature and outcome here described are novel. Gene expression analysis identified groups of AML and/or MDS lacking recurrent cytogenetic abnormalities (87%). The spectrum of the most frequently mutated genes (>10 cases) and associated gene expression subtypes is summarized in Figure 1A. TET2 (more frequent in MDS than AML, p=0.0011) and DNMT3A (more frequent in AML than MDS, p<0.0001) were the most frequently mutated genes. Interestingly, mutations in these genes promoting clonal hematopoiesis were significantly enriched in the subgroup with NPM1 mutations. Overall, NPM1 mutations occurred in 27.4% of AML and 1% of MDS and were characterized by four expression signatures with different combination of cooperating mutations in cohesin and signaling genes and outcome (Fig. 1C, gene expression, GE, groups 2, 3, 7 and 8). Co-occurring NPM1 and FLT3 mutations conferred poorer outcome compared to only NPM1, in contrast co-occurring mutations with cohesin genes had better outcome (Fig. 1D). Additional mutations that significantly co-occurred with NPM1 were in PTPN11, IDH1/2, RAD21 and SMC1A. Three gene expression clusters accounted for additional 9% of cases with mutual exclusive mutations in RUNX1,TP53 and CEBPA and co-occurring with a combination of mutations in DNA methylation, splicing and signaling genes (Fig. 1E, GE groups 4, 5 and 6). Interestingly, RUNX1 mutations were significantly associated with SRSF2 mutations but not with SF3B1, showed high expression of MN1 and poor outcome (Fig. 1F). In contrast to the distinct, mutation-associated patterns of gene expression in AML samples, the gene expression profile of MDS was less variable despite diversity in patterns of mutation. MDS was enriched in mutations of SF3B1 (27.2%), mutually exclusive with SFRS2 (14.4%) and U2AF1 (5.5%); TP53 (13.7%) and RUNX1 (10.5%) and a combination of mutations in epigenetic regulators with outcome dependent on mutational pattern (Fig. 1A, G-H). Moreover, structural variations and/or missense mutations of MECOM accounted for 2% of cases. Conclusions: the integration of mutational and expression data from a large cohort of adult pan myeloid leukemia cases enabled the definition of subtypes and constellations of mutations and have prognostic significance that transcends prior gene panel-based classification schema. Disclosures Meggendorfer: MLL Munich Leukemia Laboratory: Employment. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Baer:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Mullighan:Illumina: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: sponsored travel; Pfizer: Honoraria, Other: speaker, sponsored travel, Research Funding; AbbVie: Research Funding; Loxo Oncology: Research Funding; Amgen: Honoraria, Other: speaker, sponsored travel. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

e20679 Background: The impact of TP53 mutations in EGFR mt+ pts on PFS and OS is controversial, and different classifications of TP53 mt+ with respect to functional and potential predictive impact have been published. Therefore, we retrospectively analyzed the impact of TP53 aberrations on ORR, PFS and OS in a cohort of EGFR mt+ NSCLC IV pts (UICC 7) using different classifications of TP53 mutations. Methods: 75 EGFR mt+ NSCLC IV pts were analyzed for TP53 co-mutations. TP53 mt+ were classified according to Poeta et al. into (1) disruptive vs. non-disruptive, according to structural prediction and biophysical characteristics into (2) pathogenic vs. non-pathogenic and finally into (3) exon 8 vs. non-exon 8 mutations according to Crino et al.. The endpoints ORR according to Recist 1.1, PFS and OS were calculated by Kaplan Meier. Results: 69 of the 75 EGFR mt+ pts (92%) had a common mutation in EGFR E19/21. In 59/75 pts (79%) material was sufficient for successful TP53 analysis. TP53 mt+ were found in 29/59 pts (49%), 16/59 (27%) had a TP53 disruptive mt+, 13/59 (22%) a TP53 non-disruptive mt+ and 30/59 a TP53 WT configuration. Using the structural/biophysical classification, 7/59 (12%) had a TP53 non-pathogenic and 22/59 (37%) a TP53 pathogenic mt+. Of the 29 mutated pts, 6 had a TP53 Exon 8 mt+. Median PFS on 1st line TKI was 12 vs. 18 months for non-disruptive/disruptive mt+ vs. WT (p < 0.004), 11 vs. 17 months for pathogenic vs. non-pathogenic/WT (p < 0.0001), and 7 vs. 12 vs. 18 months for exon 8 vs. non-exon 8 vs. WT (p < 0.006). Median OS was 24 vs. 42 months in non-disruptive/disruptive mt+ vs. WT (p < 0.0009), 23 vs. 42 months in pathogenic vs. non-pathogenic/WT (p < 0.001) and 12 vs. 28 months for TP53 exon 8 vs. non-exon 8 mt+ (p < 0.024). Additionally ORR was significantly impacted by TP53 mt+. In rebiopsy samples on acquired resistance, no new TP53 mutations were observed and there were no correlations of TP53 mutations with clinical factors and the EGFR mt+ type including T790M. Conclusions: TP53 seems to be a frequent co-mutation in EGFR mt+ NSCLC and has a strong impact on all clinical endpoints on TKI therapy. These data might have an impact on the management and follow up of pts with TP53 mt+. Furthermore, there is an urgent need for further therapeutic approaches in this patient group.

Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on using the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5′ and 3′ from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments provided comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural, and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with three other commonly used driver prediction tools (FATHMM (cancer), CONDEL, and MutationTaster) significantly outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of using raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.

More than 2000 mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) have been described that confer a range of molecular cell biological and functional phenotypes. Most of these mutations lead to compromised anion conductance at the apical plasma membrane of secretory epithelia and cause cystic fibrosis (CF) with variable disease severity. Based on the molecular phenotypic complexity of CFTR mutants and their susceptibility to pharmacotherapy, it has been recognized that mutations may impose combinatorial defects in CFTR channel biology. This notion led to the conclusion that the combination of pharmacotherapies addressing single defects (e.g., transcription, translation, folding, and/or gating) may show improved clinical benefit over available low-efficacy monotherapies. Indeed, recent phase 3 clinical trials combining ivacaftor (a gating potentiator) and lumacaftor (a folding corrector) have proven efficacious in CF patients harboring the most common mutation (deletion of residue F508, ΔF508, or Phe508del). This drug combination was recently approved by the U.S. Food and Drug Administration for patients homozygous for ΔF508. Emerging studies of the structural, cell biological, and functional defects caused by rare mutations provide a new framework that reveals a mixture of deficiencies in different CFTR alleles. Establishment of a set of combinatorial categories of the previously defined basic defects in CF alleles will aid the design of even more efficacious therapeutic interventions for CF patients.

The binding affinities of protein-nucleic acid interactions could be altered due to missense mutations occurring in DNA- or RNA-binding proteins, therefore resulting in various diseases. Unfortunately, a systematic comparison and prediction of the effects of mutations on protein-DNA and protein-RNA interactions (these two mutation classes are termed MPDs and MPRs, respectively) is still lacking. Here, we demonstrated that these two classes of mutations could generate similar or different tendencies for binding free energy changes in terms of the properties of mutated residues. We then developed regression algorithms separately for MPDs and MPRs by introducing novel geometric partition-based energy features and interface-based structural features. Through feature selection and ensemble learning, similar computational frameworks that integrated energy- and nonenergy-based models were established to estimate the binding affinity changes resulting from MPDs and MPRs, but the selected features for the final models were different and therefore reflected the specificity of these two mutation classes. Furthermore, the proposed methodology was extended to the identification of mutations that significantly decreased the binding affinities. Extensive validations indicated that our algorithm generally performed better than the state-of-the-art methods on both the regression and classification tasks. The webserver and software are freely available at http://liulab.hzau.edu.cn/PEMPNI and https://github.com/hzau-liulab/PEMPNI.

Abstract Genomic analysis has greatly influenced the diagnosis and clinical management of patients affected by diverse forms of hematologic malignancies. Here, we review how genetic alterations define subclasses of patients with acute leukemias, myelodysplastic syndromes (MDS), myeloproliferative neoplasms (MPNs), non-Hodgkin lymphomas, and classical Hodgkin lymphoma. These include new subtypes of acute myeloid leukemia defined by mutations in RUNX1 or BCR-ABL1 translocations as well as a constellation of somatic structural DNA alterations in acute lymphoblastic leukemia. Among patients with MDS, detection of mutations in SF3B1 define a subgroup of patients with the ring sideroblast form of MDS and a favorable prognosis. For patients with MPNs, detection of the BCR-ABL1 fusion delineates chronic myeloid leukemia from classic BCR-ABL1− MPNs, which are largely defined by mutations in JAK2, CALR, or MPL. In the B-cell lymphomas, detection of characteristic rearrangements involving MYC in Burkitt lymphoma, BCL2 in follicular lymphoma, and MYC/BCL2/BCL6 in high-grade B-cell lymphomas are essential for diagnosis. In T-cell lymphomas, anaplastic large-cell lymphoma is defined by mutually exclusive rearrangements of ALK, DUSP22/IRF4, and TP63. Genetic alterations affecting TP53 and the mutational status of the immunoglobulin heavy-chain variable region are important in clinical management of chronic lymphocytic leukemia. Additionally, detection of BRAFV600E mutations is helpful in the diagnosis of classical hairy cell leukemia and a number of histiocytic neoplasms. Numerous additional examples provided here demonstrate how clinical evaluation of genomic alterations have refined classification of myeloid neoplasms and major forms of lymphomas arising from B, T, or natural killer cells.

Purpose Diagnosing genetic neuromuscular disorder such as muscular dystrophy is complicated when the imperfection occurs while splicing. This paper aims in predicting the type of muscular dystrophy from the gene sequences by extracting the well-defined descriptors related to splicing mutations. An automatic model is built to classify the disease through pattern recognition techniques coded in python using scikit-learn framework. Design/methodology/approach In this paper, the cloned gene sequences are synthesized based on the mutation position and its location on the chromosome by using the positional cloning approach. For instance, in the human gene mutational database (HGMD), the mutational information for splicing mutation is specified as IVS1-5 T > G indicates (IVS - intervening sequence or introns), first intron and five nucleotides before the consensus intron site AG, where the variant occurs in nucleotide G altered to T. IVS (+ve) denotes forward strand 3′– positive numbers from G of donor site invariant and IVS (−ve) denotes backward strand 5′ – negative numbers starting from G of acceptor site. The key idea in this paper is to spot out discriminative descriptors from diseased gene sequences based on splicing variants and to provide an effective machine learning solution for predicting the type of muscular dystrophy disease with the splicing mutations. Multi-class classification is worked out through data modeling of gene sequences. The synthetic mutational gene sequences are created, as the diseased gene sequences are not readily obtainable for this intricate disease. Positional cloning approach supports in generating disease gene sequences based on mutational information acquired from HGMD. SNP-, gene- and exon-based discriminative features are identified and used to train the model. An eminent muscular dystrophy disease prediction model is built using supervised learning techniques in scikit-learn environment. The data frame is built with the extracted features as numpy array. The data are normalized by transforming the feature values into the range between 0 and 1 aid in scaling the input attributes for a model. Naïve Bayes, decision tree, K-nearest neighbor and SVM learned models are developed using python library framework in scikit-learn. Findings To the best knowledge of authors, this is the foremost pattern recognition model, to classify muscular dystrophy disease pertaining to splicing mutations. Certain essential SNP-, gene- and exon-based descriptors related to splicing mutations are proposed and extracted from the cloned gene sequences. An eminent model is built using statistical learning technique through scikit-learn in the anaconda framework. This paper also deliberates the results of statistical learning carried out with the same set of gene sequences with synonymous and non-synonymous mutational descriptors. Research limitations/implications The data frame is built with the Numpy array. Normalizing the data by transforming the feature values into the range between 0 and 1 aid in scaling the input attributes for a model. Naïve Bayes, decision tree, K-nearest neighbor and SVM learned models are developed using python library framework in scikit-learn. While learning the SVM model, the cost, gamma and kernel parameters are tuned to attain good results. Scoring parameters of the classifiers are evaluated using tenfold cross-validation using metric functions of scikit-learn library. Results of the disease identification model based on non-synonymous, synonymous and splicing mutations were analyzed. Practical implications Certain essential SNP-, gene- and exon-based descriptors related to splicing mutations are proposed and extracted from the cloned gene sequences. An eminent model is built using statistical learning technique through scikit-learn in the anaconda framework. The performance of the classifiers are increased by using different estimators from the scikit-learn library. Several types of mutations such as missense, non-sense and silent mutations are also considered to build models through statistical learning technique and their results are analyzed. Originality/value To the best knowledge of authors, this is the foremost pattern recognition model, to classify muscular dystrophy disease pertaining to splicing mutations.