Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:2.1.1.67 (thiopurine methyltransferase)
 551 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

We explored the impact of mutations in the NOTCH1, FBW7 and PTEN genes on prognosis and downstream signaling in a well-defined cohort of 47 patients with pediatric T-cell acute lymphoblastic leukemia (T-ALL). In T-ALL lymphoblasts, we identified high-frequency mutations in NOTCH1 (n=16), FBW7 (n=5) and PTEN (n=26). NOTCH1 mutations resulted in 1.3- to 3.3-fold increased transactivation of an HES1 reporter construct over wild-type NOTCH1; mutant FBW7 resulted in further augmentation of reporter gene activity. NOTCH1 and FBW7 mutations were accompanied by increased median transcripts for NOTCH1 target genes (HES1, DELTEX1 and cMYC). However, none of these mutations were associated with treatment outcome. Elevated HES1, DELTEX1 and cMYC transcripts were associated with significant increases in transcript levels of several chemotherapy relevant genes, including MDR1, ABCC5, reduced folate carrier, asparagine synthetase, thiopurine methyltransferase, BCL2 and dihydrofolate reductase. PTEN transcripts positively correlated with HES1 and cMYC transcript levels. Our results suggest that (1) multiple factors should be considered with attempting to identify molecular-based prognostic factors for pediatric T-ALL, and (2) depending on the NOTCH1 signaling status, modifications in the types or dosing of standard chemotherapy drugs for T-ALL, or combinations of agents capable of targeting NOTCH1, AKT and/or mTOR with standard chemotherapy agents may be warranted.
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PMID:The impact of NOTCH1, FBW7 and PTEN mutations on prognosis and downstream signaling in pediatric T-cell acute lymphoblastic leukemia: a report from the Children's Oncology Group. 1934 1

Determining the pathogenicity of genetic variants is a critical challenge, and functional assessment is often the only option. Experimentally characterizing millions of possible missense variants in thousands of clinically important genes requires generalizable, scalable assays. We describe variant abundance by massively parallel sequencing (VAMP-seq), which measures the effects of thousands of missense variants of a protein on intracellular abundance simultaneously. We apply VAMP-seq to quantify the abundance of 7,801 single-amino-acid variants of PTEN and TPMT, proteins in which functional variants are clinically actionable. We identify 1,138 PTEN and 777 TPMT variants that result in low protein abundance, and may be pathogenic or alter drug metabolism, respectively. We observe selection for low-abundance PTEN variants in cancer, and show that p.Pro38Ser, which accounts for ~10% of PTEN missense variants in melanoma, functions via a dominant-negative mechanism. Finally, we demonstrate that VAMP-seq is applicable to other genes, highlighting its generalizability.
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PMID:Multiplex assessment of protein variant abundance by massively parallel sequencing. 2978 12

Accurately predicting the impact of point mutation on protein stability has crucial roles in protein design and engineering. In this study, we proposed a novel method (BoostDDG) to predict stability changes upon point mutations from protein sequences based on the extreme gradient boosting. We extracted features comprehensively from evolutional information and predicted structures and performed feature selection by a strategy of sequential forward selection. The features and parameters were optimized by homologue-based cross-validation to avoid overfitting. Finally, we found that 14 features from six groups led to the highest Pearson correlation coefficient (PCC) of 0.535, which is consistent with the 0.540 on an independent test. Our method was indicated to consistently outperform other sequence-based methods on three precompiled test sets, and 7363 variants on two proteins (PTEN and TPMT). These results highlighted that BoostDDG is a powerful tool for predicting stability changes upon point mutations from protein sequences.
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PMID:Accurately Predicting Mutation-Caused Stability Changes from Protein Sequences Using Extreme Gradient Boosting. 3220 53