Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:2.3.1.21 (CPT)
 4,580 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Mutation of the KRAS gene in patients with metastatic colorectal cancer(mCRC)has been established as a predictive marker of poor response to anti-EGFR cetuximab. The Japanese Society of Medical Oncology recommends that the KRAS mutation status at codon 12 and codon 13 should be genotyped by direct-sequencing or allele-specific PCR. In this study, we tested the point mutation of codon 12 and 13 in the KRAS gene by Luminex(xMAP)flow cytometry with sequence-specific oligonucleotide probes for 39 out of 64 unresectable mCRC patients enrolled from Sep 2008 to Oct 2009, who were administered cetuximab in combination with irinotecan(CPT-11)as third-line therapy. We retrospectively analyzed the relationship between KRAS mutation status and responses to combination therapy. Mutations in the KRAS gene were detected in 38. 5% of cases(codon12: 73%, codon 13: 27%), and the median follow-up time was 8. 2 months(range, 1. 4-15. 2 months). The response rates for patients with KRAS wild-type and patients with KRAS mutations were 33. 3%(95%CI 14. 5-52. 2%)and 0%(p=0. 015); the disease control rates were 75%(95%CI 57. 7-92. 3%)and 40%(95%CI, 15. 2-64. 8%; p=0. 044); the median TTF was 7 months(95%CI 4. 6-9. 3)and 2. 3 months(95%CI 1. 3-3. 2; p=0. 0007), and the median OS was 12. 9 months(95%CI 6. 7-19. 1)and 10. 8 months(95%CI 5. 0-16. 7; p=0. 15), respectively. Therefore, we concluded that the KRAS mutation in mCRC is a predictive factor for the lack of response to combination therapy with cetuximab plus CPT- 11, as reported in previous clinical studies.
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PMID:[Analysis of the correlation with KRAS gene mutation status and the benefit of cetuximab plus irinotecan as third- line chemotherapy for the Treatment of unresectable metastatic colorectal cancer]. 2182 65

In light of the increasing need by decision makers for a method of evaluating genomic applications based on the weight of evidence for their efficacy, several agencies have developed systems of classification. Here I review the horizon-scanning method for prioritizing genomics applications as described by Dotson et al. in this issue of CPT. Using the examples of the authors' Tier 1/Green classification for KRAS and Tier 2/Yellow for TPMT, I discuss differences between the guidelines issued by the Clinical Pharmacogenetics Implementation Consortium (CPIC) and those by the National Comprehensive Cancer Network (NCCN). Additionally, I offer suggestions regarding classification of the Tier 3/Red genomics applications and the reproducibility of the data-curating algorithm of the horizon-scanning method.
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PMID:A horizon-prioritizing method can identify gaps among genomic application guidelines. 2439 97

Human cancers are incredibly diverse with regard to molecular aberrations, dependence on oncogenic signaling pathways, and responses to pharmacological intervention. We wished to assess how cellular dependence on the canonical PI3K vs. MAPK pathways within HER2+ cancers affects responses to combinations of targeted therapies, and biomarkers predictive of their activity. Through an integrative analysis of mechanistic model simulations and in vitro cell line profiling, we designed a six-arm decision tree to stratify treatment of HER2+ cancers using combinations of targeted agents. Activating mutations in the PI3K and MAPK pathways (PIK3CA and KRAS), and expression of the HER3 ligand heregulin determined sensitivity to combinations of inhibitors against HER2 (lapatinib), HER3 (MM-111), AKT (MK-2206), and MEK (GSK-1120212; trametinib), in addition to the standard of care trastuzumab (Herceptin). The strategy used to identify effective combinations and predictive biomarkers in HER2-expressing tumors may be more broadly extendable to other human cancers.
CPT Pharmacometrics Syst Pharmacol 2015 Mar
PMID:Model-Based Design of a Decision Tree for Treating HER2+ Cancers Based on Genetic and Protein Biomarkers. 2622 38

KRAS has proven difficult to target pharmacologically. Two strategies have recently been described for covalently targeting the most common KRAS mutant in lung cancer, KRAS G12C. Previously, we developed a computational model of the processes that regulate Ras activation. Here, we use this model to investigate KRAS G12C covalent inhibitors. We updated the model to include Ras protein turnover, and validation demonstrates that our model performs well in areas of G12C targeting where conventional wisdom struggles. We then used the model to investigate possible strategies to improve KRAS G12C inhibitors and identified GEF loading as a mechanism that could improve efficacy. Our simulations also found resistance-promoting mutations may reverse which class of KRAS G12C inhibitor inhibits the system better, suggesting that there may be value to pursuing both types of KRAS G12C inhibitors. Overall, this work demonstrates areas in which systems biology approaches can inform Ras drug development.
CPT Pharmacometrics Syst Pharmacol 2018 05
PMID:Quantitative Systems Pharmacology Analysis of KRAS G12C Covalent Inhibitors. 2948 42