Chrome Extension
WeChat Mini Program
Use on ChatGLM
VIPCreated with Sketch.

    • The SNP Rs460089 in the Gene Promoter of the Drug Transporter OCTN1 Has Prognostic Value for Treatment-Free Remission in Chronic Myeloid Leukemia Patients Treated with Imatinib

    LEUKEMIA（2024）

    Institute of Hematology and Blood Transfusion | Institut für Medizinische Informationsverarbeitung | Department of Haematology and Oncology | Univ Klinikum Freiburg | Univ Klinikum RWTH Aachen | Onkologische Praxis Heilbronn | Universitätsklinikum Würzburg Medizinische Klinik und Poliklinik II | University Hospital Marburg | 4th Department of Internal Medicine - Hematology | Univ Helsinki | Palacky Univ | Fac Hosp Brno | Hematology Clinic | Dept. of Hematology | Department of Hematology | Hematology and Transplantology Department | Hematology and Bone Marrow Transplantation Department | Med Univ Lublin | Hematology Department | Department of General Pathology | Hematology | Experimental Hematooncology Department | Bergonie Institute Bordeaux

    Cited 0|Views3
    Abstract
    Membrane transporters are important determinants of drug bioavailability. Their expression and activity affect the intracellular drug concentration in leukemic cells impacting response to therapy. Pharmacogenomics represents genetic markers that reflect allele arrangement of genes encoding drug transporters associated with treatment response. In previous work, we identified SNP rs460089 located in the promotor of SLC22A4 gene encoding imatinib transporter OCTN1 as influential on response of patients with chronic myeloid leukemia treated with imatinib. Patients with rs460089-GC pharmacogenotype had significantly superior response to first-line imatinib treatment compared to patients with rs460089-GG. This study investigated whether pharmacogenotypes of rs460089 are associated with sustainability of treatment-free remission (TFR) in patients from the EUROpean Stop Kinase Inhibitor (EURO-SKI) trial. In the learning sample, 176 patients showed a significantly higher 6-month probability of molecular relapse free survival (MRFS) in patients with GC genotype (73%, 95% CI: 60–82%) compared to patients with GG (51%, 95% CI: 41–61%). Also over time, patients with GC genotype had significantly higher MRFS probabilities compared with patients with GG (HR: 0.474, 95% CI: 0.280-0.802, p = 0.0054). Both results were validated with data on 93 patients from the Polish STOP imatinib study. In multiple regression models, in addition to the investigated genotype, duration of TKI therapy (EURO-SKI trial) and duration of deep molecular response (Polish study) were identified as independent prognostic factors. The SNP rs460089 was found as an independent predictor of TFR.
    More
    Translated text
    Key words
    Therapeutic Targets
    PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Try using models to generate summary,it takes about 60s
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    GPU is busy, summary generation fails
    Rerequest