دورية أكاديمية
أعرض في EDS

Pathway-extended gene expression signatures integrate novel biomarkers that improve predictions of patient responses to kinase inhibitors.

التفاصيل البيبلوغرافية
العنوان: Pathway-extended gene expression signatures integrate novel biomarkers that improve predictions of patient responses to kinase inhibitors.
المؤلفون: Bagchee-Clark AJ; Department of Biochemistry, Schulich School of Medicine and Dentistry University of Western Ontario, London, Canada N6A 2C8 Canada., Mucaki EJ; Department of Biochemistry, Schulich School of Medicine and Dentistry University of Western Ontario, London, Canada N6A 2C8 Canada., Whitehead T; SHARCNET University of Western Ontario London Ontario N6A 5B7 Canada., Rogan PK; Department of Biochemistry, Schulich School of Medicine and Dentistry University of Western Ontario, London, Canada N6A 2C8 Canada.; Cytognomix Inc., 60 North Centre Road, Box 27052, London, Canada N5X 3X5 Canada.
المصدر: MedComm [MedComm (2020)] 2020 Dec 10; Vol. 1 (3), pp. 311-327. Date of Electronic Publication: 2020 Dec 10 (Print Publication: 2020).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: John Wiley & Sons, Inc Country of Publication: China NLM ID: 101769925 Publication Model: eCollection Cited Medium: Internet ISSN: 2688-2663 (Electronic) Linking ISSN: 26882663 NLM ISO Abbreviation: MedComm (2020) Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: [Beijing, China] : John Wiley & Sons, Inc. [2020]-
مستخلص: Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning to derive multi-gene expression signatures that predict individual patient responses to specific tyrosine kinase inhibitors, including erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Support vector machine (SVM) learning was used to train mathematical models that distinguished sensitivity from resistance to these drugs using a novel systems biology-based approach. This began with expression of genes previously implicated in specific drug responses, then expanded to evaluate genes whose products were related through biochemical pathways and interactions. Optimal pathway-extended SVMs predicted responses in patients at accuracies of 70% (imatinib), 71% (lapatinib), 83% (sunitinib), 83% (erlotinib), 88% (sorafenib) and 91% (gefitinib). These best performing pathway-extended models demonstrated improved balance predicting both sensitive and resistant patient categories, with many of these genes having a known role in cancer aetiology. Ensemble machine learning-based averaging of multiple pathway-extended models derived for an individual drug increased accuracy to >70% for erlotinib, gefitinib, lapatinib and sorafenib. Through incorporation of novel cancer biomarkers, machine learning-based pathway-extended signatures display strong efficacy predicting both sensitive and resistant patient responses to chemotherapy.
(© 2020 The Authors. MedComm published by Sichuan International Medical Exchange & Promotion Association (SCIMEA) and John Wiley & Sons Australia, Ltd.)
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فهرسة مساهمة: Keywords: biochemical pathways; gene signatures; machine learning; systems biology; tyrosine kinase inhibitors
تواريخ الأحداث: Date Created: 20211112 Latest Revision: 20211204
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC8491218
DOI: 10.1002/mco2.46
PMID: 34766125
قاعدة البيانات: MEDLINE
الوصف
تدمد:2688-2663
DOI:10.1002/mco2.46