乳腺癌
帕博西利布
药物基因组学
医学
癌症
肿瘤科
放射基因组学
全景望远镜
机器学习
内科学
生物信息学
转移性乳腺癌
药理学
生物
计算机科学
无线电技术
生物化学
组蛋白脱乙酰基酶
基因
放射科
组蛋白
作者
Aamir Mehmood,Sadia Nawab,Yifan Jin,Hosni M. Hassan,Aman Chandra Kaushik,Dong‐Qing Wei
出处
期刊:ACS pharmacology & translational science
[American Chemical Society]
日期:2023-02-24
卷期号:6 (3): 399-409
被引量:9
标识
DOI:10.1021/acsptsci.2c00212
摘要
Breast cancer is one of the major causes of death in women worldwide. It is a diverse illness with substantial intersubject heterogeneity, even among individuals with the same type of tumor, and customized therapy has become increasingly important in this sector. Because of the clinical and physical variability of different kinds of breast cancers, multiple staging and classification systems have been developed. As a result, these tumors exhibit a wide range of gene expression and prognostic indicators. To date, no comprehensive investigation of model training procedures on information from numerous cell line screenings has been conducted together with radiation data. We used human breast cancer cell lines and drug sensitivity information from Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases to scan for potential drugs using cell line data. The results are further validated through three machine learning approaches: Elastic Net, LASSO, and Ridge. Next, we selected top-ranked biomarkers based on their role in breast cancer and tested them further for their resistance to radiation using the data from the Cleveland database. We have identified six drugs named Palbociclib, Panobinostat, PD-0325901, PLX4720, Selumetinib, and Tanespimycin that significantly perform on breast cancer cell lines. Also, five biomarkers named TNFSF15, DCAF6, KDM6A, PHETA2, and IFNGR1 are sensitive to all six shortlisted drugs and show sensitivity to the radiations. The proposed biomarkers and drug sensitivity analysis are helpful in translational cancer studies and provide valuable insights for clinical trial design.
科研通智能强力驱动
Strongly Powered by AbleSci AI