Effects of sorafenib, a tyrosin kinase inhibitor, on adrenocortical cancer cell line

2021 ◽  
Author(s):  
Lidia Cerquetti ◽  
Barbara Bucci ◽  
Salvatore Raffa ◽  
Pina Lardo ◽  
Roberta Maggio ◽  
...  
Keyword(s):  
Cell Line ◽  
Cancer Cell ◽  
Kinase Inhibitor ◽  
Cancer Cell Line ◽  
Adrenocortical Cancer ◽  
Tyrosin Kinase Inhibitor ◽  
Tyrosin Kinase

Rho kinase inhibitor fasudil suppresses migration and invasion though down-regulating the expression of VEGF in lung cancer cell line A549

2010 ◽  
Vol 28 (2) ◽  
pp. 565-571 ◽  
Author(s):  
Fang Zhu ◽  
Zhe Zhang ◽  
Gang Wu ◽  
Zhenyu Li ◽  
Ruiguang Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Line ◽  
Cancer Cell ◽  
Kinase Inhibitor ◽  
Rho Kinase ◽  
Cancer Cell Line ◽  
Lung Cancer Cell Line ◽  
Migration And Invasion ◽  
Lung Cancer Cell ◽  
Rho Kinase Inhibitor

New mechanisms of adrenostatic compounds in a human adrenocortical cancer cell line

2000 ◽  
Vol 30 ◽  
pp. 76-82 ◽  
Author(s):  
M. Fassnacht ◽  
S. Hahner ◽  
F. Beuschlein ◽  
A. Klink ◽  
M. Reincke ◽  
...  
Keyword(s):  
Cell Line ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
Adrenocortical Cancer

c-Jun N-terminal kinase inhibitor SP600125 enhances barrier function and elongation of human pancreatic cancer cell line HPAC in a Ca-switch model

2014 ◽  
Vol 143 (5) ◽  
pp. 471-479 ◽  
Author(s):  
Takumi Konno ◽  
Takafumi Ninomiya ◽  
Takayuki Kohno ◽  
Shin Kikuchi ◽  
Norimasa Sawada ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Cell Line ◽  
Cancer Cell ◽  
Barrier Function ◽  
Pancreatic Cancer Cell ◽  
Kinase Inhibitor ◽  
Pancreatic Cancer Cell Line ◽  
Cancer Cell Line ◽  
Human Pancreatic Cancer ◽  
Human Pancreatic Cancer Cell

scholarly journals Mapping the interactome of overexpressed RAF kinase inhibitor protein in a gastric cancer cell line

BMC Cancer ◽  
2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Huan Gu ◽  
Xianquan Zhan ◽  
Guiying Zhang ◽  
Lu Yan ◽  
William CS Cho ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cell Line ◽  
Cancer Cell ◽  
Gastric Cancer Cell ◽  
Kinase Inhibitor ◽  
Gastric Cancer Cell Line ◽  
Cancer Cell Line ◽  
Inhibitor Protein ◽  
Raf Kinase ◽  
Raf Kinase Inhibitor Protein

S39, a novel Aurora B kinase inhibitor, shows potent antineoplastic activity in human Hela cervical cancer cell line

2013 ◽  
Vol 35 (6) ◽  
pp. 853-860 ◽  
Author(s):  
Jie Li ◽  
Qingyu Lang ◽  
Haoxing Zhang ◽  
Qiang Huang ◽  
Long Yu
Keyword(s):  
Cervical Cancer ◽  
Cell Line ◽  
Cancer Cell ◽  
Kinase Inhibitor ◽  
Cancer Cell Line ◽  
Antineoplastic Activity ◽  
Cervical Cancer Cell ◽  
Cervical Cancer Cell Line ◽  
Aurora B ◽  
Hela Cervical Cancer Cell

Effects of the EGFR/HER2 kinase inhibitor GW572016 on EGFR- and HER2-overexpressing breast cancer cell line proliferation, radiosensitization, and resistance

2004 ◽  
Vol 58 (2) ◽  
pp. 344-352 ◽  
Author(s):  
Hong Zhou ◽  
Yeon-Shil Kim ◽  
Aaron Peletier ◽  
Wes McCall ◽  
H.Shelton Earp ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Line ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Breast Cancer Cell Line ◽  
Kinase Inhibitor ◽  
Cancer Cell Line

scholarly journals Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines

2021 ◽  
Author(s):  
Krzysztof Koras ◽  
Ewa Kizling ◽  
Dilafruz Juraeva ◽  
Eike Staub ◽  
Ewa Szczurek
Keyword(s):  
Neural Network ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Kinase Inhibitor ◽  
Cancer Cell Line ◽  
Cancer Cell Lines ◽  
Biological Processes ◽  
Drug Sensitivity Prediction

Computational models for drug sensitivity prediction have the potential to revolutionise personalized cancer medicine. Drug sensitivity assays, as well as profiling of cancer cell lines and drugs becomes increasingly available for training such models. Machine learning methods for drug sensitivity prediction must be optimized for: (i) leveraging the wealth of information about both cancer cell lines and drugs, (ii) predictive performance and (iii) interpretability. Multiple methods were proposed for predicting drug sensitivity from cancer cell line features, some in a multi-task fashion. So far, no such model leveraged drug inhibition profiles. Recent neural network-based recommender systems arise as models capable of predicting cancer cell line response to drugs from their biological features with high prediction accuracy. These models, however, require a tailored approach to model interpretability. In this work, we develop a neural network recommender system for kinase inhibitor sensitivity prediction called DEERS. The model utilizes molecular features of the cancer cell lines and kinase inhibition profiles of the drugs. DEERS incorporates two autoencoders to project cell line and drug features into 10-dimensional hidden representations and a feed-forward neural network to combine them into response prediction. We propose a novel model interpretability approach offering the widest possible assessment of the specific genes and biological processes that underlie the action of the drugs on the cell lines. The approach considers also such genes and processes that were not included in the set of modeled features. Our approach outperforms simpler matrix factorization models, achieving R=0.82 correlation between true and predicted response for the unseen cell lines. Using the interpretability analysis, we evaluate correlation of all human genes with each of the hidden cell line dimensions. Subsequently, we identify 67 biological processes associated with these dimensions. Combined with drug response data, these associations point at the processes that drive the cell line sensitivity to particular compounds. Detailed case studies are shown for PHA-793887, XMD14-99 and Dabrafenib. Our framework provides an expressive, multitask neural network model with a custom interpretability approach for inferring underlying biological factors and explaining cancer cell response to drugs.

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