Addendum: The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Nature ◽  
2012 ◽  
Vol 492 (7428) ◽  
pp. 290-290 ◽  
Author(s):  
Jordi Barretina ◽  
Giordano Caponigro ◽  
Nicolas Stransky ◽  
Kavitha Venkatesan ◽  
Adam A. Margolin ◽  
...  
Keyword(s):  
Cell Line ◽  
Cancer Cell ◽  
Anticancer Drug ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Predictive Modelling ◽  
Cancer Cell Line Encyclopedia

scholarly journals Addendum: The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Nature ◽  
2018 ◽  
Vol 565 (7738) ◽  
pp. E5-E6 ◽  
Author(s):  
Jordi Barretina ◽  
Giordano Caponigro ◽  
Nicolas Stransky ◽  
Kavitha Venkatesan ◽  
Adam A. Margolin ◽  
...  
Keyword(s):  
Cell Line ◽  
Cancer Cell ◽  
Anticancer Drug ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Predictive Modelling ◽  
Cancer Cell Line Encyclopedia

scholarly journals The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Nature ◽  
2012 ◽  
Vol 483 (7391) ◽  
pp. 603-607 ◽  
Author(s):  
Jordi Barretina ◽  
Giordano Caponigro ◽  
Nicolas Stransky ◽  
Kavitha Venkatesan ◽  
Adam A. Margolin ◽  
...  
Keyword(s):  
Cell Line ◽  
Cancer Cell ◽  
Anticancer Drug ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Predictive Modelling ◽  
Cancer Cell Line Encyclopedia

22 The Cancer Cell Line Encyclopedia - Using Preclinical Models to Predict Anticancer Drug Sensitivity

2012 ◽  
Vol 48 ◽  
pp. S5-S6 ◽  
Author(s):  
J. Barretina ◽  
G. Caponigro ◽  
N. Stransky ◽  
K. Venkatesan ◽  
A.A. Margolin ◽  
...  
Keyword(s):  
Cell Line ◽  
Cancer Cell ◽  
Anticancer Drug ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Preclinical Models ◽  
Cancer Cell Line Encyclopedia

scholarly journals In Vitro Anticancer Drug Sensitivity Sensing through Single-Cell Raman Spectroscopy

Biosensors ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 286
Author(s):  
Jingkai Wang ◽  
Kaicheng Lin ◽  
Huijie Hu ◽  
Xingwang Qie ◽  
Wei E. Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Anticancer Drugs ◽  
Cancer Cell ◽  
Anticancer Drug ◽  
Metabolic Activity ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Sensitivity Testing

Traditional in vitro anticancer drug sensitivity testing at the population level suffers from lengthy procedures and high false positive rates. To overcome these defects, we built a confocal Raman microscopy sensing system and proposed a single-cell approach via Raman-deuterium isotope probing (Raman-DIP) as a rapid and reliable in vitro drug efficacy evaluation method. Raman-DIP detected the incorporation of deuterium into the cell, which correlated with the metabolic activity of the cell. The human non-small cell lung cancer cell line HCC827 and human breast cancer cell line MCF-7 were tested against eight different anticancer drugs. The metabolic activity of cancer cells could be detected as early as 12 h, independent of cell growth. Incubation of cells in 30% heavy water (D2O) did not show any negative effect on cell viability. Compared with traditional methods, Raman-DIP could accurately determine the drug effect, meanwhile, it could reduce the testing period from 72–144 h to 48 h. Moreover, the heterogeneity of cells responding to anticancer drugs was observed at the single-cell level. This proof-of-concept study demonstrated the potential of Raman-DIP to be a reliable tool for cancer drug discovery and drug susceptibility testing.

scholarly journals Matching anticancer compounds and tumor cell lines by neural networks with ranking loss

2022 ◽  
Vol 4 (1) ◽  
Author(s):  
Paul Prasse ◽  
Pascal Iversen ◽  
Matthias Lienhard ◽  
Kristina Thedinga ◽  
Chris Bauer ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Cell Line ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Inhibitory Concentration ◽  
Cancer Cell Line ◽  
Tumor Cell Line ◽  
Ranking Problem ◽  
Ranking Loss

ABSTRACT Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug’s inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model’s capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data.

scholarly journals Tumor Suppressors Status in Cancer Cell Line Encyclopedia

2013 ◽  
Vol 7 (4) ◽  
pp. 791-798 ◽  
Author(s):  
Dmitriy Sonkin ◽  
Mehedi Hassan ◽  
Denis J. Murphy ◽  
Tatiana V. Tatarinova
Keyword(s):  
Cell Line ◽  
Cancer Cell ◽  
Tumor Suppressors ◽  
Cancer Cell Line ◽  
Cancer Cell Line Encyclopedia
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