scholarly journals Assessment of pharmacogenomic agreement

2016 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Nehme El-Hachem ◽  
Rene Quevedo ◽  
Petr Smirnov ◽  
Anna Goldenberg ◽  
...  
Keyword(s):  
Cell Line ◽  
Reasonable Agreement ◽  
Large Scale ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Genomic Data ◽  
Clinical Settings ◽  
Response Data ◽  
Pharmacological Data

AbstractIn 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer1(GDSC) and Cancer Cell Line Encyclopedia2(CCLE), were inconsistent3. The GDSC and CCLE investigators recently reported that their respective studies exhibit reasonable agreement and yield similar molecular predictors of drug response4, seemingly contradicting our previous findings3. Reanalyzing the authors’ published methods and results, we found that their analysis failed to account for variability in the genomic data and more importantly compared different drug sensitivity measures from each study, which substantially deviate from our more stringent consistency assessment. Our comparison of the most updated genomic and pharmacological data from the GDSC and CCLE confirms our published findings that the measures of drug response reported by these two groups are not consistent5. We believe that a principled approach to assess the reproducibility of drug sensitivity predictors is necessary before envisioning their translation into clinical settings.

scholarly journals Assessment of pharmacogenomic agreement

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 825 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Nehme El-Hachem ◽  
Rene Quevedo ◽  
Petr Smirnov ◽  
Anna Goldenberg ◽  
...  
Keyword(s):  
Cell Line ◽  
Reasonable Agreement ◽  
Large Scale ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Genomic Data ◽  
Clinical Settings ◽  
Response Data ◽  
Pharmacological Data

In 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE), were inconsistent. The GDSC and CCLE investigators recently reported that their respective studies exhibit reasonable agreement and yield similar molecular predictors of drug response, seemingly contradicting our previous findings. Reanalyzing the authors’ published methods and results, we found that their analysis failed to account for variability in the genomic data and more importantly compared different drug sensitivity measures from each study, which substantially deviate from our more stringent consistency assessment. Our comparison of the most updated genomic and pharmacological data from the GDSC and CCLE confirms our published findings that the measures of drug response reported by these two groups are not consistent. We believe that a principled approach to assess the reproducibility of drug sensitivity predictors is necessary before envisioning their translation into clinical settings.

scholarly journals 3DIV update for 2021: a comprehensive resource of 3D genome and 3D cancer genome

2020 ◽  
Vol 49 (D1) ◽  
pp. D38-D46
Author(s):  
Kyukwang Kim ◽  
Insu Jang ◽  
Mooyoung Kim ◽  
Jinhyuk Choi ◽  
Min-Seo Kim ◽  
...  
Keyword(s):  
Cell Line ◽  
Large Scale ◽  
Three Dimensional ◽  
Cancer Cell Line ◽  
Cancer Genome ◽  
Structural Variations ◽  
3D Genome ◽  
Tightly Coupled ◽  
Regulatory Effects ◽  
The Impact

Abstract Three-dimensional (3D) genome organization is tightly coupled with gene regulation in various biological processes and diseases. In cancer, various types of large-scale genomic rearrangements can disrupt the 3D genome, leading to oncogenic gene expression. However, unraveling the pathogenicity of the 3D cancer genome remains a challenge since closer examinations have been greatly limited due to the lack of appropriate tools specialized for disorganized higher-order chromatin structure. Here, we updated a 3D-genome Interaction Viewer and database named 3DIV by uniformly processing ∼230 billion raw Hi-C reads to expand our contents to the 3D cancer genome. The updates of 3DIV are listed as follows: (i) the collection of 401 samples including 220 cancer cell line/tumor Hi-C data, 153 normal cell line/tissue Hi-C data, and 28 promoter capture Hi-C data, (ii) the live interactive manipulation of the 3D cancer genome to simulate the impact of structural variations and (iii) the reconstruction of Hi-C contact maps by user-defined chromosome order to investigate the 3D genome of the complex genomic rearrangement. In summary, the updated 3DIV will be the most comprehensive resource to explore the gene regulatory effects of both the normal and cancer 3D genome. ‘3DIV’ is freely available at http://3div.kr.

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 Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel

2015 ◽  
pp. btv529 ◽  
Author(s):  
Isidro Cortés-Ciriano ◽  
Gerard J. P. van Westen ◽  
Guillaume Bouvier ◽  
Michael Nilges ◽  
John P. Overington ◽  
...  
Keyword(s):  
Growth Inhibition ◽  
Cell Line ◽  
Cancer Cell ◽  
Large Scale ◽  
Cancer Cell Line ◽  
Cell Line Panel

scholarly journals Discovering long noncoding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

2019 ◽  
Vol 116 (44) ◽  
pp. 22020-22029 ◽  
Author(s):  
Aritro Nath ◽  
Eunice Y. T. Lau ◽  
Adam M. Lee ◽  
Paul Geeleher ◽  
William C. S. Cho ◽  
...  
Keyword(s):  
Anticancer Drug ◽  
Noncoding Rna ◽  
Large Scale ◽  
Drug Response ◽  
Cancer Cell Line ◽  
Systematic Evaluation ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Clinical Biomarkers ◽  
Response Predictors

Large-scale cancer cell line screens have identified thousands of protein-coding genes (PCGs) as biomarkers of anticancer drug response. However, systematic evaluation of long noncoding RNAs (lncRNAs) as pharmacogenomic biomarkers has so far proven challenging. Here, we study the contribution of lncRNAs as drug response predictors beyond spurious associations driven by correlations with proximal PCGs, tissue lineage, or established biomarkers. We show that, as a whole, the lncRNA transcriptome is equally potent as the PCG transcriptome at predicting response to hundreds of anticancer drugs. Analysis of individual lncRNAs transcripts associated with drug response reveals nearly half of the significant associations are in fact attributable to proximal cis-PCGs. However, adjusting for effects of cis-PCGs revealed significant lncRNAs that augment drug response predictions for most drugs, including those with well-established clinical biomarkers. In addition, we identify lncRNA-specific somatic alterations associated with drug response by adopting a statistical approach to determine lncRNAs carrying somatic mutations that undergo positive selection in cancer cells. Lastly, we experimentally demonstrate that 2 lncRNAs, EGFR-AS1 and MIR205HG, are functionally relevant predictors of anti-epidermal growth factor receptor (EGFR) drug response.

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