Pharmacogenomic agreement between two cancer cell line data sets

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doi:10.1038/nature15736

Cited by 355 publications

(178 citation statements)

References 10 publications

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“…Pearson correlation ( r ) coefficients of adjusted AUC IC50 calculated by this study (blue bars) were matched to the similar analyses performed by Haibe-Kains et al [5] (red bars, unadjusted AUC, Spearman correlation (ρ)), and Stransky et al [6] (green bars, unadjusted AUC, r ). B. CCLE and CTRP correlations ( r, adjusted AUC IC50 ).…”

Section: Resultsmentioning

confidence: 70%

“…This resulted in the expected poor agreement between the two datasets. To account for the analytical differences, Stransky et al capped IC 50 at a maximal drug concentration tested by the GDSC study (Supplementary Table S1) [6]. This produced a large number of capped IC 50 values, which are not supported by the experimental data for the 15 drugs shared by the two databases (Supplementary Table S2), and overestimated the correlation between IC 50 values.…”

Section: Resultsmentioning

confidence: 99%

“…These challenges were highlighted, when Haibe-Kains et al found inconsistencies in drug sensitivity data from CCLE and GDSC [5]. In response, the authors of CCLE and GDSC re-analyzed drug sensitivity data accounting for the differences in the analytical approach employed by the two studies and reported a significantly better agreement [6]. …”

Section: Introductionmentioning

confidence: 99%

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Integrating heterogeneous drug sensitivity data from cancer pharmacogenomic studies

et al. 2016

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The consistency of in vitro drug sensitivity data is of key importance for cancer pharmacogenomics. Previous attempts to correlate drug sensitivities from the large pharmacogenomics databases, such as the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC), have produced discordant results. We developed a new drug sensitivity metric, the area under the dose response curve adjusted for the range of tested drug concentrations, which allows integration of heterogeneous drug sensitivity data from the CCLE, the GDSC, and the Cancer Therapeutics Response Portal (CTRP). We show that there is moderate to good agreement of drug sensitivity data for many targeted therapies, particularly kinase inhibitors. The results of this largest cancer cell line drug sensitivity data analysis to date are accessible through the online portal, which serves as a platform for high power pharmacogenomics analysis.

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Section: Resultsmentioning

confidence: 70%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrating heterogeneous drug sensitivity data from cancer pharmacogenomic studies

et al. 2016

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“…Expression data of cell lines were downloaded from Cancer Cell Line Encyclopedia (CCLE) 46,47 and were normalized by Robust Multiarray Averaging (RMA) method. Probesets annotation and differential gene expression were carried out as described above.…”

Section: Methodsmentioning

confidence: 99%

Smoker and non-smoker lung adenocarcinoma is characterized by distinct tumor immune microenvironments

et al. 2018

OncoImmunology

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Tobacco smoking causes DNA damages in epithelial cells and immune dysfunction in the lung, which collectively contribute to lung carcinogenesis and progression. However, potential mechanisms by which tumor-infiltrating immune cells contribute to lung cancer survival and their differential contributions in ever-smokers and never-smokers are not well studied. Here, we performed integrative analysis of 11 lung cancer gene-expression datasets, including 1,111 lung adenocarcinomas and 200 adjacent normal lung samples. Distinct pathways were altered in lung carcinogenesis in ever-smokers and never-smokers. Never-smoker patients had a better outcome than ever-smoker patients. We characterized compositional patterns of 21 types of immune cells in lung adenocarcinomas and revealed the complex association between immune cell composition and clinical outcomes. Interestingly, we found two subsets of immune cells, mast cells and CD4+ memory T cells, which had completely opposite associations with outcomes in resting and activated status. We further discovered that several chemokines and their associated receptors (e.g., CXCL11-CX3CR1 axis) were selectively altered in lung tumors in response to cigarette smoking and their abundances showed stronger correlation with fractions of these immune subsets in ever-smokers than never-smokers. The status switched from the resting to activated forms in mast cells and CD4+ memory T cells might manifest some important processes induced by cigarette smoking during tumor development and progression. Our findings suggested that aberrant activation of mast cells and CD4+ memory T cells plays crucial roles in cigarette smoking-induced immune dysfunction in the lung, which contributes to tumor development and progression.

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“…Our observations that FCM displayed differential effects depending on what cell line was used, could explain differences observed in the literature when comparing normal fibroblasts to carcinoma-associated fibroblasts. According to the Cancer Cell Line Encyclopedia (CCLE) [149,150] Cisplatin is a recognized cornerstone in the treatment of lung cancer.…”

Section: Fcm Protects Against Cisplatin-induced Deathmentioning

confidence: 99%

Age-related host factors regulate lung cancer progression.

Greenwell¹

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Pharmacogenomic agreement between two cancer cell line data sets

Cited by 355 publications

References 10 publications

Integrating heterogeneous drug sensitivity data from cancer pharmacogenomic studies

Integrating heterogeneous drug sensitivity data from cancer pharmacogenomic studies

Smoker and non-smoker lung adenocarcinoma is characterized by distinct tumor immune microenvironments

Age-related host factors regulate lung cancer progression.

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