Inconsistency in large pharmacogenomic studies

Abstract

Two large-scale pharmacogenomic studies were published recently in this journal. Genomic data are well correlated between studies; however, the measured drug response data are highly discordant. Although the source of inconsistencies remains uncertain, it has potential implications for using these outcome measures to assess gene-drug associations or select potential anticancer drugs on the basis of their reported results.

Extended Data Figure 1

Intersection between the pharmacogenomic studies in terms of drugs, cell lines and genes. (a) Venn diagram reporting the number of drugs shared between CGP and CCLE studies; (b) Description of the 15 anticancer drugs screened both in CGP and CCLE studies; (c) Venn diagram reporting the number of drugs shared between CGP, CCLE and GSK studies; (d) Venn diagram reporting the number of cell lines shared by CGP and CCLE studies; (e) Number of cell lines for each tissue types among the 471 common to CGP and CCLE studies; (f) Venn diagram reporting the number of cell lines shared between CGP, CCLE and GSK studies; (g) Venn diagram reporting the number of genes whose presence of mutations was assessed both in CGP and CCLE studies; (h) Venn diagram reporting the number of genes whose expression was assessed both in CGP and CCLE studies.

Extended Data Figure 2

Box plot of the correlations of missense mutation profiles between identical cell lines in CGP and CCLE. Two-sided Wilcoxon rank sum test was used to test whether agreement was significantly higher in identical cell lines compared to different cell lines (upper right corner).

Extended Data Figure 3

Scatter plot reporting the IC₅₀ values of Camptothecin for 252 cell lines screened within the CGP project, as measured at the facilities of the Massachusetts General Hospital (MGH) and the Wellcome Trust Sanger Institute (WTSI). Spearman correlation coefficient (R_s) is reported in the upper left corner.

Extended Data Figure 4

Scatter plots reporting the drug sensitivity measurements, which are the IC₅₀ values within the range of tested concentration (thus excluding extrapolated IC₅₀ in CGP and placeholder values in CCLE) in the 471 cell lines and for each the 15 drugs investigated both in CGP and CCLE. The last bar plot (bottom right corner) reports the Spearman correlation coefficient (Rs) for each drug where significance of each correlation coefficient is reported using the symbol ‘*’ if two-sided p-value < 0.05.

Extended Data Figure 5

Scatter plots reporting the drug sensitivity (AUC) measured in the 471 cell lines and for each the 15 drugs investigated both in CGP and CCLE. The last bar plot (bottom right corner) reports the Spearman correlation coefficient (Rs) for each drug where significance of each correlation coefficient is reported using the symbol ‘*’ if two-sided p-value < 0.05.

Extended Data Figure 6

Scatter plots reporting the gene-drug associations computed with AUC, as quantified by the standardized coefficient of the gene of interest in a linear model controlled for tissue type, in the 471 cell lines and for each the 15 drugs investigated both in CGP and CCLE. The last bar plot (bottom right corner) reports the Spearman correlation coefficient (Rs) for each drug where significance of each correlation coefficient is reported using the symbol ‘*’ if two-sided p-value < 0.05.

Extended Data Figure 7

Scatter plots reporting the pathway-drug associations computed with AUC, as quantified by the standardized coefficient of the gene of interest in a linear model controlled for tissue type, in the 471 cell lines and for each the 15 drugs investigated both in CGP and CCLE. The last bar plot (bottom right corner) reports the Spearman correlation coefficient (Rs) for each drug where significance of each correlation coefficient is reported using the symbol ‘*’ if two-sided p-value < 0.05.

Extended Data Figure 8

Scatter plots reporting the mutation-drug associations computed with AUC, as quantified by the standardized coefficient of the gene of interest in a linear model controlled for tissue type, in the 471 cell lines and for each the 15 drugs investigated both in CGP and CCLE. The last bar plot (bottom right corner) reports the Spearman correlation coefficient (Rs) for each drug where significance of each correlation coefficient is reported using the symbol ‘*’ if two-sided p-value < 0.05.

Extended Data Figure 9

Comparison of drug sensitivity measured in CGP and CCLE with GSK. (a) Scatter plots reporting the drug sensitivity measurements (IC₅₀) of all drugs and cell lines screened both in CCLE and GSK datasets (2 drugs in 249 cell lines). (b) Scatter plots reporting the drug sensitivity measurements (IC₅₀) of all drugs and cell lines screened both in CCLE and GSK datasets (5 drugs in 231 cell lines).

Figure 1

Consistency between gene expression profiles of cell lines in CGP and CCLE studies.(a) Box plot representing the correlation coefficients of the biological replicates in CGP, identical and between different cell lines from CGP and CCLE datasets; (b)heatmap representing the correlations between gene expression profiles of cell lines; the order of cell lines is identical in rows (CCLE) and columns (CGP).

Figure 2

Consistency between drug sensitivity data published in CGP and CCLE studies. (a) Scatter plots reporting the drug sensitivity (IC₅₀) measured in the 471 cell lines and for the 15 drugs investigated both in CGP and CCLE. (b) Bar plot representing the Spearman correlation coefficient for IC₅₀ and AUC drug sensitivity measures; significance is reported using the symbol ‘*’ if two-sided p-value < 0.05.

Figure 3

Consistency of associations of genomics features with drug sensitivity. The bars represent the Spearman correlation coefficients computed from: (a) all and (b) significant (FDR<20%) gene-drug associations; (c) all and (d) significant (FDR<20%) pathway-drug associations, as estimated in CGP and CCLE datasets. Significance is reported using the symbol ‘*’ if two-sided p-value < 0.05.

Figure 4

Effects on consistency by intermixing CCLE and CGP data. The box plots report the correlations between: (a) all and (b) significant (FDR < 20%) gene-drug associations with IC₅₀; (c) all and (d) significant (FDR < 20%) gene-drug associations with AUC. Each box represent the datasets used to compute correlations:‘Original’ refers to the original datasets; ‘GeneCGP.fixed’ refers to [CGP_g+CGP_d] vs. [CGP_g+CCLE_d]; ‘GeneCCLE.fixed’ refers to [CCLE_g+CGP_d] vs. [CCLE_g+CCLE_d]; ‘DrugCGP.fixed’ refers to [CGP_g+CGP_d] vs. [CCLE_g+ CGP_d]; ‘DrugCCLE.fixed’ refers to [CGP_g+CCLE_d] vs. [CCLE_g+CCLE_d] where _gand _d stand for gene expression and drug sensitivity data, respectively.