Cell-Line Selectivity Improves the Predictive Power of Pharmacogenomic Analyses and Helps Identify NADPH as Biomarker for Ferroptosis Sensitivity

Abstract

Precision medicine in oncology requires not only identification of cancer-associated mutations but also effective drugs for each cancer genotype, which is still a largely unsolved problem. One approach for the latter challenge has been large-scale testing of small molecules in genetically characterized cell lines. We hypothesized that compounds with high cell-line-selective lethality exhibited consistent results across such pharmacogenomic studies. We analyzed the compound sensitivity data of 6,259 lethal compounds from the NCI-60 project. A total of 2,565 cell-line-selective lethal compounds were identified and grouped into 18 clusters based on their median growth inhibitory GI50 profiles across the 60 cell lines, which were shown to represent distinct mechanisms of action. Further transcriptome analysis revealed a biomarker, NADPH abundance, for predicting sensitivity to ferroptosis-inducing compounds, which we experimentally validated. In summary, incorporating cell-line-selectivity filters improves the predictive power of pharmacogenomic analyses and enables discovery of biomarkers that predict the sensitivity of cells to specific cell death inducers.

Keywords: NADPH; cancer pharmacology; drug mechanisms of action; ferroptosis; pharmacogenomics; tumor biomarkers.

Figure 1. Cell-line-selective lethal compounds were more consistent between two large pharmacogenomic studies

A. Cell-line selectivity of 15 lethal compounds based on AUC measure across the same 471 cell line panel in two studies, CCLE and CGP. Each point indicates one lethal compound. Colors indicate the consistency (the Spearman correlation coefficients) of pathway-compound associations between the two studies, given by Haibe-Kains et al. B. Consistency of pathway-drug associations of cell-line selective and non-selective compounds. Four lethal compounds showing larger cell-line selectivity in both studies in A were defined cell-line-selective lethal, and the others were defined non-selective. *: p-value = 0.0102 using Wilcoxon rank-sum test.

Figure 2. Scheme of the NCI-60 pharmacogenomic analysis

(i) Drug sensitivity data were filtered using the ‘cell-line selectivity’ metric, followed by model-based clustering and consolidation of clustering into 18 clusters. (ii) 13,748 genes with larger dispersion in expression were selected based on IQR of their expression across the NCI-60 cell line panel. (iii) The Spearman correlation coefficient between drug sensitivity of each lethal compound cluster and the transcriptional profile of each gene across the NCI-60 panel was computed. (iv) The correlation coefficients between compound clusters and genes were used to compute GO enrichment analysis for each compound cluster.

Figure 3. Selecting cell-line-selective lethal compounds from the NCI-60 drug sensitivity data

A. GI₅₀ profiles of 10 ferroptosis inducers. Log GI₅₀ values were centered at the median across the NCI-60 cell lines. One of them, CIL56, is known to induce dual lethal mechanisms (ferroptosis + necrotic cell death). The others induce only ferroptosis. B. The cell-line selectivity of 6,249 lethal compounds from the public dataset in comparison with ferroptosis inducers. 2,555 compounds whose cell-line selectivity score was larger than at least one ferroptosis inducer were defined as cell-line selective lethal compounds (the threshold is shown as a dashed line).

Figure 4. Clustering of cell-line-selective lethal compounds based on GI₅₀ profiles A

The GI₅₀ profiles of 53 clusters computed by model-based clustering across 2,565 cell-line selective lethal compounds. The NCI-60 cell lines are represented in rows and 53 compound clusters are in columns. T1, T2: Topoisomerases 1 and 2 inhibitors, A7: A7, guanine alkylators at N7 position, Ds: DNA synthesis inhibitors, Df: antifolates (DNA/RNA synthesis inhibitors), Fe: ferroptosis inducers, YK: Tyr kinase inhibitors. Blue and red indicates a cell line is relatively sensitive or resistant to the compound cluster, respectively. Values are median-centered log₁₀ GI₅₀ values. B. 18 clusters resulting from merging clusters g1 through g6 into six clusters in A. Bar plot on the right indicates the number of compounds assigned to each cluster.

Figure 5. GO enrichment analysis reveals GO terms associated with 18 compound clusters

A. A heatmap showing 386 GO terms associated with the GI₅₀ profiles of at least one of the 18 compound clusters (p-value < 10⁻³). Blue and yellow indicate negative and positive association, i.e., down- and up-regulation of genes in the GO term associated with resistance to the compound cluster, respectively. Bar plot on the right indicates the numbers of compounds assigned into each cluster. B. A subset of A, showing more significant association between GO terms and compound clusters (p-value < 10⁻⁷). The heatmap was further split based on categories of GO terms: BP (biological process), CC (cellular content), and MF (molecular function).

Figure 6. NAD(P)(H)-dependent oxidoreductases as biomarkers of the sensitivity to ferroptosis

A. Ten GO terms associated with ‘oxidoreductases’. B. The significance in the GO enrichment (−log₁₀ p-value) vs ‘specificity’ among the ten GO terms associated with oxidoreductases. c. Hierarchical relationship of the ten GO terms associated with oxidoreductases. D. The specificity of the NAD(P)(H)-dependent oxidoreductases (GO:0016616 + GO:0016651) against the 18 compound clusters.

Figure 7. Experimental validation of NADPH as Predictor of Sensitivity to Ferroptosis

A. Total NAD(H) and NADP(H) levels upon treatment of ferroptosis inducers with or without α-tocopherol. B. The effect of NAD⁺ kinase (NADK) knockdown on the sensitivity to ferroptosis inducers (top), NADK messenger level (bottom left) and NAD(P)(H) level. C. The relationship between the sensitivity to FIN56 and the NADP(H) level across the 12 cell line panel. D. Revised model for ferroptosis.