Systematic correlation identifies cancer drugs with the potential to induce ferroptosis
Ferroptosis is negatively regulated by the SLC7A11-GPX4 signaling axis (Fig. 1A). To systematically identify cancer drugs that modulate ferroptosis response of cancer cells, we correlated the sensitivity profiling of a previously curated small-molecule compound library (n = 481) containing FDA-approved drugs, clinical candidates and those interrogating important targets and/or cellular processes in cancer, against the transcriptomes of a cohort of pan-cancer cell lines (n = 659) [13]. This analysis revealed that gene expression (mRNA) of SLC7A11 most strongly positively correlates with the area under the curve (AUC), a measure of drug sensitivity determined by fitted concentration-response curves, of both RSL3 (Pearson correlation z-score = 9.05; p = 6.10E-05) and Erastin (Pearson correlation z-score = 7.94; p = 6.10E-05), two classical ferroptosis-inducing agents (Fig. 1B, C). This observation indicates that cancer cells with higher SLC7A11 mRNA levels have greater AUC values, and thus are less sensitive or more resistant to ferroptosis induction, which is consistent with previous studies reporting that SLC7A11 is a core negative regulator of ferroptosis and increasingly appreciated as a therapeutic target in cancers [1, 2]. Interestingly, both RSL3 and Erastin showed no significant correlation with GPX4 or SLC3A2 (data not shown), which might be due to the high abundance of the two genes in cancer cells.
Next, we systematically correlated sensitivity data (determined by AUC) of the small-molecule compounds (n = 481) with SLC7A11 gene expression across the entire cancer cell line cohort (n = 659). This analysis identified a total of 139 drug candidates whose AUC values significantly (empirical p-value < 0.01) positively correlate with SLC7A11 (Fig. 1D; Table S1), e.g., ML162, ML210, RSL3, PX-12, PRIMA-1, Piperlongumine, and Erastin that were previously shown to trigger ferroptosis [17], validating the robustness of the systematic correlation and accountability of our results.
The pattern by which these drugs cluster in the systematic correlation analysis suggests that they may share the mode of action in regulating ferroptosis [13]. Importantly, our analysis revealed a set of drug candidates with ferroptosis-activating potential that were neither previously reported nor recorded by the FerrDb (http://www.zhounan.org/ferrdb/), a manually curated dataset elaborating on ferroptosis (Table S1). Analyzing the annotated targets of the identified compounds revealed that several pathways, including ROS (reactive oxygen species) modulation, fatty acid biosynthesis regulation, MDM2-p53 signaling, receptor tyrosine kinases, NAMPT (nicotinamide phosphoribosyltransferase), ubiquitin-proteasome and, particularly, PI3K-AKT1-mTOR and epigenetic regulators, are enriched (Table S2), suggesting that these signaling cascades may be involved in ferroptosis deregulation in cancer. Supporting our findings, recent studies have demonstrated that p53 and mTOR play essential roles in regulating ferroptosis [11, 18]. Importantly, our data implicated an unexpected role for class I histone deacetylase (HDAC) family in ferroptosis regulation (Table S2). To verify this notion, we treated NSCLC cells (H1650, PC9 and HCC827) with Erastin and Vorinostat, a clinically-approved class I HDAC inhibitor, alone or in combination, which showed that the presence of Vorinostat significantly enhances the anti-proliferative effect of Erastin (Fig. 1E). Notably and consistent with our finding, previous studies showed that class I HDAC inhibitors induce ROS-dependent cell death although the underlying mechanisms were not clear [19–21].
Gene networks associated with ferroptosis sensitivity and resistance in pan-cancer
Cancer cells show high heterogeneity in response to ferroptosis-based therapeutics [7], highlighting the need for further stratification. We therefore sought to delineate the gene networks linked with ferroptosis sensitivity and resistance in cancer cells. To generalize the results and minimize drug-specific and potential off-target effects, multiple established ferroptosis-inducing molecules, namely ML162, ML210, Necrosulfonamide, PRIMA, PX-12, RSL3, and Erastin whose sensitivity most significantly correlate with SLC7A11 expression (Fig. 1D) were integrated in our analysis. Correlating drug sensitivity data with basal gene expression of pan-solid cancer cell lines (n = 659) revealed that, as expected (Fig. 1A-C), SLC7A11 re-appeared as of one of the top hits with their expression most significantly positively correlated with the AUC values of all seven drugs (Fig. S1A-E), reinforcing the robustness of our approach.
The genes significantly (empirical p-value < 0.01) correlated with the selected drugs fell into the sensitive group (high expression linked with increased ferroptosis susceptibility or decreased AUC), containing those negatively correlated with drug effects (AUC), and the resistant group whereby the expression of genes positively correlated with AUC values. Notably, ZEB1, previously shown to be a marker for sensitivity to ferroptosis [12], is negatively correlated with several ferroptosis inducers (Fig. 1B; Fig. S1A, B), while FSP1/AIFM2, an anti-ferroptotic regulator independent of GPX4 [5, 6], is in the resistant group (Fig. 2B; Fig. S1A, B). The coverage of previously identified ferroptosis regulators reiterates the validity of the systematic correlation.
To curate a generalized gene signature for ferroptosis sensitivity, we focused on the candidates common to all tested drugs. By setting a stringent threshold at an empirical p-value < 0.01, we finally delineated a set of 46 and 35 genes linked with ferroptosis sensitivity and resistance, respectively (Fig. 2). Supporting our results, two genes (ELAVL1 and ATP6V1G2) in the sensitive group (Fig. 2A) and four (SLC7A11, FSP1/AIFM2, NQO1 and SQSTM1) in the resistant group (Fig. 2B) were previously reported or curated by the FerrDb (http://www.zhounan.org/ferrdb/) [14] that fulfil the same function as assigned by our study. Notably, the functional link between ferroptosis and the vast majority of these genes (44 of 46 in the sensitive and 31 of 35 in resistant group) have not been shown. The interaction network and pathways engaged by the identified genes were shown in Fig. S2A, B. Interestingly, genes in both groups are frequently altered, despite varied degrees in different cancers (Fig. 2C, D), which may enable further stratifications for ferroptosis-based therapy. Importantly, some genes (NAMPT, IGF1R, CYP4F2, BLVRB) in the resistant group are therapeutically exploitable according to the druggable genome database (http://dgidb.org/).
Next, we applied the newly curated ferroptosis sensitivity (FS) and resistance (FR) gene signatures to a pan-cancer cohort (n = 9011) in TCGA whereby transcriptomic and clinical data are available. Low grade glioma (LGG) displays the highest FS but lowest FR score across the solid cancers (Fig. 3A, B), indicating that LGG might be particularly susceptible to ferroptosis-inducing agents. Gliomas with mutations in IDH (isocitrate dehydrogenase), which leads to loss of its normal enzymatic function and the abnormal production of oncometabolite 2-hydroxyglutarate, represent a unique subset genetically and clinically distinct from that carrying wild-type IDH, particularly in LGG [22]. Importantly, we found that IDH1/2-mutant LGG was associated with significantly higher FS score than IDH1/2-wild-type LGG (Fig. 3C), prioritizing an innovative strategy to target IDH IDH1/2-mutant LGG. Supporting our finding, recent studies showed that accumulation of oncometabolite 2-hydroxyglutarate, the product of the mutant IDH, sensitizes cells to ferroptosis [23] and shRNA-based knockdown of IDH2 increases the sensitivity to Erastin-induced ferroptosis [24].
In contrast, lung adenocarcinoma (LUAD) exhibits the highest FR score (Fig. 3B), suggesting that aberrant blockage of ferroptosis might be a key feature in LUAD. The KEAP1-NRF2 axis is well known to negatively regulate ferroptosis, and cancer cells with KEAP1 mutations are associated with increased resistance to ferroptosis [25]. In line with this notion, interrogation of a LUAD cohort in TCGA revealed that tumors with KEAP1 mutations, frequent in LUAD samples, display significantly higher FR signature scores (Fig. 3D). Importantly, a high FS score is associated with significantly better overall survival (OS) and progression-free interval (PFI), while a high FR with poor OS and PFI in patients with LGG and LUAD (Fig. 4A, B), validating the clinical relevance of the FS and FR gene signatures.
Moreover, a previous study associated susceptibility to ferroptosis with mesenchymal cell state [12]. Supporting this notion, we identified ZEB1 as one of the most strongly negatively correlated genes with RSL3, Erastin, ML162 and ML210 (Fig. 1B, C; Fig. 1A, B). We thus further investigated the link between EMT and FS signatures across the pan-cancer cohort in TCGA, and found a positive correlation of these two phenotypes in most cancer lineages (Fig. 4C).
To seek additional evidence for the applicability of the newly curated FS and FS signatures (Table S3), we prospectively probed ferroptosis response in a large cohort of pan-cancer cell lines from CCLE database (n = 890). Tumor cells of histological origins from small cell lung cancer (SCLC), which was not included in TCGA project, and sympathetic nervous tissue (autonomic ganglia cells) dominate both the top 50 and top 100 cell lines based on their FS scores, with the highest and second-highest FS signature, respectively (Fig. 5A-C). SCLC cells have significantly higher FS scores than NSCLC (p < 2.2E-16) (Fig. 5D), consistent with the results derived from cancer patients that LUAD and LUSC (lung squamous cell carcinoma), two major types of NSCLC, show low FS but high FR scores (Fig. 5A). Similarly, pheochromocytoma/ paraganglioma (PCPG) cancer, with the same origin as autonomic ganglia cells, exhibits the second-highest FS signature in patients of pan-cancer (Fig. 3A). These results reinforce the applicability and reliability of the FS/FR gene signature and further suggest that SCLC, a highly aggressive neuroendocrine lung cancer lacking targeted therapies, might particularly benefit from ferroptosis-based therapeutics.
Finally, we applied the ferroptosis gene signatures (Fig. 2A, B) to another independent study cohort of non-haematopoietic/lymphoid cancer cell lines (n = 99) treated with Erastin [4]. SCLC cell lines showed significantly higher FS but lower FR signature scores than NSCLC cells (Fig. 6A; Table S4). Importantly, sensitivity profiling revealed significantly lower AUC values of Erastin in SCLC than NSCLC cells (Fig. 6B), indicating that SCLC cells are endowed with greater sensitivity to Erastin than NSCLC, which is in line with our results obtained from the analysis of CCLE project (Fig. 5A-C). Strikingly, the FS signature score was significantly negatively correlated with the AUC value of Erastin across lung cancer (Pearson r = -0.79, p-value = 3.3e-08) and non-hematopoietic/lymphoid-derived cancer cell lines (Pearson r = -0.39, p-value = 7.3e-05) (Fig. 6C), demonstrating that cancer cells with a higher FS signature are indeed characterized by increased susceptibility to the ferroptosis inducer Erastin.
Together, our work identified new cancer drugs with the potential to relaunch ferroptosis and delineated the gene networks associated with responses to ferroptosis in pan-cancer. The applicability and credibility of our findings are demonstrated by a multitude of lines of evidence from independent study cohorts of cancer cell lines and cancer patients.