HIF-1/2α mediates sensitivity to ferroptosis in CCCs

Although ferroptosis is frequently triggered under pathological conditions such as ischemia/reperfusion and traumatic brain injuries³⁴, how intrinsic ferroptosis sensitivity arises is poorly understood. Illuminating the mechanisms underlying ferroptosis susceptibility is important for identifying the right patient cohort that would benefit from ferroptosis-inducing agents. Notably, the highly variable sensitivity to ferroptosis across CTRP models appears independent of the relative levels of GPX4 mRNA (Supplementary Fig. ^3a). While CCCs arising from different lineages remain genetically distinct, we focused on characterizing ccRCCs, the most frequent and genetically defined CCC subtype, by performing a genome-wide CRISPR suppressor/resistance screen in 786-O cells to identify mediators of ML210 sensitivity (Fig. 2a, Supplementary Data ¹–³). Among the genes required for ML210 sensitivity in all three time-points, the top hits included acyl-CoA synthetase long-chain family member 4 (ACSL4) and Kelch-like ECH associated protein 1 (KEAP1), two genes that were previously implicated in ferroptosis^35,36 (Fig. 2b); we verified both genes as required for ferroptosis in CCCs (Supplementary Fig. ^3b–i). Importantly, we found that genes related to the HIF pathway, including EPAS1 (encoding HIF-2α), EP300, FOSL1, CITED1, as well as ARNT (encoding HIF-1β) are enriched in the top screening hits in one or multiple conditions^37,38 (Fig. 2b). HIF-2α is a driver of ccRCC oncogenesis and acquisition of the clear-cell morphology^39,40, and its emergence as a ferroptosis regulator is consistent with a prior study revealing that VHL-restoration diminished the sensitivity to erastin and BSO in RCC4, another ccRCC cell line¹⁴. Gene suppression with independent sgRNA and shRNA libraries validated this pathway as mediators of ML210 sensitivity in 786-O cells (Fig. 2c, Supplementary Data ⁹ and ¹⁰). HIF-2α-dependent sensitivity to ferroptosis was also observed in ccRCC cells expressing individual HIF-2α-targeting sgRNAs and shRNAs, in single-cell EPAS1^−/− clones with or without restored EPAS1-GFP expression, as well as HIF-2α/GPX4 double knockouts (Fig. 2d–h, Supplementary Fig. ^4a–f and Supplementary Data ⁸). While loss of HIF-2α did not compromise the proliferation rate of ccRCC cells in vitro^41,42 (Supplementary Fig. ^4g), HIF-2α ablation significantly reduced lipid peroxidation levels (Supplementary Fig. ^4h–i), providing a strong indication of reduced susceptibility to ferroptosis.

Open in a separate window

Fig. 2

Genome-wide CRISPR screen identifies HIF-2α as a driver of ferroptosis susceptibility. a Experimental scheme describing the genome-wide CRISPR resistance screening to identify mediators of ML210 sensitivity in 786-O cells. b Volcano plot highlighting top enriched CRISPR hits in 786-O cells treated with ML210 for 4, 6 or 8 days. Red genes, HIF pathway genes. Purple genes, representative known ferroptosis regulators. c Relative AUC values of the Cas9/sgRNA (CRISPR) or shRNA (RNAi) transfected 786-O cells treated with a 7-point, 2-fold dilution series of ML210. The viability of cells expressing each sgRNA/shRNA (blue dots) was normalized to the respective DMSO-treated condition. AUC values were normalized to 1 as the total area-under-curve for the concentration range of ML210. d Immunoblot showing the HIF-2α/HIF-1β protein levels in control (sgNC) or EPAS1-targeting sgRNA-expressing 786-O-Cas9 and 769-P-Cas9 cells. e Viability curves of control (sgNC) or EPAS1-targeting sgRNA-expressing 786-O-Cas9 and 769-P-Cas9 cells treated with indicated concentrations of ML210 or RSL3. Representative plot of experiments repeated three times. f Immunoblot showing HIF-2α and HIF-1β protein levels in wildtype (WT, EPAS1^+/+) 786-O cells and four EPAS1^−/− single-cell clones generated by CRISPR/Cas9. g Viability curves for WT EPAS1^+/+ 786-O or EPAS1^−/− clones treated with indicated concentrations of ML210 or RSL3. Representative plot of experiments repeated three times. h Viability curves for EPAS1^−/− 786-O single-cell clones 1D7 and 1E3 expressing vector or EPAS1-GFP, then treated with indicated concentrations of ML210 or RSL3. Representative plot of experiments repeated three times. β-Actin was used as loading controls in immunoblots

The HIF-2α-induced ferroptosis sensitivity underscores a prominent example of oncogene-induced vulnerability in ccRCC. Notably, cancer cells with VHL mutations exhibited greater dependence on GPX4 than VHL wildtype cells in a pan-cancer DepMap analysis (Supplementary Fig. ^4j). Intriguingly, OCCC tumors mimic the hypoxia response in the endometrium cyst microenvironment with activated HIF-1α⁴³. Notably, HIF-1α-depletion by CRISPR diminished the sensitivity to ferroptosis in ES-2 cells (Supplementary Fig. ^4k–l). Collectively, our results indicate that the HIF pathway is a central driver of ferroptosis susceptibility in CCCs. In addition, HIF prolyl hydrolase 2 (EGLN1) diminishes ferroptosis susceptibility by destabilizing HIF-1α in A549 non-small cell lung cancer cells⁴⁴, implying a more general role of this pathway in ferroptosis in other cancer contexts.

HIF-2α selectively enriches polyunsaturated lipids in CCCs

Ferroptosis is executed by peroxidized membrane phospholipids, particularly phosphatidylethanolamines (PEs) that contain polyunsaturated fatty acyl (PUFA) chains including arachidonic acid (C20:4) and docosahexaenoic acid (C22:6)^45,46. Although HIFs drive extensive reprogramming of lipid metabolism and promote lipid storage in cancer cells, how these metabolic alterations are associated with ferroptosis sensitivity is unknown. To determine how the HIF pathway drives ferroptosis sensitivity, we performed lipidomic profiling in EPAS1^−/− single-cell 786-O clones (Supplementary Fig. ^{5a, b}, Supplementary Data ⁴). HIF-2α-depletion induced a profound shift in the lipidome of 786-O cells, with significant loss in triacylglycerols (TAGs), the major components of lipid droplets, and in phospholipids (Fig. 3a, Supplementary Fig. ^{5c, d}). Remarkably, PUFA-TAGs exhibited the most significant reduction in response to HIF-2α-depletion compared with TAGs containing saturated/monounsaturated fatty acyl chains (SFA/MUFA-TAGs) (Fig. 3a, b). Alterations in TAG saturation levels during hypoxia response was also previously noted in ccRCC cells⁴⁷. Moreover, most PEs and PE-plasmalogens (ePEs), including the ferroptosis-relevant C36:4, C38:4/5/6 and C40:6 PEs and C36:5, C38:5 and C40:7 ePEs, were significantly reduced in EPAS1^−/− cells (Fig. 3c, d). Finally, free PUFA levels were also strongly dependent on HIF-2α activity, whereas free SFA/MUFAs were less affected by HIF-2α status (Fig. 3e, Supplementary Data ⁵). Most of these alterations were reverted by HIF-2α-GFP overexpression, supporting these events as specifically driven by HIF-2α (Fig. 3a–e, Supplementary Fig. ^5a–d). Importantly, exogenous PUFA (arachidonic acid, C20:4) treatment significantly sensitized WT or HIF-2α-depleted 786-O and 769-P cells to ferroptosis (Fig. 3f). Taken together, these data suggest that HIF-2α drives ferroptosis susceptibility by selective enrichment and incorporation of PUFAs into glycerolipids (TAGs and phospholipids).

Open in a separate window

Fig. 3

HIF-2α selectively enriches polyunsaturated lipids. a Heatmap representing the relative lipid abundances in indicated cell lines. The abundance of each lipid species is normalized to the mean of that in the EPAS1^+/+ 786-O WT cells and the ratios are log2 transformed. The lipids are grouped by classes, and within each class, the lipid species are ordered first with increasing carbon number, then with increasing unsaturation levels. Abbreviations: CE, cholesterol ester; Cer, ceramide; MAG, monoacylglycerol; DAG, diacylglycerol; TAG, triacylglycerol; LPC, lysophosphatidylcholine; LPE, lysophosphatidylethanolamine; PC, phosphatidylcholine; PE, phosphatidylethanolamine; ePC, (vinyl ether-linked) PC-plasmalogen; ePE, (vinyl ether-linked) PE-plasmalogen; PI, phosphatidylinositol; PS, phosphatidylserine; SM, sphingomyelin. Blue: down-regulated relative to the WT cells, red: upregulated relative to the WT cells. The wave-like pattern in the TAG class corresponds to the more significant losses in the polyunsaturated fatty acyl (PUFA)-TAGs than saturated/monounsaturated fatty acyl (SFA/MUFA)-TAGs in response to HIF-2α-depletion. S, ferroptosis-sensitive; (R), ferroptosis-resistant. b Volcano plots showing the changes in TAGs grouped as PUFA-TAGs (red fill) and SFA/MUFA-TAGs (white fill) between the indicated cell lines. n=3, two-tailed t-test. c Volcano plots showing changes in PE and ePE lipids grouped as PUFA-PE/ePEs (red fill) and SFA/MUFA-PE/ePEs (white fill) between the indicated cell lines. d Bar graph representing the relative abundances of the indicated PUFA-PE/ePE lipids in the labeled groups. Log2 fold changes relative to 786-O WT cells are presented for each condition. n=3, error bars: ±s.d. e Bar graph representing the relative abundances of the indicated free fatty acids grouped as PUFAs or SFA/MUFAs in the indicated conditions. nd, not-detectable under the experimental condition. n=3, error bars: ±s.d. f Viability curves of 786-O-Cas9 and 769-P-Cas9 cells expressing control (sgNC) or EPAS1-targeting sgRNAs, first treated with BSA or PUFA (arachidonic acid, C20:4) for 3 days, then treated with indicated concentrations of ML210 or RSL3 for 48h. Representative plot of experiments repeated twice. g The ratios between PUFA-PE/ePE and total PE/ePE (left), and between PUFA-PC/ePC and total PC/ePCs in ccRCC tumor samples (n=49; red) and the matched normal tissues (n=49; gray) from previously reported lipidomics datasets. Student’s T-test, ***p<0.001

By interrogating a previous lipidomics dataset comprising 49 ccRCC normal/tumor tissue pairs⁴⁸, we found that human ccRCC tumors exhibited higher levels of PUFA-PE/ePEs and PUFA-PC/ePCs than normal renal tissues (Fig. 3g). These PUFA-lipids were further enriched in high-grade tumors (stage III/IV) when compared with low-grade samples (stage I/II) (Supplementary Fig. ^5e). Thus, human ccRCC tumors, which commonly possess constitutively active HIF-2α⁴⁸, are in a PUFA-lipid-enriched cell state and are likely to be sensitive to ferroptosis.

HILPDA mediates HIF-2α’s ferroptosis sensitization activity

To determine the downstream mediators of HIF-2α’s activity in stimulating the selective enrichment of PUFA-lipids and driving ferroptosis sensitivity, we first identified HIF-2α-dependent genes and then re-expressed each gene in EPAS1^−/− cells to identify candidate genes that restore sensitivity to ferroptosis (Fig. 4a). We were able to collect cDNAs for 77 of the 149 HIF-2α-activated genes identified by RNA-Seq, including 9 of the 11 lipid metabolism genes (Fig. 4b, Supplementary Data ⁶). RNA-Seq and western blot analyses also ruled out any significant changes in ACSL family expression induced by HIF-2α (Supplementary Fig. ^{6a, b}). With ALOX15 (15-Lipoxygenase-1) as a positive control, we identified hypoxia-inducible, lipid droplet-associated protein (HILPDA, also known as HIG2) and G0/G1 Switch 2 (G0S2) as top re-sensitization factors (Fig. 4c, d, Supplementary Fig. ^{6c, d}). HILPDA and G0S2 share a homologous PNPLA-binding motif (Supplementary Fig. ^6e), and each can act as a co-inhibitor of adipose triglyceride lipase (ATGL, encoded by PNPLA2)^49,50, the rate-limiting enzyme for TAG hydrolysis. In contrast, overexpressing another HIF-2α-regulated, lipid droplet-associated protein perilipin2 (PLIN2) did not alter GPX4 inhibitor sensitivity⁴⁰ (Fig. 4c, Supplementary Fig. ^6c), suggesting that specific lipid remodeling activity downstream of HILPDA/G0S2 is required for ferroptosis sensitization.

Open in a separate window

Fig. 4

HILPDA enriches polyunsaturated lipids and promotes ferroptosis sensitivity downstream of HIF-2α. a Scheme summarizing the experimental strategy for identifying the HIF-2α target genes mediating ferroptosis susceptibility in 786-O cells. b Heatmap showing the RNA-Seq analysis of WT 786-O cells and three EPAS1^−/− clones (1D1, 1D7, and 1E3). HIF-2α-dependent known lipid metabolism genes are highlighted. c Viability curves of cDNA screening results in EPAS1^−/− clones treated with a 7-point, 2-fold dilution series of ML210 or RSL3. Viability is relative to that of each cell line under zero ML210 or RSL3 treatment, respectively. n=4, error bars are omitted for visual clarity. d Viability curves of HILPDA, G0S2, or EGFP-overexpressing EPAS1^−/− 1D1 and 1D7 cells treated with ML210 or RSL3 for 48h. Viability is relative to the respective DMSO-treated conditions. n=4, Representative plot of experiments repeated three times. e qRT-PCR analysis of HILPDA mRNA levels in 786-O cells expressing shNC or shHILPDAs. B2M was used as an internal control. n=3. Student’s T-test. *p<0.05. f Immunoblot analysis of HILPDA protein levels in 786-O cells expressing shNC or shHILPDAs. β-Actin was used as a loading control. g Viability curves for 786-O cells expressing shNC or two most effective shHILPDAs under indicated concentrations of ML210 or RSL3. Viability is relative to the DMSO-treated conditions, n=4. Representative plot of experiments repeated three times. h Volcano plots showing the changes in each TAG (left panels) and PE/ePE (right panels) species grouped as PUFA-containing (red fill) or SFA/MUFA-only (white fill) lipids between the indicated cell lines. n=3. i Bar graph showing the relative abundances of the PUFA-PE/ePEs in the conditions tested. n=3. S, ferroptosis-sensitive; (R), ferroptosis-resistant. j Lipid droplet abundances analyzed by flow cytometry quantitation of BODIPY-493/503 signal in 1D1 and 1D7 EPAS1^−/− cells expressing exogenous EGFP, HILPDA, G0S2 or PLIN2. Representative plot of experiments repeated three times. k Scheme summarizing the molecular network driving the intrinsic GPX4 dependency and ferroptosis susceptibility in clear-cell carcinomas. Abbreviations: PUFA, polyunsaturated fatty acids, e.g. arachidonic acid (C20:4); TAG, triacylglycerols; PE, phosphatidylethanolamine; ePE, vinyl ether-linked PE-plasmalogens. Metabolites highlighted in red indicate promoters of ferroptosis susceptibility. Error bars: ±s.d

While qRT-PCR confirmed the HIF-2α-dependent expression of HILPDA and G0S2 (Supplementary Fig. ^6f), analyses of previous ChIP-Seq data in 786-O cells⁵¹ revealed HIF-2α/HIF-1β binding to genomic loci adjacent to PLIN2 and HILPDA but not to G0S2 (Supplementary Fig. ^6g), supporting PLIN2 and HILPDA as direct HIF-2α target genes; whereas the regulatory mechanisms of HIF-2α on G0S2 expression remain to be characterized. shRNA-mediated knockdown of endogenous HILPDA diminished GPX4 inhibitor sensitivity in 786-O cells (Fig. 4e–g). However, detecting endogenous G0S2 protein expression in CCC cells proved challenging even with multiple commercially available and validated antibodies; and four sequence-independent G0S2-targeting shRNAs did not alter the ferroptosis sensitivity in 786-O cells (Supplementary Fig. ^{6h, i}). Additionally, 786-O-Cas9 cells expressing both a HILPDA-targeting shRNA and a high-score G0S2-targeting sgRNA nearly phenocopied the ferroptosis sensitivity of cells expressing only the HILPDA-shRNA (Supplementary Fig. ^{6j, k}). Altogether, these results suggest that HILPDA is necessary and sufficient to mediate HIF-2α’s activity in driving ferroptosis susceptibility. While overexpressed G0S2 is sufficient to drive ferroptosis sensitivity, the role of endogenously expressed G0S2 protein in ferroptosis is likely insignificant.

In the cDNA screening experiment, we also included HIF-1α in the cDNA library and found that HIF-1α expression re-sensitized HIF-2α-null cells to ferroptosis (Fig. 4c). Importantly, HILPDA is also a HIF-1α target gene, and is up-regulated in OCCCs compared with other ovarian carcinomas^52,53. These observations resonate with the activity of HIF-1α in inducing ferroptosis sensitivity in OCCC cells (cf. Supplementary Fig. ^{4k, l}), and further substantiate the key role of the HIF pathway in ferroptosis susceptibility in CCCs.

Compound sources, synthesis, and treatment

ML210 and RSL3 were synthesized according to previously described protocols^15,62. The concentration range for ML210 was 0.01953–20μM in 11-concentration experiments and 0.07813–5μM in 7-concentration experiments unless otherwise indicated. The concentration range for RSL3 is 0.001953–2μM in 11-concentration experiments and 0.01563–1μM in 7-concentration experiments unless otherwise indicated. Liproxstatin-1 (Lip-1, Sigma-Aldrich SML1414) was used at 0.5–1μM. Ferrostatin-1 (Fer-1, Sigma-Aldrich, SML0583) was used at 1–5μM. Polyunsaturated acids (arachidonic acid, C20:4) (Cayman Chemicals) were conjugated with fatty-acid free BSA (Sigma-Aldrich) using previously described protocols⁶³, and were applied to cell culture media at 20μM for 3 days.

Gene-expression analysis by qRT-PCR

Total RNA was extracted from cells using RNeasy Mini kit (Qiagen) following the manufacturer’s instructions. cDNA was synthesized using the ProtoScript First Strand cDNA Synthesis kit (New England Biolabs). Quantitative PCR reaction mixtures were prepared with SYBR Green PCR Master Mix (Thermo Fisher Scientific, Applied Biosystems). PCR reactions were performed and analyzed on a LightCycler 480 Instrument (Roche). Each sample condition contains at least three biological replicates and all measurements were performed with four technical replicates. Mean and standard deviation (s.d.) of biological replicates were presented unless otherwise indicated. Gene-expression levels were first normalized to internal control genes including B2M and GAPDH and then to no-perturbation conditions unless otherwise indicated. qPCR primers used in this study are listed in Supplementary Data ⁸.

Immunoblotting and antibodies

Adherent cells were briefly washed twice with PBS and lysed with 1% SDS lysis buffer containing 10mM EDTA and 50mM Tris-HCl, pH 8.0. Lysates were collected, briefly sonicated, and incubated at 95°C for 10min, and the protein concentrations were determined by BCA Protein Assay kit (Pierce). Calibrated samples were diluted with 4× LDS sampling buffer (Novus), separated by SDS-PAGE using NuPAGE 4–12% Bis-Tris protein gels (Novus), and transferred to nitrocellulose or PVDF membranes by iBlot2 protein-transfer system (Thermo Fisher Scientific). Membranes were blocked with 50% Odyssey blocking buffer (LiCor) diluted with 0.1% Tween-20-containing TBS and immunoblotted with antibodies from Abcam, including GPX4 (Ab41787), HIF-1α (Ab51608), HILPDA (Ab78349), ACSL4 (Ab155282), and from Cell Signaling Technologies, including V5-tag (D3H8Q, #13202), HIF-1β/ARNT (D28F3, #5537), HIF-2α (D9E3, #7096), KEAP1 (D6B12, #8047), NRF2 (D1Z9C, #12721), β-Actin (8H10D10, #3700 and 13E5, #4970). All antibodies were diluted at 1:1000 for immunoblotting. Membranes were then washed with TBST and incubated with IRDye 800CW goat-anti-Rabbit or 680RD donkey-anti-Mouse secondary antibodies (LiCor). Immunoblotting images were acquired on an Odyssey equipment (LiCor) according to the manufacturer’s instructions, and analyzed in the ImageStudio software (LiCor). Unless otherwise indicated, β-Actin was used as a loading control. Raw, full scan images are presented in Supplementary Figs. ⁸ and ⁹.

CRISPR/Cas9-mediated genome editing and RNA interference

For CRISPR/Cas9-mediated genome-editing, cells were engineered for Cas9 expression with the pLX-311-Cas9 vector (Addgene 96924), which contains the blasticidin S-resistance gene driven by the SV40 promoter and the SpCas9 gene driven by the EF1α promoter. sgRNA sequences were cloned into the pLV709 doxycycline-inducible or pXPR_BRD050 constitutive sgRNA expression vectors. For shRNA-mediated RNA interference, shRNAs targeting the genes of interest were pre-cloned into constitutive shRNA expression vectors pLKO.1 or pLKO-TRC005 by the Broad Institute Genetic Perturbation Platform. Lentiviruses were generated from sgRNA/shRNA constructs in HEK-293T packaging cells in 96-well plate format using FUGENE6 (Promega) as the transfection reagent and infected cells for sgRNA or shRNA expression. Infected cells were selected with puromycin at 2μg/mL starting 48h post-infection and propagated for further analysis. Cells transduced with inducible sgRNA constructs were treated with 1μg/ml doxycycline (Sigma-Aldrich) for 7–14 days prior to gene-knockout validation by immunoblot analysis. Sequences for sgRNAs and shRNAs used are listed in Supplementary Data ⁹ and ¹⁰, respectively.

Single-cell cloning

786-O-Cas9 cells were transfected with ribonucleoprotein (RNP) complex containing EnGen Cas9 NLS, S. pyogenes (New England Biolabs), Alt-R CRISPR tracrRNA and Alt-R CRISPR crRNA (Integrated DNA Technologies) according to the manufacturer’s instructions using Lipofectamine RNAiMAX transfection reagent (Thermo Fisher Scientific). Transfected cells were sorted into 96-well plates at 1 cell/well on SONY SH800 cell sorter (SONY). Cells were allowed to grow for 7 days to become single-cell clones and analyzed by immunoblot for effective gene knockout and further analysis. The names of each clone were designated by the original plate numbers and well positions.

Genome-wide CRISPR screen and data analysis

Pooled lentiviruses for the sgRNA library was prepared using HEK-293T cells in T175 flasks as previously described⁶⁴. Briefly, the viral titer and volume was pre-determined with pilot experiments prior to screening to ensure about 30% infection rate in the screening experiment and caution was taken to minimize multiple constructs transduced into the same cell. Optimized surface area for cell growth was pre-determined in pilot experiments to avoid reaching over-confluence. Puromycin concentration was pre-determined with pilot experiments before the screen. For the screening experiment, 150 million 786-O-Cas9 cells were infected with a lentiviral library containing 77,441 sgRNA targeting ~18,000 genes in the human genome to ensure each sgRNA is represented by at least 500 cells on average⁶⁵. For the infection, cells added with the calculated lentivirus volume were supplemented with 4μg/mL of polybrene and centrifuged at 930 RCF for 2h. Fresh media was added at a 1:1 ratio post-centrifugation. Infected cells were selected with 2μg/mL of puromycin for 96h, expanded for another 4 days to reach the desired cell number and split for DMSO or 5μM ML210 treatment. At least 40 million cells were used for each treatment condition to keep the minimum presentation number for each sgRNA above 500. Cells were exposed to ML210 treatment for 4, 6, or 8 days before being cultured in drug-free media for recovery and expansion for 24h. Genomic DNA from cell pellets was purified using the QIAamp DNA Blood Maxi/Midi/Mini kits (Qiagen) according to the manufacturer’s protocols and quantified using a Nanodrop 2000 (Thermo Fisher Scientific).

The sequencing library was prepared, sequenced, and analyzed as previously described^64,66. Briefly, sgRNA cassettes were PCR-amplified and barcoded with sequencing adaptors utilizing ExTaq DNA Polymerase (Clontech). PCR products were purified with Agencourt AMPure XP SPRI beads (Beckman Coulter A63880) according to the manufacturer’s instructions, quantified using a Nanodrop 2000, pooled into a master sequencing pool, and sequenced on a HiSeq sequencer (Illumina) with 300 nt single-end reads, loaded at 60% with a 5% spike-in of PhiX DNA.

For CRISPR screen data analysis, the sgRNA sequences were mapped to a reference file containing all SpCas9 sgRNAs in the library and the sgRNA-associated barcodes were counted and mapped to the barcode reference file. The read count matrix was normalized to reads per million reads (RPM) within each condition. Normalized read counts for each sgRNA in the ML210-treated conditions were compared with those in the DMSO-treated condition. SgRNA and gene level analyses for each condition are listed in Supplementary Data ¹–³. Genes represented by 3–10 sgRNAs were included in the differential enrichment analysis. Genes of interest in each ML210-treated condition were required to have at least 2 sgRNAs exhibiting at least 2-fold enrichment with p-values <0.05. Selected genes that scored in all three ML210-treated conditions were further validated with shRNA-mediated knockdown.

Lipid peroxidation analysis by imaging and flow cytometry

For imaging, cells were plated at 5000 cells per well in a 96-well, Cell Carrier Ultra Microplate (PerkinElmer) in the appropriate cell culture media, supplemented with 1μM of liproxstatin-1 where indicated and cultured overnight. Cells were incubated with 0.1% DMSO (same volume of DMSO as samples with a compound), 10μM ML210, or 10μM ML210+1μM of liproxstatin-1 for the indicated times (1–4h). During the last hour of incubation, the media also contained 60nM DRAQ7 (Abcam), 1μg/mL Hoechst 33342 (Thermo Fisher Scientific), and 1μM BODIPY-581/591 C11 (Thermo Fisher Scientific) for live-cell imaging. Cells were imaged at ×63 magnification using an Opera Phenix High-Content Screening System (PerkinElmer, Waltham, MA) equipped with 405, 488, 560, and 647nm lasers. Image analysis was conducted with Harmony software (PerkinElmer). All images (14 images per well) were collected with the same instrument parameters and processed with the same settings to maximize the ability to compare results between conditions. 9 randomly selected images per condition were presented in Supplementary Figure ¹.

For flow cytometry analysis, 786-O, 769-P, and derivative cells were treated with DMSO or ML210 (5μM) for 90min, while for the last 45min cells were also incubated with 5μM of BODIPY-C11 dye. Before flow cytometry, cells were washed with PBS twice, stained with Hoechst 33342 for 5min, trypsinized and filtered into single-cell suspensions. Flow cytometry analysis was performed on a SONY SH800 cell sorter with standard settings, using PE-TexasRed filter for reduced BODIPY-C11 (emission: 590nm) and the FITC filter for oxidized BODIPY-C11 (emission: 510nm). A minimum of 10,000 cells were analyzed for each condition, and each experiment was independently performed at least twice and representative experimental results are shown. Data analysis was performed using the FlowJo 10 software. An example gating strategy is demonstrated in Supplementary Fig. ⁴.

CellTiter-Glo assay for viability analysis

For cellular viability assays, cells were seeded in 384-well opaque white tissue culture and assay plates (Corning) at 1000 cells/well. 18–24h after seeding, cells were treated with compounds at indicated concentrations for 48–72h. Cellular ATP levels were quantified using CellTiter-Glo (Promega) on a multi-plate reader (Envision). Relative viability was normalized to the respective DMSO-treated condition unless otherwise indicated. For data presentation, the mean and standard deviation (s.d.) for the four biological replicates of each data point in a representative experiment is presented. Sigmoidal non-linear regression models were used to compute the regression fitting curves. For plate-based screening, area-under-curve (AUC) value for each regression curve is calculated and normalized to 1 as the total AUC for the concentration ranges of ML210 or RSL3.

Lipid droplet abundance analysis by flow cytometry

786-O derivative cell lines were grown at optimal confluence and stained with BODIPY-493/503 (Thermo Fisher Scientific, Molecular Probes) at 2μM final concentration for 30min according to the manufacturer’s instructions. Cells were then briefly washed with PBS, trypsinized, collected, stained with Hoechst 33258 for 5min and filtered through a 70μm nylon filter. The resulted post-staining single-cell suspensions were analyzed on a SONY SH800 cell sorter (SONY) according to the manufacturer’s protocols. The filter used for detecting the BODIPY-493/503 signal was FITC 488nm. A minimum of 10,000 cells were analyzed for each condition, and each experiment was independently performed at least twice and representative experimental results are shown. An example gating strategy is demonstrated in Supplementary Fig. ⁷.

cDNA overexpression screening of HIF-2α-dependent genes

cDNAs for the HIF-2α-dependent genes identified by RNA-Seq were obtained from the previously described human cDNA library collection at the Broad Institute Genetic Perturbation Platform (Supplementary Data ⁶)⁶⁷. These cDNAs were constructed for mammalian expression in the pLX-TRC317 vector system. Lentiviruses were produced with the cDNA constructs in HEK293-T cells in 96-well format. EPAS1^−/− 786-O clones were infected with the cDNA lentivirus array, selected with 2μg/mL puromycin for 96h and analyzed for the cells’ ferroptosis susceptibility 7 days post-infection. Control vectors expressing EGFP were used to evaluate the infection rate. Genes of interest were identified as having significantly shifted ML210 and RSL3 sensitivity curves compared with EGFP-expressing EPAS1^−/− 786-O cells. Protein expression of the top hit genes and controls was verified by immunoblot analysis.

RNA-Seq and data analysis

RNA-Seq analysis was performed with wildtype and three EPAS1^−/− 786-O single-cell clones generated by CRISPR/Cas9 to identify the HIF-2α-responsive genes. Total RNA was extracted from adherent 786-O cells and derivatives using the RNeasy Mini Kit (Qiagen) according to the manufacturer’s instructions. The RNA sequencing library was prepared using NEB-Next Ultra RNA Library Prep Kit following the manufacturer’s recommendations. Briefly, mRNAs were first enriched with Oligo-d(T) beads and fragmented for 15min at 94°C. First strand and second strand cDNA library was subsequently synthesized, end repaired, and adenylated at 3’ ends. Universal adapter was ligated to cDNA fragments, followed by index addition and library enrichment with limited-cycle PCR. Sequencing libraries were validated using the Agilent Tapestation 4200, and quantified using a Qubit 2.0 Fluorimeter as well as by quantitative PCR. The sequencing libraries were multiplexed and clustered on one lane of a flowcell. After clustering, the flowcell was loaded on the Illumina HiSeq instrument according to the manufacturer’s instructions. The samples were sequenced using a 2×150bp paired-end configuration. Image analysis and base calling were conducted by the HiSeq Control Software. Raw sequence data generated from Illumina HiSeq was converted into fastq files and de-multiplexed using Illumina’s bcl2fastq 2.17 software. One mismatch was allowed for index sequence identification.

For RNA-Seq data analysis, raw paired-end 150bp/150bp sequencing reads were mapped to human genome build hg19 using Bowtie2 (v2.3.1) with standard settings. On average 66% of read pairs were uniquely mapped to the hg19 genome. Mapped reads were counted to gene features by the htseq-count function from HTSeq (version 0.9.1) with standard settings, normalized to library size and analyzed for differentially expressed genes with DESeq2 (Bioconductor). Heatmaps were generated using the heatmap.2 function in gplots package in R (The Comprehensive R Archive Network).

Lipidomic profiling and data analysis

Analyses of polar and non-polar lipids were conducted using an LC-MS system comprising a Shimadzu Nexera X2 U-HPLC (Shimadzu Corp.) coupled to an Exactive Plus orbitrap mass spectrometer (Thermo Fisher Scientific). Lipids were extracted from cells with 0.8mL isopropanol (HPLC Grade; Honeywell). Three replicates were analyzed for each cell line or condition. Cell extracts were centrifuged at 10,000 RCF for 10min to removed residual cellular debris prior to injecting 10μL onto an ACQUITY BEH C8 column (100×2.1mm, 1.7µm; Waters). The column was eluted isocratically with 80% mobile phase A (95:5:0.1 vol/vol/vol 10mM ammonium acetate/methanol/formic acid) for 1min followed by a linear gradient to 80% mobile-phase B (99.9:0.1 vol/vol methanol/formic acid) over 2min, a linear gradient to 100% mobile phase B over 7min, then 3min at 100% mobile-phase B. MS data were acquired using electrospray ionization in the positive-ion mode over 200–1100m/z and at 70,000 resolutions. Other MS settings were: sheath gas 50, in source CID 5eV, sweep gas 5, spray voltage 3kV, capillary temperature 300°C, S-lens RF 60, heater temperature 300°C, microscans 1, automatic gain control target 1e6, and maximum ion time 100ms. Raw data were processed using TraceFinder 3.3 (Thermo Fisher Scientific) and Progenesis QI (Nonlinear Dynamics) software for detection and integration of LC-MS peaks. Lipid identities were determined based on comparison to reference standards and reference plasma extracts and were denoted by total number of carbons in the lipid acyl chain(s) and total number of double bonds in the lipid acyl chain(s).

For lipidomics data analysis, median normalization was performed between each sample in the same experiment. Median-normalized lipidomic datasets are presented in Supplementary Data ⁴, ⁵ and ⁷. Differential-abundance analysis was performed between previously annotated lipid species (about 200 lipids were previously annotated) using two-tailed Student’s T-test. For fold-change analysis, each dataset was normalized to the mean of the WT cell condition for each lipid species, and the ratio between Test/WT was log2 transformed and presented as heatmaps, bar graphs or volcano plots. P values are adjusted for multiple-test correction using Benjamini-Hochberg correction method and presented as -log10 adj. p. For principal component plots, the log-ratios between Test/WT for each lipid were further median normalized and computed using the principal component analysis function in DESeq2 (Bioconductor) using RStudio.

Public dataset queries

The Cancer Therapeutics Response Portal (portals.broadinstitute.org/ctrp/) compound sensitivity dataset, a data matrix containing the normalized AUC values of each compound in each cell line, was downloaded from [https://ocg.cancer.gov/programs/ctd2/data-portal], (also deposited in [ftp://caftpd.nci.nih.gov/pub/OCG-DCC/CTD2/Broad/] and [https://github.com/remontoire-pac/ctrp-reference/tree/master/auc]). For the lineage/histotype-specific analyses, compounds that were profiled in at least 2/3 of the total solid cancer cell line collection, and cell lines that were profiled using at least 50% of the compounds, were included in the statistical analyses. Primary cancer types that contain >5 cell lines profiled for compound sensitivities were presented. Mann–Whitney–Wilcoxon tests were performed between cancer cell lines from each cancer type and other solid cancer cell line collections. Statistical significances were adjusted for multiple-test correction using the Benjamini-Hochberg correction method. Compound sensitivity-gene expression correlation analysis were performed with the web tools in CTRP [portals.broadinstitute.org/ctrp/]¹¹.

For gene expression analysis of CTRP cell lines, RNA-Seq dataset was downloaded from the Cancer Cell Line Encyclopedia data portal [https://portals.broadinstitute.org/ccle/data]. For Cancer Dependency Map (DepMap) datasets, dependency scores for each gene of interest, including CERES scores from CRISPR/Cas9 screening experiments and DEMETER scores for RNA interference screening experiments, were downloaded from the DepMap web portal [https://depmap.org/portal]. ChIP-Seq dataset for HIF-2α/HIF-1β in 786-O cells (GSE34871) was downloaded from Gene Expression Omnibus [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE34871].

Animal studies

All animal experiments were in compliance with relevant ethical regulations and were approved by the Institutional Animal Care and Use Committee (IACUC) of the Broad Institute. Briefly, 3–4-week-old, male athymic nude mice were used for 786-O xenograft experiments. Five million cells for each injection were trypsinized, resuspended in 50μl PBS containing 0.5μM DMSO or Lip-1, mixed with 50μl Matrigel (BD Biosciences), and implanted to mice subcutaneously. For Lip-1 treatment, Lip-1 was first dissolved in DMSO then diluted with PBS and injected into mice at 20mg/kg body weight daily. Lip-1 or vehicle treatment was continued for 10 days before withdrawal. Tumor sizes were monitored and measured on a weekly basis. Tumor volumes were quantified by measuring the length (L) and width (W) of the tumor using a caliper and calculated according to V=(L*W*W)/2.

Patient-derived cancer cell model generation

Human renal cell carcinoma samples, CCLF_KIPA_0001_T and CCLF_KIPA_0002_T, were obtained from patients, with their informed consent, at the Dana Farber Cancer Institute (DFCI); and all procedures were conducted under an Institutional Review Board (IRB)-approved protocol. Clinical information for the patient tumors were included in Supplementary Fig. ¹. For primary cell line generation, tumor resections were placed in a sterile conical tube containing DMEM media (Thermo Fisher Scientific, cat. #11995073) with 10% FBS (Sigma Aldrich, cat. #F8317), 1% penicillin–streptomycin (Thermo Fisher Scientific, cat. #15140163), 10μg/ml of gentamicin, and 250ng/ml fungizone on wet ice during transport from the operating room to the research laboratory. Resections were placed in a 15ml conical flask with 5ml DMEM media, 10% FBS, 1% penicillin–streptomycin, and the digestion enzymes regular collagenase 1ml (StemCell, cat. #07912) and dispase 1ml (StemCell Technologies, cat. #07913). The flask was placed on a rotator and incubated at 37°C for 1h. The cells were then centrifuged at 200 × g (RCF) for 5min. Cell pellets were resuspended in a 50:50 mix culture medium of Smooth Muscle Growth Medium-2 (Lonza CC-3182) and ACL4 media with 5% FBS^68,69. Later, suspended cells were plated into a 96-well plate. The medium was changed every 3 days, and cells were maintained at 37°C in a humidified 5% CO2 incubator. RCC cells were passaged using Gibco TrypLE Express (Thermo Fisher Scientific, cat. # 12604039) to detach cells when the cells reached 80–90% confluence. CCLF_KIPA_0001_N cells were prepared with a similar protocol but cultured with conditional media as previously described⁷⁰.

Statistical analysis

Data are generally expressed as mean±s.d. unless otherwise indicated. No statistical methods were used to predetermine sample sizes. Statistical significance was determined using a two-tailed Mann–Whitney–Wilcoxon test or two-tailed Student’s T-test using Prism 7 software (GraphPad Software) unless otherwise indicated. The Benjamini-Hochberg correction method was used to adjust the p-values where multi-testing corrections were involved. Statistical significance was set at p<0.05 unless otherwise indicated.

We thank Tanaz Sharifnia, Shubhroz Gill, Olivia Bare, Xi Wang, Xin Jin, Xin Rong, and Robert L. Bowman for discussion, and members of Project Achilles for sharing Cas9-expressing cell lines. We thank C. Suk-Yee Hon for project management and organization assistance. We thank the Eric Jonasch laboratory for the HIF-2α-GFP plasmid. We thank John A. Steinharter for coordinating the patient sample collection. We acknowledge funding support for M.J.P. from the NIH NHLBI award T32HL007627, for V.S.V. from the NCI SPORE P50 CA101942-13 Directors Choice Award. This work was supported by the NCI’s Cancer Target Discovery and Development (CTD²) Network (grant number U01CA217848, awarded to S.L.S.), and in part by NIH/NCI DF/HCC Kidney Cancer SPORE P50 CA101942 to S.S. and T.K.C., and by the Trust Family, the Kohlberg chair at Harvard Medical School, the Loker Pinard and the Michael Brigham Funds at the Dana-Farber Cancer Institute for Kidney Cancer Research to T.K.C.