VHL synthetic lethality screens uncover CBF-β as a negative regulator of STING

Domain: biology
Published: 2026-03-12
Editorial outcome: major_revision

Abstract
Clear cell renal cell carcinoma (ccRCC) represents the most common form of kidney cancer and is typified by biallelic inactivation of the von Hippel-Lindau (VHL) tumour suppressor gene. Here, we undertake genome-wide CRISPR/Cas9 screening to reveal synthetic lethal interactors of VHL, and uncover that loss of Core Binding Factor β (CBF-β) causes cell death in VHL-null ccRCC cell lines and impairs tumour establishment and growth in vivo. This synthetic relationship is independent of the elevated activity of hypoxia inducible factors (HIFs) in VHL-null cells, but does involve the RUNX transcription factors that are known binding partners of CBF-β. Mechanistically, CBF-β loss leads to upregulation of type I interferon signalling, and we uncover a direct inhibitory role for CBF-β at the STING locus controlling Interferon Stimulated Gene expression. Targeting CBF-β in kidney cancer both selectively induces tumour cell lethality and promotes activation of type I interferon signalling.

Introduction
Kidney cancer is a major and growing cause of morbidity and mortality worldwide, with approximately 180,000 patients dying each year from the disease1,2. Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype in adults, accounting for approximately 85% of cases3,4. While low grade tumours can be cured by partial or radical nephrectomy, the systemic medical options to manage metastatic ccRCC remain largely inadequate, and only 13% of patients with metastatic spread at the time of diagnosis survive 5 years5.

Biallelic inactivation of the VHL tumour suppressor gene at chromosome 3p25 is a hallmark event in the development of ccRCC tumours6,7. Loss-of-heterozygosity of chromosome 3p occurs in childhood or adolescence, followed decades later by mutation or epigenetic inactivation of the remaining VHL allele. The subsequent acquisition of subclonal driver mutations, including the chromatin regulators PBRM1, SETD2 and BAP1, culminates in the establishment of overt ccRCC tumours8–10. Inherited VHL mutations also underlie the hereditary autosomal dominant von Hippel-Lindau disease, in which patients develop numerous neoplasms early in life11.

VHL forms the substrate recognition component of the E3 ligase complex responsible for the ubiquitylation of prolyl-hydroxylated hypoxia inducible factor (HIF)-α subunits under conditions of abundant molecular oxygen12,13. In hypoxic environments, or upon genetic depletion of VHL, HIF-α accumulates, heterodimerises with HIF-1β/ARNT, and translocates to the nucleus, whereupon it drives a diverse transcriptional program , including genes implicated in angiogenesis and glycolytic metabolism14. The stabilisation of the HIF-2α isoform in particular accounts for many of the distinctive features of ccRCCs: vascular endothelial growth factor (VEGF) secretion results in highly angiogenic tumours, while increased fatty acid biosynthesis and impaired mitochondrial fatty acid transport cause cytoplasmic lipid deposition and the characteristic clear cell morphology15–18.

The term ‘synthetic lethality’ is used to describe the phenomenon in which the perturbation of two individually non-essential genes is lethal to a cell19. This paradigm has been used to target tumours with characteristic mutational drivers, exemplified by the employment of PARP inhibitors in the treatment of BRCA1/2-defective breast and ovarian cancers20–23. Over the last two decades, several groups have employed genetic and chemical screening platforms, computational algorithms, and hypothesis-driven approaches to identify synthetic lethal interactors of VHL24–34. However, while some hits may hold potential for the specific treatment of ccRCC, there has been little concordance between studies, presumably reflecting the diversity of cell lines used and the limitations of existing approaches, including the off-target effects of RNA interference techniques and the use of sub-optimal libraries.

Utilising pooled genome-wide CRISPR/Cas9 mutagenesis in ccRCC cell lines, we uncover synthetic lethal interactions for VHL, and find that Core Binding Factor β (CBF-β) loss selectively results in decreased tumour growth in ccRCCs. Mechanistically, CBF-β deficiency promotes cell death in ccRCCs, but we also find an unanticipated role for CBF-β in the repression of type I interferon (IFN) signalling. Loss of CBF-β results in Interferon Stimulated Gene (ISG) expression, resulting from the de-repression of STING (Stimulator of Interferon Genes) transcription. CBF-β therefore tunes cell-intrinsic type I interferon signalling, and its loss sensitises cells to a heightened STING-mediated immune response. Therefore, we propose that CBF-β offers a potential therapeutic target in ccRCC through its synthetic lethal relationship with VHL loss.

Results
Genome-wide CRISPR/Cas9 screening reveals synthetic viability interactors of VHL
To find genes which alter the viability of VHL-null cells, we employed the widely-used ccRCC models 786O and RCC4 alongside paired VHL-reconstituted lines which preferentially express different isoforms of VHL due to the use of alternative translation start sites (786O+VHL and RCC4+VHL) (Fig. 1a and Supplementary Fig. 1a)12,35,36. Pooled CRISPR/Cas9 screening was performed by transducing isogenic cell lines expressing Cas9 with the Toronto KnockOut version 3.0 (TKOv3) genome-wide sgRNA library, passaging cells until 17 population doublings had occurred, and undertaking deep sequencing of sgRNAs (Fig. 1b and Supplementary Fig. 1b)37. Identification of a curated set of core essential genes that dropped out over the duration of the culture confirmed the efficiency of the screen (Supplementary Fig. 1c and Supplementary Data 1)37. Synthetic viability interactors of VHL were identified as those targeted by sgRNA sequences which were relatively less or more abundant in the VHL-null lines at the conclusion of the screen (Fig. 1c and Supplementary Data 1). We used three different analyses to identify genes with a fitness advantage or disadvantage: BAGEL2, DrugZ and MAGeCK38–40. The positive controls ARNT/HIF1β and MTOR dropped out in the VHL-null setting, confirming the validity of our approach34,41. Alongside these expected findings we identified a putative synthetic lethal relationship between VHL and CBFB (encoding CBF-β) in both ccRCC backgrounds (Fig. 1c,d). CBF-β is best characterised as forming a transcriptional complex with RUNX proteins to activate or repress target genes42,43. Both RUNX1 and RUNX2 were identified in the RCC4 screen (Fig. 1c), consistent with a potential role of these transcriptional complexes in the synthetic viability relationship with VHL.

KEAP1 was identified as the top hit that when mutagenised conferred a proliferative advantage to VHL-null cells (Fig. 1c,d). KEAP1 ubiquitylates and traps NRF2 in the cytoplasm to prevent NRF2-mediated transcription of antioxidant factors including NQO1 and HMOX144,45, raising the possibility that upregulation of the NRF2 response may confer a survival advantage following VHL loss.

We developed a competitive growth assay with a fluorescent read-out to validate target genes (Fig. 1e). By labelling the 786O VHL-null cells with mCherry and the VHL-reconstituted cells with GFP, we could readily distinguish a growth advantage or disadvantage when both cell lines were mixed in a 1:1 ratio (Fig. 1e). Inducible depletion of CBF-β, using both sgRNA and shRNA, confirmed the VHL/CBFB synthetic lethal relationship (Fig. 1f and Supplementary Fig. 1d,e). Conversely, KEAP1 loss increased the growth of 786O cells relative to 786O+VHL cells (Fig. 1g and Supplementary Fig. 1f). We also found that NRF2 was synthetic lethal with VHL in the 786O competitive growth assay (Fig. 1g), but its modulation did not alter the levels of HIF-2α or HIF target gene expression (Supplementary Fig. 1f,g). Therefore, the KEAP1-NRF2 axis may protect against a greater degree of oxidative stress in VHL-deficient cells.

Deletion of CBFB causes cell death in VHL-null cells
We chose to focus on CBFB given the potential synthetic lethal relationship between CBFB and VHL, the additional identification of RUNX genes in the RCC4 screen, and the observation that CBF-β is abundant in ccRCC tumours (Fig. 2a), with its expression correlated strongly with poor survival outcomes in both The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases (Fig. 2b,c).

We first sought to characterise the proliferation phenotype of CBF-β loss in ccRCC cells. The growth and colony formation potential of 786O cells was significantly impaired by CBF-β depletion, but this growth defect was abrogated in the isogenic VHL-reconstituted line (Fig. 2d and Supplementary Fig. 2a). CBF-β loss also impeded growth in some other VHL-null ccRCC lines (A498 and RCC10 cells), but not in 769P ccRCC cells, or the HKC8 model of healthy proximal tubule epithelium (Supplementary Fig. 2b,c). Prior studies report differing sensitivities to HIF-2α inhibitors in ccRCC lines, and we considered whether an interaction with CBF-β and HIF-2α could explain the varied sensitivity of ccRCC lines to impaired growth with CBFB loss41. However, the functional interaction between CBFB and VHL was independent of HIF signalling, as the same phenotype remained in 786O cells following clonal knockout of HIF1β (Supplementary Fig. 2b,c, final columns), and CBFB knockout did not consistently affect the levels or transcriptional activity of HIF-2α (Supplementary Fig. 2d,e).

The proliferation defect in 786O cells could be caused by either a defect in cell cycling, or an increased rate of cell death. We excluded a significant deficiency in cell cycling as neither the distribution of cells within cycle phases nor the progression of synchronised populations through the cell cycle were affected by CBFB knockout (Fig. 2e and Supplementary Fig. 3a). Rather, the depletion of CBF-β caused cytotoxicity in VHL-null cells, demonstrated by SYTOX AADvanced staining and quantification of released lactate dehydrogenase (LDH) (Fig. 2f,g). We investigated the mode of cell death using assays of apoptosis, or specific inhibitors of necroptosis (Nec-1s), pyroptosis (VRT-043198), and ferroptosis (ferrostatin). Apoptosis assays did not demonstrate a clear induction of this pathway following CBF-β depletion (Supplementary Fig. 3b,c), and treatment with the highest tolerable doses of the various cell death inhibitors did not restore growth in the CBF-β-deficient 786O cells (Supplementary Fig. 3d). Finally, we did not observe any effect of CBFB knockout on the sensitivity of cells to the vacuolar H+-ATPase inhibitor Bafilomycin A1, suggesting that there was no involvement of autophagy-related cell death (Supplementary Fig. 3e). Therefore, while the precise modality of cell death is unclear, our studies indicate that CBF-β loss does lead to cytotoxicity in 786O cells.

To establish whether CBF-β loss also impaired tumourigenesis in vivo, we performed xenograft experiments in immunodeficient NSG mice. First, we observed that CBF-β knockout severely restricted the ability of 786O cells to develop tumours in the subcutaneous microenvironment as tumours were almost undetectable 21 days after injection (Fig. 2h). Next, to interrogate the more clinically-relevant scenario of established kidney tumours, we employed an orthotopic model of ccRCC using luciferase-expressing 786O cells harbouring doxycycline-inducible sgRNA vectors46. Upon confirmation of tumour growth in each kidney, as indicated by increased luciferase activity over time, we administered doxycycline chow to mice in order to induce CBF-β depletion in vivo. Deletion of CBF-β with two independent sgRNAs both substantially impeded tumour growth (Fig. 2i-k and Supplementary Fig. 3f) and prevented metastasis to the lungs (Fig. 2l,m), confirming the relevance of the CBF-β/VHL synthetic lethality in vivo.

CBF-β and RUNX proteins are required for the synthetic lethal interaction with VHL
Given the canonical transcriptional function of CBF-β in complex with RUNX1-3, and the manifestation of RUNX2 as a synthetic lethal hit in the RCC4 CRISPR screen (Fig. 1c), we hypothesised that CBFB loss could result in the death of VHL-null cells via impaired RUNX activity. Indeed, the combined knockout of RUNX1 and RUNX2, but not of either isoform individually, caused a similar degree of synthetic lethality to CBF-β loss in a competitive growth assay (Fig. 3a), and the deletion of all three RUNX proteins mimicked the observed CBF-β proliferation phenotype in 786O cells (Fig. 3b). However, the synthetic lethal effect of CBF-β with VHL loss could not be simply explained by a reduction of complex formation with RUNX proteins, as we noted that deletion of CBF-β caused a profound decline in RUNX1 and RUNX2 protein abundance in both 786O and 786O+VHL cells (Fig. 3c,d).

To further interrogate the role of CBF-β/RUNX dimerisation, we expressed FLAG-tagged overexpression constructs of wild-type (WT) CBF-β and an N104A-mutant form, which fails to bind the Runt homology domain of RUNX proteins47, in 786O or CBF-β-deficient 786O cells. We confirmed that the N104A mutation prevented RUNX binding by co-immunoprecipitation (Fig. 3e). Importantly, the sgRNA used to induce CBF-β deletion (CBF-β sg2) was directed against the boundary between intron 2 and exon 3 of the CBFB locus and therefore permitted knockout of the endogenous CBF-β protein while sparing the overexpression constructs. However, overexpression of either wild-type or N104A CBF-β restored the proliferation of CBF-β-null 786O cells to a similar extent (Fig. 3f,g). Moreover, we noted both CBF-β constructs restore RUNX protein levels without increasing RUNX1-3 mRNA expression (Fig. 3h,i). Together, these findings indicate that while RUNX proteins are required for the CBFB synthetic lethal growth defect in 786O cells, the direct association of CBF-β with RUNX proteins may not be necessary.

CBFB loss induces a cell-intrinsic interferon response
As CBF-β and RUNX appeared to exercise their functions at both transcriptional and translational levels, we undertook RNA sequencing (RNA-Seq) and tandem mass tag (TMT)-labelled mass spectrometry to characterise the effects of CBF-β loss in VHL-proficient and -deficient backgrounds (Fig. 4a). Strikingly, in 786O cells, CBFB knockout caused a net increase in transcription and specifically induced the expression of a plethora of ISGs exclusively in VHL-null 786O cells (Fig. 4b-d and Supplementary Data 2,3). Quantitative PCR (qPCR) analysis confirmed that the upregulation of several ISGs (IFIT1, OASL, ISG15, and RSAD2) was specific to CBF-β depletion, using both sgRNA and shRNA (Supplementary Fig. 4a-c), and prevented by reconstitution with both wild-type and N104A-mutant CBF-β-FLAG constructs (Fig. 4e and Supplementary Fig. 4d). The selective ISG stimulation in VHL-null cells was also independent of HIF activity, as it was not abolished by HIF1β knockout (Fig. 4f), and observed in other but not all ccRCC lines (Supplementary Fig. 4e).

We next determined how the induction of ISGs in CBF-β-deficient 786O and 786O+VHL cells compared with type I IFN treatment by titrating levels of IFN-β. We observed that CBF-β loss was equivalent to treatment with approximately 24 hours of 10 IU/ml IFN-β (Fig. 4g). CBF-β loss also induced ISGs non-uniformly, affecting a subset of IFN-β-responsive genes (IFI44, ISG15, and IRF9), but not MDA5 or MX1 (Fig. 4g). Given this low amplitude ISG response, we considered whether the induction of ISGs related to lentiviral transduction of our sgRNA targeting CBFB. However, ISGs were not induced if cells were transduced with sgRNAs of similar G:C content targeting alternative loci to CBFB (Supplementary Fig. 4f). Thus, CBF-β loss promotes an ISG response in 786O cells.

To further explore ISG expression in VHL-deficient renal cancer, we analysed the single cell transcriptional profiles of treatment-naive renal tumours from 12 patients, who underwent surgical resection48. Using the same type I IFN gene signatures as for the bulk RNA-Seq (Fig. 4b,c) we observed that there was considerable heterogeneity in ISG expression across cell populations within tumours (Fig. 4h and Supplementary Fig. 5). RCC cells showed only low levels of ISGs, with the bulk of ISG expression stemming from the endothelial cells and certain immune subsets (Fig. 4h and Supplementary Fig. 5). CBFB transcript levels were low across all cell types, therefore correlating CBFB expression with an ISG expression was not possible. However, these transcriptional profiles are consistent with general repression of ISGs within the tumour cells.

A STING-TBK1-IRF3 axis drives ISG expression in VHL-deficient renal cancer cells
The finding that CBFB loss resulted in type I IFN expression suggested either that CFB-β negatively regulates IFN signalling or that its loss results in damage signals detected by pattern recognition receptors (PRRs). ISG expression is classically stimulated when microbial nucleic acids are sensed by PRRs which converge, via the adaptors STING, MAVS and TRIF, on the TBK1/IKK-ε kinases to promote the phosphorylation and dimerisation of IRF349–51, activating ISGs and type I IFN expression (Fig. 5a). Upon secretion, type I IFNs then act in a paracrine or autocrine manner to initiate a JAK-STAT phosphorylation cascade which culminates in the transcription of ISGs by STAT1/2 heterodimers (in association with IRF9) or, less commonly, STAT1 homodimers (Fig. 5a)52,53. Our finding that CBFB deletion only altered the growth of 786O but not 786O+VHL cells in co-culture (Fig. 1f) suggested that type I IFN secretion was unlikely to explain the induction of ISGs, and that a cell-intrinsic pathway of direct ISG stimulation was involved. We therefore anticipated that elimination of STAT1/2 signalling would abolish the IFN response to CBF-β loss. To our surprise, however, knockout of STAT1, STAT2 or IRF9 individually, or in combination, did not prevent IFIT1 induction (Fig. 5b and Supplementary Fig. 6a-c), and we also did not observe phosphorylation of STAT1 at Tyr701 with CBFB knockout (Supplementary Fig. 6d).

We therefore examined if CBF-β loss acted upstream in ISG induction by depleting IRF3, as IRF3 homodimers have been reported to directly bind the IFN-stimulated response elements (ISREs) of canonical ISGs to stimulate their transcription independently of interferons54–56. IFIT1 expression was profoundly sensitive to IRF3 knockout in the CBF-β-null cells (Fig. 5c and Supplementary Fig. 6e). Moreover, the ISG response observed upon CBF-β knockout was entirely dependent on IRF3, and not on the closely-related, immune cell-specific IRF7 (Fig. 5c and Supplementary Fig. 6e)57, and was associated with phosphorylation of IRF3 at Ser386 (Fig. 5d and Supplementary Fig. 6f), which is essential for IRF3 activation58. To confirm that IRF3 phosphorylation was required for subsequent ISG expression, we treated CBF-β-null 786O cells with the TBK1 inhibitor, GSK8612. This treatment prevented IRF3 Ser386 phosphorylation and the subsequent ISG induction in a dose-dependent manner (Fig. 5d,e). Chromatin immunoprecipitation (ChIP) experiments showed some increased binding of IRF3 to ISREs upon CBF-β depletion (Supplementary Fig. 6g). Lastly, we established that STING, but not MAVS or TRIF, was required for the induction of ISGs following CBFB knockout (Fig. 5f,g and Supplementary Fig. 6h), identifying a STING-TBK1-IRF3 axis that is activated in a CBF-β loss-dependent manner.

As type I IFN activation can induce a plethora of cellular phenotypes, including senescence, apoptosis and cell cycle arrest, we next considered whether the activation of the STING-TBK1-IRF3 axis was sufficient to explain the synthetic lethal phenotype observed between CBFB and VHL, but STING or IRF3 depletion by themselves did not fully reverse the synthetic interaction (Supplementary Fig. 6i-k). However, IFN-β treatment did mildly increase cell death in VHL-null 786O cells compared to immortalised renal tubular epithelial cells and VHL-reconstituted 786O cells (Fig. 5h and Supplementary Fig. 6l). Therefore, while activation of the IRF3 axis cannot fully explain the synthetic lethal phenotype, ccRCCs are sensitive to type I IFN-induced cell death.

CBF-β represses STING to tune the type I interferon response
We were intrigued by the notion that CBF-β may be a negative regulator of STING. We noted that not only did CBF-β loss result in the STING-dependent induction of ISGs, but that protein levels of STING increased in CBF-β-null 786O cells (Fig. 5g). Therefore, we tested whether CBF-β depletion altered STING expression and the sensitivity of the cGAS-STING pathway to double stranded DNA (dsDNA) (Fig. 6a). Firstly, in 768O cells, we observed that STING transcription was dramatically increased upon CBFB knockout but reduced by its overexpression (Fig. 6b). Moreover, using herring testes DNA (HT-DNA), a long ‘non-self’ DNA molecule which enables specific interrogation of cGAS-STING function (Fig. 6c)59,60, we found a striking synergistic induction of ISGs following CBF-β loss: while HT-DNA alone provided only modest IFIT1 upregulation, we observed a dramatic induction in response to combined HT-DNA transfection and CBF-β sg2 transduction (Fig. 6d). This was accompanied by increased expression of IFNB1 (encoding IFN-β), despite its transcription not being affected by CBFB knockout alone. Conversely, overexpression of the wild-type CBF-β-FLAG construct blunted the response to HT-DNA (Fig. 6d).

It was plausible that CBF-β loss may contribute to STING activation through the release of dsDNA into the cytoplasm from either a mitochondrial or genomic source61–67. However, we found no evidence of mitochondrial dysfunction or superoxide generation by flow cytometry in 786O cells (Supplementary Fig. 7a,b), and neither CBF-β deletion nor overexpression affected the cellular oxygen consumption rate (Supplementary Fig. 7c). There was no increase in the leakage of DNA from mitochondria upon CBFB knockout, although interestingly this was reduced in VHL-reconstituted 786O cells (Supplementary Fig. 7d). This is consistent with a recent report that VHL loss increases mitochondrial dsDNA leakage, and may partly explain why VHL-null cells are particularly responsive to CBF-β-mediated STING induction68. Moreover, there was no overt genomic DNA damage or expression of ccRCC-associated endogenous retroviruses (ERVs) in response to transduction with CBF-β sg2 (Supplementary Fig. 7e-h and Supplementary Data 4)69–72. Indeed, while a basal level of cGAMP was required for STING activity, cGAS knockout caused only a minor reduction in CBF-β knockout-induced STING expression, confirming that the modulation of STING levels by CBF-β is largely independent of interferon signalling (Fig. 6e and Supplementary Fig. 7i). We noted that, while overexpression of STING alone was insufficient to induce ISG expression, it did sensitise cells to stimulation with dsDNA (Supplementary Fig. 8a). Overall, therefore, our results indicated that CBF-β-mediated regulation of STING levels tunes the cell-intrinsic ISG response, but that an additional stimulus is required to provoke ISG expression in the absence of CBF-β, the precise identity of which remains unclear. In support of this, equivalent experiments confirmed the synergistic induction of ISGs and IFNB1 upon CBF-β depletion and HT-DNA transfection in several other cell lines, including those of non-cancerous renal origin (Supplementary Fig. 8b-d).

The involvement of CBF-β in tuning STING transcription implied that it may be tonically repressed by CBF-β/RUNX dimers. To interrogate this, we performed ChIP sequencing (ChIP-Seq) of CBF-β in 786O cells using wildtype and overexpressed CBF-β (Fig. 7a). CBF-β was found to interact robustly at the STING locus, associated with a 5’-YGYGGTY-3’ RUNX consensus motif at the 3’-end of the gene73,74. Furthermore, analysis of published CBF-β chromatin binding patterns in SAOS2 cells revealed a strong peak at an alternative RUNX consensus motif near the transcription start site of the gene (Fig. 7b)75. Notably, we did not detect any significant binding at IFIT1 or other ISGs (Fig. 7a). ChIP-qPCR experiments validated the binding of CBF-β at both ends of the STING gene in 786O cells (Fig. 7c,d).

Next, we sought to identify the extent to which RUNX proteins are involved in this process of STING regulation. As for CBF-β itself, the overexpression of RUNX1, and to a lesser extent RUNX2, represses STING transcription (Fig. 7e,f). In addition, RUNX1 overexpression can overcome the effect of CBF-β loss on downstream ISG expression (Fig. 7e,f). Together, these findings indicate that CBF-β and RUNX transcription factors tune the level of STING through transcriptional repression. RUNX1 appears to play a dominant role in this process but requires CBF-β to exert a maximal repressive effect.

Finally, we explored whether the tonic regulation of STING by CBF-β has broader implications for type I interferon responses. CBF-β is implicated in human/simian immunodeficiency virus (HIV/SIV) infection, whereby the virally-encoded Vif associates with CBF-β to hijack a ubiquitin E3 ligase complex and degrade host APOBEC3G76,77. Besides disabling this important layer of antiviral defence, the trapping of CBF-β by Vif in the cytoplasm also prevents it from co-operating with RUNX transcription factors and exerting transcriptional activity (Fig. 7g)78. We therefore expressed GFP-Vif in 786O cells, and measured basal and dsDNA-induced type I interferon signalling. Vif expression increased ISG expression, similarly to CBF-β loss (Fig. 7h), and triggered the expression of both STING and downstream ISGs (Fig. 7h,i). Together, our findings establish CBF-β as a core and direct regulator of STING-mediated interferon signalling, with broad physiological and pathological implications in the context of anti-retroviral immunity and kidney cancer (Fig. 8).

Methods
Cell lines
Cell lines and reagents were sourced as detailed in Supplementary Table 1. 786O, 786O+VHL and 769P cells were cultured in Roswell Park Memorial Institute 1640 medium (RPMI-1640), and A498, HEK293T, HKC8, HK2, RCC4, RCC4+VHL and RCC10 cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM). All media was supplemented with 10% Fetal Calf Serum (FCS). Cells were maintained at 37°C in 5% CO . 2 μg/ml or 2.5 μg/ml Puromycin, 5 μg/ml Blasticidin, or 200 μg/ml Hygromycin was added to the media for antibiotic selection of transduced and transfected cells. Hypoxic culture was undertaken in a Whitley H35 Hypoxystation (Don Whitley) set at 1% O , 5% CO and 94% N . Cells were confirmed as mycoplasma-free with the MycoAlert detection kit (Lonza Cat#LT07-318), and authenticated by short tandem repeat profiling (Eurofins Genomics).

Plasmids
Cloning of novel gene expression plasmids was performed using Gibson assembly (NEB) of PCR amplicons and restriction digested backbone vectors, using the DNA sequences indicated in Supplementary Table 2. Plasmids were amplified in NEB 5α Competent E. coli (Cat#C2987H) grown at 37°C in LB containing appropriate antibiotics. The sources of plasmids used in this study are indicated in Supplementary Table 1.

Lentiviral production and transduction
Lentivirus was produced by transfection of HEK293T cells with the relevant vector, pMD.G, and pCMVR8.91 in six-well plates or 15 cm dishes at 60-70% confluence using Fugene 6 Transfection Reagent and Opti-MEM I. Viral supernatant was collected after 48 hours through a 0.45 μm filter and stored at −80°C. For small-scale transductions, 200 μl virus was applied to 5×104 cells in 24-well plates containing 500 μl media, and appropriate antibiotic selection performed after 48 hours until all untransduced control cells had died.

Lentiviral expression constructs were used for gene overexpression. Most overexpression assays were performed 10-14 days after transduction in comparison to controls transduced with the pHRSIN-SFFV-Puro empty vector. For STING-ALFA expression, cells transduced with pCW57.1-STING-ALFA or an empty vector were treated with 1 μg/ml doxycycline for 24 hours before harvesting.

CRISPR-Cas9 targeted gene deletions
Gene-specific sgRNA sequences listed in Supplementary Table 2 were designed using the E-CRISP or Vienna Biocenter algorithms119,120. For cloning into the pKLV-U6gRNA(BbsI)-PGKpuro2ABFP, pKLV-U6gRNA(BbsI)-PGKblast2ABFP and pSpCas9(BB)-T2A-Puro vectors, sgRNA oligonucleotides were designed with appropriate overhangs and cloned according to the Zhang lab protocol121. Cloning into tet-pLKO-sgRNA-puro for doxycycline-inducible sgRNA expression was performed using modified overhangs using the method of Huang et al.122. Multiplexed expression of sgRNAs targeting RUNX1, RUNX2 and RUNX3, or STAT1 and STAT2 from the same pKLV-U6gRNA(BbsI)-PGKpuro2ABFP vector was achieved by PCR amplification of the tRNA scaffold of pCFD5 with primers containing the relevant sgRNA sequences, followed by Gibson assembly123.

Cells were transduced with Lenti‐Cas9‐2A‐Blast, or had previously been transduced with an equivalent Hygromycin-resistant vector124, to stably express Cas9. To generate stable CRISPR/Cas9 deletion mutants, sgRNAs expressed from pSpCas9(BB)-T2A-Puro were transiently transfected into cells as described above, before dilution cloning in flat-bottomed 96-well plates. For other CRISPR/Cas9 experiments, cells were transduced with the indicated sgRNA expressed from pKLV-U6gRNA(BbsI)-PGKpuro2ABFP, pKLV-U6gRNA(BbsI)-PGKblast2ABFP, or tet-pLKO-sgRNA-puro, and assays performed 10-14 days after transduction, following antibiotic selection. All CRISPR/Cas9 experiments were controlled by transduction with an equivalent empty vector.

Pooled genome-wide CRISPR/Cas9 screening
At least 1.2×108 Cas9-expressing cells of 786O, 786O+VHL, RCC4 and RCC4+VHL backgrounds were transduced with the TKOv3 library at a multiplicity of infection of approximately 30%. Cells were expanded to 15 cm dishes after 27 hours, and selected with Puromycin for seven days. Every two or three days, cells were pooled, counted and passaged. After 17 population doublings, genomic DNA was extracted from 4×107 cells using a Puregene Cell Kit (Qiagen Cat#158043). Lentiviral sgRNA inserts were amplified in a two-step PCR using the primers detailed in Supplementary Table 2, with the second step introducing unique barcode sequences for multiplexed sequencing125. Amplicons were cleaned with Agencourt AMPure XP beads and sequenced on HiSeq 4000 or NovaSeq 6000 (Illumina) with a custom sequencing primer (Supplementary Table 2). An average of at least 400-fold (786O screen) or 500-fold (RCC4 screen) representation of each sgRNA with in the pool was maintained throughout all stages of the screens.

Sequencing data was processed and analysed using Cutadapt, HISAT2 and Bayesian Analysis of Gene Essentiality version 2 (BAGEL2) in a Snakemake-based bioinformatic pipeline (https://github.com/niekwit/crispr-screens, https://doi.org/10.5281/zenodo.10286661). Read counts for each sgRNA were compared across paired VHL-proficient and -deficient cell lines, and between late and early timepoints in each cell line, using BAGEL2, MAGeCK and DrugZ40,39,38.

shRNA-mediated RNA depletion
Oligonucleotides were designed with a TTCAAGAGA hairpin, using shRNA sequences from the Broad Institute RNAi Consortium shRNA Library (Supplementary Table 2). Sequences were cloned into pC.SIREN.Puro by digestion and ligation. Cells were transduced with the indicated vector or a scrambled shRNA control, and assays performed after at least seven days, following Puromycin selection.

Proliferation and cell death assays
Cells were seeded in six-well plates at predefined densities (1.5×104 cells/well for 786O cells, 2.5×104 cells/well for other cell lines), and manually counted with a haemocytometer after 96 hours. For the IFN-β dosage experiment, cells were seeded with the indicated concentration of IFN-β at 1×104 cells/well in 12-well plates, and final counts after 72 hours were normalised to an untreated control. For the Bafilomycin A1 experiment, 5×104 cells were seeded in six-well plates 24 hours before treatment with the indicated dose of Bafilomycin A1 or the DMSO vehicle, and counted after a further 48 hours.

Clonogenic assay
Cells were seeded in six-well plates at a density of 100 cells/well and incubated undisturbed for seven days. Colonies were fixed with 100% ice-cold methanol for 10 minutes, visualised with 0.5% crystal violet in 25% methanol, and counted.

Flow cytometry
Following appropriate staining, cells were collected in 5 ml FACS tubes, centrifuged, washed and resuspended in PBS prior to analysis on a Fortessa flow cytometer (BD Biosciences) using FACSDiva and FlowJo software. Gating strategies are displayed in Supplementary Fig. 9.

Cell death experiments employing SYTOX AADvanced and CellEvent Caspase-3/7 Green were performed according to the manufacturer’s instructions, with gates set based on empty vector-transduced or DMSO treated controls. For cell death inhibitor treatments, cells were seeded at a density of 2×104 cells/well in 12-well plates in media containing the indicated drug concentration or a DMSO vehicle. The media was exchanged after 24 hours with fresh media containing the same dose of inhibitor, and analysis performed as above after a further 24 hours.

To assess mitochondrial function, cells were seeded in six-well plates at 8×104 cells/well 24 hours prior to analysis with MitoSOX Red, MitoTracker Green FM, and tetramethylrhodamine methyl ester (TMRM). Before staining, control wells were treated with 30 minutes of Carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP) or Antimycin A. For MitoTracker Green and TMRM staining, cells were harvested, centrifuged and stained in FACS tubes for 30 minutes at 37°C. MitoSOX Red staining was performed at 37°C for 10 minutes directly in six-well plates, having washed cells in Hanks’ Balanced Salt Solution (HBSS).

For intracellular staining of γ-H2A.X, cells were permeabilised with ice-cold 90% methanol for 30 minutes, incubated at 4°C for 30 minutes with the primary antibody in a 1% BSA/PBS incubation buffer, washed in PBS, and stained with the secondary antibody at 4°C for a further 30 minutes. Cells were finally incubated in 3 μM propidium iodide/PBS for 15 minutes before flow cytometry analysis.

Competitive growth assay
Seven days post-transduction and following antibiotic selection, 3×104 clonal Cas9-expressing 786O mCherry cells were mixed with an equal number of clonal Cas9-expressing 786O+VHL GFP cells in six-well plates. For doxycycline-inducible sgRNA expression vectors, 100 ng/ml doxycycline was applied for nine days following the establishment of the co-culture. At each passage, a subset of each culture was harvested in 3.6% paraformaldehyde for flow cytometry analysis. The 786O mCherry:786O+VHL GFP cell ratio was normalised to 0.5 for the first time-point, and then to that of a relevant empty vector or scrambled shRNA control.

Cell cycle analysis
BrdU incorporation and propidium iodide staining of total cellular DNA content were used to assay cell cycle phases. For dual staining, 3.5×105 cells were seeded in 10 cm dishes. After 24 hours, cells were incubated in media containing 10 μM BrdU for one hour. Intracellular staining and flow cytometry was performed as described for γ-H2A.X, with an additional treatment step for DNA hydrolysis with 2 M hydrochloric acid for 30 minutes at room temperature immediately after methanol permeabilisation.

Cell cycle synchronisation was achieved using a double thymidine block method. 3.5×104 cells were seeded in six-well plates. Cells were treated with 2 μM thymidine after seven hours, which was replaced with thymidine-free media after 15 hours. After eight hours, media was replaced again with 2 μM thymidine for 17 hours. At the end of this second treatment, and for every two hours thereafter for 14 hours, samples were harvested in 90% methanol before propidium iodide staining and flow cytometry analysis. An asynchronous control of each experimental condition was maintained in thymidine-free media throughout.

LDH release cytotoxicity assay
5×103 cells were seeded in triplicate in 96-well plates with 100 μl of media. After 24 hours, 50 μl supernatant from each well was transferred to a new plate, and the Pierce LDH Cytotoxicity Assay Kit (ThermoFisher Cat#88953) used to assess LDH levels as per the manufacturer’s instructions, with absorbance measurements taken using a CLARIOstar Plus plate reader (BMG Labtech). Background absorbance at 680 nm was subtracted from the 490 nm signal, and readings normalised to a cell-free unconditioned media control.

DNA transfection
8×104 786O Cas9 cells were seeded in 2 ml media in six-well plates 24 hours prior to transfection with herring testes DNA (HT-DNA). Mixes of 4 μg/ml HT-DNA and 40 μg/ml Lipofectamine 2000, prepared in Opti-MEM I, were combined and gently mixed. After 30 minutes incubation at room temperature, 500 μl DNA/Lipofectamine 2000 complex was added dropwise to cells.

Real-time quantitative PCR (qPCR)
Cell lysis was performed in six-well plates at approximately 70-80% confluence. Total RNA was extracted and purified with the RNeasy Plus Mini Kit (Qiagen Cat#74136), and cDNA generated using Protoscript II Reverse Transcriptase. 20 ng template cDNA was amplified with SYBR Green PCR Master Mix on a QuantStudio 7 Real-Time PCR System (ThermoFisher), using primers specific to the mRNA interest (Supplementary Table 2). qPCR reactions for each cDNA/primer combination were performed in technical triplicates, and the mean used to calculate the relative transcript abundance with the ΔΔCt method following normalisation to the reference index of a housekeeping gene (ACTB).

RNA sequencing
Total RNA was extracted using the PureLink RNA Mini Kit (ThermoFisher Cat#12183025), including on-column DNA digestion with PureLink DNase. Library preparation and sequencing were undertaken by GENEWIZ (Azenta), using NEBNext Ultra II RNA Library Preparation Kit (NEB Cat#E7770L) for polyA transcript selection, and NovaSeq 6000 (Illumina). Bioinformatic analysis of RNA-Seq data with DESeq2 was performed as described previously124, except using Salmon for read mapping and STAR/TEtranscripts for differential expression analysis of transposable elements. Gene Set Enrichment Analysis of Hallmark gene sets was undertaken in R using the fgsea package.

ChIP-qPCR
786O cells grown to 70% confluence in 15 cm dishes were treated with 1% formaldehyde for 10 minutes to crosslink proteins to chromatin, and the reaction quenched with 0.125 M glycine for 10 minutes at room temperature. Cells were washed twice in ice-cold PBS, transferred to tubes, centrifuged at 800 rpm for 10 minutes and lysed in 500 µl of ChIP lysis buffer (50 mM Tris-HCl pH 8.1, 1% SDS, 10 mM EDTA, and 1×cOmplete EDTA-free protease inhibitor cocktail). Next, samples were incubated for 10 minutes on ice, and diluted with an equivalent volume of ChIP dilution buffer (20 mM Tris-HCl pH 8.1, 1% (v/v) Triton X-100, 2 mM EDTA, and 150 mM NaCl) prior to sonication using beads in a Bioruptor (Diagenode) for 20 cycles of 30 seconds on and 30 seconds off. Samples were centrifuged at 4°C for 10 minutes at 13,000 rpm and supernatants collected and stored at −20°C as input controls. 200 µl aliquots of the remaining samples were diluted with ChIP dilution buffer to 1 ml, and precleared with 25 µl Protein G magnetic beads at 4°C for two hours before overnight immunoprecipitation with the indicated primary antibody at 4°C. Next, 25 μl Protein G magnetic beads were added and samples incubated for a further two hours at 4°C. Beads were washed for 5 minutes with wash buffer 1 (20 mM Tris-HCl pH 8.1, 0.1% (w/v) SDS, 1% (v/v) Triton X-100, 2 mM EDTA, and 150 mM NaCl), wash buffer 2 (as for wash buffer 1 with 500 mM NaCl), wash buffer 3 (10 mM Tris-HCl pH 8.1, 0.25 M LiCl, 7 1% (v/v) NP-40, 1% (w/v) Na deoxycholate, and 1 mM EDTA), and twice with TE buffer (10 mM Tris-HCl pH 8.0, and 1 mM EDTA). Complexes were eluted in 120 μl elution buffer (1% (w/v) SDS and 0.1 M NaHCO3), and incubated overnight at 65°C with agitation with 0.2 M NaCl to reverse the formaldehyde crosslinks. Samples were incubated with 20 μg Proteinase K for 4 hours at 45°C, and with RNase H for 30 minutes at 37°C to digest protein and RNA, before DNA was purified using the DNA MinElute kit (Qiagen Cat#28206). DNA was analysed by qPCR, and results were normalised to the amplification of the input material for each sample.

ChIP sequencing
For each replicate, two 15 cm plates of 786O Cas9 cells transduced with a CBF-β-FLAG overexpression vector or an empty vector were fixed and sonicated, as for ChIP-qPCR. 1 ml sonicated sample was diluted with 4 ml ChIP dilution buffer, transferred to a 15 ml falcon tube, and pre-cleared using 60 µl IgG beads. For ChIP sequencing samples, 30 µl primary CBF-β antibody (Diagenode Cat#C15310002) was used per replicate. ChIPed DNA was eluted in 10 µl elution buffer and submitted for sequencing.

Libraries were prepared using ThruPLEX® DNA-Seq Kit and ThruPLEX DNA Unique Dual Index Kit according to the manufacturer's recommendations. The pooled libraries were sequenced on the Illumina NovaSeq X Plus system (10B flowcell, PE50 read length).

A Snakemake v8.25.5 pipeline was used to analyse ChIP-Seq data (https://doi.org/10.5281/zenodo.138015265): read quality was first assessed with FastQC v0.12.1 and MultiQC v1.21, after which the reads were quality trimmed with TrimGalore. Alignment against the human genome (hg38, build 113) was performed using Bowtie2 v2.5.3. Using SAMtools view v1.20, only fragments with MAPQ > 10 were retained for further analysis. BEDTools intersect v2.31.1 was then used to remove blacklisted regions (obtained from ENCODE, https://www.encodeproject.org/files/ENCFF356LFX). To remove duplicates, fragments were first sorted by coordinate using SAMtools sort, after which Picard MarkDuplicated v3.1.1 was used with the command line flag --REMOVE_DUPLICATES true. The resulting BAM file was indexed using the SAMtools index command. BigWig files for individual replicates were made with deepTools bamCoverage v3.5.5 with the settings binSize 10 and normalizeUsing RPKM. The Principal Component Analysis of these BigWig files was performed with deepTools multiBigWigSummary and deepTools plotPCA, while plotting was done with a custom R script. Peak calling on individual replicates was performed on the deduplicated BAM files with MACS2 callpeak v2.2.9.1 (broad peak calling) with q=0.05 and broad cutoff=0.05 and the matched input sample as a control. Consensus peaks (BED format) were generated using a custom Python script that identified regions of overlap between all replicates.

To create the coverage plot, mean BigWig files were first generated from all replicates of each condition (IP and input): first a mean Wig file was created using WiggleTools v1.2.11, which was converted to the BigWig format using wigToBigWig v4 (UCSC Genome Browser). To compute the ratio of IP over input, deeptools bigwigCompare was used with the command line flags --operation ratio --skipZeroOverZero. bigWigAverageOverBed v469 (UCSC Genome Browser) was used with the consensus peak file described above to obtain the average ratio of IP over input in the consensus peaks. GNU Awk v5.0.1 was then used to append these scores as an additional column to the consensus peak BED file. Finally, this scored BED file was used as input for the coverage plot using a custom R script that utilised the Bioconductor package ChIPseeker v1.44.0. Genome tracks were visualised with IGV v2.16.0.

Liquid-chromatography mass spectrometry (LC-MS)
2×106 cells were harvested in PBS, resuspended in 71 μl resuspension buffer (76 mM triethylammonium bicarbonate (TEAB) pH 8.5, 3 mM MgCl2, 1,400 U/ml benzonase, 7 mM tris(2-carboxyethyl)phosphine (TCEP), and 28 mM chloroacetamide), and lysed with the addition of 25 μl 20% SDS for 30 minutes at 25°C in the dark. 4 μl 1.25 M Azido-PEG3-Azide was added and samples incubated for 20 minutes at 55°C to quench the TCEP. 5 μl aliquots were compared to a standard curve of BSA using a Pierce Microplate BSA assay (ThermoFisher Cat#23252), and samples were normalised to 25 μg by dilution in a 50 mM TEAB, 5% SDS buffer.

To complete the denaturation, a 10% volume of 27.5% phosphoric acid was added to each sample, causing acidification to approximately pH 2. A 6× volume of a wash buffer (100 mM HEPES pH 7.55, 90% methanol) was added, and the solution transferred to an S-trap micro column using a positive pressure manifold (Tecan M10) and in-house fabricated adaptors. Samples were washed with 1:1 (v/v) methanol:chloroform and three times with the wash buffer. S-traps were centrifuged at 4,000 g for 2 minutes to remove residual wash buffer, and 30 μl of a digestion solution (50 mM TEAB pH 8.5, 0.1% Na deoxycholate) containing 1.25 μg Trypsin/lysC Mix was added. S-traps were incubated for 16 hours at 37°C, and peptides recovered by the addition of 25 μl digestion solution and incubation at room temperature for 15 minutes, before centrifugation at 4000 g. 40 μl 0.2% formic acid (FA) and 25 μl 50% acetonitrile (ACN) were used in the same manner to elute samples, which were subsequently dried in a vacuum centrifuge equipped with a cold trap. Samples were resuspended in 21 μl 100 mM TEAB pH 8.5. 0.5 μg unique TMTpro labels, resuspended in 9 μl anhydrous ACN, were added to each sample and incubated at room temperature for one hour. To confirm labelling efficiency of >98% and equal loading, 3 μl aliquots of each sample were taken, pooled, and analysed by LC-MS.

Samples were normalised by adjusting for the total reporter ion intensities of the initial test, and were pooled and dried in a vacuum centrifuge. Samples were acidified with approximately 200 μl 0.1% Trifluoroacetic Acid (TFA), and FA was added until the Na deoxycholate was visibly precipitated. Four volumes of ethyl acetate were added and vortexed vigorously for 10 seconds. Samples were centrifuged at 15,000 g for 5 minutes at room temperature for phase separation, and the aqueous phase transferred to a fresh tube. Samples were partially dried in a vacuum centrifuge and made up to 1 ml with 0.1% TFA. FA was added to achieve a pH <2, and the samples cleaned by solid phase extraction using a 50 mg tC18 Sep-Pak cartridge (Waters) and a positive pressure manifold: the cartridge was wetted with 1 ml methanol followed by 1 ml ACN, equilibrated with 1 ml 0.1% TFA, and each sample loaded slowly. The cartridge was washed with 1 ml 0.1% TFA, and samples were eluted in 750 μl 80% ACN, 0.1% TFA and dried under a vacuum.

Next, samples were resuspended in 40 μl ammonium formate pH 10 and transferred to a glass HPLC vial. Basic pH reverse-phase fractionation was conducted on an Ultimate 3000 UHPLC system (ThermoFisher) equipped with a 2.1 mm × 15 cm, 1.7 μm Kinetex EVO column (Phenomenex) set at a flow rate of 500 μl/minute, using solvent A (3% ACN), solvent B (100% ACN), and solvent C (200 mM ammonium formate pH 10; kept at a constant 10%). Samples were loaded in 90% solvent A for 10 minutes before they were eluted in a gradient of solvent B of 0-10% over 10 minutes, 10-34% over 21 minutes, and 34-50% over 5 minutes, followed by a 10 minute wash with 90% solvent B. 100 μl fractions were collected throughout the run, and those containing peptide (determined by ultraviolet absorbance at 280 nm) were recombined across the gradient to preserve orthogonality with on-line low pH reverse-phase separation. Combined samples were dried in a vacuum centrifuge and stored at −20°C.

Samples were analysed by LC-MS on an Orbitrap Fusion instrument on-line with an Ultimate 3000 RSLC nano UHPLC system (ThermoFisher), using trapping solvent (0.1% TFA), analytical solvent A (0.1% FA) and analytical solvent B (0.1% FA in ACN). After resuspension in 10 μl 1% TFA and 5% DMSO, 5 μl of each sample was loaded onto a 300 μm × 5 mm PepMap cartridge trap (ThermoFisher) at 10 μl/minute for 5 minutes. Samples were separated on a 75 cm × 75 μm I.D. 2 μm particle size PepMap C18 column (ThermoFisher) with a gradient of analytical solvent B at 3-10% over 10 minutes, 10-35% over 155 minutes, and 35-45% over 9 minutes, followed by washing with 95% analytical solvent B for 5 minutes and re-equilibration at 3% analytical solvent B. Eluted particles were next introduced to the MS by 2.1 kV electrospray to a 5 cm × 30 μm stainless steel emitter (PepSep). The MS was operated in SPS mode, in which MS1 Scans are obtained in the Orbitrap, CID-MS2 Scans in the Ion Trap and HCD-MS3 acquired in the Orbitrap to resolve reporter ions.

Data were analysed with Peaks 11, and .raw files searched against the SwissProt Human Database with appended common contaminants. The peptide-spectrum match FDR was controlled at 0.1% by decoy database search. Statistical analysis of the relative abundance of identified proteins in each sample was performed with the limma package in R by a moderated t-test. A q-value was computed to determine appropriate cut-off values by correcting P-values for multiple hypothesis testing with the Benjamini-Hochberg method.

SDS-PAGE immunoblotting
Cells were lysed at approximately 70-80% confluence in six-well plates with an SDS loading buffer (1% SDS, 50 mM Tris pH 7.4, 150 mM NaCl, 10% glycerol, and 2 μl/ml benzonase) and incubated on ice for 20 minutes before heating at 90°C for 5 minutes. Proteins were resolved by SDS-PAGE (Bio-Rad), and transferred to methanol-activated polyvinylidenedifluoride (PVDF) membranes. Membranes were blocked with 0.2% TWEEN-20/PBS containing 5% skimmed milk powder and 1% BSA, probed with the appropriate primary and secondary antibodies for at least one hour each, and developed with Pierce Enhanced Chemiluminescent, SuperSignal West Pico Plus Chemiluminescent, or SuperSignal West Dura Extended Duration substrates. PBS was substituted with TBS for the detection of phosphorylated proteins. β-Actin levels were used to confirm the equivalent loading of each sample. Indicated protein sizes were determined by comparison to a SeeBlue Plus2 pre-stained protein standard.

Immunoprecipitation
786O cells were lysed in 1 ml 1% Triton X-100/TBS supplemented with 1×cOmplete EDTA-free protease inhibitor cocktail at 4°C for 30 minutes. Lysates were centrifuged at 14,000 g for 15 minutes at 4°C, and the supernatant collected. 20 μl of each sample was taken in an equal volume 2×SDS loading buffer as an input, and the remainder pre-cleared with Pierce Protein G magnetic beads for one hour at 4°C. Supernatants were incubated overnight with 2 μl of the indicated primary antibody at 4°C with rotation. Next, Pierce Protein G magnetic beads were added for two hours, and samples were washed three times in 1% Triton X-100/TBS lysis buffer. Bound proteins were eluted in 2×SDS loading buffer and analysed by SDS-PAGE immunoblotting.

Subcellular fractionation for immunoblotting
2×106 cells were lysed and incubated with rotation in buffer A (0.1% IGEPAL CA-630, 10 mM HEPES, 1.5 mM MgCl , 10 mM KCl, 0.5 mM DTT, and 1×cOmplete EDTA-free protease inhibitor cocktail) for 10 minutes at 4°C before centrifugation at 1,400 g for 4 minutes at 4°C. The supernatant, representing the cytoplasmic fraction, was collected in 2×SDS loading buffer. The nuclear pellet was washed in buffer A, with centrifugation as before, and resuspended in buffer B (20 mM HEPES, 1.5 mM MgCl , 300 mM NaCl, 0.5 mM DTT, 25% glycerol, 0.2 mM EDTA, and 1×cOmplete EDTA-free protease inhibitor cocktail) for 10 minutes at 4°C. Samples were centrifuged at 1,700 g for 4 minutes at 4°C, and the soluble nucleoplasmic fraction and insoluble chromatin pellet were each collected and resuspended in 2×SDS loading buffer. Subcellular fractions were analysed by SDS-PAGE immunoblotting.

Mitochondrial DNA leakage assay
1×107 cells were harvested by trypsinisation, pelleted and washed in PBS. Pellets were resuspended in 550 μl ice-cold Cytosolic Extraction Buffer (NaCl 150 mM, HEPES 50 mM, digitonin 200 µg/ml, hexylene glycol 1M, and 1×cOmplete EDTA-free protease inhibitor cocktail) and incubated on ice for 10 minutes. Samples were centrifuged at 2,000 g for 10 minutes at 4°C. The supernatant (containing the cytosolic fraction) was transferred to a fresh tube, and the pellet (containing the reference nuclear fraction) resuspended in 550 µl RIPA lysis buffer (25 mM Tris-HCl pH 7.6, 150 mM NaCl, 1% NP-40, 0.1% Na deoxycholate, 0.1% SDS, and 1×cOmplete EDTA-free protease inhibitor cocktail). Cytosolic samples were incubated for a further 10 minutes on ice, centrifuged as before, and the supernatant isolated. Next, samples were incubated with 500 µg/ml RNase A for 1 hour at 37°C, and then with 10 mg/ml Proteinase K for a further 1 hour at 55°C. DNA was isolated by phenol-choloroform isoamyl alcohol extraction by adding 500 µl UltraPure™ Phenol:Chloroform:Isoamyl Alcohol (25:24:1, v/v), shaking vigorously for 20-30 seconds, and centrifugation at 18,300 for 15 minutes at 4°C. DNA was precipitated by transferring 400 μl of the supernatant to a fresh tube containing 500 μl 100% isopropanol. Samples were vortexed and centrifuged at 18,300 g for 10 minutes at 4°C, and the supernatant discarded. The pellet was washed with 500 μl 70% ethanol, the supernatant removed and the pellet dried at 24°C. Finally, the pellet was resuspended in 200 μl 10 mM Tris-HCl pH 8.0, and 10 ng of DNA used for qPCR analysis of mitochondrial genes. Cytoplasmic mtDNA abundance was normalised to the amount of ACTB DNA in the nuclear fraction for each sample.

Bioenergetic analysis
Oxygen consumption rates (OCRs) were measured using a Seahorse XF analyser in order to calculate basal respiration. The Mito Stress Test was performed according to the manufacturer’s instructions using Seahorse XF FluxPak consumables (Agilent Technologies). 1.2×104 cells were plated in FluxPak 96-well plates and incubated at 37°C for 24 hours. The medium was replaced with Seahorse XF RPMI, pH 7.4 supplemented with 1 mM sodium pyruvate, 2 mM L-glutamine, and 10 mM glucose, and cells were maintained at 37°C in a non-CO incubator for 1 hour before the run. Analysis was performed using the program settings: mix for 3 minutes and measure for 3 minutes ×3; inject 1.5 μM oligomycin; mix for 3 minutes and measure for 3 minutes ×3; inject 1 μM FCCP; mix for 3 minutes and measure for 3 minutes ×3; inject 1 µM Rotenone, 1 µM Antimycin A and 2.5 μM Hoechst 33342; mix for 3 minutes and measure for 3 minutes ×3. Basal respiration was calculated by subtracting the OCR following Rotenone and Antimycin A treatment from the basal OCR. Following the assay, results were normalised for cell number by quantifying Hoescht 33342 staining using a CLARIOstar Plate Reader at 355-20/455-30 nm.

Immunofluorescence
5×104 786O cells were seeded on FCS-precoated coverslips in 24-well plates. 10 µM Camptothecin was added to a control well after 24 hours, and after a further 24 hours, all coverslips were washed twice in Dulbecco’s PBS (D-PBS) and fixed with 4% paraformaldehyde for 15 minutes. Cells were washed twice in D-PBS, permeabilised in 0.1% Triton X-100 for 10 minutes, washed again, and blocked in 4% FCS in D-PBS. The coverslips were stained overnight with a 1:400 dilution of the anti-Phospho-Histone 2A.X (Ser139) primary antibody at 4°C, then washed five times with D-PBS and counterstained with a 1:800 dilution of the Alexa-Fluor 488 anti-Rabbit secondary antibody for 90 minutes at room temperature. Cells were washed five times in D-PBS and mounted in the glycerol-based mountant AF1 plus DAPI prior to imaging with a Zeiss LSM 980 with Airyscan confocal microscope. Foci were counted in a blinded an unbiased manner using Fiji (ImageJ) software.

Analysis of TCGA and CPTAC expression and survival data
CBFB mRNA expression and survival data for ccRCC tumours was obtained from The Cancer Genome Atlas (TCGA), and CBF-β protein levels and survival data obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC)7,126,127. CBF-β protein abundance was plotted for tissues from ccRCC patients for whom analysed matched normal kidney tissue was available. The R package survminer was used to perform log-rank tests and plot Kaplan-Meier curves.

Single cell expression of interferon pathway genes
Single cell data was downloaded from Li et al. (https://data.mendeley.com/datasets/g67bkbnhhg/1)48. The anndata file was first converted to a seurat object using the sceasy package in R. The single cell expression data was normalised and variance stabilised using regularised negative binomial regression using the SCTransform function from the seurat R package. Cell types annotated as either ‘Low quality’ or ‘Unknown’ were removed with the remaining cells collated into their broad principal lineages of epithelial cells, RCC cells, NK cells, myeloid cells, fibroblasts, endothelial cells, T cells and B cells. The average normalised expression data for these cell types in relation to interferon and STING pathway genes were plotted using the DotPlot function in R.

Tumour xenografts
Animal experiments were performed according to protocols approved by either the University of Cambridge Animal Welfare and Ethical Review Board in compliance with the Animals (Scientific Procedures) Act 1986 and UK Home Office regulations, or by the Institutional Animal Care and Use Committee of UT Southwestern Medical Center following NIH guidelines. The maximal tumour size and burden set by the relevant ethics committee was a tumour area of 1.2 cm2 for subcutaneous xenografts, and for orthotopic xenografts, humane endpoint criteria were used, including total body weight loss of greater than 15-20%, impaired mobility, abdominal distention, and other indications of compromised animal welfare; no animals exceeded these limits.

Female NSG mice (NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (Charles River RRID:IMSR_JAX:005557)) used for subcutaneous xenograft were housed at a density of five animals per individually ventilated cage with ad libitum access to food and water in a specific pathogen free unit. 786O Cas9 cells expressing the indicated sgRNAs were harvested and washed in D-PBS, and 1×107 cells were administered via subcutaneous injection to the dorsal flank of 8 week old NSG mice in a blinded and randomised manner between cages. Tumour volume was determined using callipers as the product of 0.5 × maximum length2 × maximum breadth, and mice were euthanised at 28 days.

Orthotopic xenografts of 786O Cas9 cells stably expressing luciferase and doxycycline-inducible sgRNA vectors were performed in 6-8 week old male and female NSG mice (UTSW Animal Resource Center) as described previously46. 1×106 viable cells were resuspended in 20 μL PBS containing 50% Matrigel and injected orthotopically into the left kidney of each NSG mouse. Following successful implantation, mice were fed Purina rodent chow #5001 with 2000 ppm doxycycline to induce sgRNA expression. Tumour growth was monitored weekly by bioluminescence imaging using the SPECTRAL AMI-HTX system. Mice were euthanised after a further eight weeks, and the tumour mass calculated by subtracting the weight of each right kidney from the corresponding left kidney. Metastasis was evaluated in isolated lungs by bioluminescence.

Quantification and statistical analysis
Quantitative data are expressed as the mean of biological repeats ± 1 standard deviation (SD) or ± 1 standard error of the mean (SEM). P-values were calculated using two-tailed Student’s t-tests, two-tailed Mann-Whitney U tests, or analysis of variance (ANOVA), as indicated in figure legends. Statistical analyses of CRISPR/Cas9 screens, RNA-Seq, mass spectrometry, and TCGA and CPTAC data are described in the relevant method sections. The number of biologically independent repeats (independent transductions for experiments involving cell culture, or number of mice for xenograft experiments) are specified in figure legends. Data presented from CRISPR/Cas9 screens are derived from a single replicate in each cell line. Figures were prepared and statistical analyses performed using GraphPad Prism or R. Software used in this study is indicated in Supplementary Table 1.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Bertlin et al describe a series of genetic screens ostensibly to discover genes that are essential in the context of VHL loss, the signal genomic lesion in kidney cancer (clear cell renal cell carcinoma, ccRCC). The authors perform genome-scale CRISPR knockout screens in two ccRCC cell lines, 786O and RCC4, which carry the signature homozygous VHL loss, and again in those same cells with exogenously expressed VHL.\nIt is not clear how well-controlled the CRISPR screens are. The authors use the TKOv3 one-component library. Some quality control figures are present in Supp Fig 1, but the relative paucity of hits (genes with Bayes Factor > 0 or some higher threshold) shown in Fig 1C suggests that quality was not optimal. It would be useful to have a precision-recall curve, using common reference sets of essential and nonessential genes (e.g. for essentials, Hart et al, G3, 2017 – the Hart et al, Cell 2015 set is not optimal – and for nonessentials, Hart et al MSB 2014). Other data, such as cell-line specific nonexpressed genes, may be substituted for nonessentials without loss of information. Comparison of the PR curves of these screens to those publicly available ccRCC genome-scale cell line screens in DepMap would provide further evidence of screen quality. This information is important, as it gives some insight into the false negative rate of a cell line screen, which is critical in evaluating whether a hit found in one screen but not another reflects underlying biology or just a missed call in the other screen.\nTo identify genes providing proliferative advantage (e.g. KEAP1 in this case), a better approach is described in Lenoir et al, Nat Comms 2021. This is far better than Bagel scores, although the outlier nature of KEAP1 knockout is a strong signal here. The cherrypicking of target genes (Fig 1d) is useful for narrowing the reader’s attention to the genes of interest but the display is not convincing. It is standard to calculate and display fold change of normalized end point cells relative to some early point (plasmid or T0), rather than read counts, alongside some negative control (again, nonessentials are useful here) whose distribution is centered around zero.\nFor comparing differential essentials between cell lines, the approach is flawed. The difference in proliferation rates between the VHL+ and VHL- cells will confound a competition assay for single gene effects. A better approach would be to use an algorithm such as DrugZ (Colic et al, Genome Med 2019; see, e.g., Karasu et al Nat Biotech 2024 for a recent example of its application) to compare endpoint readcounts of screens in VHL+ and VHL- cells.\nMy motivation for stressing controls so firmly is that the main gene chosen for follow up, CBFB, I is a very weak hit in Fig 1d. More importantly, it is not a hit in DepMap. In fact CBFB knockout shows strong phenotype in leukemia, lymphoma, and myeloma cells (Chronos score < -0.5), but is never essential in any of the >25 kidney cancer cell lines screened by DepMap. The in vivo data is compelling, but is totally inconsistent with the in vitro data that were ostensibly used to nominate CBFB for validation.\nThe authors should either perform a more realistic analysis of their screen data, or find another way to get to the CBFB hypothesis in ccRCC.", "reviewer_2": "In this study, the authors performed synthetic lethal screens and identified CBFb deletion as synthetic lethal to VHL loss. Mechanistically, they propose that CBFb negatively regulates STING. Overall, the finding that CBFb and VHL are synthetic lethal is potentially interesting. However, the mechanism underlying the role of CBFb in this synthetic lethality remains unclear, and some of the data do not support the model and appear to be conflicting. Additionally, the manuscript’s writing needs improvement, as some data contradict the conclusions presented in the figures. The primary concerns are listed below. If the authors can sufficiently address these concerns, I am willing to review a revised version of the manuscript.\nConcerns:\n1. A major issue about the study is that the results have not provided reasonable mechanistic explanation for the synthetic lethality of CBFb and VHL loss, the major topic of this study. Type I interferon activation induces a plethora of phenotypes in cells, including senescence, apoptosis, cell cycle arrest, etc. Why does CBFb deletion-induced type I IFN activation selectively kill VHL-null ccRCC? Without the mechanism, the main conclusion is not well supported.\n2. The increase in STING protein alone is not sufficient to activate the type I interferon pathway. Therefore, CBFB deletion must activate an upstream regulator of STING. The authors should employ a candidate approach to knock out potential upstream activators and elucidate the mechanism of type I IFN activation. The strategy outlined in the Cell paper, \"Inhibiting DNA Methylation Causes an Interferon Response in Cancer via dsRNA Including Endogenous Retroviruses,\" may be helpful to the authors (ScienceDirect, Volume 162, Issue 5, 27 August 2015, Pages 974-986).\n3. The data suggest that cGAS, an upstream activator of STING, is required for type I IFN activation upon CBFb depletion (Figure 6e). As cGAS is a double-stranded DNA (dsDNA) sensor, the authors should conduct experiments to identify the source of dsDNA.\n4. The whole manuscript is about ccRCC, and the GFP-Vif section in Figure 7 is irrelevant to the study. While it is acceptable if the authors aim to use GFP-Vif to degrade CBFb, this approach does not add significant value beyond the use of sgRNA or shRNA for CBFB. It remains unclear why the authors conducted an HIV-related study and how they concluded that the CBFB amount is lower than the basal conditions (as shown in the bottom of Figure 7).\n5. It is puzzling that the induction of ISGs does not require STAT1, STAT2, and IRF9 (Extended Data Fig. 4a). On page 7, first paragraph, it states, \"To our surprise, however, knockout of STAT1, STAT2, or IRF9 individually, or in combination, did not prevent IFIT1 induction (Fig. 5b and Extended Data Fig. 4a-c).\" One possibility is that the gRNAs for STAT1, STAT2, and IRF9 generated a polyclonal pool, with some cells having reduced STAT1, STAT2, or IRF9 levels, and others remaining wild type. This polyclonal nature could result in the negative outcome, particularly in the context of the autocrine and paracrine effects of IFN-B1. The authors should select at least two single clones for STAT1, STAT2, and IRF9 knockout and repeat the experiment in Extended Data Fig. 4a.\n6. For Extended Data Fig. 4, the title is “ISG transcription following CBFB deletion is mediated by the STING-TBK1-IRF3 axis.” However, the data appear to suggest otherwise. Please clarify this discrepancy.\n7. In Figure 4e, CBFb N104A rescues the effect of IFIT1 similarly to WT, suggesting that binding to RUNX proteins is not necessary for the repression of type I interferon. This result contradicts the model proposing that the CBFb-RUNX complex suppresses STING. It has been established that CBFb enhances the chromatin binding of RUNX proteins. Therefore, reconciling this result with the proposed model requires a logical explanation.\n8. In Figure 6f, the classical ChIP analysis has several issues. Firstly, it was heavily normalized. The authors should present the percentage of input on the Y-axis. Secondly, ChIP-seq or CUT&RUN analyses of CBFb or RUNX2 should be performed to unbiasedly detect the binding of CBFb and RUNX2 genome-wide.\n9. The claim that RUNX1 directly binds to the promoter region of STING is questionable because the ChIP-seq data were obtained from a public dataset involving RUNX1 in K562 cells (chronic myeloid leukemia). Transcriptional and epigenetic features are cell-line specific. Thus, binding of RUNX1 in K562 cells does not necessarily indicate binding in ccRCC cell lines. The authors should perform ChIP-seq analysis of RUNX1 in ccRCC cell lines to substantiate this claim.\n10. The authors claim that IRF3 directly induces ISGs, which contradicts existing literature. Therefore, robust data are needed to support this claim. IRF3 ChIP-seq data is required to substantiate the assertion. The authors have only performed an IRF3 knockout and demonstrated that IRF3 is necessary, which this piece of data does not indicate IRF3 directly induce ISGs.", "reviewer_3": "This study offers a novel and interesting insight into the mechanism of \"synthetic lethality\" in VHL-null kidney cancers. In this manuscript, the authors utilized a genome-wide CRISPR knockout screen to identify the synthetic lethality interactors of von Hippel-Lindau (VHL) in clear cell renal cell carcinoma (ccRCC). They found that the loss of CBF-β suppresses the growth of VHL-null tumors both in vitro and in vivo. Mechanistically, the authors demonstrated that the CBF-β/RUNX complex binds to the STING promoter, inhibiting STING transcription. The loss of CBF-β leads to cell-intrinsic STING activation, resulting in the upregulation of type I interferon and the expression of interferon-stimulated genes (ISGs), which ultimately arrests the cancer cell cycle.\nThis research demonstrated innovation and provided a potential therapeutic target for ccRCC. However, more robust and convincing evidence is required to support the hypothesis.\nMajor comments：\n1. Previous studies (Refs. 24-34 mentioned in the manuscript) have explored various ‘synthetic lethality’ interactors of VHL. So, what is the advantage of this study or the new target of CBF-b. Whether other synthetic lethality interactors of VHL (eg. ROCK1, J M Thompson, et. al, Oncogene, 2017) also have similar functions in the ccRCC case? What is the specificity of CBF-b as the ‘synthetic lethality’ interactors of VHL in the context of ccRCC?\n2. Initially, the author aimed to find the potential therapeutic targets for ccRCC and screened out CBF-b as a ‘synthetic lethality’ interactor of VHL. Can the author target CBF-b/RUNX-STING-IRF3-IFN axis for the treatment of ccRCC in a mouse model?\n3. In the manuscript, the author focuses solely on tumor cells. If the loss of CBF-b activates the STING signaling pathway in ccRCC, how does this affect other immune cells in the tumor microenvironment (TME)? Additionally, what impact could this have on T cells, natural killer (NK) cells, or other immune cell populations in systemic conditions?\n4. The author claimed that the loss of CBF-b in VHL-null 786O cells activates STING-IFN-ISG signaling. If the 786O cells produce and secrete type I interferon (IFN), why does the secreted IFN not affect the 786O+VHL cells in the co-culture experiment (Fig. 1e)? Could it be that STING activation triggers type I IFN-independent signaling pathways, such as autophagy and autophagy-mediated cell death?\n5. In Fig 1a, VHL was reconstituted in the 786O and RCC4 cell lines. Why do the Western blot bands differ in these two cell lines?\n6. In Fig. 5g, why is the STING protein absent in the 786O Cas9 (sgCtrl) cells?\n7. In Ex.Fig.6b and 6c, the knockout of CBF-b in RCC4 cells increases the mRNA level of cGAS and STING. However, in RCC10 cells, the knockout of CBF-b appears to have no effect on the regulation of either cGAS or STING levels. This suggests that the idea that CBF-b/RUNX binds to the STING promoter region to repress STING transcription may not be a universal model.\n8. Why did the author use the ChIP-Sequencing data from the K562 cell line instead of the ccRCC?\n9. In Fig. 6f, ChIP analysis using the CBF-b antibody showed that the levels of STING primers 1 and 3 were reduced in CBF-b KO cells. However, when using the RUNX2 antibody, only primer 1 exhibited a reduction, while primer 3 did not. Why？Does this suggest that primer 1 is more significant? Additionally, why is the sample size for primers 1 and 2 listed as n=5, while for primers 3 and 4, it is n=3?\nMinor point：\n1. Some of the western blot bands are too faint. For example, in Fig 4e, the band of IRF7 is barely visible, especially for the sg control.", "reviewer_4": "The manuscript by Bertlin et al. identifies the CBF-B transcriptional regulator as a synthetic lethal partner of VHL loss in kidney cancer cells. In this study, performed a pooled genome wide CRISPR/Cas9 screen to identify synthetic lethal partners of VHL loss in 2 kidney cancer cell lines. They demonstrate that loss of CBF-B selectively reduces the growth and survival of VHL deficient kidney cancer cell lines. Importantly, genetic inhibition of CBF-B reduces kidney tumor growth in vivo. Mechanistically, this study identifies CBF-B as a potential therapeutic target for the treatment of kidney cancer and identifies novel biological insights into the regulation of the STING pathway in kidney cancer. However, the manuscript requires further clarification and revision before consideration for publication. Please see the detailed comments below.\nFigure 1- CBFB was identified as a synthetic lethal partner of VHL loss in kidney cancer cells. Why is CBFB loss only lethal only in VHL null cells? The expression levels appear to be similar, but the transcriptional activity appears to be different in the VHL proficient deficient setting.\nFigure 2a- The Kaplan-Meier curve showing CBFB mRNA expression correlates with poor survival in the KIRC cohort would be nicely complemented with data showing if CBFB protein levels are either increased in VHL deficient kidney cancer compared to normal kidney tissue and/or if CBFB protein levels correlate with patient outcomes.\nFigure 2g- The kinetics of the subcutaneous tumor growth curves would be important to show here.\nFigure 3- Does rescue of RUNX expression restore growth and survival in the CBF-B knockout cells? This would be important to determine if RUNX proteins are functionally involved in the CBF-B growth and survival phenotype.\nFigure 3-4- Is it thought that RUNX is also involved in the CBF-B regulation of the ISG? It would be helpful to link the RUNX story to the STING story with data if they are proposed to be linked, or are they separate mechanisms?\nFigure 5h- A better comparison here would be to compare the sensitively of the 7860 to the 7860+VHL cells. Are the VHL deficient cells more sensitive to IFN-B mediated cell death?\nFigure 5 extended data- Does CBF-B loss in VHL deficient kidney cancer cells result in increased numbers of g-H2AX foci formation and DNA damage? The number of foci rather than total signal is important to rule out that CBF-B loss does not induce DNA damage.\nDiscussion- Can you please comment on the therapeutic potential of targeting CBF-B for cancer therapy. Are there current inhibitors, what is the potential toxicity of systemic inhibition?"}, "author_response": "Response to Reviewers’ Comments\nWe thank the reviewers for their very helpful and constructive feedback. Please find below our\nresponses to the points made.\nReviewer #1 (Remarks to the Author):\nBertlin et al describe a series of genetic screens ostensibly to discover genes that are essential in the\ncontext of VHL loss, the signal genomic lesion in kidney cancer (clear cell renal cell carcinoma,\nccRCC). The authors perform genome-scale CRISPR knockout screens in two ccRCC cell lines, 786O\nand RCC4, which carry the signature homozygous VHL loss, and again in those same cells with\nexogenously expressed VHL.\nIt is not clear how well-controlled the CRISPR screens are. The authors use the TKOv3 one-\ncomponent library. Some quality control figures are present in Supp Fig 1, but the relative paucity of\nhits (genes with Bayes Factor > 0 or some higher threshold) shown in Fig 1C suggests that quality was\nnot optimal. It would be useful to have a precision-recall curve, using common reference sets of\nessential and nonessential genes (e.g. for essentials, Hart et al, G3, 2017 – the Hart et al, Cell 2015\nset is not optimal – and for nonessentials, Hart et al MSB 2014). Other data, such as cell-line specific\nnonexpressed genes, may be substituted for nonessentials without loss of information. Comparison\nof the PR curves of these screens to those publicly available ccRCC genome-scale cell line screens in\nDepMap would provide further evidence of screen quality. This information is important, as it gives\nsome insight into the false negative rate of a cell line screen, which is critical in evaluating whether a\nhit found in one screen but not another reflects underlying biology or just a missed call in the other\nscreen.\nWe thank this reviewer for their assessment of our work. We did not include all the control figures,\nbut please see below the precision-recall (PR) curves using the reference sets for non-essential and\nessential genes as suggested. These show the PR values improving over the duration of the screens\nrelative to the sequenced plasmid, as described in Hart and Moffat, BMC Bioinformatics, 2016 (PMID:\n27083490).\nWe have updated the Venn diagrams displayed in Extended Data Fig. 1c, to include the core essential\ngene set described in Hart et al., G3, 2017 (PMID: 28655737). Again, these show robust discrimination\nof essential genes.\nWe accept that there will be false negatives from our approach, but our aim was to identify potential\nsynthetic lethal interactors of VHLwhich may be pertinent to ccRCC therapy, rather than to provide a\ncomprehensive atlas of VHL-associated synthetic lethality.\nrelative to the plasmid.\nTo identify genes providing proliferative advantage (e.g. KEAP1 in this case), a better approach is\ndescribed in Lenoir et al, Nat Comms 2021. This is far better than Bagel scores, although the outlier\nnature of KEAP1 knockout is a strong signal here.\nWe have analysed our data with both DrugZ and MAGeCK to obtain enrichment scores for genes that\nimprove survival. Please see the results below (also presented in Supplementary Table 1), which show\nthat KEAP1 sgRNAs are by far the most enriched, regardless of the analysis workflow employed.\nPairwise analyses by DrugZ (left) and MAGeCK (right) of the late timepoints of CRISPR/Cas9 screens,\nshowing gene knockouts providing a proliferative advantage in the VHL-null background.\nof interest but the display is not convincing. It is standard to calculate and display fold change of\nnormalized end point cells relative to some early point (plasmid or T0), rather than read counts,\nalongside some negative control (again, nonessentials are useful here) whose distribution is centered\naround zero.\nWe elected to display the trajectory of normalised sgRNA read counts as this includes both the desired\nfold change information, as well as a central timepoint to provide additional temporal information\nabout the depletion/enrichment of sgRNAs over the course of the screens. We have added the sgRNAs\ntargeting EGFPas negative controls to Fig. 1d. As expected, these show minimal change, with only mild\nenrichment in RCC4 cells as a result of the dropout of essential genes from the pool.\nFor comparing differential essentials between cell lines, the approach is flawed. The difference in\nproliferation rates between the VHL+ and VHL- cells will confound a competition assay for single\ngene effects. A better approach would be to use an algorithm such as DrugZ (Colic et al, Genome\nMed 2019; see, e.g., Karasu et al Nat Biotech 2024 for a recent example of its application) to\ncompare endpoint readcounts of screens in VHL+ and VHL- cells.\nWe had already used MAGeCK but chose to display the finding with BAGEL2. We have now analysed\nthe data with DrugZ and neither of these additional methods alter our findings: as displayed below,\nCBFB remains a top hit within all of these analyses. Full results are given in Supplementary Table 1.\nThe competition assays (Fig 1. f,g) are normalised to account for differential proliferation rates, and\nmCherry/GFP-expressing clonal cell lines were selected to minimise proliferation differences.\nFurthermore, the fact that guides targeting CBFB and KEAP1 produce opposite effects in these assays\nfurther reinforces our conclusions in this initial validation experiment.\nPairwise analyses by DrugZ (left) and MAGeCK (right) of the late timepoints of CRISPR/Cas9 screens,\nshowing genes which are synthetic lethal with VHL.\nMy motivation for stressing controls so firmly is that the main gene chosen for follow up, CBFB, I is a\nvery weak hit in Fig 1d. More importantly, it is not a hit in DepMap. In fact CBFB knockout shows\nstrong phenotype in leukemia, lymphoma, and myeloma cells (Chronos score < -0.5), but is never\nessential in any of the >25 kidney cancer cell lines screened by DepMap. The in vivo data is\ncompelling, but is totally inconsistent with the in vitro data that were ostensibly used to nominate\nCBFB for validation.\nWe appreciate that this reviewer found the in vivo data compelling, but we respectfully disagree with\ntheir assertion regarding the screens. We set out to take an unbiased strategy to look for\nexperimentally find synthetic lethal interactors, which is exactly why we used the dropout CRISPR\n3\napproach. As would be important for any screening approach, we go on to validate our findings. The\nscreening data is entirely consistent with the subsequent validation. Several genes and pathways\nknown to be important in targeting renal cell cancer are not hits in DepMap (e.g. TBK1, ROCK1, FTO,\nSCARB1), and DepMap is not a substitute for undertaking a screen based on VHL deficiency or\nproficiency. As discussed previously (PMID: 34244212), differences between studies may arise due to\nthe use of different cell culture media. Other possible reasons include the employment of different\nccRCC cell lines and CRISPR/RNAi libraries in screening experiments.\nThe authors should either perform a more realistic analysis of their screen data, or find another way\nto get to the CBFB hypothesis in ccRCC.\nWe disagree with this statement, and have performed a thorough analysis of the screen data.\nExperimental validation is always important following a screen: we have extensively demonstrated the\nlethal effect of CBF-β loss in ccRCC in vitro and in vivo, which is further supported by the overexpression\nof CBFB in ccRCC tumours.\nReviewer #2 (Remarks to the Author):\nIn this study, the authors performed synthetic lethal screens and identified CBFb deletion as\nsynthetic lethal to VHL loss. Mechanistically, they propose that CBFb negatively regulates STING.\nOverall, the finding that CBFb and VHL are synthetic lethal is potentially interesting. However, the\nmechanism underlying the role of CBFb in this synthetic lethality remains unclear, and some of the\ndata do not support the model and appear to be conflicting. Additionally, the manuscript’s writing\nneeds improvement, as some data contradict the conclusions presented in the figures. The primary\nconcerns are listed below. If the authors can sufficiently address these concerns, I am willing to\nreview a revised version of the manuscript.\nWe appreciate this reviewer’s interest in the work and our comments to their concerns are outlined\nbelow.\nConcerns:\n1. A major issue about the study is that the results have not provided reasonable mechanistic\nexplanation for the synthetic lethality of CBFb and VHL loss, the major topic of this study. Type I\ninterferon activation induces a plethora of phenotypes in cells, including senescence, apoptosis,\ncell cycle arrest, etc. Why does CBFb deletion-induced type I IFN activation selectively kill VHL-null ccRCC?\nWithout the mechanism, the main conclusion is not well supported.\nWe accept that the induction of a Type I IFN response does not fully explain why CBF-β depletion is\nsynthetically lethal with VHL, and we already noted this in the manuscript. However, we disagree that\nour main conclusions are not supported. We show evidence in vitro and in vivo that CBF-β loss results\nin decreased tumour growth in a VHL-dependent manner, and our data also demonstrates that CBF-β\nis a negative regulator of STING-mediated ISG induction. We did not conclude that Type I IFN activation\nwas solely sufficient to drive cell death in the VHL-null cells, and have clarified the text in this regard,\nand well as the findings of Extended Data Fig. 5i-k.\nWe now also provide further mechanistic explanations as to why VHL-null cells are more sensitive to\nCBFB knockout, and have investigated whether DNA damage and mitochondrial function is affected in\na VHL-dependent manner (Extended Data Fig. 6d-f). Interestingly, we observe that mitochondrial DNA\nleakage is higher in VHL-null cells following CBFB knockout (Extended Data Fig. 6d). This is in-keeping\nwith a recent report that demonstrates higher mtDNA leak following VHL knockout in mouse colon\n4\nadenocarcinoma cells (PMID: 39050705). It is therefore plausible that VHL-null cells may have a basal\ndegree of mitochondrial instability, rendering them susceptible to mitochondrial insultsand activation\nof the cGAS-STING pathway. We have modified the text to explain this point.\n2. The increase in STING protein alone is not sufficient to activate the type I interferon pathway.\nTherefore, CBFB deletion must activate an upstream regulator of STING. The authors should employ\na candidate approach to knock out potential upstream activators and elucidate the mechanism of\ntype I IFN activation. The strategy outlined in the Cell paper, \"Inhibiting DNA Methylation Causes an\nInterferon Response in Cancer via dsRNA Including Endogenous Retroviruses,\" may be helpful to the\nauthors (ScienceDirect, Volume 162, Issue 5, 27 August 2015, Pages 974-986).\nThe reviewer already notes that we have shown that cGAS is required (see point 3) and therefore we\nagree that upstream activation of STING occurs. We also show that transfection of dsDNA following\nCBFB loss dramatically increases the type I IFN response (Fig. 6d). However, our main point in the\nmanuscript is that CBF-β regulates the level of STING, which delineates the extent of the type I IFN\nresponse. This is entirely consistent with the original characterisation of STING activation by cGAMP\n(PMID: 21947006). We have revised the text accordingly, and highlight that the discovery of STING as\nan inducer of interferon signalling was made by modulating levels of STING protein alone (PMID:\n18724357). We have also amended the results of Extended Data Fig. 7a to clarify that, in this\nexperimental context, CBFB deletion must also activate an upstream regulator of STING, as the\nreviewer suggests.\nWe thank the reviewer for highlighting the DNMT inhibitor paper, which we were aware of. We\nconsidered whether ERVs may be relevant to the phenotype, and had already examined our RNA-Seq\ndata to see if there was any upregulation, but no clear changes were apparent (Supplementary Table\n4). We have now also performed qPCR for ccRCC-associated ERVs, but again did not observe any\nchanges upon CBFB knockout (Extended Data Fig. 6h).\n3. The data suggest that cGAS, an upstream activator of STING, is required for type I IFN activation\nupon CBFb depletion (Figure 6e). As cGAS is a double-stranded DNA (dsDNA) sensor, the authors\nshould conduct experiments to identify the source of dsDNA.\nAs discussed above, we have expanded our extensive search for the source of the dsDNA, including\nexamining whether CBF-β loss results in DNA damage, mitochondrial DNA leakage, or ERV expression\n(Extended Data Fig. 6).\n4. The whole manuscript is about ccRCC, and the GFP-Vif section in Figure 7 is irrelevant to the study.\nWhile it is acceptable if the authors aim to use GFP-Vif to degrade CBFb, this approach does not add\nsignificant value beyond the use of sgRNA or shRNA for CBFB. It remains unclear why the authors\nconducted an HIV-related study and how they concluded that the CBFB amount is lower than the\nbasal conditions (as shown in the bottom of Figure 7).\nWe used Vif not to degrade CBF-β but to sequester CBF-β, as it forms part of an E3 ligase complex with\nVif. Therefore, it is a complementary approach to demonstrate that sequestering CBF-β will also result\nin increased STING levels, and that this activity has broad potential implications beyond the kidney\ncancer field. We would therefore prefer to include this work within the manuscript.\n5. It is puzzling that the induction of ISGs does not require STAT1, STAT2, and IRF9 (Extended Data Fig.\n4a). On page 7, first paragraph, it states, \"To our surprise, however, knockout of STAT1, STAT2, or IRF9\nindividually, or in combination, did not prevent IFIT1 induction (Fig. 5b and Extended Data Fig. 4a-c).\"\nOne possibility is that the gRNAs for STAT1, STAT2, and IRF9 generated a polyclonal pool, with some\ncells having reduced STAT1, STAT2, or IRF9 levels, and others remaining wild type. This polyclonal\nnature could result in the negative outcome, particularly in the context of the autocrine and\nparacrine effects of IFN-B1. The authors should select at least two single clones for STAT1, STAT2, and\nIRF9 knockout and repeat the experiment in Extended Data Fig. 4a.\nWe were also surprised that STAT1 and STAT2 were not involved, but Fig. 5b already demonstrates that\nthe depletion of STAT1 and STAT2 ablates the Type I IFN response, as the cells were completely\nunresponsive to IFN-β treatment. Therefore, we see no reason to repeat the experiments as suggested,\nas we already show that our STAT1/STAT2 double knockout is highly efficient.\n6. For Extended Data Fig. 4, the title is “ISG transcription following CBFB deletion is mediated by the\nSTING-TBK1-IRF3 axis.” However, the data appear to suggest otherwise. Please clarify this\ndiscrepancy.\nWe apologise if we are missing something here, but we see no discrepancy here, as our findings show\nthat ISG induction is dependent on a STING-TBK1-IFR3 axis.\n7. In Figure 4e, CBFb N104A rescues the effect of IFIT1 similarly to WT, suggesting that binding to\nRUNX proteins is not necessary for the repression of type I interferon. This result contradicts the\nmodel proposing that the CBFb-RUNX complex suppresses STING. It has been established that CBFb\nenhances the chromatin binding of RUNX proteins. Therefore, reconciling this result with the\nproposed model requires a logical explanation.\nWe appreciate the uncertainty around this point, and have considered a number of plausible\nexplanations. First, in Fig. 3, we confirmed the findings of Malik et al. (2019) (PMID: 31061501) that\nCBF-β also regulates the translation of RUNX proteins: it may be that, in this context, CBF-β-RUNX\ndimerisation is dispensable for the regulation of STING, which can be achieved by RUNX alone, and\nthat the primary effect of CBFBknockout is rather to modulate total RUNX levels. Alternatively, there\nmay still be a small amount of residual binding between the CBF-β-N104A mutant and RUNX proteins,\nwhich we have been unable to detect.\nTo explore these possibilities further, we have overexpressed RUNX1 and RUNX2 and identified that\nRUNX1 in particular is capable of STING repression even in the absence of CBF-β, but that its effects\nare greatest in the presence of CBF-β (Fig. 7c,d). As we explain in the revised text, this suggests that\nRUNX1 (and to a lesser extent RUNX2) is indeed the primary effector of STING repression, CBF-β\ndimerisation is likely to be required for maximal efficiency. This would also be consistent with the\nsmall difference between WT and N104A CBF-β in the figure highlighted by the reviewer (Fig. 4e).\n8. In Figure 6f, the classical ChIP analysis has several issues. Firstly, it was heavily normalized.\nThe authors should present the percentage of input on the Y-axis. Secondly, ChIP-seq or CUT&RUN\nanalyses of CBFb or RUNX2 should be performed to unbiasedly detect the binding of CBFb and\nRUNX2 genome-wide.\nWe have modified our analysis of ChIP-qPCR experiments to display the % of input, as suggested\n(Extended Data Fig. 5g). We have also undertaken ChIP-Seq of CBF-β, alongside using overexpressed\nCBF-β to control for specific binding with the antibody. The findings of this CBF-β chromatin occupancy\nwere quite remarkable, as STING was in fact the dominant peak across the whole genome within our\nChIP-Seq analysis (Fig. 7a,b). However, CBF-β did not associate with other ISGs (Fig. 7a). These findings\nstrongly support the involvement of CBF-β in regulating the STING locus.\n6\n9. The claim that RUNX1 directly binds to the promoter region of STING is questionable because the\nChIP-seq data were obtained from a public dataset involving RUNX1 in K562 cells (chronic myeloid\nleukemia). Transcriptional and epigenetic features are cell-line specific. Thus, binding of RUNX1 in\nK562 cells does not necessarily indicate binding in ccRCC cell lines. The authors should perform ChIP-\nseq analysis of RUNX1 in ccRCC cell lines to substantiate this claim.\nWe thank the reviewer for this comment, and we have now removed the RUNX1 ChIP-seq from K562,\nand instead focused on the CBF-β ChIP-seq from the 786O, as detailed above. As our data have already\nexamined the involvement of RUNX1 in conjunction with CBF-β in ccRCC, additional RUNX1 ChIP-seq\nwill be of limited value and therefore we have not pursued this further.\n10. The authors claim that IRF3 directly induces ISGs, which contradicts existing literature. Therefore,\nrobust data are needed to support this claim. IRF3 ChIP-seq data is required to substantiate the\nassertion. The authors have only performed an IRF3 knockout and demonstrated that IRF3 is\nnecessary, which this piece of data does not indicate IRF3 directly induce ISGs.\nWe disagree with the reviewer’s point, as there is a substantial body of evidence that IRF3 homodimers\nare capable of directly stimulating ISG transcription independently of interferons. For example,\ncytomegalovirus infection can induce ISG transcription through IRF3 even in the presence of protein\nsynthesis inhibitors or IFN-β-neutralising antibodies (PMID: 9391139, PMID: 10950979, PMID:\n16379004, PMID: 30871003). IRF3 binding to the interferon-stimulated response elements (ISREs) of\ncanonical target genes has also been demonstrated previously (PMID: 31799619). We have clarified\nthe involvement of IRF3 induction of ISGs within the text. In addition, we have performed ChIP-qPCR\nof IRF3 at the ISREs of IFIT1, IFIT2, and OASL, which demonstrates increased binding upon CBF-β\nknockout (Extended Data Fig. 5g).\nReviewer #3 (Remarks to the Author):\nThis study offers a novel and interesting insight into the mechanism of \"synthetic lethality\" in VHL-\nnull kidney cancers. In this manuscript, the authors utilized a genome-wide CRISPR knockout screen\nto identify the synthetic lethality interactors of von Hippel-Lindau (VHL) in clear cell renal cell\ncarcinoma (ccRCC). They found that the loss of CBF-β suppresses the growth of VHL-null tumors both\nin vitro and in vivo. Mechanistically, the authors demonstrated that the CBF-β/RUNX complex binds\nto the STING promoter, inhibiting STING transcription. The loss of CBF-β leads to cell-intrinsic STING\nactivation, resulting in the upregulation of type I interferon and the expression of interferon-\nstimulated genes (ISGs), which ultimately arrests the cancer cell cycle.\nThis research demonstrated innovation and provided a potential therapeutic target for ccRCC.\nHowever, more robust and convincing evidence is required to support the hypothesis.\nWe thank the reviewer for their appreciation of the work, recognition of the innovative approach, and\ntheir helpful suggestions.\nMajor comments：\n1. Previous studies (Refs. 24-34 mentioned in the manuscript) have explored various ‘synthetic\nlethality’ interactors of VHL. So, what is the advantage of this study or the new target of CBF-b.\nWhether other synthetic lethality interactors of VHL (eg. ROCK1, J M Thompson, et. al, Oncogene,\n2017) also have similar functions in the ccRCC case? What is the specificity of CBF-b as the ‘synthetic\nlethality’ interactors of VHL in the context of ccRCC?\ninteractions. However, previous studies have often used focussed siRNA libraries or single ccRCC cell\nlines to interrogate synthetic lethal interactions. Hence, our approach outlined here, uses different\nccRCC lines and with genome wide libraries. CBF-β, being a top candidate in the screen, provides a\nnovel approach to target VHL loss and the dependency in ccRCC. Whether other, previously identified\nsynthetic lethal interactions should function within the same CBF-β pathway is an area that we are\ncurrently pursuing but it beyond the current scope this manuscript. Interestingly, ROCK1 has been\nreported to modulate IRF3 via phosphorylation (PMID: 34533996), so it is certainly plausible that it\nmay have effects within the IFN pathway. However, the notion of synthetic lethality does not mean\nthat all genes need to function within the same pathway.\nTo further address why VHL loss confers susceptibility to CBFB loss, we now present data which show\nthat VHL appears to protect cells from mitochondrial damage, as the leakage of mitochondrial DNA is\nspecifically reduced in VHL expressing cells following CBF-β loss (Extended Data Fig. 6), consistent with\na recent study in a mouse colon adenocarcinoma model (PMID: 39050705). It is therefore possible\nthat this relative abundance of mitochondrial dsDNA within the cytosol makes VHL-null cells more\nsusceptible to CBF-β depletion.\n2. Initially, the author aimed to find the potential therapeutic targets for ccRCC and screened out\nCBF-b as a ‘synthetic lethality’ interactor of VHL. Can the author target CBF-b/RUNX-STING-IRF3-IFN\naxis for the treatment of ccRCC in a mouse model?\nWe agree that this would be interesting but there are currently no appropriate specific inhibitors of\nCBF-β. We also do not have a GEM-ccRCC model to test this, as an immune competent mouse would\nbe important. Establishing such models is a longer-term goal, beyond the scope of this work. However,\nrecent work from the Carugo and Genovese groups (PMID: 37365326) using a GEM-RCC model identify\ndownstream signalling from the IFN receptor as an important suppressor of RCC tumour growth, which\nis entirely consistent with our conclusions. In addition, the Blyth group have shown in different GEM-\nRCC models that RUNX1 loss ablates tumour growth (PMID: 32156779). Whilst we did reference these\nstudies, we have edited the text accordingly to explain their relevance to our findings.\n3. In the manuscript, the author focuses solely on tumor cells. If the loss of CBF-b activates the STING\nsignaling pathway in ccRCC, how does this affect other immune cells in the tumor microenvironment\n(TME)? Additionally, what impact could this have on T cells, natural killer (NK) cells, or other immune\ncell populations in systemic conditions?\nThis is an interesting question but would require a GEM-ccRCC model and such work is clearly beyond\nthe scope of this manuscript. We have edited the text to emphasise the important nature of this\nreviewer’s point and to explain that it will be the subject of ongoing work.\n4. The author claimed that the loss of CBF-b in VHL-null 786O cells activates STING-IFN-ISG signaling.\nIf the 786O cells produce and secrete type I interferon (IFN), why does the secreted IFN not affect the\n786O+VHL cells in the co-culture experiment (Fig. 1e)?\nWe apologise for the confusion here. CBF-β loss leads to upregulation of ISGs but we did not detect\nType IFN secretion in the 786O cells. IFN-β was only activated following transfection of exogenous\ndsDNA. This was discussed in the text, and hence we referred to the induction of ISGs as ‘cell-intrinsic’\nbut we have clarified this point.\nCould it be that STING activation triggers type I IFN-independent signaling pathways, such as\nautophagy and autophagy-mediated cell death?\na relative protection to 786O cells following CBF-β depletion, but observed no difference. We have\nincluded this data as Extended Data Fig. 3e.\n5. In Fig 1a, VHL was reconstituted in the 786O and RCC4 cell lines. Why do the Western blot bands\ndiffer in these two cell lines?\nWe often see different translational processing of VHL between cell lines. The functions of these\nisoforms are largely overlapping (PMID:9751722), as indeed we demonstrate in Fig. 1a with respect\nto HIF regulation. We have updated the text to explain this.\n6. In Fig. 5g, why is the STING protein absent in the 786O Cas9 (sgCtrl) cells?\nSTING is still present but only observed at higher exposures. We have included a higher exposure film\nfor comparison (Fig. 5g).\n7. In Ex.Fig.6b and 6c, the knockout of CBF-b in RCC4 cells increases the mRNA level of cGAS and\nSTING. However, in RCC10 cells, the knockout of CBF-b appears to have no effect on the regulation of\neither cGAS or STING levels. This suggests that the idea that CBF-b/RUNX binds to the STING\npromoter region to repress STING transcription may not be a universal model.\nBoth the RCC4 and RCC10 cells show the same trend, with P values of 0.019 compared to 0.066, so we\ndo not see a particular concern here. However, we have not claimed that our findings would be\nuniversal. In fact, a universal action would be surprising: indeed HIF-2α inhibitors, which are now in\nclinical use, did not show universal activity in ccRCC pre-clinical models (PMID: 27595393).\n8. Why did the author use the ChIP-Sequencing data from the K562 cell line instead of the ccRCC?\nWe chose to interrogate publicly available DNA-binding data for CBF-β, which happens to have been\nperformed in K562, before validating this ourselves in RCC cells. However, as this is a point raised by\na number of reviewers, we have now run our own ChIP-Seq to demonstrate that CBF-β does indeed\nstrongly bind the STING locus in 786O cells (Fig. 7a).\n9. In Fig. 6f, ChIP analysis using the CBF-b antibody showed that the levels of STING primers 1 and 3\nwere reduced in CBF-b KO cells. However, when using the RUNX2 antibody, only primer 1 exhibited a\nreduction, while primer 3 did not. Why？Does this suggest that primer 1 is more significant?\nAdditionally, why is the sample size for primers 1 and 2 listed as n=5, while for primers 3 and 4, it is\nn=3?\nWe have now included CBF-β ChIP-seq from the 786O cells to measure CBF-β chromatin occupancy in\nan unbiased way. We have therefore removed the RUNX ChIP-PCR data, as this was an indirect measure\nof how CBF-β might bind.\nMinor point：\n1. Some of the western blot bands are too faint. For example, in Fig 4e, the band of IRF7 is barely\nvisible, especially for the sg control.\nWe have amended Fig. 5g, as discussed above in response to point 6. Regarding the IRF7 immunoblot\n(now Extended Data Fig. 5e), there is essentially no IRF7 in these cells, which was expected as IRF7 is\nonly expressed in immune cells. We have edited the text to explain this. We would be happy to present\nhigher exposures of other films if the reviewer wishes.\nThe manuscript by Bertlin et al. identifies the CBF-B transcriptional regulator as a synthetic lethal\npartner of VHL loss in kidney cancer cells. In this study, performed a pooled genome wide\nCRISPR/Cas9 screen to identify synthetic lethal partners of VHL loss in 2 kidney cancer cell lines. They\ndemonstrate that loss of CBF-B selectively reduces the growth and survival of VHL deficient kidney\ncancer cell lines. Importantly, genetic inhibition of CBF-B reduces kidney tumor growth in vivo.\nMechanistically, this study identifies CBF-B as a potential therapeutic target for the treatment of\nkidney cancer and identifies novel biological insights into the regulation of the STING pathway in\nkidney cancer. However, the manuscript requires further clarification and revision before\nconsideration for publication. Please see the detailed comments below.\nWe thank the reviewer for their appreciation of our studies. Our responses to their helpful suggestions\nare outlined below.\nFigure 1- CBFB was identified as a synthetic lethal partner of VHL loss in kidney cancer cells. Why is\nCBFB loss only lethal only in VHL null cells? The expression levels appear to be similar, but the\ntranscriptional activity appears to be different in theVHL proficient deficient setting.\nWe apologise for the confusion here. Synthetic lethality does not require different levels of expression,\nbut relies on the combined loss of two genes increasing cell death, as classically exemplified in the\ncancer field with BRCA mutations and PARP inhibition. In our case, as we acknowledge in the text, we\ndo not have the full explanation for why VHL and CBF-β are synthetic lethal, but the main cellular\nresponse altered is through type I interferon signalling. As outlined to reviewers 2 and 3, we think that\nthe relatively high levels of cytosolic dsDNA from mitochondrial leakage following CBFB loss in VHL-\nnull cells may contribute to this phenomenon (Extended Data Fig. 6d). It is likely that many of the\ntranscriptional differences which the reviewer highlights are a result of HIF signalling in VHL-null cells,\nalthough we find that both the ISG effect, and the lethality phenotype itself are HIF-independent.\nFigure 2a- The Kaplan-Meier curve showing CBFB mRNA expression correlates with poor survival in\nthe KIRC cohort would be nicely complemented with data showing if CBFB protein levels are either\nincreased in VHL deficient kidney cancer compared to normal kidney tissue and/or if CBFB protein\nlevels correlate with patient outcomes.\nWe thank the reviewer for this helpful suggestion. We have analysed data from the CPTAC ccRCC\nDiscovery study which demonstrates a similar relationship between CBF-β protein levels and patient\nsurvival to that already described for its mRNA expression (Fig. 2c). Moreover, we found that CBF-β is\noverexpressed in ccRCC tumours relative to adjacent normal kidney tissue (Fig. 2a).\nFigure 2g- The kinetics of the subcutaneous tumor growth curves would be important to show here.\nWe opted to include the data for day 28 alone for simplicity, but have now included the full growth\ncurve as suggested (Fig. 2h). Tumours in this model show an initial growth over the first two weeks\nbefore regressing slightly and then adopting a sustained growth trajectory (see PMID: 34155378 Fig.\n4n for a previous example of this). We have explained this in the updated figure legend. We elected to\nhalt the experiment at day 28 post-injection in order to obtain tissues for immunohistochemistry from\nthe CBFB knockout tumours before they fully regressed, but unfortunately there was too little tissue\nremaining to undertake this. We were, however, satisfied that the difference in tumour area between\nthe control and knockout tumours seen at both day 21 and day 28 indicated a significant impairment\nof tumour establishment. These results were strongly supported by subsequent data obtained from\nthe orthotopic xenograft model (Fig. 2i-m and Extended Data Fig. f)\nThis would be important to determine if RUNX proteins are functionally involved in the CBF-B growth\nand survival phenotype.\nWe thank the reviewer for this helpful suggestion. 786O cells do not tolerate RUNX overexpression\nwell, and expression causes a significant growth delay in routine culture. Therefore, it has not been\npossible to directly measure if they restore growth. Nevertheless, we have now confirmed that RUNX\nproteins do indeed play an important role in ISG expression, with RUNX1 overexpression reducing ISG\nexpression in CBFB depleted cells (Fig. 7c,d). Therefore, RUNX proteins and CBF-β are functionally\ninvolved in the same pathway.\nFigure 3-4- Is it thought that RUNX is also involved in the CBF-B regulation of the ISG? It would be\nhelpful to link the RUNX story to the STING story with data if they are proposed to be linked, or are\nthey separate mechanisms?\nWe are sorry that we were not sufficiently clear with our explanations. We do think that RUNX is also\ninvolved in the regulation of ISGs and have clarified this further with additional overexpression\nexperiments (Fig. 7).\nFigure 5h- A better comparison here would be to compare the sensitively of the 7860 to the\n7860+VHL cells. Are the VHL deficient cells more sensitive to IFN-B mediated cell death?\nWhile HK2 cells give a more faithful representation of normal kidney tissue, we agree that there is\nvalue in investigating the IFN-β sensitivity of VHL-reconstituted 786O cells, and have included this data\nin Extended Data Fig. 5l. VHL loss mildly increased sensitivity to IFN-β with regards to cell death,\nconsistent with our findings that IFN signalling has a propensity to result in a fitness disadvantage in\nVHL-deficient tumours.\nFigure 5 extended data- Does CBF-B loss in VHL deficient kidney cancer cells result in increased\nnumbers of g-H2AX foci formation and DNA damage? The number of foci rather than total signal is\nimportant to rule out that CBF-B loss does not induce DNA damage.\nThank you for this helpful point. We have now also examined γ-H2AX foci formation by\nimmunofluorescence as suggested, but again have not discerned any increase in DNA damage\nfollowing CBF-β loss in VHL-null cells (Extended Data Fig. 6e,f).\nDiscussion- Can you please comment on the therapeutic potential of targeting CBF-B for cancer\ntherapy. Are there current inhibitors, what is the potential toxicity of systemic inhibition?\nThank you for this suggestion and we have now included relevant comments in the Discussion.\nReviewer #2 (Remarks to the Author):\nIn this revision, the authors have incorporated new data in an effort to address several key\npoints, and the manuscript reflects some improvement over the previous version. However,\nsubstantial concerns regarding the proposed model remain unaddressed, and issues related\nto data interpretation and internal inconsistencies persist. The following points outline these\nunresolved concerns:\nOverview of the main claims in this revision\nThe revised manuscript presents a model summarized in Figure 8, which includes several\nmajor claims:\nClaim 1: CRISPR screening reveals that depletion of CBF-β is synthetically lethal in the\ncontext of VHL loss in clear cell renal cell carcinoma (ccRCC).\nClaim 2: Loss of VHL leads to increased levels (or dosage) of CBF-β (as shown by\ncomparing the middle and right panels in Figure 8), which is proposed as a partial\nmechanism underlying the synthetic lethality.\nClaim 3: CBF-β directly represses STING, which modulates the type I interferon pathway.\nCBF-β binds directly to the STING locus, and although RUNXs are involved, the interaction\nbetween CBF-β and RUNXs is not required for this repression.\nClaim 4: The authors propose that the STING–TBK1–IRF3 axis directly regulates the\nexpression of interferon-stimulated genes (ISGs). They argue that STAT1/STAT2 are not\ninvolved in ISG expression; instead, IRF3 directly induces ISGs.\nThis reviewer has no major concerns regarding Claim 1, which is supported by the screening\nresults and validation data showing that CBF-β depletion preferentially kills VHL-null cells.\nWe believe the reviewer has misunderstood several aspects of our work, leading to a\ncharacterisation of claims that differ from our actual conclusions.\nMajor Concerns\n1. Claim 2 — that loss of VHL increases the dosage of CBF-β (Fig. 8)— appears weak and\nis contradicted by the data. It seems the authors propose this mechanism to partially explain\nthe observed synthetic lethality. However, if that is the case, the data presented in Extended\nData Figures 1d, 2d, and 3c do not support the claim. In fact, CBF-β levels are slightly lower\nin VHL-null cells compared to VHL-reconstituted cells, which directly contradicts the\nproposed model.\nWe wish to clarify that we do not claim VHL loss increases CBF-β dosage. We represented\nVHL increased dosage in Fig. 8 as there is clear evidence presented in Fig. 2a that CBF-β\nis overexpressed in ccRCCs compared to normal kidney tissue, explaining why this\nmechanism may enable specific targeting of cancer cells. The reviewer appears to be\nconfusing a central point of the paper, which is the synthetic relationship between CBF-\nloss and VHL loss in ccRCC cells, with CBF-β dosage. We are happy to clarify this in Fig. 8.\n2. Claim 3 — that CBF-β directly represses STING mRNA, and although RUNXs are\ninvolved, the interaction between CBF-β and RUNXs is not required for this repression —\nlacks logical coherence and is not adequately supported by the data. In Figure 3, the authors\nattempt to demonstrate that the interaction between CBF-β and RUNXs is not required for\nISG induction (Figures 3e, 3f, and 3g), yet both CBF-β and RUNXs are independently\ninvolved. The key unresolved question is: how can this be reconciled?\nThe authors propose several possibilities. For instance, if CBF-β merely regulates RUNX\ntranslation, as suggested in their cited work (Malik, 2019), and RUNX proteins can repress\nSTING and ISGs independently of CBF-β, then the model shown in Figure 8 is inaccurate or\nunsupported — it depicts RUNXs recruiting CBF-β to the STING locus. Given that CBF-β is\nan indispensable binding partners of RUNX, this possibility is highly unlikely, if not\nimpossible.\nMoreover, in this revision, the authors claim that CBF-β directly binds to the gene body of\nSTING. However, since CBF-β lacks a DNA-binding domain, it remains unclear how it is\nrecruited to this site since the authors now try to claim that the interaction between CBF-β\nand RUNXs is dispensable for the regulation. Do RUNXs bind to the STING gene body in\nccRCC cells? If so, why is their interaction with CBF-β not required?\nAs it stands, Claim 3 is not convincingly supported by the data presented.\nWe respectfully disagree with the reviewer's interpretation of our data and conclusions. We\nexplain in the manuscript (for example in lines 313-314 and the preceding paragraph) that\nRUNX1 appears to be the dominant regulator of STING expression in this context, but that\nthe presence of CBF-β enhances its effect at a chromatin level. This conclusion is well-\nsupported by the data, as we previously explained to the reviewer in response to point 2 of\ntheir original review: '[our findings suggest] that RUNX1 (and to a lesser extent RUNX2) is\nindeed the primary effector of STING repression, but that CBF-β dimerisation is likely to be\nrequired for maximal efficiency. This is also consistent with the small difference between WT\nand N104A CBF-β in the figure highlighted by the reviewer (Fig. 4e).’\nThere are other inaccuracies in the reviewer's response to this claim. First, Fig. 3 makes no\nallusion to ISG induction, so we are unable to respond to the specific challenge regarding\nthis figure. Second, as we explain in the text (lines 358-359), CBF-β binds RUNX, but can\nperform RUNX-independent roles. Therefore, the model depicted in Fig. 8 is well-supported\nby the data we present, and the established body of RUNX/CBF-β literature. We do not\nclaim at any point that CBF-β binds DNA directly, but show that there is a dependency on\nRUNX at the chromatin level, and confirm that CBF-β overexpression can influence RUNX\ntranslation.\n3. Claim 4 — that IRF3 directly induces ISGs and that STAT1/STAT2 are not involved —\nrequires more robust experimental support. To demonstrate direct regulation by IRF3,\nthe authors performed conventional ChIP assays (Extended Data Fig. 5g). However, the\nsignal (% of input) was low and nearly indistinguishable from background noise. Among the three\ngenes tested, only IFIT2 showed a marginal yet statistically significant enrichment.\nImportantly, IFIT1 — a central model gene used throughout the manuscript — showed no\ndetectable difference, raising doubts about whether IRF3 directly regulates IFIT1 or other\nISGs.\nAt present, the data only establish that IRF3 is required for ISG expression, without clarifying\nthe underlying mechanism. To substantiate the claim of direct regulation, the authors should\nperform IRF3 ChIP-seq, which would offer a more comprehensive and convincing\nassessment of IRF3 binding across ISG loci.\nIn addition, it is worth noting that STING has been shown to activate noncanonical NF-κB\nsignaling, which promotes cell survival and metastasis (see Bakhoum et al., Nature:\n\"Chromosomal instability drives metastasis through a cytosolic DNA response\", 2018). This\nalternative pathway should be considered in the interpretation of STING’s role in ISG\nregulation and tumor biology.\nWe believe the reviewer has overlooked established literature, as referenced in our\nmanuscript (refs. 54-56), which provides considerable evidence that IRF3 can directly induce\nISGs. We also show in the revised manuscript binding of IRF3 to ISREs of key ISGs. We\nbelieve additional ChIP-seq experiments are unnecessary given the substantial supporting\nevidence in the established literature.\nFurthermore, the paper to which the reviewer alludes is not relevant in this context: the\nauthors are explicit that the activation of non-canonical NF-κB signalling by cGAS-STING in\nCIN-high cells does not result in IRF3 phosphorylation or ISG induction (Barkhoum et al.,\n2018: Extended Data Figs. 8 and 9).\nWe present robust evidence that the induction of ISGs is IRF3-driven, both by direct\ndissection of this pathway, and the exclusion of other plausible mechanisms (e.g. IFN-β\nexpression and JAK/STAT signalling).\n4. Regarding the new ChIP-seq data for CBF-β, the results appear highly unusual. Across\nseven chromosomes, only a few peaks were detected, which is atypical. A quick literature\nsearch indicates that ChIP-seq datasets for RUNX proteins and CBF-β typically yield\nthousands to tens of thousands of peaks. For a transcriptional regulator, such sparse peak\ndetection often suggests a failure in achieving specific or efficient ChIP enrichment.\nUnfortunately, it is not currently possible to assess the quality of this dataset, as the ChIP-\nseq data (GSE270776) remain private, even when accessed with the provided token:\nupszseuqvbyddsp. In contrast, the CRISPR screening dataset (GSE270775; token:\nanwvmykobbsttcl) is accessible.\nTo better evaluate the quality of the CBF-β ChIP-seq data, could the authors clarify the total\nnumber of peaks detected using an enrichment threshold of 5 and FDR < 0.01? This\ninformation would be critical for assessing the reliability and interpretability of the dataset.\nThe reviewer originally asked for CBF- ChIP-seq to align to results we showed with RUNX\npublicly available data and the CBF- ChIP-PCR experiments. We provided this with the full\nknowledge that ChIP-seq of a protein that does not directly bind DNA will result in sparse\npeaks. This was entirely expected. The finding that the STING locus was the dominant site\nfor CBF- points towards this region as having high RUNX/CBF- occupancy, as we already\ndiscuss in the text.\nWe believe the reviewer is mistaken regarding the availability of CBF-β ChIP-seq datasets.\nWe have investigated thoroughly, and not found any ChIP-seq data for CBF-β alone (ChIP-\nseq data only exists for the CBF-β-MYH11 fusion protein, which is entirely different\noncogenic fusion protein). Many RUNX ChIP-seq datasets exist, but as previously stated,\nChIP-seq of CBF- will differ as it does not directly bind DNA.\nWe are unclear why the ChIP-seq link did not work but we have verified that the new link\nbelow is working (we would of course have provided this earlier in the review process if we\nhad been alerted to the issue at the time). We also include the PCA plot below showing\nclustering of CBF- binding compared to the inputs, a table of the number of fragment\npeaks, and how we calculated the peaks with enrichment threshold > 5 and FDR < 0.01.\nhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE300828&token=kpoxqwiejbenjqn\nPCA plot for ChIP-seq. Empty Vector (EV) and CBF- overexpression conditions, with\ninputs, shown.\nSample Total fragments Fragments in peaks\nEV1 143951416 18738\nEV2 105296866 6405\nEV3 153906230 24475\nOE1 134123166 9169\nOE3 143404056 30043\nTotal number of aligned fragments and number of fragments in peaks called by\nMACS. The Empty Vector (EV) condition has one consensus peak with fold change > 5 and\nFDR < 0.01, while the Overexpression (OE) condition has 5. In both these conditions the\nhighest fold change was observed at the STING locus (15.6 and 39.9, respectively). We\ncalculated the total number of aligned fragments and the number of fragments in peaks per\nreplicate (Table 1). The numbers of reads in peaks is low, but CBF-β is not known to bind\nDNA directly, so we were most likely only able to detect loci with intense CBF-β chromatin\nbinding (i.e. STING) in our ChIP-seq experiment.\n5. Regarding Major Concern 1 (as raised in the rebuttal), this issue remains unaddressed,\npossibly due to a miscommunication. While it is acceptable that type I IFN does not fully\nexplain the observed synthetic lethality—as the authors have acknowledged—this was not\nthe central point of the previous review.\nThe key concern is that the authors claim CBF-β knockout is synthetically lethal in VHL-\ndeficient cells and CBF-β directly regulates STING. Despite the efforts to establish both\ntypes of regulation, the authors have not provided any data connecting the synthetic lethality\nto type I IFN regulation. This gap is particularly significant given that both synthetic lethality\nand type I IFN signaling are emphasized in the title and represent two major themes of the\nstudy.\nAs a transcriptional regulator, CBF-β influences a wide array of cellular pathways. Without\ngenetic evidence directly linking type I IFN signaling to the synthetic lethality phenotype,\nthe observed regulation may be incidental. To address this, the authors could perform cell death\nand in vivo tumor assays—such as those shown in Fig. 2—using STING and IRF3 wild-\ntype and knockout VHL-null ccRCC cells, which should be straightforward to implement.\nIn its current form, the manuscript presents two distinct regulatory themes—synthetic\nlethality and type I IFN signaling regulated by CBF-β —without establishing a mechanistic\nconnection between them.\nThe reviewer recognises that ‘it is acceptable that type I IFN does not fully explain the\nobserved synthetic lethality – as the authors have acknowledged’. Given the reviewer's\nacknowledgement of this point, we believe additional experiments to establish this\nconnection are beyond the scope of this study. As previously explained, investigated in\nExtended Data Fig. 5 i-k, and stated in the text, the two mechanisms are related but the\nchanges in type I IFN secretion with CBFB loss are not sufficient. We made this very clear at\nthe outset of our first submission.\n6. Furthermore, in rebuttal point 1, the authors propose: “It is therefore plausible that VHL-\nnull cells may have a basal degree of mitochondrial instability, rendering them susceptible to\nmitochondrial insults and activation of the cGAS-STING pathway (in the context of CBFB\nknockout).” However, the more pertinent and unresolved question is how CBFB knockout\nactivates cGAS, a cytosolic sensor of double-stranded DNA.\nThe authors state that CBFB knockout does not lead to mitochondrial dysfunction or\ngenomic DNA damage (new Extended Data Fig. 6a–h). In the absence of evidence for DNA\nleakage or damage, the mechanism underlying cGAS activation remains unclear. This gap is\ncritical, as it directly affects the interpretation of how CBFB loss engages the cGAS-STING\npathway in VHL-null cells and undermines the proposed model.\nWe disagree with this assessment. We show the increase STING expression occurs\nregardless of cGAS (see point 7 below). Further studies on cGAS are not critical to our\nmechanism and clearly beyond the scope of what is already a substantial manuscript.\n7. Regarding rebuttal point 3, the authors present data in Fig. 6e (right panel) showing that\ncGAS sgRNA reduces STING mRNA induction. However, this appears to be a red herring.\nThe appropriate approach to assess whether cGAS regulates STING activation is to perform\na cGAS knockout and examine phosphorylated STING or TBK1, as cGAS primarily\nregulates STING post-transcriptionally, consistent with the signal transduction model\nillustrated in Fig. 6a and Fig. 8.\nAs currently presented, the manuscript begins with a mechanistic framework in Fig. 6a, yet\ndiverges from it by investigating transcriptional regulation of STING mRNA by cGAS. If the\nauthors intend to demonstrate that cGAS regulates STING transcriptionally, this contradicts\nthe premise of Fig. 6a and reflects a conceptual inconsistency. Conversely, if they conclude\nthat cGAS does not regulate STING mRNA, this conflicts with the data shown in Fig. 6e\n(right panel), which indicates a statistically significant reduction.\nIn either case, the data concerning cGAS are problematic, and the mechanism of STING\nactivation remains insufficiently addressed. Moreover, the authors’ interpretation does not\nresolve the discrepancy between their conclusions and the presented data. In the text (lines\n292–293), they state: “cGAS knockout did not affect CBF-β knockout-induced STING\nexpression (Fig. 6e and Extended Data Fig. 6i).” Yet Fig. 6e (right panel) clearly shows a\nstatistically significant difference (p=0.012), which contradicts this claim.\nWe believe there has been a misunderstanding regarding the purpose of Fig. 6e and the\nrelationship to other experiments. The purpose of this panel is not to demonstrate that cGAS\nregulates STING transcriptionally, but the opposite. This experiment shows that the\nincrease in STING transcription upon CBF-β knockout is in fact almost completely\npreserved upon cGAS knockout, therefore showing that the effect of CBF-β on STING is\ndirect and not mediated by IFN signalling. We recognise that there is a minor reduction in\nSTING expression in the right-hand column, as would be expected given that STING is itself\nan ISG, and would be willing to qualify our statement in the text to clarify this.\n8. In this revision, the authors attempt to conclude that IFN-β (type I IFN) is not involved and\npropose a model for the direct regulation of ISGs by IRF3 (Fig. 5). However, the data\npresented—such as those concerning STAT1/STAT2—are suggestive but not definitive.\nDirect evidence would require demonstrating that IFNB gRNA has no effect, which has not\nbeen shown. JAK kinases have been shown to activate other pathways, such as PI3K-AKT\nand RAS–MAPK Pathway.\nAs explained above in response to point 3, we have presented considerable evidence that\nIRF3 directly regulates ISGs. It is important to note that IFN-β is not induced by CBFB\nknockout unless HT-DNA is also applied to the cells (Fig. 6d, Extended Data Fig. 7). IFN-\nβ therefore cannot be secreted in response to CBF-β loss alone, and so any further\nexploration of JAK signalling pathways would be futile.\nMinor Points:\n1. In Figure 3, the objective and the title is misaligned. The primary goal of Figure 3, as\nstated in the text (line 163), is to demonstrate that both CBF-β and RUNX proteins contribute\nto the synthetic lethal interaction with VHL. However, the figure title seems to focus on how\nRUNXs are regulated by CBF-β at the translational level. This emphasis appears to diverge\nfrom the main theme of the manuscript. As the authors themselves noted, the post-\ntranslational regulation of RUNXs by CBF-β has already been addressed in a previous\npublication (Malik et al., 2020, Nature Communications). It is recommended that the author\nstick to their main topic.\nOur data on the regulation of RUNX at the translational level provide important validation of\nthe previous findings of Malik et al. (to our knowledge this finding has not yet been\nreproduced by any other groups), and help to explain our results regarding RUNX and CBF-\nβ. We can revise the title accordingly.\n2. Clarification is needed for “Immune Cell-Specific IRF7”: The phrase “immune cell-specific\nIRF7” is ambiguous. Are the authors implying that IRF7 expression is restricted to immune\ncells? This interpretation appears inconsistent with their own data in Extended Data Figure\n5e, which shows IRF7 expression in cancer cells. Further clarification is warranted to resolve\nthis confusion.\nWe added the phrase ‘immune cell-specific IRF7 in response to Reviewer #3 (minor point 1),\nand note that Reviewer #3 is now satisfied with this amendment. We do not agree that our\ninterpretation is inconsistent. As explained in ref. 57, IRF7 is only expressed at significant\nlevels in immune cells; in the immunoblot (Extended Data Fig. 5e), there is only an\nextremely low level of expression in ccRCC cells, hence the very faint bands. Regardless of\nthe terminology used, the key purpose of the experiment was to show that our mechanism\nwas driven by IRF3 (Fig. 5c).\nReviewer #3 (Remarks to the Author):\nSince the author has provided clarifications and additional experiments in response to the\nreviewers' concerns, the manuscript is now deemed acceptable for publication upon re-\nevaluate.\nWe thank this reviewer for their appreciation of our work and the recommendation for\npublication.\nReviewer #4 (Remarks to the Author):\nThe authors have addressed the majority of my concerns.\nHowever, Figure 2h should be shown as the tumor volume tumor volume (mm3) = (length x\nwidth2)/2), not tumor area.\nWe thank this reviewer for the acknowledgement that we addressed their concerns, and can\ncorrect this this minor point.\nReviewer #5 (Remarks to the Author):\nBertlin et al describe a series of genetic screens ostensibly to discover genes that are\nessential in the context of VHL loss, the signal genomic lesion in kidney cancer (clear cell\nrenal cell carcinoma, ccRCC). The authors perform genome-scale CRISPR knockout\nscreens in two ccRCC cell lines, 786O and RCC4, which carry the signature homozygous\nVHL loss, and again in those same cells with exogenously expressed VHL.\nIt is not clear how well-controlled the CRISPR screens are. The authors use the TKOv3 one-\ncomponent library. Some quality control figures are present in Supp Fig 1, but the relative\npaucity of hits (genes with Bayes Factor > 0 or some higher threshold) shown in Fig 1C\nsuggests that quality was not optimal. It would be useful to have a precision-recall curve,\nusing common reference sets of essential and nonessential genes (e.g. for essentials, Hart\net al, G3, 2017 – the Hart et al, Cell 2015 set is not optimal – and for nonessentials, Hart et\nal MSB 2014). Other data, such as cell-line specific nonexpressed genes, may be\nsubstituted for nonessentials without loss of information. Comparison of the PR curves of\nthese screens to those publicly available ccRCC genome-scale cell line screens in DepMap\nwould provide further evidence of screen quality. This information is important, as it gives\nsome insight into the false negative rate of a cell line screen, which is critical in evaluating\nwhether a hit found in one screen but not another reflects underlying biology or just a missed\ncall in the other screen.\nWe thank this reviewer for their assessment of our work. We did not include all the control\nfigures, but please see below the precision-recall (PR) curves using the reference sets for\nnon-essential and essential genes as suggested. These show the PR values improving over\nthe duration of the screens relative to the sequenced plasmid, as described in Hart and\nMoffat, BMC Bioinformatics, 2016 (PMID: 27083490).\nI am satisfied with this analysis for the PR curves.\nI am wondering what the authors are plotting in Fig. 1C. Both axis say “Bayes Factor”\nand the cell line screen. It appears they are actually plotting BF((VHL-/-) – (VHL+)) to\nfind VHL SL interaction that are common to both cell lines. This will need to be\nclarified. If they are indeed plotting BF then I agree, the SL interactions they\nidentified are relatively weak since the BF between VHL-/- and VHL+ are relatively\nlow. Since the remainder of the manuscript is validaing CBFB then I think the screen\nholds for the purpose of this manuscript.\nWe thank this additional reviewer for commenting on the prior reviewer’s points, and\nagreeing that the screen identifies CBFB as a synthetic lethal interaction with VHL loss. We\nprovide comments where the reviewer requested further clarification.\nWe have updated the Venn diagrams displayed in Extended Data Fig. 1c, to include the core\nessential gene set described in Hart et al., G3, 2017 (PMID: 28655737). Again, these show\nrobust discrimination of essential genes.\nI am satisfied with the Venn diagrams\nWe accept that there will be false negatives from our approach, but our aim was to identify\npotential synthetic lethal interactors of VHL which may be pertinent to ccRCC therapy, rather\nthan to provide a comprehensive atlas of VHL-associated synthetic lethality.\nI agree with the authors on this point, they are not looking to catalog all the SL\ninteractions between with VHL-LOF, they are simply trying find some that are present\nin both cell lines which they have accomplished.\nTo identify genes providing proliferative advantage (e.g. KEAP1 in this case), a better\napproach is described in Lenoir et al, Nat Comms 2021. This is far better than Bagel scores,\nalthough the outlier nature of KEAP1 knockout is a strong signal here.\nWe have analysed our data with both DrugZ and MAGeCK to obtain enrichment scores for\ngenes that improve survival. Please see the results below (also presented in Supplementary\nTable 1), which show that KEAP1 sgRNAs are by far the most enriched, regardless of the\nanalysis workflow employed.\nI am satisfied with the DrugZ and MAGeCK analysis.\nThe cherrypicking of target genes (Fig 1d) is useful for narrowing the reader’s attention to\nthe genes of interest but the display is not convincing. It is standard to calculate and display\nfold change of normalized end point cells relative to some early point (plasmid or T0), rather\nthan read counts, alongside some negative control (again, nonessentials are useful here)\nwhose distribution is centered around zero.\nWe elected to display the trajectory of normalised sgRNA read counts as this includes both\nthe desired fold change information, as well as a central timepoint to provide additional\ntemporal information about the depletion/enrichment of sgRNAs over the course of the\nscreens. We have added the sgRNAs targeting EGFP as negative controls to Fig. 1d. As\nexpected, these show minimal change, with only mild enrichment in RCC4 cells as a result\nof the dropout of essential genes from the pool.\nI agree with reviewer 1 that displaying the normalized sgRNA read count trajectory is\nnot typical. However, Fig. 1d is essentially another way to display the screen data and\nis not critical for the remaining scope of the manuscript.\nFor comparing differential essentials between cell lines, the approach is flawed. The\ndifference in proliferation rates between the VHL+ and VHL- cells will confound a\ncompetition assay for single gene effects. A better approach would be to use an algorithm\nsuch as DrugZ (Colic et al, Genome Med 2019; see, e.g., Karasu et al Nat Biotech 2024 for\na recent example of its application) to compare endpoint readcounts of screens in VHL+ and\nVHL- cells.\nWe had already used MAGeCK but chose to display the finding with BAGEL2. We have now\nanalysed the data with DrugZ and neither of these additional methods alter our findings: as\ndisplayed below, CBFB remains a top hit within all of these analyses. Full results are given in\nSupplementary Table 1.\nI am satisfied with the DrugZ analysis\nThe competition assays (Fig 1. f,g) are normalised to account for differential proliferation\nrates, and mCherry/GFP-expressing clonal cell lines were selected to minimise proliferation\ndifferences. Furthermore, the fact that guides targeting CBFB and KEAP1 produce opposite\neffects in these assays further reinforces our conclusions in this initial validation experiment.\nI am also not convinced by the pooled competition assay result. Reviewer 1 is correct,\nthe differential growth rates of VHL-/- vs. VHL+ will confound the assay and is very\nhard to control for. Typically, these types of competition assays are done with one\ncell line comparing control sgRNA to another sgRNA. Colony formation assays are a\nbetter way to validate the SL interaction when analyzing two isogenic cell lines with\ndifferent proliferation rates and should be considered.\nTo clarify, original reviewer 1 was concerned about the use of identifying essential\ninteractions in the screen using two cell lines, hence the suggested to use DrugZ, which we\nshow still identifies CBFB. They had no criticism of the competition assay in Fig 1. f,g. In\nfact, the competition assay is well established for looking at synthetic lethal interactions,\nincluding those for VHL (e.g. PMC6913182, Fig. 3, from the Kaelin Jnr group). It is not\ndifficult to control for growth rates between the VHL -/- or VHL +/+ cells. Additionally, the\nreviewer has overlooked Fig 2d, which is already a colony formation assay and shows\nthe synthetic lethal interaction between CBFB and VHL loss.\nMy motivation for stressing controls so firmly is that the main gene chosen for follow up,\nCBFB, I is a very weak hit in Fig 1d. More importantly, it is not a hit in DepMap. In fact CBFB\nknockout shows strong phenotype in leukemia, lymphoma, and myeloma cells (Chronos\nscore < -0.5), but is never essential in any of the >25 kidney cancer cell lines screened by\nDepMap. The in vivo data is compelling, but is totally inconsistent with the in vitro data that\nwere ostensibly used to nominate CBFB for validation.\nWe appreciate that this reviewer found the in vivo data compelling, but we respectfully\ndisagree with their assertion regarding the screens. We set out to take an unbiased strategy\nto look for experimentally find synthetic lethal interactors, which is exactly why we used the\ndropout CRISPR approach. As would be important for any screening approach, we go on to\nvalidate our findings. The screening data is entirely consistent with the subsequent\nvalidation. Several genes and pathways known to be important in targeting renal cell cancer\nare not hits in DepMap (e.g. TBK1, ROCK1, FTO, SCARB1), and DepMap is not a substitute\nfor undertaking a screen based on VHL deficiency or proficiency. As discussed previously\n(PMID: 34244212), differences between studies may arise due to the use of different cell\nculture media. Other possible reasons include the employment of different ccRCC cell lines\nand CRISPR/RNAi libraries in screening experiments.\nWhile CBFB is a weak hit in the screen, I agree with the authors that lack of signal in\nDepMap does not invalideate their results. One possible explanaition is due to the\nartifical nature of adding VHL back to cancer cell lines. While targeting CBFB in\nccRCC may not be a feasible approach in the clinic, the authors have still found some\npotentially interestging bilogy to explore based on their screen resutls.\nThe authors should either perform a more realistic analysis of their screen data, or find\nanother way to get to the CBFB hypothesis in ccRCC.\nWe disagree with this statement, and have performed a thorough analysis of the screen\ndata. Experimental validation is always important following a screen: we have extensively\ndemonstrated the lethal effect of CBF-β loss in ccRCC in vitro and in vivo, which is further\nsupported by the overexpression of CBFB in ccRCC tumours.\nI am satisfied with the reviewer response.\nWe thank this reviewer for the additional review of our work and that they are satisfied with\nthe screening methodology and findings.\nResponse to Reviewer’s Comments\nReviewer #6 (Remarks to the Author):\nI find the manuscript to be technically well-executed, detailed, and to have identified a\nnovel dependency in RCC that could be therapeutically relevant.\nWe thank the reviewer for their helpful feedback and appreciation of the work. We have\naddressed the remaining issues raised.\nThere are a couple issues that need to be addressed prior to publication:\nFigure 8 does not adequately represent the findings of the manuscript as only the\ntranscriptional function of CBFb is included. The figure should be modified to show both the\ntranscriptional and translational roles (upregulation of RUNX1 in particular) of CBFb.\nWe now include the translational roles of CBFb on RUNX in Figure 8 and how this can\nfeedback onto the CBFb/RUNX complex.\nFig. 8. Model of regulation of STING expression by CBF-β/RUNX\nHIV infection Basal conditions ccRCC\nVif Immune evasion\ncGAS CBF-β CBF-β Cell viability\nP CBF-β\nP RUNX1/2 RUNX\nCBF-β\nSTING TBK1 translational RUNX\ncGAMP upregulation\nP\nIRF3 CBF-β CBF-β\nP\nSTING IRF3 & I F IS N G s s RUNX STING RUNX STING CBF-β\nRUNX\n5’-YGYGGTY-3’\nCBF-βdosage\nCBFb ChIPSeq has been performed and many peaks were identified (see for\nexample https://doi.org/10.1371/journal.pgen.1005884) as Reviewer 2 indicated, so the\nauthors’ response to the critique about the lack of peaks in their CBFb ChIPSeq data is not\ncompelling. Either the ChIPSeq should be repeated to achieve higher quality data or ChIP-\nqPCR should be done for the STING locus to confirm the presence of CBFb.\nWe thank the reviewer for highlighting the prior ChIP-seq data. To address their point we\nhave analysed the ChIP-seq data from the highlighted study (done in SAOS2 osteosarcoma\ncells) and included ChIP-PCR of the STING locus (Figure 7a-d). Our ChIP-seq data showed\nbinding at the distal end of the STING gene, whereas CBFb associated with the promoter\nregion in SAOS2 cells. However, both show CBFb occupancy at the STING locus. We next\nused primers around the promoter and distal gene regions to further analyse CBFb\noccupancy in 786O cells. These 4 different primer sets confirm the presence of CBFb at the\nSTING locus, with reduced binding following CBFb sgRNA-depletion (Figure 7a-d).\n1\n./0/N.2#PN!\"#$BN45ST8N59:NS;4<9;=N'>5?(\nFG'F\nFGF-\nFGFI\nFGF+\nFGF(\nFGFF\n!\"#$BC' !\"#$BC(\n2\n8TS54NBAN@\n./0/N.2#PN!\"#$BN45ST8N59:NS;4<9;=N'>5?(\n-G+ !\"\n-GF #$B$C'E\nIG+\nIGF\nFG+\nFGF\n!\"#$BC' !\"#$BC(\n8TS54NBAN@\nFig. 7. RUNX/CBF-βproteins directly inhibit STINGtranscription\na b ChIP-Seq: CBF-β\nChIP-Seq: CBF-β 786O Cas9 SAOS2\n(Shin et al., 2016)\nEV (IP) Chr 5 0-500 Chr 10 0-500 Input Chr 5 0-65\nEV (Input) 0-500 0-500 IP 0-65\nCBF-β-FLAG (IP) 0-500 0-500 5’-YGYGGTY-3’\nCBF-β-FLAG (Input) 0-500 0-500 STING\nc\n5’-YGYGGTY-3’ ChIP-qPCR 3 1\nSTING IFIT1 primer region 4 2\nd STING\n!\"\n#$B$C'E\n0.06\nprimers\ntupni\nfo%\nChIP: CBF-β\nP=0.0004P=0.0082\n0.1 2.5 P=0.044\n0.08 2\n1.5\nP=0.37\n0.04 1\n0.02 0.5"}, {"round": 2, "reviewer_comments": {"reviewer_2": "In this revision, the authors have incorporated new data in an effort to address several key points, and the manuscript reflects some improvement over the previous version. However, substantial concerns regarding the proposed model remain unaddressed, and issues related to data interpretation and internal inconsistencies persist. The following points outline these unresolved concerns:\nOverview of the main claims in this revision\nThe revised manuscript presents a model summarized in Figure 8, which includes several major claims:\nClaim 1: CRISPR screening reveals that depletion of CBF-β is synthetically lethal in the context of VHL loss in clear cell renal cell carcinoma (ccRCC).\nClaim 2: Loss of VHL leads to increased levels (or dosage) of CBF-β (as shown by comparing the middle and right panels in Figure 8), which is proposed as a partial mechanism underlying the synthetic lethality.\nClaim 3: CBF-β directly represses STING, which modulates the type I interferon pathway. CBF-β binds directly to the STING locus, and although RUNXs are involved, the interaction between CBF-β and RUNXs is not required for this repression.\nClaim 4: The authors propose that the STING–TBK1–IRF3 axis directly regulates the expression of interferon-stimulated genes (ISGs). They argue that STAT1/STAT2 are not involved in ISG expression; instead, IRF3 directly induces ISGs.\nThis reviewer has no major concerns regarding Claim 1, which is supported by the screening results and validation data showing that CBF-β depletion preferentially kills VHL-null cells.\nMajor Concerns\n1. Claim 2 — that loss of VHL increases the dosage of CBF-β (Fig. 8)— appears weak and is contradicted by the data. It seems the authors propose this mechanism to partially explain the observed synthetic lethality. However, if that is the case, the data presented in Extended Data Figures 1d, 2d, and 3c do not support the claim. In fact, CBF-β levels are slightly lower in VHL-null cells compared to VHL-reconstituted cells, which directly contradicts the proposed model.\n2. Claim 3 — that CBF-β directly represses STING mRNA, and although RUNXs are involved, the interaction between CBF-β and RUNXs is not required for this repression — lacks logical coherence and is not adequately supported by the data. In Figure 3, the authors attempt to demonstrate that the interaction between CBF-β and RUNXs is not required for ISG induction (Figures 3e, 3f, and 3g), yet both CBF-β and RUNXs are independently involved. The key unresolved question is: how can this be reconciled?\nThe authors propose several possibilities. For instance, if CBF-β merely regulates RUNX translation, as suggested in their cited work (Malik, 2019), and RUNX proteins can repress STING and ISGs independently of CBF-β, then the model shown in Figure 8 is inaccurate or unsupported — it depicts RUNXs recruiting CBF-β to the STING locus. Given that CBF-β is an indispensable binding partners of RUNX, this possibility is highly unlikely, if not impossible.\nMoreover, in this revision, the authors claim that CBF-β directly binds to the gene body of STING. However, since CBF-β lacks a DNA-binding domain, it remains unclear how it is recruited to this site since the authors now try to claim that the interaction between CBF-β and RUNXs is dispensable for the regulation. Do RUNXs bind to the STING gene body in ccRCC cells? If so, why is their interaction with CBF-β not required?\nAs it stands, Claim 3 is not convincingly supported by the data presented.\n3. Claim 4 — that IRF3 directly induces ISGs and that STAT1/STAT2 are not involved — requires more robust experimental support. To demonstrate direct regulation by IRF3, the authors performed conventional ChIP assays (Extended Data Fig. 5g). However, the signal (% of input) was low and nearly indistinguishable from background noise. Among the three genes tested, only IFIT2 showed a marginal yet statistically significant enrichment. Importantly, IFIT1 — a central model gene used throughout the manuscript — showed no detectable difference, raising doubts about whether IRF3 directly regulates IFIT1 or other ISGs.\nAt present, the data only establish that IRF3 is required for ISG expression, without clarifying the underlying mechanism. To substantiate the claim of direct regulation, the authors should perform IRF3 ChIP-seq, which would offer a more comprehensive and convincing assessment of IRF3 binding across ISG loci.\nIn addition, it is worth noting that STING has been shown to activate noncanonical NF-κB signaling, which promotes cell survival and metastasis (see Bakhoum et al., Nature: \"Chromosomal instability drives metastasis through a cytosolic DNA response\", 2018). This alternative pathway should be considered in the interpretation of STING’s role in ISG regulation and tumor biology.\n.\n4. Regarding the new ChIP-seq data for CBF-β, the results appear highly unusual. Across seven chromosomes, only a few peaks were detected, which is atypical. A quick literature search indicates that ChIP-seq datasets for RUNX proteins and CBF-β typically yield thousands to tens of thousands of peaks. For a transcriptional regulator, such sparse peak detection often suggests a failure in achieving specific or efficient ChIP enrichment.\nUnfortunately, it is not currently possible to assess the quality of this dataset, as the ChIP-seq data (GSE270776) remain private, even when accessed with the provided token: upszseuqvbyddsp. In contrast, the CRISPR screening dataset (GSE270775; token: anwvmykobbsttcl) is accessible.\nTo better evaluate the quality of the CBF-β ChIP-seq data, could the authors clarify the total number of peaks detected using an enrichment threshold of 5 and FDR < 0.01? This information would be critical for assessing the reliability and interpretability of the dataset.\n5. Regarding Major Concern 1 (as raised in the rebuttal), this issue remains unaddressed, possibly due to a miscommunication. While it is acceptable that type I IFN does not fully explain the observed synthetic lethality—as the authors have acknowledged—this was not the central point of the previous review.\nThe key concern is that the authors claim CBF-β knockout is synthetically lethal in VHL-deficient cells and CBF-β directly regulates STING. Despite the efforts to establish both types of regulation, the authors have not provided any data connecting the synthetic lethality to type I IFN regulation. This gap is particularly significant given that both synthetic lethality and type I IFN signaling are emphasized in the title and represent two major themes of the study.\nAs a transcriptional regulator, CBF-β influences a wide array of cellular pathways. Without genetic evidence directly linking type I IFN signaling to the synthetic lethality phenotype, the observed regulation may be incidental. To address this, the authors could perform cell death and in vivo tumor assays—such as those shown in Fig. 2—using STING and IRF3 wild-type and knockout VHL-null ccRCC cells, which should be straightforward to implement.\nIn its current form, the manuscript presents two distinct regulatory themes—synthetic lethality and type I IFN signaling regulated by CBF-β —without establishing a mechanistic connection between them.\n6. Furthermore, in rebuttal point 1, the authors propose: “It is therefore plausible that VHL-null cells may have a basal degree of mitochondrial instability, rendering them susceptible to mitochondrial insults and activation of the cGAS-STING pathway (in the context of CBFB knockout).” However, the more pertinent and unresolved question is how CBFB knockout activates cGAS, a cytosolic sensor of double-stranded DNA.\nThe authors state that CBFB knockout does not lead to mitochondrial dysfunction or genomic DNA damage (new Extended Data Fig. 6a–h). In the absence of evidence for DNA leakage or damage, the mechanism underlying cGAS activation remains unclear. This gap is critical, as it directly affects the interpretation of how CBFB loss engages the cGAS-STING pathway in VHL-null cells and undermines the proposed model.\n7. Regarding rebuttal point 3, the authors present data in Fig. 6e (right panel) showing that cGAS sgRNA reduces STING mRNA induction. However, this appears to be a red herring. The appropriate approach to assess whether cGAS regulates STING activation is to perform a cGAS knockout and examine phosphorylated STING or TBK1, as cGAS primarily regulates STING post-transcriptionally, consistent with the signal transduction model illustrated in Fig. 6a and Fig. 8.\nAs currently presented, the manuscript begins with a mechanistic framework in Fig. 6a, yet diverges from it by investigating transcriptional regulation of STING mRNA by cGAS. If the authors intend to demonstrate that cGAS regulates STING transcriptionally, this contradicts the premise of Fig. 6a and reflects a conceptual inconsistency. Conversely, if they conclude that cGAS does not regulate STING mRNA, this conflicts with the data shown in Fig. 6e (right panel), which indicates a statistically significant reduction.\nIn either case, the data concerning cGAS are problematic, and the mechanism of STING activation remains insufficiently addressed. Moreover, the authors’ interpretation does not resolve the discrepancy between their conclusions and the presented data. In the text (lines 292–293), they state: “cGAS knockout did not affect CBF-β knockout-induced STING expression (Fig. 6e and Extended Data Fig. 6i).” Yet Fig. 6e (right panel) clearly shows a statistically significant difference (p=0.012), which contradicts this claim.\n.\n8. In this revision, the authors attempt to conclude that IFN-β (type I IFN) is not involved and propose a model for the direct regulation of ISGs by IRF3 (Fig. 5). However, the data presented—such as those concerning STAT1/STAT2—are suggestive but not definitive. Direct evidence would require demonstrating that IFNB gRNA has no effect, which has not been shown. JAK kinases have been shown to activate other pathways, such as PI3K-AKT and RAS–MAPK Pathway.\nMinor Points:\n1. In Figure 3, the objective and the title is misaligned. The primary goal of Figure 3, as stated in the text (line 163), is to demonstrate that both CBF-β and RUNX proteins contribute to the synthetic lethal interaction with VHL. However, the figure title seems to focus on how RUNXs are regulated by CBF-β at the translational level. This emphasis appears to diverge from the main theme of the manuscript. As the authors themselves noted, the post-translational regulation of RUNXs by CBF-β has already been addressed in a previous publication (Malik et al., 2020, Nature Communications). It is recommended that the author stick to their main topic.\n2. Clarification is needed for “Immune Cell-Specific IRF7”: The phrase “immune cell-specific IRF7” is ambiguous. Are the authors implying that IRF7 expression is restricted to immune cells? This interpretation appears inconsistent with their own data in Extended Data Figure 5e, which shows IRF7 expression in cancer cells. Further clarification is warranted to resolve this confusion.", "reviewer_3": "Since the author has provided clarifications and additional experiments in response to the reviewers' concerns, the manuscript is now deemed acceptable for publication upon re-evaluate.", "reviewer_4": "The authors have addressed the majority of my concerns.\nHowever, Figure 2h should be shown as the tumor volume tumor volume (mm3) = (length x width2)/2), not tumor area.", "reviewer_5": "Bertlin et al describe a series of genetic screens ostensibly to discover genes that are essential in the context of VHL loss, the signal genomic lesion in kidney cancer (clear cell renal cell carcinoma, ccRCC). The authors perform genome-scale CRISPR knockout screens in two ccRCC cell lines, 786O and RCC4, which carry the signature homozygous VHL loss, and again in those same cells with exogenously expressed VHL.\nIt is not clear how well-controlled the CRISPR screens are. The authors use the TKOv3 one-component library. Some quality control figures are present in Supp Fig 1, but the relative paucity of hits (genes with Bayes Factor > 0 or some higher threshold) shown in Fig 1C suggests that quality was not optimal. It would be useful to have a precision-recall curve, using common reference sets of essential and nonessential genes (e.g. for essentials, Hart et al, G3, 2017 – the Hart et al, Cell 2015 set is not optimal – and for nonessentials, Hart et al MSB 2014). Other data, such as cell-line specific nonexpressed genes, may be substituted for nonessentials without loss of information. Comparison of the PR curves of these screens to those publicly available ccRCC genome-scale cell line screens in DepMap would provide further evidence of screen quality. This information is important, as it gives some insight into the false negative rate of a cell line screen, which is critical in evaluating whether a hit found in one screen but not another reflects underlying biology or just a missed call in the other screen.\nWe thank this reviewer for their assessment of our work. We did not include all the control figures, but please see below the precision-recall (PR) curves using the reference sets for non-essential and essential genes as suggested. These show the PR values improving over the duration of the screens relative to the sequenced plasmid, as described in Hart and Moffat, BMC Bioinformatics, 2016 (PMID: 27083490).\nI am satisfied with this analysis for the PR curves.\nI am wondering what the authors are plotting in Fig. 1C. Both axis say “Bayes Factor” and the cell line screen. It appears they are actually plotting  BF((VHL-/-) – (VHL+)) to find VHL SL interaction that are common to both cell lines. This will need to be clarified. If they are indeed plotting  BF then I agree, the SL interactions they identified are relatively weak since the  BF between VHL-/- and VHL+ are relatively low. Since the remainder of the manuscript is validaing CBFB then I think the screen holds for the purpose of this manuscript.\nWe have updated the Venn diagrams displayed in Extended Data Fig. 1c, to include the core essential gene set described in Hart et al., G3, 2017 (PMID: 28655737). Again, these show robust discrimination of essential genes.\nI am satisfied with the Venn diagrams\nWe accept that there will be false negatives from our approach, but our aim was to identify potential synthetic lethal interactors of VHL which may be pertinent to ccRCC therapy, rather than to provide a comprehensive atlas of VHL-associated synthetic lethality.\nI agree with the authors on this point, they are not looking to catalog all the SL interactions between with VHL-LOF, they are simply trying find some that are present in both cell lines which they have accomplished.\nTo identify genes providing proliferative advantage (e.g. KEAP1 in this case), a better approach is described in Lenoir et al, Nat Comms 2021. This is far better than Bagel scores, although the outlier nature of KEAP1 knockout is a strong signal here.\nWe have analysed our data with both DrugZ and MAGeCK to obtain enrichment scores for genes that improve survival. Please see the results below (also presented in Supplementary Table 1), which show that KEAP1 sgRNAs are by far the most enriched, regardless of the analysis workflow employed.\nI am satisfied with the DrugZ and MAGeCK analysis.\nThe cherrypicking of target genes (Fig 1d) is useful for narrowing the reader’s attention to the genes of interest but the display is not convincing. It is standard to calculate and display fold change of normalized end point cells relative to some early point (plasmid or T0), rather than read counts, alongside some negative control (again, nonessentials are useful here) whose distribution is centered around zero.\nWe elected to display the trajectory of normalised sgRNA read counts as this includes both the desired fold change information, as well as a central timepoint to provide additional temporal information about the depletion/enrichment of sgRNAs over the course of the screens. We have added the sgRNAs targeting EGFP as negative controls to Fig. 1d. As expected, these show minimal change, with only mild enrichment in RCC4 cells as a result of the dropout of essential genes from the pool.\nI agree with reviewer 1 that displaying the normalized sgRNA read count trajectory is not typical. However, Fig. 1d is essentially another way to display the screen data and is not critical for the remaining scope of the manuscript.\nFor comparing differential essentials between cell lines, the approach is flawed. The difference in proliferation rates between the VHL+ and VHL- cells will confound a competition assay for single gene effects. A better approach would be to use an algorithm such as DrugZ (Colic et al, Genome Med 2019; see, e.g., Karasu et al Nat Biotech 2024 for a recent example of its application) to compare endpoint readcounts of screens in VHL+ and VHL- cells.\nWe had already used MAGeCK but chose to display the finding with BAGEL2. We have now analysed the data with DrugZ and neither of these additional methods alter our findings: as displayed below, CBFB remains a top hit within all of these analyses. Full results are given in Supplementary Table 1.\nI am satisfied with the DrugZ analysis\nThe competition assays (Fig 1. f,g) are normalised to account for differential proliferation rates, and mCherry/GFP-expressing clonal cell lines were selected to minimise proliferation differences. Furthermore, the fact that guides targeting CBFB and KEAP1 produce opposite effects in these assays further reinforces our conclusions in this initial validation experiment.\nI am also not convinced by the pooled competition assay result. Reviewer 1 is correct, the differential growth rates of VHL-/- vs. VHL+ will confound the assay and is very hard to control for. Typically, these types of competition assays are done with one cell line comparing control sgRNA to another sgRNA. Colony formation assays are a better way to validate the SL interaction when analyzing two isogenic cell lines with different proliferation rates and should be considered.\nMy motivation for stressing controls so firmly is that the main gene chosen for follow up, CBFB, I is a very weak hit in Fig 1d. More importantly, it is not a hit in DepMap. In fact CBFB knockout shows strong phenotype in leukemia, lymphoma, and myeloma cells (Chronos score < -0.5), but is never essential in any of the >25 kidney cancer cell lines screened by DepMap. The in vivo data is compelling, but is totally inconsistent with the in vitro data that were ostensibly used to nominate CBFB for validation.\nWe appreciate that this reviewer found the in vivo data compelling, but we respectfully disagree with their assertion regarding the screens. We set out to take an unbiased strategy to look for experimentally find synthetic lethal interactors, which is exactly why we used the dropout CRISPR approach. As would be important for any screening approach, we go on to validate our findings. The screening data is entirely consistent with the subsequent validation. Several genes and pathways known to be important in targeting renal cell cancer are not hits in DepMap (e.g. TBK1, ROCK1, FTO, SCARB1), and DepMap is not a substitute for undertaking a screen based on VHL deficiency or proficiency. As discussed previously (PMID: 34244212), differences between studies may arise due to the use of different cell culture media. Other possible reasons include the employment of different ccRCC cell lines and CRISPR/RNAi libraries in screening experiments.\nWhile CBFB is a weak hit in the screen, I agree with the authors that lack of signal in DepMap does not invalideate their results. One possible explanaition is due to the artifical nature of adding VHL back to cancer cell lines. While targeting CBFB in ccRCC may not be a feasible approach in the clinic, the authors have still found some potentially interestging bilogy to explore based on their screen resutls.\nThe authors should either perform a more realistic analysis of their screen data, or find another way to get to the CBFB hypothesis in ccRCC.\nWe disagree with this statement, and have performed a thorough analysis of the screen data. Experimental validation is always important following a screen: we have extensively demonstrated the lethal effect of CBF-β loss in ccRCC in vitro and in vivo, which is further supported by the overexpression of CBFB in ccRCC tumours.\nI am satisfied with the reviewer response."}, "author_response": "We believe the reviewer has misunderstood several aspects of our work, leading to a\ncharacterisation of claims that differ from our actual conclusions.\nMajor Concerns\n1. Claim 2 — that loss of VHL increases the dosage of CBF-β (Fig. 8)— appears weak and\nis contradicted by the data. It seems the authors propose this mechanism to partially explain\nthe observed synthetic lethality. However, if that is the case, the data presented in Extended\nData Figures 1d, 2d, and 3c do not support the claim. In fact, CBF-β levels are slightly lower\nin VHL-null cells compared to VHL-reconstituted cells, which directly contradicts the\nproposed model.\nWe wish to clarify that we do not claim VHL loss increases CBF-β dosage. We represented\nVHL increased dosage in Fig. 8 as there is clear evidence presented in Fig. 2a that CBF-β\nis overexpressed in ccRCCs compared to normal kidney tissue, explaining why this\nmechanism may enable specific targeting of cancer cells. The reviewer appears to be\nconfusing a central point of the paper, which is the synthetic relationship between CBF-\nloss and VHL loss in ccRCC cells, with CBF-β dosage. We are happy to clarify this in Fig. 8.\n2. Claim 3 — that CBF-β directly represses STING mRNA, and although RUNXs are\ninvolved, the interaction between CBF-β and RUNXs is not required for this repression —\nlacks logical coherence and is not adequately supported by the data. In Figure 3, the authors\nattempt to demonstrate that the interaction between CBF-β and RUNXs is not required for\nISG induction (Figures 3e, 3f, and 3g), yet both CBF-β and RUNXs are independently\ninvolved. The key unresolved question is: how can this be reconciled?\nThe authors propose several possibilities. For instance, if CBF-β merely regulates RUNX\ntranslation, as suggested in their cited work (Malik, 2019), and RUNX proteins can repress\nSTING and ISGs independently of CBF-β, then the model shown in Figure 8 is inaccurate or\nunsupported — it depicts RUNXs recruiting CBF-β to the STING locus. Given that CBF-β is\nan indispensable binding partners of RUNX, this possibility is highly unlikely, if not\nimpossible.\nMoreover, in this revision, the authors claim that CBF-β directly binds to the gene body of\nSTING. However, since CBF-β lacks a DNA-binding domain, it remains unclear how it is\nrecruited to this site since the authors now try to claim that the interaction between CBF-β\nand RUNXs is dispensable for the regulation. Do RUNXs bind to the STING gene body in\nccRCC cells? If so, why is their interaction with CBF-β not required?\nAs it stands, Claim 3 is not convincingly supported by the data presented.\nWe respectfully disagree with the reviewer's interpretation of our data and conclusions. We\nexplain in the manuscript (for example in lines 313-314 and the preceding paragraph) that\nRUNX1 appears to be the dominant regulator of STING expression in this context, but that\nthe presence of CBF-β enhances its effect at a chromatin level. This conclusion is well-\nsupported by the data, as we previously explained to the reviewer in response to point 2 of\ntheir original review: '[our findings suggest] that RUNX1 (and to a lesser extent RUNX2) is\nindeed the primary effector of STING repression, but that CBF-β dimerisation is likely to be\nrequired for maximal efficiency. This is also consistent with the small difference between WT\nand N104A CBF-β in the figure highlighted by the reviewer (Fig. 4e).’\nThere are other inaccuracies in the reviewer's response to this claim. First, Fig. 3 makes no\nallusion to ISG induction, so we are unable to respond to the specific challenge regarding\nthis figure. Second, as we explain in the text (lines 358-359), CBF-β binds RUNX, but can\nperform RUNX-independent roles. Therefore, the model depicted in Fig. 8 is well-supported\nby the data we present, and the established body of RUNX/CBF-β literature. We do not\nclaim at any point that CBF-β binds DNA directly, but show that there is a dependency on\nRUNX at the chromatin level, and confirm that CBF-β overexpression can influence RUNX\ntranslation.\n3. Claim 4 — that IRF3 directly induces ISGs and that STAT1/STAT2 are not involved —\nrequires more robust experimental support. To demonstrate direct regulation by IRF3,\nthe authors performed conventional ChIP assays (Extended Data Fig. 5g). However, the\nsignal (% of input) was low and nearly indistinguishable from background noise. Among the three\ngenes tested, only IFIT2 showed a marginal yet statistically significant enrichment.\nImportantly, IFIT1 — a central model gene used throughout the manuscript — showed no\ndetectable difference, raising doubts about whether IRF3 directly regulates IFIT1 or other\nISGs.\nAt present, the data only establish that IRF3 is required for ISG expression, without clarifying\nthe underlying mechanism. To substantiate the claim of direct regulation, the authors should\nperform IRF3seq, which would offer a more comprehensive and convincing\nassessment of IRF3 binding across ISG loci.\nIn addition, it is worth noting that STING has been shown to activate noncanonical NF-κB\nsignaling, which promotes cell survival and metastasis (see Bakhoum et al., Nature:\n\"Chromosomal instability drives metastasis through a cytosolic DNA response\", 2018). This\nalternative pathway should be considered in the interpretation of STING’s role in ISG\nregulation and tumor biology.\nWe believe the reviewer has overlooked established literature, as referenced in our\nmanuscript (refs. 54-56), which provides considerable evidence that IRF3 can directly induce\nISGs. We also show in the revised manuscript binding of IRF3 to ISREs of key ISGs. We\nbelieve additional ChIP-seq experiments are unnecessary given the substantial supporting\nevidence in the established literature.\nFurthermore, the paper to which the reviewer alludes is not relevant in this context: the\nauthors are explicit that the activation of non-canonical NF-κB signalling by cGAS-STING in\nCIN-high cells does not result in IRF3 phosphorylation or ISG induction (Barkhoum et al.,\n2018: Extended Data Figs. 8 and 9).\nWe present robust evidence that the induction of ISGs is IRF3-driven, both by direct\ndissection of this pathway, and the exclusion of other plausible mechanisms (e.g. IFN-β\nexpression and JAK/STAT signalling).\n4. Regarding the new ChIP-seq data for CBF-β, the results appear highly unusual. Across\nseven chromosomes, only a few peaks were detected, which is atypical. A quick literature\nsearch indicates that ChIP-seq datasets for RUNX proteins and CBF-β typically yield\nthousands to tens of thousands of peaks. For a transcriptional regulator, such sparse peak\ndetection often suggests a failure in achieving specific or efficient ChIP enrichment.\nUnfortunately, it is not currently possible to assess the quality of this dataset, as the ChIP-\nseq data (GSE270776) remain private, even when accessed with the provided token:\nupszseuqvbyddsp. In contrast, the CRISPR screening dataset (GSE270775; token:\nanwvmykobbsttcl) is accessible.\nTo better evaluate the quality of the CBF-β ChIP-seq data, could the authors clarify the total\nnumber of peaks detected using an enrichment threshold of 5 and FDR < 0.01? This\ninformation would be critical for assessing the reliability and interpretability of the dataset.\nThe reviewer originally asked for CBF- ChIP-seq to align to results we showed with RUNX\npublicly available data and the CBF- ChIP-PCR experiments. We provided this with the full\nknowledge that ChIP-seq of a protein that does not directly bind DNA will result in sparse\npeaks. This was entirely expected. The finding that the STING locus was the dominant site\nfor CBF- points towards this region as having high RUNX/CBF- occupancy, as we already\ndiscuss in the text.\nWe believe the reviewer is mistaken regarding the availability of CBF-β ChIP-seq datasets.\nWe have investigated thoroughly, and not found any ChIP-seq data for CBF-β alone (ChIP-\nseq data only exists for the CBF-β-MYH11 fusion protein, which is entirely different\noncogenic fusion protein). Many RUNX ChIP-seq datasets exist, but as previously stated,\nChIP-seq of CBF- will differ as it does not directly bind DNA.\nWe are unclear why the ChIP-seq link did not work but we have verified that the new link\nbelow is working (we would of course have provided this earlier in the review process if we\nhad been alerted to the issue at the time). We also include the PCA plot below showing\nclustering of CBF- binding compared to the inputs, a table of the number of fragment\npeaks, and how we calculated the peaks with enrichment threshold > 5 and FDR < 0.01.\nhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE300828&token=kpoxqwiejbenjqn\nPCA plot for ChIP-seq. Empty Vector (EV) and CBF- overexpression conditions, with\ninputs, shown.\nSample Total fragments Fragments in peaks\nEV1 143951416 18738\nEV2 105296866 6405\nEV3 153906230 24475\nOE1 134123166 9169\nOE3 143404056 30043\nTotal number of aligned fragments and number of fragments in peaks called by\nMACS. The Empty Vector (EV) condition has one consensus peak with fold change > 5 and\nFDR < 0.01, while the Overexpression (OE) condition has 5. In both these conditions the\nhighest fold change was observed at the STING locus (15.6 and 39.9, respectively). We\ncalculated the total number of aligned fragments and the number of fragments in peaks per\nreplicate (Table 1). The numbers of reads in peaks is low, but CBF-β is not known to bind\nDNA directly, so we were most likely only able to detect loci with intense CBF-β chromatin\nbinding (i.e. STING) in our ChIP-seq experiment.\n5. Regarding Major Concern 1 (as raised in the rebuttal), this issue remains unaddressed,\npossibly due to a miscommunication. While it is acceptable that type I IFN does not fully\nexplain the observed synthetic lethality—as the authors have acknowledged—this was not\nthe central point of the previous review.\nThe key concern is that the authors claim CBF-β knockout is synthetically lethal in VHL-\ndeficient cells and CBF-β directly regulates STING. Despite the efforts to establish both\ntypes of regulation, the authors have not provided any data connecting the synthetic lethality\nto type I IFN regulation. This gap is particularly significant given that both synthetic lethality\nand type I IFN signaling are emphasized in the title and represent two major themes of the\nstudy.\nAs a transcriptional regulator, CBF-β influences a wide array of cellular pathways. Without\ngenetic evidence directly linking type I IFN signaling to the synthetic lethality phenotype,\nthe observed regulation may be incidental. To address this, the authors could perform cell death\nand in vivo tumor assays—such as those shown in Fig. 2—using STING and IRF3 wild-\ntype and knockout VHL-null ccRCC cells, which should be straightforward to implement.\nIn its current form, the manuscript presents two distinct regulatory themes—synthetic\nlethality and type I IFN signaling regulated by CBF-β —without establishing a mechanistic\nconnection between them.\nThe reviewer recognises that ‘it is acceptable that type I IFN does not fully explain the\nobserved synthetic lethality – as the authors have acknowledged’. Given the reviewer's\nacknowledgement of this point, we believe additional experiments to establish this\nconnection are beyond the scope of this study. As previously explained, investigated in\nExtended Data Fig. 5 i-k, and stated in the text, the two mechanisms are related but the\nchanges in type I IFN secretion with CBFB loss are not sufficient. We made this very clear at\nthe outset of our first submission.\n6. Furthermore, in rebuttal point 1, the authors propose: “It is therefore plausible that VHL-\nnull cells may have a basal degree of mitochondrial instability, rendering them susceptible to\nmitochondrial insults and activation of the cGAS-STING pathway (in the context of CBFB\nknockout).” However, the more pertinent and unresolved question is how CBFB knockout\nactivates cGAS, a cytosolic sensor of double-stranded DNA.\nThe authors state that CBFB knockout does not lead to mitochondrial dysfunction or\ngenomic DNA damage (new Extended Data Fig. 6a–h). In the absence of evidence for DNA\nleakage or damage, the mechanism underlying cGAS activation remains unclear. This gap is\ncritical, as it directly affects the interpretation of how CBFB loss engages the cGAS-STING\npathway in VHL-null cells and undermines the proposed model.\nWe disagree with this assessment. We show the increase STING expression occurs\nregardless of cGAS (see point 7 below). Further studies on cGAS are not critical to our\nmechanism and clearly beyond the scope of what is already a substantial manuscript.\n7. Regarding rebuttal point 3, the authors present data in Fig. 6e (right panel) showing that\ncGAS sgRNA reduces STING mRNA induction. However, this appears to be a red herring.\nThe appropriate approach to assess whether cGAS regulates STING activation is to perform\na cGAS knockout and examine phosphorylated STING or TBK1, as cGAS primarily\nregulates STING post-transcriptionally, consistent with the signal transduction model\nillustrated in Fig. 6a and Fig. 8.\nAs currently presented, the manuscript begins with a mechanistic framework in Fig. 6a, yet\ndiverges from it by investigating transcriptional regulation of STING mRNA by cGAS. If the\nauthors intend to demonstrate that cGAS regulates STING transcriptionally, this contradicts\nthe premise of Fig. 6a and reflects a conceptual inconsistency. Conversely, if they conclude\nthat cGAS does not regulate STING mRNA, this conflicts with the data shown in Fig. 6e\n(right panel), which indicates a statistically significant reduction.\nIn either case, the data concerning cGAS are problematic, and the mechanism of STING\nactivation remains insufficiently addressed. Moreover, the authors’ interpretation does not\nresolve the discrepancy between their conclusions and the presented data. In the text (lines\n292–293), they state: “cGAS knockout did not affect CBF-β knockout-induced STING\nexpression (Fig. 6e and Extended Data Fig. 6i).” Yet Fig. 6e (right panel) clearly shows a\nstatistically significant difference (p=0.012), which contradicts this claim.\nWe believe there has been a misunderstanding regarding the purpose of Fig. 6e and the\nrelationship to other experiments. The purpose of this panel is not to demonstrate that cGAS\nregulates STING transcriptionally, but the opposite. This experiment shows that the\nincrease in STING transcription upon CBF-β knockout is in fact almost completely\npreserved upon cGAS knockout, therefore showing that the effect of CBF-β on STING is\ndirect and not mediated by IFN signalling. We recognise that there is a minor reduction in\nSTING expression in the right-hand column, as would be expected given that STING is itself\nan ISG, and would be willing to qualify our statement in the text to clarify this.\n8. In this revision, the authors attempt to conclude that IFN-β (type I IFN) is not involved and\npropose a model for the direct regulation of ISGs by IRF3 (Fig. 5). However, the data\npresented—such as those concerning STAT1/STAT2—are suggestive but not definitive.\nDirect evidence would require demonstrating that IFNB gRNA has no effect, which has not\nbeen shown. JAK kinases have been shown to activate other pathways, such as PI3K-AKT\nand RAS–MAPK Pathway.\nAs explained above in response to point 3, we have presented considerable evidence that\nIRF3 directly regulates ISGs. It is important to note that IFN-β is not induced by CBFB\nknockout unless HT-DNA is also applied to the cells (Fig. 6d, Extended Data Fig. 7). IFN-\nβ therefore cannot be secreted in response to CBF-β loss alone, and so any further\nexploration of JAK signalling pathways would be futile.\nMinor Points:\n1. In Figure 3, the objective and the title is misaligned. The primary goal of Figure 3, as\nstated in the text (line 163), is to demonstrate that both CBF-β and RUNX proteins contribute\nto the synthetic lethal interaction with VHL. However, the figure title seems to focus on how\nRUNXs are regulated by CBF-β at the translational level. This emphasis appears to diverge\nfrom the main theme of the manuscript. As the authors themselves noted, the post-\ntranslational regulation of RUNXs by CBF-β has already been addressed in a previous\npublication (Malik et al., 2020, Nature Communications). It is recommended that the author\nstick to their main topic.\nOur data on the regulation of RUNX at the translational level provide important validation of\nthe previous findings of Malik et al. (to our knowledge this finding has not yet been\nreproduced by any other groups), and help to explain our results regarding RUNX and CBF-\nβ. We can revise the title accordingly.\n2. Clarification is needed for “Immune Cell-Specific IRF7”: The phrase “immune cell-specific\nIRF7” is ambiguous. Are the authors implying that IRF7 expression is restricted to immune\ncells? This interpretation appears inconsistent with their own data in Extended Data Figure\n5e, which shows IRF7 expression in cancer cells. Further clarification is warranted to resolve\nthis confusion.\nWe added the phrase ‘immune cell-specific IRF7 in response to Reviewer #3 (minor point 1),\nand note that Reviewer #3 is now satisfied with this amendment. We do not agree that our\ninterpretation is inconsistent. As explained in ref. 57, IRF7 is only expressed at significant\nlevels in immune cells; in the immunoblot (Extended Data Fig. 5e), there is only an\nextremely low level of expression in ccRCC cells, hence the very faint bands. Regardless of\nthe terminology used, the key purpose of the experiment was to show that our mechanism\nwas driven by IRF3 (Fig. 5c)."}, {"round": 3, "reviewer_comments": {"reviewer_6": "The author has addressed my concerns. The manuscript is acceptable for publication."}, "author_response": "We thank the reviewer for their helpful feedback and appreciation of the work. We have\naddressed the remaining issues raised.\nThere are a couple issues that need to be addressed prior to publication:\nFigure 8 does not adequately represent the findings of the manuscript as only the\ntranscriptional function of CBFb is included. The figure should be modified to show both the\ntranscriptional and translational roles (upregulation of RUNX1 in particular) of CBFb.\nWe now include the translational roles of CBFb on RUNX in Figure 8 and how this can\nfeedback onto the CBFb/RUNX complex.\nFig. 8. Model of regulation of STING expression by CBF-β/RUNX\nHIV infection Basal conditions ccRCC\nVif Immune evasion\ncGAS CBF-β CBF-β Cell viability\nP CBF-β\nP RUNX1/2 RUNX\nCBF-β\nSTING TBK1 translational RUNX\ncGAMP upregulation\nP\nIRF3 CBF-β CBF-β\nP\nSTING IRF3 & I F IS N G s s RUNX STING RUNX STING CBF-β\nRUNX\n5’-YGYGGTY-3’\nCBF-βdosage\nCBFb ChIPSeq has been performed and many peaks were identified (see for\nexample https://doi.org/10.1371/journal.pgen.1005884) as Reviewer 2 indicated, so the\nauthors’ response to the critique about the lack of peaks in their CBFb ChIPSeq data is not\ncompelling. Either the ChIPSeq should be repeated to achieve higher quality data or ChIP-\nqPCR should be done for the STING locus to confirm the presence of CBFb.\nWe thank the reviewer for highlighting the prior ChIP-seq data. To address their point we\nhave analysed the ChIP-seq data from the highlighted study (done in SAOS2 osteosarcoma\ncells) and included ChIP-PCR of the STING locus (Figure 7a-d). Our ChIP-seq data showed\nbinding at the distal end of the STING gene, whereas CBFb associated with the promoter\nregion in SAOS2 cells. However, both show CBFb occupancy at the STING locus. We next\nused primers around the promoter and distal gene regions to further analyse CBFb\noccupancy in 786O cells. These 4 different primer sets confirm the presence of CBFb at the\nSTING locus, with reduced binding following CBFb sgRNA-depletion (Figure 7a-d).\n1\n./0/N.2#PN!\"#$BN45ST8N59:NS;4<9;=N'>5?(\nFG'F\nFGF-\nFGFI\nFGF+\nFGF(\nFGFF\n!\"#$BC' !\"#$BC(\n2\n8TS54NBAN@\n./0/N.2#PN!\"#$BN45ST8N59:NS;4<9;=N'>5?(\n-G+ !\"\n-GF #$B$C'E\nIG+\nIGF\nFG+\nFGF\n!\"#$BC' !\"#$BC(\n8TS54NBAN@\nFig. 7. RUNX/CBF-βproteins directly inhibit STINGtranscription\na b ChIP-Seq: CBF-β\nChIP-Seq: CBF-β 786O Cas9 SAOS2\n(Shin et al., 2016)\nEV (IP) Chr 5 0-500 Chr 10 0-500 Input Chr 5 0-65\nEV (Input) 0-500 0-500 IP 0-65\nCBF-β-FLAG (IP) 0-500 0-500 5’-YGYGGTY-3’\nCBF-β-FLAG (Input) 0-500 0-500 STING\nc\n5’-YGYGGTY-3’ ChIP-qPCR 3 1\nSTING IFIT1 primer region 4 2\nd STING\n!\"\n#$B$C'E\n0.06\nprimers\ntupni\nfo%\nChIP: CBF-β\nP=0.0004P=0.0082\n0.1 2.5 P=0.044\n0.08 2\n1.5\nP=0.37\n0.04 1\n0.02 0.5"}]

Decision letter