cell & molecular biology review paper

Yang et al. J Exp Clin Cancer Res (2022) 41:25 https://doi.org/10.1186/s13046-022-02240-5

R E S E A R C H

Metabolic synthetic lethality by targeting NOP56 and mTOR in KRAS-mutant lung cancer Zhang Yang1†, Shun‑Qing Liang1,2†, Liang Zhao1, Haitang Yang1,3, Thomas M. Marti1, Balazs Hegedüs4, Yanyun Gao1, Bin Zheng5, Chun Chen5, Wenxiang Wang6, Patrick Dorn1, Gregor J. Kocher1, Ralph A. Schmid1* and Ren‑Wang Peng1*

Abstract Background: Oncogenic KRAS mutations are prevalent in human cancers, but effective treatment of KRAS‑mutant malignancies remains a major challenge in the clinic. Increasing evidence suggests that aberrant metabolism plays a central role in KRAS‑driven oncogenic transformation. The aim of this study is to identify selective metabolic depend‑ ency induced by mutant KRAS and to exploit it for the treatment of the disease.

Method: We performed an integrated analysis of RNAi‑ and CRISPR‑based functional genomic datasets (n = 5) to identify novel genes selectively required for KRAS‑mutant cancer. We further screened a customized library of chemi‑ cal inhibitors for candidates that are synthetic lethal with NOP56 depletion. Functional studies were carried out by genetic knockdown using siRNAs and shRNAs, knockout using CRISPR/Cas9, and/or pharmacological inhibition, fol‑ lowed by cell viability and apoptotic assays. Protein expression was determined by Western blot. Metabolic ROS was measured by flow cytometry‑based quantification.

Results: We demonstrated that nucleolar protein 5A (NOP56), a core component of small nucleolar ribonucleopro‑ tein complexes (snoRNPs) with an essential role in ribosome biogenesis, confers a metabolic dependency by regulat‑ ing ROS homeostasis in KRAS‑mutant lung cancer cells and that NOP56 depletion causes synthetic lethal susceptibility to inhibition of mTOR. Mechanistically, cancer cells with reduced NOP56 are subjected to higher levels of ROS and rely on mTOR signaling to balance oxidative stress and survive. We also discovered that IRE1α‑mediated unfolded protein response (UPR) regulates this process by activating mTOR through p38 MAPK. Consequently, co‑targeting of NOP56 and mTOR profoundly enhances KRAS‑mutant tumor cell death in vitro and in vivo.

Conclusions: Our findings reveal a previously unrecognized mechanism in which NOP56 and mTOR cooperate to play a homeostatic role in the response to oxidative stress and suggest a new rationale for the treatment of KRAS‑ mutant cancers.

Keywords: KRAS‑mutant cancer, NOP56, mTOR, ROS, Synthetic lethal vulnerability

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Background Oncogenic mutations in the RAS family (HRAS, KRAS, and NRAS) are the most common genetic alterations across human cancers and occur in approximately 25% of all tumors (COSMIC; http:// cancer. sanger. ac. uk/ cosmic). KRAS is the predominant isoform of the RAS family pro- teins activated by mutations (most frequently at codon 12, 13, and 61) in cancers and is responsible for 85% of all

Open Access

*Correspondence: [email protected]; [email protected] †Zhang Yang and Shun‑Qing Liang contributed equally to this work. 1 Division of General Thoracic Surgery and Department of BioMedical Research (DBMR), Inselspital, Bern University Hospital, University of Bern, Murtenstrasse 28, 3008 Bern, Switzerland Full list of author information is available at the end of the article

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RAS-driven cancers, particularly pancreatic, colon, and non-small cell lung cancer (NSCLC) [1]. Mutant KRAS is associated with poor prognosis and treatment resistance. However, unlike NSCLC with less frequent oncogenic drivers (e.g., EGFR, ALK, MET1, and ROS1) that respond significantly to selective kinase inhibitors [2], effec- tive therapies specifically targeting KRAS-mutant can- cers remains a challenge [2, 3]. Despite recent progress of immune checkpoint inhibitors of programmed death 1 (PD1) and the ligand PD-L1 in treating NSCLC, they fail to discriminate KRAS-mutant from other NSCLC [4]. Covalent KRAS inhibitors have demonstrated promise in preclinical models, but they are only effective for a spe- cific KRAS-G12C mutant allele and additional agents are needed to optimize the anticancer efficacy [5–7]. Target- ing KRAS downstream effectors, such as the mitogen- activated protein kinase (MAPK) RAF/MEK/ERK, has been widely pursued, but the pleiotropic nature and com- plex interplay among individual signaling cascades and toxicity ensuing from sustained inhibition of multiple KRAS effector pathways has hindered the translational potential of the strategy [8, 9]. Consequently, identifica- tion of new targets for innovative treatment strategies tailored to KRAS-mutant cancers still represents a press- ing need [3].

The concept to target KRAS synthetic lethality, prem- ised by the notion that oncogenic KRAS signaling fuels a unique cell state, manifested by adaptation to oncogenic stress and transcriptional, translational and metabolic reprogramming, and that interfering with this KRAS- driven cell state may result in selective cytotoxicity for KRAS-mutant cancer, provides an alternative strategy for treating KRAS-driven cancers [10, 11]. Indeed, exploit- ing cancer cell vulnerabilities contextually induced by mutant KRAS, in particularly the mechanisms critical for surveillance of oncogene-dependent cellular stresses (genotoxic, proteotoxic, and metabolic) that are permis- sive for strong oncogenic signaling, has not only provided promising therapeutic avenues but also a wealth of infor- mation on the fundamental principles of KRAS-induced tumorigenicity [12–14]. Activating KRAS mutations deregulate mitosis, nuclear export, redox, and mitochon- drial activity, and KRAS-mutant cancer cells have con- sequently been shown to have a greater dependency on the functions of non-oncogenes [e.g., PLK1, XPO1, and MRPL52 (a component of the mitochondrial large ribo- somal subunit)] that play critical roles in their respective processes [12–16], suggesting that targeting non-onco- gene addiction is an attractive approach for the treatment of KRAS-mutant cancer [17, 18].

NOP56 (nucleolar protein 5A or NOL5A) is a rib- onuclear protein, which, together with fibrillarin (FBL), NOP58 (nucleolar protein 58), and nonhistone

chromosome protein 2-like 1 (NHP2L1 or SNU13p, 15.5 kDa), forms the core protein set of box C/D small nucleolar ribonucleoprotein complexes (snoRNPs) that play an essential role in ribosome assembly by methylat- ing rRNA at the 2′-O-ribose and modulating ribosomal RNA (rRNA) processing [19, 20]. Recent evidence sug- gests that NOP56 and the other snoRNPs are the novel group of nucleolar proteins that promote cell transfor- mation and tumorigenesis [21, 22]. Indeed, ribosome biogenesis is the only cellular process in which a large number of genes harbor evolutionarily conserved MYC- binding sites [21]. NOP56 is overexpressed in Burkitt’s lymphoma and other cancers and serves as a marker of poor prognosis [23]. In particular, NOP56 is required for MYC-induced cell transformation and tumor growth in Burkitt’s lymphoma [21]. NOP56 may also have extra- ribosomal functions that remain to be discovered. Nevertheless, the activity of snoRNPs in oncogenic trans- formation suggests that they are promising therapeutic targets for cancer treatment [24, 25].

In this study, we reported an unexpected function of NOP56 in metabolic stress response and a previously unrecognized metabolic synthetic lethality by target- ing NOP56 and mTOR in KRAS-mutant cancers. Based on integrated analyses of RNAi- and CRISPR-mediated functional genomics [12, 16, 26], we identified NOP56 as a novel metabolic dependency of KRAS-mutant can- cer by regulating homeostasis of reactive oxygen spe- cies (ROS) that plays a well-established role in mutant KRAS-induced tumorigenesis [27–30]. Depletion of NOP56 impairs the response to oxidative stress, which renders KRAS-mutant cancer cells highly dependent on mTOR signaling for survival and particularly vulnerable to mTOR inhibition. Consequently, co-targeting NOP56 and mTOR enhances apoptotic death of KRAS-mutant lung cancer cells in  vitro and in  vivo. We further delin- eated that mTOR activation upon NOP56 depletion is mediated by IRE1α-mediated unfolded protein response (UPR). These results uncover a previously unknown mechanism by which NOP56 cooperates with UPR and mTOR to regulate metabolic stress and a novel syn- thetic lethal strategy for the treatment of KRAS-mutant cancers.

Materials and methods Cell culture and reagents Cancer cell lines used in this study (Table S1) were obtained from American Type Culture Collection (ATCC, Manassas, VA, USA). Cells were cultured in RPMI-1640 medium or Medium 199 (Cat. #8758 and #4540; Sigma-Aldrich, St. Louis, MO, USA) sup- plemented with 10% fetal bovine serum/FBS (Cat. #10270–106; Life Technologies, Grand Island, NY, USA)

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and 1% penicillin/streptomycin solution (Cat. #P0781, Sigma-Aldrich). The cells were authenticated by DNA fingerprinting and confirmed free from mycoplasma con- tamination (Microsynth, Bern, Switzerland). All inhibi- tors used in this study were listed in Table S2.

PF139 and PF563 lung cancer cells were established from lung adenocarcinoma malignant pleural effusion and pleural carcinosis specimens of a 67 year-old female patient and a 75 year-old male patient, respectively, at the time of diagnosis prior to any treatment [31]. Authenti- cation was performed by SNP based cell identification (Multiplexion, Heidelberg, Germany).

Cell viability and clonogenic survival assay Lung cancer cells seeded in 96-well plates (2500 cells/ well) were dosed 24 h later with different inhibitors for 72 h. Cell viability was determined by PrestoBlue (PB) Cell Viability Reagent (ThermoFisher Scientific) by fol- lowing the manufacturer’s instructions [14, 31]. The PB reagent was added into media directly (1:10 dilution) and incubated for 30 min-2 h and then the fluorescence was read (excitation 570 nm; emission 600 nm) at recom- mended time of incubation. The efficacy of drugs on cell growth was normalized to untreated control. Each data point was generated in triplicate and each experiment was done three times (n = 3). Best-fit curve was generated in GraphPad Prism [(log (inhibitor) vs response (−vari- able slope four parameters)]. Error bars are mean ± SD. The combination index (CI) was calculated by ComboSyn software (ComboSyn Inc., http:// www. combo syn. com/).

Clonogenic assay was done as we described previously [14, 31–33]. In brief, cells seeded in 6-well plates (3000 cells/well) were dosed 24 h later and continually treated with rapamycin for 7 days (refresh drugs every 3 days), the resulting colonies were stained with crystal violet (0.5% dissolved in 25% methanol).

Apoptosis assays Lung cancer cells were treated for 72 h with vehicle or rapamycin. After treatment, cells in the supernatant and adherent to plates were collected, washed with PBS and pooled before suspended in binding buffer and stained with the Annexin V Apoptosis Detection Kit -FITC (Cat. #88–8005; Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s instructions. Flow cytometry analysis was performed on a BD Biosciences LSRII flow cytometer.

Gene silencing by small interfering (siRNA), short hairpin RNAs (shRNA) and single‑guide RNAs (sgRNA) Transient knockdowns were mediated by siRNAs. Cells cultured in triplicate at 50–70% confluency were transfected using SiTran1.0 (TT300001; Origene

Technologies, Rockville, MD, USA) according to the manufacturer’s protocol. NOP56 (CAT#: SR307156), EIF4E (CAT#: SR320018), RPS6 (CAT#: SR304160), RAP- TOR (CAT#: SR324724), and RICTOR (CAT#: SR326062) were knocked down by specific pooled siRNA duplexes purchased from OriGene Technologies, with control siRNA Duplex as a negative control.

Stable knockdown of NOP56 was achieved via len- tiviral delivery of NOP56 Human shRNA Plasmid Kit (SHCLND_006392, MERCK). A scramble shRNA was used as a control. Lentiviral particles were generated and cells infected according to the protocol from Broad Institute. The supernatant containing lentiviruses was collected, filtered through 0.45 μM filters, and stored in aliquots at − 80 °C, or immediately used to infect recipi- ent cells. After infection, cells were selected in puromycin (1.5 μg/ml) and further passaged in culture for functional assays. NOP56 knockout was performed via a CRISPR/ Cas9 and non-homology mediated approach using the NOL5A (NOP56) Human Gene Knockout Kit (CAT#: KN411153; OriGene Technologies) according to the manufacturer’s protocol.

Quantitative real‑time PCR (qRT‑PCR) Total RNA was isolated and purified using RNeasy Mini Kit (Qiagen, Hilden, Germany). Complementary DNA was synthesized by the High capacity cDNA reverse tran- scription kit (Applied Biosystems, Foster City, CA, USA) according to manufacturer’s instructions. Real time PCR was performed in triplicate on a 7500 Fast RealTime PCR System (Applied Biosystems) using TaqMan primer/ probes (Applied Biosystems): HSPA5, Hs00607129_gH; ERN1, Hs00980095_m1; EIF2AK3, Hs00984003_m1; ATF4, Hs00909569_g1; DDIT3, Hs00358796_g1, with GAPDH (Hs02786624_g1) and ACTB (Hs01060665_g1) used as endogenous normalization controls.

Immunoblotting, immunohistochemistry and immunofluorescence Cell lysates were prepared and western blot analysis was performed as described [14, 31]. In brief, equal amounts of protein lysates resolved by SDS-PAGE (Cat. #4561033; Bio-Rad Laboratories, Hercules, CA, USA) and trans- ferred onto nitrocellulose membranes (Cat. #170–4158; Bio-Rad). Membranes were then blocked with blocking buffer (Cat. #927–4000; Li-COR Biosciences, Bad Hom- burg, Germany) for 1 h at room temperature (RT) and incubated with appropriate primary antibodies over- night at 4 °C (Table S3). IRDye 680LT-conjugated goat anti-mouse IgG (Cat. #926–68,020) and IRDye 800CW- conjugated goat anti-rabbit IgG (Cat. #926–32,211) from Li-COR Biosciences were used at 1:5000 dilutions. Finally, signals of membrane-bound secondary antibodies

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were imaged using the Odyssey Infrared Imaging System (Li-COR Biosciences).

For immunofluorescence, tumor cells grown on poly- lysine-treated coverslides were fixed with 4% paraform- aldehyde for 15 min at RT and permeabilized with cold methanol (− 20 °C) for 5 min or with 0.1% Triton X-100/ PBS at RT for 15 min before incubated overnight at 4 °C with primary antibodies (Table S3). The cells were incu- bated for 1 h at RT with Alexa Fluor 647 goat anti-mouse IgG (Cat. #A21236) or Alexa Fluor 488 goat anti-Rabbit IgG (Cat. #A11034) from Invitrogen (Eugene, OR, USA). Nuclei were counterstained by 4′,6-diamidino-2-phe- nylindole. Images were acquired on a ZEISS Axioplan 2 imaging microscope (Carl Zeiss MicroImaging, Göttin- gen, Germany) and processed using Adobe Photoshop CS6 v.13 (Adobe Systems, San Jose, CA, USA).

Immunohistochemical study was performed as we described previously [31, 32]. In brief, surgically removed xenograft tumors were formalin-fixed and paraffin- embedded (FFPE). FFPE tumors were sectioned at 4 μm, deparaffinized, rehydrated and subsequently stained with hematoxylin and eosin (H&E) and appropriate anti- bodies (Table S3) using the automated system BOND RX (Leica Biosystems, Newcastle, UK). Visualization was performed using the Bond Polymer Refine Detec- tion kit (Leica Biosystems) as instructed by the manu- facturer. Images were acquired using PANNORAMIC® whole slide scanners, processed using Case Viewer (3DHISTECH Ltd.). The staining intensities of the whole slide (two tumors/group) were quantified by QuPath software.

In vivo mouse study Mouse studies were conducted in accordance with Insti- tutional Animal Care and Ethical Committee-approved animal guidelines and protocols. All mouse experi- ments were performed in age- and gender-matched NSG (NOD-scid IL2Rγnull) as we previously described [31, 32]. Tumor cells in DMEM (H460-shScrambled or H460-shNOP56) 1:1 mixed with BD Matrigel Basement Membrane Matrix (Cat. #356231; Corning, NY, USA)

were subcutaneously inoculated in left and right flanks (0.5 × 106/injection). When tumors were palpable, mice were randomly assigned to treatment groups: 1) control; 2) rapamycin (0.1 mg/kg, i.p, 5 days/week) for 5 weeks. Tumors were measured every 3 days, with their size cal- culated as follows: (length × width2)/2. For survival anal- ysis, the mice were closely monitored on a daily basis, and the size of tumors was measured with a caliper every 4–5 days. Mice were sacrificed when the tumor volume reached 1500 mm3.

Public databases To identify synthetic lethal targets in KRAS-mutant can- cers, we interrogated functional genomics dataset of CRISPR/Cas9 knockout and RNAi/shRNA knockdown screens from published studies: whole genome RNAi screens in DLD-1 colon cancer cells [12] and in KRAS- mutant lung cancer cells (H2122, H2009, HCC44, H460, H1155) [16], genome-wide CRISPR/Cas9 loss of function screens in KRAS-mutant leukemia cells (PL-21, SKM- 1, NB4) [26]. To minimize the effects of cancer lineage and histological subtype, we selected DLD-1 (colon), H460 (large cell lung carcinoma), H2122 (lung adeno- carcinoma), SKM-1 (without PML-RARA fusion) and NB4 (PML-RARA ) for further analysis, which identified a number of common candidates (n = 21) as KRAS syn- thetic lethal partners (Fig. 1).

Interrogation of publicly available datasets was per- formed as we have described [14, 31]. Specifically, tran- scriptomic data of lung, pancreatic and colon cancer were obtained from the Cancer Genome Atlas (TCGA) (https:// portal. gdc. cancer. gov/ proje cts/ TCGA). Gene set enrichment analysis (GSEA) was performed by using GSEA software. The transcriptomic dataset (GSE15212) used for GSEA was derived from KRAS-mutant colon cancer cell line (SW480) treated with NOP56-specific siRNAs and downloaded from the Gene Expression Omnibus (GEO) database. For survival analysis, tran- scriptomic gene expression and corresponding survival data were extracted and analyzed by using the “max- stat”, “survival”, and “survminer” packages in R software

(See figure on next page.) Fig. 1 NOP56 confers a metabolic dependency in KRAS‑mutant cancers. A, Venn diagram showing common essential genes in KRAS‑mutant cancer cells. Data are based on the published studies, with the 21 common genes listed on the right. B, Network analysis of the 21 common genes by STRING. C, NOP56 mRNA expression in KRAS‑mutant lung cancer (LC), pancreatic cancer (PC) and colon cancer (CC) versus KRAS‑wild‑type (WT ) cancers in patient samples from TCGA. D, Prognostic values of NOP56 expression across TCGA lung adenocarcinoma (left), pancreatic cancer (middle) and colon cancer (right) cohorts harboring KRAS mutations. Kaplan–Meier survival analyses were stratified by the optimal cut‑off value of NOP56 mRNA levels. E, Gene set enrichment analysis (GSEA) revealed significant enrichment of oxidative phosphorylation and ROS pathway gene signatures in NOP56‑depleted KRAS‑mutant cancer cells (SW480). The GEO dataset GSE15212 was used for GSEA. F, H358 and H460 cells transfected with NOP56-specific or control siRNAs were treated (72 h post‑transfection) with 300 μM H2O2 for 6 h, followed by incubation with H2DCFDA for 30 min, and analyzed by flow cytometry. Quantification of relative ROS levels was shown in the right. Data are shown as the mean ± SD (n = 3). *P < 0.05,***P < 0.001, ****P < 0.0001 by two‑way ANOVA with Tukey’s multiple comparisons test. G, H358 and H460 cells transfected with NOP56‑specific or control siRNAs were subsequently (72 h post transfection) treated with vehicle (DMSO) or 300 μM H2O2 for 6 h before apoptotic assay. Data were shown as mean ± SD (n = 3). *P < 0.05 and ***P < 0.001 by two‑way ANOVA with Tukey’s multiple comparisons test

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Fig. 1 (See legend on previous page.)

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(version 3.6.0). Patients were divided into two groups (high_ NOP56 versus low_ NOP56) based on the optimal cutoff value of NOP56 transcripts across all patients to plot the Kaplan–Meier survival curves.

For correlative analysis of NOP56 expression with sen- sitivity (IC50) to mTOR inhibitors, gene expression data and drug response profiles were downloaded from Can- cer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases, respec- tively. Correlation analysis was performed using R soft- ware (version 3.6.0).

Statistical analysis Statistical analyses were performed using GraphPad Prism 7.01 (GraphPad Software Inc., San Diego, CA, USA) unless otherwise indicated. In all studies, data rep- resent biological replicates (n) and are depicted as mean values ± SD or mean values ± SEM as indicated in the figure legends. In all analyses, P values less than 0.05 were considered statistically significant. For the survival anal- ysis, patients were grouped by gene expression, where ‘high’ and ‘low’ expression groups were stratified by the optimal cut-off value.

Results NOP56 confers a metabolic dependency by regulating ROS homeostasis in KRAS‑mutant lung cancer To identify therapeutic vulnerabilities in KRAS-mutant cancers, we performed integrated analysis of shRNA- and CRISPR-based functional genomics of previously pub- lished studies [12, 16, 26]. To minimize lineage-specific effects, we analyzed whole-genome dropout screen data- set in KRAS-mutant lung (H460, H2122), colon (DLD-1), acute promyelocytic leukemia (NB4) and acute myeloid leukemia (SKM-1) cancer cells, which identified 21 com- mon genes whose loss of function is synthetic lethal with mutant KRAS alleles in distinct cancer lineages (Fig. 1A; Table S4). The protein products of these genes fall into several functional categories, with FBL, NOP56, PLK1 and XPO1 as a core set based on their interaction net- work (Fig.  1B). Remarkably, PLK1 and XPO1 have been reported to be selectively required for KRAS-mutant cancers by counteracting mitotic and nuclear export stress associated with KRAS-induced tumorigenesis [12, 16], and our recent study has implicated PLK1 in meta- bolic stress response of KRAS-mutant cancers [14]. FBL has also been assigned as a promising target in cancers [34, 35], suggesting the power of functional genom- ics in identifying oncogene-specific vulnerabilities and the accountability of our analyses. In the present study, we investigated the function of NOP56 in KRAS-mutant cancers.

Our investigations began with NOP56 knockdown using small interfering RNAs (siRNAs), which revealed that downregulation of NOP56 significantly inhibited the proliferation of numerous KRAS-mutant lung (H358, H460, A549, PF563, PF139), pancreatic (MIAPaCa, HPAF-II) and colon (HCT-116, DLD-1) cancer cells, which differ not only in tumor lineages and histological subtypes but also in KRAS mutations, e.g., G12C, G12D, Q61H, etc. (Fig. S1A, B; Table S1). Notably, NOP56 silencing also inhibited NRAS-mutant lung cancer H1299 cells, although the effects on EGFR-mutant (EBC-1) or FGFR1-amplified (H520) lung cancer cells were neg- ligible (Fig. S1A, B). Supporting these observations, KRAS-mutant lung, pancreatic and colon cancer showed significantly higher expression of NOP56 than KRAS-WT tumors (Fig.  1C) and patients with KRAS-mutant lung adenocarcinoma, pancreatic and colon cancer charac- terized by a higher NOP56 level are associated with sig- nificantly shorter survival (Fig.  1D). In contrast, NOP56 expression is not a prognostic marker for KRAS-mutant lung, pancreatic and colon cancers (Fig. S1C). These results indicate a unique function for NOP56 in KRAS- mutant cancers.

To explore NOP56 functions in KRAS-mutant malig- nancies, we profiled the transcriptomic gene expres- sion data of a previous study [36], whereby NOP56 in KRAS-mutant colon cancer cells (SW480) was silenced by siRNAs. Our analysis revealed that high expression of NOP56 was positively correlated with the gene signa- ture of KRAS signaling (Fig. S1D), in line with the above results (Fig.  1A-D; Fig. S1A, B), and that, importantly, siRNA-mediated NOP56 knockdown led to significant enrichment of the gene sets involved in ROS pathway (consisting of 49 genes upregulated by ROS) and oxida- tive phosphorylation (a set of 200 genes encoding pro- teins involved in oxidative phosphorylation), the latter representing a major source of ROS production (Fig. 1E), suggesting a possible role for NOP56 in the suppres- sion of metabolic ROS that is critical for KRAS-induced tumorigenesis [27–30]. Supporting this notion, NOP56 knockdown (KD) by siRNAs significantly upregulated ROS in H358 and H460 cells, and H2O2 treatment, which elevated the already high level of oxidative stress, pro- voked significantly greater apoptosis in NOP56 KD H358 and H460 cells than the control counterparts (Fig. 1F, G). These results uncover NOP56 as a metabolic dependency in KRAS-mutant cancer by exerting a previously unrec- ognized role in the surveillance of oxidative stress.

NOP56 suppression evokes IRE1α‑mediated UPR to mitigate oxidative stress Next, we investigated the mechanism that KRAS-mutant cancer cells utilize to orchestrate cytotoxic ROS upon

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NOP56 depletion. GSEA of transcriptomic dataset [36] revealed that NOP56 knockdown significantly enriched the genes involved in the unfolded protein response/UPR (a set of 113 genes upregulated during UPR) in KRAS- mutant cancer cells (Fig.  2A), suggesting that tumor cells might engage the UPR to protect from NOP56 KD- induced surge of cytotoxic ROS. To test this possibility, we knocked down NOP56 in KRAS-mutant lung cancer cells (H358, H460) by using short-hairpin RNAs (shRNA) (Fig. S2A, B). In contrast to the results from siRNA- mediated acute depletion, stable expression of two inde- pendent shRNAs showed negligible effects on H358 and H460 proliferation (Fig. S2C, D), which may be due to the activation of compensatory mechanisms. Impor- tantly, several UPR genes, in particular ERN1 and HSPA5 encoding the ER stress sensor IRE1α and the chaperon protein BiP, respectively, were markedly upregulated in NOP56-depleted H358 and H460 cells (Fig.  2B). West- ern blot confirmed the increase of BiP, IRE1α, XBP-1 s (IRE1α effector) and of the master UPR transcription fac- tor HSF-1 (heat shock factor 1) and PDI (protein disulfide isomerase), an important ER chaperone induced during ER stress by carrying out a redox reaction and respon- sible for the formation of disulfide bonds in proteins (Fig.  2C). Notably, p38 MAPK, a key stress-responsive kinase and an UPR effector [37], was highly phosphoryl- ated (activated) in NOP56-depleted H358 cells (Fig. 2C). Moreover, IRE1α KD blunted p-p38, p-AKT (T308), p-MNK1, p-eIF4E and p-S6 in NOP56-depleted H358 cells, indicating that IRE1α-mediated UPR acts upstream of p38 signaling (Fig. 2D).

Importantly, genetic (siRNA) and pharmacological (4μ8C, an inhibitor of IRE1α) inhibition of IRE1α pref- erentially impaired NOP56 KD H358 and H460 cells, manifested by significantly greater proliferative inhibition

and apoptotic induction in these cells than in control cells (Fig. 2E, F; Fig. S2E, F). Importantly, the increase of IRE1α KD-induced apoptotic cell death was paralleled by ROS upregulation, and addition of NAC, an ROS scav- enger largely dampened IRE1α KD-induced apoptosis (Fig. 2F, G), supporting a role for the UPR in response to oxidative stress. Similarly, genetic and pharmacological inhibition (with KRIBB11) of HSF1 suppressed the pro- liferation and evoked apoptotic cell death to a markedly greater extent in NOP56 KD H358 cells than in control cells (Fig. 2H-J).

The outcome of UPR ranges from adaptation to apop- tosis [38] and, as such, perturbations of ER homeostasis in cells with an already high level of ER stress, e.g., treat- ment with bortezomib and tunicamycin that induce per- sistent ER stress by targeting the 26S proteasome and the ER chaperone BiP, respectively, evoke programmed cell death [38, 39]. Indeed, NOP56 KD H358 and H460 cells with high basal levels of ROS are highly susceptible to bortezomib and Tunicamycin compared to control cells (Fig. 2K, L).

Thus, targeting NOP56 disrupts ROS homeostasis and induces IRE1α-mediated UPR in KRAS-mutant lung can- cer cells.

IRE1α‑mediated UPR fuels mTOR signaling via p38 MAPK To identify cellular processes that may present thera- peutic vulnerabilities in NOP56 KD cells, we performed synthetic lethal chemical screens with small-molecule drugs (n = 22) that interrogate various oncogenic path- ways, with the ER stress inducers (bortezomib and HA15) included as positive controls (Table S2). Our screens showed that, except for bortezomib and HA15, LY294002, AZD5363, and rapamycin, inhibitors of the PI3K/AKT/mTOR pathway, preferentially suppressed

Fig. 2 NOP56 depletion evokes IRE1α‑mediated UPR. A, NOP56 depletion led to significant enrichment of the UPR gene signature in KRAS‑mutant cancer cells. GSEA was based on the GEO dataset GSE15212. B, Transcriptional quantification (qRT‑PCR) of UPR genes in H358 and H460 cells expressing control (sh Scram) or NOP56‑specific shRNA (sh NOP56a). C, Immunoblots of H358 and H460 cells expressing scrambled control or NOP56‑specific shRNAs. D, H358 cells expressing scrambled control or NOP56‑specific shRNAs were transfected with IRE1α-specific or control siRNAs for 72 h before immunoblotting. E, Clonogenic assay of H460 and H358 cells expressing scrambled control or NOP56‑specific shRNAs after treated with indicated doses of 4μ8C (IRE1α inhibitor). Representative images are shown. F, H358 cells expressing scrambled control or NOP56‑specific shRNAs were transfected with IRE1α-specific or control siRNAs for 72 h, in the presence or absence of NAC (2.5 mM) before apoptosis assay. Data are presented as mean ± SD (n = 3). ***P < 0.001, ****P < 0.0001 and ns P>0.05 by two‑way ANOVA with Tukey’s multiple comparisons test. G, H358 cells expressing scrambled control or NOP56‑specific shRNAs were transfected with IRE1α-specific or control siRNAs for 72 h, in the presence or absence of NAC (2.5 mM). Cells were then washed, incubated with H2DCFDA for 30 min, and analyzed by flow cytometry. Quantification of relative ROS levels was shown in the right. Data are presented as mean ± SD (n = 3). ***P < 0.001, ****P < 0.0001 and ns P>0.05 by two‑way ANOVA with Tukey’s multiple comparisons test. H, H358 cells expressing scrambled control or NOP56‑specific shRNAs were transfected with HSF1-specific or control siRNAs for 72 h before apoptotic assay. I, H358 cells expressing scrambled control or NOP56-specific shRNAs were transfected with HSF1-specific or control siRNAs for 72 h before immunoblot analysis. Data are presented as mean ± SD (n = 3). ***P < 0.001, ****P < 0.0001 and ns P>0.05 by two‑way ANOVA with Tukey’s multiple comparisons test. J, Clonogenic assay of H460 and H358 cells expressing scrambled control or NOP56‑specific shRNAs after treated with the indicated doses of KRIBB11 (HSF1 inhibitor). Representative images are shown. K, L, Clonogenic assay of H460 and H358 cells expressing scrambled control or NOP56‑specific shRNAs after treated with the indicated doses of the ER stress inducer bortezomib (K) or tunicamycin (L). Representative images are shown

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Fig. 2 (See legend on previous page.)

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the viability of NOP56 KD cells, gauged by their IC50 decrease in NOP56 KD H358 and H460 cells versus con- trol cells (Fig.  3A; Fig. S3A). The greatest change in sen- sitivity was conferred by rapamycin, which was 0.7 μM and 1.0 μM in H358_shNOP56a and H358_shNOP56b but 12.0 μM in H358_Scr cells, with a selectivity index (IC50 in control cells / IC50 in NOP56 KD cells) of 17- and 12-fold, respectively (Fig.  3A; Fig. S3A). These observa- tions were validated by independent assays, in which NOP56 KD sensitized H358 and H460 cells to PI3K/ AKT inhibitors (LY294002, AZD5363), anti-mTOR drugs (rapamycin, everolimus), and ER stress inducers (bort- ezomib and HA15) (Fig.  3B,C; Fig. S3B,C). Importantly, CRISPR/Cas9-mediated knockout of NOP56 dramati- cally increased the sensitivity of KRAS-mutant (H358, H460) but not of wild-type (H520, H1703) lung cancer cells to rapamycin (Fig. S3D, E).

Moreover, examining gene expression data of KRAS- mutant cancer cells [36] revealed that NOP56 silencing significantly enriched the mTOR gene signature (Fig. 3D). Mining TCGA and Genomics of Drug Sensitivity in Can- cer (GDSC) databases showed that NOP56 expression is negatively correlated with that of mTOR pathway genes in patients with KRAS-mutant lung adenocarcinomas (Fig.  3E) and that NOP56 mRNA levels are a predictive marker of sensitivity (IC50) to rapamycin in KRAS-mutant cancer cell lines but not in KRAS-wild-type cancer cells (Fig. 3F). These data support our in vitro results (Fig. 3A- C; Fig. S3A-C) and further suggest a reciprocal interplay between NOP56 and mTOR signaling.

Indeed, siRNA-mediated NOP56 KD, which slightly increased ribonucleolar proteins (e.g., NOP58, FBL), markedly induced AKT/mTOR (p-AKT, p-mTOR, pS6), translation initiation (p-eIF4E) and the stress-responsive p38 MAPK in H358 cells in a time-dependent man- ner (Fig.  3G), as did shRNA-mediated stable NOP56

KD in H358 and H460 cells, but not in KRAS-WT lung cancer H1703 cells (Fig.  3H; Fig. S3G). Importantly, NOP56 KD sensitized KRAS-mutant lung (A549), colon (HCT-116, DLD-1, and LS174T), pancreatic (MIAPaCa, HPAF-II) and primary KRAS-mutant lung cancer cells (PF563, PF139) to rapamycin, as well as NRAS-mutant lung cancer H1299 cells but not KRAS-wild-type H2405 (BRAF-mutant), EBC-1 (EGFR-mutant), H1993 (MET amplification) and H520 (FGFR1 amplification) cells (Fig. S3H, I). These results reinforce the notion that NOP56 plays a unique role in KRAS-mutant cancer.

Our results demonstrated that NOP56 and the IRE1α- mediated UPR act upstream of p38 and mTOR signal- ing (Fig.  2C, D; Fig.  3G), suggesting a signaling cascade from the UPR to mTOR via p38 MAPK. To confirm this, we targeted p38 with the specific inhibitor SB203580, which, as expected, barely affected the upstream IRE1α- dependent UPR (p-IRE1α, XBP-1 s), but strikingly damp- ened the MNK-eIF4E axis and mTOR signaling (p-AKT, p-S6) in H358 cells (Fig.  3I). Importantly, SB203580 exposure not only decreased activity of the mTOR path- way but also increased PARP expression and promoted PARP cleavage (Fig.  3I), concurrent with substantially elevated cytotoxicity on NOP56 KD H460 and H358 cells compared to that on control cells (Fig. 3J). Together, these results unravel a signaling cascade from the IRE1α- mediated UPR to p38 MAPK and to mTOR signaling in KRAS-mutant lung cancer upon NOP56 suppression.

Synthetic lethality by targeting NOP56 and mTOR in KRAS‑mutant lung cancer Our findings that NOP56 KD cells are exposed to higher levels of metabolic ROS and display a greater dependency on UPR-activated mTOR signaling suggest a synthetic lethal vulnerability in KRAS-mutant cancer (Figs.  1, 2, 3). To test this hypothesis, we treated NOP56 KD H358

(See figure on next page.) Fig. 3 IRE1α‑mediated UPR fuels mTOR signaling via p38 MAPK. A, H358 cells expressing scrambled control or NOP56‑specific shRNAs were treated with different inhibitors, with bar graphs illustrating sensitivity increase after NOP56 KD. Fold changes of IC50 values were presented as IC50 of rapamycin in H358 cells expressing scrambled shRNA (sh_Scrambled) compared to that in H358 cells expressing NOP56‑targeted shRNAs (shNOP56 a/b). Data presented as mean (n = 2). B, Clonogenic assay of H358 and H460 cells expressing control shRNA (sh_Scr) or NOP56‑specific shRNAs (sh‑a, sh‑b) after treatment with indicated doses of rapamycin or everolimus. Representative images are shown. The heatmap (right) indicates the percentage of viable cells after the treatment, based on quantification of clonogenic results (left). Data are presented as mean ± SD (n = 3). C, Growth inhibition of H358 and H460 cells expressing NOP56‑specific shRNAs (sh NOP56a, sh NOP56b) or control shRNAs (sh Scram) after treated for 72 h with the mTOR inhibitors (rapamycin, everolimus). Data are presented as mean ± SD (n = 3). D, NOP56 silencing significantly enriched the mTOR gene signature in KRAS‑mutant cancer cells. GSEA was performed based on the GEO dataset GSE15212. E, Negative correlation of NOP56 mRNA levels with mTOR gene signature (mTOR pathway score) as determined in a TCGA cohort of patients with KRAS‑mutant lung adenocarcinoma. Pearson and Spearman coefficient, as well as the significance (p‑value), were determined using R software (Cor.test function). F, NOP56 expression is a predictive marker of sensitivity (IC50) to rapamycin in KRAS‑mutant cancer cell lines (n = 18) but not in KRAS‑wide‑type cancer cell lines (n = 82). Drug response profiles were downloaded from the GDSC (Genomics of Drug Sensitivity in Cancer) database. G, Immunoblots of H358 cells after transfection with scramble control siRNAs (si‑Control) for 72 h (−) or NOP56‑specific siRNAs (si‑NOP56) for different time points (24 h, 48 h, 72 h and 96 h). H, Immunoblots of H358 and H460 cells expressing scrambled control or NOP56‑specific shRNAs. I, Immunoblots of H358 cells expressing scramble control or the NOP56‑specific shRNAs after treated with the p38 inhibitor SB203580 (5 μM) for 24 h. J, Clonogenic assay of H460 and H358 cells expressing control or NOP56‑specific shRNAs after treated with the p38 inhibitor SB203580. Representative images are shown

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Fig. 3 (See legend on previous page.)

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and control cells with rapamycin, which, as expected, decreased mTOR effectors (e.g., p-S6, p-eIF4E) (Fig.  4A) that were the otherwise adaptively upregulated upon NOP56 depletion (Fig.  3G, H). Strikingly, rapamycin induced PARP cleavage (Cl PARP) in NOP56 KD H358 but not in control cells (Fig.  4A), indicating that con- comitant targeting of NOP56 and mTOR caused syn- thetic lethality. Similar results were observed in NOP56 KD H358 cells that were treated with the AKT inhibi- tor AZD5363 (Fig. S4A, B). Knockdown of Raptor and Rictor, key components of the mTORC1 and mTORC2, respectively, significantly suppressed the viability of NOP56 KD H358 cells despite to differential extent (Fig. S4C, D). Moreover, whereas individual S6 and eIF4E only partly contributed to the viability of NOP56 KD H358 cells, concomitant inhibition of S6 (siRNA) and eIF4E (siRNA and Briciclib, an eIF4E inhibitor) led to signifi- cantly enhanced anti-proliferative effect (Fig.  4B-G; Fig. S4E) and largely recapitulated the impact seen by mTOR inhibition with rapamycin (Fig.  4A, Fig. S4F), gauged by the extent to which apoptotic markers (CI-PARP) were induced in NOP56 KD H358 cells (Fig.  4F). These results interrogate an important role for mTOR to relay the IRE1α-mediated UPR signaling in NOP56-depleted KRAS-mutant lung cancer.

The UPR is a double-edged sword, as its outcome flips from adaption to apoptosis when malfunctional UPR, a condition at which stress stimuli are overwhelming or the UPR signal cannot be properly relayed [38, 39]. We thus assumed that the observed synthetic lethality of NOP56 and mTOR inhibition might be enabled due to malfunc- tional UPR. Indeed, co-targeting NOP56 and mTOR resulted in synergistic effects that not only increased the expression of IRE1α and p-IRE1α, indicative of hyperac- tive UPR signals, but also upregulated p-JNK, FOXO3A and BIM, a BH3 only protein and key mediator of apop- totic balance (Fig.  4H). Consistent with their pro-apop- totic roles, this increase of the JNK-FOXO3a-BIM axis was accompanied by PARP cleavage (Cl-PARP) and

significantly greater apoptotic cell death in NOP56 KD H358 and H460 cells compared to control cells (Fig.  4H, I). Importantly, IRE1α KD (siRNA) precluded the cyto- toxicity of combined NOP56 and mTOR inhibition, evi- denced by decreased levels of p-JNK, FOXO3a, BIM, Cl-PARP and of apoptotic populations in NOP56 KD H358 and H460 treated with rapamycin (Fig. 4H, I). Simi- larly, inhibiting JNK activity by the inhibitor SP600125 dampened the efficacy of rapamycin in NOP56 KD H358 and H460 cells (Fig. 4J). Thus, IRE1α-mediated UPR acti- vates mTOR, which provides a survival signal for NOP56 KD KRAS-mutant cancer cells; conversely, mTOR inhi- bition leads to overwhelmed UPR and promotes apop- totic cell death by activating the JNK-FOXO3A-BIM axis (Fig. 4K).

NOP56 and mTOR converge on a metabolic liability in KRAS‑mutant lung cancer Next, we asked if the synthetic lethality of co-targeting NOP56 and mTOR is a result of unresolvable metabolic stress. Indeed, rapamycin sensitivity of NOP56 KD H358 and H460 cells was highly correlated with ROS levels (Fig.  5A, B), and ROS scavenge by NAC significantly compromised the cytotoxicity of rapamycin (Fig.  5B), highlighting a causative link between ROS and rapamy- cin-induced apoptosis in NOP56 KD cells.

These results uncover a novel homeostatic mechanism of metabolic stress mediated by NOP56 and validate an unexpected synthetic lethality by targeting NOP56 and mTOR that aggregate a metabolic liability in KRAS- mutant lung cancer (Fig. 5C).

NOP56 downregulation plus rapamycin potently suppresses in vivo tumor growth of KRAS‑mutant lung cancer Finally, we investigated in  vivo efficacy of co-targeting NOP56 and mTOR. In a xenograft model from KRAS- mutant H460 cells, NOP56 knockdown (shNOP56) only mildly inhibited tumor growth compared to control

Fig. 4 Synthetic lethality by targeting NOP56 and mTOR in KRAS‑mutant lung cancer cells. A, Immunoblots of H358 cells expressing scramble control or NOP56‑specific shRNAs after treated with rapamycin (1 μM) for 24 h. B‑G H358 cells expressing scramble control or shNOP56‑specific shRNAs were transfected with control siRNAs or the indicated siRNAs specifically targeting S6, eIF4E, alone and in combination. The cells were then subjected to immunoblots (B, D, F) and viability assay (C, E, G) 72 h post‑transfection. Data are presented as mean ± SD (n = 3). H, H358 cells expressing scrambled control or NOP56‑specific shRNAs were transfected with IRE1α-specific or control siRNAs for 48 h, followed by treatment with rapamycin (1 μM) for 24 h before immunoblotting. I, H358 cells expressing control or NOP56-specific shRNAs were transfected with IRE1α-specific or control siRNAs for 24 h, followed by treatment with rapamycin (5 μM) for 72 h before apoptosis assay. Data are presented as mean ± SD (n = 3). *p < 0.05, ***P < 0.001 and ****P < 0.0001 by two‑way ANOVA with Tukey’s multiple comparisons test. J, H358 cells expressing control or NOP56-specific shRNAs were preincubated overnight with vehicle (DMSO) or the JNK inhibitor SP600125, followed by treatment with rapamycin for 72 h before apoptosis assay. Data are presented as mean ± SD (n = 3). **p < 0.01, ***P < 0.001 and ns P>0.05 by two‑way ANOVA with Tukey’s multiple comparisons test. K, Proposed model of cellular gauge for IRE1α‑regulated UPR. In KRAS‑mutant cancer cells, intact NOP56 keeps ROS in check so that IRE1α‑regulated UPR is minimal (basal level; left). Intermediate levels of IRE1α‑regulated UPR ensue from NOP56 depletion, which activates p38‑AKT/mTOR and promotes cell survival (middle). At “dangerous” level of ROS, IRE1α‑regulated UPR initiates JNK‑dependent apoptosis (right)

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Fig. 4 (See legend on previous page.)

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shRNA (shScrambled), as did rapamycin (Fig.  6A). However, the outcome of concomitant targeting of NOP56 and mTOR (shNOP56 plus rapamycin) was superior to that achieved by shNOP56 or rapamycin alone, leading to far more effective and potent suppres- sion of xenograft tumor growth (Fig.  6A, B). Residual tumors (after 3-week treatment) from the combina- tion group (shNOP56 plus rapamycin) were typically tiny and significantly differed from those of the other treatment groups (Fig.  6C). Immunohistochemical

(IHC) analysis revealed that the anti-tumor efficacy of combined treatment with shNOP56 and rapamycin was paralleled by marked decrease of mTOR activity (p-AKT, p-mTPOR, p-S6) and increase in apoptosis (Caspase-3) in the residual tumors (Fig. S5A).

Similar results were obtained from H358 xenografts (Fig. 6D, E) and a patient-derived xenograft (PDX) model established from the primary KRAS-mutant PF139 cells (Fig.  6F-I). In both models, NOP56 KD sensitized H358 and PF139 xenograft tumors to rapamycin, leading to

Fig. 5 NOP56 and mTOR converge on a metabolic liability in KRAS‑mutant tumor growth. A, Apoptosis assay of H358 and H460 cells expressing control or NOP56‑specific shRNAs after treatment with rapamycin (5 μM) for 72 h. Data are presented as mean of three independent experiments (n = 3). *p < 0.05, ****P < 0.0001 and ns P>0.05 by two‑way ANOVA with Tukey’s multiple comparisons test. B, H358 and H460 cells expressing control or NOP56‑specific shRNAs were treated with rapamycin (5 μM) for 24 h. Cells were then washed, incubated with H2DCFDA for 30 min, and analyzed by flow cytometry. Quantification of relative ROS levels was shown in the right. C, A working model for the function of NOP56 in KRAS‑mutant cancers

Fig. 6 NOP56 knockdown plus rapamycin inhibits KRAS‑mutant tumor growth. A, Growth curve of xenograft tumors derived from H460 cells expressing either a control or an shRNA against NOP56 (shNOP56a). Rapamycin (0.1 mg/kg) was administrated i.p. for 3 weeks (5 days/week). Data are shown as mean ± SD). ***P < 0.001, *P < 0.05 and ns (P>0.05) by two‑way ANOVA with Tukey’s multiple comparisons test. B, Relative tumor volume of H460 xenograft tumors after the treatment for 3 weeks. C, Weights of H460 xenograft tumors after the treatment for 3 weeks. ***P < 0.001 by one‑way ANOVA with Tukey’s multiple comparisons test. D, Growth curve of xenograft tumors derived from H358 cells expressing either a control or an shRNA against NOP56 (shNOP56a). **P < 0.01 by two‑way ANOVA with Tukey’s multiple comparisons test. E, Kaplan‑Meier survival curve of mice with H358 xenografts from the experiment in D. F, Growth curve of PDX tumors derived from primary KRAS‑mutant PF139 lung cancer cells expressing either control or NOP56‑specific shRNAs. Data are shown as mean ± SD). ***P < 0.001 and *P < 0.05 by two‑way ANOVA with Tukey’s multiple comparisons test. Immunoblots of PF139 cells expressing NOP56‑specific or scrambled shRNAs was also shown. G, Relative tumor volume of PF139 xenografts after 3 weeks of treatment. H, Weights of PF139 xenograft tumors after treated for 3 weeks. ***P < 0.001, *P < 0.05 and ns P>0.05 by two‑way ANOVA with Tukey’s multiple comparisons test. I, H&E and IHC of p‑AKT(T308), p‑mTOR(S2448), p‑S6(S235/236), Ki67 and Caspase‑3) in PF139 xenograft tumors after the treatment. Scale bars 100 μm

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Fig. 6 (See legend on previous page.)

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potent suppression of tumor growth (Fig.  6D, F-H) and significant improvement of mouse survival (Fig. 6E). IHC of the PF139 residual tumors revealed that NOP56 deple- tion plus rapamycin strikingly suppressed tumor cell pro- liferation (Ki-67) and dampened mTOR activity (p-AKT, p-mTOR and p-S6), but increased Caspase-3 cleavage (Fig.  6I), which is consistent with the in  vitro results (Figs. 4, 5) and our observations on H460 xenografts (Fig. S5A). Notably, rapamycin showed little beneficial effects in NOP56 KD KRAS wild-type H1703 xenografts (Fig. S5B, C), which mirrors the in  vitro data and reinforces the selective activity of co-targeting NOP56/mTOR in KRAS-mutant lung cancer.

Together, these results support a model that NOP56 downregulation induces a metabolic vulnerability to mTOR inhibition, which presents a new and rational strategy for treating KRAS-mutant lung cancer.

Discussion In the present study, we have uncovered a new and unanticipated mechanism by which NOP56 and mTOR signaling cooperate in metabolic stress response in KRAS-mutant lung cancer. We show that NOP56 sup- presses ROS, and its depletion induces synthetic lethal susceptibility to inhibition of mTOR that is otherwise essential for counterbalance of the resurge of cytotoxic ROS evoked by NOP56 downregulation. We also dis- cover that mTOR activation is driven by IRE1α-mediated UPR via p38 MAPK. These findings support a model that NOP56 plays a role in the surveillance of ROS homeosta- sis and suggest that concomitant blockage of NOP56 and mTOR signaling has the potential to selectively target KRAS-mutant lung cancer. As multiple mTOR inhibitors (e.g., rapamycin and everolimus) are clinically approved drugs, our observations have immediate translational significance.

Despite decades-long steady efforts, therapeutic tar- geting of KRAS-mutant cancers has remained an over- arching challenge in clinical oncology [3]. A promising strategy to target KRAS-driven tumors is to exploit can- cer cell vulnerabilities contextually co-opted by mutant KRAS, in light of the concept that mutant KRAS alter physiological biochemical networks and induces cellu- lar stresses, rendering KRAS-mutant cancer particularly susceptible to inhibition of stress-remedy mechanisms [12–14]. Empowered by CRISPR- and shRNA-based functional genomics, a plethora of novel factors required for KRAS-mutant cancer cells have been identified [12– 16, 26], although the long-sought-after universal syn- thetic lethal targets for KRAS-driven pan-cancers are still at large. By implementing integrated analysis of func- tional genomic datasets (n = 5) derived from shRNA- and CRISPR-based screens [12, 16, 26], we revealed

that NOP56 confers a metabolic requirement for KRAS-mutant cancer by regulating ROS homeostasis. A functional link between NOP56 and mutant KRAS is supported by a multitude of lines of evidence, i.e., the ele- vated expression of NOP56 in KRAS-mutant tumors, the prognostic significance of NOP56 expression in patients with KRAS-mutant but not wild-type cancers, and selec- tive damage on KRAS-mutant cancer cells incurred by NOP56 downregulation.

Moreover, our results uncover a reciprocal interaction of NOP56 and mTOR signaling and suggest that com- bined inhibition of NOP56/mTOR is a rational strategy to combat KRAS-mutant cancer. Supporting our find- ings, mRNA levels of NOP56 significantly correlate with that of mTOR pathway genes in KRAS-mutant cancer cells and lung adenocarcinomas, and NOP56 expression is a predictive marker of sensitivity to mTOR inhibi- tors in KRAS-mutant but not KRAS-wild-type cancer cells. Importantly, NOP56 knockdown sensitizes KRAS- mutant cancer cells to mTOR inhibitors in  vitro and in vivo, which is not true for KRAS-wild-type tumor cells. Despite potential toxicological challenges as ribosome biogenesis is also an important physiological process, modulation of NOP56 activity may afford a therapeu- tic window for targeted inhibition of mTOR in KRAS- mutant cancers.

An increasingly growing body of evidence suggests that oncogenic KRAS signaling rewires metabolic pathways to meet the energetic and biosynthetic demands of cancer cells [27–30, 40]. In particular, increased ROS produc- tion, which has been shown to be functionally required for KRAS-mediated tumorigenicity [27, 28], is a key metabolic manifestation associated with KRAS-mutant cancer cells [27–30]. Since excess ROS is harmful, cancer cells must leverage ROS levels to favor tumor progression but prevent cell death [14, 27–30, 41]. Here, we reported, for the first time, a role for NOP56 in metabolic ROS response in KRAS-mutant lung cancer. NOP56 is a key component of box C/D snoRNPs that regulates ribosome assembly. This process has been shown to be deregulated in tumors with increased requirement for protein syn- thesis, providing cancer vulnerabilities for therapeutic avenues [21–24, 42–45]. Our results are also in line with previous studies reporting cancer subtype-specific altera- tions in ribosome assembly and biogenesis processes [44, 45]. In addition, recent evidence suggests that snoRNPs may also be involved in other processes independent of their functions in ribosome biogenesis [46]. Future stud- ies will be necessary to clarify whether the newly identi- fied metabolic role of NOP56 in KRAS-mutant cancer is related to its canonical role.

Our finding that IRE1α-mediated UPR connects the NOP56 function in ROS scavenge with mTOR

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signaling, a master regulator of cellular metabolism [47], provides mechanistic insights about the syn- thetic lethality of co-targeting NOP56 and mTOR, which is supported the observation that challenges to ribosome biogenesis result in acute loss of pro- teostasis [48]. IRE1α-mediated UPR, whilst initially protective, turns to be pro-apoptotic if ROS-induced metabolic stress is prolonged and persists [38, 39]. We further reveal that the overwhelming metabolic stress incurred by NOP56 depletion and mTOR inhi- bition activates the JNK-BIM axis, a stress-responsive pathway that promotes cell-cycle arrest and apoptosis [37]. The observation that UPR is a key component of the homeostatic mechanism in response to ROS resurge evoked by NOP56 knockdown is consist- ent with the notion that UPR plays an import role in homeostasis regulation including ROS and that chal- lenges to ribosome biogenesis result in acute loss of proteostasis [49]. As metabolic ROS is causally linked to mutant KRAS-induced tumorigenicity and requires homeostatic mechanisms to maintain ROS levels within a threshold favorable for tumor devel- opment [14, 27–30, 41], the identification of NOP56 and mTOR converging on a role as ROS scavengers reveals an unanticipated metabolic vulnerability in KRAS-mutant cancers.

Conclusion In summary, we have uncovered an unexpected role for NOP56 in the surveillance of metabolic ROS in KRAS-mutant lung cancer. We have also revealed a novel synthetic lethality between NOP56 depletion and mTOR inhibitors that occurs by impeding the homeostatic mechanism of ROS in KRAS-mutant can- cer cells. Moreover, we have demonstrated that mTOR activation upon NOP56 depletion is driven by IRE1α- mediated UPR. These results shed light on the mecha- nisms underlying KRAS-induced metabolic rewiring, reveals an unanticipated metabolic vulnerability in KRAS-mutant lung cancer, and suggest a new ration- ale for the treatment of the disease. Because KRAS alterations are implicated in a broad spectrum of human malignancies, our findings may also be appli- cable to other lineages of cancer with high frequencies of KRAS alterations.

Abbreviations GSEA: Gene Set Enrichment Analysis; IRE1α: Inositol‑requiring enzyme 1α; KRAS: Kirsten rat sarcoma viral oncogene homolog; mTOR: Mechanistic target of rapamycin; NOP56: Nucleolar protein 5A; PARP: Poly (ADP‑ribose) polymer‑ ase; RNAi: RNA interference; ROS: Reactive oxygen species; TCGA : The Cancer Genome Atlas; UPR: Unfolded protein response.

Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s13046‑ 022‑ 02240‑5.

Additional file 1: Figure S1. NOP56 knockdown inhibits proliferation of KRAS‑mutant cancer cells. A, Immunoblots of KRAS‑mutant and KRAS‑wild type cancer cells that were transfected with NOP56‑specific siRNAs (si‑ NOP56) or scramble control siRNAs (si‑Control). B, KRAS mutant and KRAS wild type cancer cells were transfected with control siRNAs or NOP56‑ specific siRNAs. Cell viability was determined 72 h post transfection. Data are presented as mean ± SD (n = 3). *p < 0.05, **p < 0.01, ***P < 0.001, ****P < 0.0001 and ns P>0.05 by two‑way ANOVA with Tukey’s multiple comparisons test. C, NOP56 is not a biomarker of survival in patients with KRAS‑wild‑type lung adenocarcinoma (LC), pancreatic cancer (PC) and colon cancer (CC). Kaplan–Meier survival analyses of patient cohorts in TCGA were stratified by the optimal cut‑off value of the mRNA level of NOP56. D, Gene set enrichment analysis (GSEA) of a TCGA cohort of patients with KRAS‑mutant lung (n = 141), pancreatic (n = 133) and colon cancer (n = 170). Figure S2. Stable expression of NOP56-specific shRNAs activates IRE1α‑mediated UPR. A, Immunoblots of H358 and H460 cells expressing scrambled control or NOP56 shRNAs. B, Immunofluorescence of H358 and H460 cells that express scrambled control or NOP56 shRNAs. The NOP56 signal is indicated by arrowheads. C, The cell viability curve of H358 and H460 cells expressing scramble control shRNA or the NOP56‑ targeted shRNAs was measured at the indicated time points. D, Clongenic assay of H358 and H460 cells expressing scramble control or NOP56‑tar‑ geted shRNAs. Quantification of clongenic assay were shown underneath. Data are presented as mean ± SD (n = 3). E, Growth inhibition of H358 and H460 cells expressing control shRNA or NOP56‑targeted shRNA (3000 cells/well) treated for 72 h with the indicated doses of an IRE1α inhibitor (4μ8C). Data are presented as mean ± SD (n = 3). F, Apoptosis assay of H460 cells expressing scrambled control or NOP56‑targeted shRNAs after transfection with IRE1α-specific or control siRNAs for 72 h. Data are presented as mean ± SD (n = 3). ***P < 0.001 and ns P>0.05 by two‑way ANOVA with Tukey’s multiple comparisons test. Figure S3. NOP56 KD renders KRAS‑mutant lung cancer cells susceptible to mTOR inhibition. A, Bar graphs illustrating the change of sensitivity to different inhibitors in H460 cells after NOP56 knockdown. Data are presented as IC50 values of the indicated inhibitors in H460 cells expressing scramble control shRNAs compared to IC50 in H460 cells expressing NOP56‑targeted shRNAs. Data are shown as mean (n = 2). B, Viability assay of H460 and H358 cells expressing control shRNA or NOP56‑targeted shRNA (3000 cells/well) after treated for 72 h with the indicated doses of PI3K inhibitor (LY294002) and AKT inhibitor (AZD5363). Data are presented as mean ± SD (n = 3). C, Viability assay of H460 and H358 cells expressing control shRNA or NOP56‑targeted shRNA (3000 cells/well) after treated for 72 h with the indicated doses of BiP inhibitor (HA15) and ER stress inducer (bortezomib). Data are presented as mean ± SD (n = 3). D, Immunoblots of KRAS‑mutant (H358, H460) and wild‑type (H1703, H520) cells expressing NOP56‑specific sgRNAs. E, Viability assay of the cells expressing control or NOP56‑ specific sgRNAs after treated with rapamycin for 72 h. Data are shown as mean ± SD (n = 3). F, NOP56 is negatively correlated with PI3K/AKT/ mTOR pathway genes (PI3KCA, PDPK1, PIK3R1) in KRAS‑mutant lung cancer patients. Pearson and Spearman coefficient and significance (p‑value) are analyzed using R software (Cor.test function). G, Immunoblots of H460 and H1703 cells expressing control or NOP56-targeted shRNAs. H, I, Viability assay of KRAS‑mutant (H) and wildtype (I) cancer cells expressing control or NOP56‑specific siRNAs after treated with rapamycin. The assay was performed 72 h after drug treatment (96 h after siRNA transfection). Figure S4. NOP56 KD activates and induces dependence on the mTOR pathway in KRAS‑mutant cancer cells. A, Immunoblots of H358 cells expressing control or NOP56‑targeted shRNAs after treated with the AKT inhibitor (AZD5363) for 24 h. B, Clongenic assay of H358 cells express‑ ing control or NOP56‑specific shRNAs after treated with indicated doses of AZD5363. Representative images are shown. C, D, Immunoblots (C) and viability assay (D) of H358 cells expressing control or NOP56-specific shRNAs after transfected with raptor‑ or rictor‑specific or control siRNAs for 72 h. Data are presented as mean ± SD (n = 3). E, Clongenic assay of H358 and H460 cells expressing control shRNA or NOP56‑specific shRNAs after

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treatment with indicated doses of eIF4E inhibitor (Briciclib). Representative images are shown. F, Immunoblots of H460 cells expressing control or NOP56‑target shRNAs after treated with rapamycin (1 μM) for 24 h. Figure S5. In vivo activity and selectivity of co‑targeting NOP56 and mTOR in KRAS‑mutant lung cancer. A, H&E and IHC analysis of p‑AKT(T308), p‑mTOR(S2448),p‑S6(S235/236), Ki67 and Caspase‑3) in residual H460 xenograft tumors after the indicated treatment. Scale bars 100 μm. B, Tumor volume of H1703 xenografts in immunocompromised (NSG) mice. H1703 cells were transduced with either a control or an shRNA against NOP56 (shNOP56a). Tumors were measured every 5 days with a caliper. C, Kaplan‑Meier survival curve of mice harboring H1703 xenografts from the experiment shown in B.

Additional file 2: Table S1. Cell lines used in this study. Table S2. Inhibi‑ tors used for synthetic lethal chemical screens. Table S3. Antibodies used in this study. Table S4.KRAS synthetic lethal (SL) genes.

Acknowledgements We gratefully acknowledge Christelle Dubey (Division of Thoracic Surgery, Inselspital, Bern University Hospital) for technical support, especially with ani‑ mal studies and CRISPR‑based knockout of NOP56. We thank the West‑Ger‑ man Biobank Essen (WBE) for the collaboration in establishment of the PF139 and PF526 lung cancer cells. The Translational Research Unit at the Institute of Pathology, University of Bern is acknowledged for assistance of IHC staining.

Authors’ contributions ZY, SQL designed and performed the experiments, analyzed the data and wrote the manuscript. LZ, HY performed the experiments and analyzed the data. TMM, BH, YG, BZ, CC and WW analyzed the data and edited the manu‑ script. PD and GJK provided conceptual inputs, analyzed the data and edited the manuscript. RAS provided financial support and edit the manuscript. RWP conceived the project, supervised the study and wrote the paper. All authors read and approved the final version of the manuscript.

Funding This study was supported by a grant from Swiss National Science Founda‑ tion (SNSF #310030_192648; to R‑W. Peng) and PhD fellowships from China Scholarship Council (ZY, LZ, YG).

Availability of data and materials All data generated or analysed during this study are included in this published article and its supplementary information files.

Declarations

Ethics approval and consent to participate The establishment of PF139 and PF526 cells was approved by the Ethics Com‑ mittee of the University Hospital Essen (#18–8208‑BO), Germany, with written consents obtained from the patients. The study was performed in accord‑ ance with the Declaration of Helsinki. Mouse studies were approved by the Veterinary Office of Canton Bern, Switzerland, and conducted in accordance with Institutional Animal Care.

Consent for publication Not applicable.

Competing interests The authors declare no competing interests.

Author details 1 Division of General Thoracic Surgery and Department of BioMedical Research (DBMR), Inselspital, Bern University Hospital, University of Bern, Murtenstrasse 28, 3008 Bern, Switzerland. 2 Current address: University of Massachusetts Medical School, Worcester, MA 01605, USA. 3 Current address: Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong Univer‑ sity, Shanghai 200030, China. 4 Department of Thoracic Surgery, University Medicine Essen – Ruhrlandklinik, University Duisburg‑Essen, Essen, Germany. 5 Department of Thoracic surgery, Fujian Medical University Union Hospital, Fuzhou City, Fujian, China. 6 Thoracic Surgery Department 2, Hunan Cancer

Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China.

Received: 23 May 2021 Accepted: 1 January 2022

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