Epigenetic CRISPR Screens Identify Npm1 as a Therapeutic Vulnerability in Non–Small Cell Lung Cancer

Lung cancer is one of the most common and deadly cancers worldwide (1). Approximately 85% of all lung cancer cases are non–small cell lung cancers (NSCLC; ref. 2). Although tyrosine kinase inhibitors (TKI) and immunotherapy have contributed to significant survival benefits in some patients, the overall survival rates for NSCLCs remain low. In particular, patients with NSCLCs that are driven by KRAS mutations are often unresponsive to TKIs and have a poor prognosis (2). For patients with NSCLC, who harbor mutations in the EGFR or in anaplastic lymphoma kinase (ALK) fusions, targeted therapeutics have achieved responses in up to 80% of cases; in contrast, targeted therapy against mutant KRAS-driven tumors has proved challenging (3). Indeed, although allele-specific inhibitors for the KRAS^G12C mutant have entered phase I clinical trials, a general strategy that targets all KRAS mutants remains elusive (4). There is, therefore, an urgent need to identify new drug targets and develop therapeutic strategies to benefit a broader patient population, especially those with KRAS mutations.

Chromatin-modifying and chromatin-interacting proteins (also known as epigenetic regulators) play important roles in tumor initiation and progression. Epigenetic regulators are frequently dysregulated in cancer and provide a repertoire of potential therapeutic targets (5). Small-molecule inhibitors of epigenetic regulators (such as pan-BET inhibitors, DNMT inhibitors, and HDAC inhibitors) have been exploited as cancer therapeutics and entered various phases of clinical trials (6, 7). The FDA has approved the use of DNMT inhibitors (azacitidine and decitabine) for the treatment of myelodysplastic syndrome, and HDAC inhibitors (vorinostat, romidepsin, and belinostat) to treat cutaneous T-cell lymphoma (7). Despite the clinical success of small-molecule inhibitors of epigenetic regulators in blood cancers, the therapeutic potential of targeting epigenetic regulators in NSCLC remains underexplored. In view of the therapeutic potential in targeting epigenetic regulators in NSCLC, we sought to systematically study their functional roles in NSCLC progression. We performed in vitro and in vivo epigenome-wide CRISPR loss-of-function screens in a mouse Kras-mutant lung ADC model and identified both novel and established vulnerabilities in NSCLCs. Subsequent functional studies uncovered Npm1 as a druggable vulnerability, providing a therapeutic opportunity for patients with NSCLC who harbor KRAS mutations.

HEK-293T cells and 3T3 cells were cultured in DMEM (Gibco) with 10% FBS. Mouse cell lines KP (Kras^G12D; P53^−/−), KP-2 (Kras^G12D; P53^−/−), KL (Kras^G12D; Lkb1^−/−), and PLP (Pten^−/−; Lkb1^−/−; P53^−/−), and human cell lines A549, H441, H358, A427, H460, H157, H2030, H2009, H23, H2228, and Beas2B were cultured in RPMI1640 (Gibco) with 10% FBS. All cell lines used in this study were tested as Mycoplasma negative using Universal Mycoplasma Detection Kit (ATCC 30-1012K).

Plasmids pLenti-Cas9-Puro, pXPR-GFP-Blast, pLKO.1-Tet-on, PSPAX2, and PMD2.G were purchased from Addgene. The shRNAs specific for mouse Npm1 and human NPM1 were cloned into pLKO.1-Tet-on vector with the AgeI/EcoRI sites. The target sequences are as follows:

• shNpm1-3: 5′-GCAGAGTCTGAAGATGAAGAT-3′

• shNpm1-4: 5′-CTATGAAGGCAGTCCAATTAA-3′

• shNpm1-5: 5′-GTGGAAGCCAAGTTCATTAAT-3′

• shNPM1-1: 5′-GCGCCAGTGAAGAAATCTATA-3′

• shNPM1-3: 5′-CCTAGTTCTGTAGAAGACATT-3′

The shRNAs specific for mouse Pgk1 and Pdk2 were purchased from Sigma.

To generate lentivirus, HEK-293T cells were cotransfected with pLenti-Cas9, pXPR-GFP-sgRNA-Blast, or pLKO.1-Tet-on-shRNA plasmid and packaging plasmids PSPAX2 and PMD2.G using Lipofectamine 3000 (Invitrogen). Viral particles released into the cell culture supernatant were filtered with 0.45-μm filters (Corning) to remove cellular debris. KP cells were transduced by culturing with viral supernatants in the presence of polybrene (Sigma) to increase infection efficiency. Stable cell lines were selected and maintained in cell culture media containing 2 μg/mL puromycin or 5 μg/mL blasticidin.

The construction of single guide RNA (sgRNA) library of epigenome as described previously (8). We obtained sgRNA oligo pools from the Belfer Center for Applied Cancer Science at the Dana-Farber Cancer Institute (Boston, MA; ref. 9). The library contains 7,780 sgRNAs, including sgRNAs that target 524 epigenetic regulators, 173 control genes (e.g., essential genes and immune modulators), and 723 nontargeting sgRNAs. For each gene, there are 8–12 sgRNAs. Additional details of the library are included in Supplementary Table S1. The sgRNA library was inserted into the pXPR-GFP-Blast vector using the Gibson assembly kit (NEB), expanded by transformation into electrocompetent cells (Invitrogen) by electroporation. Library representation was maintained at least 1,000× at each step of the preparation process.

As described previously (8), KP-Cas9 clones with validated Cas9 activity were transduced at an MOI of 0.2 with lentivirus produced from the libraries with at least 1,000-fold coverage (cells per construct) in each infection replicate. Transduced KP-Cas9 cells were expanded in vitro for two weeks and then subcutaneously implanted into B6-Rag1^−/− mice and C57BL/6 mice. Genomic DNA from tumors and from cells were extracted using the DNA Blood Midi kit (Qiagen). PCR was used to amplify the sgRNA cassette and next-generation sequencing (NGS) sequencing was performed on an Illumina HiSeq to determine sgRNA abundance. In vivo screens in this study shared the data with our previous study (8).

Adaptor sequences were trimmed using cutadapt (v1.18), and untrimmed reads were removed. Sequences after the 20 base gRNAs were cut using fastx-toolkit (v0.0.13; http://hannonlab.cshl.edu/fastx_toolkit/index.html), gRNAs were mapped to the annotation file (0 mismatch), and read count tables were created. The count tables were normalized on the basis of their library size factors using DESeq2 (10), and differential expression analysis was performed. MAGeCK (0.5.8; ref. 11) was used to normalize the count table based on median normalization and fold changes and significance of changes in the conditions was calculated for genes and sgRNAs.

Cells were trypsinized to produce a single-cell suspension. 1,000 cells were counted and plated in each well of 6-well plate. Medium was changed every two days. For in vitro induction of Npm1/NPM1 knockdown, 100 ng/μL doxycycline was supplemented in cell culture medium. When the control wells reached the adequate confluency, cells were fixed with 70% ethanol for 10 minutes, and cells were stained with 0.5% crystal violet (dissolved in 20% methanol) for 5 minutes and washed. Photos were taken and quantified using ImageJ.

All mouse work was reviewed and approved by the Institutional Animal Care and Use Committee at either NYU School of Medicine or Dana-Farber Cancer Institute (Boston, MA). Specific pathogen–free facilities were used for housing and care of all mice. Six-week-old male B6-Rag1^−/−, B6 WT, and nude mice were purchased from Jackson Laboratories. For in vivo screens, 8.0 × 10⁶ library-transduced KP-Cas9 cells were resuspended in 200 μL PBS and subcutaneously inoculated into the flanks of B6-Rag1^−/− mice and B6 WT mice. For the treatment study, 1.0 × 10⁶ cells were subcutaneously inoculated into the flanks of B6 WT mice or nude mice. For in vivo induction of Npm1/NPM1 knockdown, doxycycline-containing food was used. Tumor size was measured using calipers to collect maximal tumor length and width. Tumor volume was estimated with the following formula: (L × W²)/2. Tail vein injection was also used as an orthotopic model, and 1.0 × 10⁶ cells in 300 μL PBS were injected. MRI was used to monitor tumor formation and progression in an orthotopic model. Randomization of mouse groups was performed when appropriate. CO₂ inhalation was used to euthanize mice.

Animals were anesthetized with isoflurane to perform lung MRI using BioSpec USR70/30 horizontal bore system (Bruker) to scan 24 consecutive sections. Tumor volume within the whole lung was quantified using 3D slicer software to reconstruct MRI volumetric measurements as described previously (12). Acquisition of the MRI signal was adapted according to cardiac and respiratory cycles to minimize motion effects during imaging.

RNA sequencing (RNA-seq) of KP cells with or without Npm1 knockdown was performed at the NYU Langone Medical Center Genome Technology Core. STAR 2.4.2a (13) was used to align the RNA-seq samples to the reference mouse genome (mm9) and to count the number of reads mapping to each gene in the ensembl GRCm38.80 gene model. Differential expression between the different groups was performed through the use of DESeq2 (14). Differential expression analysis was done using R (v.3.5.1; http://www.R-project.org/) and the DESeq2 package (v.1.10.0).

Gene-set enrichment analysis was done using Gene Set Enrichment Analysis (GSEA; v.3.0) and gene sets from MSigDB (v.5.0). We used the “preranked” algorithm to analyze gene lists ranked by the negative decadic logarithm of P values multiplied by the value of log₂FC obtained from the differential-expression analysis with DESeq2.

KP tumor cells with or without Npm1 knockdown were stained with fluorochrome-coupled antibodies against mouse Sca-1 (clone D7, BioLegend). Cells were imaged on a BD Biosciences LSRFortessa and analyzed with FlowJo software.

Cells were lysed in RIPA buffer (Pierce) containing protease/phosphatase inhibitor cocktail (Thermo Scientific). Protein concentration was measured using a BCA assay (Pierce). Equivalent amounts of each sample were loaded on 4%–12% Bis-Tris gels (Invitrogen), transferred to nitrocellulose membranes, and immunoblotted with antibodies directed against Npm1 (sc-271737, Santa Cruz Biotechnology), Pgk1 (PA5-28612, Thermo Fisher Scientific), Pdk2 (PA5-28612, Thermo Fisher Scientific), VINC (4650s, Cell Signaling Technology), and β-actin (Ab8227, Abcam). IRDye 800–labeled goat anti-rabbit IgG and IRDye 680–labeled goat anti-mouse IgG secondary antibodies were purchased from LI-COR Biosciences, and membranes were subjected to an Odyssey detection system (LI-COR Biosciences).

Total RNA was extracted from cells using an RNeasy Plus Mini Kit (Qiagen), and cDNA was generated with a High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Quantitative PCR was performed using SYBR Green PCR Master Mix (Applied Biosystems), and transcript levels were normalized to actin as an internal control. Samples were run in triplicate. For detection of mouse Npm1, the forward primer was 5′-TCCTGGAGGTGGTAACAAGG-3′ and the reverse primer was 5′-ACCCTTTGATCTCGGTGTTG-3′. For detection of mouse Carm1, the forward primer was 5′-ACCACACGGACTTCAAGGAC-3′ and the reverse primer was 5′-CTCTTCACCAGGACCTCTGC-3′. For internal control Actb, the forward primer was 5′-CTGTCCCTGTATGCCTCTG-3′ and the reverse primer was 5′-ATGTCACGCACGATTTCC-3′.

We analyzed the publicly available genome-scale CRISPR-KO and small interfering RNA (RNAi) data from more than 102 human NSCLC cell lines from the Cancer Dependency Map (DepMap; ref. 15). Datasets were downloaded from the webpage of DepMap (https://depmap.org/portal/download/), which included cell line metadata, mutation data, as well as CRISPR and combined RNAi results published in Q2 of 2019. Data analysis were performed in R 3.5.1.

Pearson correlations between basal gene expression of NPM1 and small-molecule inhibitor sensitivity for adherent cell lines were determined from the Cancer Therapeutics Response Portal (CTRP; http://portals.broadinstitute.org/ctrp.v2.1/).

The bioenergetic function of cells in response to Npm1 knockdown was determined using a Seahorse Bioscience XF24/96 Extracellular Flux Analyzer (Seahorse Bioscience). Cells were seeded in specialized V7 Seahorse tissue culture plates for 24 hours. One hour prior to experiment, cells were washed and changed to XF Base medium adjusted to pH 7.4. For oxidative phosphorylation (OXPHOS) experiments medium was supplemented with either pyruvate (1 mmol/L), l-glutamine (2 mmol/L), and glucose (10 mmol/L; shNMP1 experiment) or l-glutamine (2 mmol/L) and glucose (10 mmol/L; shPDK2 experiments). Three baseline oxygen consumption rate (OCR) measurements were taken, followed by three measurements after each injection of oligomycin (1 mmol/L), FCCP (1 mmol/L), and rotenone and antimycin A (0.5 mmol/L), respectively. For glycolysis experiments, medium was supplemented with l-glutamine (1 mmol/L). Three baseline measurements were taken for extracellular acidification rate (ECAR), followed by three measurements after each injection of glucose (10 mmol/L), oligomycin (1 mmol/L) and 2-DG (50 mmol/L) respectively. For all analyses, values were normalized to protein concentration before baseline measurements were subtracted.

All statistical analyses were carried out using GraphPad Prism 7. Data was analyzed by Student t test (two tailed) unless otherwise specified. Error bars represent SEM. Kaplan–Meier survival plots were obtained using the GEPIA (Gene Expression Profiling Interactive Analysis) online tool (16). Log-rank P < 0.05 was considered statistically significant.

NGS data for CRISPR screens and RNA-seq data have been deposited in the National Center for Biotechnology Information's Gene Expression Omnibus and are accessible through GEO Series accession number GSE127232 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE127232), and GSE133555 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133555).

The CRISPR-Cas9 knockout screen is a powerful approach in identifying genes that are essential for tumor initiation and growth (17). To identify novel epigenetic gene candidates that are critical for NSCLC progression, we performed pooled CRISPR loss-of-function screens using the KP mouse lung ADC model (Fig. 1A). As described previously (8), we first generated KP single clones with stable expression of Cas9 (KP-Cas9 clones), plus an epigenetic-focused sgRNA library (Supplementary Table S1; Supplementary Fig. S1A). For the in vitro screen, we transduced the KP-Cas9 single clones with the lentiviral sgRNA library, cultured the KP-Cas9-library pools, and harvested cells every 2 weeks for the extraction of genomic DNA. For the in vivo screen as described previously (8), we injected early passage of the KP-Cas9-library cell pool into B6 Rag1^−/− mice and B6 wild-type (WT) mice, harvested the tumors before necrosis for the extraction of genomic DNA. Next, we amplified the sgRNA constructs from the genomic DNA, performed NGS sequencing, and analyzed depletion/enrichment of the sgRNAs (Fig. 1B–D; Supplementary Table S1; Supplementary Fig. S1B–S1D). The control sgRNAs, which targeted some well-known tumor suppressor genes (such as Rb1, Pten, Fbxw7, and Nf2), were significantly enriched in the in vitro as well as in vivo CRISPR screens (Fig. 1B and C; Supplementary Fig. S1B and S1C), suggesting that they confer a proliferation advantage to tumor cells, thus validating our screening strategy.

The sgRNAs of some candidate genes were significantly depleted, indicating that genetic ablation or chemical inhibition of the corresponding gene products would likely inhibit cell proliferation and tumor growth. Among the top-ranked candidates were polo like kinase 1 (Plk1), bromodomain-containing protein 4 (Brd4), and histone deacetylase 3 (Hdac3), all of which serve reported oncogenic functions in lung cancer (18–20). Notably, we also identified previously underexplored therapeutic targets such as histone chaperone nucleophosmin 1 (Npm1) and coactivator-associated arginine methyltransferase 1 (Carm1; Fig. 1B–E; Supplementary Fig. S1B–S1G; Supplementary Table S2).

Npm1 and Carm1 are among the top gene candidates that appear to be druggable targets in some hematologic and solid malignancies (21, 22). To further explore the therapeutic potential of targeting these epigenetic regulators in NSCLC, we queried the clinically relevant data of patients with cancer and human cancer cell lines using publicly available information such as The Cancer Genome Atlas (TCGA) database and The Human Protein Atlas (HPA) portal. Data mining from 962 patients with NSCLC in TCGA revealed that high expression of NPM1 is positively correlated with a lower overall survival (Fig. 1F), while expression of CARM1 is not significantly correlated with overall survival (Supplementary Fig. S1H). In the HPA portal, lung tumor tissue revealed a higher expression of NPM1 than normal lung tissue (Supplementary Fig. S1I), suggesting that tumor cells might be more addicted to NPM1 expression. Also, in the HPA portal, the expression of NPM1 is correlated with a lower overall survival of patients with renal, liver, head and neck cancers, while the expression of CARM1 is linked to a lower overall survival of patients with renal and urothelial cancers (23). These data suggest that NPM1 and CARM1 are prognostic markers in specific types of malignancies.

In parallel, we used pharmacologic tools to help select candidate genes for follow-up functional and mechanistic studies. The PLK1 inhibitor BI-6727, the BET inhibitor JQ1, and the HDAC1/3 inhibitor MS-275 are established chemical probes that serve as positive controls in an in vitro colony formation assay. As expected, each of these inhibitors significantly hindered cell growth in the KP lung ADC model (Supplementary Fig. S2A–S2C). The small-molecule chemical probes NSC348884 (24) and TP-064 (22) were selected to pharmacologically disrupt the functions of the Npm1 and Carm1, respectively. Colony formation and the CCK-8 assay showed that both chemical probes significantly inhibited tumor cell proliferation in vitro (Supplementary Fig. S2D–S2H), implying that the epigenetic regulators Npm1 and Carm1 are pharmacologically targetable vulnerabilities in NSCLCs. These results highlight the need for more in-depth follow-up studies.

In addition to bioinformatic and pharmacologic approaches, we also employed a genetic approach that consists of a doxycycline-inducible short hairpin RNA (shRNA) system to select one of the candidate genes for further functional and mechanistic studies. Although Npm1 knockdown moderately inhibited cell proliferation of the mouse KP cells, it dramatically inhibited in vitro colony formation (Fig. 2A and B; Supplementary Fig. 3A). Meanwhile, Carm1 knockdown showed a moderate inhibitory effect on in vitro colony formation (Supplementary Fig. S3B and S3C). An allograft assay in B6 WT mice showed that Npm1 knockdown markedly decreased KP tumor growth (Supplementary Fig. S3D and S3E) while Carm1 knockdown resulted in a significant, but relatively less profound, effect on growth inhibition (Supplementary Fig. S3F and S3G). These bioinformatic, pharmacologic, and genetic findings prompted further exploration of the therapeutic potential of targeting Npm1 in NSCLCs.

To further validate NPM1 as a therapeutic vulnerability in NSCLCs, we perturbed the function of NPM1 in vitro and in vivo using genetic and pharmacologic tools. Genetically, we explored the effect of shNPM1 in human NSCLC cell lines with more heterogeneous genetic backgrounds, and we found that NPM1 knockdown significantly inhibits in vitro colony formation of human NSCLC cell lines (A549, H441, H358, and A427; Fig. 2C–H; Supplementary Fig. S4A–S4D). Furthermore, knockdown of Npm1 significantly inhibited tumor growth in three different mouse tumor models: the KP allograft model (Supplementary Fig. S3D and S3E), the KP xenograft model (Fig. 2I and J), and the A549 xenograft model (Fig. 2K and L).

Pharmacologically, NSC348884 treatment profoundly inhibits in vitro colony formation of 2 mouse lung ADC cell lines [KP and KL (Kras^G12D; Lkb1^−/−)] and eight human NSCLC cell lines (Supplementary Fig. S5A–S5J). However, we also noticed that NSC348884 showed acute and significant toxicity to nontumorous cell lines (3T3, 293T, and Beas2B) (Supplementary Fig. S5K–S5M), hinting that NSC348884 may not be an ideal chemical probe to disrupt the function of Npm1. Although in vivo NSC348884 treatment at a dose of 2 mg/kg (once daily) significantly inhibited tumor growth in two different mouse tumor allograft models: the KP subcutaneous injection model (Supplementary Fig. S6A–S6C) and the orthotopic model (intravenous injection; Supplementary Fig. S6D–S6F), lethal dose was observed at 3–5 mg/kg (once daily). Collectively, these data indicate that NPM1 is a viable therapeutic target for NSCLCs and that the search of a less toxic NPM1 inhibitor is warranted.

We reasoned that NPM1 dependence in NSCLC could be greater in KRAS-mutant cells than in KRAS-WT cells. To address this question, we analyzed the publicly available genome-scale CRISPR-KO and RNAi data from more than 102 human NSCLC cell lines from the Cancer Dependency Map (DepMap; ref. 15) and found that NPM1 is a vulnerability in NSCLC (Fig. 3A). Notably, KRAS-mutant cell lines have a more negative dependency score (and thus a higher sensitivity) following NPM1 knockout or knockdown compared with KRAS-WT cell lines (Fig. 3B and C). Consistent with the bioinformatic analysis of the DepMap, we found that Npm1 knockdown dramatically inhibits in vitro colony formation in the Kras-mutated mouse KP cells, but less so in the Kras-WT PLP (p53^−/−; Lkb1^−/−; Pten^−/−) cells (Fig. 3D–G). These results indicate that targeting NPM1 could be an effective and precise therapeutic strategy for patients with NSCLC that harbor KRAS mutations.

However, KRAS mutation status is not the single factor that determines the sensitivity to NPM1 targeting. We identified another KP cell line (KP-2), which has very low level of Npm1 expression (Supplementary Fig. S7A). As the previously used KP cell line has very high Npm1 expression (Supplementary Fig. S7A), we therefore had a good system to compare the sensitivities of Npm1 targeting in Npm1-high and Npm1-low tumor cells. By comparing these two lines, we found that KP-2 showed significantly less sensitivity to Npm1 knockdown (Supplementary Fig. S7B and S7C). These data suggest that the sensitivity of Npm1 targeting is also determined by the Npm1 expression levels.

To explore the mechanistic linkage between Npm1 depletion and tumor progression, we performed RNA-seq to identify genes and pathways that are responsive to Npm1 knockdown (Supplementary Table S3). GSEA showed that the aerobic glycolysis pathway is among the most significantly enriched gene sets of the control cells, indicating that this pathway is downregulated in Npm1 knockdown tumor cells (Fig. 4A). In contrast, the OXPHOS gene set is significantly enriched following Npm1 knockdown (Fig. 4B).

We used Seahorse assays to measure glycolytic activity and mitochondrial respiration, and revealed that Npm1 knockdown in tumor cells significantly enhanced the basal mitochondrial oxygen consumption rate (OCR) and reduced the extracellular acidification rate (ECAR; Fig. 4C and D). Although Npm1 knockdown in immortalized normal cells (3T3 cells) showed marginal effect to inhibit cell proliferation (Supplementary Fig. S8A), it has a minimal impact on basal OXPHOS (Supplementary Fig. S8B). Collectively, these data imply that Npm1 depletion in mouse tumor cells leads to reduced glycolytic flux and elevates mitochondrial respiration, further supporting that energy metabolism is rewired from aerobic glycolysis to OXPHOS upon Npm1 depletion in NSCLC cells.

We next sought to explore the mechanism that underlies the influence of NPM1-mediated rewiring of energy metabolism on NSCLC progression. We interrogated the CTRP (25, 26), which correlates genetic, lineage, and other cellular features of cancer cell lines to small-molecule sensitivity. The sensitivity data from 656 adherent cancer cell lines across a set of 481 small-molecule probes unveiled an association between high expression of NPM1 and the pharmacologic inhibition (UNC0638 and BIX-01294) of the epigenetic regulator protein lysine methyltransferase G9a (Supplementary Fig. S9). G9a is a suppressor of aggressive lung tumor–propagating cells (TPC) and depletion of G9a promotes tumor progression and metastasis (5). These data led us to hypothesize that the depletion and/or functional disruption of NPM1 may lead to the impairment of TPCs, and thereby retarding lung tumor progression. To test this possibility, we analyzed the cell surface markers of TPCs (27) and found that following knockdown of Npm1, the expression of stem cells antigen-1 (Sca-1) is indeed significantly decreased in tumor cells, but not in normal cells (Fig. 5A; Supplementary Fig. S10). These data suggest that the depletion of Npm1 significantly impairs the TPC phenotype. To confirm that cells with high Sca-1 expression represents a stemness phenotype, we used flow cytometry to sort the populations with high expression (top 10%) of Sca-1 and low expression (bottom 10%) of Sca-1, and used these cells in an in vitro colony formation assay. The population with high expression of Sca-1 demonstrated a much stronger ability to form colonies in vitro (Fig. 5B), suggesting that the in vitro colony formation assay can be effectively used to evaluate TPC phenotype.

Previous studies showed that the rewiring of metabolism leads to the differentiation of cancer stem cells, thus causing tumor regression (28). We thus hypothesized that metabolic rewiring will impair the TPC phenotype and retard tumor progression. To test this hypothesis, we cultured the KP cells by substituting glucose with galactose in RPMI1640 medium, to induce a metabolic switch from aerobic glycolysis to OXPHOS (29). Tumor cells cultured in galactose-substituted medium displayed reduced Sca-1 expression, as indicated by FACS (Fig. 5C and D) and a lower colony formation capacity in vitro (Fig. 5E).

Pyruvate dehydrogenase kinases (PDK) negatively regulate the activity of pyruvate dehydrogenase complex (PDC) by phosphorylating the E1 subunit of PDC and suppress the conversion of pyruvate to acetyl-CoA (30). PGK1 is an important enzyme in the metabolic glycolysis pathway, but PGK1 also phosphorylates and activates PDK1 through its protein kinase activity (31). PDK2 inhibition was reported to enhance the tricarboxylic acid cycle (TCA cycle; ref. 28). To further confirm the functions of Pgk1 or Pdk2 in regulating the glycolysis or OXPHOS, we performed Pgk1 or Pdk2 knockdown in KP cells (Supplementary Fig. S11A and S11B). Seahorse assay showed that Pgk1 knockdown significantly reduced the ECAR, and Pdk2 knockdown significantly enhanced the OCR (Supplementary Fig. S11C and S11D). Pgk1 or Pdk2 knockdown only showed marginal effect to inhibit tumor cell proliferation (Supplementary Fig. S11E); however, it showed significant effect to suppress Sca-I expression (Supplementary Fig. S11F). These data further suggest that the decreased glycolysis or enhanced OXPHOS leads to the reduction of TPC phenotype.

CTRP analysis also revealed that pharmacologic inhibition of PDK2 by the small-molecule probe AZD7545 (32) is negatively associated with a high level of expression of NPM1 (Supplementary Fig. S9). Because PDK2 is a key regulator of glycolysis and oxidative phosphorylation, we used AZD7545 to switch the energy metabolism of the cells from aerobic glycolysis to OXPHOS. We found that PDK2 inhibition by AZD7545 significantly reduces Sca-1 expression and impairs in vitro colony formation (Supplementary Fig. S12A and S12B), hinting that the metabolism homeostasis plays an important role in the maintenance of TPC phenotype and NSCLC progression. Given that NPM1 regulates the switch between glycolysis and OXPHOS, we propose a regulatory mechanism in which the depletion of NPM1 leads to the energy metabolism rewiring, thereby impairing the TPC phenotype, and attenuating NSCLC progression (Fig. 5F).

In summary, we used pooled epigenome-wide CRISPR knockout screens in vitro and in vivo to identify novel epigenetic regulators that modulate NSCLC progression. Functional studies and mechanistic assessments confirmed that depletion of Npm1 attenuates NSCLC progression by rewiring energy metabolism and impairing the tumor-propagating cell phenotype in a mouse lung tumor model. Our findings provide a preclinical rationale for targeting NPM1 as a therapeutic strategy for patients with NSCLC.

Low survival rates and unpredictable treatment outcomes associated with NSCLC have driven the search for new therapeutic targets and agents (2). CRISPR knockout screen is a powerful approach for identifying new therapeutic targets in various cancers (17); for example, by using an in vivo CRISPR screen, we have successfully identified the histone chaperone Asf1a as an immunotherapeutic target in lung ADC (8). Here, we used in vitro and in vivo epigenome-wide CRISPR screens to identify novel regulators of tumor growth in a KRAS-mutant NSCLC model (Fig. 1). Notably, we used a xenograft model for in vivo screening, which more accurately represented the tumor microenvironment compared with cell culture models, and thereby facilitating the search for new therapeutic targets, especially those associated with metabolism (33).

We used various in vitro and in vivo tumor models to demonstrate that the genetic ablation of Npm1 significantly inhibits tumor growth, showing that NPM1 is a therapeutically tractable target in NSCLC (Fig. 2; Supplementary Fig S4A–S4D). In addition, mining the DepMap database uncovered that NSCLC cell lines that harbor a KRAS-mutation are particularly sensitive to NPM1 depletion, and this finding is confirmed experimentally by in vitro colony formation assay (Fig. 3). Currently, no effective therapy exists for KRAS-mutant NSCLCs (which represent approximately 15–25% of patients with ADC; ref. 34), and our findings highlight NPM1 as a promising therapeutic target in this highly aggressive subset of NSCLCs.

NPM1 (also known as B23) is a multifunctional nucleolar protein involved in many cellular processes such as ribosome biogenesis, nucleocytoplasmic transport, transcriptional regulation, and chromatin remodeling as a histone chaperone (35–37). NPM1 is mutated in one-third of acute myeloid leukemias (AML) and has been an attractive therapeutic target (38). Functionally, NPM1 mutations result in abnormal cytoplasmic localization of the mutant protein and play a protumorigenic function in AML (38). NPM1 also promotes aerobic glycolysis and tumor progression in patients with pancreatic cancer (39). However, NPM1 mutation rate is very low in lung cancer (0.55%-4.46%, cBioPortal), and based on the HPA database, NPM1 is highly expressed in the nuclear in lung cancer cells. In comparison with AML, lung cancer is derived from different cell lineage and thus NPM1 may function differently in the context of lung cancer. The oncogenic role of NPM1 is often associated with its binding to tumor suppressor genes such as p14^ARF (40–43) and p53 (44–46), and the direct interaction of NPM1 with p53 downregulates the transcriptional activity of p53 (46). However, p53 is not expressed in the KP lung ADC model in this study, so NPM1 could play context-dependent roles in tumorigenesis and tumor maintenance. Moreover, in the nucleus of AML, wild-type NPM1 interacts directly with HAUSP, therefore preventing PTEN deubiquitination and promoting its shuttling into the cytoplasm (47). However, we did not observe the change of PTEN at protein level nor the deenrichment of PI3K-AKT and mTOR gene sets in GSEA analysis (Supplementary Table S3). Importantly, as NPM1 interacts directly with many cellular proteins via multimodal interactions (48), we could not rule out the possibility that the knockdown of NPM1 led to the inactivation of some of the NPM1-interacting proteins, thereby confounding the phenotypes observed upon the knockdown of NPM1. Therefore, further studies are needed to explore other possible mechanisms regarding the therapeutic targeting of NPM1 in NSCLC.

There were claims that the pharmacologic inhibition of NPM1 by small molecules could sensitize cells to radiotherapy in NSCLCs (49, 50) and induces apoptosis in solid tumors (24). However, detailed characterizations of the target-specificity and protein binding affinities of these small-molecules are lacking. While our in vitro data showed that Npm1 knockdown had moderate effect on cell proliferation over eight days (Supplementary Fig. 3A), NSC348884 treatment led to acute and significant cell death, suggesting that the putative small-molecule NPM1 inhibitor NSC348884 has strong toxicity and potentially off-target effects. In addition, our in vivo treatment study (in B6 WT mice) showed that NSC348884 led to obvious weight loss at a dosage of 2 mg/kg (once daily; Supplementary Fig. S6D), and caused lethality at a dosage of 3–5 mg/kg (once daily), indicating that NSC348884 may not be suitable for in vivo studies. To validate NPM1 as a therapeutic target pharmacologically, a NPM1 inhibitor with higher target specificity and less toxicity is urgently needed.

Transcriptomic analysis and subsequent functional validations revealed that depletion of Npm1 reprograms NSCLC metabolism from aerobic glycolysis to OXPHOS and reduces the population of Sca-1⁺ TPCs, which are aggressive tumor cells that exhibit regenerative and proliferative behaviors (Fig. 4; ref. 27). Despite the significant translational potential of targeting TPCs, an in-depth understanding of the energy metabolism of TPCs is lacking (51) and the selective eradication of TPCs is challenging (52). Here, we demonstrate that Npm1 governs energy metabolism in TPCs, suggesting that NPM1 is a metabolic vulnerability in lung tumorigenesis.

Aberrant cell metabolism is a hallmark of cancer, as exemplified by the Warburg effect (53, 54). Otto Warburg reported more than 60 years ago that tumor cells rely heavily on glycolysis even in the presence of abundant oxygen, termed aerobic glycolysis (55). Although aerobic glycolysis seems to be an inefficient way of energy production, it may facilitate the uptake and incorporation of nutrients into the biomass needed to produce a new cell (54). In view of the specific metabolic needs of rapidly proliferating cancer cells, targeting metabolic dependencies in these cells has been put forward as a selective anticancer strategy (56, 57). The PDK-PDC axis is proposed as a therapeutic target in cancers (58) and the suppression of PDK2 has been shown to overcome resistance to paclitaxel treatment in lung cancer (59). However, to the best of our knowledge, no established linkage exists between energy metabolism and TPCs in NSCLC. Here, we target Npm1 to induce the rewiring of energy metabolism and a reduction in the population of Sca-1⁺ TPCs, thereby attenuating lung tumor progression (Fig. 5).

In sum, this study demonstrates that in vitro and in vivo epigenome-wide CRISPR knockout screens are powerful tools for target identification, and that targeting NPM1 is a potential therapeutic opportunity in NSCLCs.

W.-L. Ng received consultation fees from Aptorum Group Limited and Videns Incorporation Limited regarding the development of MRI probes for Alzheimer's diseases, which is outside the scope of the submitted work. He is also a cofounder of DeepGlyco Limited, a startup company that focuses on the development of antidiabetic agents, which is outside the scope of the submitted work. R. Possemato reports grants from NIH, ACS, FARA, Pew Scholars, and Gabrielle's Angels during the conduct of the study. J. Qi reports grants from NIH (R01CA222218) during the conduct of the study, funding from Zentalis (consultant), and funding from Epiphanse (cofounder) outside the submitted work. K.-K. Wong reports grants from NCI during the conduct of the study. No potential conflicts of interest were disclosed by the other authors.

F. Li: Conceptualization, data curation, formal analysis, validation, investigation, visualization, methodology, writing-original draft, writing-review and editing. W.-L. Ng: Conceptualization, data curation, formal analysis, validation, investigation, visualization, methodology, writing-original draft, writing-review and editing. T.A. Luster: Conceptualization, supervision, writing-review and editing. H. Hu: Data curation, investigation, writing-review and editing. V.O. Sviderskiy: Resources, data curation, investigation, methodology, writing-review and editing. C.M. Dowling: Resources, data curation, investigation, methodology, writing-review and editing. K.E.R. Hollinshead: Resources, data curation, investigation, methodology, writing-review and editing. P. Zouitine: Data curation, investigation. H. Zhang: Investigation, writing-review and editing. Q. Huang: Data curation, validation, investigation, writing-review and editing. M. Ranieri: Data curation, investigation, writing-review and editing. W. Wang: Resources, investigation, methodology. Z. Fang: Data curation, investigation, methodology. T. Chen: Resources, investigation, methodology. J. Deng: Investigation, methodology. K. Zhao: Data curation, investigation, writing-review and editing. H.-C. So: Data curation, investigation, writing-review and editing. A. Khodadadi-Jamayran: Data curation, investigation, methodology. M. Xu: Data curation, investigation, methodology. A. Karatza: Investigation. V. Pyon: Investigation. S. Li: Resources. Y. Pan: Resources, writing-review and editing. K. Labbe: Resources, investigation, project administration. C. Almonte: Resources, investigation, project administration. J.T. Poirier: Supervision, writing-review and editing. G. Miller: Resources, methodology. R. Possemato: Resources, supervision, methodology, writing-review and editing. J. Qi: Conceptualization, supervision, funding acquisition, writing-review and editing. K.-K. Wong: Conceptualization, supervision, funding acquisition, writing-review and editing.

We thank the NYU Langone Medical Center and Dana-Farber Cancer Institute Animal Resources Facility staff for their support of the animal studies. We thank the NYU Langone Medical Center and Dana-Farber Cancer Institute Genome Technology Core for NGS sequencing. We thank the NYU Langone Medical Center Genome Technology Center for RNA-seq. We thank the NYU Langone Medical Center Cytometry & Cell Sorting Laboratory for FACS analyses and cell sorting service. We thank the NYU Langone Medical Center Applied Bioinformatics Laboratories for bioinformatics analyses. We thank the NYU Langone Medical Center Preclinical Imaging Laboratory for providing MRI equipment. We thank Dr. Sonal Jhaveri (DFCI, Boston, MA) for help editing a draft of this manuscript. This work was supported by NIH grant CA222218-A1 (to J. Qi). R. Possemato received grants from NIH, ACS, FARA, Pew Scholars, and Gabrielle's Angels. K.-K. Wong received grants from NCI.

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