In vivo CRISPR screening unveils histone demethylase UTX as an important epigenetic regulator in lung tumorigenesis

Edited by Ronald A. DePinho, The University of Texas MD Anderson Cancer Center, Houston, TX, and approved March 19, 2018 (received for review September 20, 2017)
April 9, 2018
115 (17) E3978-E3986

Significance

Tumor suppressor genes (TSGs) play important roles in lung cancer initiation, progression, and even metastasis. Here, we take advantage of the clustered regularly interspaced short palindromic repeats/Cas9-mediated screening in vivo technique to identify multiple tumor suppressor genes contributing to lung cancer malignant progression. Using genetically engineered mouse models, we further confirm the tumor-suppressive role of epigenetic regulator UTX and provide therapeutic implications for UTX-deficient lung tumors. Thus, our work provides a systematic screening of TSGs in vivo and demonstrates UTX functions as the important epigenetic regulator in lung tumorigenesis.

Abstract

Lung cancer is the leading cause of cancer-related death worldwide. Inactivation of tumor suppressor genes (TSGs) promotes lung cancer malignant progression. Here, we take advantage of the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-mediated somatic gene knockout in a KrasG12D/+ mouse model to identify bona fide TSGs. From individual knockout of 55 potential TSGs, we identify five genes, including Utx, Ptip, Acp5, Acacb, and Clu, whose knockout significantly promotes lung tumorigenesis. These candidate genes are frequently down-regulated in human lung cancer specimens and significantly associated with survival in patients with lung cancer. Through crossing the conditional Utx knockout allele to the KrasG12D/+ mouse model, we further find that Utx deletion dramatically promotes lung cancer progression. The tumor-promotive effect of Utx knockout in vivo is mainly mediated through an increase of the EZH2 level, which up-regulates the H3K27me3 level. Moreover, the Utx-knockout lung tumors are preferentially sensitive to EZH2 inhibitor treatment. Collectively, our study provides a systematic screening of TSGs in vivo and identifies UTX as an important epigenetic regulator in lung tumorigenesis.
Lung cancer is one of the deadliest diseases worldwide, with very high incidence and mortality (1). Tumor suppressor genes (TSGs) play important roles in lung cancer initiation, progression, and even metastasis. Cancer genomic studies have provided a comprehensive spectrum of thousands of potentially important genetic alterations of TSGs (2, 3). Except for a few well-studied TSGs like RB1, TP53, STK11, and PTEN, most of these genetic aberrations still remain to be functionally validated and characterized. To achieve this, the major existing challenge is to define bona fide TSGs, especially using in vivo systems.
Genetically engineered mouse models (GEMMs) are often applied to validate those potentially important molecular alterations in vivo. Systematic screening of TSGs using GEMMs seems impractical due to the highly expensive and time-consuming process. This demands a more efficient and less costly technique. Recently, the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 system has been proven to be a powerful genome-editing tool, making it possible for systematic TSG screening (4, 5). Indeed, recent studies have established highly efficient knockout of somatic genes in mouse cancer models (6, 7). The Cas9 nuclease can be directed to a specific gene locus by single-guide RNA (sgRNA) via 20 bases matched to targeted genomic loci and can create double strand breaks, which are then repaired by nonhomologous end-joining (8). Using this technique, insertions or deletions (in-dels) could be introduced into the targeted locus and result in the loss of gene function if it occurs in the gene-coding region. Previous studies have shown that loss of LKB1 or TTF1 in mouse lungs via CRISPR/Cas9-mediated somatic genome editing significantly promotes lung cancer malignant progression (6, 9), similar to the observations from GEMMs (10, 11). Thus, this type of somatic gene knockout technique makes it feasible to efficiently and systematically identify potential TSGs in vivo.
KRAS oncogenic mutations are frequently detected in human lung cancer, and the KrasG12D/+ mouse model has been widely used for investigating the function of TSGs (12). Using CRISPR-mediated somatic gene knockout in the KrasG12D/+ mouse model, we have screened a total of 55 potential TSGs individually. Our data show that somatic knockout of any of five genes, including Utx, Ptip, Acp5, Acacb, and Clu, significantly promotes Kras-driven lung tumor progression. These five genes are frequently down-regulated in human lung cancer specimens and significantly associated with patient survival. Importantly, conditional knockout of the epigenetic regulator Utx dramatically accelerates lung tumorigenesis in the KrasG12D/+ mouse model. Our data also demonstrates that loss of UTX results in increased EZH2 expression in Kras-driven lung cancer and EZH2 inhibitor preferentially suppresses the growth of Utx-knockout lung tumors.

Results

Identification of TSGs via CRISPR Screening in Vivo.

To perform CRISPR/Cas9-mediated screening in vivo, we first generated a list of potential TSG candidates based on integrative analyses of gene expression profiling, copy number variation from Chinese lung cancer microarray data, and survival correlation analyses from the prediction of clinical outcomes from genomic profiles (PRECOG) dataset (https://precog.stanford.edu/). Based on the literature and gene mutation status from the catalogue of somatic mutations in cancer (COSMIC) website (cancer.sanger.ac.uk/cosmic), we further narrowed down the list to 55 potential TSGs with relative unknown function (Table S1). We found that 30 of these genes were located in a recurrent genomic deletion locus (Table S1). Further analyses using the Broad lung adenocarcinoma dataset (13) showed that the inactivating mutations of these genes tended to be concurrent with KRAS mutations (Table S1). We therefore chose these 55 candidate genes for further individual CRISPR/Cas9 screening in the KrasG12D/+ mouse model (Fig. 1A). To ensure optimal gene disruption in vivo, we designed three sgRNAs for each gene and performed in vitro screening first using the LentiCRISPRv2 plasmid (Cas9-sgRNA-Puro system) in KrasG12D/+ mouse embryonic fibroblasts (MEFs) (14) (Fig. 1B and Table S2).
Fig. 1.
Identification of TSGs through CRISPR/Cas9 screening in a Kras-driven lung cancer mouse model. (A) Schematic illustration of identification of potential TSGs through bioinformatics analysis and knowledge-based selection. (B) Schematic illustration of CRISPR/Cas9 screening of TSGs in a Kras-driven lung cancer mouse model. (C) Quantification of tumor burden in KrasG12D/+ mice virally infected with pSECC-sgRNA individually targeting 55 potential TSGs. Each gene was quantified in at least four mice. The mice were pathologically analyzed at 10 wk after lentiviral treatment. Data are shown as mean ± SEM. *P < 0.05, t test. (D) Quantification of surface tumor number in indicated groups. Data are shown as mean ± SEM. *P < 0.05, **P < 0.01; t test. Quantification of total number (E) and size (F) of lung tumors in indicated groups is shown. Data are shown as mean ± SEM. *P < 0.05, **P < 0.01; t test. (G) Representative photographs of hematoxylin and eosin (H&E) staining of mouse lungs from indicated groups. (Scale bars, 50 μm.)
With this, we were able to select the most efficient sgRNA with minimal off-targeting effect for further study and then cloned the optimal sgRNA into the pSECC plasmid (Cas9-sgRNA-Cre system), which could allow us to produce the lentiviruses for specific knockout of individual genes with simultaneous Cre expression in the KrasG12D/+ mouse model as previously described (6) (Fig. 1B and Table S2). We have previously shown that somatic loss-of-function mutation of LKB1 is frequently detected in human lung cancer and knockout of Lkb1 significantly promotes tumor progression in the Kras-driven mouse model (10). Thus, we used Lkb1 as our positive control for this in vivo screening of potential TSGs (Fig. 1B and Table S2). We treated the KrasG12D/+ mouse model with nasal inhalation of 2 × 104 pfus of lentiviruses targeting either Lkb1 or Tomato (serving as a negative control) (6) and analyzed lung tumor formation at 10 wk after lentiviral infection (Fig. 1B and Table S2). Consistent with a previous report (9), pSECC-sgLkb1 lentiviral infection significantly promoted an increase of the burden, number, and size of lung tumors in contrast to pSECC-sgTom lentiviral infection (Fig. 1 C–F). Moreover, lung adenocarcinoma was detectable in the pSECC-sgLkb1 group, whereas the pSECC-sgTom group mainly showed early lung cancer lesions like atypical adenomatous hyperplasia (AAH) (Fig. 1G). These data supported that this system could potentially be applied to TSG screening in vivo.
Following the same protocol, we then performed the individual knockout of these 55 candidate genes and analyzed the lung tumor formation (Table S3). Comparative analyses showed that deletion of any of five genes, including Utx, Ptip, Acp5, Acacb, and Clu, significantly increased the lung tumor burden (Fig. 1C). Consistently, lung tumors detectable on the lung surface were found to be significantly increased (Fig. 1D). This was further supported by the notable increase of average tumor number and size through detailed pathological analyses (Fig. 1 E–G). Genomic DNA sequencing data showed the clear genome editing in targeted alleles without notable off-targeting effects (Fig. S1 and Tables S4 and S5). Through real-time PCR quantification analyses of paired Chinese lung cancer and pathologically normal lungs, we found that the expression of these five genes was significantly decreased in cancer specimens (Fig. 2A and Fig. S2 AD). Moreover, low expression of each TSG was associated with poor survival of patients with lung cancer (Fig. 2 B and C and Fig. S2 EH). Taken together, these data identified five TSGs that might contribute to lung tumorigenesis through in vivo CRISPR screening.
Fig. 2.
Conditional deletion of Utx promotes lung cancer progression in a KrasG12D/+ mouse model. (A) Real-time PCR detection of UTX mRNA level in human lung cancer specimens in contrast to paired pathologically normal lungs (n = 30). Relative mRNA quantity refers to the ratio of indicated gene expression to β-actin level. Data are shown as mean ± SEM. **P < 0.01, t test. Low expression of UTX (B) was significantly associated with poor survival of patients with lung cancer (C). The patients diagnosed with lung cancer (n = 443, number of events = 237) were classified into a high- or low-risk group according to their PI (red curve in C denotes the high-risk group, blue curve in C denotes the low-risk group). In B, a box plot illustrates significantly high expression of UTX in the low-risk group (blue dots) compared with that in low-risk group (red dots). Horizontal lines represent group medians. Boxes illustrate 25–75% quartiles. Vertical lines represent range (maximum and minimum). The gene expression values were normalized and log2-transformed. (D) Representative photographs of lung hematoxylin and eosin (H&E) staining from K (n = 6) and KU (n = 6) mice after 16 wk of Ad-Cre treatment. (Scale bar, 400 μm.) (E) Representative photographs of H&E staining of lung tumors from K (n = 6) and KU mice (n = 6) at 16 wk after Ad-Cre treatment. (Scale bars, 50 μm.) Quantification of tumor burden (F), average tumor number (G), and average tumor size (H) in K (n = 6) and KU mice (n = 6) at 16 wk after Ad-Cre treatment is shown. Data are shown as mean ± SEM. **P < 0.01, ***P < 0.0001; t test. (I) Distribution of tumor grades in K (n = 6) and KU mice (n = 6) at 16 wk after Ad-Cre treatment. G1, grade 1; G2, grade 2; G3, grade 3; G4, grade 4. Representative IHC staining (J) and statistical analysis (K) of Ki67 expression in K and KU mice at 16 wk after Ad-Cre treatment are shown. Data are shown as mean ± SEM. ***P < 0.0001, t test. (Scale bars, 50 μm.) (L) Kaplan–Meier survival analysis of K (n = 6) and KU (n = 13) mice.

Utx Knockout Promotes Lung Tumor Progression in the KrasG12D/+ Mouse Model.

Epigenetic regulation, such as H3K27 methylation deregulation, is known to play important roles in cancer malignant progression (15). Interestingly, two identified TSGs, including UTX and PTIP, were located at the same histone methyltransferase MLL3/MLL4 complex (16, 17). Importantly, UTX, also named KDM6A, is considered to be responsible for H3K27me3 demethylation (16, 17) and counteracts the methyltransferase function of Polycomb Repressive Complex 2 (PRC2) during cancer malignant progression (15). We then focused on UTX and investigated the potential function of UTX in vivo. For this, we crossed the conditional UtxL/L allele (referred to as U mice) (18) with the KrasG12D/+ allele (referred to as K mice) and generated the compound alleles KrasG12D/+;UtxL/L (referred to as KU mice), which could become a homozygous deletion of Utx when the mice were given Cre recombinase adenovirus (Ad-Cre). We then delivered 2 × 106 pfus of Ad-Cre to the KU mice via nasal inhalation and analyzed lung tumor formation after 16 wk of Ad-Cre treatment (19). Strikingly, homozygous deletion of Utx significantly promoted Kras-driven lung cancer progression (Fig. 2 D and E). Statistical analyses showed that deletion of Utx dramatically increased the Kras-driven lung tumor burden (Fig. 2F), as well as the tumor number and size (Fig. 2 G and H). Moreover, grade 3 and grade 4 lung tumors, including adenocarcinoma and invasive adenocarcinoma (20, 21), were only detectable in the KU mouse model (Fig. 2I). Furthermore, the KU tumors also displayed high cell proliferation (Fig. 2 J and K). Consistently, the medium survival of the KU mouse model was about 17 wk, in comparison to 30 wk for the K mouse model (Fig. 2L). We further established mouse primary lung cancer cells from lung tumors in the KrasG12D/+;Trp53L/L;UtxL/L (KPU) mouse model (Fig. S3A). Using these KPU primary cancer cells, we found that deletion of Utx significantly promoted cell proliferation, which could be inhibited by reexpression of Utx (Fig. S3 B and C). These data collectively supported the tumor-suppressive role of the epigenetic regulator UTX in lung tumorigenesis.

Utx Knockout Increases H3K27me3 Level Potentially Through EZH2 Up-Regulation.

Given that UTX functions as histone H3K27me3 demethylase (15), we next examined the H3K27me3 level in Utx-knockout lung tumors. As expected, Utx deletion resulted in a significant increase of the H3K27me3 level in KU lung tumors (Fig. 3 A and B and Fig. S4 A and C). Previous studies have indicated that UTX loss promoted the activity of the PRC2 complex, the machinery mainly responsible for H3K27 methylation, which contains the enzymatic subunit EZH2 and other cofactors, including SUZ12 and EED (15, 22). In consideration of the increased H3K27me3 level in KU lung tumors, we next tested the expression of all the core PRC2 components, including EZH1, EZH2, SUZ12, and EED, and another H3K27me3 demethylase, KDM6B (17, 23). RNA sequencing data showed that EZH2 is the only gene with significantly expressed up-regulation in KU lung tumors (Fig. 3C). We further confirmed this up-regulation of EZH2 through immunohistochemistry (IHC) staining and Western blotting analyses (Fig. 3 DF and Fig. S4 A and B). A previous study has demonstrated that H3K27 methylation silenced the expression of several classic tumor suppressors, such as CDKN2A and CDKN2B, that are importantly involved in the cell cycle (24). We found that KU lung tumors showed obvious down-regulation of Cdkn2a and Cdkn2b (Fig. 3 G and H). This might explain the high cell proliferation in KU tumors (Fig. 2 J and K). To ask whether the regulation of EZH2 and H3K27me3, as well as downstream signaling, was due to tumor malignant progression, we comparatively analyzed lung tumors from the KU mouse model, along with KrasG12D/+;Trp53L/L (KP) and KrasG12D/+;Lkb1L/L (KL) mouse models, whose tumors are well known for high malignancy (10, 20). We observed that the KU lung tumors consistently displayed the highest levels of EZH2 and H3K27me3 and the lowest gene expression of Cdkn2a and Cdkn2b (Fig. S5). These results indicated that the up-regulation of EZH2 and H3K27me3 in KU lung tumors might be ascribed to Utx knockout.
Fig. 3.
Utx knockout in lung tumors increases H3K27me3 level through EZH2 up-regulation. Representative IHC staining (A) and statistical analysis (B) of H3K27me3 expression in K and KU mice at 16 wk after Ad-Cre treatment are shown. (Scale bars, 50 μm.) (C) RNA-sequencing data showing the differential expression of H3K27me3-related genes from K and KU lung tumors at 16 wk after Ad-Cre treatment. Representative IHC staining (D) and statistical analysis (E) of EZH2 expression in lung tumors from K and KU mice at 16 wk after Ad-Cre treatment are shown. (Scale bars, 50 μm.) (F) Western blotting for detection of UTX and EZH2 expression in K (n = 3) and KU (n = 3) lung tumors. Real-time PCR detection of Cdkn2a (G) and Cdkn2b (H) mRNA level in K (n = 3) and KU (n = 3) lung tumors is shown. Relative mRNA quantity refers to the ratio of indicated gene expression to β-actin level. Data are shown as mean ± SEM. *P < 0.05, t test. Representative IHC staining (I) and Kendall’s tau correlation analysis (JL) of UTX, EZH2, and H3K27me3 expression in a human lung cancer tissue microarray are shown. (Scale bars, 50 μm.) The immunostaining scores of 0, 1, and 2 refer to negative staining, low staining, and high staining, respectively. Tk, Kendall’s tau correlation analysis.
To determine the correlation between UTX, EZH2, and H3K27me3 levels in human lung cancer samples, we performed analyses of a panel of 162 human clinical samples (Fig. 3I and Fig. S6). We observed low expression of UTX in most of the specimens (Table S6). Importantly, UTX level was found to reversely correlate with EZH2 level in human lung cancer samples (Fig. 3J). Moreover, H3K27me3 level was positively correlated with EZH2 level (Fig. 3K). We also detected a negative correlation between H3K27me3 and UTX levels (Fig. 3L). Despite the well-known low mutation rate of KRAS in Chinese lung cancer specimens (25, 26), we found that all of the six lung tumors with KRAS mutations showed either low or no UTX immunostaining (Table S6). Among these samples, three (50%) showed a high EZH2 level and five (83.3%) showed a high H3K27me3 level (Table S6). Together, these data demonstrated that Utx knockout resulted in an increase of H3K27me3 level, potentially through up-relation of EZH2 level.

Targeting EZH2 Suppresses the Growth of UTX-Deficient Lung Tumor.

Therapeutics toward epigenetic regulation have been developed for lung cancer (27, 28). To explore whether Utx-mutant lung tumors would benefit from EZH2 inhibition, we assessed the therapeutic efficacy of the EZH2 inhibitor JQEZ5 in vivo. JQEZ5 is known to function well for treating EZH2-overexpressed lung cancer in a mouse model (29). We then treated KU and K mice with JQEZ5 for 2 wk (Fig. 4A). No efficacy of JQEZ5 was detected in the K mouse model, and the lung tumors even grew bigger during the treatment process (Fig. S7). In contrast, JQEZ5 treatment resulted in significant tumor regression in the KU mouse model (Fig. 4B). The KU mice treated with JQEZ5 exhibited a significantly decreased tumor burden (Fig. 4C). Moreover, the tumor number and size were also decreased in the JQEZ5 treatment group (Fig. 4 D and E). As expected (29), JQEZ5 treatment resulted in a decrease of the H3K27me3 level (Fig. 4F). Furthermore, we detected a significant decrease of cell proliferation and increase of cell apoptosis in the JQEZ5 treatment group (Fig. 4 GI). Collectively, these results demonstrated that loss of Utx was vulnerable to EZH2 inhibition and JQEZ5 holds therapeutic implications for UTX-deficient lung tumors.
Fig. 4.
EZH2 inhibition preferentially suppresses the growth of UTX-deficient lung tumors. (A) Schematic illustration of JQEZ5 treatment in KU mice. (B) Representative photographs of hematoxylin and eosin (H&E) stained lung paraffin sections from KU mice treated with vehicle (n = 5) or JQEZ5 (n = 5). (Magnification:1×.) Quantification of tumor burden (C), average tumor number (D), and average tumor size (E) in KU mice treated with vehicle or JQEZ5 is shown. Data are shown as mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.0001; t test. (F) Representative IHC staining of H3K27me3 expression in lung tumors from KU mice treated with vehicle or JQEZ5. (Scale bars, 50 μm.) Representative IHC staining (G) and statistical analysis of Ki67-positive (H) and Cleaved Caspase-3 (CC3)–positive (I) cells in KU mice treated with vehicle or JQEZ5 are shown. Arrowheads indicate CC3-positive cells. Data are shown as mean ± SEM. ***P < 0.0001, t test. (Scale bars, 50 μm.) (J) Schematic illustration of the role of UTX in a KrasG12D/+ mouse model. (Left) In the UTX wild-type context, homeostasis for the H3K27me3 level is maintained by coordination between demethylase UTX and methyltransferase EZH2. (Center) Balance is disrupted when UTX is mutated and the high H3K27me3 level is potentially driven by up-regulated EZH2 expression. (Right) EZH2 inhibitor treatment decreases the expression of H3K27me3.

Discussion

Identification of novel TSGs has always been a hot spot of cancer research. Given the emergence of a tremendous amount of cancer genomic alterations, traditional methods, such as the GEMM, clearly could not meet the demand. Taking advantage of the widely used KrasG12D/+ mouse model as the system, we here systematically assess the functions of potential TSGs in lung tumorigenesis using the CRISPR/Cas9-mediated screening in vivo technique. From this, we are able to identify multiple TSGs contributing to lung cancer malignant progression.
Although CRISPR/Cas9-based libraries have been developed for screening in vitro and ex vivo (14, 30), there is still a shortage of systematic CRISPR screenings in lung cancer studies using well-established mouse models. Our work demonstrates the power of the screening strategy in the context of conventional lung cancer mouse models. For in vivo screening, we chose the sgRNA with high efficiency and minimal off-targeting effects from in vitro experiments. Furthermore, we performed somatic gene knockout individually instead of using a mixed library to avoid potential gene cross-talk. The five TSGs we identified from this screening are frequently down-regulated in human lung cancer and significantly associated with patient survival. Among these genes, ACACB (also known as ACC2) is responsible for fatty acid metabolism, which is a downstream target of the LKB1-AMPK pathway (31). A previous study has demonstrated the tumor-suppressive role of ACACB in some contexts (32), consistent with our in vivo data. ACP5 is reported to be frequently down-regulated in hepatocellular carcinoma (33), indicating its tumor-suppressive function. Several studies have investigated the role of CLU in lung cancer (34). Although in vitro and in vivo studies show controversial functions of CLU, low CLU level is associated with poor patient survival (34). UTX and PTIP are considered to be important epigenetic regulators located at the same histone methyltransferase MLL3/MLL4 complex (16). PTIP harbors no enzymatic activity and is reported to link DNA-binding proteins to the H3K4 methyltransferase complex (16, 35). UTX is the major demethylase of H3K27me3 (15), and a previous study has shown that loss of UTX results in deregulation of the cell cycle via RB-dependent pathways (36). Moreover, UTX has been reported to regulate the development of invariant natural killer T cells via the epigenetic program (37). Interestingly, UTX somatic mutations are frequently detected in various epithelial cancers, including multiple myeloma, esophageal squamous cell carcinoma, and lung cancer (38). Previous studies have demonstrated that UTX functions as a tumor suppressor in T cell acute lymphoblastic leukemia (T-ALL) and bladder cancer (22, 39, 40). Inactivating UTX mutations are observed in human T-ALL specimens, and UTX loss accelerates T-ALL malignant progression (40). Similarly, we find that UTX is often down-regulated in human lung cancer samples and significantly correlates with patient survival. Moreover, Utx knockout promotes lung cancer malignant progression in the Kras-driven mouse model. These data demonstrate that an in vivo CRISPR screening strategy is feasible and highly efficient.
Previous studies have shown that deregulation of PRC2 is implicated in multiple cancers, including lung cancer and leukemia (41, 42). Our work demonstrates that loss of UTX results in an increase of H3K27me3 level, potentially through up-regulation of EZH2, which is the enzymatic subunit of PRC2. Emerging evidence has revealed that epigenetic therapies could be extraordinarily effective for a subset of patients with lung cancer (2729). Our study reveals that the Utx-mutant mouse lung tumors are sensitive to EZH2 inhibition, consistent with studies in T-ALL and bladder cancer (22, 29, 40). This is very helpful since direct targeting of UTX to boost its activation seems impractical for now. In the KrasG12D/+ mouse model, homeostasis of the H3K27me3 level is maintained by the coordination between the demethylase UTX and PRC2 complex containing methyltransferase EZH2 (Fig. 4J). However, this balance is disrupted when Utx is mutated and the high H3K27me3 level is potentially driven by up-regulated EZH2 expression through a currently unknown mechanism (Fig. 4J). The increased H3K27me3 modification then silences several TSGs, including CDKN2A and CDKN2B, and contributes to tumor progression. Under the circumstance of UTX loss, EZH2 inhibitor works effectively to suppress lung tumor growth (Fig. 4J). The high efficacy of EZH2 inhibitor in the KU mouse model provides therapeutic implications for human lung cancer with KRAS mutations exhibiting a low UTX level. However, KRAS tumors harboring wild-type UTX are more advanced after EZH2 inhibitor treatment, indicating the treatment would be dangerous for certain patients with lung cancer. In future studies, it will be interesting to ask whether EZH2 inhibitor also works well to suppress UTX-low lung cancer independent of the KRAS mutation status.

Materials and Methods

Human Lung Cancer Specimen Analysis.

Human lung cancer clinical samples were collected with the approval of the Institutional Review Committee of Shanghai Cancer Hospital at Fudan University as previously described (19). Written informed consent was obtained from all of the patients. All cases were rereviewed by pathologists from the Department of Pathology at Shanghai Cancer Hospital for confirmation of tumor histology and tumor content. The datasets used in this study have been deposited in the Gene Expression Omnibus (GEO) repository under accession nos. GSE74095 and GSE77684. Human Exon 1.0 profiling data of 76 Chinese lung adenocarcinomas were quantified with Affymetrix Power Tools (v1.18.0) and summarized as gene-level expression (“rma-sketch”). Differential expression calls for each tumor sample were obtained by comparison with normal samples, using the twofold expression change threshold. SNP 6.0 genotyping data of the same samples were preprocessed with PennCNV (v1.0.0) and annotated to gene copy number estimates. To identify genes located in frequently deleted regions, raw copy number signals (log ratios) were first segmented with the circular binary segmentation algorithm, and recurrent copy number alteration peaks were called with GISTIC (v2.0.23). To prioritize deletions accompanied by an underexpression effect, concurrent samples between differential expression calls and copy number alterations were counted and tested for enrichment with Fisher’s exact test. A set of 67 genes with concurrent allele loss and down-regulated gene expression (five or more samples) was selected and merged with another set of 43 genes that were identified (Z-scores < 0) to be the favorable prognosis genes in eight lung cancer datasets according to PRECOG data analyses (Lung_cancer_ADENO_200, Lung_cancer_ADENO_206, Lung_cancer_ADENO_216, Lung_cancer_ADENO_221, Lung_cancer_SCC_234, Lung_cancer_SCC_243, Lung_cancer_SCC_251, and Lung_cancer_SCC_258). The statistical associations between genes and clinical outcomes were assessed by Z-scores generated by PRECOG (43). Based on the literature and COSMIC mutational status, we further narrowed down the list to 55 potential TSGs with relative unknown function and high mutational frequency. Using the Broad lung adenocarcinoma dataset in cBioPortal (13), we analyzed the mutations of these genes for the tendency toward concurrence with KRAS mutations. Moreover, we also mapped these genes to the areas of recurrent deletion. The clinical data for UTX were retrieved from the Director’s Challenge (44). The univariate Cox regression analysis was applied to produce the prognostic index (PI). The patients were split into two risk groups based on PI rank. Then, the likelihood ratio test and log-rank test were performed on the PI to generate the corresponding P value and Kaplan–Meier curves from the two risk groups to compare the differences in survival. A human lung cancer tissue microarray (detailed information is provided in Table S6) was provided by the Department of Pathology at Shanghai Cancer Hospital, Fudan University, Shanghai, China.

CRISPR Plasmids Construction and Lentivirus Production.

The pSECC and LentiCRISPRv2 plasmids were generously provided by F. J. Sanchez-Rivera, Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA and T. Jacks, Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA. All of the sgRNAs were designed using optimized CRISPR Design (crispr.mit.edu/) except for sgTomato and sgLkb1, which were previously reported (6, 9) (the sequences of all sgRNAs for in vivo screening are shown in Table S2). The top three exonic off-targeting sites were predicted using optimized CRISPR Design (Table S4). Cloning methods were optimized following established protocols described elsewhere (4, 14). Lentiviruses were produced by cotransfection of HEK-293T cells with pSECC or LentiCRISPRv2 constructs and packaging vectors (psPAX2 and pMD2.G). For pSECC lentiviruses, supernatant was collected 48 h and 72 h posttransfection, concentrated by ultracentrifugation at 50,000 × g for 2 h, and resuspended overnight in an appropriate volume of OptiMEM (Gibco).

Mouse Colony, Treatment, and Tumor Analysis.

KrasG12D/+, Trp53L/L, Lkb1L/L, and UtxL/L mice were originally generously provided by T. Jacks, Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, R. Depinho, The University of Texas MD Anderson Cancer Center, Houston, and Kai Ge, National Institutes of Health, Bethesda. All mice were housed in a specific pathogen-free environment at the Shanghai Institute of Biochemistry and Cell Biology and treated in strict accordance with protocols approved by the Institutional Animal Care and Use Committee of the Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. For CRISPR/Cas9 screening, KrasG12D/+ mice at 6–8 wk of age were treated with 2 × 104 pfus of pSECC-sgRNA lentiviruses via nasal inhalation and analyses were performed after 10 wk. Either KrasG12D/+ or KrasG12D/+;UtxL/L mice at 6–8 wk of age were treated with 2 × 106 pfus of Ad-Cre via nasal inhalation as previously described, unless especially noted (19), and analyses were performed after 16 wk. Gross inspection and histopathological examination were performed during the analyses of mice. Lung tumors were dissected for molecular and pathological analysis. Tumor burden and total number and size of lung tumors, as well as surface lung tumors, were analyzed as previously described (19). Tumor grades were analyzed as follows (20, 21): grade 1, AAH or AAH progressing to a small adenoma; grade 2, adenoma; grade 3, adenocarcinoma with pleomorphic nuclei or mixed cellular phenotypes; and grade 4, invasive adenocarcinoma and adenocarcinoma with glandular/acinar architecture.

Deep Sequencing and Bioinformatics Analysis.

The genomic DNA was isolated from either freshly dissected lung tumors or lobes using a QIAamp DNA Mini Kit (Qiagen). For each target gene or for potential off-target sites, a genomic region containing the target sequence (the primers are shown in Table S5) was PCR-amplified (KOD-Plus-Neo DNA polymerase; TOYOBO) and gel-purified, followed by high-throughput sequencing analysis (HiSeq 2500; Illumina). Briefly, reference sequences of the genomic region containing the target sequence were indexed using the Burrows–Wheeler Aligner (BWA; version 0.7.12) (45), and sequences were aligned with the BWA-maximal exact match algorithm. Samtools (version 1.2) (46) and NGSUtils/BAMutils software (version 0.5.7) (47) were used to detect total mutations and in-dels per position. The datasets used in this study have been deposited in the Sequence Read Archive repository under accession no. SRP069330.

Cell Culture, Plasmids, and Lentiviral Infection.

Non-small cell lung cancer (NSCLC) cell lines, KrasG12D/+ MEFs, KP cells, KPU cells, and HEK-293T cells were all cultured in DMEM supplemented with 8% FBS. NSCLC cell lines and HEK-293T cells were obtained from the American Type Culture Collection. KrasG12D/+ MEFs were generated from KrasG12D/+ mice, KP cells were generated from KrasG12D/+;Trp53L/L mice, and KPU cells were generated from KrasG12D/+;Trp53L/L;UtxL/L mice. Genotyping was performed using the following primers: Utx, U1 (5′-AACAAAAACCCAGGCTTTATTCAC-3′) and U2 (5′-AGTTTCAGGATACCTTTACTATAAG-3′). All cell lines used in this study have been tested for mycoplasma contamination.
The ORF of an indicated gene was built in expression vector pCDH-EF1-Puro. Lentiviruses were produced by cotransfection of HEK-293T cells with pCDH or LentiCRISPRv2 constructs and packaging vectors (psPAX2 and pMD2.G). The progeny viruses released from HEK-293T cells were filtered, collected, and used to infect NSCLC cell lines, KPU cells, and KrasG12D/+ MEFs. Cells infected with lentiviruses were then selected by appropriate concentration of puromycin.

RT-PCR and Real-Time PCR.

Total RNA was isolated using TRIzol reagent (Invitrogen) and retrotranscribed into first-strand cDNA using a RevertAid First Strand cDNA Synthesis Kit (Fermentas). The cDNAs were subjected to real-time PCR with gene-specific primers on a 7500 Fast Real-Time PCR System (Applied Biosystems) using SYBR-Green Master PCR mix (Roche). GAPDH (human) and Actin (mouse) served as internal controls. Primers used for real-time PCR were as follows: UTX, 5′-GACATTGAGGGAAGCTCTCA-3′ and 5′-ACTTGCATCAGGTCCTCCAT-3′; PTIP, 5′-ACAATGCACTAGCCTCACACA-3′ and 5′-ACACTGAACGGACAGAATCAC-3′; ACP5, 5′-TGCAAGATGAGAATGGCGTG-3′ and 5′-CAAAGCCACCCAGTGAGTCT-3′; ACACB, 5′-AGAAGACAAGAAGCAGGCAAAC-3′ and 5′-GTAGACTCACGAGATGAGCCA-3′; CLU, 5′-CCAATCAGGGAAGTAAGTACGTC-3′ and 5′-CTTGCGCTCTTCGTTTGTTTT-3′; GAPDH, 5′-AGGTGAAGGTCGGAGTCAAC-3′ and 5′-AGTTGAGGTCAATGAAGGGG-3′; Cdkn2a, 5′-CGCAGGTTCTTGGTCACTGT-3′ and 5′-TGTTCACGAAAGCCAGAGCG-3′; Cdkn2b, 5′-CCCTGCCACCCTTACCAGA-3′ and 5′-CAGATACCTCGCAATGTCACG-3′; and Actin, 5′-CAGCCTTCCTTCTTGGGTAT-3′ and 5′-GGTCTTTACGGATGTCAACG-3′.

Western Blotting.

Western blotting was performed as previously described (19). Briefly, cells were lysed in lysis buffer and subjected to Western blot analysis with following primary antibodies: UTX (33510; Cell Signaling), EZH2 (D221769; Sangon), and ACTIN (A2228; Sigma).

In Vitro Functional Assays.

For 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays, cells were seeded on 96-well plates, and the viability of cells was measured by MTT daily for 5 d. For soft agar assays, virally infected cells were suspended in growth medium with 0.4% agar on top of 1% agar base in six-well plates. After 2–3 wk of culture, colonies were stained with 0.004% crystal violet and counted using ImageJ software (NIH). All experiments were performed in triplicate.

In Vivo Treatment Studies.

KrasG12D/+ and KrasG12D/+;UtxL/L mice at 6–8 wk of age were treated with 2 × 107 pfus of Ad-Cre via nasal inhalation. For treatment of KU mice with the EZH2 inhibitor JQEZ5, we randomized these mice into two groups. One group was given the vehicle, while the other group was given JQEZ5. After 2 wk of treatment, mice were killed and lung tissues were collected and fixed. For treatment of K mice, we evaluated these mice by magnetic resonance imaging (MRI) to determine the tumor volume. Mice received JQEZ5 by i.p. injection for 2 wk (75 mg/kg each day), as previously reported (29). JQEZ5 was initially dissolved in DMSO as a stock and diluted at a ratio of 1:10 in 10% (2-Hydroxypropyl)-β-cyclodextrin (H107; Sigma-Aldrich) for injection.

MRI Tumor Quantification.

Mice were evaluated by MRI to determine the tumor volume. Tumor volume changes were analyzed by Sante DICOM Viewer Free software.

IHC.

IHC staining was performed as previously described (19). The following antibodies were used: UTX (33510; Cell Signaling), H3K27me3 (A2363; ABclonal), EZH2 (D221769; Sangon), Ki67 (NCL-Ki67p; Leica Biosystems), and Cleaved Caspase-3 (9661; Cell Signaling). With regard to statistical analysis of H3K27me3 and EZH2 staining, “low” means the percentage of positive cells in one tumor <50% and “high” means the percentage of positive cells in one tumor >50%. Then, the numbers of high and low tumors were counted for analysis.

RNA Sequencing.

Total RNA was isolated using TRIzol reagent from tumors of KrasG12D/+ and KrasG12D/+;UtxL/L mice at 16 wk after Ad-Cre treatment. Sequencing data have been deposited in the GEO database under the accession no. GSE93302.

Statistical Analysis.

All experimental data were analyzed by Student’s t test (two-tailed), and P < 0.05 was considered to be significant. All error bars indicate SEM.

Data Availability

Data deposition: The datasets reported in this paper have been deposited in the Gene Expression Omnibus (GEO) database, https://www.ncbi.nlm.nih.gov/geo (accession nos. GSE74095, GSE77684, and GSE93302) and in the Sequence Read Archive (SRA) (accession no. SRP069330).

Acknowledgments

We thank Drs. T. Jacks and K. Wong for providing the KrasG12D/+ and Trp53L/L mice, Dr. R. Depinho for providing the Lkb1L/L mice. We thank Drs. T. Jacks, W. Xue, and F. J. Sanchez-Rivera for providing the pSECC and LentiCRISPRv2 plasmids. This work was supported by the National Basic Research Program of China (Grant 2017YFA0505501), Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDB19020201), National Natural Science Foundation of China (Grants 81325015, 81430066, 91731314, 31621003, 31370747, 81402276, 81402371, 81401898, 81402498, 81101583, and 81372509), Science and Technology Commission of Shanghai Municipality (Grant 15XD1504000), China Postdoctoral Science Foundation (Grant 2015M581673), Chinese Academy of Science Taiwan Young Scholar Visiting Program (Grant 2015TW1SB0001), National Science Foundation (Grant 81472606), Science and Technology Program of Guangzhou (Grant 201803010124), and National Key R&D Program of China (Grant 2016YFC0905500).

Supporting Information

Supporting Information (PDF)

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Information & Authors

Information

Published in

Go to Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences
Vol. 115 | No. 17
April 24, 2018
PubMed: 29632194

Classifications

Data Availability

Data deposition: The datasets reported in this paper have been deposited in the Gene Expression Omnibus (GEO) database, https://www.ncbi.nlm.nih.gov/geo (accession nos. GSE74095, GSE77684, and GSE93302) and in the Sequence Read Archive (SRA) (accession no. SRP069330).

Submission history

Published online: April 9, 2018
Published in issue: April 24, 2018

Keywords

  1. tumor suppressor genes
  2. CRISPR/Cas9 in vivo knockout
  3. non-small cell lung cancer
  4. UTX
  5. EZH2 inhibitor

Acknowledgments

We thank Drs. T. Jacks and K. Wong for providing the KrasG12D/+ and Trp53L/L mice, Dr. R. Depinho for providing the Lkb1L/L mice. We thank Drs. T. Jacks, W. Xue, and F. J. Sanchez-Rivera for providing the pSECC and LentiCRISPRv2 plasmids. This work was supported by the National Basic Research Program of China (Grant 2017YFA0505501), Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDB19020201), National Natural Science Foundation of China (Grants 81325015, 81430066, 91731314, 31621003, 31370747, 81402276, 81402371, 81401898, 81402498, 81101583, and 81372509), Science and Technology Commission of Shanghai Municipality (Grant 15XD1504000), China Postdoctoral Science Foundation (Grant 2015M581673), Chinese Academy of Science Taiwan Young Scholar Visiting Program (Grant 2015TW1SB0001), National Science Foundation (Grant 81472606), Science and Technology Program of Guangzhou (Grant 201803010124), and National Key R&D Program of China (Grant 2016YFC0905500).

Notes

This article is a PNAS Direct Submission.

Authors

Affiliations

Qibiao Wu1
State Key Laboratory of Cell Biology, Innovation Center for Cell Signaling Network, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China;
Yahui Tian1
Institute of Life and Health Engineering, Jinan University, Guangzhou, Guangdong 510632, China;
Jian Zhang1
State Key Laboratory of Cell Biology, Innovation Center for Cell Signaling Network, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China;
Xinyuan Tong1
State Key Laboratory of Cell Biology, Innovation Center for Cell Signaling Network, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China;
Hsinyi Huang
State Key Laboratory of Cell Biology, Innovation Center for Cell Signaling Network, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China;
Shuai Li
Laura & Isaac Perlmutter Cancer Center, NYU Langone Medical Center, New York, NY 10016;
Hong Zhao
Institute of Life and Health Engineering, Jinan University, Guangzhou, Guangdong 510632, China;
Ying Tang
State Key Laboratory of Cell Biology, Innovation Center for Cell Signaling Network, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China;
Chongze Yuan
Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai 200031, China;
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200031, China;
Kun Wang
Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
Zhaoyuan Fang
State Key Laboratory of Cell Biology, Innovation Center for Cell Signaling Network, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China;
Lei Gao
Institute of Life and Health Engineering, Jinan University, Guangzhou, Guangdong 510632, China;
Xin Hu
The University of Texas Graduate School in Biomedical Sciences, Houston, TX 77030;
Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030;
Fuming Li
State Key Laboratory of Cell Biology, Innovation Center for Cell Signaling Network, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China;
Zhen Qin
State Key Laboratory of Cell Biology, Innovation Center for Cell Signaling Network, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China;
Shun Yao
State Key Laboratory of Cell Biology, Innovation Center for Cell Signaling Network, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China;
Ting Chen
Laura & Isaac Perlmutter Cancer Center, NYU Langone Medical Center, New York, NY 10016;
Haiquan Chen
Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai 200031, China;
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200031, China;
Gong Zhang
Institute of Life and Health Engineering, Jinan University, Guangzhou, Guangdong 510632, China;
Wanting Liu
Institute of Life and Health Engineering, Jinan University, Guangzhou, Guangdong 510632, China;
Yihua Sun
Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai 200031, China;
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200031, China;
Luonan Chen
State Key Laboratory of Cell Biology, Innovation Center for Cell Signaling Network, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China;
Kwok-Kin Wong
Laura & Isaac Perlmutter Cancer Center, NYU Langone Medical Center, New York, NY 10016;
Laboratory of Endocrinology and Receptor Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892;
Institute of Life and Health Engineering, Jinan University, Guangzhou, Guangdong 510632, China;
State Key Laboratory of Cell Biology, Innovation Center for Cell Signaling Network, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China;
School of Life Science and Technology, ShanghaiTech University, Shanghai 200120, China

Notes

2
To whom correspondence may be addressed. Email: [email protected] or [email protected].
Author contributions: Q.W., Liang Chen, and H.J. designed research; Q.W., Y. Tian, J.Z., X.T., H.H., S.L., H.Z., C.Y., K.W., L.G., F.L., Z.Q., S.Y., and T.C. performed research; H.C., G.Z., W.L., Y.S., Luonan Chen, K.-K.W., and K.G. contributed new reagents/analytic tools; Q.W., J.Z., X.T., H.Z., Y. Tang, Z.F., and X.H. analyzed data; and Q.W., Liang Chen, and H.J. wrote the paper.
1
Q.W., Y. Tian, J.Z., and X.T. contributed equally to this work.

Competing Interests

The authors declare no conflict of interest.

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    In vivo CRISPR screening unveils histone demethylase UTX as an important epigenetic regulator in lung tumorigenesis
    Proceedings of the National Academy of Sciences
    • Vol. 115
    • No. 17
    • pp. 4295-E4143

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