NRF2-Driven KEAP1 Transcription in Human Lung Cancer Yijun Tian1, Qian Liu1, Shengnan Yu2, Qian Chu2, Yuan Chen2, Kongming Wu2,3, and Liang Wang1,4

Published OnlineFirst June 22, 2020; DOI: 10.1158/1541-7786.MCR-20-0108

MOLECULAR CANCER RESEARCH | CANCER GENES AND NETWORKS

ABSTRACT ◥ Constitutive NRF2 activation by disrupted KEAP1-NRF2 inter- sequencing data demonstrated consistent NRF2 occupancy to action has been reported in a variety of human cancers. However, KEAP1 promoter. Deleting NRF2 binding site significantly reduced studies focusing on NRF2-driven KEAP1 expression under human baseline and inducible KEAP1 promoter activity and KEAP1 mRNA cancer contexts are still uncommon. We examined mRNA expres- expression. By incorporating tumor tissue KEAP1 mRNA expression correlation between NRF2 and KEAP1 in multiple human sions in estimating NRF2 signaling disruptions, we found increased cancers. We measured KEAP1 mRNA and protein alterations in TXN/KEAP1 mRNA ratio in cases with NRF2 gain or KEAP1 loss response to the activation or silencing of NRF2. We queried and decreased NRF2/KEAP1 mRNA ratio in cases with NRF2– chromatin immunoprecipitation sequencing (ChIP-seq) datasets KEAP1 somatic mutations. In TCGA PanCancer datasets, we also to identify NRF2 binding to KEAP1 promoters in human cells. We identified that cases with loss-of-function mutations in NRF2 used reporter assay and CRISPR editing to assess KEAP1 promoter pathway recurrently appeared above the NRF2-KEAP1 mRNA activity and mRNA abundance change. To determine specimen expression regression lines. Moreover, compared with previous implication of the feedback pattern, we used gene expression ratio to NRF2 signatures, the ratio-based strategy showed better predictive predict NRF2 signal disruption as well as patients’ prognosis. performance in survival analysis with multiple squamous cell lung Correlation analysis showed KEAP1 mRNA expression was in cancer cohort validations. positive association with NRF2 in multiple squamous cell cancers. The positive correlations were consistent across all squamous cell Implications: NRF2-driven KEAP1 transcription is a crucial com- lung cancer cohorts, but not in adenocarcinomas. In human lung ponent of NRF2 signaling modulation. This hidden circuit will cells, NRF2 interventions significantly altered KEAP1 mRNA and provide in-depth insight into novel cancer prevention and thera- protein expressions. ChIP-quantitative PCR (ChIP-qPCR) and peutic strategies.

Introduction INRF2) gene (6). It has been reported that KEAP1 anchors NRF2 in the cytoplasm and mediates its degradation (9). By forming For decades, cancer prevention and therapeutic breakthroughs a “Hinge and Latch” structure through homodimerization and occur in pace with deepened insights about cancer genome (1–3). interaction with NRF2 ETGE and DLG domain, KEAP1 restricts Among the endeavors to decoding cancer genomics, the landmark NRF2 activation under basal conditions (10). In the presence program, The Cancer Genome Atlas (TCGA), generated abundant of oxidative or electrophilic stress, NRF2 factor evades from ubi- profiling from over 20,000 primary tumor tissues spanning 32 quitination and translocates into the nucleus. The in-nucleus cancer types and broadened cancer research boundaries with mas- NRF2 binds to specific regulatory sequences named antioxidant- sive discoveries (4, 5). In these discoveries, a pivotal factor regu- responsive elements (ARE) to transactivate cytoprotective and lating redox homeostasis, nuclear factor erythroid-2 related factor 2 antioxidant genes (11). (NFE2L2, which is generally referred to NRF2), has shown frequent However, two interesting studies have also identified a hidden activation in nearly 30% squamous cell lung cancers (SQC; circuit in NRF2 regulations. In the mouse Keap1 (INrf2) gene, Lee refs. 6–8). These NRF2 activations are mainly caused by somatic and colleagues (12) found that an AREs located on a negative strand mutations and copy number variations in itself and its gatekeeper can subtly connect Nrf2 activation to Keap1 transcription. When gene Kelch-like ECH-associated protein 1 (KEAP1, also named as examining NRF2 occupancies in human lymphocytes, Chorley and colleagues identified an approximately 700 bp locus within the KEAP1 promoter region was consistently top rank enriched, even 1Department of Tumor Biology, Moffitt Cancer Center, Tampa, Florida. 2Depart- atthewhole-genomescale(13).Thesebasicfindings have depicted a ment of Oncology, Tongji Hospital of Tongji Medical College, Wuhan, P.R. China. mutually influenced pattern between NRF2 and KEAP1. Because 3Department of Oncology, The First Affiliated Hospital of Zhengzhou University NRF2 shows oncogenic functions in human cells (14, 15) and 4 & Henan Cancer Hospital, Zhengzhou, P.R. China. Department of Pathology, displays a high frequency of aberrant activation in tumors (16); Medical College of Wisconsin, Milwaukee, Wisconsin. these results also suggested the potential regulatory role of NRF2– Note: Supplementary data for this article are available at Molecular Cancer KEAP1 axis in human cancers. To identify the presence and Research Online (http://mcr.aacrjournals.org/). extents of this feedback regulation under human cancer contexts, Corresponding Authors: Liang Wang, Moffitt Cancer Center, 12902 Magnolia we reconsidered transcriptome associations between NRF2 Drive, Tampa, FL 33612. Phone: 813-745-4955; Fax: 813-745-6606; E-mail: and KEAP1 in TCGA PanCancer and currently available lung fi Liang.Wang@mof tt.org; and Kongming Wu, Tongji Hospital of Tongji Medical cancer RNA profiling datasets. We functionally characterized College, Building 303, 1095 Jiefang Avenue, Wuhan 430030, P.R. China. Phone: 8613-5171-96182; Fax: 8627-8366-3476; E-mail: [email protected] NRF2-driven KEAP1 expression in lung cancer cells and further leveraged KEAP1 expression to enhance prediction of NRF2 sig- Mol Cancer Res 2020;XX:XX–XX naling disruption. This work will give new perspectives to pinpoint doi: 10.1158/1541-7786.MCR-20-0108 NRF2 regulation and pave the way toward possible pharmaceutical 2020 American Association for Cancer Research. interventions.

AACRJournals.org | OF1

Tian et al.

Materials and Methods Master Mix (Thermo Fisher Scientific) on the ABI 7900 HT platform. TCGA PanCancer dataset query and somatic mutation The primers were listed in Supplementary Table S2. annotation Total protein was extracted and electrophoresed, as described previously (27). SuperSignal West Pico Chemiluminescent Substrate NRF2, KEAP1, and NRF2 downstream gene expression (Thermo Fisher Scientific) was used to generate luminescent signals on [“mRNA Expression, RSEM (Batch normalized from Illumina HiSeq_ the Syngene fluorescence imaging system. Captured images were RNASeqV2)”], copy number variation [“Copy-number Alterations aligned in Photoshop and cropped in Microsoft PowerPoint. (OQL is not in effect)”], and mutations [“Mutations (OQL is not in effect)”] of 10,967 tumor samples from 32 TCGA PanCancer Luciferase reporter assay atlas studies were downloaded from cBioPortal (https://www.cbiopor The cells were seeded into a 24-well plate one day before per-well tal.org/; refs. 4, 5) on September 26, 2019. Annotations of NRF2 and 250 ng of the pGL3 plasmid was transfected by using Lipofectamine KEAP1 somatic mutation were based on previous publications 2000 (27). The tBHQ was added to the replaced medium after (6, 17–25). Truncating mutations in KEAP1 were bona fide regarded transfection for 36 hours, whereas empty vector (200 ng) or NRF2 as loss-of-function mutations. A summary of the NRF2 and KEAP1 ORF (290 ng) was cotransfected from the beginning. After 12 hours somatic mutation annotations can be found in Supplementary of tBHQ treatment or a total of 72 hours for cotransfection, the cells Table S1. were lysed for the luciferase assay according to FireflyLuciferase fi Regents and cell culture Glow Assay Kit protocol. Luminescence was measured by in nite 200Pro. After subtracting the empty vector value, gene promoter The antibodies against NRF2 were purchased from Abcam (for activities were represented by relative luminescence unit fold Western blot analysis, ab62352) and Active Motif [for chromatin change. immunoprecipitation–quantitative PCR (ChIP-qPCR), 61599]. Anti- bodies against KEAP1 (10503-2-AP) and b-actin (60008-1-Ig) were Functional validation of AREs by CRISPR-Cas9–based genome purchased from ProteinTech Group. IgG isotype control antibodies editing were purchased from Abcam (ab171870, Cambridge). Dimethyl sulf- Flanking sequence (20 bp) around the putative ARE was exam- oxide (DMSO) and tert-butylhydroquinone (tBHQ) were purchased ined for sgRNA using the CRISPR software (28). Guide RNA with the from Sigma-Aldrich. Firefly luciferase Glow Assay Kit was purchased best off-target score was synthesized along with a hU6 promoter and a from Thermo Fisher Scientific. scaffold as a gblock fragment (https://benchling.com/rrm38/f/h4fdY A549 (RRID: CVCL_0023), H292 (RRID: CVCL_0455), HEK293FT FOi-protocols/prt-10T3UWFo-detailed-gblocks-based-crispr-proto (RRID: CVCL_6911), SK-MES-1 (RRID: CVCL_0630), BEAS-2B col/edit; refs. 29, 30; the guide RNA sequence can be found in (RRID: CVCL_0168), and H460 (RRID: CVCL_0459) cells were Supplementary Table S2). The gblock fragment was further ampli- obtained from the ATCC and verified with short tandem repeat (STR) fied with Phusion High-Fidelity DNA Polymerase (Thermo Fisher profiling prior to use. All cell lines were disposed and replaced with Scientific) and cleaned with MinElute PCR Purification Kit (Qia- low passage aliquots after being subcultured for 15 times. All lung gen). To edit, gblock fragments and Cas9 plasmid (pSpCas9-2A- cells were grown in RPMI1640, expect for SK-MES-1 was grown in GFP, Addgene, #48138; ref. 31) by a molar ratio of 8:1 using MEM. The HEK293FT cells were cultured in DMEM. The complete Lipofectamine 2000, along with a DARE promoter sequence as media were supplemented with 10% FBS (Thermo Fisher Scientific) repair template donor. At 24-hour posttransfection, GFP-positive and were all free of sodium pyruvate. All cell lines were examined for cells were sorted by FACSMELODY (BD Biosciences). The genomic Mycoplasma contamination with Venor GeM Mycoplasma Detection DNA of the sorted cells was extracted for the T7E1 assay to verify Kit (Sigma-Aldrich). sgRNA-directed cleavage. Meanwhile, the sorted cells were seeded into a 96-well plate to obtain single-cell clones. DirectPCR Lysis Plasmid construction and siRNA design Reagent (Viagen Biotech) was used to prepare the template for Promoter sequence of KEAP1 gene was searched from The Light- Sanger sequencing. The sgRNA sequences and T7E1 primers were Switch Promoter Reporter GoClone Collection (https://switchgearge listed in Supplementary Table S2. nomics.com/products/promoter-reporter-collection, SWITCHGEAR GENOMIC). A 329-bp subsequence containing the putative AREs was Confirmation of NRF2 binding to KEAP1 promoter by ChIP amplified from the genomic DNA of HEK293FT cells. This promoter analysis was further cloned into a pGL3-basic vector between the KpnI and To confirm the NRF2 binding to KEAP1 promoter, we reanalyzed BglII sites. Q5 Site-Directed Mutagenesis Kit (New England Biolabs) the ChIP-seq dataset evaluated NRF2 occupancies in human genome. was used to introduce ARE- and SP1-binding site deletions to the Raw FASTQ files were downloaded from the European Nucleotide pGL3-KEAP1 plasmid. Small interfering RNA (siRNA) targeting Archive (ENA) database (13, 32, 33). Relevant and qualified sequence NRF2 was the same as described in a previous publication (26). The files were aligned to hg38 human genome using bowtie2. Unique promoter sequences, primers, RNA oligos, and guide RNA sequences alignments were sorted and indexed, and then fed to the Model-based were listed in Supplementary Table S2. Analysis of ChIP-Seq 2 (MASC2; ref. 34) callpeak function with a false positive rate threshold of <0.01. After obtaining significantly enriched Real-time PCR, Western blot analysis, and immunofluorescence loci coordinates, the alignments were visualized by Integrative Geno- staining mics Viewer (35). For ENCODE NRF2 ChIP-seq data, we used Total RNA was extracted from cells using the RNeasy Mini Kit MACS2 to call peak between NRF2 ChIP and Input sequencing (QIAGEN). One microgram of total RNA was reverse transcribed by alignment file to identify potential peaks. For sulforaphane (SFN, SuperScript IV VILO Master Mix (Thermo Fisher Scientific). Quan- commonly use NRF2 activator) treated ChIP-seq data, we used tification reactions were performed with PowerUp SYBR Green MACS2 to call peak between DMSO and SFN treatment group to

OF2 Mol Cancer Res; 2020 MOLECULAR CANCER RESEARCH

NRF2 Activates KEAP1 Expression in Human Cancers

identify potential peaks. ChIP-seq datasets used were listed in Sup- KEAP1 was introduced. Z denotes gene E expression, and f denotes its plementary Table S3. mRNA transcribed from unit nuclear NRF2 protein. This hypothesis To further verify NRF2 binding in lung cancer cells, we performed can be written as Eq. (B): ChIP-qPCR in H292, SK-MES-1, H520, and A549 cells. After 8-hour ðÞaX mbY f ¼ Z ðBÞ treatment with 0.1% DMSO or 60 mmol/L tertiary butylhydroquinone (tBHQ), chromatin was prepared from above cell lines with ChIP-IT This equation can be transformed together with Eq. (A) into: High Sensitivity kit (Active Motif, 53040) and used for four independent ChIP reactions against IgG control and NRF2 antibodies, respec- Z=Y ¼ f =e tively. To assess NRF2 binding at NQO1, KEAP1, and TXN genes, we used previously published primers (13), and our designs to amplify From this equation, NRF2 disequilibrium caused by KEAP1 dele- fi fl AREs containing as well as a gene-specific negative control (NC) tion and NRF2 ampli cation can be re ected as increases in the ratio region. We performed quantification reactions with input or ChIPed between the downstream Gene E and KEAP1 mRNA. We eventually DNA by PowerUp SYBR Green Master Mix on CFX96 qPCR instru- selected a well-known NRF2 target gene: TXN as the KEAP1 footprint fi ment (Bio-Rad). For each gene, enrichments at ARE-containing for below reasons: Similar coef cient of variation (CV) for mRNA regions were calculated on the basis of gene-specific NC after nor- expression measured by microarray as well as RNA-seq across each malized to the respective input control. The primers used were listed in dataset (Supplementary Fig. S1A and S1B); TXN and KEAP1 both have fi Supplementary Table S2. similar high con dence NRF2 occupancy near respective transcription start site (Supplementary Fig. S1C). Correlation analysis between KEAP1 and NRF2 expression in On the basis of above considerations, we used ROC curve in testing lung cancer TXN/KEAP1 and NRF2/KEAP1 ratio for predicting NRF2 signal To interrogate the RNA expression correlation between KEAP1 disruptions (copy number variations and somatic mutations). We fi and NRF2, we retrieved transcriptomic data from both Gene Expres- also strati ed SQC patients by upper 25% TXN/KEAP1 or lower 25% – sion Omnibus (GEO) and TCGA. For Affymetrix microarrays, NRF2/KEAP1 and used Kaplan Meier curve to test potential prog- we used JetSet (36) probeset expression of KEAP1 (202417_at), TXN nostic associations. (216609_at), and NRF2 (201146_at) to represent bona fide RNA In previous published NRF2 signatures, Singh and colleagues (40) abundances. For Illumina or other microarrays, we selected the indicated 14 genes under NRF2 control. Cescon and colleagues (41) probeset with the maximum average expression across samples to summarized 27 genes altered in NRF2 activation SQC cases. Rodrigo fi fi represent gene RNA abundance. and colleagues (42) identi ed 108 high con dence NRF2 target genes. To perform a summary statistics meta-analysis, we calculated the Notably, none of above gene signatures included KEAP1 as NRF2 Pearson correlation coefficient from each original gene expression targets. To evaluate prognostic performance of these NRF2 signatures fi in SQC (GSE3141, GSE37745, and TCGA-LUSC), we clustered dataset (log2 transformed). We used each coef cient and respective sample size for pooled analysis (37) in ADC and SQC, respectively. patients of each cohort by each gene signature expression (center and This strategy has been proven (38) to be decent to investigate the standardized by mean and SD) into high and low NRF2 activity groups. correlation between expression profiles of a gene pair across multiple We used time-dependent areas under ROCs (43) to compare the ratio – fi microarray studies. The heterogeneity test (39) in SQC (heterogeneity and above NRF2 signature based strati cation. A follow-up limit of I2 ¼ 0) has indicated little correlation variability between datasets came 60 months was set for the datasets with high overall survival censor rate > from array and sequencing platforms, suggesting the results cannot be ( 40%). No follow-up limits were set to relapse-free survival (RFS), fi explained by chances. progression-free survival (PFS), disease-speci c survival (DSS), and disease-free survival (DFS) data. The characteristics of datasets used Estimation of NRF2 disruption based on KEAP1 expression were listed in Supplementary Table S4. Previously published NRF2 If nuclear NRF2 protein indeed regulates KEAP1 mRNA expression, signatures were listed in Supplementary Table S6. a dynamic equilibrium between NRF2 and KEAP1 can be written as Eq. Statistical analysis (A): Thedatawereexpressedasthemean SEM. The unpaired t test ðÞ ¼ ð Þ aX mbY e Y A was used to compare differences between groups after determining the homogeneity of variance. Two-way ANOVA was used to In Eq. (A), a and b denote NRF2 and KEAP1 protein translated from compare differences between groups in the time-series data. The per respective unit mRNAs; e denotes the KEAP1 mRNA transcribed log-rank test was used to compare survival between stratified from per unit of the functional nuclear NRF2 protein; m denotes NRF2 patients. Two-tailed P values less than 0.05 indicated statistically protein degraded by per unit of the KEAP1 protein; X and Y denote significant differences. NRF2 and KEAP1 mRNA abundance. This equation can be further transformed to: Availability of data and materials X=Y ¼ 1=ae þ mb=a All data generated or analyzed in this study are included either in this article or in the supplementary information files. In the transformed equation, NRF2-KEAP1 disequilibrium caused by loss-of-function mutation of NRF2 (elevated “a”) and KEAP1 (decreased “mb”) can be reflected by the decrease of NRF2/KEAP1 Results mRNA ratio. NRF2 and KEAP1 expression are significantly correlated in In the above equations, disequilibrium caused by KEAP1 deletion squamous cell cancers and NRF2 amplification were not included. To include this situation, To evaluate NRF2 expression and its tissue distribution in human another downstream gene E that expressed in a similar pattern with cancers, we ranked TCGA PanCancer datasets by respective median

AACRJournals.org Mol Cancer Res; 2020 OF3

Tian et al.

A TCGA-PanCancer studies

16 Amplification & gain Diploid 14 Deep & shallow deletion

NRF2 mRNA expression 8

Log 6 DLBC TGCT AM PCPG UV AC UCS PRAD LIHC CHOL THYM BRCA LG SARC UCEC COAD OV L SKCM PAA BL KIRC GB KIRP STAD ME KIC THCA CE ESCA HNS LUSC UAD CA L M C G M SO H SC D C BCDELUAD LUSC ESCA: squamous HNSC F CESC: squamous 16 r = 0.009 16 r = 0.50 16 r = 0.49 16 r = 0.33 16 r = 0.20 P = 0.84 P < 1e-15 P = 7.13e-7 P = 5e-15 P = 0.0014 14 14 n = 510 n = 484 14 n = 94 14 n = 515 14 n = 243 12 12 12 12 12

10 10 10 10 10 mRNA expression mRNA expression mRNA expression mRNA expression mRNA expression 8 8 8 8 8

KEAP1 6 KEAP1 6 KEAP1 6 KEAP1 6 KEAP1 6 8 10121416 8 10121416 8 10121416 6 8 10121416 6 8 10121416 NRF2 mRNA expression NRF2 mRNA expression NRF2 mRNA expression NRF2 mRNA expression NRF2 mRNA expression GHNRF2-KEAP1 correlation in ADC NRF2-KEAP1 correlation in SQC Dataset Sample Size Coefficient 95% CI WeightDataset Sample Size Coefficient 95% CI Weight Bild 58 0.29 (0.04; 0.51) 4.1% Bild 53 0.55 (0.33; 0.72) 3.7% Lee 63 0.17 (−0.08; 0.40) 4.4% Lee 75 0.59 (0.42; 0.72) 5.3% Kuner 40 −0.03 (−0.34; 0.28) 3.2% Kuner 18 0.51 (0.06; 0.79) 1.1% Hou 45 0.05 (−0.24; 0.34) 3.5% Hou 27 0.56 (0.23; 0.77) 1.8% Zhu 71 0.03 (−0.21; 0.26) 4.7% Zhu 52 0.50 (0.27; 0.68) 3.6% Micke 50 −0.02 (−0.29; 0.26) 3.8% Micke 28 0.39 (0.02; 0.67) 1.8% Fujiwara 9 0.60 (−0.11; 0.90) 0.7% Fujiwara 48 0.47 (0.22; 0.67) 3.3% Rousseaux 85 0.12 (−0.09; 0.33) 5.1% Rousseaux 61 0.55 (0.34; 0.70) 4.3% Botling 106 0.20 (0.01; 0.37) 5.7% Botling 66 0.45 (0.24; 0.63) 4.6% Tarca 77 0.17 (−0.06; 0.38) 4.9% Tarca 73 0.61 (0.45; 0.74) 5.1% Tang 133 0.30 (0.14; 0.45) 6.2% Tang 43 0.56 (0.32; 0.74) 2.9% Der 128 −0.16 (−0.32; 0.01) 6.1% Der 43 0.50 (0.24; 0.70) 2.9% TCGA−LUAD 510 0.01 (−0.08; 0.09) 8.5% TCGA−LUSC 484 0.50 (0.43; 0.57) 36.6% Beer 86 0.25 (0.04; 0.44) 5.1% Raponi 130 0.58 (0.46; 0.69) 9.3% Landi 58 −0.02 (−0.27; 0.24) 4.1% Wilkerson 56 0.44 (0.20; 0.63) 3.9% Shedden 443 −0.09 (−0.18; 0.00) 8.3% Meyerson 1350.43 (0.28; 0.56) 9.7% Tomida 117 −0.16 (−0.34; 0.02) 5.9% Lu* 60 0.05 (−0.21; 0.30) 4.2% Fixed effect model 0.50 (0.47; 0.55) 100.0% Selamat 58 −0.20 (−0.44; 0.06) 4.1% Total 1409 Heterogeneity Okayama 226 0.10 (−0.03; 0.23) 7.3% I2 = 0%, t2 = 0 Total 2428 −0.5 0 0.5 Random effect model 0.06 (−0.01; 0.13) 100.0% Heterogeneity −0.5 0 0.5 I2 = 60%, t2 = 0.01

Figure 1. NRF2 and KEAP1 mRNA correlations in human cancers. A, NRF2 mRNA expression across TCGA PanCancer datasets. Scatter plot with NRF2 and KEAP1 mRNA expression in lung adenocarcinoma (LUAD, B), squamous cell lung cancer (LUSC, C), esophageal squamous cell carcinoma (ESCA: squamous, D), head and neck squamous cell carcinoma (HNSC, E), cervical squamous cell carcinoma (CESC: squamous, F). G, Pooled correlation coefﬁcient between NRF2 and KEAP1 mRNA expression in lung adenocarcinoma (ADC). , Three bronchioloalveolar carcinomas and one squamous carcinoma included in Lu's dataset. H, Pooled correlation coefﬁcient between NRF2 and KEAP1 mRNA expression in SQC.

NRF2 expression (Fig. 1A). We found that four cancer types with Studies in dashed boxes indicated those measured RNA profiling squamous histology expressed substantially higher NRF2 mRNA. for ADC and SQC at the same time. This high-level mRNA was positively correlated with NRF2 copy number gain. Furthermore, Pearson correlation analysis showed NRF2 regulates endogenous KEAP1 expression in human lung that NRF2 and KEAP1 were significantly correlated in the four cells squamous cancer types (Fig. 1C–F), but not in lung adenocarci- Previous studies showed NRF2 feedback in KEAP1 activation in noma (TCGA-LUAD, Fig. 1B). This correlation was further con- mouse hepatoma cells (12) and human lymphoid cells (13). To test if firmed by meta-analysis in NSCLC datasets (Fig. 1H and G). NRF2 activation also regulated KEAP1 expression in human lung cell

OF4 Mol Cancer Res; 2020 MOLECULAR CANCER RESEARCH

NRF2 Activates KEAP1 Expression in Human Cancers

lines, we used two well-known NRF2 activator drugs, tBHQ (44) and interesting fact in H292 cells was the mild increased (1.7-fold) dimethylformamide (DMF; ref. 45) to treat H292 cells, which was occupancy for KEAP1 promoter and steady occupancies for NQO1 previously reported to have low NRF2 activity (26, 46). We found that and TXN during tBHQ treatment, suggesting NRF2 activation was 60 mmol/L tBHQ and DMF induced KEAP1 mRNA expression after tightly restricted by the feedback regulation, which might be the reason 12-hour treatment (Fig. 2A). Correspondingly, in a well-known that we saw transient induction and rapid reduction of KEAP1 protein NRF2-addictive cell line A549, silencing NRF2 by siRNA significantly in NRF2 activator–treated Western blot. In addition, with the highest reduced KEAP1 mRNA expression (Fig. 2B). The same responses were baseline NRF2 occupancies in KEAP1 ARE-containing promoter, detected at protein level in above settings (Fig. 2C–E), as well as in SK- tBHQ treatment did not induce significant NRF2 binding increase in MES-1 (SQC, Fig. 2F), H520 (SQC, Supplementary Fig. S5A), and A549 cells (Fig. 3H). BEAS-2B (normal human bronchial epithelium) cells (Fig. 2G). Besides, in A549 and H460 cells (both with KEAP1 loss-of-function NRF2-binding site is crucial to KEAP1 transcription mutation), KEAP1 protein (Fig. 2H; Supplementary Fig. S5B) did not To identify NRF2-binding motif in KEAP1 promoter region, we elevate after tBHQ treatment. Western blot bands in Fig. 2 and screened for TF binding in the JASPAR database (48). In accordance Supplementary Fig. S5 were quantified in Supplementary Fig. S5C with Chorley and colleagues (13), we verified the existence of a NRF2- and S5D. binding site (ARE, score: 15.16, relative score: 0.98), as well as a SP1- binding site (score: 13.428, relative score: 0.95; Fig. 4A). After cloning NRF2 directly binds to KEAP1 promoters in human cell lines this promoter into luciferase reporter plasmid, we first tested activity of To investigate possible NRF2 binding at human KEAP1 promoter, this promoter during tBHQ treatment. Interestingly, in KEAP1 wild- we retrieved publicly available NRF2 ChIP-seq datasets from type H292 cells, KEAP1 promoter activities increased up to 2-fold ENCODE (https://www.encodeproject.org/; ref. 47). As expected, we within the first 6-hour treatment, then decreased after 12 hours. found a confined region in KEAP1 promoter with variable NRF2 However, in KEAP1-mutated A549 cells, the activities remain unal- binding enrichment across four human cell lines (Fig. 3A). In addition, tered (Fig. 4B). We further used site-directed mutagenesis to introduce in another 2 NRF2 ChIP-seq datasets, SFN treatment increased NRF2 three putative TF site deletion to this promoter and compared the occupancy in the same region in BEAS-2B cells (Fig. 3B) and activity alteration. In H292 cells, full-length (FL) and DSP1 promoter immortalized lymphocytes (Fig. 3C). To identify significantly activities were significantly enhanced by tBHQ treatment or by NRF2 enriched regions in lymphocytes, we used MACS2 to callpeak between overexpression. However, deletion of ARE (DARE) or double deletion the SFN treatment and DMSO control. We found that KEAP1 (DD) abolished this enhancement (Fig. 4C). In A549 cells, we only promoter loci ranked third (FDR q value) in all enriched regions. observed a significant reduction of baseline promoter activity of DARE (Fig. 3D). and DD. The inducible activity absence suggested oversaturated To further identify NRF2 binding at human KEAP1 promoter, we KEAP1 promoter activity in A549 cells (Fig. 4D). performed ChIP-qPCR with NRF2 antibody in four lung cancer cells, To further test the effect of ARE locus on endogenous KEAP1 including two SQC lines (SK-MES-1 and H520). We observed repro- expression, we applied the CRISPR-Cas9–based editing technology to ducible baseline (0.1% DMSO) NRF2 occupancies in ARE-containing introduce an ARE deletion in KEAP1 promoters. After established promoter regions of NQO1, KEAP1, and TXN genes (Fig. 3E–H). One multiple ARE-deleted single-cell clones verified by Sanger sequencing

H292 60 μmol/L tBHQ E A549 A H292 B A549 C KEAP1 wt KEAP1 p.G333C NC siNRF2 0 h 1 h 3 h 6 h 12 h 24 h P = 9.45e-5 NC 4.0 DMSO 1.5 siNRF2 DMF KEAP1 68 kDa NRF2 100 kDa P = 0.012 tBHQ 1.2 P = 0.01 3.0 KEAP1 68 kDa P β = 0.0011 0.9 -Actin 43 kDa KEAP1 2.0 H292 β-Actin 43 kDa 0.6 KEAP1 wt 60 μmol/L DMF 1.0 D 0.3 0 h1 h3 h6 h12 h 24 h Relative mRNA expression mRNA 0.0 0.0 KEAP1 68 kDa

Relative mRNA expression Relative mRNA NRF2 KEAP1

β-Actin 43 kDa

SK-MES-1 BEAS-2B 60 μmol/L tBHQ 60 μmol/L tBHQ 60 μmol/L tBHQ FGHKEAP1 wt KEAP1 wt A549 H460 KEAP1 p.G333C KEAP1 p.D236H 0 h 1 h 3 h 6 h 12 h 24 h 0 h 1 h 3 h 6 h 12 h 24 h 0 h 12 h 0 h 12 h KEAP1 68 kDa KEAP1 68 kDa KEAP1 68 kDa

β-Actin 43 kDa β-Actin 43 kDa β-Actin 43 kDa

Figure 2. KEAP1 expression alterations under NRF2 intervention in human lung cell lines. A, KEAP1 mRNA alteration in H292 cells after DMF and tBHQ treatment for 24 hours. B, NRF2 and KEAP1 mRNA alterations in A549 cells after NRF2 siRNA transfection for 72 hours. C and D, Changes of KEAP1 protein abundance of H292 cells treated by tert-butylhydroquinone (tBHQ, 60 mmol/L, C) or dimethylformamide (DMF, 60 mmol/L, D) within 24 hours. E, Changes of NRF2 and KEAP1 protein abundance in A549 cells transfected with scramble control (NC) or NRF2 siRNA for 72 hours. Changes of KEAP1 protein abundance in SK-MES-1 (F) and BEAS-2B (G) cells treated by tBHQ (60 mmol/L) within 24 hours. H, Changes of NRF2 and KEAP1 protein abundance in A549 and H460 cells treated with DMSO or tBHQ (60 mmol/L) for 12 hours. The 0-hour time point indicates cells treated with 0.1% DMSO. DMSO and tBHQ treatments were given in 10% FBS medium without sodium pyruvate. The P values were calculated using the two-tailed t test.

AACRJournals.org Mol Cancer Res; 2020 OF5

Tian et al.

A ENCODE NRF2 ChIP-seq collection (Santa cruz biotech sc-13032)

IMR90 [0-328] human embryonic lung fibroblasts FDR q = 1e-354

A549 [0-328] human lung carcinoma FDR q = 1e-139

HepG2 [0-328] human hepatocellular carcinoma FDR q = 1e-228

HELA [0-328] human cervival adenocarcinoma FDR q = 1e-47

B PRJNA305423 NRF2 ChIP-seq (Abcam ab62352) in BEAS-2B cells SFN [0-32]

DMSO [0-32]

C PRJNA161889 NRF2 ChIP-seq (Abcam ab62352) in lymphocytes SFN [0-646]

DMSO [0-646]

NM_203500 NM_012289 D Top altered NRF2 binding loci during NRF2 activator SFN treatment Inducible binding Chromosome Start End Pileup -Log (P value) -Log (q value) Regulated gene 10 foldchange 10 3 53,270,085 53,270,474 72 64.91 15.19 56.78 TKT 8 30,727,704 30,728,097 48 64.25 25.11 56.33 GSR 19 10,502,601 10,503,002 68 59.80 14.36 52.54 KEAP1 22 35,371,858 35,372,275 68 59.81 14.36 52.54 Z82244.2 14 95,668,338 95,668,782 27 50.42 23.73 43.60 TCL6 16 69,726,802 69,727,205 76 48.55 8.94 41.79 NQO1 9 110,256,729 110,257,123 68 48.48 10.29 41.72 TXN 20 653,305 653,695 38 47.56 19.99 40.83 SRXN1 E H292 F H520 150 DMSO-IgG tBHQ-IgG 200 DMSO-IgG tBHQ-IgG DMSO-NRF2 tBHQ-NRF2 P = 0.82 DMSO-NRF2 tBHQ-NRF2 P = 0.003 P = 0.61 150 P = 0.069 100 P = 0.053 P = 0.003 100 50 50 Gene specific NRF2 Gene specific NRF2 0 occupancies (IP/Input) occupancies (IP/Input) 0 NQO1-NC NQO1-ARE KEAP1-NC KEAP1-ARE TXN-NC TXN-ARE NQO1-NC NQO1-ARE KEAP1-NC KEAP1-ARE TXN-NC TXN-ARE GHSK-MES-1 A549 500 DMSO-IgG tBHQ-IgG 250 DMSO-IgG tBHQ-IgG DMSO-NRF2 tBHQ-NRF2 P = 0.073 DMSO-NRF2 tBHQ-NRF2 P 400 200 = 0.38 P = 0.56 P 300 = 0.004 150 P = 0.97 200 100 P = 0.029 100 50 Gene specific NRF2 Gene specific NRF2 occupancies (IP/Input) 0 0 occupancies (IP/Input) NQO1-NC NQO1-ARE KEAP1-NC KEAP1-ARE TXN-NC TXN-ARE NQO1-NC NQO1-ARE KEAP1-NC KEAP1-ARE TXN-NC TXN-ARE

Figure 3. NRF2 occupancies on KEAP1 promoter in human cell lines. A, NRF2 occupancy in KEAP1 promoter of IMR90, A549, HepG2, and HELA cells, Sequencing data come from ENCODE NRF2 ChIP-seq collection. B, NRF2 occupancy in KEAP1 promoter of BEAS-2B cells treated with DMSO or SFN (10 mmol/L), sequencing data come from SRA accession PRJNA305423. C, NRF2 occupancy in KEAP1 promoter of immortalized lymphocytes treated with DMSO or SFN (10 mmol/L), sequencing data come from SRA accession PRJNA161889. D, Top-ranked enriched regions with the NRF2 antibody in SFN-treated immortalized lymphocytes. NRF2 ChIP-qPCR at NQO1, KEAP1,andTXN promoter region in lung cancer cells H292 (E), H520 (F), SK-MES-1 (G), and A549 (H). Quantification data were normalized to sample specific input and then normalized to gene-specific negative control (NC) at IgG isotype control. P values represented comparisons of foldchanges between DMSO-NRF2 and tBHQ-NRF2 group at each ARE-containing promoters. Bars represented an average of four independent ChIP experiments SEM.

OF6 Mol Cancer Res; 2020 MOLECULAR CANCER RESEARCH

NRF2 Activates KEAP1 Expression in Human Cancers

ABH292 A549 3.0 1.5 P = 0.019 SP1 binding site ARE: NRF2 binding site P Full-length = 0.034 ΔSp1 2.0 1.0 ΔARE ΔΔ 1.0 0.5 TF name Score Relative score Start End Predict site sequence NRF2 15.16 0.98 10502793 10502803 ATGACTAAGCA Relative luciferase activity Relative luciferase activity SP1 13.428 0.95 10502941 10502951 CCCCCGCCCCG 0.0 0.0 0 h 1 h 3 h 6 h 12 h 24 h 0 h 1 h 3 h 6 h 12 h 24 h CD60 μmol/L tBHQ 60 μmol/L tBHQ H292 H292 A549 A549 2.0 2.0 1.5 1.5 P P = 0.04 = 0.011 P P = 8.3E-5 = 0.03 P = 0.00012 P = 0.0016 P P = 0.02 1.5 1.5 = 0.02 DMSO Vector 1.0 DMSO 1.0 Vector tBHQ NRF2 tBHQ NRF2 1.0 1.0

0.5 0.5 0.5 0.5 Relative luciferase activity Relative luciferase activity Relative luciferase activity Relative luciferase activity 0.0 0.0 0.0 0.0 FL ΔSP1ΔARE ΔΔ FL ΔSP1ΔARE ΔΔ FL ΔSP1ΔARE ΔΔ FL ΔSP1ΔARE ΔΔ E P1C3 Non-edited genome P3C12 ARE

P4C10

P3E11

FGHEK293FT H292 H H292 0.20 0.004 2.5 ΔARE (n =5) P = 0.0081 P = 0.0051 P =0.026 n P3C12 CTL ( = 4) mRNA mRNA 2.0 0.15 0.003 P P =0.043 =0.027 P =0.018 P3E11 1.5 P =0.032 0.002 0.10 P4C10 1.0 P1C3 0.05 0.001 0.5 Relative KEAP1/ACTB Relative KEAP1/ACTB Relative mRNA expression 0.00 0.000 0.0 ΔARE CTL ΔARE CTL TKT NQO1 GCLC SRXN1 TXN n = 7 n = 6 n = 5 n = 4 I H292 J H292 P3C12 P4C10 3.0 KEAP1 60 μmol/L tBHQ renew at 30 h 140 kDa dimer 2.0 P < 0.0001 KEAP1 68 kDa 1.0

Relative KEAP1 P3C12 NRF2 100 kDa mRNA expression P4C10 0.0 01224364860 β-Actin 43 kDa 60 μmol/L tBHQ treatment (hours)

Figure 4. KEAP1 promoter reporter assay and KEAP1 mRNA quantiﬁcation in ARE edited clones. A, In silico predicted NRF2- (ARE) and SP1-binding sites and KEAP1 promoter reporter variants. B, Alteration of full-length KEAP1 promoter activity in H292 and A549 cells treated with tBHQ (60 mmol/L) within 24 hours. Relative luciferase activity fold changes driven by different mutants of KEAP1 promoters after chemical (12 hours) or genetic (48 hours) NRF2 activation in H292 (C) and A549 (D) cells. Data represent the mean SEM (n ¼ 3). NC, scramble control. NRF2, pcDNA3.1-NRF2-ORF. Vector, pcDNA3.1-empty vector. E, The schematic bar plot shows homozygous ARE-deleted (DARE) clones selected from CRISPR edited HEK293FT and H292 cells. Baseline KEAP1 mRNA expressions in DARE and CTL clones from HEK293FT (F) and H292 (G) cells. Error bars represent the mean SEM. The unpaired t-test was used to compare between-group differences. H, NRF2 downstream gene expression between DARE and CTL clones in H292 cells. I, KEAP1 and NRF2 protein expression levels in one DARE (P4C10) and CTL (P3C12) clone. J, KEAP1 mRNA quantiﬁcation in DARE (P4C10) and CTL (P3C12) H292 clones treated with 60 mmol/L tBHQ within 60 hours. Fresh media containing 60 mmol/L tBHQ were replenished at 30-hour time point. Data represent the mean SEM (n ¼ 3). Two-way ANOVA was used to test between-group differences.

AACRJournals.org Mol Cancer Res; 2020 OF7

Tian et al.

(Fig. 4E), we used qPCR to examine KEAP1 mRNA abundance in To better demonstrate the performance of ratio-based strategy in these single-cell clones. We found robust baseline KEAP1 mRNA predicting patient survival, we used the Kaplan–Meier curve and reduction in both heterozygous and homozygous ARE deletion time-dependent ROC curve in each dataset for visualization of this mutants when compared with the nonedited controls (CTL; Fig. 4F difference. Compared with previous NRF2 signatures (refs. 40–42; and 4G). Furthermore, qPCR showed higher expression of well-known green, red, and purple lines), the ratio-based strategy (black, blue, NRF2 downstream genes in subclones with ARE deletion than CTL in and yellow lines) showed better Kaplan–Meier curve separation in H292 cells (Fig. 4H). To confirm NRF2 and KEAP1 alterations at multiple SQC datasets with various prognostic settings (Fig. 6A–D, protein level, we selected a homozygous ARE deletion subclone left). The time-dependent ROC analysis demonstrated better accu- (P4C10) and a CTL (P3C12) subclone from H292 for Western blotting racy of ratio-based strategy in predicting endpoint over gene analysis. In P4C10, expression of the KEAP1 protein and its dimer signatures during the whole follow-up in each study (higher AUC were attenuated, whereas NRF2 protein abundance was increased of black, blue, and yellow curve; Fig. 6A–D, right). In other SQC (Fig. 4I, bands were quantified in Supplementary S5E). During tBHQ cohorts, the ratio-based strategy exhibited a consistent trend in treatment, KEAP1 mRNA in P4C10 subclone remained stable for predicting patients' survival, which demonstrated that higher TXN/ 60 hours compared with CTL (Fig. 4J, P < 0.0001). KEAP1 and lower NRF2/KEAP1 were negative biomarkers under RNA profiling measurement by different platforms (Supplementary KEAP1-based gene expression ratio predicts NRF2 disruption Fig. S3A–S3J). On the basis of the dynamic balance between NRF2 and KEAP1 gene (Fig. 5A), we assumed that KEAP1 deletion and NRF2 amplification might lead to an increased TXN/KEAP1 ratio [see method Eq. (B)], Discussion whereas NRF2-KEAP1 loss-of-function mutation may lead to a As a crucial transcription factor modulating antioxidant response decreased NRF2/KEAP1 ratio [see method Eq. (A)]. To confirm this and metabolism reaction, NRF2 is activated in several types of human assumption, in TCGA PanCancer, LUSC, and LUAD datasets, we squamous cell cancers (49). It is well known that KEAP1 protein observed the expected TXN/KEAP1 ratio increase (Fig. 5B and D). In mediates NRF2 protein degradation and inhibits its nuclear translo- the two datasets with the highest NRF2-KEAP1 somatic mutation cation. According to the TCGA Research Network publications in the frequencies, the observations also conformed to our assumptions four datasets (7, 50–52), NRF2 alterations are recurrently present in (Fig. 5C and E). Thanks to cBioportal coexpression module, we used squamous cancer types and may have resulted from NRF2 and KEAP1 linear regression equations between NRF2 and KEAP1 mRNA expres- gene somatic mutations and copy number variations (Supplementary sions to test which the NRF2-KEAP1 mutations were above the trend Fig. S4). Consistent with a recent study (8), human lung adenocar- lines (Fig. 5F). Interestingly, NRF2 mutations recurrently presented cinoma was mainly featured with KEAP1 somatic mutation and copy above the trend lines are mostly activation mutations in DLG and number deletion, while squamous cell lung carcinoma was more ETGE domains (20, 25). KEAP1 mutations recurrently presented commonly with NRF2 mutations and copy number amplifications. above the trend lines are likely to lead to function loss The correlation in SQC suggested that KEAP1 expressions were (refs. 17, 20, 25; Fig. 5I). To be brief, the more times one mutation responding to the increase NRF2 expressions, which were mostly presented above the NRF2-KEAP1 regression line, the more possible it driven by NRF2 copy number amplification and further magnified could be functional (x2 P ¼ 1.42e-20; Fig. 5G). The same tendencies in combination with NRF2 activation mutation. However, the absence were observed in the other two separate cell line datasets (Fig. 5H). The of NRF2-KEAP1 mRNA correlation in lung adenocarcinoma speci- full above the lines analysis results can be accessed in Supplementary mens did not mean the regulation vanished in lung adenocarcinoma Table S5. cells. This is supported by the observation in lung adenocarcinoma that mutated KEAP1 present higher mRNA expression (Fig. 5F). Notably, KEAP1-based gene expression ratio is prognostic in SQC KEAP1-truncating cases do not comply with this observation (yellow datasets dots labeled in Fig. 5F), which can be partially explained by two recent NRF2 downstream gene signatures have been used to infer NRF2 researches about the impact of truncated mutations that can lead to activation and tested using unsupervised clustering method in nonsense-mediated mRNA decay (NMD) and thus decrease the gene clinical samples to discover potential prognostic markers (38–40). expression (53, 54). However, this strategy does not seem to be promising in SQC The NRF2 driven KEAP1 expression at mRNA and protein levels in datasets (41). Here, we thus used KEAP1-based gene expression multiple human lung cell lines proved that activation of NRF2 could ratio as an alternative strategy to identify patients with SQC with increase KEAP1 transcription. The highly consistent binding region in poor prognosis. To determine abnormal TXN/KEAP1 and NRF2/ KEAP1 promoter indicates that NRF2 feedback is conserved among KEAP1 ratio, we compared these ratios between SQC and paired human cell types. Practically, we observed three different responding normal tissues. We found that the maximum TXN/KEAP1 ratio in patterns in different lung cell lines. The fast-in-fast-out pattern of normal lung was approximately equal to upper quartile of the ratio KEAP1 protein alteration in H292 cells suggested tightly controlled in SQC. On the other side, the minimum NRF2/KEAP1 ratio in NRF2 activation in this cell line, which was also observed in BEAS-2B normal lung was roughly equal to lower quartile of the ratio in SQC, cells. The steadily increasing pattern of KEAP1 protein alteration in respectively (Supplementary Fig. S2A). SQC cell lines (SK-MES-1, Fig. 2F; H520, Supplementary Fig. S5D) To characterize molecular signatures in patients with different ratio suggested delayed feedback control of NRF2 activation. Moreover, the quartiles, we compared gene expression profiles in outlier patients with KEAP1 protein Western blot, KEAP1 promoter luciferase assay and either higher TXN/KEAP1 or lower NRF2/KEAP1 ratio to the remain- ChIP-qPCR results in A549 cells (KEAP1 p.G333C, loss-of-function ing (Supplementary Fig. S2B). GSEA analysis in Molecular Signatures mutation) imply the existence of a completely broken balance between Database (MSigDB) demonstrated that reactive oxygen species hall- NRF2 signaling and loss of functional KEAP1 mutation, which can mark (Supplementary Fig. S2C) and oncogenic NRF2 pathway (Sup- reflect as oversaturated NRF2 occupancies at KEAP1 promoter plementary Fig. S2D) were significantly enriched in outlier patients. regions.

OF8 Mol Cancer Res; 2020 MOLECULAR CANCER RESEARCH

NRF2 Activates KEAP1 Expression in Human Cancers

NRF2 amplification A Equilibrium state NRF2-KEAP1 mutation or KEAP1 deletion TXN mRNA TXN mRNA TXN mRNA

NRF2 NRF2 NRF2 Nuclear Nuclear Nuclear KEAP1 KEAP1 KEAP1

KEAP1 mRNA KEAP1 mRNA KEAP1 mRNA

BCTXN/KEAP1 ratio predict KEAP1 deletion and NRF2 amplification NRF2/KEAP1 ratio predict NRF2-KEAP1 mutation PanCancer TCGA-LUSC TCGA-LUAD TCGA-LUSC TCGA-LUAD 100 100 100 100 100

80 80 80 80 80

60 60 60 60 60

40 40 40 40 40 Sensitivity (%) Sensitivity (%) Sensitivity (%) Sensitivity (%) Sensitivity (%) 20 20 AUC = 0.66, P < 1e-15 20 AUC = 0.67, P = 1e-10 20 AUC = 0.62, P = 6.9e-6 20 AUC = 0.61, P = 1.3e-4 AUC = 0.70, P = 2.3e-10 95% CI = 0.65−0.67 95% CI = 0.62−0.72 95% CI = 0.57−0.67 95% CI = 0.56−0.68 95% CI = 0.63−0.76 0 0 0 0 0 020406080100 020406080100 020406080100 020406080100 0 20 40 60 80 100 1-Specificity (%) 1-Specificity (%) 1-Specificity (%) 1-Specificity (%) 1-Specificity (%) DE KEAP1 del or NRF2 amp n = 2842 KEAP1 del or NRF2 amp n = 281 KEAP1 del or NRF2 amp n = 330 25 NRF2-KEAP1 mutation n = 118 NRF2-KEAP1 mutation n = 107 15 30 40 40 KEAP1 not del and NRF2 not amp n = 7047 KEAP1 and NRF2 not altered n = 203 KEAP1 not del and NRF2 not amp n = 177 NRF2-KEAP1 mutation n = 363 NRF2-KEAP1 mutation n = 403 20 30 30 10 20 15 20 20 10 5 10 10 5 10 Frequency (%) Frequency (%) Frequency (%) Frequency (%) Frequency (%) 0 0 0 0 0 −4.25 −2.25 −0.25 1.75 2.75 −20 246 −1.5 0.5 2.5 4.5 6.5 −1.2 0.4 2.0 3.6 −1.2 0.4 2.0 3.6 5.2 TXN/KEAP1 TXN/KEAP1 TXN/KEAP1 NRF2/KEAP1 NRF2/KEAP1 Log2 ( ) Log2 ( ) Log2 ( ) Log2 ( ) Log2 ( )

NRF2 mutation KEAP1 mutation KEAP1 truncation FGBoth mutated Neither mutated Functional mutation TCGA-LUSC TCGA-LUAD Above line times Netrual or unknow mutation 15 13 “Above the line” “Above the line” Never P = 1.42e-20 14 12 Once 100% Twice E41* 2% 80% 13 11 3 2% 1% 2% 4 60% 12 10 5 Q201* 7% K287* 6+ 40% 11 E441* E449* 9 Q193* 34% Y345* K323* Percentage 20% 10 8 Q75* 0% 9 e 3 5 7 4 6+ Never Once Twic 8 6 52% mRNA expression KEAP1 mRNA mRNA expression KEAP1 mRNA Above line times 10 11 12 13 14 15 16 10 11 12 13 14 15 NRF2 mRNA expression NRF2 mRNA expression I Top rank above line mutations H CCLE 2019 NSCLC cell lines Coldren 2006 NSCLC cell lines (RNA-seq) (Microarray) NRF2 mutation Times above Presence KEAP1 mutation Times above Presence No mutation R34G R470C KEAP1 mutation 20 20 5 5 Unknown E82D 14 14 G480W 4 4 KEAP1/NRF2 ratio V271L 0.5 D29H 13 16 4 5 1.0 1.5 E79Q R554Q 2.0 13 14 3 3 E79K 6 7 S45F 3 3 G31A 6 6 X570_splice 3 5 No mutation D29Y 5 5 D236N 2 3 NRF2 mutation R34P 5 7 E117K 2 2 KEAP1 mutation

mRNA expression mRNA D29N G333C mRNA expression mRNA KEAP1/NRF2 ratio 4 5 2 2 1 G81S 4 5 G333S 2 2 2 G81V 4 4 G417V 2 2 P r n R34Q 4 6 G423V 2 2 P = 0.0007 r = 0.30 n = 126 KEAP1 = 0.0189 = 0.37 = 41 KEAP1 W24R 4 4 L115Q 2 2 L30F P278L NRF2 mRNA expression NRF2 mRNA expression 4 6 2 2

Figure 5. Inferring NRF2 signaling disruption by ratio-based strategy. A, Schematic for inferred NRF2-KEAP1 disequilibrium caused by NRF2-KEAP1 alterations. B, ROC curve of TXN/KEAP1 ratio predicting NRF2 amplification and KEAP1 deletion in TCGA PanCancer (left), LUSC (center), and LUAD (right) datasets. C, ROC curve of NRF2/KEAP1 ratio predicting NRF2-KEAP1 mutations in LUSC (left) and LUAD (right) datasets. D, Distribution of TXN/KEAP1 ratio for cases with or without NRF2 amplification or KEAP1 deletion in TCGA PanCancer (left), LUSC (center), and LUAD (right) datasets. E, Distribution of NRF2/KEAP1 ratio for cases with or without NRF2-KEAP1 mutations in LUSC (left) and LUAD (right) datasets. F, Visualization of “Above the line” analysis in LUSC (left) and LUAD (right) datasets. G, Associations between times of “above the line” in TCGA PanCancer and NRF2-KEAP1 mutation annotations. H, “Above the line” profiling in NSCLC cell line dataset CCLE 2019 (left) and Coldren 2006 (right). I, Top-rank mutations with above line and presence times in TCGA PanCancer datasets.

AACRJournals.org Mol Cancer Res; 2020 OF9

Tian et al.

ABTCGA-LUSC DFS TCGA-LUSC DSS P = 0.99 HR = 0.99 P = 2e-4 HR = 2.65 P = 0.49 HR = 0.85 P = 0.0016 HR = 2.04 Singh’s NRF2 target Two ratios Singh’s NRF2 target Two ratios 95% CI = 0.59−1.69 95% CI = 1.55−4.53 95% CI = 0.54−1.35 95% CI = 1.30−3.20 P = 0.82 HR = 0.94 NRF2/KEAP1 P = 0.046 HR = 1.71 P = 0.16 HR = 0.71 NRF2/KEAP1 P = 0.38 HR = 1.24 Cescon’s signature Cescon’s signature 95% CI = 0.55−1.60 ratio 95% CI = 1.01−2.9 95% CI = 0.44−1.15 ratio 95% CI = 0.77−2.02 P = 0.98 HR = 1.00 TXN/KEAP1 P = 0.03 HR = 1.81 P = 0.43 HR = 0.83 TXN/KEAP1 P = 6e-4 HR = 2.13 Rodrigo’s signature Rodrigo’s signature 95% CI = 0.6−1.69 ratio 95% CI = 1.05−3.12 95% CI = 0.53−1.31 ratio 95% CI = 1.37−3.32 1.0 1.0 1.0 1.0

0.9 0.8 0.9 0.8 0.8 0.8 0.6 0.6 0.7

0.7 AUC(t)

0.4 AUC(t) 0.4 0.6 0.6 Survival fraction 0.2 0.5 Survival fraction 0.2 0.5

0.0 0.4 0.0 0.4 0 50 100 150 10 20 30 40 50 60 70 0 50 100 150 10 20 30 40 50 60 70 Time (months) Time (months) Time (months) Time (months)

CDGSE3141-SQC OS GSE37745 SQC OS P = 0.14 HR = 0.55 P = 0.005 HR = 3.00 P = 0.36 HR = 0.77 P = 0.021 HR = 1.90 Singh’s NRF2 target Two ratios Singh’s NRF2 target Two ratios 95% CI = 0.25−1.22 95% CI = 1.35−6.70 95% CI = 0.45−1.34 95% CI = 1.09−3.29 P = 0.15 HR = 0.56 NRF2/KEAP1 P = 3e-4 HR = 3.90 P = 0.40 HR = 1.27 NRF2/KEAP1 P = 0.33 HR = 1.35 Cescon’s signature Cescon’s signature 95% CI = 0.25−1.26 ratio 95% CI = 1.76−8.65 95% CI = 0.73−1.20 ratio 95% CI = 0.74−2.46 P = 0.14 HR = 0.55 TXN/KEAP1 P = 0.64 HR = 1.22 P = 0.044 HR = 0.57 TXN/KEAP1 P = 0.06 HR = 1.78 Rodrigo’s signature Rodrigo’s signature 95% CI = 0.25−1.22 ratio 95% CI = 0.53−2.82 95% CI = 0.32−0.99 ratio 95% CI = 0.98−3.25 1.0 1.0 1.0 1.0 0.9 0.8 0.8 0.9 0.8 0.6 0.6 0.8 0.7 0.7 0.4 0.6 AUC(t) 0.4 AUC(t) 0.5 0.6 0.2 0.2 Survival fraction 0.4 Survival fraction 0.5 0.0 0.0 0.3 0204060 80 10 20 30 40 50 60 70 01k2k3k 4k 5k 0.5k 1k 1.5k 3.5k2k4k Time (months) Time (months) Time (days) Time (days)

Figure 6. Evaluation of prognosis performance with previous NRF2 signatures and gene ratios in SQC cohorts. A, Kaplan–Meier curve (left) and time-dependent ROC analysis (right) in LUSC comparing DFS between previous published NRF2 signatures and gene ratio strategy. B, Kaplan–Meier curve (left) and time-dependent ROC analysis (right) in LUSC comparing DSS between previous published NRF2 signatures and gene ratio strategy. C, Kaplan–Meier curve (left) and time-dependent ROC analysis (right) in Bild SQC cohort comparing overall survival (OS) between previous published NRF2 signatures and gene ratio strategy. D, Kaplan–Meier curve (left) and time-dependent ROC analysis (right) in Botling SQC comparing OS between previous published NRF2 signatures and gene ratio strategy.

Interestingly, negative feedback may generally function as a reason that we observed good separation by TXN/KEAP1 ratio in cushion to alleviate the effect of functional mutations (55). multiple SQC datasets (Supplementary Fig. S3D–S3I). Although previous studies demonstrated that NRF2 and KEAP1 Current RNA profiling technologies detect certain transcript somatic mutations were responsible for constitutively NRF2 acti- presence from a snapshot of the whole transcriptome. However, vation in human cancers (40, 41), thoroughly functional charac- the real transcriptome undergoes continuously changing and com- terization of these candidate variants remained as a great chal- plicated interactions (57). Although technologies are improving lenge (20). Here, by perceiving the nature of KEAP1 mRNA now and then, standardizations between different platforms and compensation in response to NRF2 activation, we could anticipate batches still challenge the application of certain gene signature to more experimental or algorithm designs in prioritizing mutations clinical settings (58). Under this circumstance, knowing transcrip- causing NRF2 signaling disequilibrium. tome regulatory seems considerably important for noise reduction Another NRF2 downstream gene TXN (Thioredoxin) mRNA and alteration identification. In gene-ratio–based strategy, dividing expression in the model may help define copy number changes of two genes within each sample effectively removed between-sample NRF2 and KEAP1. TXN protein catalyzes dithiol-disulfide exchange variation and system noise, which made the prediction more reactions, which is crucial to maintain intracellular redox balance (56). accurate and reliable (59, 60). By applying gene ratios to evaluate Similar to KEAP1, the NRF2 binding site of TXN was also in the NRF2-KEAP1 disequilibrium, we can expect to dissect functional proximal–promoter region, right near its first exon (Supplementary disruptive somatic mutations in this pathway. More importantly, Fig. S1C). This ARE pattern similarity might partially explain the because NRF2 activation leads to chemotherapy resistance in parallel expression between the two genes (Supplementary Fig. S1A multiple cancer types (61), this strategy could also provide quan- and S1B) and suggested TXN/KEAP1 ratio could be a good conse- titative and reproducible information in identifying intrinsic non- quential indicator of NRF2-KEAP1 disequilibrium, which was the responding patients.

OF10 Mol Cancer Res; 2020 MOLECULAR CANCER RESEARCH

NRF2 Activates KEAP1 Expression in Human Cancers

In summary, we characterized NRF2-driven KEAP1 expression review and editing. Q. Liu: Data curation, investigation. S. Yu: Investigation. Q. Chu: in human cancer contexts and proposed a new perspective in under- Funding acquisition. Y. Chen: Resources. K. Wu: Supervision, funding acquisition. standing NRF2 signaling regulation. Considering that NRF2 alteration L. Wang: Data curation, formal analysis, supervision, funding acquisition. is a frequently altered signaling pathway in human cancers, our work will help pinpoint NRF2-KEAP1 disruption and promote drug dis- Acknowledgments covery designed for targeting this pathway. We want to give our thanks to Dr. Kunnimalaiyaan (Muthusamy Kunnima- laiyaan) at the Medical College of Wisconsin for his time spent in discussing this Disclosure of Potential Conflicts of Interest project. This study was supported by Natural Science Foundation of China (grant nos. 81572608 and 81874120, to K. Wu and Q. Chu), Advancing a Healthier K. Wu reports grants from Natural Science Foundation of China (grant nos. Wisconsin Fund (Project # 5520227), and Moffitt Cancer Center Faculty Startup 81572608, 81874120) during the conduct of the study. No potential conflicts of Fund (to L. Wang). interest were disclosed by the other authors.

Disclaimer The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked The funders had no role in study design, data collection, and analysis, decision to advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate publish, or preparation of the manuscript. this fact. Authors’ Contributions Y. Tian: Conceptualization, resources, data curation, software, validation, Received January 31, 2020; revised May 18, 2020; accepted June 16, 2020; investigation, visualization, writing-original draft, project administration, writing- published ﬁrst June 22, 2020.

References 1. Herbst RS, Khuri FR, Fossella FV, Glisson BS, Kies MS, Pisters KM, et al. ZD1839 18. Fukutomi T, Takagi K, Mizushima T, Ohuchi N, Yamamoto M. Kinetic, (Iressa) in non-small-cell lung cancer. Clin Lung Cancer 2001;3:27–32. thermodynamic, and structural characterizations of the association between 2. Koivunen JP, Mermel C, Zejnullahu K, Murphy C, Lifshits E, Holmes AJ, et al. Nrf2-DLGex degron and Keap1. Mol Cell Biol 2014;34:832–46. EML4-ALK fusion gene and efficacy of an ALK kinase inhibitor in lung cancer. 19. Hast BE, Cloer EW, Goldfarb D, Li H, Siesser PF, Yan F, et al. Cancer-derived Clin Cancer Res 2008;14:4275–83. mutations in KEAP1 impair NRF2 degradation but not ubiquitination. 3. Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, et al. Cancer Res 2014;74:808–17. Cancer immunology. Mutational landscape determines sensitivity to PD-1 20. Kerins MJ, Ooi A. A catalogue of somatic NRF2 gain-of-function mutations in blockade in non-small cell lung cancer. Science 2015;348:124–8. cancer. Sci Rep 2018;8:12846. 4. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio 21. Kim E, Ilic N, Shrestha Y, Zou L, Kamburov A, Zhu C, et al. Systematic functional cancer genomics portal: an open platform for exploring multidimensional cancer interrogation of rare cancer variants identifies oncogenic alleles. Cancer Discov genomics data. Cancer Discov 2012;2:401–4. 2016;6:714–26. 5. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative 22. Kobayashi M, Itoh K, Suzuki T, Osanai H, Nishikawa K, Katoh Y, et al. analysis of complex cancer genomics and clinical profiles using the cBioPortal. Identification of the interactive interface and phylogenic conservation of the Sci Signal 2013;6:pl1. Nrf2-Keap1 system. Genes Cells 2002;7:807–20. 6. Ohta T, Iijima K, Miyamoto M, Nakahara I, Tanaka H, Ohtsuji M, et al. Loss of 23. Lee DF, Kuo HP, Liu M, Chou CK, Xia W, Du Y, et al. KEAP1 E3 ligase-mediated Keap1 function activates Nrf2 and provides advantages for lung cancer cell downregulation of NF-kappaB signaling by targeting IKKbeta. Mol Cell 2009;36: growth. Cancer Res 2008;68:1303–9. 131–40. 7. Cancer Genome Atlas Research Network. Comprehensive genomic character- 24. Shibata T, Kokubu A, Gotoh M, Ojima H, Ohta T, Yamamoto M, et al. Genetic ization of squamous cell lung cancers. Nature 2012;489:519–25. alteration of Keap1 confers constitutive Nrf2 activation and resistance to 8. Frank R, Scheffler M, Merkelbach-Bruse S, Ihle MA, Kron A, Rauer M, chemotherapy in gallbladder cancer. Gastroenterology 2008;135:1358–68. et al. Clinical and pathological characteristics of KEAP1- and NFE2L2- 25. Shibata T, Ohta T, Tong KI, Kokubu A, Odogawa R, Tsuta K, et al. Cancer related mutated non-small cell lung carcinoma (NSCLC). Clin Cancer Res 2018; mutations in NRF2 impair its recognition by Keap1-Cul3 E3 ligase and promote 24:3087–96. malignancy. Proc Natl Acad Sci U S A 2008;105:13568–73. 9. Tian Y, Liu Q, He X, Yuan X, Chen Y, Chu Q, et al. Emerging roles of Nrf2 signal 26. Tian Y, Wu K, Liu Q, Han N, Zhang L, Chu Q, et al. Modification of platinum in non-small cell lung cancer. J Hematol Oncol 2016;9:14. sensitivity by KEAP1/NRF2 signals in non-small cell lung cancer. J Hematol 10. Tong KI, Padmanabhan B, Kobayashi A, Shang C, Hirotsu Y, Yokoyama S, et al. Oncol 2016;9:83. Different electrostatic potentials define ETGE and DLG motifs as hinge and latch 27. Liu Q, Li A, Yu S, Qin S, Han N, Pestell RG, et al. DACH1 antagonizes CXCL8 to in oxidative stress response. Mol Cell Biol 2007;27:7511–21. repress tumorigenesis of lung adenocarcinoma and improve prognosis. 11. Ma Q. Role of nrf2 in oxidative stress and toxicity. Annu Rev Pharmacol Toxicol J Hematol Oncol 2018;11:53. 2013;53:401–26. 28. Haeussler M, Schonig K, Eckert H, Eschstruth A, Mianne J, Renaud JB, et al. 12. Lee OH, Jain AK, Papusha V, Jaiswal AK. An auto-regulatory loop between stress Evaluation of off-target and on-target scoring algorithms and integration into the sensors INrf2 and Nrf2 controls their cellular abundance. J Biol Chem 2007;282: guide RNA selection tool CRISPOR. Genome Biol 2016;17:148. 36412–20. 29. Arbab M, Srinivasan S, Hashimoto T, Geijsen N, Sherwood RI. Cloning-free 13. Chorley BN, Campbell MR, Wang X, Karaca M, Sambandan D, Bangura F, et al. CRISPR. Stem Cell Reports 2015;5:908–17. Identification of novel NRF2-regulated genes by ChIP-Seq: influence on retinoid 30. Chen B, Gilbert LA, Cimini BA, Schnitzbauer J, Zhang W, Li GW, et al. Dynamic X receptor alpha. Nucleic Acids Res 2012;40:7416–29. imaging of genomic loci in living human cells by an optimized CRISPR/Cas 14. Na HK, Surh YJ. Oncogenic potential of Nrf2 and its principal target protein system. Cell 2013;155:1479–91. heme oxygenase-1. Free Radic Biol Med 2014;67:353–65. 31. Ran FA, Hsu PD, Wright J, Agarwala V, Scott DA, Zhang F. Genome engineering 15. Sanghvi VR, Leibold J, Mina M, Mohan P, Berishaj M, Li Z, et al. The oncogenic using the CRISPR-Cas9 system. Nat Protoc 2013;8:2281–308. action of NRF2 depends on de-glycation by fructosamine-3-kinase. Cell 2019; 32. Wang X, Campbell MR, Lacher SE, Cho HY, Wan M, Crowl CL, et al. A 178:807–19. polymorphic antioxidant response element links NRF2/sMAF binding to 16. Menegon S, Columbano A, Giordano S. The dual roles of NRF2 in cancer. enhanced MAPT expression and reduced risk of parkinsonian disorders. Trends Mol Med 2016;22:578–93. Cell Rep 2016;15:830–42. 17. Berger AH, Brooks AN, Wu X, Shrestha Y, Chouinard C, Piccioni F, et al. High- 33. Levings DC, Wang X, Kohlhase D, Bell DA, Slattery M. A distinct class of throughput phenotyping of lung cancer somatic mutations. Cancer Cell 2016;30: antioxidant response elements is consistently activated in tumors with NRF2 214–28. mutations. Redox Biol 2018;19:235–49.

AACRJournals.org Mol Cancer Res; 2020 OF11

Tian et al.

34. Feng J, Liu T, Qin B, Zhang Y, Liu XS. Identifying ChIP-seq enrichment using 48. Fornes O, Castro-Mondragon JA, Khan A, van der Lee R, Zhang X, Richmond MACS. Nat Protoc 2012;7:1728–40. PA, et al. JASPAR 2020: update of the open-access database of transcription 35. Robinson JT, Thorvaldsdottir H, Wenger AM, Zehir A, Mesirov JP. Variant factor binding profiles. Nucleic Acids Res 2020;48:D87–92. review with the integrative genomics viewer. Cancer Res 2017;77:e31–4. 49. Dotto GP, Rustgi AK. Squamous cell cancers: a unified perspective on biology 36. Li Q, Birkbak NJ, Gyorffy B, Szallasi Z, Eklund AC. Jetset: selecting the and genetics. Cancer Cell 2016;29:622–37. optimal microarray probe set to represent a gene. BMC Bioinformatics 2011; 50. Cancer Genome Atlas Network. Comprehensive genomic characterization of 12:474. head and neck squamous cell carcinomas. Nature 2015;517:576–82. 37. Balduzzi S, Rucker G, Schwarzer G. How to perform a meta-analysis with R: a 51. Cancer Genome Atlas Research Network, Albert Einstein College of Medicine, practical tutorial. Evid Based Ment Health 2019;22:153–60. Analytical Biological Services, Barretos Cancer Hospital, Baylor College of 38. Almeida-de-Macedo MM, Ransom N, Feng Y, Hurst J, Wurtele ES. Medicine, Beckman Research Institute of City of Hope, , et al. et al. Integrated Comprehensive analysis of correlation coefficients estimated from genomic and molecular characterization of cervical cancer. Nature 2017;543: pooling heterogeneous microarray data. BMC Bioinformatics 2013;14: 378–84. 214. 52. Cancer Genome Atlas Research Network, Analysis Working Group, Asan 39. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in University, BC Cancer Agency, Brigham and Women's Hospital, Broad Institute, meta-analyses. BMJ 2003;327:557–60. , et al. et al. Integrated genomic characterization of oesophageal carcinoma. 40. Singh A, Misra V, Thimmulappa RK, Lee H, Ames S, Hoque MO, et al. Nature 2017;541:169–75. Dysfunctional KEAP1-NRF2 interaction in non-small-cell lung cancer. 53. Jia P, Zhao Z. Impacts of somatic mutations on gene expression: an association PLoS Med 2006;3:e420. perspective. Brief Bioinform 2017;18:413–25. 41. Cescon DW, She D, Sakashita S, Zhu CQ, Pintilie M, Shepherd FA, et al. NRF2 54. You KT, Li LS, Kim NG, Kang HJ, Koh KH, Chwae YJ, et al. Selective pathway activation and adjuvant chemotherapy benefit in lung squamous cell translational repression of truncated proteins from frameshift mutation- carcinoma. Clin Cancer Res 2015;21:2499–505. derived mRNAs in tumors. PLoS Biol 2007;5:e109. 42. Romero R, Sayin VI, Davidson SM, Bauer MR, Singh SX, LeBoeuf SE, et al. Keap1 55. Marciano DC, Lua RC, Herman C, Lichtarge O. Cooperativity of negative loss promotes Kras-driven lung cancer and results in dependence on glutami- autoregulation confers increased mutational robustness. Phys Rev Lett 2016; nolysis. Nat Med 2017;23:1362–8. 116:258104. 43. Blanche P, Dartigues JF, Jacqmin-Gadda H. Estimating and compar- 56. Mochizuki A, Saso A, Zhao Q, Kubo S, Nishida N, Shimada I. Balanced ing time-dependent areas under receiver operating characteristic regulation of redox status of intracellular thioredoxin revealed by in-cell NMR. curves for censored event times with competing risks. Stat Med J Am Chem Soc 2018;140:3784–90. 2013;32:5381–97. 57. Kim K, Zakharkin SO, Allison DB. Expectations, validity, and reality in gene 44. Wang H, Liu K, Geng M, Gao P, Wu X, Hai Y, et al. RXRalpha inhibits the NRF2- expression profiling. J Clin Epidemiol 2010;63:950–9. ARE signaling pathway through a direct interaction with the Neh7 domain of 58. Michiels S, Ternes N, Rotolo F. Statistical controversies in clinical research: NRF2. Cancer Res 2013;73:3097–108. prognostic gene signatures are not (yet) useful in clinical practice. Ann Oncol 45. Saidu NE, Noe G, Cerles O, Cabel L, Kavian-Tessler N, Chouzenoux S, et al. 2016;27:2160–7. Dimethyl fumarate controls the NRF2/DJ-1 axis in cancer cells: therapeutic 59. Reddy A, Growney JD, Wilson NS, Emery CM, Johnson JA, Ward R, et al. Gene applications. Mol Cancer Ther 2017;16:529–39. expression ratios lead to accurate and translatable predictors of DR5 agonism 46. Homma S, Ishii Y, Morishima Y, Yamadori T, Matsuno Y, Haraguchi N, et al. across multiple tumor lineages. PLoS One 2015;10:e0138486. Nrf2 enhances cell proliferation and resistance to anticancer drugs in human 60. Price ND, Trent J, El-Naggar AK, Cogdell D, Taylor E, Hunt KK, et al. Highly lung cancer. Clin Cancer Res 2009;15:3423–32. accurate two-gene classifier for differentiating gastrointestinal stromal tumors 47. Davis CA, Hitz BC, Sloan CA, Chan ET, Davidson JM, Gabdank I, et al. The and leiomyosarcomas. Proc Natl Acad Sci U S A 2007;104:3414–9. encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids 61. Rojo de la Vega M, Chapman E, Zhang DD. NRF2 and the hallmarks of cancer. Res 2018;46:D794–801. Cancer Cell 2018;34:21–43.

OF12 Mol Cancer Res; 2020 MOLECULAR CANCER RESEARCH

NRF2-Driven KEAP1 Transcription in Human Lung Cancer

Yijun Tian, Qian Liu, Shengnan Yu, et al.

Mol Cancer Res Published OnlineFirst June 22, 2020.

Updated version Access the most recent version of this article at: doi:10.1158/1541-7786.MCR-20-0108

Supplementary Access the most recent supplemental material at: Material http://mcr.aacrjournals.org/content/suppl/2020/06/20/1541-7786.MCR-20-0108.DC1

E-mail alerts Sign up to receive free email-alerts related to this article or journal.

Reprints and To order reprints of this article or to subscribe to the journal, contact the AACR Publications Subscriptions Department at [email protected].

Permissions To request permission to re-use all or part of this article, use this link http://mcr.aacrjournals.org/content/early/2020/07/13/1541-7786.MCR-20-0108. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC) Rightslink site.