Integrated Analysis of Transcriptomes of Cancer Cell Lines and Patient Samples Reveals STK11/LKB1–Driven Regulation of Camp Phosphodiesterase-4D

Published OnlineFirst August 13, 2014; DOI: 10.1158/1535-7163.MCT-14-0297

Molecular Cancer Models and Technologies Therapeutics

Integrated Analysis of Transcriptomes of Cancer Cell Lines and Patient Samples Reveals STK11/LKB1–Driven Regulation of cAMP Phosphodiesterase-4D

Ningning He1,2, Nayoung Kim1,2, Mee Song1, Choa Park1, Somin Kim1, Eun Young Park2, Hwa Young Yim2, Kyunga Kim3, Jong Hoon Park2, Keun Il Kim2, Fan Zhang4, Gordon B. Mills4, and Sukjoon Yoon1,2

Abstract The recent proliferation of data on large collections of well-characterized cancer cell lines linked to therapeutic drug responses has made it possible to identify lineage- and mutation-specific transcriptional markers that can help optimize implementation of anticancer agents. Here, we leverage these resources to systematically investigate the presence of mutation-specific transcription markers in a wide variety of cancer lineages and genotypes. Sensitivity and specificity of potential transcriptional biomarkers were simultaneously analyzed in 19 cell lineages grouped into 228 categories based on the mutational genotypes of 12 cancer-related genes. Among a total of 1,455 category-specific expression patterns, the expression of cAMP phosphodiesterase-4D (PDE4D) with 11 isoforms, one of the PDE4(A-D) subfamilies, was predicted to be regulated by a mutant form of serine/threonine kinase 11 (STK11)/liver kinase B1 (LKB1) present in lung cancer. STK11/ LKB1 is the primary upstream kinase of adenine monophosphate–activated protein kinase (AMPK). Subse- quently, we found that the knockdown of PDE4D gene expression inhibited proliferation of STK11-mutated lung cancer lines. Furthermore, challenge with a panel of PDE4-specific inhibitors was shown to selectively reduce the growth of STK11-mutated lung cancer lines. Thus, we show that multidimensional analysis of a well-characterized large-scale panel of cancer cell lines provides unprecedented opportunities for the identification of unexpected oncogenic mechanisms and mutation-specific drug targets. Mol Cancer Ther; 13(10); 1–11. 2014 AACR.

Introduction and metastasis (2). Moreover, mutational status of specific The limited success of chemotherapeutics has led to the cancer lineages can affect the sensitivity to or resistance development of cancer therapies that block the growth against drugs (3). Thus, the variation in genotype of and progression of cancer by targeting and interfering different cancer lineages is important in the context of with specific molecules involved in tumorigenesis. How- the heterogeneity of the anticancer drug response. ever, many targeted therapeutics that specifically target Recently, there has been an increase in the availability of tumor-associated components have not measured up to large-scale human cell line and tissue data in multilevel expectations in clinical studies (1). DNA abnormalities experiments such as sequencing, gene expression, protein and genetic alterations contribute to all aspects of cancer regulation, and compound response. These diverse data- development, including tumor initiation, progression, sets can be used to identify mutation- and lineage-specific cancer signatures that provide new insights into targeted cancer therapies. For example, a public multiple cell line

1 high-throughput screening dataset, called NCI-60 project, Center for Advanced Bioinformatics and Systems Medicine, Sookmyung Women's University, Seoul, Republic of Korea. 2Department of Biological was developed by the U.S. National Cancer Institute (NCI; Sciences, Sookmyung Women's University, Seoul, Republic of Korea. Bethesda, MD), to analyze 60 cancer cell lines as a pre- 3Department of Statistics, Sookmyung Women's University, Seoul, Repub- lic of Korea. 4Systems Biology, University of Texas MD Anderson Cancer dictive model system of gene/protein expression and Center, Houston, Texas. drug response (4). The GlaxoSmithKline dataset, which Note: Supplementary data for this article are available at Molecular Cancer contains microarray and drug screening data for more Therapeutics Online (http://mct.aacrjournals.org/). than 300 cancer cell lines, has been made available N. He and N. Kim contributed equally to this article. through the NCI’s cancer Bioinformatics Grid (5, 6). A collection known as the Cancer Cell Line Encyclopedia Corresponding Author: Sukjoon Yoon, Center for Advanced Bioinformat- ics and Systems Medicine, Sookmyung Women's University, Hyochang- (CCLE) furnishes gene expression data for 947 cancer cell won-gil 52, Yongsan-gu, Seoul 140-742, Republic of Korea. Phone: 82-2- lines as well as chemical screening data, and has expanded 710-9415; Fax: 82-2-2077-7322; E-mail: [email protected] the understanding of preclinical cancer cell line models in doi: 10.1158/1535-7163.MCT-14-0297 the context of mutational status (7). The mutational gen- 2014 American Association for Cancer Research. otypes and drug sensitivity of human cancer cell lines

www.aacrjournals.org OF1

He et al.

have been well characterized in the Sanger COSMIC values existed over 150 cell lines. For validation, the project (8). GSE6135 dataset constructed from a total of 12 STK11 We have previously used the GlaxoSmithKline cell line [liver kinase B1 (LKB1)] wild-type and mutant samples datasets to assess mutation-oriented integration of mul- was obtained from the NCBI Gene Expression Omnibus tilevel "omics" and chemical screening data, in the context (GEO) ftp. Gene expression levels were compared of mutations that might be the source of oncogenic sig- between parental A549 and H2126 lung cancer cell lines, naling (9). If a mutation is the direct driver of cancer which express STK11 homozygous-mutant proteins, and progression, blocking the functional consequences of the are null for STK11, respectively, and the same cell lines mutation or regulation of downstream markers may have expressing wild-type STK11 (18), using the GEO2R tool. better chance to inhibit cancer progression. For example, To analyze tissue-based gene expression profile, data driver mutations such EGFR, PIK3CA, BRAF, ALK, HER2, from patients’ lung adenocarcinoma (LUAD) samples AKT1, MAP2K1, and MET in NSCLC have been reported were obtained from the TCGA. These data (level 3) have as potential targets of anticancer agents with some clinical 355 and 490 (updated on 2013) tumor samples analyzed relevance (10). Thus, it is important to distinguish muta- with the Illumina HiSeq 200 RNA-Sequencing platform. tion-driven markers from mutation-associated markers. The expression signal of each gene is represented with Analysis of DNA microarray data of cancer cell lines has upper quartile normalized RNA-Seq by expectation max- generated a large number of transcription signatures that imization (RSEM) count estimates. Fold change values per may have direct or indirect relationships with a cancer sample of a gene were calculated by median value of total lineage or oncogenic mutation (11). samples. In the present study, we applied two orthogonal metrics to independently measure gene expression and relations Statistical analysis with specific mutations in the context of cellular lineage To compare gene expression levels of various geno- among a large number of samples. With this approach, types and/or lineages, fold change and t test P values we were able to identify mutation-driven signatures in were calculated using microarray gene expression data. specific cellular lineages. To validate the identified tran- The log2 fold change of a probe is given by the difference scriptional signatures, we then applied the signature to between the average of cell lines for each category and The Cancer Genome Atlas (TCGA) datasets (12), which median value of total cell lines. To determine statistical provide mutational genotypes and RNA expression significance, we calculated P value from t statistic. To levels for hundreds of human cancer tissues in major describe the selective association between gene expres- cancer lineages. Especially, we focused on the mutation sion and genotype, we also calculated enrichment score of serine/threonine kinase 11 (STK11), which was known using an odds ratio between the observed odds and as a major tumor suppressor and the upstream kinase expected odds. The observed odds is the ratio for the > of adenine monophosphate–activated protein kinase number of differentially expressed (log2 fold change 1 (AMPK). Its significance for cancer and cell metabolism and P < 0.01) cell lines with specific genotype via the has been well known (13, 14). In this study, phosphodi- number of cell lines with specific genotype. The expected esterase-4D (PDE4D) expression showed significant asso- odds is the ratio for the number of differentially expressed STK11 > P < ciation with mutation in lung cancer. PDE4D is one (log2 fold change 1 and 0.01) cell lines versus the of PDE4 subfamilies (A/B/C/D). PDE4 enzymes break number of total cell lines except missing values. In addi- down cAMP and sequestered species underpin cAMP tion, the probability of an odds ratio was calculated by signal compartmentalization in cells, such that each pro- Fisher exact test using the R open-source computing duces a series of isoforms with distinct functional roles language, version 2.15. Fisher exact test uses hypergeo- (15–17). As a key candidate mutational target, PDE4D was metric distribution to determine the statistical significance demonstrated experimentally to be directly regulated by of the agreement between individual question pairs (19). the predicted mutation and lineage characteristics and potentially plays a role in mutation-specific cancer regu- Cell line culture and siRNA transfection lation. The ability to identify and validate candidate NCI-60 lung cancer cell lines (NCI-H460 and A549) targets of tissue-specific mutational events supports the were obtained from Developmental Therapeutics Pro- power of the proposed data mining strategy. gram NCI/NIH (DTP) on January 13, 2010. NCI-60 lung cancer cell lines (NCI-H322M and NCI-H226) were Materials and Methods obtained from the U.S. NCI (DTP) on February 7, 2012. Data acquisition NCI-H1993, NCI-H1395, NCI-H82, and NCI-H524 were Microarray gene expression data for 318 cell lines were obtained from American Type Culture Collection (ATCC) obtained from Bioinformatics Grid (caBIG) of the NCI (6). on January 6, 2012. All cells were grown in RPMI medium This caArray_GSK dataset has 950 arrays performed in (GIBCO) with 10% FBS (GIBCO) and 1% penicillin/strep- triplicate for each of 318 cell lines. The experiments were tavidin (GIBCO), and maintained at 37C in a humidified 5 carried out with the Affymetrix U133 Plus 2.0 Array chip, atmosphere at 5% CO2. For siRNA transfection, 2 10 which includes 54,613 probes. However, for the refined cells per well were plated in a 6-well plate. After adhering analysis, we extracted 22,357 probes, whose expression for 24 hours, 45 nmol/L of target siRNA (Santa Cruz

OF2 Mol Cancer Ther; 13(10) October 2014 Molecular Cancer Therapeutics

STK11-Driven Regulation of PDE4D

Biotechnology) were added in transfection medium for 6 Western blot analysis hours at 37 CinaCO2 incubator. After transfection, cells After 48-hour target siRNA treatment, cell samples were supplemented with RPMI containing FBS and cul- were harvested and the protein supernatants were tured at 37 C/5% CO2 for up to 48 hours before harvest isolated using cell lysis buffer (Cell Signaling Technol- and RNA extraction. ogy; #9803) with added phenylmethylsulfonyl fluoride Lung normal cell lines (LL24 and LL86) were obtained (PMSF). The total protein content (30 mg) from cell from the Korean Cell Line Bank (KCLB) on December 11, lysates was separated using SDS–PAGE (8%) and trans- 2013. CCD-19Lu was obtained from ATCC on November ferred to a 0.45-mm nitrocellulose membrane (Millipore) 14, 2013. All cells were grown in RPMI medium (GIBCO) for 1 hour. The membranes were washed with TBS-T with 10% FBS (GIBCO) and 1% penicillin/streptavidin containing 5% (w/v)BSA.Themembraneswereincu- (GIBCO), and maintained at 37 C in a humidified atmo- bated overnight with specific AMPK-a (Cell Signaling sphere at 5% CO2. For reverse siRNA transfection, 1,500 Technology), GAPDH (Cell Signaling Technology), cells per well were plated in a 384-well plate, which PDE4D (Abcam plc.), and a-actin (Cell Signaling Tech- contained 10 nmol/L target siRNAs of PLK1, UBB1 and nology) were exposed to secondary antibodies coupled PDE4D (Thermo Fisher Scientific, Dharmacon Products) to horseradish peroxidase for 2 hours at room temper- separately. After cultured for 72 hours at 37 C/5% CO2, ature. The membranes were then washed three times the cell viability was detected using the CellTiter-Blue Cell with TBS-T at room temperature. Immunoreactivity Viability Assay (Promega). was detected using an enhanced chemiluminescent substrate from Thermo and analyzed with an LAS3000 Quantitative real-time PCR analysis Luminescent image analyzer from Fuji Film. Total RNA was extracted in 6-well plates using TRIzol. Synthesis of cDNA and PCR amplification was carried out Results with the SuperScript One-step RT-PCR Platinum Taq Kit The availability of DNA microarray data from a large (Invitrogen). Quantitative real-time PCR for PDE4D well-characterized collection of cancer cell lines pro- (Hs01579625_m1, TaqMan) and STK11 (Hs00176092_m1, vides the opportunity to identify markers allowing TaqMan) was carried out on an Applied Biosystems 7500 classification of cancers into "categories" driven by their using a TaqMan real-time detection protocol. combined lineage and mutational status. We applied two complementary, orthogonal metrics to determining Long-term cell viability and apoptosis assays the sensitivity and specificity of transcriptome markers A total of five PDE4D inhibitors were screened on in these different categories. Fold change reflects the STK11 wide-type (NCI-H82 and NCI-H524) and muta- sensitivity of a transcriptional probe in each category tional (NCI-H1993 and NCI-H1395) cell lines. Roli- against all other samples, while the enrichment score pram and roflumilast were purchased from Selleck quantifies the specificity of expression of a transcript Chemicals. L-454560, cilomilast, and lirimilast were within a specific category (see Materials and Methods purchased from Axon Medchem BV. Cells were seeded for details). Because these two metrics for gene expres- in a 96-well plate with density of the optimized cell sion were not correlated (Fig. 1A), they allow us to number (1,000 cells/well for 3-day treatment, 330/well prioritize category-associated markers for further for 7-day treatment, and 165/well for 10-day treat- investigation. ment). After 24 hours of seeding, cells were treated In this study, cancer categories were defined by 19 with diluted chemicals at 20 mmol/L working concen- lineages and 12 mutated cancer genes with sufficient tration. Cells were incubated for another 3, 7, and 10 frequency to allow a robust analysis with sufficient power days, respectively, and then measured for the viability to identify potential cancer drivers (Supplementary Table using the CellTiter-Blue Cell Viability Assay (Pro- S1). Through combining lineage and mutation across the mega). We kept the culture medium fresh by replacing sample sets, a total of 228 categories were generated, each every 3 or 4 days. Apoptotic cells were identified using of which included at least three cell lines. Among genes a terminal deoxynucleotidyl transferase dUTP nick that were significantly over- or underexpressed in a given end labeling (TUNEL)-based cell detection kit, POD category, only 10% to 20% were specific to each category (Roche Applied Science). Cells (A549 and H226) were (Table 1). Combined categories (two mutations or lineage- fixed in slides with 4% paraformaldehyde (Ducsan) for mutation) included expression markers with higher sen- 1 hour, washed with PBS (Welgene), and then incu- sitivity and specificity than those in single categories (Fig. bated for 10 minutes with 3% H2O2 in methanol (tri- 1B). In particular, categories defined by a combination of sodium citrate dihydrate; Junsei) at room temperature. lineage and mutation status identified markers with high After washing with PBS again, cells were incubated 2 sensitivity and specificity (Fig. 1B and Table 1). These minutes on ice with 0.1% Trion X-100 in 0.1% sodium selected markers (a total of 1,455 genes) provide a starting citrate. Then, slides were incubated with the TUNEL point to integrate the interaction of lineage and mutational reaction mixture for 60 minutes at 37C, and then status on gene expression as a functional readout for rinsed with FBS. Stained slides were examined under mutation-specific cancer progression (complete lists light microscopy. available in Supplementary Table S2).

www.aacrjournals.org Mol Cancer Ther; 13(10) October 2014 OF3

He et al.

A B 1442 2789 220 860 4 383 206 1656 2 319 3 2 0 enrichment 046 fold change 2

2 113 163 2 −2 Log Log 1347 796 1 fold-change (sensitivity) 2 Log −6 −4 −2 Single Single Lineage Lineage Double Double mutation mutation −2 0 2 4 mutations mutations Combination Log2 enrichment (specificity) Combination

C 204*

100

80 Lung CNS 60 Colon Breast 40 Liver 20 Leukemia Pancreatic Lineage

No. of transcript markers 0 Skin RB1 NRAS PTEN PTEN KRAS KRAS BRAF BRAF STK11 STK11 PIK3CA PIK3CA CDKN2A CTNNB1 Mutation

Figure 1. Lineage- and genotype-specific gene expression. A, sensitivity (fold change) and specificity (enrichment) of 22,357 genes (blue) expressed in 228 cancer lineage and mutation combinations. Red, genes with significant (P < 0.01 and Fisher t < 0.01) expression in both fold change and enrichment score. B, the average expression (fold change and enrichment) and the variation of transcript markers in different cell line categories. Details of the type of categorization are available in Supplementary Table S1. C, distribution of 1,455 category-specific transcript markers defined in Table 1 (the complete list of 1,455 markers are available in Supplementary Table S2). , For example, 204 gene probes were significantly over- (or under-) expressed in lung cell lines with RB1 mutation (the y-axis scale is not presented for this case). Their expression is also significantly specific to RB1 and lung category in terms of enrichment score.

Combining cancer lineage with mutational status between lineage and mutation using lung cancer as a revealed a varied distribution of expression markers model. (Fig.1C).Importantly,theeffectsofparticularmuta- To determine the relevance of markers identified in tions were most clearly evident in specific lineages. lung cancer cell lines, we extended our analysis to clinical For example, RB1-andSTK11-associated gene expres- tumor samples. Together with KRAS (20) and CDKN2A sion changes were exclusive to lung cancer cell lines, (21), STK11 (18) was consistently found to be one of three while BRAF-associated markers were dominant in major mutations in non–small cell lung cancer (NSCLC) skin cancer cell lines. Other mutations such as those subtype of tissue and cell line samples (Fig. 2A). RB1 (22) in CDKN2A were associated with expression markers and PTEN (23) mutations were dominantly found in small across multiple cell lineages. Lung cell lines showed cell lung cancer (SCLC) subtype. In addition, NSCLC type the greatest number of markers, most of which were is histologically classified into LUAD and lung squamous found in cells harboring RB1, STK11,orPTEN muta- cell carcinoma (LUSC) subtypes. Thus, the relative fre- tions, prompting us to further investigate interactions quency of mutations was also comparatively analyzed on

OF4 Mol Cancer Ther; 13(10) October 2014 Molecular Cancer Therapeutics

STK11-Driven Regulation of PDE4D

Table 1. Occurrence of transcript markers that signiﬁcantly associate with lineage and/or genotype categories

Combination of a Single Combination of mutation and a Types of categories Lineage mutation two mutations lineage Number of categories 19 12 22 228 Number of genes with >2-fold change (P < 0.01) in a category 4,055 905 1,574 11,519 Number of category-speciﬁca genes with >2-fold change (P < 0.01) 739 114 135 1,455

NOTE: A total of 22,357 gene probes in the DNA microarray were analyzed against 318 cancer cell lines (caArray_GSK dataset). Each category includes at least 3 cell lines. aThe category speciﬁcity was deﬁned as enrichment score of >2(P < 0.01) in the given category.

LUAD and LUSC subtypes of tissue samples (Fig. 2A). mutation. Most of STK11-mutant cell lines were lung cell Cancer cell lines did not include enough LUSC subtype lines and only four cell lines originated from leukemia, samples in datasets. Among three major mutations (i.e., cervical, and ovarian cancer (Fig. 2E). Although the over- KRAS, CDKN2A, and STK11) in NSCLC, KRAS and STK11 expression of PDE4D was generally associated with mutations were dominantly found in LUAD, while STK11 mutation in most cell lines, only lung category was CDKN2A was dominant in LUSC subtype of cancer appropriate for the association study with statistical con- tissues. Although the importance of STK11 mutations in fidence. PDE4D has been implicated in cancer cell prolif- lung cancer has been addressed previously (24), thera- eration and metastasis (27, 28). The inhibition of PDE4 peutic targets specific to or driven by STK11 mutations activity suppressed cell growth and induced apoptosis in have not been identified. In the present analysis, a total of human leukemia cells (29). Furthermore, the PDE4 was 32 expression gene probes showed alterations with sig- considered as a target to regulate the malignancies of nificant sensitivity and specificity in lung cell lines har- cancers including leukemia and colon (30, 31). Thus, we boring STK11 mutations (Fig. 2B). Because TP53 muta- explored whether the previously undetected link between tions are widely observed in cancer cell lines including STK11 and PDE4D could contribute to lung cancer pro- lung cancer cell lines (25), we analyzed whether or not the gression and provide a tractable therapeutic target in lung 32 gene expression changes associated with STK11 muta- cancer cell lines. tion were also associated with TP53 mutational status. We Lung cell lines with STK11 mutations showed higher found that the expression of 16 of 32 gene probes (16 levels of PDE4D expression than cell lines without STK11 probes representing 11 unique genes) associated with the mutations, and cells from other lineages with STK11 STK11 mutations was independent of TP53 genotype, and mutations (Figs. 2E and 3A). The association of PDE4D is likely driven by STK11 (Fig. 2C). expression with STK11 mutation was conserved in patient To confirm the direct association of selected markers lung cancer samples (Fig. 3B), implying that PDE4D plays with the STK11 genotype, we analyzed transcriptome a critical role in STK11-dependent progression of lung data from two STK11-defective lung cancer cell lines, cancers. Furthermore, PDE4D have 11 types of isoforms A549 and H2126, as well as cells stably expressing (1–11). The expressional change of isoforms was con- wild-type STK11 (18). We determined the fold change of firmed using the TCGA RNA-sequencing data (Fig. 3C genes in parental cells expressing mutated STK11,as and D). Generally, the expression of all PDE4D isoforms compared with wild-type STK11 (Fig. 2D). Among the was significantly enriched and increased in STK11- 11 STK11 category-specific genes identified above, only mutant lung cancers except PDE4D4 and PDE4D7. the expression of PDE4D showed the expected pattern in As predicted by the expression patterns shown in Fig. response to the expression of wild-type STK11. PDE4D 2D, the expression of wild-type STK11 in lung cancer cells expression was significantly downregulated by transfec- with aberrant STK11 function resulted in markedly tion of wild-type STK11 into STK11-mutant lines, while it decreased PDE4D levels (Fig. 4A). The raw DNA micro- was upregulated in STK11-mutant lung cell lines (Fig. 2D). array data were obtained from Ji and colleagues (18). The overexpression of PDE4D has been observed in mul- Furthermore, knockdown of STK11 in wild-type, but not tiple types of cancer cells originating from the central STK11-mutant, lung cell lines resulted in increased nervous system (CNS), lung, breast, and melanomas PDE4D expression (Fig. 4B). In addition, knockdown of (26). But, so far, there has been no report that the elevated AMPK, which is a direct downstream target of STK11, also expression of PDE4D is mutation-specific in cancer. In the increased PDE4D expression in wild-type, but not STK11- present study, a total of 318 cancer cell lines were analyzed mutant, cell lines (Fig. 4C). In the Western blot analysis of to find association between STK11 and PDE4D. Among PDE4D protein expression, the efficacy of AMPK knock- them, there were 17 cancer cell lines harboring STK11 down was also reproduced at protein expression level

www.aacrjournals.org Mol Cancer Ther; 13(10) October 2014 OF5

He et al.

A B 4 *0.18 15,882 NSCLC tissues 179 SCLC tissues 3 0.04 2

0.02 1

0 0 –1 fold chnage (FC) fold chnage 0.3 538 LUAD tissues 2 –2 P > 0.01 in FC and ES 178 LUSC tissues Log P < 0.01 in FC 0.2 –3 P < 0.01 in ES P < 0.01 in FC and ES 0.1 –4 012345

Relative frequency 0 Log2 enrichment score

0.6 46 NSCLC cell lines C 4

0.4 25 SCLC c el l l i n es 3 PDE4D WT 0.2 2

TP53 1 0 0

RB1 43210–1–2–3–4 PTEN BRAF KRAS NRAS STK11 –1 PIK3CA CTNNB1 CDKN2A –2 fold chagne in in fold chagne 2 TP53-dependent –3

Log TP53-independent –4

Log 2 fold change in TP53 MT

D 12 E

5 8 Lung (13) 4 Others (4) PDE4D 3 4 2 1 0 0 3210–1–2–3 –1 OV: Caov-3 OV: LC: NCI-H1355 LC: NCI-H2126 LC: NCI-H1993 LC: NCI-H1623 LC: NCI-H1395 LC: NCI-H2030 LC: NCI-H2122 LC: NCI-H1666 LC: NCI-H838 LC: NCI-H23 LC: LC: UMC-11 LC: CV: SW756 CV: SiHa LE: MOLT-4 LC: A427 LC: –4 PDE4D –2 LC: DMS-53 fold change of 2 –8 Log fold change in STK11 recovered 2 –12 Log

Log2 fold change in STK11 MT STK11-mutant cancer cell lines

Figure 2. STK11 mutation-specific gene expression in lung cancer. A, comparative frequency of mutations in lung tissue samples (COSMIC DB and TCGA dataset) and cell lines (caArray_ GlaxoSmithKline dataset). The relative frequency represents the ratio of the mutation frequency over the total number of samples. Raw data for NSCLC and SCLC tissue samples were retrieved from Sanger COSMIC website. Raw data for LUAD and LUSC tissue samples were retrieved from the TCGA website. B, sensitivity and specificity of STK11-associated markers in lung cancer cell lines. Filled red circles, 32 expressed gene probes with significant fold change and enrichment. List of 32 gene probes are available in Supplementary Table S3. C, TP53 dependency of STK11-specific markers. Red circles, 14 gene expressions that have less than 2-fold change in one of TP53WT or MT lung categories. Filled red circles, 16 gene expression patterns with over 2-fold changes on both TP53WT or MT lung categories. See Supplementary Table S3 for the list. D, comparison of gene expression between parental STK11-mutant A549, H2126 lung cancer cell lines and same cell lines stable expressing WT STK11 by gene recovery (raw data extracted from GSE6135 dataset; see Materials and Methods for detail). The fold change is the average for A549 and H2126 cell lines. Blue spots, 350 gene probes of STK11-dependent expression in both caArray_GSK and GSE6135 datasets. Red spots, the TP53-independent STK11 markers selected in C. E, gene expression of PDE4D in all STK11-mutant cell lines from caArray_GSK dataset. CV, cervical cancer; LC, lung cancer; LE, leukemia; OV, ovarian cancer.

OF6 Mol Cancer Ther; 13(10) October 2014 Molecular Cancer Therapeutics

STK11-Driven Regulation of PDE4D

A B

4 All cell lines 4 Lung 3 Lung 3 Lung and STK11 MT PDE4D 2 STK11 MT 2 1 Lung and STK11 MT 1 0 0 –1 –1 fold change of of fold change fold change of PDE4D 2

–2 2 –2 –3 –3 Log Log 0 50 100 150 200 250 0 50 100 150 200 250 300 350 Rank of cell lines Rank of patient tissues C D 2.5 2

2 1.5 ** ** ** * ** ** 1.5 1 ** ** * 1 0.5 fold change change fold 2 0.5 0 Log Enrichment score 0 –0.5 PDE4D1 PDE4D2 PDE4D3 PDE4D4 PDE4D5 PDE4D6 PDE4D7 PDE4D8 PDE4D9 PDE4D1 PDE4D2 PDE4D3 PDE4D4 PDE4D5 PDE4D6 PDE4D7 PDE4D8 PDE4D9

Figure 3. Gene expression patterns of PDE4D isoforms along lung cancer cell lines and patient samples. A, PDE4D expression in 257 cancer cell lines. B, PDE4D expression in 355 human LUAD samples. Enrichment score (C) and differential expression of PDE4D (D; refs. 1–9) isoforms for STK11 mutations in 490 human LUAD samples. Enrichment score was calculated using odds ratio between the observed odds and expected odds. The observed odds is the ratio for the number of differentially expressed (log2 fold change >1) cell lines with STK11 mutant via the number of cell lines with it. The expected odds is the ratio for the number of differentially expressed (log2 fold change >1) cell lines versus the number of total cell lines except missing values. The fold change represented the difference of expression between mutant and wild-type cell lines. , P < 0.01; , P < 0.001, compared with STK11 wild-type cell lines.

(Fig. 4D). AMPK knockdown was selectively effective in siRNA treatment showed similar patterns to those of cell increasing the PDE4D protein expression in STK11 wild- growth assay (Fig. 5C), that is, siRNA knockdown of type cell lines. These results indicate that the regulation of PDE4D induced apoptosis more effectively in STK11 PDE4D expression is at the downstream of STK11 and mutants than in wild-type cells. AMPK signaling. A number of PDE4D inhibitors are in clinical trials (32) Previously, PDE4D has been known as a tumor-pro- offering a potential therapeutic opportunity for STK11- moting factor (26). We investigated whether inhibition of mutant lung cancers. For example, rolipram, an anti- PDE4D would selectively inhibit the growth of cancer inflammatory drug, has anticancer efficacy (33, 34), but cells with aberrant STK11. When we knocked down it is not currently used because of the difficulty of iden- PDE4D in a series of lung cell lines, cell growth was tifying patients likely to benefit. In the present study, a significantly decreased in STK11-mutant cell lines but not total of five selective PDE4D inhibitors—cilomilast, roflu- wild-type cancer lines (Fig. 5A). Thus PDE4D appears to milast, rolipram (35), lirimilast (36), and L454560 (37)— play an important role in cancer survival or proliferation were tested at 20 mmol/L concentration. Generally, all the in STK11-mutated lung cancer cells. In addition, three five PDE4D inhibitors showed differential efficacy on the lung normal cell lines (LL24, LL86, and CCD-19Lu) were proliferation of STK11-mutant cell lines in a time-depen- tested for treatment with siPDE4D, no significant dent manner (Fig. 5D). The result of 10-day viability decreased cell growth were observed in comparison with assays showed that the sensitivity of STK11-mutant cell positive control siRNAs (Fig. 5B). Together with Fig. 5A, lines was significantly increased for cilomilast, lirimilast, this result shows that PDE4D knockdown is selectively rolipram, and L454560 more than STK11 wild-type cell effective in STK11-mutant cancer cells, while it is less lines (Fig. 5D). We thus expect that PDE4D inhibition with effective in STK11 wild-type cancer cells or noncancer effective drugs may provide efficacious treatment of normal cells. Furthermore, the induction of apoptosis by patient tumors harboring STK11 mutations.

www.aacrjournals.org Mol Cancer Ther; 13(10) October 2014 OF7

He et al.

A 3 ** B STK11siRNA treatment 3 ** 2 STK11 recovered 2 1

(fold change) 1 PDE4D expression (fold change) A549 STK11-WT H2126 STK11-WT 0 H2126 STK11-WT A549 STK11-KD A549 parental cell H2126 parental cell PDE4D expression 0 NCI-H460 NCI-H1993 NCI-H226 NCI-H524 NCI-H322M A549

1 2 STK11 MT STK11 WT

C D AMPK siRNA treatment NCI-H460 NCI-H1993 AMPK siRNA treatment siRNA 3 ** 3 Control AMPK Control AMPK 2 PDE4D 2 ACTIN 1 1 (fold change)

PDE4D expression NCI-H226 NCI-H82 NCI-H1993 NCI-H226 NCI-H460 NCI-H524 NCI-H322M 0 (fold change) A549 NCI-H460 NCI-H1993 NCI-H226

0 NCI-H82 siRNA

Control AMPK Control AMPK PDE4D protein expression PDE4D ACTIN STK11 MT STK11 WT STK11 MT STK11 WT

Figure 4. Change of PDE4D gene expression by STK11 and AMPK in lung. A, DNA microarray data for PDE4D expression in STK11-recovered cell lines (gray) and parental cell lines with no STK11 function (black). , P < 0.01 compared with STK11-recovered cell lines. Raw data were retrieved from GSE6135 dataset in GEO database. B, qPCR analysis of the change of PDE4D expression by STK11 siRNA treatment on STK11-mutant cell lines (black) and on STK11 wild-type cell lines (gray). , P < 0.01 compared with STK11-mutant cell lines. C, qPCR analysis of the change of PDE4D expression by AMPK siRNA treatment on STK11-mutant cell lines (black) and STK11 wild-type cell lines (gray). The fold change of PDE4D expression was measured on the basis of the nontreated control cell lines. STK11 mutational status in the tested cell lines was conﬁrmed by cDNA sequencing for all cell lines (Supplementary Table S4). Error bars represent the standard deviation in three repeats. , P < 0.01 compared with STK11-mutant cell lines. Data for transfection efﬁcacy of siRNAs are available both in forms of qPCR (Supplementary Fig. S1) and Western blot analysis (Supplementary Fig. S2). D, Western blot analysis of the change of PDE4D expression by AMPK siRNA treatment on STK11-mutant and wild- type cell lines.

In this study, we demonstrated that classification of cell nation with cancer lineage, using two metrics reflecting lines by a combination of lineage and mutational status sensitivity and specificity independently. provides a powerful approach to identify sensitive and Previous work has reported that genetic variants of the specific transcriptional markers. Furthermore, these STK11–AMPK signaling pathway can influence therapeu- approaches were able to reveal nonintuitive downstream tic effects in diseases such as type II diabetes (40). STK11 mediators and elicit potential mutation-specific antican- encodes a serine/threonine kinase that functions as a cer therapies. Thus, an integrative analysis of highly tumor suppressor and regulates cell polarity. Loss-of- characterized cell lines and human tissue samples has function somatic mutations of STK11 have been found in the potential to uncover novel mechanisms of carcinogen- approximately 30% of lung cancer, and have been pro- esis, and therapeutic strategies. posed to promote metastasis (41). In the present study, we searched for transcriptional markers directly associated Discussion with STK11 mutations in lung cancer cells. Among several High-throughput cancer cell line screening provides a STK11-specific signatures we identified, PDE4D was up- strategy to identify transcription markers to help guide or downregulated depending on whether the STK11 gene targeted cancer therapy (38, 39). Here, we present a was wild-type or mutant. Although STK11 is a tumor computational strategy to profile unique transcriptional suppressor with a mutational frequency of about 20% to signatures associated with somatic mutations in combi- 30% in NSCLC (42), PDE4D was deleted in only 3% of lung

OF8 Mol Cancer Ther; 13(10) October 2014 Molecular Cancer Therapeutics

STK11-Driven Regulation of PDE4D

A B *** siPLK1 siUBB1 siPDE4D siPDE4D 100 *** *** 100 80 *** 80 60 40 60 20 40 % of cell growth % of cell growth 0 20 NCI-H322M A549 NCI-H1993 NCI-H1395 NCI-H460 NCI-H226 NCI-H524 NCI-H82 0 LL24 LL86 19Lu CCD-

C D Day3 40 * STK11mt * Day7 * 30 15 STK11wt Day10 cells 20 * ** 10 ** 10 0 Sensitivity on on Sensitivity 5 STK11mt –10

Apoptosis (%) –20 0 L454560 Lirimilast Rolipram Cilomilast Control Roflumilast siPDE4D

PDE4D inhibitors

Figure 5. STK11-dependent cell growth regulation by PDE4D inhibition. A, cell growth change by siPDE4D treatment (48 hours, 45 nmol/L) on lung cell lines with STK11-mutant (black) versus wild-type (gray). Percentage of each cell growth was calculated by siNC treatment. , P < 0.001 compared with STK11- mutant cell lines. The cell viability was determined using the MTT Assay Kit. B, cell growth change by siPDE4D treatment (72 hours, 10 nmol/L) on three lung normal cell lines (CCD-19Lu, LL24, and LL86). siPLK1 and siUBB1 were used as positive controls. Percentage of each cell growth was calculated by siNC treatment. , P < 0.001 compared with siPLK1 treatment. C, comparison of apoptotic potential of siPDE4D treatment on lung cell lines with STK11 mutant (black) versus wild-type (gray). , P < 0.05 compared with control siRNA treatment. A549 was used as STK11-mutant cell line and H226 was used as STK11 wild-type cell line. D, average percentage difference of cell growth by PDE4D inhibitors between STK11 mutant and wild-type cell lines. The sensitivity on STK11mt cell lines represents the difference of average percentage growth between STK11-mutant (NCI-H1993 and NCI-H1395) and wild-type cell lines (NCI-H82 and NCI-H524). Average percentage growth of each cell line by a PDE4D inhibitor was based on the DMSO control. Cells were treated with a 20 mmol/L of PDE4D inhibitors for 3, 7, and 10 days incubation, respectively. The cell viability was determined using CellTiter-Blue fluorescence. , P < 0.01 and , P < 0.05 in the difference between STK11-mutant and wild-type cell lines. All cells used in the experiment were cultured in normal conditioned RPMI medium with 10% FBS and 1% penicillin/streptavidin. cancer cell lines (43). Further analysis of somatic copy proliferation (45). Also, it has been demonstrated that number alterations showed that about 4% of solid tumor regulation of PDE4D9 by a group of kinases, of which primary specimens from the TCGA project had homozy- AMPK is a key species, could influence cell-cycle pro- gous deletion. Whether PDE4D functions as to increase or gression, particularly through mitosis. This study showed decrease tumorigenesis is likely context dependent and a connection between PDE4D and AMPK in cancer/cell- potentially dependent on whether STK11 is intact. Indeed, cycle dynamics (46). Further depletion of PDE4D can PDE4 has previously been implicated in cancer patho- cause apoptosis (10%–40%) in multiple types of cancer physiology by altering proliferation and angiogenesis in cells which have elevated expression level of PDE4D (26). lung cancer (44). The association of PDE4D expression We experimentally confirmed that the knockdown of and cancer progression has been reported in multiple PDE4D gene expression suppressed proliferation of types of cancer (26). Recently, PDE4D7, one of PDE4D STK11-mutated cell lines. Conversely, we also found that isoforms, has been reported as an androgen-independent rescue of STK11 activity in mutant and siRNA-silenced marker with a role in regulation of prostate cancer cell cells decreased the expression of PDE4D. These results

www.aacrjournals.org Mol Cancer Ther; 13(10) October 2014 OF9

He et al.

demonstrated that PDE4D can be a transcriptional bio- that the therapeutic potential of PDE4D-targeted therapy marker capable of detecting STK11 mutation in lung should be further assessed in STK11 mutant–type lung cancers. It further represents a possible route to therapeu- cancer. Therefore, in addition to describing a method to tic application for regulating lung cancer progression. In identify therapeutic biomarkers, our approach may pro- addition, our results are consistent with STK11-mediated vide useful clues for effective novel targeted therapies. activation of AMPK contributing to carcinogenesis (47). We found the knockdown of AMPK and STK11 resulted in Disclosure of Potential Conflicts of Interest similar effects on PDE4D expression. No potential conflicts of interest were disclosed. The knockdown of PDE4D gene expression induced selective reduction of cell growth in STK11 mutants (Fig. Authors' Contributions 4A). This indicated that PDE4D expression was important Conception and design: S. Yoon Development of methodology: N. He, S. Yoon for the growth of cells, when normal STK11 signaling was Acquisition of data (provided animals, acquired and managed patients, lost. These observations were confirmed in apoptosis provided facilities, etc.): M. Song, C. Park, S. Kim, H.Y. Yim, J.H. Park, PDE4D K.I. Kim assays (Fig. 4C). In addition to expression, Analysis and interpretation of data (e.g., statistical analysis, biostatis- STK11-dependant AMPK signaling seems to be important tics, computational analysis): N. He, N. Kim, K. Kim, F. Zhang, S. Yoon in the survival/growth of STK11-mutant cancer cells. Our Writing, review, and/or revision of the manuscript: N. He, N. Kim, STK11 G.B. Mills, S. Yoon study has demonstrated that mutant is closely Administrative, technical, or material support (i.e., reporting or orga- associated with enhanced PDE4D expression in NSCLC. nizing data, constructing databases): E.Y. Park Previous studies give some clues about how STK11– Study supervision: J.H. Park, G.B. Mills, S. Yoon AMPK signaling is linked to PDE4D regulation as well as the potential mechanism of apoptosis induction. Acknowledgments The authors thank Hyun-Young Lee for her professional work in PDE4D inhibitors could competitively inhibit cAMP- improving the graphic quality and visibility of the figures. degrading phosphodiesterases, leading to elevated cAMP level (48). cAMP is known to activate protein kinase A Grant Support (PKA)—an AMP-dependent enzyme, then activate This work was supported by the National Research Foundation of enzymes related to metabolic response and regulate gene Korea (KRF) grants, Bio and Medical Technology Development Program expression by the activation of transcription factors (such (NRF-2012M3A9B6055398) and National Leading Research Lab (NLRL) program (NRF-2011-0028816), funded by Korea government (MEST). S. as CREB) (49). An increased intracellular cAMP has also Yoon received the NRF-2012M3A9B6055398 and NRF-2011-0028816 been shown to increase the activity of AMPK (50), which is grants. G.B. Mills receives sponsored research support from GlaxoSmithKline. the direct downstream target of STK11 (LKB1) (47). Fur- The costs of publication of this article were defrayed in part by the ther mechanistic studies will be required to understand payment of page charges. This article must therefore be hereby marked the relationship among STK11, AMPK, and PDE4D in advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. cancer progression. Most of PDE4D inhibitors were more STK11 STK11 active in -mutant cell lines than wild-type Received April 4, 2014; revised July 16, 2014; accepted August 1, 2014; cells (Fig. 4D and E). Collectively, our findings suggest published OnlineFirst August 13, 2014.

References 1. Grothey A. Recognizing and managing toxicities of molecular targeted 8. Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, Beare D, et al. therapies for colorectal cancer. Oncology 2006;20:21–8. COSMIC: mining complete cancer genomes in the catalogue 2. Khan S, Coulson JM, Woll PJ. Genetic abnormalities in plasma DNA of of somatic mutations in cancer. Nucleic Acids Res 2011;39: patients with lung cancer and other respiratory diseases. Int J Cancer D945–50. 2004;110:891–5. 9. Kim N, He N, Kim C, Zhang F, Lu Y, Yu Q, et al. Systematic analysis of 3. Prahallad A, Sun C, Huang S, Di Nicolantonio F, Salazar R, Zecchin genotype-specific drug responses in cancer. Int J Cancer 2012;131: D, et al. Unresponsiveness of colon cancer to BRAF(V600E) inhi- 2456–64. bition through feedback activation of EGFR. Nature 2012;483: 10. Pao W, Girard N. New driver mutations in non–small-cell lung cancer. 100–3. Lancet Oncol 2011;12:175–80. 4. He N, Wang X, Kim N, Lim JS, Yoon S. 3D shape-based analysis of cell 11. Kim C, Choi J, Park H, Park Y, Park J, Park T, et al. Global analysis of line–specific compound response in cancers. J Mol Graph Model microarray data reveals intrinsic properties in gene expression and 2013;43:41–6. tissue selectivity. Bioinformatics 2010;26:1723–30. 5. McDermott U, Sharma SV, Dowell L, Greninger P, Montagut C, Lamb J, 12. Chin L, Andersen JN, Futreal PA. Cancer genomics: from discovery et al. Identification of genotype-correlated sensitivity to selective science to personalized medicine. Nat Med 2011;17:297–303. kinase inhibitors by using high-throughput tumor cell line profiling. 13. Zhao RX, Xu ZX. Targeting the LKB1 tumor suppressor. Curr Drug Proc Natl Acad Sci U S A 2007;104:19936–41. Targets 2014;15:32–52. 6. Greshock J, Bachman KE, Degenhardt YY, Jing J, Wen YH, Eastman S, 14. Korsse SE, Peppelenbosch MP, van Veelen W. Targeting LKB1 sig- et al. Molecular target class is predictive of in vitro response profile. naling in cancer. Biochim Biophys Acta 2013;1835:194–210. Cancer Res 2010;70:3677–86. 15. Houslay MD. Underpinning compartmentalised cAMP signalling 7. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, through targeted cAMP breakdown. Trends Biochem Sci 2010;35: Kim S, et al. The Cancer Cell Line Encyclopedia enables predic- 91–100. tive modelling of anticancer drug sensitivity. Nature 2012;483: 16. Keravis T, Lugnier C. Cyclic nucleotide phosphodiesterases (PDE) and 603–7. peptide motifs. Curr Pharm Des 2010;16:1114–25.

OF10 Mol Cancer Ther; 13(10) October 2014 Molecular Cancer Therapeutics

STK11-Driven Regulation of PDE4D

17. Houslay MD, Adams DR. PDE4 cAMP phosphodiesterases: modular idine and novel derivatives free of positional isomers. Potent inhibitors enzymes that orchestrate signalling cross-talk, desensitization and of cAMP-specific phosphodiesterase and of malignant tumor cell compartmentalization. Biochem J 2003;370:1–18. growth. J Med Chem 1998;41:4733–43. 18. Ji H, Ramsey MR, Hayes DN, Fan C, McNamara K, Kozlowski P, et al. 35. Lipworth BJ. Phosphodiesterase-4 inhibitors for asthma and chronic LKB1 modulates lung cancer differentiation and metastasis. Nature obstructive pulmonary disease. Lancet 2005;365:167–75. 2007;448:807–10. 36. Sturton G, Fitzgerald M. Phosphodiesterase 4 inhibitors for the treat- 19. Agresti A. Categorical data analysis. 2nd ed. New York; Chichester: ment of COPD. Chest 2002;121:192S-6S. Wiley-Interscience; 2002. 37. Huang Z, Dias R, Jones T, Liu S, Styhler A, Claveau D, et al. L-454,560, 20. Eberhard DA, Johnson BE, Amler LC, Goddard AD, Heldens SL, Herbst a potent and selective PDE4 inhibitor with in vivo efficacy in animal RS, et al. Mutations in the epidermal growth factor receptor and in models of asthma and cognition. Biochem Pharmacol 2007;73: KRAS are predictive and prognostic indicators in patients with non– 1971–81. small-cell lung cancer treated with chemotherapy alone and in com- 38. Bull JH, Ellison G, Patel A, Muir G, Walker M, Underwood M, et al. bination with erlotinib. J Clin Oncol 2005;23:5900–9. Identification of potential diagnostic markers of prostate cancer and 21. Foulkes WD, Flanders TY, Pollock PM, Hayward NK. The CDKN2A prostatic intraepithelial neoplasia using cDNA microarray. Br J Cancer (p16) gene and human cancer. Mol Med 1997;3:5–20. 2001;84:1512–9. 22. Meuwissen R, Linn SC, Linnoila RI, Zevenhoven J, Mooi WJ, Berns A. 39. Matsumura N, Zembutsu H, Yamaguchi K, Sasaki K, Tsuruma T, Induction of small cell lung cancer by somatic inactivation of both Nishidate T, et al. Identification of novel molecular markers for detec- Trp53 and Rb1 in a conditional mouse model. Cancer Cell 2003;4: tion of gastric cancer cells in the peripheral blood circulation using 181–9. genome-wide microarray analysis. Exp Ther Med 2011;2:705–13. 23. Soria JC, Lee HY, Lee JI, Wang L, Issa JP, Kemp BL, et al. Lack of PTEN 40. Choi J, He N, Sung MK, Yang Y, Yoon S. Sanguinarine is an allosteric expression in non–small cell lung cancer could be related to promoter activator of AMP-activated protein kinase. Biochem Biophys Res methylation. Clin Cancer Res 2002;8:1178–84. Commun 2011;413:259–63. 24. Sanchez-Cespedes M, Parrella P, Esteller M, Nomoto S, Trink B, 41. Sung JS, Whang YM, Park KH, Ryu JS, Choi JG, Seo JH, et al. No Engles JM, et al. Inactivation of LKB1/STK11 is a common event in association between promoter polymorphism of STK11 gene and lung adenocarcinomas of the lung. Cancer Res 2002;62:3659–62. cancer risk in the Korean population. Cancer Res Treat 2009;41:211–7. 25. Hainaut P, Hollstein M. p53 and human cancer: the first ten thousand 42. Minna JD, Roth JA, Gazdar AF. Focus on lung cancer. Cancer Cell mutations. Adv Cancer Res 2000;77:81–137. 2002;1:49–52. 26. Lin DC, Xu L, Ding LW, Sharma A, Liu LZ, Yang H, et al. Genomic and 43. Kohno T, Otsuka A, Girard L, Sato M, Iwakawa R, Ogiwara H, et al. A functional characterizations of phosphodiesterase subtype 4D in catalog of genes homozygously deleted in human lung cancer and the human cancers. Proc Natl Acad Sci U S A 2013;110:6109–14. candidacy of PTPRD as a tumor suppressor gene. Genes Chromo- 27. Pullamsetti SS, Banat GA, Schmall A, Szibor M, Pomagruk D, Hanze J, somes Cancer 2010;49:342–52. et al. Phosphodiesterase-4 promotes proliferation and angiogenesis of 44. Pullamsetti SS, Banat GA, Schmall A, Szibor M, Pomagruk D, Hanze J, lung cancer by crosstalk with HIF. Oncogene 2012;32:1121–34. et al. Phosphodiesterase-4 promotes proliferation and angiogenesis of 28. Rahrmann EP, Collier LS, Knutson TP, Doyal ME, Kuslak SL, Green LE, lung cancer by crosstalk with HIF. Oncogene 2013;32:1121–34. et al. Identification of PDE4D as a proliferation promoting factor in 45. Henderson DJ, Byrne A, Dulla K, Jenster G, Hoffmann R, Baillie GS, prostate cancer using a Sleeping Beauty transposon-based somatic et al. The cAMP phosphodiesterase-4D7 (PDE4D7) is downregulated mutagenesis screen. Cancer Res 2009;69:4388–97. in androgen-independent prostate cancer cells and mediates prolif- 29. Ogawa R, Streiff MB, Bugayenko A, Kato GJ. Inhibition of PDE4 eration by compartmentalising cAMP at the plasma membrane of phosphodiesterase activity induces growth suppression, apoptosis, VCaP prostate cancer cells. Br J Cancer 2014;110:1278–87. glucocorticoid sensitivity, p53, and p21(WAF1/CIP1) proteins in 46. Sheppard CL, Lee LC, Hill EV, Henderson DJ, Anthony DF, Houslay human acute lymphoblastic leukemia cells. Blood 2002;99:3390–7. DM, et al. Mitotic activation of the DISC1-inducible cyclic AMP phos- 30. Lerner A, Epstein PM. Cyclic nucleotide phosphodiesterases as tar- phodiesterase-4D9 (PDE4D9), through multi-site phosphorylation, gets for treatment of haematological malignancies. Biochem J influences cell cycle progression. Cell Signal 2014;26:1958–74. 2006;393:21–41. 47. Dong LX, Sun LL, Zhang X, Pan L, Lian LJ, Chen Z, et al. Negative 31. Murata K, Sudo T, Kameyama M, Fukuoka H, Muka M, Doki Y, et al. regulation of mTOR activity by LKB1-AMPK signaling in non–small cell Cyclic AMP specific phosphodiesterase activity and colon cancer cell lung cancer cells. Acta Pharmacol Sin 2013;34:314–8. motility. Clin Exp Metastasis 2000;18:599–604. 48. Sha H, Ma Q, Jha RK, Wang Z. Resveratrol ameliorates lung injury via 32. Griswold DE, Webb EF, Breton J, White JR, Marshall PJ, Torphy TJ. inhibition of apoptosis in rats with severe acute pancreatitis. Exp Lung Effect of selective phosphodiesterase type IV inhibitor, rolipram, on Res 2009;35:344–58. fluid and cellular phases of inflammatory response. Inflammation 49. Meinkoth JL, Alberts AS, Went W, Fantozzi D, Taylor SS, Hagiwara M, 1993;17:333–44. et al. Signal transduction through the cAMP-dependent protein kinase. 33. Drees M, Zimmermann R, Eisenbrand G. 30,50-Cyclic nucleotide phos- Mol Cell Biochem 1993;127–128:179–86. phodiesterase in tumor cells as potential target for tumor growth 50. Omar B, Zmuda-Trzebiatowska E, Manganiello V, Goransson O, inhibition. Cancer Res 1993;53:3058–61. Degerman E. Regulation of AMP-activated protein kinase by cAMP 34. Merz KH, Marko D, Regiert T, Reiss G, Frank W, Eisenbrand G. in adipocytes: roles for phosphodiesterases, protein kinase B, protein Synthesis of 7-benzylamino-6-chloro-2-piperazino-4-pyrrolidinopter- kinase A, Epac and lipolysis. Cell Signal 2009;21:760–6.

www.aacrjournals.org Mol Cancer Ther; 13(10) October 2014 OF11

Integrated Analysis of Transcriptomes of Cancer Cell Lines and Patient Samples Reveals STK11/LKB1−Driven Regulation of cAMP Phosphodiesterase-4D

Ningning He, Nayoung Kim, Mee Song, et al.

Mol Cancer Ther Published OnlineFirst August 13, 2014.

Updated version Access the most recent version of this article at: doi:10.1158/1535-7163.MCT-14-0297

Supplementary Access the most recent supplemental material at: Material http://mct.aacrjournals.org/content/suppl/2014/08/16/1535-7163.MCT-14-0297.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://mct.aacrjournals.org/content/early/2014/09/18/1535-7163.MCT-14-0297. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC) Rightslink site.