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International Journal of Oncology logoLink to International Journal of Oncology
. 2015 Nov 20;48(1):367–375. doi: 10.3892/ijo.2015.3262

Differential regulation and synthetic lethality of exclusive RB1 and CDKN2A mutations in lung cancer

Nayoung Kim 1,3, Mee Song 1, Somin Kim 1, Yujeong Seo 3, Yonghwan Kim 3, Sukjoon Yoon 1,2,✉
PMCID: PMC6903902  PMID: 26647789

Abstract

Genetic alterations in lung cancer are distinctly represented in non-small cell lung carcinoma (NSCLC) and small cell lung carcinoma (SCLC). Mutation of the RB1 and CDKN2A genes, which are tightly associated with cell cycle regulation, is exclusive to SCLC and NSCLC cells, respectively. Through the systematic analysis of transcriptome and proteome datasets for 318 cancer cell lines, we characterized differential gene expression and protein regulation in RB1-mutant SCLC and CDKN2A-mutant NSCLC. Many of the genes and proteins associated with RB1-mutant SCLC cell lines belong to functional categories of gene expression and transcription, whereas those associated with CDKN2A-mutant NSCLC cell lines were enriched in gene sets of the extracellular matrix and focal adhesion. These results indicate that the loss of RB1 and CDKN2A function induces distinctively different signaling cascades in SCLC and NSCLC cells. In addition, knockdown of the RB1 gene in CKDN2A-mutant cell lines (and vice versa) synergistically inhibits cancer cell proliferation. The present study on the exclusive role of RB1 and CDKN2A mutations in lung cancer subtypes demonstrates a synthetic lethal strategy for cancer regulation.

Keywords: cancer cell line panel, RB1, CDKN2A, gene expression, reverse-phase protein array, lung cancer

Introduction

Understanding heterogeneous genetic alterations in tumors is recognized as a key factor in advancing cancer therapy (1–3). Lung cancers are classified into two subtypes, non-small cell lung carcinoma (NSCLC) and small cell lung carcinoma (SCLC), which harbor exclusive specific mutations: RB1 in SCLC and CDKN2A (p16INK4A) in NSCLC (4). Both the RB1 and CDKN2A genes are tightly associated with cell cycle regulation, and CDKN2A regulates RB1 phosphorylation through cyclin E and D1 (5,6). The crucial role of RB1 as a regulator in cell cycle progression has been intensively investigated (7–9). Accumulated data have demonstrated mutually exclusive mutation patterns for genes encoding proteins that function in the same biological pathway. For instance, mutations of the KRAS or BRAF gene, which are downstream of the EGFR signaling pathway, have not been found in EGFR-mutated NSCLC (10,11), and co-mutations of the TP53 and PIK3CA pair (12) or the RB1 and CDKN2A pair (13) rarely occur in the same tumors. However, the biological meaning of such mutually exclusive mutation patterns is not fully understood, even though this exclusiveness does serve as an attractive target for the development of novel therapeutics (14).

An understanding of differential regulation along with distinct mutations in RB1 and CDKN2A is required to identify molecular characteristics of the progression of SCLC and NSCLC subtypes. Large-scale cell line-based high-throughput transcriptome and proteome datasets facilitate the understanding of molecular characterization of cancers through genome-wide functional analyses. The National Cancer Institute (NCI) released well-annotated sets of both DNA microarray data to detect the gene expression and reverse-phase protein array (RPPA) data to detect the total protein and phosphorylation on 60 well-characterized cancer cell lines (15). Diverse omics datasets on an expanded panel of >300 cancer cell lines were also generated by GlaxoSmithKline (GSK) (16). Together with these datasets, the extensive mutation profile on individual cell lines is available from the COSMIC (Catalogue of Somatic Mutations in Cancer) database (17). Through mutation-oriented association studies on cell line-based omics data, we have reported new targets and mechanisms for cancer regulation (3,18,19).

In the present study, the regulation of gene and protein levels driven by RB1 or CDKN2A mutations in lung cancer was analyzed using transcriptome and proteome datasets obtained from 318 diverse cancer cell lines. We attempted to identify the differentially regulated gene/protein signatures and functional pathways specific to RB1 and CDKN2A mutations. Furthermore, we experimentally investigated whether double or complementary knockdown of RB1 or CDKN2A gene expression has a specific effect on the reciprocal mutant subtype in lung cancer cell lines. We expect that this study will provide a useful resource for the regulation of lung cancer progression using synergistic mechanisms of exclusive RB1 or CDKN2A mutations.

Materials and methods

Data acquisition

The large-scale transcriptome dataset on 318 cancer cell lines was obtained from the Cancer Biomedical Informatics Grid (caBIG) website (https://cabig.nci.nih.gov/caArray_GSKdata) (16). This dataset, also known as the GlaxoSmithKline (GSK) dataset, has 950 arrays performed in triplicate for each cell line with the Affymetrix U133 Plus 2.0 Array chip. It was normalized to MAS5 and then transformed to a log2 scale.

The reverse-phase protein array (RPPA) dataset to detect protein expression and phosphorylation was generated in the Functional Proteomics Core of the M.D. Anderson Cancer Center using a total of 179 cancer cell lines, which were included in the transcriptome dataset. These cell lines were purchased from several vendors (American Type Culture Collection; Developmental Therapeutics Program, National Cancer Institute; German Resource Centre for Biological Material and European Collection of Animal Cell Cultures) and grown in standard culture media as recommended by the vendor. The genetic identity of cell lines was determined by cross comparing all cell lines in this set (16,20). The cells were maintained in RPMI-1640 supplemented with 5% fetal bovine serum at 37°C in a humidified 5% CO2 atmosphere. Proteins were harvested when the cells reached ~70% confluence. The cells were lysed in buffer containing 1% Triton X-100, 50 mM HEPES pH 7.4, 150 mM NaCl, 1.5 mM MgCl2, 1 mM ethylene glycol tetraacetic acid, 100 mM NaF, 10 mM NaPPi, 10% glycerol, 1 mM Na3VO4 and complete protease inhibitor cocktail (Roche Diagnostics). Protein supernatants were isolated using standard methods (21), and the protein concentration was determined using the bicinchoninic acid assay (22). Samples were diluted to a uniform protein concentration and denatured in 1% sodium dodecyl sulfate for 10 min at 95°C. Samples were stored at −80°C until use. RPPA analysis was performed as described previously (21,23,24). A logarithmic value reflecting the relative amount of each protein in each sample was generated for subsequent analyses. The RPPA analysis was performed using a total of 115 antibodies.

The annotation of somatic mutation on all cell lines was organized by the COSMIIC (Catalogue of Somatic Mutations in Cancer) database (http://cancer.sanger.ac.uk/cosmic) (17).

Enrichment analysis of somatic mutations

To describe the selectivity of mutation occurrence, we calculated enrichment scores using an odds ratio between the observed odds and expected odds. The observed odds score is the ratio for the number of mutated cell lines in a specific cancer type via the number of cell lines in a specific cancer type. The expected odds score is the ratio for the number of mutated cell lines vs. the total number of cell lines. In addition, the probability of an odds ratio was calculated by the Fisher exact test using the R open-source computing language, version 2.15. The Fisher exact test uses a hypergeometric distribution to determine the significance of the agreement between individual question pairs (25).

Mutation-specific gene and protein expression analysis

For the selection of RB1 and CDKN2A mutation-specific gene and protein expression markers together with excluding the subtype-dependent expressions, lung cancer cell lines were classified into two groups: NSCLC and SCLC. Then, we divided the cell lines of each subtype into two groups based on the mutational status of RB1 and CDKN2A. CDKN2A-mutant and wild-type cell lines were mainly considered in the NSCLC type. RB1-mutant and wild-type cell lines were considered in the SCLC type. As a result, in the transcriptome dataset, we classified 9, 16, 22 and 24 cell line samples into the following four groups, respectively: RB1wt SCLC; RB1mt SCLC; CDKN2Awt NSCLC; and CDKN2Amt NSCLC. In the RPPA dataset, we classified 4, 7, 4 and 16 cell line samples into four groups, respectively: RB1wt SCLC; RB1mt SCLC; CDKN2Awt NSCLC; and CDKN2Amt NSCLC. The gene expression was detected using a log2 fold change value for the average difference of mutant and wild-type cell lines. The significance was confirmed by a t-test.

The patterns of gene expression were analyzed through a hierarchical clustering method. The clustering and its visualization on a heatmap were performed using the software QCanvas (26). QCanvas can be downloaded freely from the website http://compbio.sookmyung.ac.kr/~qcanvas.

Gene set enrichment analysis

Pathway analysis was performed using the GSEA (Gene Set Enrichment Analysis) method (27). Gene sets, integrated from Reactome, PID, KEGG, and Biocarta database, were obtained from the online pathway database, MSigDB v3.1 (http://www.broadinstitute.org/gsea/msigdb). The significantly (p<0.01) enriched gene sets among the results of the GSEA were reorganized based on major functional categories in each database.

Cell culture

NCI-60 lung cancer cell lines (NCI-H460, A549, NCI-H322M, NCI-H226, EKVX, and NCI-H23) were obtained from National Cancer Institute (NCI DTP), USA. NCI-H1993, NCI-H1935, NCI-H82 and NCI-H524 were obtained from American Type Culture Collection (ATCC). All cells were grown in RPMI-1640 medium (HyClone, USA) with 10% FBS (HyClone) and 1% penicillin/streptomycin (Gibco, USA), and maintained at 37°C in a humidified atmosphere at 5% CO2

siRNA transfection and cell viability assay

To detect cell viability after siRNA transfection, the cells were seeded in a 96-well plate at a density of 5,000 cells per well. After adhering for 24 h, target siRNAs were added in transfection medium (Gibco) for 6 h at 37°C in a CO2 incubator. siRB1 (L-003296-02), siCDKN2A (L-011007-00) and non-targeting siRNA (D-001810-10) were purchased from Dharmacon Inc. (Lafayette, CO, USA). After being cultured for 72 h at 37°C, 5% CO2, cell viability was detected using a CellTiter-Blue Cell Viability Assay (Promega, Madison, WI, USA).

Results and Discussion

RB1 and CDKN2A mutations in SCLC and NSCLC cell lines

Genetic alterations affecting the same biological pathway are generally not found in the same cancer cell. Accordingly, exclusive mutation patterns of RB1 and CDKN2A genes have been observed in the lung cancer subtypes SCLC and NSCLC (4,13). Based on the analysis of mutation frequencies across 318 cell lines, we found the general exclusiveness of RB1 and CDKN2A mutations in diverse cancer lineages (Fig. 1A). RB1 mutations were significantly enriched in urinary tract and lung cancer cell lines yet rarely found in liver, renal, pancreatic and skin cancers, in which CDKN2A mutations were frequent. Furthermore, among 71 lung cancer cell lines, 25 SCLC-derived cells were significantly enriched with RB1 mutations, whereas 46 NSCLCs predominantly contained STK11, KRAS and CDKN2A mutations (Fig. 1B). Taken together, the mutations of RB1 and CDKN2A genes, which belong to a common functional pathway, were clearly exclusive from each other among frequently mutated genes in diverse cancer cell lines (Fig. 1C).

Figure 1.

Figure 1

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RB1 and CDKN2A mutation frequency in the cancer cell line panel. (A) Enrichment score of RB1 and CDKN2A mutation frequency among diverse cancer lineages. The enrichment score was calculated by the odds ratio between the observed and expected odds. The observed odds ratio is the ratio for RB1 or CDKN2A mutation among each cancer type. The expected odds ratio is the ratio for RB1 or CDKN2A mutation among all 318 cell lines. (B) The enrichment score of major mutation frequency in non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). The observed odds ratio is the ratio for each mutation frequency in 46 NSCLC or 25 SCLC cell lines. The expected odds ratio is the ratio for each mutation frequency in all 318 cell lines. (C) The distribution of major gene mutations in lung cancer cell lines. Statistical significance of *p<0.05 and **p<0.01, respectively.

Differential gene expression profiles between RB1mt SCLCs and CDKN2Amt NSCLCs

To find lineage-independent, mutation-specific gene expression patterns, we classified 9, 16, 22 and 24 cell line samples into four groups, RB1wt SCLC, RB1mt SCLC, CDKN2Awt NSCLC and CDKN2Amt NSCLC, and analyzed the group-specific gene expression patterns using DNA microarray data. There was no general correlation of gene expression between the SCLC and NSCLC cell lines (Fig. 2A), and significantly enriched gene sets were also different between the lung subtypes. However, RB1mt SCLC and CDKN2Amt NSCLC cells showed a negative correlation in gene expression (Fig. 2B), whereas RB1wt SCLC and CDKN2Awt NSCLC exhibited a positive correlation (Fig. 2C). This observation indicated that RB1 and CDKN2A mutations caused lineage-specific distinctive changes in gene expression.

Figure 2.

Figure 2

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Comparison of gene expression in SCLC and NSCLC with the mutational status of RB1 and CDKN2A. (A) The expression change of a total of 22,357 gene probes in SCLC and NSCLC cell lines. Major categories of the gene sets significantly (p<0.01) over-enriched for each gene expression were analyzed using gene set enrichment analysis (GSEA). Gene sets from different pathway DBs (orange, Reactome; sky-blue, PID; purple, KEGG; and green, Biocarta) were collected. The number indicates the number of gene sets per category. (B) Comparison of gene expressional change in RB1-mutated SCLC and CDKN2A-mutated NSCLC. Red represents 1,208 RB1-mutated SCLC-specific gene signatures and blue represents 159 RB1-mutated SCLC-specific gene signatures (>2-fold change and p<0.01). A total of 42 gene probes were commonly selected between them. (C) Comparison of gene expression change in SCLC and NSCLC cell lines with RB1 and CDKN2A wild-type, respectively. The colored gene signatures were consistent with (B). The expressional change for each gene probe was calculated by the log2 fold change via the median across 318 cell lines. The r value represents the Pearson correlation coefficient.

Our analysis showed that the lineage difference was generally more important than RB1 and CDKN2A mutational status in the differential gene expression pattern (Fig. 3A). Thus, we attempted to identify RB1mt- and CDKN2Amt-specific gene signatures by separately analyzing SCLC and NSCLC cells (Fig. 3B). As a result, we were able to identify distinct mutation-specific gene signatures for which expression was significantly regulated (>2-fold change and p<0.05) in each subtype (Tables I and II). Of note, the significantly over-enriched (p<0.01) gene sets (functional categories of selected gene signatures) generally did not overlap between the two mutation groups (Fig. 3B). The upregulated gene sets with RB1 mutation in SCLC cell lines mainly belonged to functional categories of transcription. The hit list included known target genes of E2F, which are released and activated upon RB1 inactivation (28). The upregulated genes upon CDKN2A mutation in NSCLC cell lines were largely enriched in the gene sets of extracellular matrix and metabolism. Genes related to the extracellular matrix are known to be important factors for enhancing tumorigenicity and promoting metastasis (29). Although CDKN2A and RB1 are known to function in the same pathway of cell cycle regulation, inactivation of the mutations might have a different functional role in cancer development or progression in SCLC and NSCLC subtypes.

Figure 3.

Figure 3

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RB1mt- and CDKN2Amt-specific gene signatures in SCLC and NSCLC cells. (A) Enrichment score (ES) map of significantly (p<0.01) over-enriched gene sets in SCLC and NSCLC cell lines. Each subtype was further divided by RB1 or CDKN2A mutational status. The color index on the right side represents the functional category of clustered gene sets. The individual gene expression level in Fig. 2 was used for gene set enrichment analysis (GSEA). (B) Comparison of gene expression change along RB1 and CDKN2A mutations. The red color represents 159 transcription markers specific to the RB1 mutation, and blue represents 122 transcription markers specific to the CDKN2A mutation (>2-fold change and p<0.05). The complete list of the selected markers is available in Table I and II. The scale of the plot is log2 fold change of differential gene expression. It was calculated by the differences of average log2 gene expression between mutation and wild-type cell lines in a given subtype. Major categories of the gene sets significantly (p<0.01) over-enriched for each gene expression were analyzed by GSEA. Gene sets from different pathway DBs (orange, Reactome; sky-blue, PID; purple, KEGG; and green, Biocarta) were collected. The value next to each category indicates the number of sub-gene sets. The r value represents the Pearson correlation coefficient.

Table I.

The RB1mt-specific gene signatures in SCLC.

Upregulated genes specific to RB1mt Downregulated genes specific to RB1mt
ProbeID Symbol log2 fold change p-value ProbeID Symbol log2 fold change p-value
231736_x_at MGST1 2.083 0.001 202834_at AGT −3.059 0
218847_at IGF2BP2 2.058 0.011 1566764_at MACC1 −2.16 0.035
202620_s_at PLOD2 2.01 0.002 205501_at PDE10A −2.159 0.004
213139_at SNAI2 2.002 0.016 204044_at QPRT −2.149 0.004
206332_s_at IFI16 1.879 0.03 239503_at Unknown −2.041 0
235763_at SLC44A5 1.829 0.009 208891_at DUSP6 −2.019 0.006
204646_at DPYD 1.828 0.005 1560652_at Unknown −1.943 0.019
202016_at MEST 1.817 0.003 203881_s_at DMD −1.937 0.006
226225_at MCC 1.717 0.045 208892_s_at DUSP6 −1.921 0.005
217028_at CXCR4 1.675 0.023 206218_at MAGEB2 −1.732 0.013
214597_at SSTR2 1.655 0.038 203132_at RB1 −1.709 0.006
210839_s_at ENPP2 1.557 0.04 205305_at FGL1 −1.67 0.006
203038_at PTPRK 1.531 0.001 201328_at ETS2 −1.663 0.005
222553_x_at OXR1 1.528 0.003 205110_s_at FGF13 −1.651 0.036
1558217_at SLFN13 1.515 0.045 209365_s_at ECM1 −1.61 0.014
1565162_s_at MGST1 1.493 0.016 210102_at VWA5A −1.597 0.005
204620_s_at VCAN 1.47 0.011 209468_at LRP5 −1.583 0.001
221731_x_at VCAN 1.44 0.018 1558882_at LOC401233 −1.574 0.032
218197_s_at OXR1 1.409 0.006 219750_at TMEM144 −1.572 0.034
205229_s_at COCH 1.338 0.006 223748_at SLC4A11 −1.552 0.002
203184_at FBN2 1.338 0.026 205601_s_at HOXB5 −1.511 0.023
205027_s_at MAP3K8 1.311 0.004 209803_s_at PHLDA2 −1.495 0.038
204030_s_at SCHIP1 1.308 0.038 212268_at SERPINB1 −1.467 0.001
241400_at Unknown 1.296 0.006 1569191_at ZNF826 −1.448 0.022
1555788_a_at TRIB3 1.274 0.034 212188_at KCTD12 −1.43 0.002
211675_s_at MDFIC 1.272 0.012 241672_at C13orf36 −1.414 0.033
229465_s_at PTPRS 1.255 0.008 219305_x_at FBXO2 −1.337 0.015
225093_at UTRN 1.255 0.042 1554472_a_at PHF20L1 −1.317 0
205122_at TMEFF1 1.251 0.01 203028_s_at CYBA −1.308 0.047
219489_s_at NXN 1.238 0.035 228726_at Unknown −1.303 0.01
225056_at SIPA1L2 1.237 0.011 204158_s_at TCIRG1 −1.302 0.006
208949_s_at LGALS3 1.235 0.021 211538_s_at HSPA2 −1.279 0.035
201063_at RCN1 1.229 0.033 220082_at PPP1R14D −1.259 0.008
235244_at CCDC58 1.184 0.032 203005_at LTBR −1.257 0.011
210978_s_at TAGLN2 1.184 0.005 229964_at C9orf152 −1.23 0.036
233903_s_at SGEF 1.182 0.003 203961_at NEBL −1.212 0.032
205123_s_at TMEFF1 1.177 0.019 224577_at ERGIC1 −1.206 0.002
200897_s_at PALLD 1.164 0.018 238021_s_at CRNDE −1.189 0.022
200916_at TAGLN2 1.161 0.015 223041_at CD99L2 −1.182 0.001
215127_s_at RBMS1 1.143 0.03 205586_x_at VGF −1.182 0.008
202887_s_at DDIT4 1.141 0.005 239278_at Unknown −1.163 0.013
212636_at QKI 1.137 0.014 213689_x_at FAM69A −1.157 0.005
214877_at CDKAL1 1.134 0.03 232099_at PCDHB16 −1.153 0.028
227197_at SGEF 1.129 0.005 219256_s_at SH3TC1 −1.153 0.005
224918_x_at MGST1 1.12 0.02 227943_at Unknown −1.141 0.004
227522_at CMBL 1.08 0.007 210538_s_at BIRC3 −1.138 0.024
206385_s_at ANK3 1.073 0.042 1568838_at LOC100132169 −1.117 0.032
226464_at C3orf58 1.072 0.01 229872_s_at LOC100132999 −1.099 0.021
1568720_at ZNF506 1.054 0.04 1555579_s_at PTPRM −1.09 0.043
201656_at ITGA6 1.04 0.028 224997_x_at H19 −1.082 0.032
212190_at SERPINE2 1.034 0.037 213005_s_at KANK1 −1.081 0.01
204995_at CDK5R1 1.022 0.017 219371_s_at KLF2 −1.076 0.013
210512_s_at VEGFA 1.02 0.037 37408_at MRC2 −1.074 0.01
226419_s_at FLJ44342 1.015 0.001 224391_s_at SIAE −1.059 0.01
210735_s_at CA12 1.011 0.032 201329_s_at ETS2 −1.053 0.022
65588_at LOC388796 1.002 0.009 205016_at TGFA −1.049 0.007
213857_s_at CD47 1.001 0.002 227384_s_at LOC727820 −1.043 0.002
208622_s_at EZR 1.001 0.001 228010_at PPP2R2C −1.033 0.031
209500_x_at TNFSF12/TNFSF13 −1.031 0.019
224576_at ERGIC1 −1.031 0.007
236719_at Unknown −1.025 0.004
227001_at NIPAL2 −1.021 0.006
230722_at BNC2 −1.019 0.047
204682_at LTBP2 −1.007 0.024
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Table II.

The CDKN2Amt-specific gene signatures in NSCLC.

Upregulated genes specific to CDKN2Amt Downregulated genes specific to CDKN2Amt
ProbeID Symbol log2 fold change p-value ProbeID Symbol log2 fold change p-value
236694_at CYorf15A 2.585 0.001 228956_at UGT8 −1.618 0.001
211980_at COL4A1 1.978 0.006 209644_x_at CDKN2A −1.52 0.004
213725_x_at XYLT1 1.654 0.023 225681_at CTHRC1 −1.396 0.014
204971_at CSTA 1.615 0.022 1554242_a_at COCH −1.373 0.008
209970_x_at CASP1 1.566 0.001 218820_at C14orf132 −1.199 0.012
225688_s_at PHLDB2 1.412 0.017 200884_at CKB −1.191 0.001
202638_s_at ICAM1 1.388 0.011 227623_at Unknown −1.156 0.018
222453_at CYBRD1 1.377 0.016 207558_s_at PITX2 −1.151 0.014
1562102_at AKR1C1 1.344 0.048 236302_at PPM1E −1.151 0.009
208782_at FSTL1 1.312 0.014 209198_s_at SYT11 −1.145 0.007
211340_s_at MCAM 1.299 0.002 1560023_x_at Unknown −1.097 0.01
210004_at OLR1 1.299 0.01 214321_at NOV −1.094 0.035
202008_s_at NID1 1.286 0.004 205229_s_at COCH −1.061 0.031
202350_s_at MATN2 1.197 0.012 223551_at PKIB −1.045 0.046
239999_at Unknown 1.126 0.033 230130_at Unknown −1.04 0.049
205407_at RECK 1.117 0.014 212706_at LOC100286937/LOC100287164/RASA4 −1.019 0.003
203304_at BAMBI 1.113 0.012
228698_at SOX7 1.104 0.014
227051_at Unknown 1.088 0.036
201939_at PLK2 1.082 0.017
209087_x_at MCAM 1.081 0.007
206165_s_at CLCA2 1.067 0.025
227178_at CUGBP2 1.067 0.012
227253_at CP 1.044 0.015
212262_at QKI 1.043 0.002
202998_s_at LOXL2 1.039 0.006
214022_s_at IFITM1 1.021 0.034
211366_x_at CASP1 1.019 0.001
222446_s_at BACE2 1.014 0.009
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Specific change in total proteins and phosphoproteins in RB1 and CDKN2A mutations

We characterized the differential regulation of RB1 and CDKN2A mutations at the protein level using RPPA data of 77 pan- and 38 phospho-antibodies for 89 proteins across 179 cancer cell lines. Consistent with the patterns of gene expression data, the overall protein expression and phosphorylation status were inversely correlated between RB1mt SCLC and CDKN2Amt NSCLC cell lines (Fig. 4). Thus, the mutational effect of RB1 and CDKN2A genes were separately analyzed in SCLC and NSCLC cell lines (Fig. 5). The results showed that β-catenin was commonly over expressed in both RB1 and CDKN2A mutants. Wnt/β-catenin overexpression has been extensively reported in lung cancer (30), and the overexpression of β-catenin might be maintained by the mutational effect of both RB1 and CDKN2A genes. The RB1 mutation specifically regulated PTEN, STAT, mTOR, p53 expression and MAPK phosphorylation in SCLC cells. However, the CDKN2A mutation altered the expression of JNK2 and cKIT and the phosphorylation status of AKT, STAT3 and AMPKa.

Figure 4.

Figure 4

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Comparison of protein expression and phosphorylation in SCLC and NSCLC with the mutational status of RB1 and CDKN2A. Protein expression change of a total of 77 pan-antibodies was compared between (A) SCLC and NSCLC, (B) RB1-mutated SCLC and CDKN2A-mutated NSCLC, and (C) SCLC and NSCLC with RB1 and CDKN2A wild-type cell lines, respectively. Protein phosphorylation change of a total of 38 phospho-antibodies was compared between (D) SCLC and NSCLC, (E) RB1-mutated SCLC and CDKN2A-mutated NSCLC, and (F) SCLC and NSCLC with RB1 and CDKN2A wild-type cell lines, respectively. The phosphorylation change for each protein phospho-antibody was calculated by the log2 fold change via the median across 179 cell lines. The r value represents the Pearson correlation coefficient.

Figure 5.

Figure 5

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Protein and phosphoprotein signatures specific to RB1 or CDKN2A mutations. (A) Comparison of differential protein expression (77 pan-antibody data) between RB1 and CDKN2A mutations. (B) Comparison of differential protein phosphorylation (38 phospho-antibody data) between RB1 and CDKN2A mutations. The red color represents protein markers specific to the RB1 mutation, and blue represents protein markers specific to the CDKN2A mutation (>1-fold change and p<0.05). The scale of the plots is the log2 fold change of protein expression and phosphorylation, respectively. The value was calculated by the differences of average log2 expression or phosphorylation level between mutation and wild-type cell lines in the given subtype. The r value represents the Pearson correlation coefficient.

MAPK (T202), which is significantly (p<0.05) phosphorylated in RB1-mutated SCLC cancer cell lines, has an important role in transcriptional regulation of targeting transcription factors such as c-Jun, c-Fos, and c-Myc (31). This observation is consistent with the DNA microarray data (Fig. 3B) for RB1mt SCLC cells, which are enriched in the functional categories of transcription. AKT is specifically phosphorylated (S473, T308) in CDKN2Amt NSCLC and related to focal adhesion (32), which is the enriched gene set of CDKN2Amt NSCLC from DNA microarray analysis. Furthermore, PTEN, which was overexpressed in RB1mt SCLC cells (Fig. 5A), is a well-known negative regulator of AKT activation (33), suggesting that AKT-mediated signaling might be exclusively activated by CDKN2Amt in NSCLC, not by RB1mt in SCLC. Both proteome and transcriptome data analyses demonstrated that exclusive RB1 and CDKN2A mutations in different subtypes of lung cancer included a differential change of gene expression and protein regulation, even though RB1 and CDKN2A are in the same cell cycle-related pathway.

Synthetic lethality of reciprocal regulation of RB1 and CDKN2A expression

Through the systematic analysis of transcriptome and proteome data, we found unique mRNA and protein regulation patterns induced by the mutation of either the RB1 gene or the CDKN2A gene (Fig. 6A). Furthermore, we investigated the synergistic negative effect on cancer growth by simultaneous functional loss (or knockdown) of these two genes. We performed a viability assay with diverse lung cancer cell lines with the combined knockdown of RB1 and CDKN2A genes using siRNA-mediated gene depletion. As a result, the knockdown of one of these genes decreased the viability of cells harboring a mutation of the other gene (Fig. 6B). The viability of CDKN2A-mutant cell lines was significantly decreased by knockdown of RB1; similarly, RB1-mutant cell lines were inhibited by CDKN2A depletion. Consistently, the simultaneous depletion of RB1 and CDKN2A genes significantly decreased the viability of lung cell lines harboring wild-types of these genes (Fig. 6C). However, the single knockdown of either the RB1 gene or the CDKN2A gene did not effectively reduce viability in these wild-type cell lines. In conclusion, the functional inhibition of the RB1 or CDKN2A gene in CDKN2Amt or RB1mt cancer cells, respectively, might be a promising therapeutic approach in SCLC or NSCLC lung cancers. The present study on differential proteome and transcriptome profiles between two mutant groups provides mechanistic insights into the synthetic lethality of RB1 and CDKN2A mutations.

Figure 6.

Figure 6

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Differential viability change of lung cancer subtypes induced by reciprocal knockdown of the RB1 and/or CDKN2A genes. (A) A schematic summary of biological functions dysregulated by the mutation status of RB1 and CDKN2A. (B) The effects of siRB1 and siCDKN2A in lung cell lines with CDKN2A mutations (NCI-H82 and NCI-H524) and RB1 mutations (NCI-H460, A549, NCI-H322M, and NCI-H226), respectively. (C) The effect of single- or double-knockdown of RB1 and CDKN2A gene expression on the viability of RB1- and CDKN2A-positive cell lines (EKVX, NCI-H23, NCI-H1395, and NCI-H1993). The cells were incubated in siRB1 and siCDKN2A and cultured for 72 h. Cell viability was determined using a CellTiter-Blue assay. The siRNA-mediated gene depletion efficacy of RB1 or CDKN2A in each tested cell line was evaluated (data not shown). The % cell viability was calculated using siNC as the negative control for each siRNA treatment. *p<0.05 and **p<0.01 between the compared groups.

Acknowledgements

This study was supported by Sookmyung Women's University Research Grant 1-1303-0160.

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