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We have previously mapped a major susceptibility locus influencing familial lung cancer risk to chromosome 6q23–25. However, the causal gene at this locus remains undetermined. In this study, we further refined this locus to identify a single candidate gene, by fine mapping using microsatellite markers and association studies using high-density SNPs. This region-wide scan across 6q23-25 found significant association between lung cancer susceptibility and three SNPs in the first intron of the RGS17. Association of two SNPs, rs4083914 and rs9479510, was further confirmed in two independent familial lung cancer populations. Quantitative RT-PCR analysis of matched tumor and normal human tissues found that RGS17 transcript accumulation is highly increased in sporadic lung tumors. Human lung tumor cell proliferation is inhibited upon knockdown of RGS17 transcript and enhanced upon overexpression of RGS17. Our findings indicate that RGS17 may influence familial susceptibility to lung cancer through its affects on cell proliferation.
Lung cancer can occur sporadically in people with no known family history of lung cancer or it can be familial, occurring in multiple members of the same family. Initial evidence of a genetic basis for susceptibility to lung cancer came from observations of individual differences in susceptibility to the same environmental risk factors(1–3), familial aggregation of lung cancer after accounting for personal smoking (4), increased risk of lung cancer mortality in siblings (5), and segregation analyses (6–11). Despite increasing knowledge of genetic influences on lung cancer susceptibility, no specific causal genes have been identified. A recent genome-wide linkage study by the Genetic Epidemiology of Lung Cancer Consortium (GELCC) mapped a major susceptibility locus to 6q23-25 (12). An analysis of 52 extended pedigrees with 3 or more first-degree relatives with lung cancer produced a maximum heterogeneity LOD score (HLOD) of 2.79 at 155 cM (marker D6S2436) on chromosome 6q. Further analysis of a subset of 23 multigenerational pedigrees containing 5 or more family members affected with lung cancer yielded a multipoint HLOD score of 4.26 at the same position.
To identify the causal gene in the 6q susceptibility locus, the present study employed a combination of linkage fine mapping and region-wide SNP association analysis. We have identified common variants in RGS17 that associate with familial lung cancer and validated these in two independent samples of unrelated familial cases and controls. This established RGS17 as a candidate familial lung cancer susceptibility gene for this major locus on Chromosome 6q. RGS17 encodes a recently identified member of the regulator of G-protein signaling (RGS) family. RGS proteins negatively regulate G-protein related signaling at least in part by accelerating the GTPase activity of Gα subunits. We demonstrated that RGS17 is highly expressed in tumor tissues and that loss of RGS17 transcript inhibits the growth of xenografted tumors and the proliferation of tumor cells, while overexpression of RGS17 increases the rate of proliferation of tumor cells.
The goal of this study, which was to identify candidate genes for lung cancer susceptibility on Chromosome 6q, was realized with the identification of RGS17 as a familial lung cancer associated gene. Furthermore, this study demonstrates that RGS17 is commonly overexpressed in lung tumors and that expression of RGS17 induces a proliferative phenotype in lung tumor cells.
A total of six multigenerational families (with 5 or more affected members) were chosen for fine mapping the 6q linkage. The LOD scores for these families in the initial linkage study at the D6S2436 position are: Family 12 (LOD = 0.83), Family 33 (LOD = 0.94), Family 35 (LOD = 0.871), Family 47 (LOD = 0.678), Family 100 (LOD = 0.24), Family 102 (LOD = 0.6) (12). Twenty six microsatellite markers (including seven from the original linkage study) used for mapping were: D6S2437, D6S1040, D6S262, D6S1038, D6S1272, D6S1009, D6S250, D6S1055, C6S1848, D6S971, G15833, D6S960, D6S495, D6S2442, D6S2436, D6S442, D6S969, D6S1035, D6S955, D6S1008, D6S1277, D6S1273, D6S392, D6S297, D6S1697 and D6S1027. Genotyping was performed as previously described and LOD scores for individual families were estimated with Simwalk2 under the autosomal dominant model as used previously (12). Haplotypes were inferred with Simwalk2 (13, 14) for all genotyped affected members from each of 6 families, with the largest common haplotypes indicated. The haplotype shared by affected members within families varied in length and position.
For high-density SNP association mapping, we used 24 6q-linked cases (from pedigrees with a positive LOD at 155 cM), and 72 unrelated non-cancer controls (both spouses and non-spouses) from the GELCC collection. To ensure genetic independence among subjects, one case was selected from each family, while multiple controls from the same families were allowed as long as they had no blood relationship with any selected cases from the relevant families. In this initial screen, 87.5% and 12.5% of the cases are current/former smokers and nonsmokers, respectively, with an average age of 61.2 (± 11.5). In controls, 67.5% and 29.7% subjects are current/former smokers and nonsmokers, respectively, with an average age of 70.9 (± 12.8).
For the GELCC familial validation study, we genotyped a separate sample of 226 familial lung cases and 313 controls from GELCC resources. Each familial case was chosen from one high-risk lung cancer family that has three or more affected members. Most of these families did not have linkage information ascertained due to the paucity of biospecimens. In these samples, only a blood sample of one affected member was collected. It is anticipated that some of these families are not 6q-linked which may dilute the association. Non-cancer controls were obtained from a combination of GELCC resources, Coriell Institute for Medical Research (CIMR) (Camden, NJ), and the Fernald Medical Monitoring Program (FMMP). In order to minimize possible effects of cigarette smoking and age, we selected mainly smokers of older age as controls, except spouse controls. In the cases, 83.6% and 15.5% of the subjects are current/former smokers and nonsmokers, respectively, with an average age of 61.6 (± 10.6). In the controls, 74.7% and 22.4% of the subjects are current/former smokers and nonsmokers, respectively, with an average age of 75.0 (± 9.4). To maintain the homogeneous population samples, only Caucasians from the GELCC, CIMR and FMMP collections were used for the association analysis in the initial screen and the validation study. We detected no population stratification in these GELCC subjects, as shown by linkage agglomerative clustering implemented in PLINK software (15). For the Mayo Clinic validation study, we genotyped 154 familial lung cancer cases and 325 non-cancer controls from Mayo Clinic (Rochester, MN). These familial lung cancer cases were chosen from families that have three or more first-degree relatives with lung cancer. These samples are part of the Mayo Clinic Lung Cancer Cohort (MCLCC) (NIH CA77118, CA80127, and CA84354) collected from an ongoing case-control study (16). In the cases, 85.7% and 14.3% are current/former smokers and nonsmokers, respectively, with an average age of 62.9 (± 9.5). In the controls, 65.2% and 19.7% are current/former smokers and nonsmokers, respectively, with an average age of 75.5 (± 7.3). Basic characteristics of study subjects with familial lung cancer and corresponding controls are detailed in Table S1.
For the sporadic validation studies, samples from the MCLCC study (Mayo) were used, including 553 sporadic cases and 627 controls (16, 17). Shanghai samples were collected from the Shanghai Women’s Health Study (Shanghai, China), an on-going prospective cohort study of approximately 75,000 adult women, including 197 sporadic female cases and 410 female controls (18). All Chinese samples are female nonsmokers, which may be particularly informative in studying main gene effects. Basic characteristics of Mayo Clinic and Shanghai study subjects with sporadic lung cancer and corresponding controls are detailed in Table S2.
The Affymetrix 500K SNP chipset, including two chips (Nsp and Sty), was used to genotype 24 cases and 72 controls. SNP genotyping was performed by the Vanderbilt University Microarray Shared Resource at Nashville, TN following the Affymetrix protocol (www.affymetrix.com). A confidence score of 0.33 was used for genotype calling, using the Bayesian Robust Linear Model with Mahalanobis (BRLMM) algorithm (19), at which an average call rate of 96.9% was obtained across all case and control samples. The SNP-Genotyping Core at Washington University using the Sequenom platform carried out Genotyping for the validation samples.
Hardy-Weinberg equilibrium for each SNP was examined with software hweStrata implementing an exact test method proposed by (20), under stratum number K=1. SNPs with an exact p value ≤ 0.01 in controls were excluded in the association analysis. Due to the relatively small sample sizes, the Fisher exact test was used for assessing associations between SNPs and lung cancer. Conforming to the linkage study (12), the autosomal dominant model was used for coding marker genotypes, which involved two steps: 1) identifying, for each SNP, the putative “disease allele” (denoted as D) as the one that is more common in cases than in controls; 2) forming a 2×2 contingency table by combining genotypes DD and Dd into one group and taking genotype dd as the other group. For validation samples, the same Fisher exact test with the autosomal dominant model described above was applied to the association analysis. Results from multiple case-control groups were combined using a Mantel-Hazenszel model in which the groups were allowed to have different population frequencies for alleles and genotypes but were assumed to have common relative risks (21).
The GELCC has not collected paired tumor and normal tissues from GELCC cases and thus does not have RNA for qRT-PCR, therefore we employed available sporadic adenocarcinoma and adenosquamous carcinoma tumor samples for RGS17 expression analysis. RNA from 13 paired lung tumors and normal tissues was obtained from the Tissue Procurement Core at WUSTL. cDNA from normal human tissues was obtained from BD Biosciences, San Jose, CA. Quantitative real-time PCR (qRT-PCR) was conducted using the method as described previously (22). Briefly, two micrograms of total RNA per sample were converted to cDNA using the SuperScript First-Strand Synthesis system for RT-PCR (Invitrogen). Quantitative RT-PCR assay was done using the SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA). One microliter of cDNA was added to a 25 μL total volume reaction mixture containing water, SYBR Green PCR Master Mix, and primers. Each real-time assay was done in duplicate on a BioRad MyIQ machine. Data were collected and analyzed with Stratagene Mx3000 software. Gene β-actin was used as an internal control to compute the relative expression level (ΔCT) for each sample. The fold change of gene expression in tumor tissues as compared to the paired normal tissues was calculated as 2d, where d = ΔCT normal − ΔCT tumor. Pairwise Wilcoxon signed-rank test was carried out to assess the overall statistical significance of the difference in gene expression levels between the paired tumor and normal tissues.
Non-small cell lung cancer cell lines (n=56) and normal controls (n=8, NHBEC, SAEC, HBEC2-5) were from our Hamon Center collection at UT Southwestern Medical Center. They were tested and found free of mycoplasma contamination and their identities were verified by DNA fingerprinting. Normal lung tissues (n = 29) were obtained from David Lam at Hong Kong University and from William Gerald at Memorial Sloan Kettering Cancer Center in New York. RNAs were labeled and hybridized to Affymetrix GeneChips according to the manufacturer’s protocol (http://www.affymetrix.com). Microarrays used were HG-U133-Plus2 (54,675 elements; 29,180 unique genes) and HG-U133A and HG-U133B (together 44,928 elements; 23,583 unique genes). When comparing different GeneChips, U133A and U133B were pooled together using their 100 common control genes and U133-Plus2 and U133A&B were analyzed using their 43,680 common genes. Microarray analysis was performed using in-house Visual Basic software MATRIX 1.41 that incorporates a connection to the statistical programming language R. Array data were first processed with the mas method of the R package affy which yield PM-corrected signals similar to MAS5 (MicroArray Suite, Affymetrix) processing. The data was then log-transformed, thresholded to a log signal value of 5, and quantile-normalized. Classes of samples were compared by calculating log2 ratios and T tests for each gene. All genes on the arrays were BLAST-verified and annotated using recent versions of public NCBI databases.
The tumor cell line H1299 was cultured in RPMI-1640 plus 10% FBS (Gibco). Cells were transduced with a lentiviral short-hairpin RNA (shRNA) construct based on the pLKo1 vector and designed to specifically target RGS17 transcripts (Open Biosystems). Vector with no knock-down or vector knocking down RGS17 transcript were first made in Phoenix cells (Orbigen) and then transduced into H1299 cells with 8 ug/ml Polybrene (Sigma). Media was replaced 24 hours after transduction, and cells were split 1:4 48 hours after transduction. At 72 hours post transduction, cells harboring lentiviral constructs were selected with 1 μg/ml puromycin for 2–4 days, until mock infected cells were dead. Surviving cells were pooled and plated at the indicated densities.
For overexpression in H1299 cells, an N-terminal three hemagglutinin tagged (HA3) full length RGS17 cDNA was cloned into pCDNA3.1 for expression of HA-tagged RGS17 protein. Cells at ~60% confluence were transfected with 8ug of vector with 3HA alone or that expressing 3HA-RGS17 using Geneporter II reagents (Gene Therapy Systems, San Diego, CA). Twenty four hours post transfection cells were split 1:4 and placed on 2 μg/ml puromycin. Cells were selected for 3 days until mock transfected cells were dead. Cells were pooled and plated at 500 cells per well in a 12-well tissue culture dish. P-values were determined by one-tailed Student’s T-Test. Cells were quantitatively assayed for viable cell numbers in triplicate.
Cells stably expressing shRNA vectors or HA3-RGS17 as described above were seeded onto 6-well tissue culture dishes at a density of 500 cells per well. Cells were assayed for viable cell numbers using the CellTiter 96 Non-Radioactive Cell Proliferation Assay kit (Promega) periodically over 10 days in culture. P-values were determined by one-tailed Student’s T-Test.
Low density colony formation assays were performed as previously described (Sato, 2006). Briefly, cells stably expressing shRNA vectors as described above were seeded onto 100 mM tissue culture dishes at a density of 100 cells per dish. Colonies were stained with crystal violet after 10 days in culture. Visible colonies were counted. P-values were determined by one-tailed Student’s T-Test.
Tumor cells stably expressing shRNA vectors as described above were cultured, counted and resuspended in serum free media at a concentration of 1.5 × 107 cells/mL. A volume of 200 μL (3 × 106 cells respectively) were injected subcutaneously into the right (vector) or left (shRNA) flank of athymic nude mice at an age of 4–6 weeks. The health of these mice was monitored 3 times weekly and tumor sizes were measured periodically until sacrifice at 2 – 4 weeks post injection depending on the growth rate of the tumors. Tumor volume was determined by the formula (l × w × h). P-values were determined by one-tailed Student’s t-test.
To further narrow the broad 6q23-25 linkage region, fine linkage mapping using additional microsatellite markers was employed. Mapping with increased marker density can capture more recombination events, and thus add resolution to the linkage region. We chose 6 multigenerational families with 5 or more affected first degree relatives for fine mapping studies (Figure 1A, B). Of the 52 GELCC families used in the original linkage mapping, these 6 families were chosen for further microsatellite mapping because they had the largest number of affecteds per pedigree (i.e. 5 or more) and they exhibited the strongest linkage at marker D6S2436 (peak marker in 6q23-25 region). We estimate that these would be the most representative of informative families linked to the 6q susceptibility gene. The LOD scores for each individual high risk family ranged from 0.24 to 0.94 at marker D6S2436 (12). Twenty-six microsatellite markers (including 7 markers used in the previous linkage study) selected from the Marshfield Map were genotyped in these six families with an increased marker density averaging 2.4 cM per marker. After genotyping, LOD scores for individual families were estimated with Simwalk2 under the autosomal dominant model as used previously (12). Haplotypes were inferred with Simwalk2 for all genotyped affected members from each of 6 families, with the largest common haplotypes indicated (13, 14). The haplotype shared by affected members within families varied in length and position. The common region of haplotype sharing by affected members across all the families covers a region of ~3 cM centering on the marker D6S2442, spanning 152.0 to 154.2 Mb on chromosome 6q (Figure 1B). As a result, this region of haplotype sharing includes 12 annotated genes (NCBI Build 36.3).
Simultaneously, we used the Affymetrix 500K chipset to ascertain SNP information on 24 6q-linked unrelated cases and 72 unrelated non-cancer controls. To minimize the chance of missing the causal gene(s), the association analysis was performed on an expanded region of one-HLOD support interval, spanning 144 Mb to 164 Mb on 6q23-25 (Figure 2A). A total of 114 annotated genes reside in this region (NCBI Build 36.3). Each case is Caucasian and was chosen from one pedigree with a positive LOD at 155 cM. Even though such a small sample size was insufficient for a genome-wide scan, prior linkage evidence to 6q permitted a region-wide threshold across 6q to be used (12). A total of 3,957 SNPs were extracted from the Affymetrix 500K chipset for the 6q region. After exclusion of SNPs significantly deviating from Hardy-Weinberg equilibrium (P ≤ 0.01) in the Caucasian control sample, or with a minor allele frequency < 0.05, 3,169 polymorphic SNPs were retained for association analysis. The average SNP coverage was one SNP per 6.3 kb. Under an autosomal dominant model as described and utilized in the previous linkage study (12), we identified three SNPs with the strongest association on 6q23-25: rs6901126 (P = 1.27 × 10−4), rs4083914 (P = 1.31 × 10−4), and rs9479510 (P = 2.43 × 10−4) (Figure 2B, C). These SNPs reside in a linkage disequilibrium (LD) block of 43 kb within the first intron of the RGS17 gene (Figure 2D) and support our fine linkage mapping observations where specific haplotypes are shared by affected members within each of the families spanning the interval from 152.0 to 154.2 Mb on 6q25 (NCBI Build 36.3) (Figure 1B). RGS17 encodes a recently identified member of the regulator of G-protein signaling (RGS) family. RGS proteins negatively regulate G-protein related signaling at least in part by accelerating the GTPase activity of Gα subunits (23, 24).
To validate the association signal from the 6q-linked case/control samples, we genotyped three SNPs with the lowest P values from the initial screen in two independent familial lung cancer samples from the GELCC and Mayo Clinic (Rochester, MN). These lung cancer cases in the GELCC and Mayo Clinic collections are derived from families that have three or more first-degree relatives with lung cancer. In the GELCC samples, there are 226 familial lung cancer cases and 313 non-cancer controls, all of Caucasian descent. All three SNPs were detected to be significantly associated with lung cancer in the GELCC samples (Table 1), and the observed risk allele for the three SNPs was the same as in the initial screening sample. In the Mayo Clinic samples, there are 154 familial cases and 325 controls and we observed that two out of the three SNPs (rs4083914, P = 0.033 and rs9479510, P = 0.035) to be significantly associated with familial lung cancer. In the combined data sets (404 cases and 710 controls), all three SNPs showed significant association with lung cancer with odds ratios around 1.8 (Table 1).
Less than 5% of lung cancer cases diagnosed are familial in origin, which prohibits extensive validation of putative genetic risk factors for familial lung cancer because of limited biospecimen availability. We instead sought to determine whether this gene is also associated with sporadic lung cancer cases and genotyped 553 sporadic cases and 627 controls of Caucasian descent from Mayo Clinic and 197 Chinese sporadic female cases and 410 female controls from Shanghai, China. However, no RGS17 SNPs showed significant associations in these sporadic case populations (rs6901126, P = 0.403; rs4083914, P = 0.804; rs9479510, P = 0.951) (Table S3). This suggests a role for RGS17 in lung cancer that may be very different in sporadic cases versus familial cases, as in the case with the susceptibility gene p53 (25). This gene is mutated both somatically and in the germline, each of which plays roles in the development of various cancers. Hence, the specific mechanism of RGS17 dysfunction as it relates to cancer may be very different in sporadic cases as opposed to familial cases. This familial specificity of RGS17 was also supported by a comparative linkage analysis of different risk lung cancer families. High LOD score was detected in 6q when analyzing families with five or more first-degree relatives with lung cancer, while 6q linkage signal was not detectable in families with three or fewer first-degree relatives with lung cancer (Figure S1).
In order to investigate possible pathogenic changes in RGS17 in our familial group, we performed direct sequencing of the RGS17 protein coding sequences in 10 familial lung cancer cases containing risk alleles implicated in the above association analysis. We did not uncover any coding mutations in RGS17, which might suggest that another mechanism such as changes in gene expression underlie disease susceptibility. RGS17 has been shown to be expressed in both central nervous and peripheral tissues, including the lung (23). We examined the gene expression levels of RGS17 in paired tumor and normal tissues from 13 sporadic lung cancer patients (9 adenocarcinoma and 4 adenosquamous carcinoma) using qRT-PCR, and observed significant overexpression in the tumor tissue (Figure 3A). Of these 13 paired tumors, 10 (77%) exhibited increased expression of RGS17 and the average difference in expression of RGS17 in these tumor tissues versus matched normal controls was 9.1 fold (pairwise Wilcoxon signed-rank test P = 0.009). Smoking status of these samples was unavailable. In an expanded set of 61 sporadic lung tumors of various pathologies RGS17 transcript was increased in 80% of lung tumors over matched normal lung tissue tumors by an average of 8.3 fold (p = 1.36 × 10−9) confirming our observations in the original 13 samples (see Supporting Data – Section #1). Among these samples there is no statistical difference in RGS17 induction between adenocarcinomas (n = 18) and adenosquamous carcinomas (n = 8) as measured by a students t-test (p > 0.1).
We performed Affymetrix gene chip expression analysis on 56 NSCLC cell lines and 37 normal samples (29 normal lung tissue and 8 normal lung cell lines). This data revealed a significant increase in RGS17 expression as determined by Wilcoxon test, P = 8.1 × 10−7 (Figure S3).
In order to evaluate the effect of RGS17 on the growth properties of tumor cells, a lentiviral shRNA construct was utilized to stably knock-down RGS17 transcript levels a human lung tumor cell line. Human H1299 NSCLC cells were chosen due to the high expression of RGS17 in this cell line as measured by expression microarray (Figure S3). RGS17 transcript accumulation was effectively reduced in H1299 human lung tumor cells and knock-down of RGS17 transcript resulted in a decrease in the proliferative rate of these cells in culture as measured by an MTT cell proliferation assay over 10 days (Figure 3B). Showing similar proliferative effects using two distinct shRNA constructs in H1299 cells minimized the possibility of off-target effects with shRNA knock-down. Colony formation assays also clearly show decreased proliferative capacity of H1299 cells with RGS17 knock-down (Figure 3C). Knockdown in cancer cell lines Hct116 (colon carcinoma) and DU145 (prostate carcinoma) has resulted in decreased proliferative rates in both cell lines (see Supporting data – Section #2). Knockdown was also attempted in the A549 lung cancer cell line, however sufficient knockdown of RGS17 transcript was not achieved in these cells. The in vivo significance of the proliferative effects of RGS17 knock-down was further established using an athymic nude mouse tumorigenesis assay. Mice were injected subcutaneously with H1299 human lung tumor cells stably expressing shRNA as described above and monitored for tumor growth over 4 weeks. The rate of growth and tumor load was decreased significantly by RGS17 knock-down (Figure 3D, E). Average tumor weight was reduced from 148 mg to 23 mg (P = 0.03), and average tumor volume was reduced from 385 to 47 mm3 (P < 0.01) with RGS17 knock-down (Figure 3E). Furthermore, exogenous overexpression of HA-tagged RGS17 in H1299 cells enhanced cell proliferation consistent with a role of RGS17 in tumor cell proliferation (Figure S2).
Our statistical and biological analyses have strongly implicated RGS17 as a candidate for the lung cancer susceptibility locus at 6q23-25. Although we are not able to analyze RGS17 expression levels in our familial lung cancer cases because of limited biospecimen availability, our data did indicate high RGS17 transcript upregulation in sporadic lung tumors and cell lines, and strong effects on cell proliferation through knock-down and over-expression in a lung cancer cell line. We hypothesize that there exists a rare variant or variants, which lie on the same haplotype detected and defined by the significantly associated SNPs. This rare, highly penetrant genetic lesion is postulated to affect RGS17 expression and lung cancer susceptibility.
Our future efforts to identify causal variants are currently focused on a re-sequencing analysis of the LD block 12 (Figure 2D). This 43 kb region contains the core promoter, non-coding exon 1 and part of intron 1. Several CpG islands are clustered around exon 1. We intend to address the methylation status of four CpG islands located around exon 1, in sporadic lung tumor expression. Because expression data cannot be obtained from familial samples due to biospecimen availability, it will be necessary to determine meaningful changes in the RGS17 gene in familial germline DNA using these types of experiments until appropriate familial specimens become available for the analysis of expression. The elucidation of specific mechanisms by which RGS17 confers accelerated growth and other tumor phenotypes using cell and molecular biology studies must be pursued, and will be seminal in influencing the diagnostic, preventive and therapeutic applications of this research. These studies constitute ongoing and long-term goals generated by the work presented here, and will elucidate the mechanism by which RGS17 affects familial lung cancer susceptibility.
Recent reports have linked RGS domain containing genes to cancer. One study describes SNPs in PDZ-RhoGEF, containing an RGS domain, which modulates the risk of lung cancer in Mexican Americans (26). Another such study describes the identification of a functional polymorphism in the 3′UTR of RGS6 that is associated with bladder cancer risk, and was shown to affect protein translation (27). There is also evidence that RGS17 reduces dopamine-D2/Gαi-mediated inhibition of cAMP formation and abolishes thyrotropin-releasing hormone receptor/Gαq-mediated calcium mobilization (24). D2 dopamine receptor agonists exhibits anti-proliferative effects in lung tumors and lung cancer cell lines associated with decreased cAMP accumulation (28, 29). It is possible that RGS17 is involved in addiction/reward signaling pathways, as the D2 dopamine gene (DRD2) appears to be associated with smoking addiction. A recent paper also found that RGS17 may work on opioid receptor function (30). Previous work has shown that lung cancers of both non-small cell and small cell histologic types can express high affinity opioid receptors including mu receptors, opioid peptides and also nAChR receptors (31). Lung cancer growth is inhibited and apoptosis induced by opioids including mu agonists while nicotine acting through nAChRs antagonizes this effect providing a growth regulatory loop that is antagonized by nicotine (31, 32). Recently, PET imaging studies have provided in vivo evidence for the presence of delta and mu opioid receptor types in small cell, squamous and adenocarcinomas of the lung (33). Because of the negative action of RGS17 on opioid receptors it is possible that over expression of RGS17 could act through inhibiting a growth regulatory pathway provided by opioid receptors. In a recent study, we demonstrate that RGS17 induces CREB phosphorylation and CREB responsive gene expression (James et al. unpublished data). RGS17 also enhances forskolin mediated cAMP production, forskolin induced gene expression and forskolin induced proliferation. Furthermore, PKA inhibition causes growth arrest of lung tumor cells, which is partially restored by RGS17 overexpresion. Thus, RGS17 appears to be a potential oncoprotein promoting proliferation through cAMP-PKA-CREB signaling. The identification of RGS17 as the major candidate gene for familial lung cancer susceptibility on chromosome 6q has important implications for the diagnosis and treatment of lung cancer as well as delineation of the mechanisms underlying both familial and sporadic lung cancer.
NIH grants U01CA76293 (Genetic Epidemiology of Lung Cancer Consortium), R01CA058554, R01CA093643, R01CA099147, R01CA099187, R01ES012063, R01ES013340, R03CA77118, R01CA80127, P30ES06096, P50CA70907 (Specialized Program of Research Excellence), N01HG65404, N01-PC35145, P30CA22453, R01CA63700, DE-FGB-95ER62060, Mayo Clinic intramural research funds, and Department of Defense VITAL grant. This study was supported in part by NIH, the Intramural Research Programs of the National Cancer Institute, and the National Human Genome Research Institute.
We thank the FMMP for sharing their biospecimens and data with us. We are grateful to the lung cancer families who participated in this research, and for the high caliber service of Vanderbilt University Microarray Shared Resource, Washington University Genotyping Core, Mayo Clinic Genotyping Shared Resource (supported in part by P30CA 15083) and to Julie Clark, Qiong Chen, Shaw Levy, Mark Watson and Jennifer Baker for their assistance in various aspects of this work. We would also like to thank David Lam (University of Hong Kong) and William Gerald (Memorial Sloan Kettering Cancer Center) for microarray data on the normal lung tissue samples.
Statement of Translational Relevance: Lung cancer is the leading cause of cancer morbidity and mortality in developed nations. Although tobacco smoke is the main environmental influence, a genetic component to susceptibility also exists. A previous study has mapped a major susceptibility locus influencing familial lung cancer risk to chromosome 6q23–25. However, the susceptibility gene at this locus remains unresolved. Through a combination of genetic fine mapping and association studies we identified RGS17 as the major candidate susceptibility gene. Knowledge of the molecular and genetic mechanisms of lung cancer will better diagnosis and treatment in the future.