Uncovering SNP (single nucleotide polymorphisms)-environment interactions can generate new hypotheses about the function of poorly characterized genetic variants and environmental factors, like pesticides. We evaluated SNP-environment interactions between 30 confirmed prostate cancer susceptibility loci and 45 pesticides and prostate cancer risk in 776 cases and 1,444 controls in the Agricultural Health Study. We used unconditional logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Multiplicative SNP-pesticide interactions were calculated using a likelihood ratio test. After correction for multiple tests using the False Discovery Rate method, two interactions remained noteworthy. Among men carrying two T alleles at rs2710647 in EH domain binding protein 1 (EHBP1) SNP, the risk of prostate cancer in those with high malathion use was 3.43 times those with no use (95% CI: 1.44–8.15) (P-interaction = 0.003). Among men carrying two A alleles at rs7679673 in TET2, the risk of prostate cancer associated with high aldrin use was 3.67 times those with no use (95% CI: 1.43, 9.41) (P-interaction = 0.006). In contrast, associations were null for other genotypes. Although additional studies are needed and the exact mechanisms are unknown, this study suggests known genetic susceptibility loci may modify the risk between pesticide use and prostate cancer.
Citation: Koutros S, Berndt SI, Hughes Barry K, Andreotti G, Hoppin JA, et al. (2013) Genetic Susceptibility Loci, Pesticide Exposure and Prostate Cancer Risk. PLoS ONE 8(4): e58195. doi:10.1371/journal.pone.0058195
Editor: Michael Scheurer, Baylor College of Medicine, United States of America
Received: August 2, 2012; Accepted: February 3, 2013; Published: April 4, 2013
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: This research was supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics (Z01CP010119) and National Institute of Environmental Health Sciences (Z01ES049030). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Occupational exposure to pesticides has been associated with increased prostate cancer risk in many epidemiologic studies –. Specifically, several groups or chemicals classes have been linked to prostate cancer, including triazine herbicides , , , organochlorine insecticides (OC) –, and organophosphate insecticides (OP) , , , but none of the associations are conclusive and it is unclear which specific pesticides might be driving the group findings. In the Agricultural Health Study (AHS), a prospective cohort of licensed private and commercial pesticide applicators from Iowa and North Carolina, we have consistently observed an excess of prostate cancer among AHS men compared to men in the general populations of Iowa and North Carolina ,  and with continued follow-up of this cohort we recently reported an excess risk of prostate cancer associated with four insecticides, fonofos (OP), terbufos (OP), malathion (OP), and aldrin (OC) .
Genome-wide association studies (GWAS) have identified several independent single nucleotide polymorphisms (SNPs) as risk factors for prostate cancer –. Since these discoveries, large epidemiologic studies have also attempted to uncover SNP-environment interaction in the hopes of generating new hypotheses about the function of many of these gene poor regions. Similarly, SNP-environment interactions help inform our understanding of potential mechanisms by which an environmental factor might influence risk. We previously reported interactions between the organophosphate (OP) insecticide fonofos and known susceptibility loci in the 8q24 region and significant increased risks of prostate cancer, suggesting that variants identified from GWAS may interact with environmental factors . With increasing information about the function of the 8q24 region in cancer development –, this finding provides valuable information about how pesticide use might act to influence prostate cancer risk.
In this study, we use newly genotyped data in 32 prostate GWAS SNPs to continue to explore possible SNP-pesticide interactions and risk of prostate cancer in 2,220 AHS subjects included in a nested case-control study.
Materials and Methods
The AHS is a prospective cohort study that includes 55,747 male licensed pesticide applicators in Iowa and North Carolina, recruited from 1993 through 1997 . During a follow-up interview conducted in 1999–2003, applicators were asked for a mouthwash rinse sample to provide DNA from buccal cells. Approximately 72% of all applicators who completed the follow-up interview returned a buccal sample. In addition, applicators with incident prostate cancer who had not returned a sample at follow-up were asked separately to provide one, with 307/561 (55%) returning a sample. White male pesticide applicators diagnosed with incident prostate cancer between 1993 and 2004 were included in the current nested case-control study. Eligibility, inclusion and exclusion criteria have been previously described . Briefly, cancer cases were coded using the International Classification of Diseases for Oncology, 2nd edition, and stage (local, regional, distant, unstaged) and grade (well differentiated, moderately differentiated, poorly differentiated, undifferentiated, missing) were abstracted by the state cancer registries in Iowa and North Carolina. Eligible controls were frequency matched 2:1 to cases by date of birth (+/− 1 year). Controls were white, male applicators who provided buccal cell material, were alive and not lost to follow-up at the time of case diagnosis, and had no previous cancer diagnosis except non-melanoma skin cancer. Based on these inclusion criteria, 841 cases (66% of total white cases in the cohort as of 2004) and 1,659 controls were identified (total N = 2,500). Due to genotyping space limitations 164 controls were excluded. Of the remaining samples, 108 were removed due to insufficient or poor DNA quality (N = 20; 14 cases, 6 controls) or <90% completion rate (i.e. more than 10% of the SNP assays failed for a given sample, N = 88; 47 cases, 41 controls). We further identified 5 individuals who were suspected to be non-white (<80% European ancestry using STRUCTURE software  or significant deviation from the first two components in principal components analysis ) leaving a final sample size of 776 cases and 1,444 controls. Participants provided written informed consent, and the study protocol was approved by the institutional review boards of the National Institutes of Health, the University of Iowa, and other contractors in compliance with all applicable requirements of the United States.
Genotyping and Quality Control
Thirty-two SNPs not previously genotyped in the AHS but reported as susceptibility loci from GWAS of prostate cancer – were evaluated. Genotyping was performed at NCI's Core Genotyping Facility (http://cgf.nci.nih.gov/operations/uniplex-genotyping.html) , using Applied Biosystems TaqMan® SNP Genotyping Assays. SNPs with low completion rate (<90% of samples) were excluded (rs1465618 and rs4962416). The mean genotyping rate was 96% for the remaining 30 SNPs. No SNPs showed evidence of deviation from Hardy-Weinberg proportions given alpha = 0.05/30 = 0.0017 after Bonferroni correction. Blinded duplicate samples (5%) were also included and concordance of these samples was 100%.
Unconditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between SNPs and prostate cancer and the interaction between SNPs and pesticide use with prostate cancer risk. For SNP associations, genotypes were coded as counts of the risk allele assuming a log-additive model and models were adjusted for age (10 yr-intervals) and state (Iowa or North Carolina). Information on lifetime use of 50 pesticides was captured in two self-administered questionnaires completed during cohort enrollment. All nested case-control study participants completed the first (enrollment) questionnaire, which inquired about ever/never use of the 50 pesticides, as well as duration and frequency of use for a subset of 22 of the pesticides, while 1,439 of these men (60.4% of cases and 67.2% of controls), completed the second (take-home) questionnaire, which inquired about use of the remaining 28 pesticides. Pesticides with a prevalence of use less than 5% in the current nested case-control subgroup were excluded leaving 45 pesticides for analysis (17 herbicides, 21 insecticides, 2 fumigants, and 5 fungicides); a list of all 45 pesticides and their prevalence of use is presented in Table S1. Cumulative lifetime exposure to each pesticide was assessed by intensity-weighted lifetime exposure days and categorized into three groups (non-exposed, low, and high, with low and high divided at the median among controls for each pesticide). SNP-pesticide interactions, adjusted for age and state, were examined in a multiplicative model using the three-level pesticide variable and assuming the dominant genetic model for SNPs. The P-value (1 df) for each SNP-pesticide interaction was computed by comparing nested models with and without the cross-product terms using a likelihood ratio test. SNP-pesticide combinations with a P-interaction<0.05 and a significant increased risk (α = 0.05) of prostate cancer following a monotonic pattern with increasing pesticide exposure in one genotype group and no significant association in the other group are presented. We also evaluated pesticide interactions with a cumulative score variable (continuous and categorical) by coding genotypes as zero, one, or two risk alleles to assess the contribution of multiple independent SNPs (n = 26, including those from 8q24 ) and prostate cancer risk. All P-values are two-sided and all analyses were performed using AHS data release version P1REL0712.04.
We applied the false discovery rate (FDR) (Benjamini – Hochberg adjustment) method to account for the expected proportion of false discoveries. FDR values were calculated separately for each pesticide from the results of 30 tests (i.e., total number of SNPs evaluated) in the evaluation of the association between each SNP-pesticide interaction and the risk of prostate cancer. Interactions were deemed noteworthy at an FDR = 0.20 level.
Applicators in the current study were representative of applicators in the larger cohort with respect to a variety of demographic characteristics . Also, cases were similar in age, state of residence, and applicator type to controls in the study but had a higher proportion of first-degree relatives with a family history of prostate cancer compared with controls (16.7% versus 10.0%, Table 1).
Table 1. Selected characteristics of prostate nested case-control participants.doi:10.1371/journal.pone.0058195.t001
All observed associations for the 30 SNPs and prostate cancer were in the same direction as reported in GWAS of prostate cancer , , –, ,  (Table 2), except for rs12500426 (PDLIM5) for which the opposite allele was observed to be the risk allele compared to the initial report . Among the 30 genotyped SNPs, the strongest association was with the MSMB SNP rs10993994 (p-trend = 0.0002, Table 2). Additionally, there were eight loci with 0.001<P-trend <0.01 (rs1859962, rs5759167, rs2710647, rs4430796, rs7841060, rs902774, rs17632542, rs16901979) and three loci with 0.01<P-trend <0.05 (rs10896449, rs266849, rs10486567).
Table 2. Risk of prostate cancer in the AHS for previously reported susceptibility loci identified from genome wide association studies of prostate cancer.doi:10.1371/journal.pone.0058195.t002
Stratified odds ratios for the association between pesticide use and prostate cancer for interactions <0.05 and a significant increased risk of prostate cancer following a monotonic pattern are presented in Table 3. Among men carrying two T alleles at rs2710647 in EH domain binding protein 1 (EHBP1), the risk of prostate cancer in those with low malathion use was 2.17 times those with no use (95% CI: 0.91, 5.14) and in those with high malathion use was 3.43 times those with no use (95% CI: 1.44–8.15) (P-interaction = 0.003). Among men carrying two A alleles at rs7679673 in TET2, the risk of prostate cancer associated with low aldrin use was 1.86 times those with no use (95% CI: 0.73, 4.75) and for high aldrin use was 3.67 times those with no use (95% CI: 1.43, 9.41) (P-interaction = 0.006). In contrast, associations were null for other genotypes. After correction for multiple tests, both of these interactions remained noteworthy at the FDR = 0.20 level.
Table 3. Stratified odds ratios and 95% CI, adjusted for age and state, for associations between pesticides and prostate cancer.doi:10.1371/journal.pone.0058195.t003
Among men carrying the variant allele at the PDLIM5 SNPs rs1859962 or rs17021918 increased prostate cancer risk was observed with high compared to no terbufos use (OR = 2.05, 95% CI: 1.16–3.64, P-interaction = 0.037), (OR = 1.59, 95% CI: 1.03–2.45, P-interaction = 0.042), respectively (Table 3). Although nominally significant without adjustment for multiple testing, these interactions were not noteworthy after adjustment using the FDR method.
No interactions were observed between cumulative genetic score and pesticide use in relation to prostate cancer risk (data not shown).
We observed four quantitative interactions between GWAS loci and select pesticide use and risk of prostate cancer. Two of these, malathion-rs2710647 and aldrin-rs7679673, were noteworthy at the FDR = 0.20 level after correction for multiple testing. Additional interactions with terbufos were also observed with a lesser level of significance. Interestingly, all of the observed interactions are with pesticides that have been implicated in the AHS as risk factors for aggressive prostate cancer .
The interactions with the OP insecticides malathion and terbufos were in one nongenic region on chromosome 17q24 and two gene regions, EHBP1 and PDLIM5. The function of the rs1859962 SNP, which is located in a nongenic region, is not known. Although the nearest protein-coding regions, KCNJ2 and SOX9, are ~1Mb away, SOX9 is involved in prostate epithelial differentiation and observed to promote prostate tumor cell proliferation when upregulated , . EHBP1 encodes an Eps15 homology domain binding protein, which is involved in clathrin-mediated endocytosis, a process fundamental to neurotransmission, signal transduction and the regulation of many plasma membrane activities. Alterations (fusions, somatic mutations, over and under-expression) of clathrin-mediated endocytosis proteins have been reported in numerous cancers, including prostate cancer . PDLIM5 (PDZ and LIM domain 5, also called ENH or ENH1) is a PDZ-LIM protein. PDZ-LIM proteins can act as signal modulators, influence actin dynamics, regulate cell architecture, and control gene transcription . Misregulated PDZ-LIM proteins have been shown to promote tumor cell invasion and metastasis in prostate tumors and prostate cancer cell lines , . Interestingly, the OP pesticides malathion and terbufos are acetylcholinesterase (enzyme that degrades the neurotransmitter acetylcholine) inhibitors. PDLIM5 is observed to be expressed in various brain regions and is localized in presynaptic nerve terminals where neurotransmitter vesicles are stored . Although it is not clear how pesticides may interact with these variants to increase the risk of prostate cancer, it is possible that exposure to these pesticides may alter important signal transduction pathways and/or compromise cellular morphology to promote the development of carcinogenesis.
Another interaction was observed for the organochlorine (OC) insecticide aldrin and SNP rs7679673 on chromosome 4. This SNP is located between two gene regions, TET2, a gene recently characterized as a tumor suppressor gene involved in the pathogenesis of several hematopoietic diseases , and PP2A, a gene implicated in androgen regulation in prostate cancer cell lines . Organochlorine pesticides, like aldrin, have been implicated as endocrine disrupting chemicals and may alter androgen levels to influence prostate cancer risk . Although there is no direct information about the function of rs7679673, this variant has been shown to be associated with earlier onset of disease and to have a stronger association with prostate cancer among those with a family history of prostate cancer , . In the AHS, we observed a significant interaction between aldrin and family history of prostate cancer . Small numbers in the current analysis preclude evaluation of the effect of family history on the aldrin-rs7679673-prostate cancer association (3-way interaction).
Although we observed interesting interactions, the sample size for the current study is limited. This limited sample size is reflected by the small cell counts for some gene-exposure groups and in the inability to achieve the same magnitude of effect observed in GWAS for all SNP associations. This does not negate the importance of these SNPs in our population because they are known risk variants for prostate cancer as established by GWAS. We also considered many interactions in the current analysis thus our findings may be due to chance, however, after adjusting for multiple comparisons some interactions stood out. When interpreting the meaningfulness of an interaction between two factors, more credence is given to a positive interaction when each component has been shown to be a risk factor for the disease independently. Thus, it is important to note that the interactions we found in our study were observed between well-established GWAS loci and use of two specific pesticides (malathion and aldrin) that have independently been associated with prostate cancer in the AHS .
For many gene-exposure studies, a key limitation is the quality of the exposure information. In the AHS, we have high quality information on lifetime use of specific pesticides from several detailed questionnaires, which has been shown to be reliable , . Few studies have the ability to examine interactions between pesticide exposure and genetic risk factors for prostate cancer, thus replication of these findings may be difficult.
In conclusion, we observed several positive interactions between pesticide use and GWAS loci. Interactions between the chromosome 2p15 SNP rs2710647 and malathion, as well as the chromosome 4q24 SNP rs7679673 and aldrin, were noteworthy after correction for multiple testing. Although additional studies are needed and the exact mechanisms by which these variants may interact with these specific pesticides are unknown, our study raises some intriguing questions about interplay of genetic and environmental risk factors for prostate cancer.
List of 45 chemicals evaluated for interaction.
Conceived and designed the experiments: SK SIB MCRA LEBF. Performed the experiments: SK MY LAB JY. Analyzed the data: SK. Contributed reagents/materials/analysis tools: MY LAB JY. Wrote the paper: SK SIB KHB GA JAH DPS MCRA LEBF.
- 1. Band PR, Abanto Z, Bert J, Lang B, Fang R, et al. (2011) Prostate cancer risk and exposure to pesticides in British Columbia farmers. Prostate 71: 168–183.
- 2. Blair A, Dosemeci M, Heineman EF (1993) Cancer and other causes of death among male and female farmers from twenty-three states. Am J Ind Med 23: 729–742.
- 3. Blair A, Zahm SH, Pearce NE, Heineman EF, Fraumeni JF Jr (1992) Clues to cancer etiology from studies of farmers. Scand J Work Environ Health 18: 209–215.
- 4. Dosemeci M, Hoover RN, Blair A, Figgs LW, Devesa S, et al. (1994) Farming and prostate cancer among African-Americans in the southeastern United States. J Natl Cancer Inst 86: 1718–1719.
- 5. Meyer TE, Coker AL, Sanderson M, Symanski E (2007) A case-control study of farming and prostate cancer in African-American and Caucasian men. Occup Environ Med 64: 155–160.
- 6. Van Maele-Fabry G, Willems JL (2004) Prostate cancer among pesticide applicators: a meta-analysis. Int Arch Occup Environ Health 77: 559–570.
- 7. MacLennan PA, Delzell E, Sathiakumar N, Myers SL, Cheng H, et al. (2002) Cancer incidence among triazine herbicide manufacturing workers. J Occup Environ Med 44: 1048–1058.
- 8. Mills PK, Yang R (2003) Prostate cancer risk in California farm workers. J Occup Environ Med 45: 249–258.
- 9. Multigner L, Ndong JR, Giusti A, Romana M, Delacroix-Maillard H, et al. (2010) Chlordecone exposure and risk of prostate cancer. J Clin Oncol 28: 3457–3462.
- 10. Settimi L, Masina A, Andrion A, Axelson O (2003) Prostate cancer and exposure to pesticides in agricultural settings. Int J Cancer 104: 458–461.
- 11. Xu X, Dailey AB, Talbott EO, Ilacqua VA, Kearney G, et al. (2010) Associations of serum concentrations of organochlorine pesticides with breast cancer and prostate cancer in U.S. adults. Environ Health Perspect 118: 60–66.
- 12. Alavanja MC, Samanic C, Dosemeci M, Lubin J, Tarone R, et al. (2003) Use of agricultural pesticides and prostate cancer risk in the Agricultural Health Study cohort. Am J Epidemiol 157: 800–814.
- 13. Mahajan R, Blair A, Lynch CF, Schroeder P, Hoppin JA, et al. (2006) Fonofos exposure and cancer incidence in the agricultural health study. Environ Health Perspect 114: 1838–1842.
- 14. Alavanja MC, Sandler DP, Lynch CF, Knott C, Lubin JH, et al. (2005) Cancer incidence in the agricultural health study. Scand J Work Environ Health 31 Suppl 139–45.
- 15. Koutros S, Alavanja MC, Lubin JH, Sandler DP, Hoppin JA, et al. (2010) An update of cancer incidence in the Agricultural Health Study. J Occup Environ Med 52: 1098–1105.
- 16. Koutros S, Beane Freeman LE, Lubin JH, Heltshe SL, Andreotti G, et al. (2013) Risk of Total and Aggressive Prostate Cancer and Pesticide Use in the Agricultural Health Study. Am J Epidemiol 177(1): 59–74.
- 17. Eeles RA, Kote-Jarai Z, Al Olama AA, Giles GG, Guy M, et al. (2009) Identification of seven new prostate cancer susceptibility loci through a genome-wide association study. Nat Genet 41: 1116–1121.
- 18. Eeles RA, Kote-Jarai Z, Giles GG, Olama AA, Guy M, et al. (2008) Multiple newly identified loci associated with prostate cancer susceptibility. Nat Genet 40: 316–321.
- 19. Gudmundsson J, Sulem P, Gudbjartsson DF, Blondal T, Gylfason A, et al. (2009) Genome-wide association and replication studies identify four variants associated with prostate cancer susceptibility. Nat Genet 41: 1122–1126.
- 20. Gudmundsson J, Sulem P, Rafnar T, Bergthorsson JT, Manolescu A, et al. (2008) Common sequence variants on 2p15 and Xp11.22 confer susceptibility to prostate cancer. Nat Genet 40: 281–283.
- 21. Gudmundsson J, Sulem P, Steinthorsdottir V, Bergthorsson JT, Thorleifsson G, et al. (2007) Two variants on chromosome 17 confer prostate cancer risk, and the one in TCF2 protects against type 2 diabetes. Nat Genet 39: 977–983.
- 22. Kote-Jarai Z, Amin Al Olama A, Leongamornlert D, Tymrakiewicz M, Saunders E, et al. (2011) Identification of a novel prostate cancer susceptibility variant in the KLK3 gene transcript. Hum Genet 129: 687–694.
- 23. Parikh H, Wang Z, Pettigrew KA, Jia J, Daugherty S, et al. (2011) Fine mapping the KLK3 locus on chromosome 19q13.33 associated with prostate cancer susceptibility and PSA levels. Hum Genet 129: 675–685.
- 24. Schumacher FR, Berndt SI, Siddiq A, Jacobs KB, Wang Z, et al. (2011) Genome-wide association study identifies new prostate cancer susceptibility loci. Hum Mol Genet 20(19): 3867–3875.
- 25. Sun J, Zheng SL, Wiklund F, Isaacs SD, Li G, et al. (2009) Sequence variants at 22q13 are associated with prostate cancer risk. Cancer Res 69: 10–15.
- 26. Thomas G, Jacobs KB, Yeager M, Kraft P, Wacholder S, et al. (2008) Multiple loci identified in a genome-wide association study of prostate cancer. Nat Genet 40: 310–315.
- 27. Yeager M, Chatterjee N, Ciampa J, Jacobs KB, Gonzalez-Bosquet J, et al. (2009) Identification of a new prostate cancer susceptibility locus on chromosome 8q24. Nat Genet 41: 1055–1057.
- 28. Koutros S, Beane Freeman LE, Berndt SI, Andreotti G, Lubin JH, et al. (2010) Pesticide use modifies the association between genetic variants on chromosome 8q24 and prostate cancer. Cancer Res 70: 9224–9233.
- 29. Ahmadiyeh N, Pomerantz MM, Grisanzio C, Herman P, Jia L, et al. (2010) 8q24 prostate, breast, and colon cancer risk loci show tissue-specific long-range interaction with MYC. Proc Natl Acad Sci USA 107: 9742–9746.
- 30. Meyer KB, Maia AT, O'Reilly M, Ghoussaini M, Prathalingam R, et al. (2011) A functional variant at a prostate cancer predisposition locus at 8q24 is associated with PVT1 expression. PLoS Genet 7: e1002165.
- 31. Sotelo J, Esposito D, Duhagon MA, Banfield K, Mehalko J, et al. (2010) Long-range enhancers on 8q24 regulate c-Myc. ProcNatlAcadSciUSA 107: 3001–3005.
- 32. Alavanja MC, Sandler DP, McMaster SB, Zahm SH, McDonnell CJ, et al. (1996) The Agricultural Health Study. Environ Health Perspect 104: 362–369.
- 33. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155: 945–959.
- 34. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38: 904–909.
- 35. Packer BR, Yeager M, Burdett L, Welch R, Beerman M, et al. (2006) SNP500Cancer: a public resource for sequence validation, assay development, and frequency analysis for genetic variation in candidate genes. Nucleic Acids Res 34: D617–D621.
- 36. Gudmundsson J, Besenbacher S, Sulem P, Gudbjartsson DF, Olafsson I, et al. (2010) Genetic correction of PSA values using sequence variants associated with PSA levels. Sci Transl Med 2: 62ra92.
- 37. Gudmundsson J, Sulem P, Manolescu A, Amundadottir LT, Gudbjartsson D, et al. (2007) Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24. Nat Genet 39: 631–637.
- 38. Thomsen MK, Ambroisine L, Wynn S, Cheah KS, Foster CS, et al. (2010) SOX9 elevation in the prostate promotes proliferation and cooperates with PTEN loss to drive tumor formation. Cancer Res 70: 979–987.
- 39. Thomsen MK, Francis JC, Swain A (2008) The role of Sox9 in prostate development. Differentiation 76: 728–735.
- 40. McMahon HT, Boucrot E (2011) Molecular mechanism and physiological functions of clathrin-mediated endocytosis. Nat Rev Mol Cell Biol 12: 517–533.
- 41. Krcmery J, Camarata T, Kulisz A, Simon HG (2010) Nucleocytoplasmic functions of the PDZ-LIM protein family: new insights into organ development. Bioessays 32: 100–108.
- 42. Davila M, Frost AR, Grizzle WE, Chakrabarti R (2003) LIM kinase 1 is essential for the invasive growth of prostate epithelial cells: implications in prostate cancer. J Biol Chem 278: 36868–36875.
- 43. Davila M, Jhala D, Ghosh D, Grizzle WE, Chakrabarti R (2007) Expression of LIM kinase 1 is associated with reversible G1/S phase arrest, chromosomal instability and prostate cancer. Mol Cancer 6: 40.
- 44. Maeno-Hikichi Y, Chang S, Matsumura K, Lai M, Lin H, et al. (2003) A PKC epsilon-ENH-channel complex specifically modulates N-type Ca2+ channels. Nat Neurosci 6: 468–475.
- 45. Delhommeau F, Dupont S, Della VV, James C, Trannoy S, et al. (2009) Mutation in TET2 in myeloid cancers. N Engl J Med 360: 2289–2301.
- 46. Chen S, Kesler CT, Paschal BM, Balk SP (2009) Androgen receptor phosphorylation and activity are regulated by an association with protein phosphatase 1. J Biol Chem 284: 25576–25584.
- 47. Diamanti-Kandarakis E, Bourguignon JP, Giudice LC, Hauser R, Prins GS, et al. (2009) Endocrine-disrupting chemicals: an Endocrine Society scientific statement. Endocr Rev 30: 293–342.
- 48. Pomerantz MM, Werner L, Xie W, Regan MM, Lee GS, et al. (2011) Association of prostate cancer risk Loci with disease aggressiveness and prostate cancer-specific mortality. Cancer Prev Res (Phila) 4: 719–728.
- 49. Blair A, Tarone R, Sandler D, Lynch CF, Rowland A, et al. (2002) Reliability of reporting on life-style and agricultural factors by a sample of participants in the Agricultural Health Study from Iowa. Epidemiology 13: 94–99.
- 50. Hoppin JA, Yucel F, Dosemeci M, Sandler DP (2002) Accuracy of self-reported pesticide use duration information from licensed pesticide applicators in the Agricultural Health Study. J Expo Anal Environ Epidemiol 12: 313–318.