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Inherited Variation in the Androgen Pathway Is Associated With the Efficacy of Androgen-Deprivation Therapy in Men With Prostate Cancer

Robert W. Ross, William K. Oh, Wanling Xie, Mark Pomerantz, Mari Nakabayashi, Oliver Sartor, Mary-Ellen Taplin, Meredith M. Regan, Philip W. Kantoff, Matthew Freedman

From the Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA

Corresponding author: Matthew Freedman, MD, Dana-Farber Cancer Institute, Dana 710-C, 44 Binney St, Boston, MA 02115; e-mail: freedman{at}broad.mit.edu

	ABSTRACT

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Purpose Androgen-deprivation therapy (ADT) is the most common and effective systemic therapy for advanced prostate cancer. We hypothesized that germline genetic variation in the androgen axis would improve the efficacy of ADT.

Patients and Methods A cohort of 529 men with advanced prostate cancer treated with ADT was genotyped for 129 DNA polymorphisms distributed across 20 genes involved in androgen metabolism.

Results Three polymorphisms in separate genes (CYP19A1, HSD3B1, and HSD17B4) were significantly (P < .01) associated with time to progression (TTP) during ADT, remaining so in multivariate analyses and after correcting for the number of hypotheses tested. Individuals carrying more than one of the polymorphisms associated with improved TTP demonstrated a better response to therapy than individuals carrying zero or one (P < .0001).

Conclusion This report is the first to examine the influence of inherited variation in the androgen metabolic pathway on the efficacy of ADT, establishing the importance of pharmacogenomics on individual's response to this therapy. At least two potential clinical benefits may be realized from this study. The first is prognostic –genotyping patients at these loci may yield important information that could improve efficacy prediction. The second is therapeutic –these results shed light on the pathways that govern response to ADT. Drugs could be developed (or may already exist) to inhibit or augment these targets to improve ADT efficacy.

	INTRODUCTION

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Androgen-deprivation therapy (ADT) is the most important treatment for advanced prostate cancer. It has been the most effective and widely used systemic therapy for prostate cancer for the last 50 years.¹ The natural history of castration-sensitive prostate cancer is eventual resistance to ADT and tumor growth despite castrate levels of serum testosterone. Progression despite maintenance of a castrate state is termed castration-resistant prostate cancer and is fatal. Because the efficacy of ADT is related to its ability to deprive the tumor of androgen, genes involved in the androgen metabolism pathway are strong candidates for influencing response to therapy. In this study, we evaluated polymorphisms within the androgen pathway in a cohort of men treated with ADT.

	PATIENTS AND METHODS

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Patient Selection
This cohort was generated from the Prostate Clinical Research Information System (CRIS) at Dana-Farber Cancer Institute (DFCI; Boston, MA). The CRIS system consists of data-entry software, a central data repository, collection of patient data including comprehensive follow-up of all patients, and tightly integrated security measures. This system has recently been described elsewhere.² Data are stored in an Oracle relational database (Oracle, Inc, Redwood Shores, CA). All patients seen at DFCI and Brigham and Women's Hospital (Boston, MA) with a diagnosis of prostate cancer are approached to participate. The consent rate for patients is 86%.

Five hundred ninety-five patients were initially identified with prostate cancer and had been treated with ADT (orchiectomy or luteinizing hormone–releasing hormone, with or without an antiandrogen) for nonlocalized, hormone-sensitive prostate cancer; had consented to provide information and tissue; and had undergone blood collection for research purposes. Patients were excluded if there were no prostate-specific antigen (PSA) data after ADT initiation, if there were systemic data problems on the quality-control analysis, or if they demonstrated failure for more than 50% of the assays in the genotyping. Applying these filters resulted in a cohort of 529 patients included for this analysis.

Clinical Data and Outcomes Collection
Data were collected on patient and disease baseline characteristics, ADT treatment, and treatment outcome. Three doctors trained in genitourinary oncology (M.P., R.W.R. and M.N.) reviewed the primary medical record on 9% of the cohort and independently evaluated each of the variables studied. Fourteen data points on 48 patients (a total of 672 data points) were reviewed. Overall, the data from the database were 91% concordant with the data generated from this review.

The primary outcome variable was time to progression (TTP) during treatment with ADT. Progression was defined as two rises in PSA (at least 1 week apart) greater than a nadir value while receiving ADT. These rises did not need to be sequential, but the first rise needed to be greater than the nadir PSA plus 0.02 ng/mL and the second rise needed to be greater than the nadir and also greater than the first rise. Initiation of secondary hormone for rising PSA before fulfillment of the definition of progression was also considered as a progression event, with the date of starting secondary hormone as the progression date.

TTP during ADT was defined as the duration of time from ADT initiation to the date of ADT progression or the date of initiation of secondary hormonal therapy (for 30 patients), or was censored among patients who did not progress at the date last known progression free or the date of death (for 18 patients) who died without progression.

DNA Isolation and Polymorphism Analysis
Genomic DNA was prepared from peripheral blood using QIAamp DNA Blood mini kit (QIAGEN Inc, Valencia, CA). DNA was stored at 4°C. Genotyping was carried out at the Broad Institute Center for Genotyping and Analysis using Sequenom iPLEX matrix-assisted laser desorption/ionization (MALDI)-time of flight mass spectrometry technology (Carlsbad, CA; http://www.sequenom.com/seq_genotyping.html/). Assays were designed using Sequenom's MassARRAY AssignDesigner application, version 3.0. Polymerase chain reaction (PCR) and single base extension (SBE) oligonucleotides were synthesized by Integrated DNA Technologies (Coralville, IA). Up to 24 single-nucleotide polymorphism (SNP) assays were combined into one multiplex pool. All reactions were carried out in 384-well format. PCR amplification was performed using an input of 10 ng of DNA in a final volume of 6 µL. The PCR products were subsequently treated with shrimp alkaline phosphatase, and the SBE reactions were initiated using iPLEX enzyme and mass-modified terminators (both Sequenom). After desalting, 7 nL of the SBE reaction was spotted onto a SpectroCHIP (Sequenom) preloaded with 7 nL of 3-hydroxypicolinic acid matrix. The SpectroCHIPs were analyzed in automated mode by a MassArray Compact solid phase laser mass spectrometer system (Bruker Daltonics, Billerica, MA). The resulting spectra were analyzed and genotypes called in real-time by the SpectroCaller algorithm, followed by additional user-initiated analysis with the SpectroTyper v.3.4A (Sequenom). The error rate on this platform is estimated to be less than 0.03%. Primers and probes for each SNP are available on request from the corresponding author.

For the microsatellite analysis, DNA samples were whole genome amplified using illustra GenomiPhi V2 according to the manufacturer's recommended protocol (GE Healthcare, Chalfont St Giles, United Kingdom). They were then quantified using RNAseP Gene Expression Assay according to the manufacturer's recommended protocol (Applied Biosystems [ABI], Foster City, CA). PCR was performed using 2-µm final concentration both forward and reverse primer, 1.5 mmol/L Mg, and 0.2 units of Taq Platinum Polymerase (Invitrogen, Carlsbad, CA) in a final volume of 10 µL. After an initial denaturation of 5 minutes at 94°C, samples were run through 35 cycles of 94°C for 40 seconds, the proper annealing temperature (AR, 66.7°C and Cyp19, 59°C) for 30 seconds, and 72°C for 30 seconds, followed by a final extension step of 72°C for 10 minutes. One microliter of PCR product was added to a mixture of Hi-Di Formamide (ABI) and ROX 400HD Standard (ABI). Samples were then denatured for 5 minutes at 95°C and then placed on ice for 3 minutes, followed by capillary electrophoresis on an ABI 3730XL. Genotypes were scored using ABI GeneMapper 4.0. Primers sequences are available on request from the corresponding author.

Polymorphism Selection
Searching the literature, we identified genes involved in androgen synthesis and metabolism. We also identified microsatellite repeats within these genes that have been associated with prostate cancer risk in previous studies. We identified SNPs within or near these genes from http://www.hapmap.org/ (Tables A1 and A2, online only). Polymorphisms were selected from each gene provided that the minor allele frequency was greater than 0.05 in the CEU population. We ran the program Tagger and selected tags to capture the unmeasured variants r² more than 0.8.³ All nonsynonymous SNPs with a minor allele frequency more than 0.02 were also selected. The CEU HapMap population (individuals of Northern and Western European ancestry) was used to select tags. Two hundred twenty-two SNPs in 23 genes were initially selected for analysis on the basis of their known participation in androgen synthesis and metabolism. All SNPs that did not conform to Hardy-Weinberg equilibrium (P < .01) were removed (n = 29). Any SNP below a genotyping call rate of 80%, or minor allele frequency less than 5%, was also removed from analysis (n = 66). Thus, a total of 127 SNPs in 20 genes were included for statistical analysis.

Statistical Analysis
Patient clinical characteristics were summarized as number and percentage of patients or median and range of values; continuous factors were dichotomized at the median value within the cohort, with the exception of PSA at diagnosis, which was dichotomized at 20 ng/mL because of its known correlation with prostate-cancer–specific survival. The distribution of and median TTP were estimated using the Kaplan-Meier method, with 95% CIs for the median. TTP between clinical groups was compared using the log-rank test.

Each SNP was treated as a categoric variable with a common homozygote, a rare homozygote, and a heterozygote. A rare homozygote was combined with a heterozygote if the frequency of the rare homozygote was below 0.05 (for 66 SNPs). Test of the association between 127 individual SNPs and TTP used the log-rank test. We also combined a homozygote and a heterozygote if they showed a similar effect on the outcome (TTP), which significantly differed from the other homozygous genotype (for three SNPs). As we were testing 127 different SNPs, we reported q value, a measure of significance in terms of the false-discovery rate as proposed by Storey and Tibshirani for multiple comparisons.⁴ The q value represents the expected proportion of false-positive results when announcing a test significant.

For a subset of SNPs selected on the basis of log-rank test and false-discovery rate, we performed Cox regression models to adjust for clinical variables for their associations with TTP. Clinical variables were compared across genotype for the selected SNPs using Pearson ² tests for categoric variables and Wilcoxon rank sum tests for continuous variables. The statistical analysis was performed using SAS version 9 (SAS Institute, Cary, NC). All P values are two sided. q values were estimated using R q-value package (http://faculty.washington.edu/jstorey/qvalue/) on the basis of the observed distribution of P values from the log-rank test for 127 individual SNPs.

	RESULTS

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All 529 patients in this study were evaluated at some point in their clinical course at DFCI. This cohort was treated with ADT between 1988 and 2006. Median follow-up after ADT initiation was 5.1 year (range, 1 month to 17 years). Table 1 presents the patient and disease characteristics of this cohort. Median TTP during ADT in this cohort was 22.7 months (95% CI, 19.0 to 26.1 month). Three hundred eighty-five patients progressed and 144 were progression free. Biopsy Gleason score, type of primary therapy, whether ADT was used as part of local therapy, metastatic status at the initiation of ADT, and serum PSA at the initiation of ADT were significantly associated (P < .05) with TTP during ADT (data not shown).

View larger version (14K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 1. Kaplan-Meier curves of time to progression during androgen-deprivation therapy, stratified by (A) genotype at rs1870050 (CYP19A1); (B) genotype at rs1856888 (HSD3B1); (C) and genotype at rs7737181 (HSD17B4).

Each SNP added information beyond the clinical variables hypothesized to influence TTP. We performed a multivariate analysis, adjusting for the clinical factors associated with TTP during ADT. The association between each of the three SNPs and ADT TTP remained significant (P < .01, Table 2). After adjusting for these clinical factors, the hazard ratios for progression on ADT were 0.60, 0.58, and 0.70 for genotype at rs1870050, rs1856888, and rs7737181, respectively (Table 2).

There was also an additive effect of combinations of genotypes across the three loci. The population was stratified by the number of favorable genotypes an individual carried. In this analysis, a favorable genotype was defined as the homozygote genotype at the SNP in question associated with improved TTP during ADT (AA at rs1870050, GG at rs1856888, and CC at rs7737181). Table 3 displays the statistically significant association between number of favorable genotypes with TTP during ADT, with 22-month difference between the median TTP for those with zero favorable genotypes at the three loci in question versus those with more than one favorable genotype. Figure 2 displays the Kaplan-Meier curve of TTP during ADT for zero, one, or more than one favorable genotypes at these loci.

View larger version (17K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 2. Kaplan-Meier curve of time to progression during androgen-deprivation therapy for patients with 0, 1, or > 1 favorable genotype at the three genetic loci of interest.

	DISCUSSION

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CYP19A1, which encodes aromatase, is a key enzyme that converts testosterone to estrogen in men. Several studies have evaluated prostate cancer risk and polymorphisms in CYP19A1 (the tetranucleotide repeat in intron 4 and Arg264Cys polymorphism in exon 7).^5,6 Tsuchiya et al⁷ found that the tetranucleotide repeat in CYP19A1 (more than seven repeats) was associated with decreased survival in men with metastatic prostate cancer (median survival, 41 v 61 months, respectively). We did not observe an association between the CYP19A1 tetranucleotide repeat and TTP during ADT. HSD3B enzymes catalyze androstenedione production and deactivate dihydroxytestosterone.^8,9 Human type I (HSD3B1) is predominantly expressed in the skin, prostate, and peripheral tissues, whereas type II is expressed in the adrenals, ovaries, and testis, and both are located within a potential prostate cancer susceptibility locus.¹⁰ Recently, variation within HSD3B1 has been associated with prostate cancer susceptibility.¹¹ Finally, HSD17B4 catalyzes the conversion of estradiol to estrone and 5-androstendiol to dehydroepiandrosterone and is expressed at its highest levels in the breast, prostate, liver, and heart.¹² Higher expression of HSD17B4 within prostate cancer has been associated with higher Gleason grade.^13,14 It is possible that the effect of these germline variations on ADT efficacy may be a result of their influence on gene expression within prostate tumor tissue itself, altering local androgen concentrations.^15,16

There are at least two potential clinical benefits to the identification of genetic polymorphisms associated with the activity of any therapy. The first is prognostic: Genotyping patients beginning ADT at these genetic loci may provide important and unique information. For example, a study of chemotherapy in castration-sensitive prostate cancer might limit eligibility to patients with zero favorable genotypes present to enrich for a particularly poor prognostic cohort. The second is therapeutic: If these polymorphisms have functional consequences for these enzymes, then that provides powerful evidence that the activity of that enzyme can influence the activity of ADT. Drugs could be developed to inhibit or augment the enzyme activity to improve ADT efficacy. Some of these drugs already exist. Aromatase inhibitors are widely used in breast cancer, and although the studies in castration-resistant prostate cancer are unimpressive, they have never been studied in combination with ADT in castration-sensitive disease. Thus, this data provides at least two avenues of investigation: Validation of this finding in an independent clinical cohort and cellular and molecular experiments to identify whether this genetic variability has any functional consequence.

A possible criticism of this study is the use of a TTP end point based on serum PSA. We chose this end point because of its biologic and clinical relevance. Usually, the first indication of the failure of ADT and the development of castration-resistant disease is a rising PSA. Thus, for a hypothesis that focuses specifically on one type of therapy (ADT, the most common systemic therapy for prostate cancer), the end point of PSA TTP most closely identifies the timing of the failure of this therapy. Moreover, clinically, a rising PSA on ADT typically triggers a change in therapy.

Another potential criticism is the number of hypotheses tested in this study. We evaluated 129 different polymorphisms in a study of 529 participants. To explicitly account for multiple hypothesis testing, we reported false-discovery rates (q values), as proposed by Storey and Tibshirani.⁴ The q value represents the rate at which significant features are truly null. Thus, for variation at rs7737181, a q value of 0.38 means that of characteristics that are called significant at this q value, 38% are false discoveries. Correction for multiple hypothesis testing is a critical step for interpreting the significance of findings when many possible hypotheses exist and only a small fraction of them are likely to be truly positive.

We recently found an association between clinical variables and TTP during ADT in this cohort.¹⁷ An important question is whether the polymorphism data add useful prognostic information to the clinical risk factors for progression (and vice versa). In multivariate modeling, the polymorphism data retained its association with the efficacy of ADT while controlling for the clinical risk factors (biopsy Gleason score, type of primary therapy, whether ADT was used as part of local therapy, metastatic status and PSA at initiation of ADT), suggesting that patient genotype adds information above and beyond traditional factors.

In summary, this is the first study to systematically study inherited variation in the androgen pathway in relation to the efficacy of ADT in men with castration-sensitive prostate cancer. We found that three polymorphisms were associated with ADT TTP, even after adjusting for multiple hypothesis testing and clinical predictors of outcome. Moreover, the more favorable genotypes a man carries, the longer the TTP. Studies to validate these findings are necessary.

	AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
The author(s) indicated no potential conflicts of interest.

	AUTHOR CONTRIBUTIONS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
Conception and design: Robert W. Ross, William K. Oh, Mari Nakabayashi, Matthew Freedman

Financial support: Robert W. Ross, William K. Oh

Administrative support: Robert W. Ross, William K. Oh

Provision of study materials or patients: Robert W. Ross, William K. Oh, Wanling Xie, Mark Pomerantz, Mari Nakabayashi, Oliver Sartor, Mary-Ellen Taplin, Meredith M. Regan, Philip W. Kantoff, Matthew Freedman

Collection and assembly of data: Robert W. Ross, William K. Oh, Wanling Xie, Mark Pomerantz, Mari Nakabayashi, Oliver Sartor, Mary-Ellen Taplin, Meredith M. Regan, Philip W. Kantoff, Matthew Freedman

Data analysis and interpretation: Robert W. Ross, William K. Oh, Wanling Xie, Mark Pomerantz, Mari Nakabayashi, Oliver Sartor, Mary-Ellen Taplin, Meredith M. Regan, Philip W. Kantoff, Matthew Freedman

Manuscript writing: Robert W. Ross, William K. Oh, Matthew Freedman

Final approval of manuscript: Robert W. Ross, William K. Oh, Wanling Xie, Mark Pomerantz, Mari Nakabayashi, Oliver Sartor, Mary-Ellen Taplin, Meredith M. Regan, Philip W. Kantoff, Matthew Freedman

	Appendix

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	ACKNOWLEDGMENTS

 
We thank Miyako Abe, Carrie Sougnez, and Kristen Waraska for their diligent genotyping labors.

	NOTES

 
Supported by a Dunkin Donuts Rising Star Award (R.W.R.), the Gelb Center for Translational Research (P.W.K., W.K.O.), Bing Sound Wong Fund for Prostate Cancer Research (W.K.O.) and the Dana-Farber/Harvard Cancer Center Prostate Cancer SPORE (National Cancer Institute Grant No. 5P50CA90381). The Broad Institute Center for Genotyping and Analysis is supported by Grant No. U54 RR020278-01 from the National Center for Research Resources.

P.W.K. and M.F. contributed equally to this work.

Presented in part at the American Society of Clinical Oncology Prostate Cancer Symposium, February 22-24, 2007, Orlando, FL.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

	REFERENCES

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
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2. Oh WK, Hayes J, Evan C, et al: Development of an integrated prostate cancer research information system. Clin Genitourin Cancer 5:61-66, 2006

3. de Bakker PI, Burtt NP, Graham RR, et al: Transferability of tag SNPs in genetic association studies in multiple populations. Nat Genet 38:1298-1303, 2006[CrossRef][Medline]

4. Storey JD, Tibshirani R: Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 100:9440-9445, 2003[Abstract/Free Full Text]

5. Latil AG, Azzouzi R, Cancel GS, et al: Prostate carcinoma risk and allelic variants of genes involved in androgen biosynthesis and metabolism pathways. Cancer 92:1130-1137, 2001[CrossRef][Medline]

6. Modugno F, Weissfeld JL, Trump DL, et al: Allelic variants of aromatase and the androgen and estrogen receptors: Toward a multigenic model of prostate cancer risk. Clin Cancer Res 7:3092-3096, 2001[Abstract/Free Full Text]

7. Tsuchiya N, Wang L, Suzuki H, et al: Impact of IGF-I and CYP19 gene polymorphisms on the survival of patients with metastatic prostate cancer. J Clin Oncol 24:1982-1989, 2006[Abstract/Free Full Text]

8. Roberts RO, Bergstralh EJ, Farmer SA, et al: Polymorphisms in genes involved in sex hormone metabolism may increase risk of benign prostatic hyperplasia. Prostate 66:392-404, 2006[CrossRef][Medline]

9. Simard J, Ricketts ML, Gingras S, et al: Molecular biology of the 3beta-hydroxysteroid dehydrogenase/delta5-delta4 isomerase gene family. Endocr Rev 26:525-582, 2005[Abstract/Free Full Text]

10. Xu J, Zheng SL, Chang B, et al: Linkage of prostate cancer susceptibility loci to chromosome 1. Hum Genet 108:335-345, 2001[CrossRef][Medline]

11. Chang BL, Zheng SL, Hawkins GA, et al: Joint effect of HSD3B1 and HSD3B2 genes is associated with hereditary and sporadic prostate cancer susceptibility. Cancer Res 62:1784-1789, 2002[Abstract/Free Full Text]

12. de Launoit Y, Adamski J: Unique multifunctional HSD17B4 gene product: 17beta-hydroxysteroid dehydrogenase 4 and D-3-hydroxyacyl-coenzyme A dehydrogenase/hydratase involved in Zellweger syndrome. J Mol Endocrinol 22:227-240, 1999[Abstract]

13. True L, Coleman I, Hawley S, et al: A molecular correlate to the Gleason grading system for prostate adenocarcinoma. Proc Natl Acad Sci U S A 103:10991-10996, 2006[Abstract/Free Full Text]

14. Zha S, Ferdinandusse S, Hicks JL, et al: Peroxisomal branched chain fatty acid beta-oxidation pathway is upregulated in prostate cancer. Prostate 63:316-323, 2005[CrossRef][Medline]

15. Ellem SJ, Schmitt JF, Pedersen JS, et al: Local aromatase expression in human prostate is altered in malignancy. J Clin Endocrinol Metab 89:2434-2441, 2004[Abstract/Free Full Text]

16. Stanbrough M, Bubley GJ, Ross K, et al:Increased expression of genes converting adrenal androgens to testosterone in androgen-independent prostate cancer. Cancer Res 66:2815-2825, 2006[Abstract/Free Full Text]

17. Ross RW, Xie W, Regan MM, et al: Efficacy of androgen deprivation therapy in patients with advanced prostate cancer: Association between Gleason score, prostate specific antigen level, and prior ADT exposure with duration of ADT effect. Cancer (in press)

Submitted July 24, 2007; accepted November 2, 2007.

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This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a colleague Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Save to my personal folders Download to citation manager Rights & Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Ross, R. W. Articles by Freedman, M. Search for Related Content PubMed PubMed Citation Articles by Ross, R. W. Articles by Freedman, M. Social Bookmarking                            What's this?