Search

Prostate cancer exerts a greater toll on African American men than on White men of European descent (hereafter referred to as European American men): the disparity in incidence and mortality is greater than that of any other common cancer. The disproportionate impact of prostate cancer on Black men has been attributed to the genetics of African ancestry, to diet and lifestyle risk factors, and to unequal access to quality health care. In this Review, all of these influences are considered in the context of the evolving understanding that chronic or recurrent inflammatory processes drive prostatic carcinogenesis. Studies of inherited susceptibility highlight the contributions of genes involved in prostate cell and tissue repair (BRCA1/2, ATM) and regeneration (HOXB13 and MYC). Social determinants of health appear to accentuate these genetic influences by fueling prostate inflammation and associated cell and genome damage. Molecular characterization of the prostate cancers that arise in Black versus White men further implicates this inflammatory microenvironment in disease behavior. Yet, when Black and White men with similar grade and stage of prostate cancer are treated equally, they exhibit equivalent outcomes. The central role of prostate inflammation in prostate cancer development and progression augments the impact of the social determinants of health on disease pathogenesis. And, when coupled with poorer access to high-quality treatment, these inequities result in a disparate burden of prostate cancer on African American men.

Additional file 4. (Supplemental Figures): Supplementary figure 1. Correlation of MR-IVW estimates (betas) using the two different instrument definitions. Y axis represents the MR IVW estimates using the cis-eQTL and X axis represents the MR IVW estimates using the cis-pQTL instrument definition. Supplementary figure 2. Associations that showed MR evidence for both causality and colocalization (posterior probability>0.8) are plotted, within ��500 kb of the gene locus of the exposure cytokine.

Additional file 1. Members from the PRACTICAL Consortium, CRUK, BPC3, CAPS and PEGASUS.

Additional file 1. Supplementary Methods.

Supplemental tables for an article

Appendix - ICD codes used in cost model as described in methods. (PDF 464 kb)

Spearmanâ s rank correlation of sample value and FFPE block age. Table S2. Spearmanâ s rank correlation of sample value and RNA Integrity Number. Figure S1. Association of RIN and Sample Block Age. No association was observed between sample age and RNA Integrity Number (RIN). (DOCX 258 kb)

BackgroundWe previously reported that both intraprostatic inflammation and SNPs in genes involved in the immune response are associated with prostate cancer risk and disease grade. In the present study, we evaluated the association between these SNPs and intraprostatic inflammation in men without a prostate cancer diagnosis.MethodsIncluded in this cross-sectional study were 205 white controls from a case-control study nested in the placebo arm of the Prostate Cancer Prevention Trial. We analyzed inflammation data from the review of H&E-stained prostate tissue sections from biopsies performed per protocol at the end of the trial irrespective of clinical indication, and data for 16 SNPs in key genes involved in the immune response (IL1β, IL2, IL4, IL6, IL8, IL10, IL12(p40), IFNG, MSR1, RNASEL, TLR4, TNFA; 7 tagSNPs in IL10). Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between carrying at least one minor allele and having at least one biopsy core (of a mean of three reviewed) with inflammation.ResultsNone of the SNPs evaluated was statistically significantly associated with having at least one core with inflammation. However, possible inverse associations were present for carrying the minor allele of rs2069762 (G) in IL2 (OR = 0.51, 95%CI 0.25-1.02); carrying two copies of the minor allele of rs1800871 (T) of IL10 (OR = 0.29, 95%CI 0.08-1.00); and carrying the minor allele of rs486907 (A) in RNASEL (OR = 0.52, 95%CI 0.26-1.06). After creating a genetic risk score from the three SNPs possibly associated with inflammation, the odds of inflammation increased with increasing number of risk alleles (P-trend = 0.008).ConclusionWhile our findings do not generally support a cross-sectional link between individual SNPs in key genes involved in the immune response and intraprostatic inflammation in men without a prostate cancer diagnosis, they do suggest that some of these variants when in combination may be associated with intraprostatic inflammation in benign tissue.

BackgroundA previous analysis of the placebo arm of the Prostate Cancer Prevention Trial (PCPT) reported 82% overall prevalence of intraprostatic inflammation and identified a link between inflammation and higher-grade prostate cancer and serum PSA. Here, we studied these associations in the PCPT finasteride arm.MethodsProstate cancer cases (N = 197) detected either on a clinically indicated biopsy or on protocol-directed end-of-study biopsy, and frequency-matched controls (N = 248) with no cancer on an end-of-study biopsy were sampled from the finasteride arm. Inflammation in benign prostate tissue was visually assessed using digital images of hematoxylin and eosin-stained sections. Logistic regression was used for statistical analysis.ResultsIn the finasteride arm, 91.6% of prostate cancer cases and 92.4% of controls had at least one biopsy core with inflammation in benign areas (P < 0.001 for difference compared with placebo arm). Overall, the odds of prostate cancer did not differ by prevalence [OR, 0.90; 95% confidence interval (CI), 0.44-1.84] or extent (P trend = 0.68) of inflammation. Inflammation was not associated with higher-grade disease (prevalence: OR, 1.07; 95% CI, 0.43-2.69). Furthermore, mean PSA concentration did not differ by the prevalence or extent of inflammation in either cases or controls.ConclusionThe prevalence of intraprostatic inflammation was higher in the finasteride than placebo arm of the PCPT, with no association with higher-grade prostate cancer.ImpactFinasteride may attenuate the association between inflammation and higher-grade prostate cancer. Moreover, the missing link between intraprostatic inflammation and PSA suggests that finasteride may reduce inflammation-associated PSA elevation.

BackgroundIndividual studies have suggested that circulating carotenoids, retinol, or tocopherols may be associated with prostate cancer risk, but the studies have not been large enough to provide precise estimates of associations, particularly by stage and grade of disease.ObjectiveThe objective of this study was to conduct a pooled analysis of the associations of the concentrations of 7 carotenoids, retinol, α-tocopherol, and γ-tocopherol with risk of prostate cancer and to describe whether any associations differ by stage or grade of the disease or other factors.DesignPrincipal investigators of prospective studies provided individual participant data for prostate cancer cases and controls. Risk by study-specific fifths of each biomarker was estimated by using multivariable-adjusted conditional logistic regression in matched case-control sets.ResultsData were available for up to 11,239 cases (including 1654 advanced stage and 1741 aggressive) and 18,541 controls from 15 studies. Lycopene was not associated with overall risk of prostate cancer, but there was statistically significant heterogeneity by stage of disease, and the OR for aggressive disease for the highest compared with the lowest fifth of lycopene was 0.65 (95% CI: 0.46, 0.91; P-trend = 0.032). No other carotenoid was significantly associated with overall risk of prostate cancer or with risk of advanced-stage or aggressive disease. For retinol, the OR for the highest compared with the lowest fifth was 1.13 (95% CI: 1.04, 1.22; P-trend = 0.015). For α-tocopherol, the OR for the highest compared with the lowest fifth was 0.86 (95% CI: 0.78, 0.94; P-trend < 0.001), with significant heterogeneity by stage of disease; the OR for aggressive prostate cancer was 0.74 (95% CI: 0.59, 0.92; P-trend = 0.001). γ-Tocopherol was not associated with risk.ConclusionsOverall prostate cancer risk was positively associated with retinol and inversely associated with α-tocopherol, and risk of aggressive prostate cancer was inversely associated with lycopene and α-tocopherol. Whether these associations reflect causal relations is unclear.

BackgroundTelomeres are repetitive nucleoproteins that help maintain chromosomal stability by inhibiting exonucleolytic degradation, prohibiting inappropriate homologous recombination, and preventing chromosomal fusions by suppressing double-strand break signals. We recently observed that men treated for clinically localized prostate cancer with shorter telomeres in their cancer-associated stromal cells, in combination with greater variation in cancer cell telomere lengths, were significantly more likely to progress to distant metastases, and die from their disease. Here, we hypothesized that shorter stromal cell telomere length would be associated with prostate cancer risk at time of biopsy.MethodsTelomere-specific fluorescence in situ hybridization (FISH) analysis was performed in normal-appearing stromal, basal epithelial, and luminal epithelial cells in biopsies from men randomized to the placebo arm of the Prostate Cancer Prevention Trial. Prostate cancer cases (N = 32) were either detected on a biopsy performed for cause or at the end of the study per trial protocol, and controls (N = 50), defined as negative for cancer on an end-of-study biopsy performed per trial protocol (e.g., irrespective of indication), were sampled. Logistic regression was used to estimate the association between mean telomere length of the particular cell populations, cell-to-cell telomere length variability, and risk of prostate cancer.ResultsMen with short stromal cell telomere lengths (below median) had 2.66 (95% CI 1.04-3.06; P = 0.04) times the odds of prostate cancer compared with men who had longer lengths (at or above median). Conversely, we did not observe statistically significant associations for short telomere lengths in normal-appearing basal (OR = 2.15, 95% CI 0.86-5.39; P= 0 .10) or luminal (OR = 1.15, 95% CI 0.47-2.80; P = 0.77) cells.ConclusionsThese findings suggest that telomere shortening in normal stromal cells is associated with prostate cancer risk. It is essential to extend and validate these findings, while also identifying the cellular milieu that comprises the subset of cells with short telomeres within the prostate tumor microenvironment.

Obesity and inactivity have been associated with advanced-stage prostate cancer, and poor prostate cancer outcomes, though the underlying mechanism(s) is unknown. To determine whether telomere shortening, which has been associated with lethal prostate cancer, may be a potential underlying mechanism, we prospectively evaluated the association between measures of adiposity, physical activity, and telomere length in 596 participants in the Health Professionals Follow-up Study, who were surgically treated for prostate cancer. Using tissue microarrays, we measured telomere length in cancer and benign cells using a telomere-specific FISH assay. Adiposity and activity were assessed via questionnaire within 2 years of diagnosis. Adjusting for age, pathologic stage, and grade, the median and SD of the per cell telomere signals were determined for each man for stromal cells and cancer cells by adiposity and activity categories. Overweight/obese men (54%) were similar to normal weight men on most factors, but had higher Gleason sum and lower activity levels. Overweight/obese men had 7.4% shorter telomeres in stromal cells than normal weight men (P = 0.06). The least active men had shorter telomeres in stromal cells than more active men (Ptrend = 0.002). Men who were overweight/obese and the least active had the shortest telomeres in stromal cells (20.7% shorter; P = 0.0005) compared with normal weight men who were the most active. Cancer cell telomere length and telomere length variability did not differ by measures of adiposity or activity. Telomere shortening in prostate cells may be one mechanism through which lifestyle influences prostate cancer risk and outcomes.

Physical activity has been associated with lower risks of breast and colorectal cancer in epidemiological studies; however, it is unknown if these associations are causal or confounded. In two-sample Mendelian randomisation analyses, using summary genetic data from the UK Biobank and GWA consortia, we found that a one standard deviation increment in average acceleration was associated with lower risks of breast cancer (odds ratio [OR]: 0.51, 95% confidence interval [CI]: 0.27 to 0.98, P-value = 0.04) and colorectal cancer (OR: 0.66, 95% CI: 0.48 to 0.90, P-value = 0.01). We found similar magnitude inverse associations for estrogen positive (ER+ve) breast cancer and for colon cancer. Our results support a potentially causal relationship between higher physical activity levels and lower risks of breast cancer and colorectal cancer. Based on these data, the promotion of physical activity is probably an effective strategy in the primary prevention of these commonly diagnosed cancers.

Additional file 2: Figure S1. Scatter plot of SNP-BMI and SNP-CRC associations. Figure S2. Scatter plot of SNP-BMI and SNP-CRC associations (female specific). Figure S3. Scatter plot of SNP-BMI and SNP-CRC associations (male specific). Figure S4. Scatter plot of SNP-BMI and SNP-colon cancer associations. Figure S5. Scatter plot of SNP-BMI and SNP-proximal colon cancer associations. Figure S6. Scatter plot of SNP-BMI and SNP-distal colon cancer associations. Figure S7. Scatter plot of SNP-BMI and SNP-rectal cancer associations. Figure S8. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on CRC. Figure S9. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on CRC (female specific). Figure S10. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on CRC (male specific). Figure S11. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on colon cancer. Figure S12. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on proximal colon cancer. Figure S13. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on distal colon cancer. Figure S14. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on rectal cancer. Figure S15. Leave-one-out plot showing the association between BMI and CRC, following SNP-by-SNP removal from the model. Figure S16. Leave-one-out plot showing the association between BMI and CRC (femalespecific), following SNP-by-SNP removal from the model. Figure S17. Leave-one-out plot showing the association between BMI and CRC (malespecific), following SNP-by-SNP removal from the model. Figure S18. Leave-one-out plot showing the association between BMI and colon cancer, following SNP-by-SNP removal from the model. Figure S19. Leave-one-out plot showing the association between BMI and proximal colon cancer, following SNP-by-SNP removal from the model. Figure S20. Leave-one-out plot showing the association between BMI and distal colon cancer, following SNP-by-SNP removal from the model. Figure S21. Leave-one-out plot showing the association between BMI and rectal cancer, following SNP-by-SNP removal from the model. Figure S22. Scatter plot of SNP-WHR and SNP-CRC associations. Figure S23. Scatter plot of SNP-WHR and SNP-CRC associations (female specific). Figure S24. Scatter plot of SNP-WHR and SNP-CRC associations (male specific). Figure S25. Scatter plot of SNP-WHR and SNP-colon cancer associations. Figure S26. Scatter plot of SNP-WHR and SNP-proximal colon cancer associations. Figure S27. Scatter plot of SNP-WHR and SNP-distal colon cancer associations. Figure S28. Scatter plot of SNP-WHR and SNP-rectal cancer associations. Figure S29. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on CRC. Figure S30. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on CRC (female specific). Figure S31. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on CRC (male specific). Figure S32. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on colon cancer. Figure S33. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on proximal colon cancer. Figure S34. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on distal colon cancer. Figure S35. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on rectal cancer. Figure S36. Leave-one-out plot showing the association between WHR and CRC, following SNP-by-SNP removal from the model. Figure S37. Leave-one-out plot showing the association between WHR and CRC, following SNP-by-SNP removal from the model (female specific). Figure S38. Leave-one-out plot showing the association between WHR and CRC, following SNP-by-SNP removal from the model (male specific). Figure S39. Leave-one-out plot showing the association between WHR and colon cancer, following SNP-by-SNP removal from the model. Figure S40. Leave-one-out plot showing the association between WHR and proximal colon cancer, following SNP-by-SNP removal from the model. Figure S41. Leave-one-out plot showing the association between WHR and distal colon cancer, following SNP-by-SNP removal from the model. Figure S42. Leave-one-out plot showing the association between WHR and rectal cancer, following SNP-by-SNP removal from the model. Figure S43. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 1 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S44. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 2 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S45. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 3 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S46. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 4 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S47. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 5 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S48. Power curves for MR analyses, based on samples sizes for colorectal cancer in the present study (black), Thrift et al., 2015 (blue) and Jarvis et al., 2016 (purple). Upper and lower power curves describe genetic instruments explaining 5% and 0.3% of variance respectively for each study.

Salicylic acid (SA) has observationally been shown to decrease colorectal cancer (CRC) risk. Aspirin (acetylsalicylic acid, that rapidly deacetylates to SA) is an effective primary and secondary chemopreventive agent. Through a Mendelian randomization (MR) approach, we aimed to address whether levels of SA affected CRC risk, stratifying by aspirin use. A two-sample MR analysis was performed using GWAS summary statistics of SA (INTERVAL and EPIC-Norfolk, N = 14,149) and CRC (CCFR, CORECT, GECCO and UK Biobank, 55,168 cases and 65,160 controls). The DACHS study (4410 cases and 3441 controls) was used for replication and stratification of aspirin-use. SNPs proxying SA were selected via three methods: (1) functional SNPs that influence the activity of aspirin-metabolising enzymes; (2) pathway SNPs present in enzymes' coding regions; and (3) genome-wide significant SNPs. We found no association between functional SNPs and SA levels. The pathway and genome-wide SNPs showed no association between SA and CRC risk (OR: 1.03, 95% CI: 0.84-1.27 and OR: 1.08, 95% CI: 0.86-1.34, respectively). Results remained unchanged upon aspirin use stratification. We found little evidence to suggest that an SD increase in genetically predicted SA protects against CRC risk in the general population and upon stratification by aspirin use.

Physical activity has been associated with lower risks of breast and colorectal cancer in epidemiological studies; however, it is unknown if these associations are causal or confounded. In two-sample Mendelian randomisation analyses, using summary genetic data from the UK Biobank and GWA consortia, we found that a one standard deviation increment in average acceleration was associated with lower risks of breast cancer (odds ratio [OR]: 0.51, 95% confidence interval [CI]: 0.27 to 0.98, P-value = 0.04) and colorectal cancer (OR: 0.66, 95% CI: 0.48 to 0.90, P-value = 0.01). We found similar magnitude inverse associations for estrogen positive (ER+ve) breast cancer and for colon cancer. Our results support a potentially causal relationship between higher physical activity levels and lower risks of breast cancer and colorectal cancer. Based on these data, the promotion of physical activity is probably an effective strategy in the primary prevention of these commonly diagnosed cancers.

BACKGROUND: MicroRNAs (miRNAs) are small non-coding RNAs that regulate a broad array of cellular and disease processes. Several miRNAs are differentially expressed in cancer and many are being considered as biomarkers for predicting clinical outcomes. Here we quantified the expression of three miRNAs, miR-21, miR-141, and miR-221, from prostate cancer surgical specimens and evaluated their association with disease recurrence after primary therapy. METHODSA pilot nested case-control study was designed from a large cohort of men who underwent radical prostatectomy between 1993 and 2001. Total RNA was extracted from malignant prostate tissue of 59 cases (recurrence) and 59 controls. Cases and controls were matched on age, race, pathologic stage, and grade. The relative expression of each miRNA was then determined for each sample by quantitative real-time RT-PCR. Conditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) of recurrence for tertiles of miRNA expression. We noted block storage time effects and thus, used separate tertile cutpoints based on the controls by calendar year of prostatectomy. RESULTSLower miR-221 expression was associated with a higher risk of recurrence; the ORs were 3.21 for the lowest tertile and 2.63 for the middle tertile compared with the highest tertile of expression (P-trend=0.02). This pattern was unchanged after multivariable adjustment (P-trend=0.05). No statistically significant trends were observed for miR-21 or miR-141 after multivariable adjustment. CONCLUSIONSBased on this small pilot study, men with localized prostate cancers with lower miR-221 expression may have a greater risk for recurrence after surgery Funding for this project was supported by grants from the National Institutes of Health 5R01CA143299, 5P50CA058236, Patrick C. Walsh Prostate Cancer Research Fund, the Department of Defense Prostate Cancer Research Program Award numbers W81XWH-10-2- 0056, W81XWH-05-1-0030 and W81XWH-10-2-0046 Prostate Cancer Biorepository Network (PCBN).numbers W81XWH-10-2-0056, W81XWH-05-1-0030 and W81XWH-10-2-0046 Prostate Cancer Biorepository Network (PCBN)

Purpose To report age-specific serum estradiol concentration in nonsmoking, lean US men without comorbidities. We provide concentrations from 30 and 15 to 20 years ago given previously described declines in serum estradiol in US men over time. Methods We used data from the Third National Health and Nutrition Examination Survey (NHANES III; 1988 to 1991) and continuous NHANES (1999 to 2004). Serum estradiol and SHBG were previously measured by competitive electrochemiluminescence immunoassays. Free estradiol was estimated from estradiol, SHBG, and albumin. By age, we calculated median concentrations overall and for nonsmoking, lean (body mass index Results Overall, respective total estradiol medians for men ages 20 to 39, 40 to 59, and ≥60 years old were 37.0, 33.9, and 33.5 pg/mL in NHANES III and 31.3, 30.5, and 27.0 pg/mL in continuous NHANES. In nonsmoking, lean men without comorbidities, respective total estradiol medians were 32.0, 32.1, and 32.0 pg/mL in NHANES III and 29.1, 22.7, and 26.1 pg/mL in continuous NHANES. Overall, respective free estradiol medians were 0.82, 0.72, and 0.64 pg/mL in NHANES III and 0.67, 0.61, and 0.47 pg/mL in continuous NHANES. In nonsmoking, lean men without comorbidities, respective free estradiol medians were 0.64, 0.67, and 0.62 pg/mL in NHANES III and 0.58, 0.42, and 0.40 pg/mL continuous NHANES. Conclusion We report US nationally representative serum estradiol concentrations in healthy men, which could be used for targeting estradiol during testosterone supplementation and for general good health.

Context Testosterone deficiency prevalence increases with age, comorbidities, and obesity. Objective To inform clinical guidelines for testosterone deficiency management and development of targets for nonpharmacologic intervention trials for these men, we determined serum testosterone in never-smoking, lean men without select comorbidities in nationally representative surveys. Design, Setting, Participants We used cross-sectional data for never-smoking, lean men ≥20 years without diabetes, myocardial infarction, congestive heart failure, stroke, or cancer, without use of hormone-influencing medications, and participated in morning sessions of National Health and Nutrition Examination Survey (NHANES) III (phase I 1988–1991) or continuous NHANES (1999–2004). By age, we determined median total testosterone (ng/mL) measured previously by a Food and Drug Administration-approved immunoassay and median estimated free testosterone concentration. Results In NHANES III, in never-smoking, lean men without comorbidities, median (25th, 75th percentile) testosterone was 4% to 9% higher than all men—20 to 39 years: 6.24 (5.16, 7.51), 40 to 59: 5.37 (3.83, 6.49), and ≥60: 4.61 (4.01, 5.18). In continuous NHANES, in never-smoking, lean men without comorbidities, levels were 13% to 24% higher than all men—20 to 39 years: 6.26 (5.32, 7.27), 40 to 59: 5.86 (4.91, 6.55), and ≥60: 4.22 (3.74, 5.73). In never-smoking, lean men without comorbidities, median estimated free testosterone was similar to (NHANES III) or slightly higher than (continuous NHANES) in all men. Conclusions These nationally representative data document testosterone levels (immunoassay) in never-smoking, lean men without select comorbidities 30 and 15 to 20 years ago. This information can be incorporated into guidelines for testosterone deficiency management and used to develop targets for nonpharmacologic intervention trials for testosterone deficiency.