Jennifer A. Sinnott

Effect of dietary polyunsaturated fatty acids on castration-resistant Pten-null prostate cancer

A common treatment of advanced prostate cancer involves the deprivation of androgens. Despite the initial response to hormonal therapy, eventually all the patients relapse. In the present study, we sought to determine whether dietary polyunsaturated fatty acid (PUFA) affects the development of castration-resistant prostate cancer. Cell culture, patient tissue microarray, allograft, xenograft, prostate-specific Pten knockout and omega-3 desaturase transgenic mouse models in conjunction with dietary manipulation, gene knockdown and knockout approaches were used to determine the effect of dietary PUFA on castration-resistant Pten-null prostate cancer. We found that deletion of Pten increased androgen receptor (AR) expression and Pten-null prostate cells were castration resistant. Omega-3 PUFA slowed down the growth of castration-resistant tumors as compared with omega-6 PUFA. Omega-3 PUFA decreased AR protein to a similar extent in tumor cell cytosolic and nuclear fractions but had no effect on AR messenger RNA level. Omega-3 PUFA treatment appeared to accelerate AR protein degradation, which could be blocked by proteasome inhibitor MG132. Knockdown of AR significantly slowed down prostate cancer cell proliferation in the absence of androgens. Our data suggest that omega-3 PUFA inhibits castration-resistant prostate cancer in part by accelerating proteasome-dependent degradation of the AR protein. Dietary omega-3 PUFA supplementation in conjunction with androgen ablation may significantly delay the development of castration-resistant prostate cancer in patients compared with androgen ablation alone.

Toll-like Receptor Signaling Pathway Variants and Prostate Cancer Mortality

An understanding of factors associated with prostate cancer (PCa) mortality is increasingly important given the biological heterogeneity of disease. Previous studies have shown that genetic variation in the Toll-like receptor (TLR) signaling pathway is associated with PCa incidence, but any role in progression and mortality is unclear. Among 1,252 PCa cases from the Cancer Prostate in Sweden study, we conducted time-to-event analyses of PCa mortality for 99 individual tagging SNPs and haploytpes from 20 genes in the TLR pathway. Cox proportional hazards models were used to estimate hazard ratios (HR) and 99% confidence intervals (99% CI). Global P values were estimated from a likelihood ratio test. During a median follow-up of 5.1 years, 191 PCa deaths occurred. Controlling for age and geographic location, two polymorphisms were statistically significantly associated with PCa mortality (P < 0.01). Compared with homozygous wild-type carriers of the TLR-9 polymorphism (rs187084), the HR (99% CI) was 1.57 (1.02, 2.41) for heterozygotes and 1.02 (0.57, 1.84) for rare homozygotes (P = 0.009). For a MIC-1 SNP (rs1227732), the HR comparing carriers of at least one copy of the minor allele to wild-type homozygotes was 0.54 (99% CI: 0.34, 0.87). Only the MIC-1 SNP remained significant after additional adjustment for treatment. No significant associations were observed for common haplotypes and PCa mortality. This study highlights the importance of studies of PCa mortality because risk factors for incidence and mortality may differ. (Cancer Epidemiol Biomarkers Prev 2009;18(6):1859–63)

Ejaculation Frequency and Risk of Prostate Cancer: Updated Results with an Additional Decade of Follow-up

BACKGROUND Evidence suggests that ejaculation frequency may be inversely related to the risk of prostate cancer (PCa), a disease for which few modifiable risk factors have been identified. OBJECTIVE To incorporate an additional 10 yr of follow-up into an original analysis and to comprehensively evaluate the association between ejaculation frequency and PCa, accounting for screening, clinically relevant disease subgroups, and the impact of mortality from other causes. DESIGN, SETTING, AND PARTICIPANTS A prospective cohort study of participants in the Health Professionals Follow-up Study utilizing self-reported data on average monthly ejaculation frequency. The study includes 31925 men who answered questions on ejaculation frequency on a 1992 questionnaire and followed through to 2010. The average monthly ejaculation frequency was assessed at three time points: age 20-29 yr, age 40-49 yr, and the year before questionnaire distribution. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Incidence of total PCa and clinically relevant disease subgroups. Cox models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS AND LIMITATIONS During 480831 person-years, 3839 men were diagnosed with PCa. Ejaculation frequency at age 40-49 yr was positively associated with age-standardized body mass index, physical activity, divorce, history of sexually transmitted infections, and consumption of total calories and alcohol. Prostate-specific antigen (PSA) test utilization by 2008, number of PSA tests, and frequency of prostate biopsy were similar across frequency categories. In multivariable analyses, the hazard ratio for PCa incidence for ≥21 compared to 4-7 ejaculations per month was 0.81 (95% confidence interval [CI] 0.72-0.92; p<0.0001 for trend) for frequency at age 20-29 yr and 0.78 (95% CI 0.69-0.89; p<0.0001 for trend) for frequency at age 40-49 yr. Associations were driven by low-risk disease, were similar when restricted to a PSA-screened cohort, and were unlikely to be explained by competing causes of death. CONCLUSIONS These findings provide additional evidence of a beneficial role of more frequent ejaculation throughout adult life in the etiology of PCa, particularly for low-risk disease. PATIENT SUMMARY We evaluated whether ejaculation frequency throughout adulthood is related to prostate cancer risk in a large US-based study. We found that men reporting higher compared to lower ejaculatory frequency in adulthood were less likely to be subsequently diagnosed with prostate cancer.

Improving the Power of Genetic Association Tests with Imperfect Phenotype Derived from Electronic Medical Records

To reduce costs and improve clinical relevance of genetic studies, there has been increasing interest in performing such studies in hospital-based cohorts by linking phenotypes extracted from electronic medical records (EMRs) to genotypes assessed in routinely collected medical samples. A fundamental difficulty in implementing such studies is extracting accurate information about disease outcomes and important clinical covariates from large numbers of EMRs. Recently, numerous algorithms have been developed to infer phenotypes by combining information from multiple structured and unstructured variables extracted from EMRs. Although these algorithms are quite accurate, they typically do not provide perfect classification due to the difficulty in inferring meaning from the text. Some algorithms can produce for each patient a probability that the patient is a disease case. This probability can be thresholded to define case–control status, and this estimated case–control status has been used to replicate known genetic associations in EMR-based studies. However, using the estimated disease status in place of true disease status results in outcome misclassification, which can diminish test power and bias odds ratio estimates. We propose to instead directly model the algorithm-derived probability of being a case. We demonstrate how our approach improves test power and effect estimation in simulation studies, and we describe its performance in a study of rheumatoid arthritis. Our work provides an easily implemented solution to a major practical challenge that arises in the use of EMR data, which can facilitate the use of EMR infrastructure for more powerful, cost-effective, and diverse genetic studies.

Measuring PI3K Activation: Clinicopathologic, Immunohistochemical, and RNA Expression Analysis in Prostate Cancer

Assessing the extent of PI3K pathway activity in cancer is vital to predicting sensitivity to PI3K-targeting drugs, but the best biomarker of PI3K pathway activity in archival tumor specimens is unclear. Here, PI3K pathway activation was assessed, in clinical tissue from 1,021 men with prostate cancers, using multiple pathway nodes that include PTEN, phosphorylated AKT (pAKT), phosphorylated ribosomal protein S6 (pS6), and stathmin. Based on these markers, a 9-point score of PI3K activation was created using the combined intensity of the 4-markers and analyzed its association with proliferation (Ki67), apoptosis (TUNEL), and androgen receptor (AR) status, as well as pathologic features and cancer-specific outcomes. In addition, the PI3K activation score was compared with mRNA expression profiling data for a large subset of men. Interestingly, those tumors with higher PI3K activation scores also had higher Gleason grade (P = 0.006), increased AR (r = 0.37; P < 0.001) and Ki67 (r = 0.24; P < 0.001), and decreased TUNEL (r = −0.12; P = 0.003). Although the PI3K activation score was not associated with an increased risk of lethal outcome, a significant interaction between lethal outcome, Gleason and high PI3K score (P = 0.03) was observed. Finally, enrichment of PI3K-specific pathways was found in the mRNA expression patterns differentiating the low and high PI3K activation scores; thus, the 4-marker IHC score of PI3K pathway activity correlates with features of PI3K activation. Implications: The relationship of this activation score to sensitivity to anti-PI3K agents remains to be tested but may provide more precision guidance when selecting patients for these therapies. Mol Cancer Res; 13(10); 1431–40. ©2015 AACR.

Molecular differences in transition zone and peripheral zone prostate tumors

Prostate tumors arise primarily in the peripheral zone (PZ) of the prostate, but 20-30% arise in the transition zone (TZ). Zone of origin may have prognostic value or reflect distinct molecular subtypes; however, it can be difficult to determine in practice. Using whole-genome gene expression, we built a signature of zone using normal tissue from five individuals and found that it successfully classified nine tumors of known zone. Hypothesizing that this signature captures tumor zone of origin, we assessed its relationship with clinical factors among 369 tumors of unknown zone from radical prostatectomies (RPs) and found that tumors that molecularly resembled TZ tumors showed lower mortality (P = 0.09) that was explained by lower Gleason scores (P = 0.009). We further applied the signature to an earlier study of 88 RP and 333 transurethral resection of the prostate (TURP) tumor samples, also of unknown zone, with gene expression on ~6000 genes. We had observed previously substantial expression differences between RP and TURP specimens, and hypothesized that this might be because RPs capture primarily PZ tumors, whereas TURPs capture more TZ tumors. Our signature distinguished these two groups, with an area under the receiver operating characteristic curve of 87% (P < 0.0001). Our findings that zonal differences in normal tissue persist in tumor tissue and that these differences are associated with Gleason score and sample type suggest that subtypes potentially resulting from different etiologic pathways might arise in these zones. Zone of origin may be important to consider in prostate tumor biomarker research.

Sniffing out significant “Pee values”: genome wide association study of asparagus anosmia

Objective To determine the inherited factors associated with the ability to smell asparagus metabolites in urine. Design Genome wide association study. Setting Nurses’ Health Study and Health Professionals Follow-up Study cohorts. Participants 6909 men and women of European-American descent with available genetic data from genome wide association studies. Main outcome measure Participants were characterized as asparagus smellers if they strongly agreed with the prompt “after eating asparagus, you notice a strong characteristic odor in your urine,” and anosmic if otherwise. We calculated per-allele estimates of asparagus anosmia for about nine million single nucleotide polymorphisms using logistic regression. P values <5×10-8 were considered as genome wide significant. Results 58.0% of men (n=1449/2500) and 61.5% of women (n=2712/4409) had anosmia. 871 single nucleotide polymorphisms reached genome wide significance for asparagus anosmia, all in a region on chromosome 1 (1q44: 248139851-248595299) containing multiple genes in the olfactory receptor 2 (OR2) family. Conditional analyses revealed three independent markers associated with asparagus anosmia: rs13373863, rs71538191, and rs6689553. Conclusion A large proportion of people have asparagus anosmia. Genetic variation near multiple olfactory receptor genes is associated with the ability of an individual to smell the metabolites of asparagus in urine. Future replication studies are necessary before considering targeted therapies to help anosmic people discover what they are missing.

Inference for survival prediction under the regularized Cox model

When a moderate number of potential predictors are available and a survival model is fit with regularization to achieve variable selection, providing accurate inference on the predicted survival can be challenging. We investigate inference on the predicted survival estimated after fitting a Cox model under regularization guaranteeing the oracle property. We demonstrate that existing asymptotic formulas for the standard errors of the coefficients tend to underestimate the variability for some coefficients, while typical resampling such as the bootstrap tends to overestimate it; these approaches can both lead to inaccurate variance estimation for predicted survival functions. We propose a two-stage adaptation of a resampling approach that brings the estimated error in line with the truth. In stage 1, we estimate the coefficients in the observed data set and in [Formula: see text] resampled data sets, and allow the resampled coefficient estimates to vote on whether each coefficient should be 0. For those coefficients voted as zero, we set both the point and interval estimates to [Formula: see text] In stage 2, to make inference about coefficients not voted as zero in stage 1, we refit the penalized model in the observed data and in the [Formula: see text] resampled data sets with only variables corresponding to those coefficients. We demonstrate that ensemble voting-based point and interval estimators of the coefficients perform well in finite samples, and prove that the point estimator maintains the oracle property. We extend this approach to derive inference procedures for survival functions and demonstrate that our proposed interval estimation procedures substantially outperform estimators based on asymptotic inference or standard bootstrap. We further illustrate our proposed procedures to predict breast cancer survival in a gene expression study.

Life after endometrial cancer: A systematic review of patient-reported outcomes

BACKGROUND Women with endometrial cancer (EC) are the second largest population of female cancer survivors in the United States. However, the outcomes of EC survivors, from the patient perspective, are not well-understood. Therefore, we conducted a systematic review of patient-reported outcomes (PROs) following an EC diagnosis. METHODS We searched MEDLINE, EMBASE, Scopus, CINAHL, and reference lists to identify published observational studies that examined PROs among women with EC. Reviewers independently reviewed eligible full-text study articles and conducted data extraction. We qualitatively summarized included articles according to exposures [e.g. body mass index (BMI), treatment, etc.] or specific PROs (e.g. sexual function). RESULTS Of 1722 unique studies, 102 full-text articles were reviewed, of which a total of 27 studies fulfilled the inclusion criteria. The most commonly used PRO questionnaires were the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30) (n=9), Short Form 36 Questionnaire (SF-36, n=8), the Functional Assessment of Cancer Therapy-General (FACT-G, n=5), and the Female Sexual Function Index (FSFI, n=4). Obesity was associated with lower quality of life (QOL) and physical functioning. Treatment type affected several outcomes. Laparoscopy generally resulted in better QOL outcomes than laparotomy. Likewise, vaginal brachytherapy was associated with better outcomes compared to external beam radiation. Sexual function outcomes were dependent on age, time since diagnosis, and having consulted a physician before engaging in sexual activities. In addition, a physical activity intervention was associated with improved sexual interest but not sexual function. CONCLUSIONS Our review provides insight into the experience of EC survivors from the patient perspective. Factors that contribute to QOL, such as pain, fatigue, emotional and social functioning, should be monitored following an EC diagnosis.

Pathway aggregation for survival prediction via multiple kernel learning

Attempts to predict prognosis in cancer patients using high-dimensional genomic data such as gene expression in tumor tissue can be made difficult by the large number of features and the potential complexity of the relationship between features and the outcome. Integrating prior biological knowledge into risk prediction with such data by grouping genomic features into pathways and networks reduces the dimensionality of the problem and could improve prediction accuracy. Additionally, such knowledge-based models may be more biologically grounded and interpretable. Prediction could potentially be further improved by allowing for complex nonlinear pathway effects. The kernel machine framework has been proposed as an effective approach for modeling the nonlinear and interactive effects of genes in pathways for both censored and noncensored outcomes. When multiple pathways are under consideration, one may efficiently select informative pathways and aggregate their signals via multiple kernel learning (MKL), which has been proposed for prediction of noncensored outcomes. In this paper, we propose MKL methods for censored survival outcomes. We derive our approach for a general survival modeling framework with a convex objective function and illustrate its application under the Cox proportional hazards and semiparametric accelerated failure time models. Numerical studies demonstrate that the proposed MKL-based prediction methods work well in finite sample and can potentially outperform models constructed assuming linear effects or ignoring the group knowledge. The methods are illustrated with an application to 2 cancer data sets.