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Background Although unopposed estrogen exposure is considered a major driver of endometrial carcinogenesis, chronic inflammation and insulin resistance and hyperinsulinemia are also major endometrial cancer risk factors. However, it is unclear whether diets with inflammatory or insulinemic potential are associated with risk of endometrial cancer. Methods We followed 48 330 women from the Nurses' Health Study (1984-2016) and 85 426 women from the Nurses' Health Study II (1989-2017). Using food frequency questionnaires, we calculated repeated measures of empirical dietary inflammatory pattern (EDIP) and empirical dietary index for hyperinsulinemia (EDIH) scores, which characterize the potential of the whole diet to modulate circulating biomarkers of inflammation or C-peptide, respectively. We used multivariable-adjusted Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for type I endometrial cancer risk. Results We documented 1462 type I endometrial cancer cases over 2 823 221 person-years of follow-up. In the pooled multivariable-adjusted analyses, women in the highest compared with lowest quintiles were at higher risk of type I endometrial cancer (EDIP HRQ5vsQ1 = 1.46, 95% CI = 1.24 to 1.73; P trend <.001; EDIH HRQ5vsQ1 = 1.58, 95% CI = 1.34 to 1.87; P trend <.001). Additional adjustment for body mass index attenuated the associations (EDIP HR = 1.03, 95% CI = 0.87 to 1.22; EDIH HR = 1.01, 95% CI = 0.85 to 1.21), and mediation analyses showed that body mass index may explain 60.4% (95% CI = 37.4% to 79.6%; P <.001) and 71.8% (95% CI = 41.0% to 90.4%; P <.001) of the association of endometrial cancer with EDIP and EDIH, respectively. Conclusions In this large cohort study, higher dietary inflammatory and insulinemic potential were each associated with increased endometrial cancer incidence, and this association may be almost entirely mediated by adiposity. [ABSTRACT FROM AUTHOR]

Circulating insulin-like growth factor-1 (IGF-1) is consistently associated with prostate cancer risk. IGF-1 binds to IGF-1 receptor (IGF1R) and insulin receptor (IR), activating cancer hallmark pathways. Experimental evidence suggests that TMPRSS2:ERG may interact with IGF/insulin signaling to influence progression. We investigated IGF1R and IR expression and its association with lethal prostate cancer among 769 men. Protein expression of IGF1R, IR and ERG (i.e. a surrogate of ERG fusion genes) were assayed by immunohistochemistry. Cox models estimated hazard ratios (HR) and 95% confidence intervals (CI) adjusted for clinical characteristics. Among patients, 29% had strong tumor IGF1R expression and 10% had strong IR expression. During a mean follow-up of 13.2 years through 2012, 80 men (11%) developed lethal disease. Tumors with strong IGF1R or IR expression showed increased cell proliferation, decreased apoptosis and a higher prevalence of ERG. In multivariable models, strong IGF1R was associated with a borderline increased risk of lethal prostate cancer (HR 1.7; 95% CI 0.9–3.1). The association appeared greater in ERG-positive tumors (HR 2.8; 95% CI 0.9–8.4) than in ERG-negative tumors (HR 1.3; 95% CI 0.6–3.0, p-heterogeneity 0.08). There was no association between IR and lethal prostate cancer (HR 0.8; 95% CI 0.4–1.9). These results suggest that tumor IGF1R expression may play a role in prostate cancer progression to a lethal phenotype and that ERG-positive tumors may be more sensitive to IGF signaling. These data may improve our understanding of IGF signaling in prostate cancer and suggest therapeutic options for disease subtypes. [ABSTRACT FROM AUTHOR]

Objective: Standard approaches for large scale phenotypic screens using electronic health record (EHR) data apply thresholds, such as ≥2 diagnosis codes, to define subjects as having a phenotype. However, the variation in the accuracy of diagnosis codes can impair the power of such screens. Our objective was to develop and evaluate an approach which converts diagnosis codes into a probability of a phenotype (PheProb). We hypothesized that this alternate approach for defining phenotypes would improve power for genetic association studies.Methods: The PheProb approach employs unsupervised clustering to separate patients into 2 groups based on diagnosis codes. Subjects are assigned a probability of having the phenotype based on the number of diagnosis codes. This approach was developed using simulated EHR data and tested in a real world EHR cohort. In the latter, we tested the association between low density lipoprotein cholesterol (LDL-C) genetic risk alleles known for association with hyperlipidemia and hyperlipidemia codes (ICD-9 272.x). PheProb and thresholding approaches were compared.Results: Among n = 1462 subjects in the real world EHR cohort, the threshold-based p-values for association between the genetic risk score (GRS) and hyperlipidemia were 0.126 (≥1 code), 0.123 (≥2 codes), and 0.142 (≥3 codes). The PheProb approach produced the expected significant association between the GRS and hyperlipidemia: p = .001.Conclusions: PheProb improves statistical power for association studies relative to standard thresholding approaches by leveraging information about the phenotype in the billing code counts. The PheProb approach has direct applications where efficient approaches are required, such as in Phenome-Wide Association Studies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To investigate the role of adiponectin receptor 2 (AdipoR2) in aggressive prostate cancer we used immunohistochemistry to characterize AdipoR2 protein expression in tumor tissue for 866 men with prostate cancer from the Physicians’ Health Study and the Health Professionals Follow-up Study. AdipoR2 tumor expression was not associated with measures of obesity, pathological tumor stage or prostate-specific antigen (PSA) at diagnosis. However, AdipoR2 expression was positively associated with proliferation as measured by Ki-67 expression quartiles (P-trend < 0.0001), with expression of fatty acid synthase (P-trend = 0.001), and with two measures of angiogenesis (P-trend < 0.1). An inverse association was observed with apoptosis as assessed by the TUNEL assay (P-trend = 0.006). Using Cox proportional hazards regression and controlling for age at diagnosis, Gleason score, year of diagnosis category, cohort and baseline BMI, we identified a statistically significant trend for the association between quartile of AdipoR2 expression and lethal prostate cancer (P-trend = 0.02). The hazard ratio for lethal prostate cancer for the two highest quartiles, as compared to the two lowest quartiles, of AdipoR2 expression was 1.9 (95% confidence interval [CI]: 1.2–3.0). Results were similar when additionally controlling for categories of PSA at diagnosis and Ki-67 expression quartiles. These results strengthen the evidence for the role of AdipoR2 in prostate cancer progression. [ABSTRACT FROM AUTHOR]

Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai, Tonini, and Lin, 2011). In this article, we derive testing and prediction methods for KM regression under the accelerated failure time (AFT) model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest, it may be unclear in advance which kernel to use for testing and estimation. We propose a robust Omnibus Test that combines information across kernels, and an approach for selecting the best kernel for estimation. The methods are illustrated with an application in breast cancer. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]