Travis Gerke

Prostate Cancer (PCa) Risk Variants and Risk of Fatal PCa in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium

BACKGROUND Screening and diagnosis of prostate cancer (PCa) is hampered by an inability to predict who has the potential to develop fatal disease and who has indolent cancer. Studies have identified multiple genetic risk loci for PCa incidence, but it is unknown whether they could be used as biomarkers for PCa-specific mortality (PCSM). OBJECTIVE To examine the association of 47 established PCa risk single-nucleotide polymorphisms (SNPs) with PCSM. DESIGN, SETTING, AND PARTICIPANTS We included 10 487 men who had PCa and 11 024 controls, with a median follow-up of 8.3 yr, during which 1053 PCa deaths occurred. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS The main outcome was PCSM. The risk allele was defined as the allele associated with an increased risk for PCa in the literature. We used Cox proportional hazards regression to calculate the hazard ratios of each SNP with time to progression to PCSM after diagnosis. We also used logistic regression to calculate odds ratios for each risk SNP, comparing fatal PCa cases to controls. RESULTS AND LIMITATIONS Among the cases, we found that 8 of the 47 SNPs were significantly associated (p<0.05) with time to PCSM. The risk allele of rs11672691 (intergenic) was associated with an increased risk for PCSM, while 7 SNPs had risk alleles inversely associated (rs13385191 [C2orf43], rs17021918 [PDLIM5], rs10486567 [JAZF1], rs6465657 [LMTK2], rs7127900 (intergenic), rs2735839 [KLK3], rs10993994 [MSMB], rs13385191 [C2orf43]). In the case-control analysis, 22 SNPs were associated (p<0.05) with the risk of fatal PCa, but most did not differentiate between fatal and nonfatal PCa. Rs11672691 and rs10993994 were associated with both fatal and nonfatal PCa, while rs6465657, rs7127900, rs2735839, and rs13385191 were associated with nonfatal PCa only. CONCLUSIONS Eight established risk loci were associated with progression to PCSM after diagnosis. Twenty-two SNPs were associated with fatal PCa incidence, but most did not differentiate between fatal and nonfatal PCa. The relatively small magnitudes of the associations do not translate well into risk prediction, but these findings merit further follow-up, because they may yield important clues about the complex biology of fatal PCa. PATIENT SUMMARY In this report, we assessed whether established PCa risk variants could predict PCSM. We found eight risk variants associated with PCSM: One predicted an increased risk of PCSM, while seven were associated with decreased risk. Larger studies that focus on fatal PCa are needed to identify more markers that could aid prediction.

Circulating vitamin D, vitamin D-related genetic variation, and risk of fatal prostate cancer in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium.

Evidence from experimental animal and cell line studies supports a beneficial role for vitamin D in prostate cancer (PCa). Although the results from human studies have been mainly null for overall PCa risk, there may be a benefit for survival. This study assessed the associations of circulating 25‐hydroxyvitamin D (25(OH)D) and common variations in key vitamin D–related genes with fatal PCa.

Interrogation of ERG gene rearrangements in prostate cancer identifies a prognostic 10-gene signature with relevant implication to patients' clinical outcome

ERG‐gene rearrangement defines a distinct molecular subtype of PCA with potential biological and clinical implications. To identify a molecular signature reflective of the downstream effects of ERG‐mediated transcriptional regulation with prognostic implication in patients with prostate cancer (PCA).

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.

Perineural Invasion and Risk of Lethal Prostate Cancer

Background: Prostate cancer has a propensity to invade and grow along nerves, a phenomenon called perineural invasion (PNI). Recent studies suggest that the presence of PNI in prostate cancer has been associated with cancer aggressiveness. Methods: We investigated the association between PNI and lethal prostate cancer in untreated and treated prostate cancer cohorts: the Swedish Watchful Waiting Cohort of 615 men who underwent watchful waiting, and the U.S. Health Professionals Follow-Up Study of 849 men treated with radical prostatectomy. One pathologist performed a standardized histopathologic review assessing PNI and Gleason grade. Patients were followed from diagnosis until metastasis or death. Results: The prevalence of PNI was 7% and 44% in the untreated and treated cohorts, respectively. PNI was more common in high Gleason grade tumors in both cohorts. PNI was associated with enhanced tumor angiogenesis, but not tumor proliferation or apoptosis. In the Swedish study, PNI was associated with lethal prostate cancer [OR 7.4; 95% confidence interval (CI), 3.6–16.6; P < 0.001]. A positive, although not statistically significant, association persisted after adjustment for age, Gleason grade, and tumor volume (OR 1.9; 95% CI, 0.8–5.1; P = 0.17). In the U.S. study, PNI predicted lethal prostate cancer independent of clinical factors (HR 1.8; 95% CI, 1.0, 3.3; P =0.04). Conclusions: These data support the hypothesis that perineural invasion creates a microenvironment that promotes cancer aggressiveness. Impact: Our findings suggest that PNI should be a standardized component of histopathologic review, and highlights a mechanism underlying prostate cancer metastasis. Cancer Epidemiol Biomarkers Prev; 26(5); 719–26. ©2017 AACR.

The ABC model of prostate cancer: A conceptual framework for the design and interpretation of prognostic studies

There has been limited success in identifying prognostic biomarkers in prostate cancer. A partial explanation may be that insufficient emphasis has been put on clearly defining what type of marker or patient category a biomarker study aims to identify and how different cohort characteristics affect the ability to identify such a marker. In this article, the authors put forth the ABC model of prostate cancer, which defines 3 groups of patients with localized disease that an investigator may seek to identify: patients who, within a given time frame, will not develop metastases even if untreated (category A), will not develop metastases because of radical treatment (category B), or will develop metastases despite radical treatment (category C). The authors demonstrate that follow‐up time and prostate‐specific antigen screening intensity influence the prevalence of patients in categories A, B, and C in a study cohort, and that prognostic markers must be tested in both treated and untreated cohorts to accurately distinguish the 3 groups. The authors suggest that more emphasis should be put on considering these factors when planning, conducting, and interpreting the results from prostate cancer biomarker studies, and propose the ABC model as a framework to aid in that process. Cancer 2017;123:1490–1496.

Nonparametric Regression for Estimation of Spatiotemporal Mountain Glacier Retreat From Satellite Images

Historical variations in the extent of mountain glaciers give insight into natural and forced changes of these bellwethers of the climate. Because of the limited number of ground observations relative to the number of glaciers, it is useful to develop techniques that permit for the monitoring of glacier systems using satellite imagery. Here, we propose a new approach for identifying the glacier terminus over time from Landsat images. The proposed method permits for detecting inflection points in multispectral satellite imagery taken along a glaciers flow path in order to identify candidate terminus locations. A gated tracking algorithm is then applied to identify the best candidate for the glacier terminus location through time. Finally, the long-term trend of the terminus position is estimated with uncertainty bounds. This is achieved by applying nonparametric regression to the temporal sequence of estimated terminus locations. The method is shown to give results consistent with ground-based observations for the Franz Josef and Gorner glaciers and is further applied to estimate the retreat of Viedma, a glacier with no available ground measurements.

Height, obesity, and the risk ofTMPRSS2:ERG-defined prostate cancer

Background: The largest molecular subtype of primary prostate cancer is defined by the TMPRSS2:ERG gene fusion. Few studies, however, have investigated etiologic differences by TMPRSS2:ERG status. Because the fusion is hormone-regulated and a mans hormonal milieu varies by height and obesity status, we hypothesized that both may be differentially associated with risk of TMPRSS2:ERG-defined disease. Methods: Our study included 49,372 men from the prospective Health Professionals Follow-up Study. Participants reported height and weight at baseline in 1986 and updated weight biennially thereafter through 2009. Tumor ERG protein expression (a TMPRSS2:ERG marker) was immunohistochemically assessed. We used multivariable competing risks models to calculate HRs and 95% confidence intervals (CIs) for the risk of ERG-positive and ERG-negative prostate cancer. Results: During 23 years of follow-up, we identified 5,847 incident prostate cancers, among which 913 were ERG-assayed. Taller height was associated with an increased risk of ERG-positive disease only [per 5 inches HR 1.24; 95% confidence interval (CI), 1.03–1.50; Pheterogeneity = 0.07]. Higher body mass index (BMI) at baseline (per 5 kg/m2 HR 0.75; 95% CI, 0.61–0.91; Pheterogeneity = 0.02) and updated BMI over time (per 5 kg/m2 HR 0.86; 95% CI, 0.74–1.00; Pheterogeneity = 0.07) were associated with a reduced risk of ERG-positive disease only. Conclusions: Our results indicate that anthropometrics may be uniquely associated with TMPRSS2:ERG-positive prostate cancer; taller height may be associated with greater risk, whereas obesity may be associated with lower risk. Impact: Our study provides strong rationale for further investigations of other prostate cancer risk factors that may be distinctly associated with subtypes. Cancer Epidemiol Biomarkers Prev; 27(2); 193–200. ©2017 AACR.

Trends in Gene Expression Profiling for Prostate Cancer Risk Assessment: A Systematic Review

Objectives: The aim of the study is to review biotechnology advances in gene expression profiling on prostate cancer (PCa), focusing on experimental platform development and gene discovery, in relation to different study designs and outcomes in order to understand how they can be exploited to improve PCa diagnosis and clinical management. Methods: We conducted a systematic literature review on gene expression profiling studies through PubMed/MEDLINE and Web of Science between 2000 and 2016. Tissue biopsy and clinical gene profiling studies with different outcomes (e.g., recurrence, survival) were included. Results: Over 3,000 papers were screened and 137 full-text articles were selected. In terms of technology used, microarray is still the most popular technique, increasing from 50 to 70% between 2010 and 2015, but there has been a rise in the number of studies using RNA sequencing (13% in 2015). Sample sizes have increased, as well as the number of genes that can be screened all at once, but we have also observed more focused targeting in more recent studies. Qualitative analysis on the specific genes found associated with PCa risk or clinical outcomes revealed a large variety of gene candidates, with a few consistent cross-studies. Conclusions: The last 15 years of research in gene expression in PCa have brought a large volume of data and information that has been decoded only in part, but advancements in high-throughput sequencing technology are increasing the amount of data that can be generated. The variety of findings warrants the execution of both validation studies and meta-analyses. Genetic biomarkers have tremendous potential for early diagnosis of PCa and, if coupled with other diagnostics (e.g., imaging), can effectively be used to concretize less-invasive, personalized prediction of PCa risk and progression.