Objective. Vietnamese American women are at the greatest risk for cervical cancer but have the lowest cervical cancer screening rates. This study was to determine whether demographic and acculturation, healthcare access, and knowledge and beliefs are associated with a prior history of cervical cancer screening among Vietnamese women. Methods. Vietnamese women (n = 1450) from 30 Vietnamese community-based organizations located in Pennsylvania and New Jersey participated in the study and completed baseline assessments. Logistic regression analyses were performed. Results. Overall levels of knowledge about cervical cancer screening and human papillomavirus (HPV) are low. Factors in knowledge, attitude, and beliefs domains were significantly associated with Pap test behavior. In multivariate analyses, physician recommendation for screening and having health insurance were positively associated with prior screening. Conclusion. Understanding the factors that are associated with cervical cancer screening will inform the development of culturally appropriate intervention strategies that would potentially lead to increasing cervical cancer screening rates among Vietnamese women.

Background

Prostate cancer is the second leading cause of cancer-related deaths in men, accounting for over 30,000 deaths annually. The purpose of this study was to test whether variation in selected candidate genes in biological pathways of interest for prostate cancer progression could help distinguish patients at higher risk for fatal prostate cancer.

Methods

In this hypothesis-driven study, we genotyped 937 single nucleotide polymorphisms (SNPs) in 156 candidate genes in a population-based cohort of 1,309 prostate cancer patients. We identified 22 top-ranking SNPs (P ≤0.01, FDR ≤0.70) associated with prostate cancer-specific mortality (PCSM). A subsequent validation study was completed in an independent population-based cohort of 2,875 prostate cancer patients.

Results

Five SNPs were validated (P ≤0.05) as being significantly associated with PCSM, one each in the LEPR, CRY1, RNASEL, IL4, and ARVCF genes. Compared to patients with 0–2 of the at-risk genotypes those with 4–5 at-risk genotypes had a 50% (95% CI, 1.2–1.9) higher risk of PCSM and risk increased with the number of at-risk genotypes carried (Ptrend = 0.001), adjusting for clinicopathological factors known to influence prognosis.

Conclusion

Five genetic markers were validated to be associated with lethal prostate cancer.

Impact

This is the first population-based study to demonstrate that germline genetic variants provide prognostic information for prostate cancer-specific survival. The clinical utility of this five-SNP panel to stratify patients at higher risk for adverse outcomes should be evaluated.

Summary

Detection of lung cancer at early stages could potentially increase survival rates. One promising approach is the application of suitable lung cancer-specific biomarkers to specimens obtained by non-invasive methods. Thus far, clinically useful biomarkers that have high sensitivity have proven elusive. Certain genes, which are involved in cellular pathways such as signal transduction, apoptosis, cell to cell communication, cell cycles and cytokine signaling are down-regulated in cancers and may be considered as potential tumor suppressor genes. Aberrant promoter hypermethylation is a major mechanism for silencing tumor suppressor genes in many kinds of human cancers. Using quantitative real time PCR, we tested 11 genes (3-OST-2, RASSF1A, DcR1, DcR2, P16, DAPK, APC, ECAD, HCAD, SOCS1, SOCS3) for levels of methylation within their promoter sequences in non-small cell lung cancers (NSCLC), adjacent non-malignant lung tissues, in peripheral blood mononuclear cells (PBMC) from cancer free patients, in sputum of cancer patients and controls. Of all the 11 genes tested 3-OST-2 showed the highest levels of promoter methylation in tumors combined with lowest levels of promoter methylation in control tissues. 3-OST-2 followed by, RASSF1A showed increased levels of methylation with advanced tumor stage (P<0.05). Thus, quantitative analysis of 3-OST-2 and RASSF1A methylation appears to be a promising biomarker assay for NSCLC and should be further explored in a clinical study. Our preliminary data on the analysis of sputum DNA specimens from cancer patients further support these observations.

Background

Of the 200,000 U.S. men annually diagnosed with prostate cancer, approximately 20–30% will have clinically aggressive disease. While factors such as Gleason score and tumor stage are used to assess prognosis, there are no biomarkers to identify men at greater risk for developing aggressive prostate cancer. We therefore undertook a search for genetic variants associated with risk of more aggressive disease.

Methods

A genome-wide scan was conducted in 202 prostate cancer cases with a more aggressive phenotype and 100 randomly sampled, age-matched PSA-screened negative controls. Analysis of 387,384 autosomal SNPs was followed by validation testing in an independent set of 527 cases with more aggressive and 595 cases with less aggressive prostate cancer, and 1,167 age-matched controls.

Results

A variant on 15q13, rs6497287, was confirmed to be most strongly associated with more aggressive (pdiscovery=5.20×10−5, pvalidation=0.004) than less aggressive disease (p=0.14). Another SNP on 3q26, rs3774315, was found to be associated with prostate cancer risk however the association was not stronger for more aggressive disease.

Conclusions

This study provides suggestive evidence for a genetic predisposition to more aggressive prostate cancer and highlights the fact that larger studies are warranted to confirm this supposition and identify further risk variants.

Impact

These findings raise the possibility that assessment of genetic variation may one day be useful to discern men at higher risk for developing clinically significant prostate cancer.

One of the challenges with functional data is incorporating geometric structure, or local correlation, into the analysis. This structure is inherent in the output from an increasing number of biomedical technologies, and a functional linear model is often used to estimate the relationship between the predictor functions and scalar responses. Common approaches to the problem of estimating a coefficient function typically involve two stages: regularization and estimation. Regularization is usually done via dimension reduction, projecting onto a predefined span of basis functions or a reduced set of eigenvectors (principal components). In contrast, we present a unified approach that directly incorporates geometric structure into the estimation process by exploiting the joint eigenproperties of the predictors and a linear penalty operator. In this sense, the components in the regression are ‘partially empirical’ and the framework is provided by the generalized singular value decomposition (GSVD). The form of the penalized estimation is not new, but the GSVD clarifies the process and informs the choice of penalty by making explicit the joint influence of the penalty and predictors on the bias, variance and performance of the estimated coefficient function. Laboratory spectroscopy data and simulations are used to illustrate the concepts.

BACKGROUND

This report and a companion report describe a validation of the ability of serum proteomic profiling via SELDI-TOF mass spectrometry to detect prostatic cancer. Details of this 3-stage process have been described. This report describes the development of the algorithm and results of the blinded test for stage 1.

METHODS

We derived the decision algorithm used in this study from the analysis of serum samples from patients with prostate cancer (n = 181) and benign prostatic hyperplasia (BPH) (n = 143) and normal controls (n = 220). We also derived a validation test set from a separate, geographically diverse set of serum samples from 42 prostate cancer patients and 42 controls without prostate cancer. Aliquots were subjected to randomization and blinded analysis, and data from each laboratory site were subjected to the decision algorithm and decoded.

RESULTS

Using the data collected from the validation test set, the decision algorithm was unsuccessful in separating cancer from controls with any predictive utility. Analysis of the experimental data revealed potential sources of bias.

CONCLUSION

The ability of the decision algorithm to successfully differentiate between prostate cancer, BPH, and control samples using data derived from serum protein profiling was compromised by bias.

Background

Chronic inflammation is an important mechanism for the development and progression of prostate cancer. To better understand the potential relationship between genes in the inflammation pathway and prostate cancer (PC) risk, we evaluated variants in 16 candidate genes.

Methods

A total of 143 tagging and amino acid altering single nucleotide polymorphisms (SNPs) were genotyped in Caucasian and African American men participating in one of two population-based, case-control studies (n = 1,458 cases and 1,351 controls). The relative risk of prostate cancer was estimated using logistic and polytomous regression models.

Results

Ten SNPs in seven genes (CXCL12, IL4, IL6, IL6ST, PTGS2, STAT3, and TNF) were nominally associated (p<0.05) with risk of PC in Caucasians. The most significant effect on risk was seen with rs11574783 in the IL6ST gene (odds ratio, OR=0.08, 95% CI 0.01–0.63). Cumulatively, four SNPs in genes IL4, IL6ST, PTGS2, and STAT3 conferred a three-fold elevation in PC risk among men carrying the maximum number of high-risk alleles (OR=2.97, 95% CI 1.41–6.25, ptrend = 0.0003). Risk estimates for seven SNPs varied significantly according to disease aggressiveness (phomogeneity<0.05), with SNPs in AKT1, PIK3R1 and STAT3 independently associated with more aggressive PC; OR=5.1 (95% CI 2.29–11.40, ptrend = 3.8×10−5) for carriers of all high-risk genotypes.

Conclusions

These results suggest that variants in genes within the inflammation pathway may play a role in the development of PC, however further studies are needed to replicate our findings.

Impact

These results underline the potential importance of the inflammation pathway in PC development and progression.

Background

Alpha-methylacyl CoA racemase (AMACR) is an enzyme involved in fatty acids metabolism. One of AMACRs primary substrates, phytanic acid, is principally obtained from dietary red meat/dairy which are associated with prostate cancer (PCa) risk. AMACR is also a tumor tissue biomarker over-expressed in PCa. In this study, we explored the potential relationship between AMACR polymorphisms, red meat/dairy intake and PCa risk.

Methods

Caucasian participants from two population-based PCa case-control studies were included. AMACR SNPs were selected to capture variation across the gene and regulatory regions. Red meat and dairy intake was determined from food frequency questionnaires. The odds ratio (OR) of PCa (overall and by disease aggressiveness) was estimated by logistic and polytomous regression. Potential interactions between genotypes and dietary exposures were evaluated.

Results

Data from 1,309 cases and 1,267 controls were analyzed. Carriers of the variant T allele (rs2287939) had an OR of 0.81 (95% CI 0.68-0.97) for less aggressive PCa, but no alteration in risk for more aggressive PCa. Red meat consumption was positively associated with PCa risk, and the association was stronger for more aggressive disease (lowest vs. highest tertile OR= 1.55, 95% CI 1.10-2.20). No effect modification of AMACR polymorphisms by either dietary red meat or dairy intake on PCa risk was observed.

Conclusions

Prostate cancer risk varied by level of red meat intake and by one AMACR SNP, but there was no evidence for gene-environment interaction. These findings suggest that the effects of AMACR polymorphisms and red meat and dairy on PCa risk are independent.

Recent evidence suggests that the observed clinical distinctions between lung tumors in smokers and never smokers (NS) extend beyond specific gene mutations, such as EGFR, EML4-ALK, and KRAS, some of which have been translated into targeted therapies. However, the molecular alterations identified thus far cannot explain all of the clinical and biological disparities observed in lung tumors of NS and smokers. To this end, we performed an unbiased genome-wide, comparative study to identify novel genomic aberrations that differ between smokers and NS.

High resolution whole genome DNA copy number profiling of 69 lung adenocarcinomas from smokers (n = 39) and NS (n = 30) revealed both global and regional disparities in the tumor genomes of these two groups. We found that NS lung tumors had a greater proportion of their genomes altered than those of smokers. Moreover, copy number gains on chromosomes 5q, 7p, and 16p occurred more frequently in NS. We validated our findings in two independently generated public datasets. Our findings provide a novel line of evidence distinguishing genetic differences between smoker and NS lung tumors, namely, that the extent of segmental genomic alterations is greater in NS tumors. Collectively, our findings provide evidence that these lung tumors are globally and genetically different, which implies they are likely driven by distinct molecular mechanisms.

Inflammation plays a role in the progression to cancer and it is linked to the presence of senescent cells. Ulcerative colitis (UC) is a chronic inflammatory disease that predisposes to colorectal cancer. Tumorigenesis in this setting is associated with telomere shortening that can be observed in the non-dysplastic epithelium of UC patients with high-grade dysplasia (HGD) or cancer (UC Progressors). We hypothesized that a pre-neoplastic field of inflammation, telomere shortening, and senescence underlies tumor progression in UC Progressors. Multiple biopsies of varying histological grade were collected along the colon of 9 UC Progressors and analyzed for telomere length, DNA damage, senescence, p53, p16, and chronic and acute inflammation. Twenty biopsies from 4 UC non- Progressors and 21 biopsies from control individuals without UC were also analyzed. Short telomeres and increased DNA damage, senescence, and infiltrating leukocytes were observed in biopsies located less than 10 cm from HGD or cancer. Low-grade dysplasia had the shortest telomeres along with the highest levels of senescence and infiltrating leukocytes, whereas HGD biopsies showed the opposite pattern. p16 and p53 expression was low in non-dysplastic biopsies, but progressively increased in LGD and HGD. Additionally, high levels of infiltrating leukocytes were associated with telomere shortening, senescence, and reduced p53 expression. These results suggest that dysplasia arises in a pre-neoplastic field of chronic inflammation which leads to telomere shortening, DNA damage, and senescence. Our findings argue that senescence acts as a tumor suppressor mechanism that is abrogated during the transition from LGD to HGD in ulcerative colitis.

Background

Polymorphisms in sex hormone receptor-encoding genes may alter the activity of sex hormone receptors and thereby affect susceptibility to breast cancer and related outcomes.

Methods

In a case-control study of women from Shanghai, China, we examined the risk of breast cancer and fibrocystic breast conditions associated with the ESR1 PvuII (rs2234693) and XbaI (rs9340799) and AR CAG repeat ((CAG)n) and GGC repeat ((GGC)n) polymorphisms among 614 women with breast cancer, 467 women with fibrocystic conditions, and 879 women without breast disease. We also evaluated whether risk differed by the presence/absence of proliferative changes (in the extratumoral epithelium or fibrocystic lesion), menopausal status, or body mass index (BMI). Age-adjusted odds ratios (OR) and 95% confidence intervals (95% CI) were calculated using logistic regression.

Results

Only associations with AR (CAG)n and (GGC)n genotypes were detected. Allocating AR (CAG)n genotypes into six categories, with the (CAG)22–24/(CAG)22–24 genotype category designated as the reference group, the (CAG)>24/(CAG)>24 genotype category was associated with an increased risk of fibrocystic breast conditions (OR, 1.8; 95% CI, 1.1–3.0). Relative to the AR (GGC)17/(GGC)17 genotype, the (GGC)17/(GGC)14 genotype was associated with elevated risks of incident breast cancer (OR, 2.6; 95% CI, 1.3–5.4) and fibrocystic conditions (OR, 2.3; 95% CI, 1.1–4.5). Results did not differ according to proliferation status, menopausal status, or BMI.

Conclusion

Although these data lend support for a link between AR variation and breast disease development, given the low frequency of the putative risk-conferring genotypes and other constraints, further confirmation of our results is needed.

The CA 19-9 assay detects a carbohydrate antigen on multiple protein carriers, some of which may be preferential carriers of the antigen in cancer. We tested the hypothesis that the measurement of the CA 19-9 antigen on individual proteins could improve performance over the standard CA 19-9 assay. We used antibody arrays to measure the levels of the CA 19-9 antigen on multiple proteins in serum or plasma samples from patients with pancreatic adenocarcinoma or pancreatitis. Sample sets from three different institutions were examined, comprising 531 individual samples. The measurement of the CA 19-9 antigen on any individual protein did not improve upon the performance of the standard CA 19-9 assay (82% sensitivity at 75% specificity for early-stage cancer), owing to diversity among patients in their CA 19-9 protein carriers. However, a subset of cancer patients with no elevation in the standard CA 19-9 assay showed elevations of the CA 19-9 antigen specifically on the proteins MUC5AC or MUC16 in all sample sets. By combining measurements of the standard CA 19-9 assay with detection of CA 19-9 on MUC5AC and MUC16, the sensitivity of cancer detection was improved relative to CA 19-9 alone in each sample set, achieving 67–80% sensitivity at 98% specificity. This finding demonstrates the value of measuring glycans on specific proteins for improving biomarker performance. Diagnostic tests with improved sensitivity for detecting pancreatic cancer could have important applications for improving the treatment and management of patients suffering from this disease.

Summary

A marker's capacity to predict risk of a disease depends on disease prevalence in the target population and its classification accuracy, i.e. its ability to discriminate diseased subjects from non-diseased subjects. The latter is often considered an intrinsic property of the marker; it is independent of disease prevalence and hence more likely to be similar across populations than risk prediction measures. In this paper, we are interested in evaluating the population-specific performance of a risk prediction marker in terms of positive predictive value (PPV) and negative predictive value (NPV) at given thresholds, when samples are available from the target population as well as from another population. A default strategy is to estimate PPV and NPV using samples from the target population only. However, when the marker's classification accuracy as characterized by a specific point on the receiver operating characteristics (ROC) curve is similar across populations, borrowing information across populations allows increased efficiency in estimating PPV and NPV. We develop estimators that optimally combine information across populations. We apply this methodology to a cross-sectional study where we evaluate PCA3 as a risk prediction marker for prostate cancer among subjects with or without previous negative biopsy.

Background

Observational studies linking vitamin D deficiency with increased prostate cancer mortality and the pleiotropic anticancer effects of vitamin D in malignant prostate cell lines have initiated trials examining potential therapeutic benefits of vitamin D metabolites. There have been some successes but efforts have been hindered by risk of inducing hypercalcemia. A limited number of studies have investigated associations between variants in vitamin D pathway genes with aggressive forms of prostate cancer. Increased understanding of relevant germline genetic variation with disease outcome could aid in development of vitamin D-based therapies.

Methods

We undertook a comprehensive analysis of 48 tagging single nucleotide polymorphisms (tagSNPs) in genes encoding for vitamin D receptor (VDR), vitamin D activating enzyme 1-α-hydroxylase (CYP27B1), and deactivating enzyme 24-hydroxylase (CYP24A1) in a cohort of 1,294 Caucasian cases with an average of 8 years of follow-up. Disease recurrence/ progression and prostate cancer-specific mortality risks were estimated using adjusted Cox proportional hazards regression.

Results

There were 139 cases with recurrence/progression events and 57 cases who died of prostate cancer. Significantly altered risks of recurrence/progression were observed in relation to genotype for two VDR tagSNPs (rs6823 and rs2071358) and two CYP24A1 tagSNPs (rs927650 and rs2762939). Three VDR tagSNPs (rs3782905, rs7299460 and rs11168314), one CYP27B1 tagSNP (rs3782130) and five CYP24A1 tagSNPs (rs3787557, rs4809960, rs2296241, rs2585428, and rs6022999) significantly altered risks of prostate cancer death.

Conclusions

Genetic variations in vitamin D pathway genes were found to alter both risk of recurrence/progression and prostate cancer-specific mortality.

Recent interest has focused on the role that inflammation may play in the development of prostate cancer and whether use of aspirin or other nonsteroidal antiinflammatory drugs (NSAIDs) affects risk. In a population-based case-control study designed to investigate the relation between these medications and prostate cancer risk, detailed exposure data were analyzed from 1,001 cases diagnosed with prostate cancer between January 1, 2002, and December 31, 2005, and 942 age-matched controls from King County, Washington. A significant 21% reduction in the risk of prostate cancer was observed among current users of aspirin compared with nonusers (95% confidence interval (CI): 0.65, 0.96). Long-term use of aspirin (>5 years: odds ratio = 0.76, 95% CI: 0.61, 0.96) and daily use of low-dose aspirin (odds ratio = 0.71, 95% CI: 0.56, 0.90) were also associated with decreased risk. There was no evidence that the association with aspirin use varied by disease aggressiveness, but there was effect modification (Pinteraction = 0.02) with a genetic variant in prostaglandin-endoperoxide synthase 2 (PTGS2) (rs12042763). Prostate cancer risk was not related to use of either nonaspirin NSAIDs or acetaminophen. These results contribute further evidence that aspirin may have chemopreventive activity against prostate cancer and highlight the need for additional research.

Classification and association models differ fundamentally in objectives, measurements, and clinical context specificity. Association studies aim to identify biomarker association with disease in a study population and provide etiologic insights. Common association measurements are odds ratio, hazard ratio, and correlation coefficient. Classification studies aim to evaluate biomarker use in aiding specific clinical decisions for individual patients. Common classification measurements are sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Good association is usually a necessary, but not a sufficient, condition for good classification. Methods for developing classification models have mainly used that criteria for association models and therefore are not optimal for classification purposes. We suggest that developing classification models by focusing on the region of receiver operating characteristic (ROC) curve relevant to the intended clinical application optimizes the model for the intended application setting.

OBJECTIVE

Cytochrome P450 17α-hydroxylases-C-17,20-lyase (CYP17) is a key enzyme involved with the androgen biosynthesis pathway and has recently been targeted for therapy in men with advanced prostate cancer (PCa). However, studies relating prostate cancer outcomes with CYP17 gene variants have conflicting results. In this study we analyzed Single Nucleotide Polymorphisms (SNPs) spanning the CYP17 gene for association with PCa survival.

METHODS

The cohort was comprised of Caucasian men, aged 40–64, diagnosed with PCa between 1993–1996 in King County, Washington who participated in a population-based case-control study. CYP17 SNPs were selected to capture variation across the gene and known regulatory regions. PCa-specific mortality (PCSM) was obtained by linking to the SEER cancer registry. Recurrence/progression of PCa was determined from patient survey data and medical records. Cox proportional hazards regression analysis was used to generate hazard ratios for patient outcomes.

RESULTS

Genotypes were available for 598 cases. With a median follow-up of 13.2 years, 44 PCa deaths were observed. Recurrence/progression events were observed in 30% of subjects. No genetic association with disease progression were identified. However, men with the variant A allele in rs10883783 had a 56% risk reduction in PCSM (HR 0.44, 95% CI 0.21–0.98).

CONCLUSION

These data suggest that genetic variation in the CYP17 gene in Caucasian men is associated with PCa survival.

Background

The occurrence of diabetes has greatly increased in low- and middle-income countries, particularly in Asia, as has the prevalence of overweight and obesity; in European-derived populations, overweight and obesity are established causes of diabetes. The shape of the association of overweight and obesity with diabetes risk and its overall impact have not been adequately studied in Asia.

Methods and Findings

A pooled cross-sectional analysis was conducted to evaluate the association between baseline body mass index (BMI, measured as weight in kg divided by the square of height in m) and self-reported diabetes status in over 900,000 individuals recruited in 18 cohorts from Bangladesh, China, India, Japan, Korea, Singapore and Taiwan. Logistic regression models were fitted to calculate cohort-specific odds ratios (OR) of diabetes for categories of increasing BMI, after adjustment for potential confounding factors. OR were pooled across cohorts using a random-effects meta-analysis. The sex- and age-adjusted prevalence of diabetes was 4.3% in the overall population, ranging from 0.5% to 8.2% across participating cohorts. Using the category 22.5–24.9 Kg/m2 as reference, the OR for diabetes spanned from 0.58 (95% confidence interval [CI] 0.31, 0.76) for BMI lower than 15.0 kg/m2 to 2.23 (95% CI 1.86, 2.67) for BMI higher than 34.9 kg/m2. The positive association between BMI and diabetes prevalence was present in all cohorts and in all subgroups of the study population, although the association was stronger in individuals below age 50 at baseline (p-value of interaction<0.001), in cohorts from India and Bangladesh (p<0.001), in individuals with low education (p-value 0.02), and in smokers (p-value 0.03); no differences were observed by gender, urban residence, or alcohol drinking.

Conclusions

This study estimated the shape and the strength of the association between BMI and prevalence of diabetes in Asian populations and identified patterns of the association by age, country, and other risk factors for diabetes.

Background

Caveolin-1 (cav-1) is overexpressed by metastatic prostate cancer (PC) cells. Pre-operative serum cav-1 levels have been shown to be a prognostic marker for PC recurrence. This study evaluated the relationship between post-treatment serum cav-1 levels and single nucleotide polymorphisms (SNPs) in the cav-1 and -2 genes with risk of PC, aggressive PC, PC recurrence or death.

Methods

Two case-control studies of PC among men in Washington State were combined for this analysis. Cases (n=1,458) were diagnosed in 1993–96 or 2002–05 and identified via a SEER cancer registry. Age-matched controls (n=1,351) were identified via random digit dialing. Logistic regression assessed the relationship between exposures (19 haplotype-tagging SNPs from all subjects and post-treatment serum cav-1 levels from a sample of 202 cases and 226 controls) and PC risk and aggressive PC. Cox proportional hazards regression assessed the relationship between exposures and PC recurrence and death.

Results

Rs9920 in cav-1 was associated with an increased relative risk of overall PC (ORCT+CC=1.37, 95%CI=1.12, 1.68) and aggressive PC (ORCT+CC=1.57, 95%CI=1.20, 2.06), but not with PC recurrence or death. High post-treatment serum cav-1 levels were not associated with PC risk, aggressive PC, or PC-specific death, but approached a significant inverse association with PC recurrence (hazard ratio=0.69, 95%CI=0.47, 1.00).

Conclusions

We found modest evidence for an association with a variant in the cav-1 gene and risk of overall PC and aggressive PC, which merits further study. We found no evidence that higher post-treatment serum cav-1 is associated with risk of aggressive PC or adverse PC outcomes.

Background

The free PSA isoform, [-2]proPSA, has been shown to be associated with prostate cancer. The study objective was to characterize the clinical utility of serum [-2]proPSA for prostate cancer detection and assess its association with aggressive disease.

Methods

From among 669 subjects in a prospective prostate cancer detection study at four NCI Early Detection Research Network clinical validation centers, 566 were eligible. Serum PSA, free PSA, and [-2]proPSA were measured (Beckman Coulter Access 2 Analyzer).

Results

245 (43%) of the 566 participants had prostate cancer on biopsy. At 70% specificity, sensitivity of %[-2]proPSA ([-2]proPSA/fPSA) was 54% (95% CI:48%–61%, null hypothesis 40%). Including %[-2]proPSA in a multivariate prediction model incorporating PSA and %fPSA improved the performance (p<0.01). In the 2–4 ng/mL PSA range, %[-2]proPSA outperformed %fPSA (ROC AUC= 0.73 vs. 0.61, p=0.01). At 80% sensitivity, %[-2]proPSA had significantly higher specificity (51.6%, 95% CI:41.2%–61.8%) than PSA (29.9%, 95% CI:21.0%–40.0%) and %fPSA (28.9%, 95% CI:20.1%–39.0%). In the 2–10 ng/mL PSA range, a multivariate model had significant improvement (AUC=0.76) over individual PSA forms (p<0.01-<0.0001). At 80% sensitivity, specificity of %[-2]proPSA (44.9%, 95% CI:38.4%–51.5%) was significantly higher than PSA (30.8%, 95% CI:24.9%–37.1%) and relatively higher than %fPSA (34.6%, 95% CI:28.5%–41.4%). %[-2]proPSA increased with increasing Gleason score (p<0.001) and was higher in aggressive cancers (p=0.03).

Conclusions

In this prospective study, %[-2]proPSA demonstrated potential clinical utility for improving prostate cancer detection and was related to the risk of aggressive disease.

Impact

The addition of %[-2]proPSA could impact the early detection of prostate cancer.

Objective

Current 2007 Partin Tables restricts the application of total PSA (tPSA) as a non-continuous biomarker by creating “groups” for the risk stratification with tPSA value of 0-2.5, 2.6-4.0, 4.1-6.0, 6.1-10.0, and >10.0 ng/ml. Hence we developed a “2010 Partin Nomogram” with tPSA as a continuous biomarker and employ “predictiveness curve” to calculate the percentile risk of a patient among the cohort.

Patients and Method

5730 and 1646 men were treated with radical prostatectomy (without neoadjuvant therapy) between 2000 and 2005 at the Johns Hopkins Hospital (JHH) and University Clinic Hamburg-Eppendorf (UCHE) respectively. Multinomial logistic regression analysis was performed to create a model for predicting risk of the four non-ordered pathologic stages i.e. organ-confined disease (OC), extraprostatic extension (EPE), seminal vesicle (SV+) and lymph node (LN+) involvement. Patient-specific risk was modelled as a function of the B-spline basis of tPSA (knots at the 1st, 2nd, and 3rd quartiles), clinical stage (T1c, T2a, and T2b/T2c) and biopsy Gleason score (5-6, 3+4=7, 4+3=7, 8-10).

Results

The “2010 Partin Nomogram” calculates patient-specific absolute risk for all four pathological outcomes (OC, EPE, SV+, LN+) given a patient's pre-operative clinical stage, tPSA, and biopsy Gleason score. While having comparable performance in terms of calibration and discriminatory power, this new model provides a more accurate prediction of patients’ pathological stage compared to the 2007 Partin Tables model. Further, the use of “predictiveness curves” has made possible to obtain percentile risk of a patient among the cohort and to also gauge the impact of risk thresholds for making decision regarding radical prostatectomy.

Conclusions

The “2010 Partin Nomogram” using tPSA as a continuous biomarker together with the corresponding “predictiveness curve” will aid clinicians and patients to make improved treatment decisions.

Boosting is an important tool in classification methodology. It combines the performance of many weak classifiers to produce a powerful committee, and its validity can be explained by additive modeling and maximum likelihood. The method has very general applications, especially for high-dimensional predictors. For example, it can be applied to distinguish cancer samples from healthy control samples by using antibody microarray data. Microarray data are often high-dimensional and many of them are incomplete. One natural idea is to impute a missing variable based on the observed predictors. However, the calculation of imputation for high-dimensional predictors with missing data may be rather tedious. In this paper, we propose 2 conditional mean imputation methods. They can be applied to the situation even when a complete-case subset does not exist. Simulation results indicate that the proposed methods are superior than other naive methods. We apply the methods to a pancreatic cancer study in which serum protein microarrays are used for classification.

Summary

Consider a continuous marker for predicting a binary outcome. For example, serum concentration of prostate specific antigen (PSA) may be used to calculate the risk of finding prostate cancer in a biopsy. In this paper we argue that the predictive capacity of a marker has to do with the population distribution of risk given the marker and suggest a graphical tool, the predictiveness curve, that displays this distribution. The display provides a common meaningful scale for comparing markers that may not be comparable on their original scales. Some existing measures of predictiveness are shown to be summary indices derived from the predictiveness curve. We develop methods for making inference about the predictiveness curve, for making pointwise comparisons between two curves and for evaluating covariate effects. Applications to risk prediction markers in cancer and cystic fibrosis are discussed.

Purpose

Reports on biochemical recurrence after prostate cancer (PCa) primary therapy have shown differences between Gleason 4+3 and 3+4 tumors. These findings have not been explored for PCa-specific mortality (PCSM). In this population-based cohort, we determine PCa outcomes at different Gleason scores, in particular the different Gleason 7 patterns.

Methods

Men aged 40–64 diagnosed with PCa between 1993–1996 in King County, Washington comprised the cohort. Recurrence/progression was determined by follow-up survey and medical records review. Mortality and cause of death were obtained from the SEER registry. Outcomes were determined with Cox proportional hazards regression analysis.

Results

Of 753 men with PCa, 65 PCa-specific deaths occurred during a median follow-up of 13.2 years. The 10-year PCa-specific survival rates for Gleason ≤6, 3+4, 4+3, and 8–10 were 98.4%, 92.1%, 76.5% and 69.9%, respectively. Compared to patients with Gleason 3+4 disease, those with Gleason 4+3 tumors had an increased risk of PCSM in both the unadjusted (HR 2.80, 95%CI 1.26 – 6.18) and multivariate models (HR 2.12, 95% CI 0.87–5.17, p=0.1). In men undergoing curative therapy with radical prostatectomy or radiation therapy, there was an increased risk of recurrence/progression (HR 2.1, 95% CI 1.1–4.0) and PCSM (HR 3.2, 95% CI 1.0–9.7) in those with Gleason 4+3 compared to 3+4 tumors in the multivariate models. No difference in PCSM was seen between Gleason 4+3 and 8 – 10 tumors.

Conclusion

Gleason 7 PCa tumors exhibit a heterogeneous behavior with Gleason 3+4 and 4+3 tumors conferring different PCSM. These data provide important information for counseling patients with Gleason 7 PCa on the natural history of their disease and may inform treatment decisions.