Serum autoantibodies, directed against oncogenic proteins, have been frequently detected in the sera of breast cancer patients. It is unknown whether serum antibodies that are identified in patients with established disease could also be detected in patients with newly diagnosed disease or even predate the diagnosis of breast cancer. Using sera collected at the time of treatment, at the time of diagnosis, or prior to the time of diagnosis, the current study aimed to address the temporal relationship between breast cancer development and serum antibody response. Starting from serum antibodies to eight known breast cancer antigens, we first identified four serum antibodies, HER-2/neu, p53, CEA, and cyclin B1, which are significantly increased in the sera collected from breast cancer patients at the time of treatment. These antibodies were also elevated in breast cancer sera collected at the time of diagnosis. Lastly, comparison of antibody responses in pre-diagnostic samples from women prior to the development of breast cancer and in controls demonstrated that antibodies to the HER-2/neu and p53 can be detected in sera that were collected on average more than 150 days before a breast cancer diagnosis. These results demonstrated that serum autoantibodies commonly reported in sera from patients with established disease can also be detected in pre-diagnostic sera and may be useful for the early detection of breast cancer.
serum antibody; breast cancer; early detection
The receiver operating characteristic (ROC) curve, the positive predictive value (PPV) curve and the negative predictive value (NPV) curve are three measures of performance for a continuous diagnostic biomarker. The ROC, PPV and NPV curves are often estimated empirically to avoid assumptions about the distributional form of the biomarkers. Recently, there has been a push to incorporate group sequential methods into the design of diagnostic biomarker studies. A thorough understanding of the asymptotic properties of the sequential empirical ROC, PPV and NPV curves will provide more flexibility when designing group sequential diagnostic biomarker studies. In this paper we derive asymptotic theory for the sequential empirical ROC, PPV and NPV curves under case-control sampling using sequential empirical process theory. We show that the sequential empirical ROC, PPV and NPV curves converge to the sum of independent Kiefer processes and show how these results can be used to derive asymptotic results for summaries of the sequential empirical ROC, PPV and NPV curves.
Group Sequential Methods; Empirical Process Theory; Diagnostic Testing
Selecting controls that match cases on risk factors for the outcome is a pervasive practice in biomarker research studies. Yet, such matching biases estimates of biomarker prediction performance. The magnitudes of bias are unknown.
We examined the prediction performance of biomarkers and improvements in prediction gained by adding biomarkers to risk factor information. Data simulated from bivariate normal statistical models and data from a study to identify critically ill patients were used. We compared true performance with that estimated from case-control studies that do or do not use matching. Receiver operating characteristic curves quantified performance. We propose a new statistical method to estimate prediction performance from matched studies when data on the matching factors are available for subjects in the population.
Performance estimated with standard analyses can be grossly biased by matching especially when biomarkers are highly correlated with matching risk factors. In our studies, the performance of the biomarker alone was underestimated while the improvement in performance gained by adding the marker to risk factors was overestimated by 2 to 10 fold. We found examples where the relative ranking of two biomarkers for prediction was inappropriately reversed by use of a matched design. The new approach to estimation corrected for bias in matched studies.
To properly gauge prediction performance in the population or the improvement gained by adding a biomarker to known risk factors, matched case-control studies must be supplemented with risk factor information from the population and must be analyzed with nonstandard statistical methods.
design; diagnosis; prediction; prognosis; receiver operating characteristic curve
Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [−2]proPSA measured on an external case control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network.
Calibration; Discrimination; Net Benefit, Risk Prediction; Validation; Prostate Cancer Prevention Trial
BACKGROUND AND AIMS
Cystic lesions of the pancreas are increasingly being recognized due to the widespread use of high resolution abdominal imaging. Since certain cyst types are precursors to invasive cancer, this situation presents an opportunity to intervene prior to malignant progression. Effective implementation of that strategy has been hampered by difficulties in clearly distinguishing cystic lesions with no malignant potential from those with malignant potential. Here we explored whether glycosylation variants on specific proteins in cyst fluid samples could serve as biomarkers to aid in this diagnosis.
We utilized a novel antibody-lectin sandwich microarray method to measure the protein expression and glycosylation of MUC1, MUC5AC, MUC16, CEA, and other proteins implicated in pancreatic neoplasia in cyst fluid samples. Fifty-three cyst fluid samples were obtained from patients with mucinous cystic neoplasms (MCN, n = 17), intraductal papillary mucinous neoplasms (IPMN, n = 15), serous cystadenomas (SC, n = 12), or pseudocysts (PC, n = 9), with confirmation of histologic diagnosis at surgical resection.
The detection of a glycan variant on MUC5AC using the lectin wheat-germ agglutinin discriminated mucin-producing cystic tumors (MCNs + IPMNs) from benign cystic lesions (SC + PC) with a 78% sensitivity at 80% specificity, and when used in combination with cyst fluid CA 19-9 gave a sensitivity of 87% at 86% specificity. These biomarkers performed better than cyst fluid CEA (37%/80% sensitivity/specificity).
These results demonstrate the value of glycan variants for biomarker discovery and suggest that these biomarkers could greatly enhance the accuracy of differentiating pancreatic cystic tumors. Validation studies will be required to determine the clinical value of these markers.
We assessed the independent predictive value of prostate volume, number of biopsy cores and AUASS (American Urological Association symptom score) compared to risk factors included in the PCPTRC (Prostate Cancer Prevention Trial risk calculator for prostate cancer) and PCPTHG (Prostate Cancer Prevention Trial risk calculator for high grade cancer [Gleason grade 7 or greater]).
Materials and Methods
Of 5,519 PCPT (Prostate Cancer Prevention Trial) participants the 4,958 used to construct the PCPTRC with AUASS and prostate specific antigen 10 ng/ml or less were included on logistic regression analysis. Risk algorithms were evaluated in 571 EDRN (Early Detection Research Network) participants using the ROC AUC.
A total of 1,094 participants (22.1%) had prostate cancer, of whom 232 (21.2%) had high grade disease. For prostate cancer prediction higher prostate specific antigen, abnormal digital rectal examination, family history of prostate cancer and number of cores were associated with increased risk, while volume was associated with decreased risk. Excluding prostate volume and number of cores, a history of negative biopsy and increased AUASS were also associated with lower risk. For high grade cancer higher prostate specific antigen, abnormal digital rectal examination, black race and number of cores were associated with increased risk and volume, while AUASS was associated with decreased risk. The AUC of the PCPTRC adjusted for volume and number of cores was 72.7% using EDRN data and 68.2% when adjusted for AUASS alone vs 67.6% for the PCPTRC. For high grade disease the AUC was 74.8% and 74.0%, respectively, vs 73.5% for the PCPTHG.
Adjusted PCPT risk calculators for volume, number of cores and AUASS improve cancer detection.
prostate; prostatic neoplasms; risk; algorithms; early detection of cancer
To develop more targeted intervention strategies, an important research goal is to identify markers predictive of clinical events. A crucial step towards this goal is to characterize the clinical performance of a marker for predicting different types of events. In this manuscript, we present statistical methods for evaluating the performance of a prognostic marker in predicting multiple competing events. To capture the potential time-varying predictive performance of the marker and incorporate competing risks, we define time- and cause-specific accuracy summaries by stratifying cases based on causes of failure. Such definition would allow one to evaluate the predictive accuracy of a marker for each type of event and compare its predictiveness across event types. Extending the nonparametric crude cause-specific ROC curve estimators by Saha and Heagerty (2010), we develop inference procedures for a range of cause-specific accuracy summaries. To estimate the accuracy measures and assess how covariates may affect the accuracy of a marker under the competing risk setting, we consider two forms of semiparametric models through the cause-specific hazard framework. These approaches enable a flexible modeling of the relationships between the marker and failure times for each cause, while efficiently accommodating additional covariates. We investigate the asymptotic property of the proposed accuracy estimators and demonstrate the finite sample performance of these estimators through simulation studies. The proposed procedures are illustrated with data from a prostate cancer prognostic study.
Biomarker evaluation; Cause-specific Hazard; Competing risk; Negative predictive value; Positive predictive value; Receiver Operating Characteristics Curve (ROC curve); Survival analysis
Oral cancer is the sixth most common cancer with a five-year survival rate of approximately 60%. Presently there are no scientifically credible early detection techniques beyond conventional clinical oral examination. The goal of this study is to validate if the 7 mRNAs and 3 proteins previously reported biomarkers are capable of discriminating patients with oral squamous cell carcinomas (OSCC) from healthy subjects in independent cohorts and by a National Cancer Institute (NCI)- Early Detection Research Network (EDRN) Biomarker Reference Laboratory (BRL).
395 subjects from 5 independent cohorts based on case-controlled design were investigated by 2 independent laboratories, UCLA discovery laboratory and NCI-EDRN Biomarker Reference Laboratory (BRL).
Expression of all 7 mRNA and 3 protein markers was increased in OSCC versus controls in all 5 cohorts. With respect to individual marker performance across the 5 cohorts, the increase in IL-8 and SAT were statistically significant and remained top performers across different cohorts in terms of sensitivity and specificity. A previously identified multiple marker model demonstrated an area under the receiver operating characteristic (ROC)-curve for prediction of OSCC status ranging from of 0.74 to 0.86 across the cohorts.
The validation of these biomarkers demonstrated their feasibility in the discrimination of OSCC from healthy controls. Established assay technologies are robust enough to perform independently. Individual cutoff values for each of these markers and for the combined predictive model need to be further defined in large clinical studies.
Salivary proteomic and transcriptomic biomarkers can discriminate oral cancer from control subjects.
Longitudinal blood collections from cohort studies provide the means to search for proteins associated with disease prior to clinical diagnosis. We investigated plasma samples from the Women’s Health Initiative (WHI) cohort to determine quantitative differences in plasma proteins between subjects subsequently diagnosed with colorectal cancer (CRC) and matched controls that remained cancer free during the period of follow-up. Proteomic analysis of WHI samples collected prior to diagnosis of CRC resulted in the identification of six proteins with significantly (p <0.05) elevated concentrations in cases compared to controls. Proteomic analysis of two colorectal cancer cell lines showed 5 of the 6 proteins were produced by cancer cells. MAPRE1, IGFBP2, LRG1 and CEA were individually assayed by enzyme linked immunosorbent assay (ELISA) in 58 pairs of newly diagnosed CRC samples and controls and yielded significant elevations (p <0.05) among cases relative to controls. A combination of these four markers resulted in an ROC with an AUC=0.841 and 57% sensitivity at 95% specificity. This combination rule was tested in an independent set of WHI samples collected within 7 months prior to diagnosis from cases and matched controls resulting in 41% sensitivity at 95% specificity. A panel consisting of CEA, MAPRE1, IGFBP2 and LRG1 has predictive value in pre-diagnostic colorectal cancer plasmas.
colorectal cancer; risk markers; Pre-Diagnostic samples
antibody; diagnosis; risk
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.
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.
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.
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.
Five genetic markers were validated to be associated with lethal prostate cancer.
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.
Prostate cancer-specific mortality; survival; genetic variants; single nucleotide polymorphisms; hazard ratio
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.
Real time PCR; Tumor suppressor gene; Non-small cell lung cancer
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.
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.
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.
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.
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.
prostate cancer; aggressive prostate cancer; GWAS; genetic variants
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.
Penalized regression; generalized singular value decomposition; regularization; functional data
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.
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.
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.
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.
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.
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.
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.
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.
These results underline the potential importance of the inflammation pathway in PC development and progression.
prostate cancer; genetic association; inflammation; genetic variation
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.
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.
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.
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.
ulcerative colitis; telomere length; inflammation; senescence; colon
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.
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.
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.
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.
Estrogen receptor alpha; androgen receptor; genetic polymorphism; breast neoplasm; fibrocystic breast disease; Chinese
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.
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.
Biomarker; Classification; NPV; PPV; Sensitivity; Specificity
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.
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.
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.
Genetic variations in vitamin D pathway genes were found to alter both risk of recurrence/progression and prostate cancer-specific mortality.
Vitamin D Receptor; 1-alpha-Hydroxylase; 24-Hydroxylase; Prostatic Neoplasms; Outcomes
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.
anti-inflammatory agents, non-steroidal; aspirin; odds ratio; polymorphism, genetic; prostaglandin-endoperoxide synthases; prostatic neoplasms