Most studies that have evaluated the association between the body-mass index (BMI) and the risks of death from any cause and from specific causes have been conducted in populations of European origin.
We performed pooled analyses to evaluate the association between BMI and the risk of death among more than 1.1 million persons recruited in 19 cohorts in Asia. The analyses included approximately 120,700 deaths that occurred during a mean follow-up period of 9.2 years. Cox regression models were used to adjust for confounding factors.
In the cohorts of East Asians, including Chinese, Japanese, and Koreans, the lowest risk of death was seen among persons with a BMI (the weight in kilograms divided by the square of the height in meters) in the range of 22.6 to 27.5. The risk was elevated among persons with BMI levels either higher or lower than that range — by a factor of up to 1.5 among those with a BMI of more than 35.0 and by a factor of 2.8 among those with a BMI of 15.0 or less. A similar U-shaped association was seen between BMI and the risks of death from cancer, from cardiovascular diseases, and from other causes. In the cohorts comprising Indians and Bangladeshis, the risks of death from any cause and from causes other than cancer or cardiovascular disease were increased among persons with a BMI of 20.0 or less, as compared with those with a BMI of 22.6 to 25.0, whereas there was no excess risk of either death from any cause or cause-specific death associated with a high BMI.
Underweight was associated with a substantially increased risk of death in all Asian populations. The excess risk of death associated with a high BMI, however, was seen among East Asians but not among Indians and Bangladeshis.
Active surveillance is used to manage low risk prostate cancer. Both PCA3 and TMRPSS2-ERG are promising biomarkers that may be associated with aggressive disease. This study examines the correlation of these biomarkers with higher cancer volume and grade determined at the time of biopsy in an active surveillance cohort.
Post-DRE urine was collected prospectively as part of the multi-institutional Canary Prostate Active Surveillance Study (PASS). PCA3 and TMPRSS2-ERG levels were analyzed in urine collected at study entry. Biomarker scores were correlated to clinical and pathologic variables.
In 387 men, both PCA3 and TMPRSS2-ERG scores were significantly associated with higher volume disease. For a negative repeat biopsy, and 1–10%, 11–33%, ≥34% positive cores, median PCA3 and TMPRSS2-ERG scores increased incrementally (P < 0.005). Both PCA3 and TMPRSS2-ERG scores were also significantly associated with presence of high grade disease. For a negative repeat biopsy, Gleason 6 and Gleason ≥7 cancers, the median PCA3 and TMPRSS2-ERG scores also increased incrementally (P = 0.02 and P = 0.001, respectively). Using the marker scores as a continuous variables, the odds ratio for a biopsy in which cancer was detected versus a negative repeat biopsy (ref) on modeling was 1.41 (95% CI 1.07–1.85), P = 0.01 for PCA3 and 1.28 (95% CI 1.10–1.49), P = 0.001 for TMPRSS2-ERG.
For men on active surveillance both PCA3 and TMPRSS2-ERG appear to stratify risk of having aggressive cancer as defined by tumor volume or Gleason score.
prostate cancer; active surveillance; biomarkers
While several studies showed that selenium may prevent prostate cancer (PCa), few studies have evaluated variation in selenoenzyme genes in relation to PCa risk and survival.
We studied common variants in seven selenoenzymes genes in relation to risk of PCa and PCa-specific mortality (PCSM). In a population-based case-control study of men of European ancestry (1,309 cases, 1,266 controls), we evaluated 35 common, tagging single nucleotide polymorphisms (SNPs) in GPX1 (n = 2), GPX2 (n = 4), GPX3 (n = 6), GPX4 (n = 6), SEP15 (n = 4), SEPP1 (n = 6), and TXNRD1 (n = 7) in relation to PCa risk, and among cases, associations between these variants and risk of PCSM. We used logistic regression and Cox proportional hazards regression to estimate the relative risk of PCa and PCSM, respectively.
Of the SNPs examined, only GPX1 rs3448 was associated with overall PCa risk with an odds ratio of 0.62 for TT versus CC (95% confidence interval, 0.44–0.88). SNPs in GPX2, GPX3, GPX4, SEP15, and SEPP1 had different risk estimates for PCa in subgroups based on stage and grade. We observed associations between SNPs in GPX4 and TXNRD1 and risk of PCSM. None of these associations, however, remained significant after adjustment for multiple comparisons.
We found evidence that genetic variation in a subset of selenoenzyme genes may alter risk of PCa and PCSM. These results need validation in additional subsets.
prostate cancer; risk; mortality; selenoenzyme genes; genetic variation
Vietnamese American women represent one of the ethnic subgroups at great risk for cervical cancer in the United States. The underutilization of cervical cancer screening and the vulnerability of Vietnamese American women to cervical cancer may be compounded by their health beliefs.
The objective of this study was to explore the associations between factors of the Health Belief Model (HBM) and cervical cancer screening among Vietnamese American women.
Vietnamese American women (n=1,450) were enrolled into the randomized controlled trial (RCT) study who were recruited from 30 Vietnamese community-based organizations located in Pennsylvania and New Jersey. Participants completed baseline assessments of demographic and acculturation variables, health care access factors, and constructs of the HBM, as well as health behaviors in either English or Vietnamese.
The rate of those who had ever undergone cervical cancer screening was 53% (769/1450) among the participants. After adjusting for sociodemographic variables, the significant associated factors from HBM included: believing themselves at risk and more likely than average women to get cervical cancer; believing that cervical cancer changes life; believing a Pap test is important for staying healthy, not understanding what is done during a Pap test, being scared to know having cervical cancer; taking a Pap test is embarrassing; not being available by doctors at convenient times; having too much time for a test; believing no need for a Pap test when feeling well; and being confident in getting a test.
Understanding how health beliefs may be associated with cervical cancer screening among underserved Vietnamese American women is essential for identifying the subgroup of women who are most at risk for cervical cancer and would benefit from intervention programs to increase screening rates.
This study was to identify a biomarker that could improve α-fetoprotein (AFP) performance in hepatocellular carcinoma (HCC) surveillance among patients with cirrhosis. We performed proteomic profiling of plasma from patients with cirrhosis or HCC and validated selected candidate HCC biomarkers in two geographically distinct cohorts in order to include HCC of different etiologies. Mass spectrometry profiling of highly fractionated plasma from 18 cirrhosis and 17 HCC patients identified osteopontin (OPN) as significantly upregulated in HCC cases compared to cirrhosis controls. OPN levels were subsequently measured in 312 plasma samples collected from 131 HCC patients, 76 cirrhosis patients, 52 chronic hepatitis C (CHC) and B (CHB) patients and 53 healthy controls, in two independent cohorts. OPN plasma levels were significantly elevated in HCC patients compared to cirrhosis, CHC, CHB or healthy controls, in both cohorts. OPN alone or in combination with AFP had significantly better area under the receiver operating characteristic curve compared to AFP in comparing cirrhosis and HCC in both cohorts. OPN overall performance remained higher than AFP in comparing cirrhosis and the following HCC groups: HCV-related HCC, HBV-associated HCC and early HCC. OPN had also a good sensitivity in AFP negative HCC. In a pilot prospective study including 22 patients who developed HCC during follow-up, OPN was already elevated a year prior to diagnosis. Conclusion: OPN was more sensitive than AFP for the diagnosis of HCC in all studied HCC groups. In addition, OPN performance remained intact in samples collected a year prior to diagnosis.
biomarker; HCC; early detection; OPN
Tissue microarrays provide unique resources for rapid evaluation and validation of tissue biomarkers. The Canary Foundation Retrospective Prostate Tissue Microarray Resource used a rigorous statistical design, quota sampling, a variation of the case-cohort study, to select patients for inclusion in a multicenter, retrospective prostate cancer tissue microarray cohort. The study is designed to definitively validate tissue biomarkers of prostate cancer recurrence after radical prostatectomy. Tissue samples from over 1,000 participants treated for prostate cancer with radical prostatectomy between 1995 and 2004 were selected at six participating institutions in the United States and Canada. This design captured the heterogeneity of screening and clinical practices in the contemporary North American population. Standardized clinical data were collected in a centralized database. The project has been informative in several respects. The scale and complexity of assembling tissue microarrays (TMAs) with over 200 cases at each of six sites involved unanticipated levels of effort and time. Our statistical design promises to provide a model for outcome-based studies where tissue localization methods are applied to high-density tissue microarrays.
Prostate Cancer; Prognosis; Tissue Microarray; quota sampling
Through mediation of estrogen receptors, estradiol has been shown to have both carcinogenic and anti-carcinogenic effects on the prostate. We performed a population-based case-control study to investigate variants in estrogen-related genes ESR1, ESR2, CYP19A1, CYP1A1, and CYP1B1 and the potential association with risk of prostate cancer.
Materials and Methods
We evaluated prostate cancer risk conferred by 73 single nucleotide polymorphisms in 1,304 incident prostate cancer cases and 1,266 age-matched controls. Analysis included stratification by clinical features and assessment of environmental modifiers.
There was evidence of altered risk of developing prostate cancer for variants in ESR1, CYP1A1, and CYP1B1, however, only CYP1B1 rs1056836 retained significance after adjustment for multiple comparisons. An association with risk for more aggressive prostate cancer was observed for variants in ESR1, ESR2, and CYP19A1, but none was significant after adjustment for multiple comparisons. There was no effect modification by obesity.
Germline genetic variation of these estrogen pathway genes may contribute to risk of prostate cancer. Additional studies to validate these results and examine the functional consequence of validated variants are warranted.
Estrogen Receptor; Cytochrome P450; Aromatase; Prostate Neoplasm; Polymorphism
The mission of the National Cancer Institute’s Early Detection Research Network (EDRN) is to identify and validate cancer biomarkers for clinical use. Since its inception, EDRN investigators have learned a great deal about the process of validating biomarkers for clinical use. Translational research requires a broad spectrum of research expertise, and coordinating collaborative activities can be challenging. The EDRN has developed a robust triage and validation system that serves the roles of both “facilitator” and “brake”.
The system consists of (i) establishing a reference set of specimens collected under Prospective-specimen-collection-Retrospective-Blinded-Evaluation (PRoBE) design criteria; (ii) using the reference set to pre-validate candidate biomarkers before committing to full scale validation; (iii) doing full scale validation for those markers that succeed in pre-validation; and (iv) ensuring that the reference set is sufficiently large in numbers and volumes of sample that future candidate biomarkers can also be studied with it. This system provides rigorous and efficient evaluation of candidate biomarkers and biomarker panels. Reference sets should also be constructed to enable high quality biomarker discovery research.
We describe the process of establishing our system and hope it will serve as an example of how to validate biomarkers for clinical application. We also describe the biospecimen reference sets that are available in the EDRN and hope this will encourage the biomarker research community, from academia or industry, to use this resource to advance their biomarkers into clinical use.
Screening; Diagnosis; Prognosis; Prediction; Validation; Pre-validation
We aimed to examine the association between BMI and the risk of death from pancreas cancer in a pooled analysis of data from the Asia Cohort Consortium.
The data for this pooled-analysis included 883,529 men and women from 16 cohort studies in Asian countries. Cox proportional-hazards models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for pancreas cancer mortality in relation to BMI. Seven predefined BMI categories (<18.5, 18.5–19.9, 20.0–22.4, 22.5–24.9, 25.0–27.4, 27.5–29.9, ≥30) were used in the analysis, with BMI of 22.5–24.9 serving as the reference group. The multivariable analyses were adjusted for known risk factors, including age, smoking, and history of diabetes.
We found no statistically significant overall association between each BMI category and risk of death from pancreas cancer in all Asians, and obesity was unrelated to mortality risk in both East Asians and South Asians. Age, smoking, and history of diabetes did not modify the association between BMI and risk of death from pancreas cancer. In planned subgroup analyses among East Asians, an increased risk of death from pancreas cancer among those with a BMI<18.5 was observed for individuals with a history of diabetes; HR = 2.01(95%CI: 1.01–4.00) (p for interaction=0.07).
The data do not support an association between BMI and risk of death from pancreas cancer in these Asian populations.
body mass index; insulin resistance; obesity; overweight; pancreatic cancer
Active surveillance is a management plan for localized prostate cancer that offers selective delayed intervention upon indication of disease progression, allowing patients to delay or avoid treatment and associated side-effects. Outcomes from centers that promote active surveillance are favorable, with high rates of disease-specific survival. However, there remains a need for prognostic variables or biomarkers that distinguish with high specificity the aggressive cancers that progress on surveillance from the indolent cancers. The Canary Prostate Active Surveillance Study(PASS) is a multicenter study and biorepository that will discover and confirm biomarkers of aggressive disease as defined by histologic, PSA, or clinical criteria.
Prostate cancer; Active Surveillance; Clinical Trial
Two different approaches to analysis of data from diagnostic biomarker studies are commonly employed. Logistic regression is used to fit models for probability of disease given marker values while ROC curves and risk distributions are used to evaluate classification performance. In this paper we present a method that simultaneously accomplishes both tasks. The key step is to standardize markers relative to the non-diseased population before including them in the logistic regression model. Among the advantages of this method are: (i) ensuring that results from regression and performance assessments are consistent with each other; (ii) allowing covariate adjustment and covariate effects on ROC curves to be handled in a familiar way, and (iii) providing a mechanism to incorporate important assumptions about structure in the ROC curve into the fitted risk model. We develop the method in detail for the problem of combining biomarker datasets derived from multiple studies, populations or biomarker measurement platforms, when ROC curves are similar across data sources. The methods are applicable to both cohort and case-control sampling designs. The dataset motivating this application concerns Prostate Cancer Antigen 3 (PCA3) for diagnosis of prostate cancer in patients with or without previous negative biopsy where the ROC curves for PCA3 are found to be the same in the two populations. Estimated constrained maximum likelihood and empirical likelihood estimators are derived. The estimators are compared in simulation studies and the methods are illustrated with the PCA3 dataset.
constrained likelihood; empirical likelihood; logistic regression; predictiveness curve; ROC curve
It is currently not known whether most lung cancers detected by computerized tomography (CT) screening are aggressive and likely to be fatal if left untreated, or if a sizable fraction are indolent and unlikely to cause death during the natural lifetime of the individual. We developed a longitudinal biologically-based model of the relationship between individual smoking histories and the probability for lung cancer incidence, CT screen detection, lung cancer mortality, and other-cause mortality. The longitudinal model relates these different outcomes to an underlying lung cancer disease pathway and an effective other-cause mortality pathway, which are both influenced by the individual smoking history. The longitudinal analysis provides additional information over that available if these outcomes were analyzed separately, including testing if the number of CT detected and histologically-confirmed lung cancers is consistent with the expected number of lung cancers “in the pipeline”. We assume indolent nodules undergo Gompertz growth and are detectable by CT, but do not grow large enough to contribute significantly to symptom-based lung cancer incidence or mortality. Likelihood-based model calibration was done jointly to data from 6,878 heavy smokers without asbestos exposure in the control (placebo) arm of the Carotene and Retinol Efficacy Trial (CARET); and to 3,642 heavy smokers with comparable smoking histories in the Pittsburgh Lung Screening Study (PLuSS), a single-arm prospective trial of low-dose spiral CT screening for diagnosis of lung cancer. Model calibration was checked using data from two other single-arm prospective CT screening trials, the New York University Lung Cancer Biomarker Center (NYU) (n=1,021), and Moffitt Cancer Center (Moffitt) cohorts (n=677). In the PLuSS cohort, we estimate that at the end of year 2, after the baseline and first annual CT exam, that 33.0 (26.9, 36.9)% of diagnosed lung cancers among females and 7.0 (4.9, 11.7)% among males were overdiagnosed due to being indolent cancers. At the end of the PLuSS study, with maximum follow-up of 5.8 years, we estimate that due to early detection by CT and limited follow-up, an additional 2.2 (2.0, 2.4) % of all diagnosed cancers among females and 7.1 (6.7, 8.0) % among males would not have been diagnosed in the absence of CT screening. We also find a higher apparent cure rate for lung cancer among CARET females than males, consistent with the larger indolent fraction of CT detected and histologically confirmed lung cancers among PLuSS females. This suggests that there are significant gender differences in the aggressiveness of lung cancer. Females may have an inherently higher proportion of indolent lung cancers than males, or aggressive lung cancers may be brought into check by the immune system more frequently among females than males.
CT screening; multistage; longitudinal model; lung cancer
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