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1.  Biospecimen Reporting for Improved Study Quality (BRISQ) 
Journal of proteome research  2011;10(8):3429-3438.
Human biospecimens are subject to a number of different collection, processing, and storage factors that can significantly alter their molecular composition and consistency. These biospecimen preanalytical factors, in turn, influence experimental outcomes and the ability to reproduce scientific results. Currently, the extent and type of information specific to the biospecimen preanalytical conditions reported in scientific publications and regulatory submissions varies widely. To improve the quality of research utilizing human tissues it is critical that information regarding the handling of biospecimens be reported in a thorough, accurate, and standardized manner. The Biospecimen Reporting for Improved Study Quality (BRISQ) recommendations outlined herein are intended to apply to any study in which human biospecimens are used. The purpose of reporting these details is to supply others, from researchers to regulators, with more consistent and standardized information to better evaluate, interpret, compare, and reproduce the experimental results. The BRISQ guidelines are proposed as an important and timely resource tool to strengthen communication and publications around biospecimen-related research and help reassure patient contributors and the advocacy community that the contributions are valued and respected.
doi:10.1021/pr200021n
PMCID: PMC3169291  PMID: 21574648
2.  Biospecimen Reporting for Improved Study Quality 
Human biospecimens are subject to a number of different collection, processing, and storage factors that can significantly alter their molecular composition and consistency. These biospecimen preanalytical factors, in turn, influence experimental outcomes and the ability to reproduce scientific results. Currently, the extent and type of information specific to the biospecimen preanalytical conditions reported in scientific publications and regulatory submissions varies widely. To improve the quality of research utilizing human tissues, it is critical that information regarding the handling of biospecimens be reported in a thorough, accurate, and standardized manner. The Biospecimen Reporting for Improved Study Quality recommendations outlined herein are intended to apply to any study in which human biospecimens are used. The purpose of reporting these details is to supply others, from researchers to regulators, with more consistent and standardized information to better evaluate, interpret, compare, and reproduce the experimental results. The Biospecimen Reporting for Improved Study Quality guidelines are proposed as an important and timely resource tool to strengthen communication and publications around biospecimen-related research and help reassure patient contributors and the advocacy community that the contributions are valued and respected.
doi:10.1089/bio.2010.0036
PMCID: PMC3142856  PMID: 21826252
3.  Ovarian Cancer Biomarker Performance in Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Specimens 
Establishing a cancer screening biomarker’s intended performance requires “phase III” specimens obtained in asymptomatic individuals before clinical diagnosis rather than “phase II” specimens obtained from symptomatic individuals at diagnosis. We used specimens from the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial to evaluate ovarian cancer biomarkers previously assessed in phase II sets.
Phase II specimens from 180 ovarian cancer cases and 660 benign disease or general population controls were assembled from four Early Detection Research Network (EDRN) or Ovarian Cancer Specialized Program of Research Excellence (SPORE) sites and used to rank 49 biomarkers. Thirty-five markers, including 6 additional markers from a fifth site, were then evaluated in PLCO proximate specimens from 118 women with ovarian cancer and 474 matched controls.
Top markers in phase II specimens included CA125, HE4, transthyretin, CA15.3, and CA72.4 with sensitivity at 95% specificity ranging from 0.73 to 0.40. Except for transthyretin, these markers had similar or better sensitivity when moving to phase III specimens that had been drawn within six months of the clinical diagnosis. Performance of all markers declined in phase III specimens more remote than 6 months from diagnosis.
Despite many promising new markers for ovarian cancer, CA125 remains the single-best biomarker in the phase II and phase III specimens tested in this study.
doi:10.1158/1940-6207.CAPR-10-0195
PMCID: PMC3085251  PMID: 21372036
Ovarian neoplasms; CA125; HE4; Screening tests; CA72.4
4.  A Framework for Evaluating Biomarkers for Early Detection: Validation of Biomarker Panels for Ovarian Cancer 
A panel of biomarkers may improve predictive performance over individual markers. Although many biomarker panels have been described for ovarian cancer, few studies used pre-diagnostic samples to assess the potential of the panels for early detection. We conducted a multi-site systematic evaluation of biomarker panels using pre-diagnostic serum samples from the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) screening trial.
Using a nested case-control design, levels of 28 biomarkers were measured laboratory-blinded in 118 serum samples obtained before cancer diagnosis and 951 serum samples from matched controls. Five predictive models, each containing 6–8 biomarkers, were evaluated according to a pre-determined analysis plan. Three sequential analyses were conducted: blinded validation of previously established models (Step 1); simultaneous split-sample discovery and validation of models (Step 2); and exploratory discovery of new models (Step 3). Sensitivity, specificity, sensitivity at 98% specificity, and AUC were computed for the models and CA125 alone among 67 cases diagnosed within one year of blood draw and 476 matched controls. In Step 1, one model showed comparable performance to CA125, with sensitivity, specificity and AUC at 69.2%, 96.6% and 0.892, respectively. Remaining models had poorer performance than CA125 alone. In Step 2, we observed a similar pattern. In Step 3, a model derived from all 28 markers failed to show improvement over CA125.
Thus, biomarker panels discovered in diagnostic samples may not validate in pre-diagnostic samples; utilizing pre-diagnostic samples for discovery may be helpful in developing validated early detection panels.
doi:10.1158/1940-6207.CAPR-10-0193
PMCID: PMC3057372  PMID: 21372037
Early Detection; Screening; Biomarkers; Validation; Study Design
5.  Building Multi-Marker Algorithms for Disease Prediction—The Role of Correlations Among Markers 
Biomarker Insights  2011;6:83-93.
A widely held viewpoint in the field of predictive biomarkers for disease holds that no single marker can provide high enough discrimination and that a panel of markers, combined in some type of algorithm, will be needed. Motivated by a recent study where 27 additional markers for ovarian cancer, many of which had good predictive value alone, failed to substantially increase the predictive ability of the primary marker of CA125, we explore the effect of additional markers on the area under the ROC curve (AUC). We develop a statistical model based on the multivariate normal distribution and linear algorithms and use it to explore how the magnitude and direction of statistical correlation among the markers (in diseased and in non-diseased) is critical in determining the added predictive value of additional markers. We show mathematically and empirically that if the additional marker(s) is negatively correlated with the primary marker, then it will always be able to provide increased AUC when combined with the primary marker (as compared to that obtained with the primary marker alone), even if it has little predictive ability on its own. In contrast, if the additional marker(s) is positively correlated with the primary marker, then it is unlikely to substantially increase the AUC when combined with the primary marker, even when it has good predictive ability on its own. Thus, univariate analyses alone may not be the best approach in choosing which markers to combine in a predictive panel of markers; patterns of statistical correlation should be considered in ranking top-performing biomarkers.
doi:10.4137/BMI.S7513
PMCID: PMC3169344  PMID: 21918599
correlation; ROC AUC; biomarkers; multivariate normal distribution; linear algorithm

Results 1-5 (5)