Targeted Mass Spectrometry of Longitudinal Patient Sera Reveals LTBP1 as a Potential Surveillance Biomarker for High-Grade Serous Ovarian Carcinoma.

High-grade serous ovarian carcinoma (HGSC) is the most prevalent subtype of epithelial ovarian cancer. The combination of a high rate of recurrence and novel therapies in HGSC necessitates an accurate assessment of the disease. Currently, HGSC response to treatment and recurrence are monitored via immunoassay of serum levels of the glycoprotein CA125. CA125 levels predictably rise at HGSC recurrence; however, it is likely that the disease is progressing even before it is detectable through CA125. This may explain why treating solely based on CA125 increase has not been associated with improved outcomes. Thus, additional biomarkers that monitor HGSC progression and cancer recurrence are needed. For this purpose, we developed a scheduled parallel reaction monitoring mass spectrometry (PRM-MS) assay for the quantification of four previously identified HGSC-derived glycopeptides (from proteins FGL2, LGALS3BP, LTBP1, and TIMP1). We applied the assay to quantify their longitudinal expression profiles in 212 serum samples taken from 34 HGSC patients during disease progression. Analyses revealed that LTBP1 best-mirrored tumor load, dropping as a result of cancer treatment in 31 out of 34 patients and rising at HGSC recurrence in 28 patients. Additionally, LTBP1 rose earlier during remission than CA125 in 11 out of 25 platinum-sensitive patients with an average lead time of 116.4 days, making LTBP1 a promising candidate for monitoring of HGSC recurrence.

Senescence-Targeted and NAD+-Dependent SIRT1-Activated Nanoplatform to Counteract Stem Cell Senescence for Promoting Aged Bone Regeneration.

Age-related bone defects are a leading cause of disability and mortality in elderly individuals, and targeted therapy to delay the senescence of bone marrow-derived mesenchymal stem cells (MSCs) has emerged as a promising strategy to rejuvenate bone regeneration in aged scenarios. More specifically, activating the nicotinamide adenine dinucleotide (NAD+)-dependent sirtuin 1 (SIRT1) pathway is demonstrated to effectively counteract MSC senescence and thus promote osteogenesis. Herein, based on an inventively identified senescent MSC-specific surface marker Kremen1, a senescence-targeted and NAD+ dependent SIRT1 activated nanoplatform is fabricated with a dual delivery of resveratrol (RSV) (SIRT1 promoter) and nicotinamide riboside (NR, NAD+ precursor). This targeting nanoplatform exhibits a strong affinity for senescent MSCs through conjugation with anti-Kremen1 antibodies and enables specifically responsive release of NR and RSV in lysosomes via senescence-associated ß-galactosidase-stimulated enzymatic hydrolysis of the hydrophilic chain. Furthermore, this nanoplatform performs well in promoting aged bone formation both in vitro and in vivo by boosting NAD+, activating SIRT1, and delaying MSC senescence. For the first time, a novel senescent MSC-specific surface marker is identified and aged bone repair is rejuvenated by delaying senescence of MSCs using an active targeting platform. This discovery opens up new insights for nanotherapeutics aimed at age-related diseases.

Contributions of the ADNI Biostatistics Core.

The goal of the Biostatistics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI) has been to ensure that sound study designs and statistical methods are used to meet the overall goals of ADNI. We have supported the creation of a well-validated and well-curated longitudinal database of clinical and biomarker information on ADNI participants and helped to make this accessible and usable for researchers. We have developed a statistical methodology for characterizing the trajectories of clinical and biomarker change for ADNI participants across the spectrum from cognitively normal to dementia, including multivariate patterns and evidence for heterogeneity in cognitive aging. We have applied these methods and adapted them to improve clinical trial design. ADNI-4 will offer us a chance to help extend these efforts to a more diverse cohort with an even richer panel of biomarker data to support better knowledge of and treatment for Alzheimer's disease and related dementias. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Biostatistics Core provides study design and analytic support to ADNI investigators. Core members develop and apply novel statistical methodology to work with ADNI data and support clinical trial design. The Core contributes to the standardization, validation, and harmonization of biomarker data. The Core serves as a resource to the wider research community to address questions related to the data and study as a whole.

Stress testing the Centiloid: Precision and variability of PET quantification of amyloid pathology.

INTRODUCTION: Assessing the potential sources of bias and variability of the Centiloid (CL) scale is fundamental for its appropriate clinical application. METHODS: We included 533 participants from AMYloid imaging to Prevent Alzheimer's Disease (AMYPAD DPMS) and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts. Thirty-two CL pipelines were created using different combinations of reference region (RR), RR and target types, and quantification spaces. Generalized estimating equations stratified by amyloid positivity were used to assess the impact of the quantification pipeline, radiotracer, age, brain atrophy, and harmonization status on CL. RESULTS: RR selection and RR type impact CL the most, particularly in amyloid-negative individuals. The standard CL pipeline with the whole cerebellum as RR is robust against brain atrophy and differences in image resolution, with 95% confidence intervals below ± 3.95 CL for amyloid beta positivity cutoffs (CL < 24). DISCUSSION: The standard CL pipeline is recommended for most scenarios. Confidence intervals should be considered when operationalizing CL cutoffs in clinical and research settings. HIGHLIGHTS: We developed a framework for evaluating Centiloid (CL) variability to different factors. Reference region selection and delineation had the highest impact on CL values. Whole cerebellum (WCB) and whole cerebellum plus brainstem (WCB+BSTM) as reference regions yielded consistent results across tracers. The standard CL pipeline is robust against atrophy and image resolution variation. Estimated within- and between-pipeline variability (95% confidence interval) in absolute CL units.

Surveillance and treatment of primary hepatocellular carcinoma (aka. STOP HCC): protocol for a prospective cohort study of high-risk patients for HCC using GALAD-score.

BACKGROUND: Vietnam and Saudi Arabia have high disease burden of primary hepatocellular carcinoma (HCC). Early detection in asymptomatic patients at risk for HCC is a strategy to improve survival outcomes in HCC management. GALAD score, a serum-based panel, has demonstrated promising clinical utility in HCC management. However, in order to ascertain its potential role in the surveillance of the early detection of HCC, GALAD needs to be validated prospectively for clinical surveillance of HCC (i.e., phase IV biomarker validation study). Thus, we propose to conduct a phase IV biomarker validation study to prospectively survey a cohort of patients with advanced fibrosis or compensated cirrhosis, irrespective of etiologies, using semi-annual abdominal ultrasound and GALAD score for five years. METHODS: We plan to recruit a cohort of 1,600 patients, male or female, with advanced fibrosis or cirrhosis (i.e., F3 or F4) and MELD ≤ 15, in Vietnam and Saudi Arabia (n = 800 each). Individuals with a liver mass ≥ 1 cm in diameter, elevated alpha-fetoprotein (AFP) (≥ 9 ng/mL), and/or elevated GALAD score (≥ -0.63) will be scanned with dynamic contrast-enhanced magnetic resonance imaging (MRI), and a diagnosis of HCC will be made by Liver Imaging Reporting and Data System (LiRADS) assessment (LiRADS-5). Additionally, those who do not exhibit abnormal imaging findings, elevated AFP titer, and/or elevated GALAD score will obtain a dynamic contrast-enhanced MRI annually for five years to assess for HCC. Only MRI nearest to the time of GALAD score measurement, ultrasound and/or AFP evaluation will be included in the diagnostic validation analysis. MRI will be replaced with an abdominal computed tomography scan when MRI results are poor due to patient conditions such as movement etc. Gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced MRI will not be carried out in study sites in both countries. Bootstrap resampling technique will be used to account for repeated measures to estimate standard errors and confidence intervals. Additionally, we will use the Cox proportional hazards regression model with covariates tailored to the hypothesis under investigation for time-to-HCC data as predicted by time-varying biomarker data. DISCUSSION: The present work will evaluate the performance of GALAD score in early detection of liver cancer. Furthermore, by leveraging the prospective cohort, we will establish a biorepository of longitudinally collected biospecimens from patients with advanced fibrosis or cirrhosis to be used as a reference set for future research in early detection of HCC in the two countries. TRIAL REGISTRATION: Name of the registry: ClinicalTrials.gov Registration date: 22 April 2022 Trial registration number: NCT05342350 URL of trial registry record.

Strategies for validating biomarkers using data from a reference set.

Candidate biomarkers discovered in the laboratory need to be rigorously validated before advancing to clinical application. However, it is often expensive and time-consuming to collect the high quality specimens needed for validation; moreover, such specimens are often limited in volume. The Early Detection Research Network has developed valuable specimen reference sets that can be used by multiple labs for biomarker validation. To optimize the chance of successful validation, it is critical to efficiently utilize the limited specimens in these reference sets on promising candidate biomarkers. Towards this end, we propose a novel two-stage validation strategy that partitions the samples in the reference set into two groups for sequential validation. The proposed strategy adopts the group sequential testing method to control for the type I error rate and rotates group membership to maximize the usage of available samples. We develop analytical formulas for performance parameters of this strategy in terms of the expected numbers of biomarkers that can be evaluated and the truly useful biomarkers that can be successfully validated, which can provide valuable guidance for future study design. The performance of our proposed strategy for validating biomarkers with respect to the points on the receiver operating characteristic curve are evaluated via extensive simulation studies and compared with the default strategy of validating each biomarker using all samples in the reference set. Different types of early stopping rules and boundary shapes in the group sequential testing method are considered. Compared with the default strategy, our proposed strategy makes more efficient use of the limited resources in the reference set by allowing more candidate biomarkers to be evaluated, giving a better chance of having truly useful biomarkers successfully validated.

Prediction and Ranking of Biomarkers Using multiple UniReD.

Protein-protein interactions (PPIs) are of key importance for understanding how cells and organisms function. Thus, in recent decades, many approaches have been developed for the identification and discovery of such interactions. These approaches addressed the problem of PPI identification either by an experimental point of view or by a computational one. Here, we present an updated version of UniReD, a computational prediction tool which takes advantage of biomedical literature aiming to extract documented, already published protein associations and predict undocumented ones. The usefulness of this computational tool has been previously evaluated by experimentally validating predicted interactions and by benchmarking it against public databases of experimentally validated PPIs. In its updated form, UniReD allows the user to provide a list of proteins of known implication in, e.g., a particular disease, as well as another list of proteins that are potentially associated with the proteins of the first list. UniReD then automatically analyzes both lists and ranks the proteins of the second list by their association with the proteins of the first list, thus serving as a potential biomarker discovery/validation tool.

From laboratory to clinic: Translation of extracellular vesicle based cancer biomarkers.

The past decade has witnessed a rapid growth in the field of extracellular vesicle (EV) based biomarkers for the diagnosis and monitoring of cancer. Several studies have reported novel EV based biomarkers, but the technical and clinical validation phase has been hampered by general challenges common to biomedical research field as well as specific challenges inherent to the nanoparticle field. This has led to more common failures than success stories in the biomarker discovery pipeline. As a result, more attention must be focused on the process of biomarker discovery, verification, and validation to allow for translation and application of novel EV based research to patient care. Herein, we briefly discuss the hurdles and potential solutions in EV biomarker discovery and verification and validation, and clinical translation.

An examination of a novel multipanel of CSF biomarkers in the Alzheimer's disease clinical and pathological continuum.

INTRODUCTION: This study examines the utility of a multipanel of cerebrospinal fluid (CSF) biomarkers complementing Alzheimer's disease (AD) biomarkers in a clinical research sample. We compared biomarkers across groups defined by clinical diagnosis and pTau181 /Aß42 status (+/-) and explored their value in predicting cognition. METHODS: CSF biomarkers amyloid beta (Aß)42 , pTau181 , tTau, Aß40 , neurogranin, neurofilament light (NfL), α-synuclein, glial fibrillary acidic protein (GFAP), chitinase-3-like protein 1 (YKL-40), soluble triggering receptor expressed on myeloid cells 2 (sTREM2), S100 calcium binding protein B (S100B), and interleukin 6 (IL6), were measured with the NeuroToolKit (NTK) for 720 adults ages 40 to 93 years (mean age = 63.9 years, standard deviation [SD] = 9.0; 50 with dementia; 54 with mild cognitive impairment [MCI], 616 unimpaired). RESULTS: Neurodegeneration and glial activation biomarkers were elevated in pTau181 /Aß42 + MCI/dementia participants relative to all pTau181 /Aß42 - participants. Neurodegeneration biomarkers increased with clinical severity among pTau181 /Aß42 + participants and predicted worse cognitive performance. Glial activation biomarkers were unrelated to cognitive performance. DISCUSSION: The NTK contains promising markers that improve the pathophysiological characterization of AD. Neurodegeneration biomarkers beyond tTau improved statistical prediction of cognition and disease stages.

Targeted and Untargeted Proteomics Approaches in Biomarker Development.

An enormous amount of research effort has been devoted to biomarker discovery and validation. With the completion of the human genome, proteomics is now playing an increasing role in this search for new and better biomarkers. Here, what leads to successful biomarker development is reviewed and how these features may be applied in the context of proteomic biomarker research is considered. The "fit-for-purpose" approach to biomarker development suggests that untargeted proteomic approaches may be better suited for early stages of biomarker discovery, while targeted approaches are preferred for validation and implementation. A systematic screening of published biomarker articles using MS-based proteomics reveals that while both targeted and untargeted technologies are used in proteomic biomarker development, most researchers do not combine these approaches. i) The reasons for this discrepancy, (ii) how proteomic technologies can overcome technical challenges that seem to limit their translation into the clinic, and (iii) how MS can improve, complement, or replace existing clinically important assays in the future are discussed.

Quality Control and Outlier Detection of Targeted Mass Spectrometry Data from Multiplex Protein Panels.

Increased throughput as well as increased multiplexing of liquid chromatography coupled to selected reaction monitoring mass spectrometry (LC-SRM-MS) assays for protein quantification challenges routine data analysis. Despite the measurement of multiple transitions from multiple peptides, for clinical applications a single (quantifier) transition from one (quantifier) signature peptide is used to represent the protein quantity with most data used solely to validate the quantifier result. To support the generation of reliable protein results from multiplexed LC-SRM-MS assays with large sample numbers, we developed a data analysis process for quality control and outlier detection using data from an 11-protein multiplex LC-SRM-MS method for dried blood samples (195 492 chromatographic peaks from 1481 samples * 11 proteins * 2 peptides * 3 transitions * 2 isotopologues). The 2-tiered data analysis process detects outliers for ion transition ratio, peptide ratio, and % difference between duplicates, applying less stringent criteria to samples with a small % difference between duplicates (Tier 1) and more stringent criteria to samples with unassessed or a large % difference between duplicates (Tier 2). After manual peak review, 1127 samples (76%) were selected based on the sample quality. The data analysis process thereafter automatically selected quantifier transitions/peptides, removed quality control failures and outliers (8%), averaged duplicates, and generated a comprehensive report listing 6085 quality controlled protein-level results. The proposed data analysis process serves as a starting point toward standardized data analysis of multiplexed LC-SRM-MS protein assays.

Duchenne Muscular Dystrophy: recent advances in protein biomarkers and the clinical application.

INTRODUCTION: Early biomarker discovery studies have praised the value of their emerging results, predicting an unprecedented impact on health care. Biomarkers are expected to provide tests with increased specificity and sensitivity compared to existing measures, improve the decision-making process, and accelerate the development of therapies. For rare disorders, like Duchenne Muscular Dystrophy (DMD) such biomarkers can assist the development of therapies, therefore also helping to find a cure for the disease. AREA COVERED: State-of-the-art technologies have been used to identify blood biomarkers for DMD and efforts have been coordinated to develop and promote translation of biomarkers for clinical practice. Biomarker translation to clinical practice is however, adjoined by challenges related to the complexity of the disease, involving numerous biological processes, and the limited sample resources. This review highlights the current progress on the development of biomarkers, describing the proteomics technologies used, the most promising findings and the challenges encountered. EXPERT OPINION: Strategies for effective use of samples combined with orthogonal proteomics methods for protein quantification are essential for translating biomarkers to the patient's bed side. Progress is achieved only if strong evidence is provided that the biomarker constitutes a reliable indicator of the patient's health status for a specific context of use.

A five-CpG DNA methylation score to predict metastatic-lethal outcomes in men treated with radical prostatectomy for localized prostate cancer.

BACKGROUND: Prognostic biomarkers for localized prostate cancer (PCa) could improve personalized medicine. Our group previously identified a panel of differentially methylated CpGs in primary tumor tissue that predict disease aggressiveness, and here we further validate these biomarkers. METHODS: Pyrosequencing was used to assess CpG methylation of eight biomarkers previously identified using the HumanMethylation450 array; CpGs with strongly correlated (r >0.70) results were considered technically validated. Logistic regression incorporating the validated CpGs and Gleason sum was used to define and lock a final model to stratify men with metastatic-lethal versus non-recurrent PCa in a training dataset. Coefficients from the final model were then used to construct a DNA methylation score, which was evaluated by logistic regression and Receiver Operating Characteristic (ROC) curve analyses in an independent testing dataset. RESULTS: Five CpGs were technically validated and all were retained (P < 0.05) in the final model. The 5-CpG and Gleason sum coefficients were used to calculate a methylation score, which was higher in men with metastatic-lethal progression (P = 6.8 × 10-6 ) in the testing dataset. For each unit increase in the score there was a four-fold increase in risk of metastatic-lethal events (odds ratio, OR = 4.0, 95%CI = 1.8-14.3). At 95% specificity, sensitivity was 74% for the score compared to 53% for Gleason sum alone. The score demonstrated better prediction performance (AUC = 0.91; pAUC = 0.037) compared to Gleason sum alone (AUC = 0.87; pAUC = 0.025). CONCLUSIONS: The DNA methylation score improved upon Gleason sum for predicting metastatic-lethal progression and holds promise for risk stratification of men with aggressive tumors. This prognostic score warrants further evaluation as a tool for improving patient outcomes.

The Terry Fox Research Institute Canadian Prostate Cancer Biomarker Network: an analysis of a pan-Canadian multi-center cohort for biomarker validation.

BACKGROUND: Refinement of parameters defining prostate cancer (PC) prognosis are urgently needed to identify patients with indolent versus aggressive disease. The Canadian Prostate Cancer Biomaker Network (CPCBN) consists of researchers from four Canadian provinces to create a validation cohort to address issues dealing with PC diagnosis and management. METHODS: A total of 1512 radical prostatectomy (RP) specimens from five different biorepositories affiliated with teaching hospitals were selected to constitute the cohort. Tumoral and adjacent benign tissues were arrayed on tissue microarrays (TMAs). A patient clinical database was developed and includes data on diagnosis, treatment and clinical outcome. RESULTS: Mean age at diagnosis of patients in the cohort was 61 years. Of these patients, 31% had a low grade (≤6) Gleason score (GS), 55% had GS 7 (40% of 3 + 4 and 15% of 4 + 3) and 14% had high GS (≥8) PC. The median follow-up of the cohort was 113 months. A total of 34% had a biochemical relapse, 4% developed bone metastasis and 3% of patients died from PC while 9% died of other causes. Pathological review of the TMAs confirmed the presence of tumor and benign tissue cores for > 94% of patients. Immunohistochemistry and FISH analyses, performed on a small set of specimens, showed high quality results and no biorepository-specific bias. CONCLUSIONS: The CPCBN RP cohort is representative of real world PC disease observed in the Canadian population. The frequency of biochemical relapse and bone metastasis as events allows for a precise assessment of the prognostic value of biomarkers. This resource is available, in a step-wise manner, for researchers who intend to validate prognostic biomarkers in PC. Combining multiple biomarkers with clinical and pathologic parameters that are predictive of outcome will aid in clinical decision-making for patients treated for PC.

CSF biomarkers of Alzheimer's disease concord with amyloid-ß PET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts.

INTRODUCTION: We studied whether fully automated Elecsys cerebrospinal fluid (CSF) immunoassay results were concordant with positron emission tomography (PET) and predicted clinical progression, even with cutoffs established in an independent cohort. METHODS: Cutoffs for Elecsys amyloid-ß1-42 (Aß), total tau/Aß(1-42), and phosphorylated tau/Aß(1-42) were defined against [18F]flutemetamol PET in Swedish BioFINDER (n = 277) and validated against [18F]florbetapir PET in Alzheimer's Disease Neuroimaging Initiative (n = 646). Clinical progression in patients with mild cognitive impairment (n = 619) was studied. RESULTS: CSF total tau/Aß(1-42) and phosphorylated tau/Aß(1-42) ratios were highly concordant with PET classification in BioFINDER (overall percent agreement: 90%; area under the curve: 94%). The CSF biomarker statuses established by predefined cutoffs were highly concordant with PET classification in Alzheimer's Disease Neuroimaging Initiative (overall percent agreement: 89%-90%; area under the curves: 96%) and predicted greater 2-year clinical decline in patients with mild cognitive impairment. Strikingly, tau/Aß ratios were as accurate as semiquantitative PET image assessment in predicting visual read-based outcomes. DISCUSSION: Elecsys CSF biomarker assays may provide reliable alternatives to PET in Alzheimer's disease diagnosis.

Toward an integrated pipeline for protein biomarker development.

Protein biomarker development is a multidisciplinary task involving basic, translational and clinical research. Integration of multidisciplinary efforts in a single pipeline is challenging, but crucial to facilitate rational discovery of protein biomarkers and alleviate existing disappointments in the field. In this review, we discuss in detail individual phases of biomarker development pipeline, such as biomarker candidate identification, verification and validation. We focus on mass spectrometry as a principal technique for protein identification and quantification, and discuss complementary -omics approaches for selection of biomarker candidates. Proteomic samples, protein-based clinical laboratory tests and limitations of biomarker development are reviewed in detail, and critical assessment of all phases of biomarker development pipeline is provided. This article is part of a Special Issue entitled: Medical Proteomics.

Evaluating a 4-marker signature of aggressive prostate cancer using time-dependent AUC.

BACKGROUND: We previously identified a protein tumor signature of PTEN, SMAD4, SPP1, and CCND1 that, together with clinical features, was associated with lethal outcomes among prostate cancer patients. In the current study, we sought to validate the molecular model using time-dependent measures of AUC and predictive values for discriminating lethal from non-lethal prostate cancer. METHODS: Using data from the initial study, we fit survival models for men with prostate cancer who were participants in the Physicians' Health Study (PHS; n = 276). Based on these models, we generated prognostic risk scores in an independent population, the Health Professionals Follow-up Study (HPFS; n = 347) to evaluate external validity. In each cohort, men were followed prospectively from cancer diagnosis through 2011 for development of distant metastasis or cancer mortality. We measured protein tumor expression of PTEN, SMAD4, SPP1, and CCND1 on tissue microarrays. RESULTS: During a median of 11.9 and 14.3 years follow-up in the PHS and HPFS cohorts, 24 and 32 men (9%) developed lethal disease. When used as a prognostic factor in a new population, addition of the four markers to clinical variables did not improve discriminatory accuracy through 15 years of follow-up. CONCLUSIONS: Although the four markers have been identified as key biological mediators in metastatic progression, they do not provide independent, long-term prognostic information beyond clinical factors when measured at diagnosis. This finding may underscore the broad heterogeneity in aggressive prostate tumors and highlight the challenges that may result from overfitting in discovery-based research.

A Dual-Carbon-and-Nitrogen Stable Isotope Ratio Model Is Not Superior to a Single-Carbon Stable Isotope Ratio Model for Predicting Added Sugar Intake in Southwest Virginian Adults.

BACKGROUND: An objective measure of added sugar (AS) and sugar-sweetened beverage (SSB) intake is needed. The δ(13)C value of finger-stick blood is a novel validated biomarker of AS/SSB intake; however, nonsweetener corn products and animal protein also carry a δ(13)C value similar to AS sources, which may affect blood δ(13)C values. The δ(15)N value of blood has been proposed as a "correction factor" for animal protein intake. OBJECTIVES: The objectives were to 1) identify foods associated with δ(13)C and δ(15)N blood values, 2) determine the contribution of nonsweetener corn to the diet relative to AS intake, and 3) determine if the dual-isotope model (δ(13)C and δ(15)N) is a better predictor of AS/SSB intake than δ(13)C alone. METHODS: A cross-sectional sample of southwest Virginian adults (n = 257; aged 42 ± 15 y; 74% overweight/obese) underwent dietary intake assessments and provided finger-stick blood samples, which were analyzed for δ(13)C and δ(15)N values by using natural abundance stable isotope mass spectrometry. Statistical analyses included ANOVAs, paired-samples t tests, and multiple linear regressions. RESULTS: The mean ± SD daily AS intake was 88 ± 59 g and nonsweetener corn intake was 13 ± 13 g. The mean δ(13)C value was -19.1 ± 0.9‰, which was significantly correlated with AS and SSB intakes (r = 0.32 and 0.39, respectively; P ≤ 0.01). The δ(13)C value and nonsweetener corn intake and the δ(15)N value and animal protein intake were not correlated. AS intake was significantly greater than nonsweetener corn intake (mean difference = 76.2 ± 57.2 g; P ≤ 0.001). The δ(13)C value was predictive of AS/SSB intake (ß range: 0.28-0.35; P ≤ 0.01); however, δ(15)N was not predictive and minimal increases in R(2) values were observed when the δ(15)N value was added to the model. CONCLUSIONS: The data do not provide evidence that the dual-isotope method is superior for predicting AS/SSB intakes within a southwest Virginian population. Our results support the potential of the δ(13)C value of finger-stick blood to serve as an objective measure of AS/SSB intake. This trial was registered at clinicaltrials.gov as NCT02193009.

Randomized reverse marker strategy design for prospective biomarker validation.

We describe a novel study design for validating marker-based treatment strategies meant to select among possible therapeutic options using a biologic marker. Studying existing designs in realistic scenarios, we demonstrate that this design is more than four times more efficient for testing the interaction between a marker and its intended treatment. Our analysis employs a simple parametric framework that uncovers systematic biases in currently proposed designs and suggests how they may be accommodated or enumerated. In the context of markers for choosing a treatment for recurrent ovarian cancer, our proposal requires sample sizes on the order of recently completed phases II and III studies making validation studies for this clinical decision scenario viable.

Two-stage adaptive cutoff design for building and validating a prognostic biomarker signature.

Cancer biomarkers are frequently evaluated using archived specimens collected from previously conducted therapeutic trials. Routine collection and banking of high quality specimens is an expensive and time-consuming process. Therefore, care should be taken to preserve these precious resources. Here, we propose a novel two-stage adaptive cutoff design that affords the possibility to stop the biomarker study early if an evaluation of the model performance is unsatisfactory at an early stage, thereby allowing one to preserve the remaining specimens for future research. In addition, our design integrates important elements necessary to meet statistical rigor and practical demands for developing and validating a prognostic biomarker signature, including maintaining strict separation between the datasets used to build and evaluate the model and producing a locked-down signature to facilitate future validation. We conduct simulation studies to evaluate the operating characteristics of the proposed design. We show that under the null hypothesis when the model performance is deemed undesirable, the proposed design maintains type I error at the nominal level, has high probabilities of terminating the study early, and results in substantial savings in specimens. Under the alternative hypothesis, power is generally high when the total sample size and the targeted degree of improvement in prediction accuracy are reasonably large. We illustrate the use of the procedure with a dataset in patients with diffuse large-B-cell lymphoma. The practical aspects of the proposed designs are discussed. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.