The incorporation of biomarkers into the drug development process will improve understanding of how new therapeutics work and allow for more accurate identification of patients who will benefit from those therapies. Strategically planned biomarker evaluations in phase II studies may allow for the design of more efficient phase III trials and better screening of therapeutics for entry into phase III development, hopefully leading to increased chances of positive phase III trial results. Some examples of roles that a biomarker can play in a phase II trial include predictor of response or resistance to specific therapies, patient enrichment, correlative endpoint, or surrogate endpoint. Considerations for using biomarkers most effectively in these roles are discussed in the context of several examples. The substantial technical, logistic, and ethical challenges that can be faced when trying to incorporate biomarkers into phase II trials are also addressed. A rational and coordinated approach to the inclusion of biomarker studies throughout the drug development process will be the key to attaining the goal of personalized medicine.

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.

Background

The Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) checklist consists of 20 items to report for published tumor marker prognostic studies. It was developed to address widespread deficiencies in the reporting of such studies. In this paper we expand on the REMARK checklist to enhance its use and effectiveness through better understanding of the intent of each item and why the information is important to report.

Methods

REMARK recommends including a transparent and full description of research goals and hypotheses, subject selection, specimen and assay considerations, marker measurement methods, statistical design and analysis, and study results. Each checklist item is explained and accompanied by published examples of good reporting, and relevant empirical evidence of the quality of reporting. We give prominence to discussion of the 'REMARK profile', a suggested tabular format for summarizing key study details.

Summary

The paper provides a comprehensive overview to educate on good reporting and provide a valuable reference for the many issues to consider when designing, conducting, and analyzing tumor marker studies and prognostic studies in medicine in general.

To encourage dissemination of the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration, this article has also been published in PLoS Medicine.

The REMARK “elaboration and explanation” guideline, by Doug Altman and colleagues, provides a detailed reference for authors on important issues to consider when designing, conducting, and analyzing tumor marker prognostic studies.

Exciting new technologies for assessing markers in human specimens are now available to evaluate unprecedented types and numbers of variations in DNA, RNA, proteins, or biological structures such as chromosomes. These markers, whether viewed individually, or collectively as a 'signature', have the potential to be useful for disease risk assessment, screening, early detection, prognosis, therapy selection, and monitoring for therapy effectiveness or disease recurrence. Successful translation from basic research findings to clinically useful test requires basic, translational, and regulatory sciences and a collaborative effort among individuals with varied types of expertise including laboratory scientists, technology developers, clinicians, statisticians, and bioinformaticians. The focus of this commentary is the many statistical challenges in translational marker research, specifically in the development and validation of marker-based tests that have clinical utility for therapeutic decision-making.

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.

Purpose

The molecular drivers that determine histology in lung cancer are largely unknown. We investigated whether microRNA (miR) expression profiles can differentiate histological subtypes and predict survival for non-small cell lung cancer.

Experimental design

We analyzed miR expression in 165 adenocarcinoma (AD) and 125 squamous cell carcinoma (SQ) tissue samples from the Environmental And Genetics in Lung cancer Etiology (EAGLE) study using a custom oligo array with 440 human mature antisense miRs. We compared miR expression profiles using t-tests and F-tests and accounted for multiple testing using global permutation tests. We assessed the association of miR expression with tobacco smoking using Spearman correlation coefficients and linear regression models, and with clinical outcome using log-rank tests, Cox proportional hazards and survival risk prediction models, accounting for demographic and tumor characteristics.

Results

MiR expression profiles strongly differed between AD and SQ (global p<0.0001), particularly in the early stages, and included miRs located on chromosome loci most often altered in lung cancer (e.g., 3p21-22). Most miRs, including all members of the let-7 family, were down-regulated in SQ. Major findings were confirmed by QRT-PCR in EAGLE samples and in an independent set of lung cancer cases. In SQ, low expression of miRs down-regulated in the histology comparison was associated with 1.2 to 3.6-fold increased mortality risk. A 5-miR signature significantly predicted survival for SQ.

Conclusions

We identified a miR expression profile that strongly differentiated AD from SQ and had prognostic implications. These findings may lead to histology-based therapeutic approaches.

Clinical biomarker tests that aid in making treatment decisions will play an important role in achieving personalized medicine for cancer patients. Definitive evaluation of the clinical utility of these biomarkers requires conducting large randomized clinical trials (RCTs). Efficient RCT design is therefore crucial for timely introduction of these medical advances into clinical practice, and a variety of designs have been proposed for this purpose. To guide design and interpretation of RCTs evaluating biomarkers, we present an in-depth comparison of advantages and disadvantages of the commonly used designs. Key aspects of the discussion include efficiency comparisons and special interim monitoring issues that arise because of the complexity of these RCTs. Important ongoing and completed trials are used as examples. We conclude that, in most settings, randomized biomarker-stratified designs (ie, designs that use the biomarker to guide analysis but not treatment assignment) should be used to obtain a rigorous assessment of biomarker clinical utility.

Carbohydrate antigen arrays (glycan arrays) have been recently developed for the high-throughput analysis of carbohydrate macromolecule interactions. When profiling serum, information about experimental variability, inter-individual biological variability, and intra-individual temporal variability is critical. In this report, we describe the characterization of a carbohydrate antigen array and assay for profiling human serum. Through optimization of assay conditions and development of a normalization strategy, we obtain highly reproducible results with a within-experiment coefficient of variation (CV) of 10.8% and an overall CV (across multiple batches of slides and days) of 28.5%. We also report antibody profiles for 48 human subjects and evaluate for the first time the effects of age, race, sex, geographic location, and blood type on antibody profiles for a large set of carbohydrate antigens. We found significant dependence on age and blood type of antibody levels for a variety of carbohydrates. Finally, we conducted a longitudinal study with a separate group of 7 serum donors to evaluate the variation in anti-carbohydrate antibody levels within an individual over a period ranging from 3 to 13 weeks and found that, for nearly all antigens on our array, antibody levels are generally stable over this period. The results presented here provide the most comprehensive evaluation of experimental and biological variation reported to date for a glycan array and have significant implications for studies involving human serum profiling.

Background

MiR arrays distinguish themselves from gene expression arrays by their more limited number of probes, and the shorter and less flexible sequence in probe design. Robust data processing and analysis methods tailored to the unique characteristics of miR arrays are greatly needed. Assumptions underlying commonly used normalization methods for gene expression microarrays containing tens of thousands or more probes may not hold for miR microarrays. Findings from previous studies have sometimes been inconclusive or contradictory. Further studies to determine optimal normalization methods for miR microarrays are needed.

Methods

We evaluated many different normalization methods for data generated with a custom-made two channel miR microarray using two data sets that have technical replicates from several different cell lines. The impact of each normalization method was examined on both within miR error variance (between replicate arrays) and between miR variance to determine which normalization methods minimized differences between replicate samples while preserving differences between biologically distinct miRs.

Results

Lowess normalization generally did not perform as well as the other methods, and quantile normalization based on an invariant set showed the best performance in many cases unless restricted to a very small invariant set. Global median and global mean methods performed reasonably well in both data sets and have the advantage of computational simplicity.

Conclusions

Researchers need to consider carefully which assumptions underlying the different normalization methods appear most reasonable for their experimental setting and possibly consider more than one normalization approach to determine the sensitivity of their results to normalization method used.

A workshop sponsored by the National Cancer Institute and the US Food and Drug Administration addressed past lessons learned and ongoing challenges faced in biomarker development and drug and biomarker codevelopment. Participants agreed that critical decision points in the product life cycle depend on the level of understanding of the biology of the target and its interaction with the drug, the preanalytical and analytical factors affecting biomarker assay performance, and the clinical disease process. The more known about the biology and the greater the strength of association between an analytical signal and clinical result, the more efficient and less risky the development process will be. Rapid entry into clinical practice will only be achieved by using a rigorous scientific approach, including careful specimen collection and standardized and quality-controlled data collection. Early interaction with appropriate regulatory bodies will ensure studies are appropriately designed and biomarker test performance is well characterized.

TOC Summary: Atypical BSE is probably not sporadic and not related to sporadic Creutzfeldt-Jakob disease.

Strategies to investigate the possible existence of sporadic bovine spongiform encephalopathy (BSE) require systematic testing programs to identify cases in countries considered to have little or no risk of orally acquired disease or to detect a stable occurrence of atypical cases in countries in which orally acquired disease is disappearing. To achieve 95% statistical confidence that the prevalence for sporadic BSE is no greater than 1 per million (i.e., the annual incidence of sporadic Creutzfeldt-Jakob disease [CJD] in humans) would require negative tests in 3 million randomly selected older cattle. A link between BSE and sporadic CJD has been suggested on the basis of laboratory studies but is unsupported by epidemiologic observation. Such a link might yet be established by the discovery of a specific molecular marker or of particular combinations of trends over time of typical and atypical BSE and various subtypes of sporadic CJD, as their numbers are influenced by a continuation of current public health measures that exclude high-risk bovine tissues from the animal and human food chains.