Biobanks Can Make or Break Biomarkers

Previously, the NCI raised an alarm about the wide range of biospecimen quality used for research programs seeking novel biomarkers for diseases and the resulting tests that aid in the diagnosis of those diseases. During the NCI “Biospecimen Best Practices Forum” held in Seatle in January the warning was repeated.

The NCI identified the absence of standardized, high-quality biospecimens as a major issue in the progress of cancer research. Last year, the institution updated its first-generation guidelines for biorepositories with the publication of “Best Practices for Biospecimen Resources.” The document defines state-of-the-science biospecimen resource practices.

The call for standardization in any industry necessarily follows in the wake of fast-breaking adoption of technology that pushes the boundaries of existing standards or simply passes them by in a rush to market. Pharmaceutical and biotechnology companies are rushing headlong toward a ready market for personalized medicine by pushing accelerated data processing as well as today’s high-throughput processing for biological specimens. The pressure is on research teams to rapidly identify blockbusters for disease diagnosis among a myriad of metabolites, proteins, and genes.

Key Criteria for Selection

Where selecting a biorepository would seem to be limited to selecting a resource provider, experience from biomarker-discovery programs shows a biobank provider becomes an essential partner in project development and plays a critical role in the speed of development and discovery. Ultimately, it is inextricably linked to the quality of the findings. It is not an exaggeration to say a good biorepository can accelerate research by several years and an unfortunate choice can have the opposite effect.

On the front end, a biorepository needs to be capable of providing valid specimens with informed consent firmly attached. Next, the biobank should hold a depth of clinical and demographic data on each specimen to verify initial discoveries and support validation. Finally, the bioresource should be able to offer a selection of same-patient tissues for cross-reference research to validate findings. In the case of blood-based collections, the availability of tightly matched healthy control samples is essential.

Ethical collection, specimen integrity, and data quality are three categories of investigation when looking at candidate biobanks. Evaluation should be conducted well ahead of launching research activities.

Ethical collection is often summarized in three letters: IRB (Institutional Review Board). Impaneled to protect the rights and welfare of human subjects, IRB review is federally mandated for research on humans. So, while that upstream conformance to IRB protocols by a biorepository would seem a given, it should be challenged and verified. To put it another way, failure to assure proper patient-informed consent for specimen research opens the door to claims against eventual intellectual property.

The collection, handling, and storage of all biological specimens with integrity relies on known science that can be verified. Most reliable will be the provision of genetic samples that are essentially fixed and subject to fewer variables than the tissues and sera needed for proteomic and metabolomic experimentation. Proteins and metabolites can be ephemeral and rely on uniformity in procedures on the part of the biorepository, which can be verified to a high degree of confidence.

Equally critical but highly variable is data quality, which begins in the collection process and extends to the storage and processing capabilities of databanks. Uniformity of data procedures through standardization is the key challenge identified in the NCI Best Practices report.

The most important factor in this equation is the staff gathering the samples and their level of training and experience. For example, BioServe’s sera and tissue samples are drawn from multiple sites around the world and are collected in stages with significant variations in collection controls and standard operating procedures.

Extensive demographic and clinical data is ideally required for every sample, including the sample donor’s detailed medical history, family history, and the lifestyle choices. To illustrate the essential role of data quality and data retrieval for discovery programs, consider the case study from Phenomenome Discoveries in its search for a novel biomarker for colorectal cancer (CRC).

A targeted high-throughput screening method found that six novel molecules with formulae resembling the gamma isoform of vitamin E, tocopherol, were deficient in the serum from CRC patients and not controls. The resulting dataset of 900 accurate molecular masses was visualized using principal component analysis, which indicated a robust differentiation between controls and CRC cases.

The program rapidly achieved a milestone due to the ability to search datasets against multiple, matched biological materials in BioServe’s global repository to validate findings. Demonstrating that the novel metabolites are absent in both tissue samples and serum of diseased patients but not in matched healthy controls allowed a convincing correlation to be made.

Uniformity in Datasets

Uniformity in datasets supporting samples allowed the research team to tease out all factors contributing to the appearance of candidate biomarkers that were not related to the targeted disease state. Again applying biological materials from BioServe, a cross-reference investigation of diseased patient samples with other disease groups revealed that some of the markers are shared in both ovarian and breast cancer samples and therefore, not specific to CRC.

The excitement that built over one promising metabolite was tempered when the research team went back to the datasets and found that patients in the identified subgroup were taking the same over-the-counter medication. This ability to reach back into detailed records across all samples was highly valued as it not only averted the embarrassment of premature elation but ultimately built the research team’s confidence in the programs’ findings, which did indeed result in the discovery of a novel biomarker set for CRC.

Based upon this data, Phenomenome was confident in going forward to develop a diagnostic test, which is undergoing clinical trials in Japan this year.

The critical interplay of the three categories proposed in this article—ethical collection, specimen integrity, and data quality—for front-end evaluation of biorepositories can be seen converging during any investigation or discovery in the omics field. These three criteria come into play again as the research program reaches its conclusion with the final validation of findings. These criteria become critical determinants that can give a research team confidence in hitting project milestones and result in findings with powerful validation.

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Kevin Krenitsky, M.D., is CEO of BioServe. Web: www.bioserve.com. E-mail: kkrenitsky@bioserve.com.