Error: please reset date.
Time: 12:00 am -
Location: Richmond Road Diagnostic and Treatment Centre - Room 4005


Joseph Roberts


Driving population metrics into biobanking quality


Human biospecimens are essential for biomarker research towards the goal of personalized medicine. The three-fold increase in the number of human biobanks, over the past decade, has enabled thousands of biomarker studies to be conducted. However, the success rate for the replication and validation of biomarker “discovery” studies has been very low and few biomarkers have reached clinical implementation. Extensive methodological research has attempted to address the potential reasons for these failures but has focused almost exclusively on specimen quality. Researchers have reported the impact of collection, processing, storage and handling of biospecimen and published standard operating procedures that can minimize the impact of these factors on the pre-analytic variation in biospecimens. However, much less attention has been given to the representativeness of biospecimens with regard to the populations from which they were collected. Perfectly collected, processed, stored, handled, distributed and analyzed biospecimens can still provide misleading results if they do not represent the disease or population from which they were selected.

Many biobanks are based on opportunistic or convenience collections or restricted by the eligibility criteria of clinical trials. Opportunistic or non population-based biospecimen collection can introduce selection bias to the point that the collection of specimens does not accurately reflect the disease burden or source population. Biobanks have a duty to provide specimens and data that will facilitate the most valid research. It is difficult to assess the value of observational research if the population on which it is based has not been adequately described. Biobanks must acknowledge that the specimens, that they collect and distribute, represent observational data. Consequently, biobanks should help downstream researchers address the specific methodological issues and biases that are common to all observational research. Failure to consider the requirements and address the limitations of observational data will continue to compromise the translation of seemingly promising discovery studies into viable clinical tests.