"Transcriptomics & Functional Genomics"
Welcome to Transcriptomics & Functional Genomics News Letter No 6. This editions focus is on Biomarkers.
Currently only the focus topic from each newsletter is being made available on the internet (please note this material is covered by copy right and permission should be sought to reproduce any content). The full newsletter is available internally via the intranet as a pdf. If you are interested in advertising a seminar or promotion via the newsletter or sponsorship please contact : Dr K Laing (Senior Scientist Intracellullar Pathogen Cooperative).
The term “Biomarker” is rapidly becoming one of the most frequently used buzz words both in industry and academic circles. Like many buzz words its come to mean all things to all people. We take a brief look at Biomarkers and the role of the new technologies in Biomarker Discovery and what role they play in the development new drugs and therapies.
I hope that this edition will introduce some of the common methods and concepts routinely in use within SGUL and elsewhere to enable this type of sample to be used.
If you are interested in other topics cover in other newsletters go to:
Ken Laing Intracellular Pathogen Co-operative, Cellular & Molecular Medicine, St George’s University of London
Biomarkers: The answer to all our questions or Just another buzz word
You only have to speak to an industrialist for a couple of minutes or survey the types of industry enquiries that come into St George’s and the subject of biomarkers and biomarker discovery seem to crop up again and again. If you do a PubMed search for “Biomarker” you’ll retrieve over 330,000 publications including 34,000 reviews on the touching on the subject. Irrespective of whether we are working in transcriptomics and functional genomics or proteomics and metabolomics the term applies. So what do we mean by a biomarker! Of course its easy enough to expand it to get to “Biological Marker” but how exactly do with define this.
A biomarker at its most simplistic is a biological characteristic or feature that represents a biological state or process (table 1) but more often than not a single biomarker does not accurately reflect such states or processes and combinations or patterns of Biomarkers can better represent them. Such correlates may vary wildly in nature and complexity ranging from something as simple as weight as a measure of obesity to imaging markers to complex patterns of gene regulation, gene expression or serum protein levels to a disease state. So in essence almost all of us in some way or other have embarked upon biomarker discovery at some time in our careers and many of us are unwittingly working on biomarker discovery.
Talk of biomarkers is particularly prevalent in the new technology driven fields of transcriptomics, genomics, proteomics and metabolomics in part because of the suitability of these “omics” to the discovery of novel patterns of biological markers of disease but also the rapid generation of large comparative data sets using these techniques. The term was officially agreed and defined by the Biomarker Definitions Working Group1 and entered into the drug regulatory framework somewhere around the beginning of the millennium. However, others have subsequently suggested qualifications of what is a biomarker since they argue that a biomarker can only be defined as such once it has been extensively validated and proven to represent its purported state, process or condition. Since most biomarkers never reach such rarified heights, in 2003 Janet Woodcock at the FDA introduced the self-explanatory qualifiers “valid biomarker”, “known valid biomarker” and “probable valid biomarker”.
Biomarkers are used in all stages of drug development3 from “pre-development”, “Discovery”, pre-clinical”, clinical” and “market validation” but also have particular value in diagnostics and prognostics aiding clinical decision making in the choice of therapy (fig 1). One of the aims of biomarker discovery is the advancement of personalized point-of-care medicine5, but has as yet to fulfill such promise. Diagnostic biomarkers are used, at least in the clinical, setting to discriminate specific diseased states or conditions from the normal “healthy” individual and are therapy independent. Such biomarkers are primarily tools to aid clinical decision making leading to appropriate and improved diagnosis and therapy. Such markers can be derived from a wide range of studies such as transcriptional or protein expression studies, mechanistic studies, animal models or epidemiological studies. Prognostic biomarkers in contrast are used to predict outcome or more generally the course of a disease and are again therapy independent, however, their application aims to provide clinical benefit by predicting survival, choice of initial treatment, stratification of patients in clinical trials, accurate communication among healthcare providers, and uniform reporting of outcomes.
They may also be derived from similar types of studies but are more reliant upon animal models and epidemiological studies since they require an outcome measure although biomarkers themselves can be used as surrogate end points. Transcriptional profiling of Diffuse large B-cell lymphoma (DLBCL) in recent years has lead to a large number of prospective prognostic markers, whilst these have established a molecular classification and survival prediction models of DLBCL they have not entered routine clinical practice5 for various reasons. Stratification biomarkers are used to identify patients likely to respond to a specific therapy or conversely identify those likely to suffer adverse side affects prior to administration of a drug. This type of biomarker is often identified during pre-clinical studies, clinical trials or during epidemiological studies. They are particularly valuable in patient selection for clinical trials although stratification of patients can exclude groups who would benefit from a therapy that do not fit the predicted profile. Pharmacodynamic or pharmacokinetic (PK/PD) biomarkers are used to correlate drug response or activity with the drug or drug metabolite concentrations and when incorporated into clinical trials facilitate optimal drug dose scheduling. PK/PD biomarkers can help with predicting drug safety and efficacy, reduce the high levels of drug attrition during development, accelerate drug approval, and decrease the overall costs of drug development4. Such biomarkers are often derived from metabolomic studies particularly in animal models since drug metabolism and stability in vivo are important factors. Efficacy and Toxicity biomarkers are essentially positive and negative outcome measures but are used in different ways and for different purposes. The former monitors beneficial effects of a drug on a biological target, system or on a medical condition and these are derived from studies specifically looking for down-stream consequences of treatment usually in a concentration dependant manner. Such measures can be identified using the whole range of molecular techniques and experimental approaches mentioned previously including clinical trials. Toxicity biomarkers on the other hand monitor adverse effects on unintended cellular processes, cells, tissues or organs and relate the behavior of such systems with the concentration or activity of a drug or treatment. They are used in drug screening, pre-clinical and clinical trials but again can be derived from a wide range of experimental techniques or approaches.
But for most of us at least the discovery of targets key to disease processes or markers of disease states are the aims of biomarker discovery. Allowing us to understand the underlying mechanisms by which cellular and molecular processes are purtabated. With this aim gene expression analysis using microarrays and the newer high-through put real-time qRT-PCR techniques such as the ABI7900HT fast system becoming available in the market but not yet available at St George’s or mass spectrometry are all key techniques to pattern discovery and are the means to biomarker discovery. These techniques are currently being put to good use by numerous groups across St George’s and are easily implemented in an experimental setting given the level of expertise, support and availability of equipment at St George’s through groups such as BUGS/Intracellular Cooperative in med micro and core facilities such as biomics. Biomarker validation and follow through of biomarkers to the clinic, however, remains relatively rare for various reasons and this currently is no different at St George’s to elsewhere. The use of biomarkers as surrogate end points in pre-clinical and early clinical trials is topical given the spectacular failure of the recent phase I clinical trial of TGN 1412 at Northwick Park. Such techniques employing toxicity or stratification biomarkers to identify potential adverse drug effects may in the future help in early detection and better understanding of side affects. By far the biggest hurdle to entry of biomarkers into clinical practice is the stringent requirement for validation and is a primary reason for the failure thus far for the use of prognostic biomarkers in DLBCL6. However, validation of biomarkers as a diagnostic, prognostic or a surrogate end point in well-stratified populations is a relatively simple but exacting task to undertake given the funding and logistical support to do so, but perhaps the “golden goal” for biomarker discovery is probably validating disease markers in non-presenting populations as in “life long” Cohort studies, this would allow the validation of early markers prior to clinical onset of disease and St George’s is ideally positioned in this respect.