Over recent years the role of biomarkers in anticancer drug development has expanded across a spectrum of applications ranging from research tool during early discovery to surrogate endpoint in the clinic. However, in Europe when biomarker measurements are performed on samples collected from subjects entered into clinical trials of new investigational agents, laboratories conducting these analyses become subject to the Clinical Trials Regulations. While these regulations are not specific in their requirements of research laboratories, quality assurance and in particular assay validation are essential. This review, therefore, focuses on a discussion of current thinking in biomarker assay validation. Five categories define the majority of biomarker assays from 'absolute quantitation' to 'categorical'. Validation must therefore take account of both the position of the biomarker in the spectrum towards clinical end point and the level of quantitation inherent in the methodology. Biomarker assay validation should be performed ideally in stages on 'a fit for purpose' basis avoiding unnecessarily dogmatic adherence to rigid guidelines but with careful monitoring of progress at the end of each stage. These principles are illustrated with two specific examples: (a) absolute quantitation of protein biomarkers by mass spectrometry and (b) the M30 and M65 ELISA assays as surrogate end points of cell death.

Applications of genomic and proteomic technologies have seen a major increase, resulting in an explosion in the amount of highly dimensional and complex data being generated. Subsequently this has increased the effort by the bioinformatics community to develop novel computational approaches that allow for meaningful information to be extracted. This information must be of biological relevance and thus correlate to disease phenotypes of interest. Artificial neural networks are a form of machine learning from the field of artificial intelligence with proven pattern recognition capabilities and have been utilized in many areas of bioinformatics. This is due to their ability to cope with highly dimensional complex datasets such as those developed by protein mass spectrometry and DNA microarray experiments. As such, neural networks have been applied to problems such as disease classification and identification of biomarkers. This review introduces and describes the concepts related to neural networks, the advantages and caveats to their use, examples of their applications in mass spectrometry and microarray research (with a particular focus on cancer studies), and illustrations from recent literature showing where neural networks have performed well in comparison to other machine learning methods. This should form the necessary background knowledge and information enabling researchers with an interest in these methodologies, but not necessarily from a machine learning background, to apply the concepts to their own datasets, thus maximizing the information gain from these complex biological systems.

The potential for development of personalised medicine through the characterisation of novel biomarkers is an exciting prospect for improved patient care. Recent advances in mass spectrometric (MS) techniques, liquid phase analyte separation and bioinformatic tools for high throughput now mean that this goal may soon become a reality. However, there are challenges to be overcome for the identification and validation of robust biomarkers. Bio-fluids such as plasma and serum are a rich source of protein, many of which may reflect disease status, and due to the ease of sampling and handling, novel blood borne biomarkers are very much sought after. MS-based methods for high throughput protein identification and quantification are now available such that the issues arising from the huge dynamic range of proteins present in plasma may be overcome, allowing deep mining of the blood proteome to reveal novel biomarker signatures for clinical use. In addition, the development of sensitive MS-based methods for biomarker validation may bypass the bottleneck created by the need for generation and usage of reliable antibodies prior to large scale screening. In this review, we discuss the MS-based methods that are available for clinical proteomic analysis and highlight the progress made and future challenges faced in this cutting edge area of research.