Biomarker Classification: what are we talking about?

The lack of consistent nomenclature can make the field of biomarker confusing at time.  Here, I would like to propose a classification that reflects an emerging consensus among my peers.

First, any attempt to classify biomarkers based on detection technology is, in my mind, inappropriate since the technology component of biomarkers should be seen as an enabling factor and not a defining one.  In addition, some biomarkers can be measured using different methods.  For example the biomarker HER2 associated with breast cancer was first measured using RNA expression and then was translated to immunohistochemistry.  Similarly, in Alzheimer’s disease, the levels of b amyloid fragments in cerebrospinal fluid are routinely measured using mass-spectrometry and immunoassays.  Actually, the classification of biomarker based on technology is more a reflection of the historical tendency of organizing biomarker discovery and development groups based on technical specialties (e.g. genomics, proteomics, genetic) than a conscious attempt to classify biomarkers in a logical manner.

Fundamentally, biomarkers can be subdivided into two main categories based on the origin of the core stimulus producing the biological effect evaluated by the biomarker.

Figure 1        

 Figure 2

1. 1.    Drug - Target centric biomarkers for which the stimulus is extrinsic to the system (fig. 1), enabling experimental control of the dose and duration of the stimulus.  These biomarkers can be further classified based on the biological “distance” between the observation and the core stimulus

a.    Target engagement: drug binding to its target

b.    Proximal pharmacodynamic: direct biological effect of a drug binding its target

c.    Distal pharmacodynamic: indirect or secondary effect of a drug binding its target

d.    Activity: integrated effect of a drug on tissues, organs, and/or the entire system

Note: this classification also applies to negative or undesired drug effects translating into: off-target engagement, proximal and distal toxicodynamic, and toxicity.

1. 2.    Patient - disease centric biomarkers for which the stimulus is intrinsic to the system (fig. 2), only allowing observation of the duration of the stimulus and in some cases observation of the dose/magnitude of the stimulus (in many cases, the nature of the core stimulus of diseases remains unknown).  These biomarkers can be further classified based on the nature the information produced:

a.    Trait: stable predictor of disease risk or response to treatment (e.g. genotype, liver CYP expression)

b.    State (diagnostic): evolving predictor of a disease stage (e.g. most medical diagnostics).

c.    Rate (prognostic): evolving predictor of a disease course (e.g. Oncotype Dx [breast cancer recurrence], CSF Ab42 [transition from mild cognitive impairment to Alzheimer’s disease])

Beyond the fundamental scientific differences between these two main biomarkers classes, the general development processes applicable to these two classes of biomarkers are quite different too.  The development of Drug-Target centric biomarkers is essentially an internal translational process from discovery biology to nonclinical development and ultimately to clinical development.  In contrast, the development of Patient – Disease centric biomarkers takes place in a limited translational space.  Indeed, until a project reaches the stage of clinical development, internal access to patients is limited or non-existent, implying that early development of Patient-Disease biomarkers must occur through external collaborations such as pre-competitive initiatives.

Thierry Sornasse for Integrated Biomarker Strategy