Biomarkers and surrogate endpoints

Abstract

When David Beckham leaves the field towards the end of a match, the man who replaces him is a surrogate. and although I suspect that many footballers, if asked, would say that Surrogate is a town in Yorkshire, the word actually comes from the Latin word subrogare, to substitute. The use of the term 'surrogate marker' in medicine dates from the late 1980s [1], but it had been preceded by some years by the term 'biomarker'[2] and was succeeded and replaced by yet another term, 'surrogate endpoint'[3]. To see where these terms stand in relation to each other we need to define them from the bottom up. A surrogate endpoint has been defined as 'a biomarker intended to substitute for a clinical endpoint', the latter being 'a characteristic or variable that reflects how a patient feels, functions, or survives'[4]. So, what is a biomarker? Well, that has been defined as 'a characteristic that is objectively measured and evaluated as an indication of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention'[4]. Although what the marker marks is clearly defined here as being intrinsic, that is not true of the marker itself, which can be intrinsic or extrinsic. This leads me to propose a nosography of biomarkers, as listed in Table 1. The categories in the table could be further subdivided according to whether the markers are being used for diagnosis, staging, or monitoring of disease or for determining its response to therapy. Biomarkers are often cheaper and easier to measure than 'true' endpoints. For example, it is easier to measure a patient's blood pressure than to use echocardiography to measure left ventricular function, and it is much easier to do echocardiography than to measure morbidity and mortality from hypertension in the long term. Biomarkers can also be measured more quickly and earlier. Blood pressure can be measured today, whereas it takes several years to collect mortality data. In clinical trials the use of biomarkers leads to smaller sample sizes. For example, to determine the effect of a new drug on blood pressure a relatively small sample size of say 100–200 patients would be needed and the trial would be relatively quick (1–2 years). To study the prevention of deaths from strokes a much larger study group would be needed and the trial would take many years. There may also be ethical problems associated with measuring true endpoints. For example, in paracetamol overdose it is unethical to wait for evidence of liver damage before deciding whether or not to treat a patient; instead a pharmacological biomarker, the plasma paracetamol concentration, is used to predict whether treatment is required. There are many links in the chain of events that leads from the pathogenesis of a disease to its clinical manifestations; biomarkers can be used at any point in the chain, at the molecular, cellular, or organ levels. Likewise, a therapy might be developed to attack any one of these links, in order to try to manipulate the disease, symptomatically or therapeutically. Any measurement short of the actual outcome could be regarded as a surrogate endpoint biomarker. However, although all surrogate endpoints are biomarkers, not all biomarkers are useful surrogate endpoints. The ideal biomarker is one through which the disease comes about or through which an intervention alters the disease. For example, the serum cholesterol concentration should be an excellent diagnostic marker for cardiovascular disease; however, there is no clear cut-off point, and only about 10% of those who are going to have a stroke or heart attack have a serum cholesterol concentration above the reference range. But even if cholesterol is not a good diagnostic marker, it can still be used as a marker of therapeutic response to cholesterol lowering drugs. Other useful biomarkers are not directly related to the clinical endpoint, but are affected in parallel with the disease. In some cases they are good diagnostic markers but not good markers of progress (for example, prostate specific antigen in prostatic cancer), or conversely they may be good markers of progress but not helpful diagnostically (for example carcinoembryonic antigen in ovarian carcinoma). In looking for criteria for deciding which biomarkers are good candidates for surrogate endpoints we can turn to the guidelines that Austin Bradford Hill propounded for helping to analyse association in determining causation (Table 2) [5, 6]. He propounded these guidelines in the context of environmental causes of disease, but they can be used in other spheres [7]. Whenever a biomarker conforms to these guidelines, it is more likely to be useful. Note that simply because a biomarker fulfills the guidelines it will not necessarily be useful; it merely makes it more likely to be useful. Surrogate endpoints are most likely to be useful when the pathophysiology of the disease and the mechanism of action of the intervention are thoroughly underst