Surrogate Biomarkers: Biomarkers are easier to measure and can be used as screening or surrogate measures for more sophisticated, more accurate, but more cumbersome measures. For example, while the gold standard of diagnosis in oncology is a pathologic tissue review, a highly elevated prostate-specific antigen (PSA) level in the right clinical setting can be diagnostic of prostate cancer. Although the value of PSA level as a diagnostic biomarker is limited by its sensitivity and specificity, it can be an excellent surrogate biomarker for monitoring prostate cancer response to treatment. Surrogate biomarkers can be diagnostic, prognostic, or predictive. In clinical trials, the term 'surrogate endpoints' are also used. Surrogate biomarkers can be surrogate endpoints, but not all surrogate endpoints are surrogate biomarkers. For example, the imaging endpoints (e.g. MRI, CT Scan) may be used as surrogate endpoints, but they are not surrogate biomarkers.
Diagnostic biomarkers indicate if a disease already exists. They are often used for screening for diseases such as cancer. If diagnostic biomarkers are used for screening, they must have good sensitivity and specificity and also must be sufficiently noninvasive and inexpensive to allow widespread applicability.
Prognostic biomarkers indicate how a disease may develop in an individual case regardless of the type of treatment and show the progression of disease with or without treatment. In other words, prognostic biomarkers refer to markers that correlate with the natural progression or aggressiveness of a disease. In oncology, prognostic biomarkers are useful for informing patients about the risk of recurrence or median survival for their particular type of malignancy and for minimizing confounding factors when analyzing clinical trial cohorts or when prospectively stratifying patients in randomized clinical trials.
Predictive biomarkers are defined by their role in predicting a response to a given treatment. Therefore, these are most useful if they can be assessed before the initiation of treatment. Predictive biomarkers help to assess the most likely response to a particular treatment type. If we are looking surrogate endpoints for efficacy measure in clinical trials, predictive biomarkers are most useful.
When we discuss the biomarkers, it is necessary to distinguish whether or not they are diagnostic biomarkers, prognostic biomarkers, or predictive biomarkers.
Medscape has an article by Tezak, Kondratovich, and Mansfield "US FDA and Personalized Medicine: In vitro Diagnostic Regulatory Perspective". The article included the following diagram to distinguish the differences between prognostic biomarkers and predictive biomarkers.
Predictive versus prognostic biomarkers.One of the slides from Roche is also a good summary for the differences among three type of biomarkers.
Marker-positive population is marked in red, and marker-negative population is marked in blue. The figures only illustrate a few simple ways in which biomarker–therapy–outcome interactions might occur. Other factors (such as risk:benefit ratio, safety concerns, availability of other treatment and so on) that may affect assessment of the biomarker and therapeutic effect are not taken into account. (A) No biomarker effect tested. The effect of T versus S is assessed. T shows improved outcome (green arrow) compared with the S in all comers. (B) Prognostic biomarker. Only S is used to assess the effect of biomarker; the effect of therapy is not assessed. When the same type of care is used (regardless of whether there is treatment or no treatment), marker-positive population (dashed red line) shows better outcome than the marker-negative population (dashed blue line). Biomarker shows prognostic effect (yellow arrow). (C) Prognostic biomarker. The effect of S versus T is assessed in both biomarker-positive (red) and biomarker-negative population (blue). Similar therapy versus standard-of-care effect size is observed (green arrows), regardless of biomarker status. For the purposes of the point described, the therapeutic effect is the same, for example, in an 'absolute' survival sense (the green arrows are the same length). Biomarker-positive population has better outcome than biomarker-negative population (yellow arrows) regardless of whether the S or T is used. The biomarker shows prognostic effect, and there is no predictive biomarker effect (i.e., treatment effect is independent of marker status). (D) Predictive biomarker. The effect of S versus T is assessed, in both biomarker-positive (red) and biomarker-negative population (blue). T does not appear to improve patient outcomes over S in the marker-negative population (circled green arrow between blue lines T and S). T shows large improvement in patient outcomes when compared with S in marker-positive population (green arrow between T and S red lines). Biomarker shows predictive effect. (E) No biomarker effect. The effect of S versus T is assessed, in both biomarker-positive (red) and biomarker-negative population (blue). Similar therapy versus standard-of-care effect size is observed (green arrow), regardless of biomarker status, and T shows improved patient outcomes when compared with S. There appears to be no biomarker effect on patient outcomes in either S or T arm (marked by yellow circles). There is no predictive or prognostic biomarker effect.
Figures are simplified illustrations of the relevant points, and not depictions of biological data.
S: Standard of care; T: New therapy.
References:
- FDA Biomarkers and Surrogate Endpoints
- Prognostic and Predictive Biomarkers: Tools in Personalized Oncology
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