US FDA and Personalized Medicine: In vitro Diagnostic Regulatory Perspective

Živana Težak; Marina V Kondratovich; Elizabeth Mansfield


Personalized Medicine. 2010;7(5):517-530. 

In This Article

Prognostic & Predictive Markers

Although all of the above-mentioned uses of IVDs in the context of therapeutic use are important, we will discuss prognostic and predictive markers here in more detail, owing to their relatively large contribution to date in the FDA's therapeutic-related IVD reviews, as well as the critical importance of understanding the distinction between these two types of markers and what information they provide (Figure 1).

Figure 1.

Predictive versus prognostic 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.

Prognostic markers are useful to assess the risk of disease recurrence, by comparing the outcome for marker-positive and marker-negative patients, regardless of the treatment (Figure 1B), where intervention (e.g., drug therapy) is not a variable.[14] A prognostic marker can be defined as either a single trait or signature of traits that separates different populations with respect to the risk of an outcome of interest in absence of treatment, or despite nontargeted 'standard' treatment. Conversely, predictive markers compare intervention effect (i.e., treatment vs control) for marker-positive versus marker-negative patients, and predict differential effect of treatment on the outcome (Figure 1D). Predictive markers can be defined as a single trait or signature of traits that separate different populations with respect to the outcome of interest in response to a particular targeted treatment. Figure 1 illustrates the effects of predictive and prognostic markers on therapeutic trial outcomes. In Figure 1D there is predictive biomarker effect; in Figure 1C there is no predictive biomarker effect, but there is prognostic effect; and in Figure 1E there is neither a predictive nor a prognostic biomarker effect. A predictive marker implies relative sensitivity or resistance to specific treatments (or adverse events), and can ultimately be useful for selecting or avoiding specific therapy.

If a predictive marker is used to guide treatment decisions, some of the issues to be considered include the following:

  • Differences between assays and test systems may yield inconsistent or nongeneralizable results across different platforms. Therefore, even if the marker has clear clinical significance, the specific test used for measuring the biomarker may make a (sometimes large) difference in test results for the same patient;

  • Although some markers may have well-accepted or plausible mechanistic significance, the clinical significance of the marker will need to be demonstrated in a prospective manner as applied to a specific therapeutic;

  • The findings from a specific study often cannot be extrapolated to different study populations (e.g., various human subpopulations with different genetic backgrounds).


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