Statistical issues in identifying, validating, and using molecular markers in clinical trials
Authors: Marc Buyse
Journal: Clinical Cancer Research (2008)

ED06-01    Definitions   A biomarker can be formally defined as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention”. Biomarkers can include biochemical markers, cellular markers, cytokines, genetic markers, gene expression profiles, imaging markers, physiological markers, or any other measurements made on the tumor. They can be measured once before a treatment is administered, or repeatedly before, during and after the treatment is administered, in which case interest focuses on changes in the biomarkers over time.  Biomarkers can be classified into three broad groups:  •  Prognostic  biomarkers, which affect the outcome of patients in terms of a clinical endpoint.  •  Predictive  biomarkers, which affect the effect of a specific treatment on a clinical endpoint.  •  Surrogate  biomarkers, which  may  replace a clinical endpoint in clinical trials carried out to evaluate the effect of a specific treatment.   Identification and validation of biomarkers   Prognostic biomarkers.  A prognostic biomarker affects the clinical outcome regardless of treatment. Hence the baseline value of the biomarker, or changes in the biomarker over time, should be correlated with the clinical endpoint in untreated or in treated patients. This condition is straightforward to establish: the prognostic impact of a biomarker can be investigated retrospectively in a series of patients providing a sufficient number of clinical events are available. In addition, a prognostic biomarker will be of clinical interest only if its impact on the clinical endpoint of interest is large enough, hence the number of patients required will often be smaller than what is required to establish or confirm a treatment benefit.  The ideal setting to identify and validate a prognostic biomarker is a randomized therapeutic trial, in which all procedures to measure the biomarker are specified in the protocol (biological material collected, methods of preservation, assays and quantitation methods used, quality control procedures, reproducibility assessments, etc.) and in which all patients fulfill well-defined characteristics and are treated and followed according to a pre-determined schedule.  One key issue is the choice of appropriate statistic(s) to quantify the impact of a prognostic biomarker on the clinical outcome of interest. The p-value of a test comparing the clinical outcome in two groups of patients, one with and another without the biomarker, provides insufficient evidence that the biomarker is of any clinical use. Measures that quantify the  magnitude  of effect of the biomarker on the clinical endpoint, such as the odds ratio (for tumor response and other dichotomous endpoints) or the hazard ratio (for survival time and other time-related endpoints), along with their confidence limits, are far more informative. However these statistics are not sufficient to gauge the performance of the prognostic biomarker. Measures of predictive accuracy are needed as well, and these are usually provided by the sensitivity and specificity of the biomarker, or equivalently by its positive and negative predictive value. If the biomarker is a continuous measurement, ROC (Receiver Operating Characteristics) curves are informative. Last but not least, a biomarker is of interest only if it provides  additional  prognostic value, over and above that of all easily measured clinical and pathological characteristics of the patients.   Predictive biomarkers.  A predictive biomarker modifies the effect of treatment on the clinical outcome. Hence, the baseline value of the biomarker, or changes in the biomarker over time, should be correlated with the effect of treatment on the clinical endpoint of interest. This condition is far more difficult to establish than a mere prognostic impact of the biomarker on the endpoint. It is a common misconception that a biomarker having prognostic value in a group of treated patients is predictive, even if the biomarker does not have prognostic value in another group of untreated patients. Indeed, the lack of impact in one group and the impact in another may be due to different selection criteria and other confounding factors that make such an indirect comparison untenable. The most reliable way to formally identify and validate a predictive factor is through a comparative trial, where patients are randomized either to a standard treatment or to an experimental treatment for which a predictive biomarker is thought to exist, since such a design makes treated and untreated patients comparable.  The statistical evidence required to establish that a biomarker is truly predictive is an open question. The most convincing situation would be one in which an interaction test between the effect of treatment and the biomarker status reaches statistical significance. The major problem of interaction tests is that they lack power, so that a very large trial would be required for the test to reach significance. Often biological considerations will alleviate the need for definite statistical evidence. For instance, it is acceptable to consider HER2-neu status as a predictive biomarker for herceptin, even though no study was carried out to formally test for an interaction between the biomarker and the targeted agent.   Surrogate biomarkers.  A surrogate biomarker must be able to replace a clinical endpoint for the purposes of evaluating the effect of an experimental treatment as compared with a standard treatment. The requirements for a biomarker to be considered a valid surrogate have been a theme of intense debate in the statistical literature over the last years. In early discussions about surrogate biomarkers, a common misconception was that it was sufficient for a biomarker to be prognostic for the clinical endpoint to establish surrogacy. It was later argued that the presence of an association between the biomarker and the clinical endpoint, no matter how strong, does not imply surrogacy. An additional key requirement is that there is also an association between treatment-induced  changes  in the biomarker and corresponding  changes  in the risk of the clinical endpoint.  The best theoretical setting to validate a potential surrogate biomarker consists of several large-scale randomized clinical trials in the context of a meta-analysis, or a large randomized trial that can be broken down in smaller, clinically meaningful units, such as countries or clinical sites. In order for a surrogate biomarker to be used in the clinical development of a new drug, surrogacy would have to be demonstrated across a range of treatments testing multiple new drugs, or one new drug compared with multiple comparators. This again suggests recourse to a meta-analysis of several randomized trials.   Uses of biomarkers  Biomarkers are already in common use in patient management: PSA is used to monitor progression of disease in prostate cancer, CEA in colorectal cancer, etc. This does not automatically imply that these biomarkers are useful for clinical research purposes. In terms of clinical development of new treatments, biomarkers can be used to select patients eligible for clinical trials, to stratify patients at entry in clinical trials, to monitor patients and guide treatment decisions, or to substitute for a clinical endpoint in the evaluation of the effects of new treatments.   Prognostic and predictive biomarkers.  Prognostic biomarkers are useful primarily to adjust the therapy for individual patients, with patients of poor prognosis being treated more aggressively than patients of good prognosis. Although prognostic biomarkers can be useful, it is mostly predictive biomarkers that have the potential of changing oncology practice in the near future. Indeed, the presence of unknown predictive factors in a patient population can profoundly affect the statistical power of trials aimed at showing the benefit of new treatments for these patients. Conversely, if a biomarker could reliably identify a subset of patients who derive the most benefit (or the least toxicity) from a new treatment, then clinical trials could be restricted to this subset, and the treatment of future patients could be targeted at this subpopulation only.   Surrogate biomarkers.  The search for prognostic or predictive biomarkers has been intense over the last years, but the real long-term hope is to identify a surrogate biomarker, i.e. a biomarker capable, in and of itself, to predict that a new treatment will have a desired effect on the ultimate endpoint of interest. Currently, no such biomarkers have been identified in oncology. One of the most intensely studied cancer biomarker is prostate-specific antigen (PSA). Changes in PSA often antedate changes in bone scan, and they have long been used as an indicator of response in patients with androgen-independent prostate cancer. Notwithstanding the usefulness of PSA for patient management, it was recently shown that PSA could not be considered a surrogate for long-term clinical endpoints. Similarly, tumor shrinkage was shown not to be an acceptable surrogate endpoint for overall survival in advanced colorectal cancer. Even so, biomarkers (such as tumor response) have often been used for drug approval in situations where the number of patients and / or the follow-up required to show the treatment effect on the long-term clinical endpoints would have led to unrealistic trials.