Quantitative assessment of a prognostic or predictive biomarker panel

Methods for assessing whether a single biomarker is prognostic or predictive in the context of a control and experimental treatment are well known. With a panel of biomarkers, each component biomarker potentially measuring sensitivity to a different drug, it is not obvious how to extend these methods. We consider two situations, which lead to different ways of defining whether a biomarker panel is prognostic or predictive. In one, there are multiple experimental targeted treatments, each with an associated biomarker assay of the relevant target in the panel, along with a control treatment; the extension of the single-biomarker scenario to this situation is straightforward. In the other situation, there are many (nontargeted) treatments and a single assay that can be used to assess the sensitivity of the patient's tumor to the different treatments. In addition to evaluating previous approaches to this situation, we propose using regression models with varying assumptions to assess such panel biomarkers. Missing biomarker data can be problematic with the regression models, and, after demonstrating that a multiple imputation procedure does not work, we suggest a modified regression model that can accommodate some forms of missing data. We also address the notions of qualitative interactions in the biomarker panel setting.