The term biomarker signature describes the behavior of a set of biomarkers that define a signature to maximize the prediction performance. We examine the behavior of specific biomarkers as a set that consistently fluctuate together to maximize the accuracy on predicting the disease-related outcome. How we apply a biomarker signature depends on the prediction problem. A prognostic biomarker signature is used to predict the disease progression, a risk biomarker signature is used to identify sets of subjects that are likely to develop a disease, and a predictive biomarker signature is used to determine the patients that are likely to respond to a particular treatment. Predictive biomarker signatures are used often in oncology to stratify patients with a specific cancer into sub-populations and develop targeted therapies for the diseased population subtypes defined by the biomarker signature.
Development Challenge Our client was developing a new drug for complex neurodegenerative disease in pre-clinical development. The drug may be only effective for a particular subgroup of patients. Our client needed to generate a hypothesis on the molecular pathway and the targeted drug activity and identify a biomarker signature defining potential response to the new drug.
Cytel Solution with Advanced Analytical Expertise Cytel data scientists were engaged to narrow down and define the relationship of a pool of 35 molecular variables : Step 1: Create visualization tools using Principal Component Analysis ( PCA) to narrow down the candidate biomarkers Step 2: Refine biomarkers further using unsupervised learning clustering algorithms. The goal was to minimize cluster complexity and heterogeneity. Step 3: Validate initial findings using a new dataset and semi-supervised clustering. Outcome The analysis produced a biomarker signature that was provided to the client for in-vivo validation. Using these techniques, the client was able to improve their chances of targeting the most promising subgroup for their therapy.
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