Probabilistic Boolean Modeling of Pre‐clinical Tumor Models for Biomarker Identification in Cancer Drug Development. Issue 10 (18th October 2021)

• Record Type: Journal Article
• Authors: Berlow, Noah E.
• Is Part Of: Current protocols. Volume 1:Issue 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-18
• Subjects: bioinformatics -- biomarker development -- pre‐clinical studies -- probabilistic Boolean modeling; Life sciences -- Laboratory manuals -- Periodicals; Biology -- Laboratory manuals -- Periodicals; Life sciences -- Technique -- Periodicals; Biology -- Technique -- Periodicals; 570.028
• DOI: 10.1002/cpz1.269
• Languages: English
• ISSNs: 2691-1299

Abstract

Abstract: As high‐throughput sequencing experiments become more widely used in pre‐clinical and clinical settings, pharmacogenetic and pharmacogenomic biomarker development plays an increasingly important role in oncology drug development pipelines and programs. Consequently, computer‐based learning approaches have entered into use at multiple stages in pre‐clinical and clinical pipelines. However, few approaches are available to identify interpretable and implementable biomarkers of response early in the drug development process when only small pre‐clinical data packages are available. To address the need for early‐stage biomarker development using pre‐clinical tumor models, we have adapted the previously published Probabilistic Target Inhibitor Map (PTIM) platform to the challenge of biomarker hypothesis development, and denoted this approach the Probabilistic Target Map‐Biomarker (PTM‐Biomarker). In this article, we detail the history and design philosophy of PTM‐Biomarker, and present two case studies using the biomarker discovery tool to illustrate its utility in guiding cancer drug development. © 2021 Wiley Periodicals LLC.