Probabilistic Boolean Modeling of Pre-clinical Tumor Models for Biomarker Identification in Cancer Drug Development.
Curr Protoc
; 1(10): e269, 2021 Oct.
Article
em En
| MEDLINE
| ID: mdl-34661991
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias
/
Antineoplásicos
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Curr Protoc
Ano de publicação:
2021
Tipo de documento:
Article