Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics

Hiroaki Imoto; Sawa Yamashiro; Ken Murakami; Mariko Okada

doi:10.1016/j.xpro.2022.101619

Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics

STAR Protoc. 2022 Aug 11;3(3):101619. doi: 10.1016/j.xpro.2022.101619. eCollection 2022 Sep 16.

Authors

Hiroaki Imoto¹, Sawa Yamashiro², Ken Murakami², Mariko Okada³

Affiliations

¹ Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan. Electronic address: himoto@protein.osaka-u.ac.jp.
² Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.
³ Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan; Center for Drug Design and Research, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka 567-0085, Japan. Electronic address: mokada@protein.osaka-u.ac.jp.

Abstract

Personalized kinetic models can predict potential biomarkers and drug targets. Here, we provide a step-by-step approach for building an executable mathematical model from text and integrating transcriptomic datasets. We additionally describe the steps to personalize the mechanistic model and to stratify patients with triple-negative breast cancer (TNBC) based on in silico signaling dynamics. This protocol can also be applied to any signaling pathway for patient-specific modeling. For complete details on the use and execution of this protocol, please refer to Imoto et al. (2022).

Keywords: Bioinformatics; Cancer; Computer sciences; Gene expression; Genomics; Signal transduction; Systems biology.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Signal Transduction / genetics
Transcriptome / genetics
Triple Negative Breast Neoplasms* / diagnosis