# Self-consistent signal transduction analysis for modeling context-specific signaling cascades and perturbations

## TL;DR

This paper introduces a new method to model how signals move through biological networks using patient-specific data, helping understand how drugs affect cancer pathways.

## Contribution

The novel method, self-consistent signal transduction analysis, uses genome-scale data to model individualized signaling and drug effects.

## Key findings

- The method identifies clinically impactful effects of estrogen receptor inhibitors on genes like GREB1, HK1, and AKT1.
- It reveals how drug perturbations modulate proliferative signals in breast cancer patients.
- The approach provides insights into how network dysregulation leads to disease and how therapies can alter these networks.

## Abstract

Biological signal transduction networks are central to information processing and regulation of gene expression across all domains of life. Dysregulation is known to cause a wide array of diseases, including cancers. Here I introduce self-consistent signal transduction analysis, which utilizes genome-scale -omics data (specifically transcriptomics and/or proteomics) in order to predict the flow of information through these networks in an individualized manner. I apply the method to the study of endocrine therapy in breast cancer patients, and show that drugs that inhibit estrogen receptor α elicit a wide array of antitumoral effects, and that their most clinically-impactful ones are through the modulation of proliferative signals that control the genes GREB1, HK1, AKT1, MAPK1, AKT2, and NQO1. This method offers researchers a valuable tool in understanding how and why dysregulation occurs, and how perturbations to the network (such as targeted therapies) effect the network itself, and ultimately patient outcomes.