Oncogenic FGFR4 signaling represents a potential therapeutic target in various cancer types, including triple-negative breast cancer and hepatocellular carcinoma. However, resistance to FGFR4 single-agent therapy remains a major challenge, emphasizing the need for effective combinatorial treatments. Our study sought to develop a comprehensive computational model of FGFR4 signaling and to provide network-level insights into resistance mechanisms driven by signaling dynamics. An integrated approach, combining computational network modeling with experimental validation, uncovered potent AKT reactivation following FGFR4 targeting in triple-negative breast cancer cells. Analyzing the effects of cotargeting specific network nodes by systematically simulating the model predicted synergy of cotargeting FGFR4 and AKT or specific ErbB kinases, which was subsequently confirmed through experimental validation; however, cotargeting FGFR4 and PI3K was not synergistic. Protein expression data from hundreds of cancer cell lines was incorporated to adapt the model to diverse cellular contexts. This revealed that although AKT rebound was common, it was not a general phenomenon. For example, ERK reactivation occurred in certain cell types, including an FGFR4-driven hepatocellular carcinoma cell line, in which there is a synergistic effect of cotargeting FGFR4 and MEK but not AKT. In summary, this study offers key insights into drug-induced network remodeling and the role of protein expression heterogeneity in targeted therapy responses. These findings underscore the utility of computational network modeling for designing cell type-selective combination therapies and enhancing precision cancer treatment.  Significance: Computational predictive modeling of signaling networks can decipher mechanisms of cancer cell resistance to targeted therapies and enable identification of more effective cancer type-specific combination treatment strategies.

Download full-text PDF

Source
http://dx.doi.org/10.1158/0008-5472.CAN-23-3409DOI Listing

Publication Analysis

Top Keywords

cotargeting fgfr4
12
modeling signaling
8
cell type-selective
8
fgfr4 signaling
8
types including
8
triple-negative breast
8
breast cancer
8
hepatocellular carcinoma
8
computational network
8
network modeling
8

Similar Publications

Oncogenic FGFR4 signaling represents a potential therapeutic target in various cancer types, including triple-negative breast cancer and hepatocellular carcinoma. However, resistance to FGFR4 single-agent therapy remains a major challenge, emphasizing the need for effective combinatorial treatments. Our study sought to develop a comprehensive computational model of FGFR4 signaling and to provide network-level insights into resistance mechanisms driven by signaling dynamics.

View Article and Find Full Text PDF

FGF19 Is Coamplified With CCND1 to Promote Proliferation in Lung Squamous Cell Carcinoma and Their Combined Inhibition Shows Improved Efficacy.

Front Oncol

April 2022

State Key Laboratory of Oncogenes and Related Genes, Ren Ji Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Lung squamous cell carcinoma (LUSC) remains as a major cause of cancer-associated mortality with few therapeutic options. Continued research on new driver genes is particularly important. FGF19, a fibroblast growth factor, is frequently observed as amplified in human LUSC, which is also associated with multiple genomic gains and losses.

View Article and Find Full Text PDF

Background: Two promising therapeutic strategies in oncology are chimeric antigen receptor-T cell (CAR-T) therapies and antibody-drug conjugates (ADCs). To be effective and safe, these immunotherapies require surface antigens to be sufficiently expressed in tumors and less or not expressed in normal tissues. To identify new targets for ADCs and CAR-T specifically targeting breast cancer (BC) molecular and pathology-based subtypes, we propose a novel in silico strategy based on multiple publicly available datasets and provide a comprehensive explanation of the workflow for a further implementation.

View Article and Find Full Text PDF

Object: The authors have previously reported that erlotinib, an EGFR tyrosine kinase inhibitor, exerts widely variable antiproliferative effects on 9 human glioblastoma multiforme (GBM) cell lines in vitro and in vivo. These effects were independent of EGFR baseline expression levels, raising the possibility that more complex genetic properties form the molecular basis of the erlotinib-sensitive and erlotinib-resistant GBM phenotypes. The aim of the present study was to determine candidate genes for mediating the cellular response of human GBMs to erlotinib.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!