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Assessment of Resistance to Tyrosine Kinase Inhibitors by an Interrogation of Signal Transduction Pathways by Antibody Arrays
Published on: September 19, 2018
Enabling precision medicine by unravelling disease pathophysiology: quantifying signal transduction pathway activity
Anja van de Stolpe1, Laurent Holtzer2, Henk van Ooijen2
1Philips Research, High Tech Campus 11, 5656 AE, Eindhoven, The Netherlands. Anja.van.de.stolpe@philips.com.
Abstract:
Signal transduction pathways are important in physiology and pathophysiology. Targeted drugs aim at modifying pathogenic pathway activity, e.g., in cancer. Optimal treatment choice requires assays to measure pathway activity in individual patient tissue or cell samples. We developed a method enabling quantitative measurement of functional pathway activity based on Bayesian computational model inference of pathway activity from measurements of mRNA levels of target genes of the pathway-associated transcription factor. Oestrogen receptor, Wnt, and PI3K-FOXO pathway assays have been described previously. Here, we report model development for androgen receptor, Hedgehog, TGFβ, and NFκB pathway assays, biological validation on multiple cell types, and analysis of data from published clinical studies (multiple sclerosis, amyotrophic lateral sclerosis, contact dermatitis, Ewing sarcoma, lymphoma, medulloblastoma, ependymoma, skin and prostate cancer). Multiple pathway analysis of clinical prostate cancer (PCa) studies showed increased AR activity in hyperplasia and primary PCa but variable AR activity in castrate resistant (CR) PCa, loss of TGFβ activity in PCa, increased Wnt activity in TMPRSS2:ERG fusion protein-positive PCa, active PI3K pathway in advanced PCa, and active PI3K and NFκB as potential hormonal resistance pathways. Potential value for future clinical practice includes disease subtyping and prediction and targeted therapy response prediction and monitoring.
Insights
This study introduces a novel Bayesian method to measure signal pathway activity using mRNA levels. This approach aids in understanding disease mechanisms and predicting targeted therapy response in various cancers.
Area of Science:
- Molecular biology
- Computational biology
- Oncology
Background:
- Signal transduction pathways are crucial in health and disease, particularly in cancer.
- Targeted therapies require accurate measurement of pathway activity in patient samples for optimal treatment selection.
Purpose of the Study:
- To develop and validate a quantitative assay for measuring functional pathway activity using Bayesian inference.
- To model and analyze key signaling pathways including androgen receptor (AR), Hedgehog, TGFβ, and NFκB.
Main Methods:
- Bayesian computational model inference applied to mRNA levels of target genes.
- Development of new assays for AR, Hedgehog, TGFβ, and NFκB pathways.
- Biological validation across multiple cell types and analysis of clinical study data.
Main Results:
- Demonstrated increased AR activity in early prostate cancer (PCa) and variable activity in castrate-resistant PCa.
- Observed loss of TGFβ activity and increased Wnt activity in specific PCa subtypes.
- Identified active PI3K and NFκB pathways as potential drivers of hormonal resistance in advanced PCa.
Conclusions:
- The developed method provides a quantitative measure of pathway activity from mRNA data.
- Pathway activity profiling can aid in disease subtyping and predicting targeted therapy response.
- This approach holds potential for clinical applications in personalized cancer medicine.
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