Model‐Based Design of a Decision Tree for Treating HER2+ Cancers Based on Genetic and Protein Biomarkers

Human cancers are incredibly diverse with regard to molecular aberrations, dependence on oncogenic signaling pathways, and responses to pharmacological intervention. We wished to assess how cellular dependence on the canonical PI3K vs. MAPK pathways within HER2+ cancers affects responses to combinat...

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Bibliographic Details
Published in:CPT: pharmacometrics and systems pharmacology 2015-03, Vol.4 (3), p.212-220
Main Authors: Kirouac, DC, Lahdenranta, J, Du, J, Yarar, D, Onsum, MD, Nielsen, UB, McDonagh, CF
Format: Article
Language:English
Subjects:
Biomarkers
Cancer therapies
Cell growth
Decision trees
Drugs
Genomes
Kinases
Ligands
Monoclonal antibodies
Mutation
Oncology
Ordinary differential equations
Original
Patients
Pharmaceuticals
Proteins
Signal transduction
Targeted cancer therapy
Tumors
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Summary:Human cancers are incredibly diverse with regard to molecular aberrations, dependence on oncogenic signaling pathways, and responses to pharmacological intervention. We wished to assess how cellular dependence on the canonical PI3K vs. MAPK pathways within HER2+ cancers affects responses to combinations of targeted therapies, and biomarkers predictive of their activity. Through an integrative analysis of mechanistic model simulations and in vitro cell line profiling, we designed a six‐arm decision tree to stratify treatment of HER2+ cancers using combinations of targeted agents. Activating mutations in the PI3K and MAPK pathways (PIK3CA and KRAS), and expression of the HER3 ligand heregulin determined sensitivity to combinations of inhibitors against HER2 (lapatinib), HER3 (MM‐111), AKT (MK‐2206), and MEK (GSK‐1120212; trametinib), in addition to the standard of care trastuzumab (Herceptin). The strategy used to identify effective combinations and predictive biomarkers in HER2‐expressing tumors may be more broadly extendable to other human cancers.
ISSN:2163-8306
2163-8306
DOI:10.1002/psp4.19