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Model-Based Design of a Decision Tree for Treating HER2+ Cancers Based on Genetic and Protein Biomarkers
D C Kirouac1, J Lahdenranta1, J Du1
1Merrimack Pharmaceuticals Cambridge, Massachusetts, USA.
Abstract:
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.
Insights
This study explores how PI3K and MAPK pathway dependence in HER2+ cancers impacts targeted therapy response. It identifies predictive biomarkers for personalized treatment strategies.
Area of Science:
- Oncology
- Molecular Biology
- Pharmacology
Background:
- Human cancers exhibit diverse molecular alterations and pathway dependencies.
- Understanding cellular dependence on PI3K vs. MAPK pathways is crucial for HER2+ cancer treatment.
- Targeted therapies offer promise but require predictive biomarkers for efficacy.
Purpose of the Study:
- To assess how cellular dependence on PI3K vs. MAPK pathways influences response to combination targeted therapies in HER2+ cancers.
- To identify biomarkers predictive of targeted therapy activity in HER2+ cancers.
- To develop a decision tree for stratifying HER2+ cancer treatment.
Main Methods:
- Integrative analysis combining mechanistic model simulations and in vitro cell line profiling.
- Development of a six-arm decision tree for treatment stratification.
- Evaluation of combinations of inhibitors targeting HER2, HER3, AKT, and MEK pathways.
Main Results:
- Activating mutations in PIK3CA and KRAS, and heregulin expression predicted sensitivity to specific targeted therapy combinations.
- Identified key molecular aberrations and pathway dependencies influencing treatment response.
- Established a framework for stratifying HER2+ cancer treatment based on predictive biomarkers.
Conclusions:
- Cellular dependence on PI3K and MAPK pathways dictates response to targeted therapy combinations in HER2+ cancers.
- Predictive biomarkers, including PIK3CA/KRAS mutations and heregulin, can guide treatment selection.
- The developed strategy holds potential for broader application in other cancer types.
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