Model‐Based Design of a Decision Tree for Treating HER2+ Cancers Based on Genetic and Protein Biomarkers
Open accessAbstract
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.
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The literature search excluded many relevant publications that do not include "systems" or "QSP" in their title and highly impactful models published elsewhere.
“As a caveat, this excludes many relevant publications that do not include “systems” or “QSP” in their title10, 11 and highly impactful models published elsewhere.12”
From Reproducibility of Quantitative Systems Pharmacology Models: Current Challenges and Future Opportunities (2019) · cites this paper as [10]
The Abstract (page unavailable) describes a mechanistic model used to design a six-arm treatment decision tree, so the paper is an example of a model publication. Its title contains neither “systems” nor “QSP.” Our copy does not establish that the model was highly impactful or clarify what “published elsewhere” means relative to the citing paper’s search. (LLM, conf=0.82)
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References 50 · Cited by 5
- Authors
- Kirouac, D., Lahdenranta, J., Du, J., Yarar, D., Onsum, M., Nielsen, U., & McDonagh, C.
- Affiliations
- Merrimack PharmaceuticalsCambridge Massachusetts USA
- Merrimack Pharmaceuticals (United States) (US)
- ORCID
- Lahdenranta, J
- Venue
- CPT: Pharmacometrics & Systems Pharmacology
- Publisher
- Wiley
- Published
- 2015-03
- Volume
- 4
- Issue
- 3
- Pages
- 212-220
- ISSN
- 2163-8306 (ISSN-L)
- PMC
- PMC4394616
- OpenAlex
- W1563330829
- Cited by
- 5
- References
- 50
- Citations by year
- 2019: 3 · 2017: 1 · 2016: 1
- FWCI
- 0.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- CPT Pharmacometrics & Systems Pharmacology (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Merrimack Pharmaceuticals Inc.
- Topics
- Health Sciences › Medicine › Oncology › HER2/EGFR in Cancer Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › PI3K/AKT/mTOR signaling in cancer
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Treatments and Mutations
- Keywords
- HER2-positive cancer, decision tree, PI3K signaling, MAPK signaling, PIK3CA mutation, KRAS mutation, neuregulin-1, MM.111, AKT inhibitors, MEK inhibitors, trastuzumab, combination targeted therapy, predictive biomarkers, treatment stratification
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