ArticleBiotechnology and bioengineering2012
A multivariate model of ErbB network composition predicts ovarian cancer cell response to canertinib.
Article in Biotechnology and bioengineering, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 29 citations in OpenAlex.
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- Cellular context alters EGF-induced ERK dynamics and reveals potential crosstalk with GDF-15.Biomicrofluidics · 2022Article
- Topological defects in the mesothelium suppress ovarian cancer cell clearance.APL bioengineering · 2021Article
- Ovarian cancer cells direct monocyte differentiation through a non-canonical pathway.BMC cancer · 2020Article
- Tumor cell sensitivity to vemurafenib can be predicted from protein expression in a BRAF-V600E basket trial setting.BMC cancer · 2019Article
- Human epidermal growth factor receptor targeted inhibitors for the treatment of ovarian cancer.Cancer biology & medicine · 2018Article
- Alternatively-Activated Macrophages Upregulate Mesothelial Expression of P-Selectin to Enhance Adhesion of Ovarian Cancer Cells.Cancer research · 2018Article
- Mechanistic modeling quantifies the influence of tumor growth kinetics on the response to anti-angiogenic treatment.PLoS computational biology · 2017Article
- Article
- Hierarchy of cellular decisions in collective behavior: Implications for wound healing.Scientific reports · 2016Article
- High-grade serous ovarian cancer cell lines exhibit heterogeneous responses to growth factor stimulation.Cancer cell international · 2015Article
- Analysis of the quantitative balance between insulin-like growth factor (IGF)-1 ligand, receptor, and binding protein levels to predict cell sensitivity and therapeutic efficacy.BMC systems biology · 2014Article
- Using partial least squares regression to analyze cellular response data.Science signaling · 2013Article
- Bioinformatics and variability in drug response: a protein structural perspective.Journal of the Royal Society, Interface · 2012Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Identifying the optimal treatment strategy for cancer is an important challenge, particularly for complex diseases like epithelial ovarian cancer (EOC) that are prone to recurrence. In this study we developed a quantitative, multivariate model to predict the extent of ovarian cancer cell death following treatment with an ErbB inhibitor (canertinib, CI-1033). A partial least squares regression model related the levels of ErbB receptors and ligands at the time of treatment to sensitivity to CI-1033. In this way, the model mimics the clinical problem by incorporating only information that would be available at the time of drug treatment. The full model was able to fit the training set data and was predictive. Model analysis demonstrated the importance of including both ligand and receptor levels in this approach, consistent with reports of the role of ErbB autocrine loops in EOC. A reduced multi-protein model was able to predict CI-1033 sensitivity of six distinct EOC cell lines derived from the three subtypes of EOC, suggesting that quantitatively characterizing the ErbB network could be used to broadly predict EOC response to CI-1033. Ultimately, this systems biology approach examining multiple proteins has the potential to uncover multivariate functions to identify subsets of tumors that are most likely to respond to a targeted therapy.
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Registered trials
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