Engineered Multivalency Enhances Affibody-Based HER3 Inhibition and Downregulation in Cancer Cells.
John S SchardtJinan M OubaidSonya C WilliamsJames L HowardChloe M AloimonosMichelle L BookstaverTek N LamichhaneSonja SokicMariya S LiyasovaMaura O'NeillThorkell AndressonArif HussainStanley LipkowitzSteven M JayPublished in: Molecular pharmaceutics (2017)
The receptor tyrosine kinase HER3 has emerged as a therapeutic target in ovarian, prostate, breast, lung, and other cancers due to its ability to potently activate the PI3K/Akt pathway, especially via dimerization with HER2, as well as for its role in mediating drug resistance. Enhanced efficacy of HER3-targeted therapeutics would therefore benefit a wide range of patients. This study evaluated the potential of multivalent presentation, through protein engineering, to enhance the effectiveness of HER3-targeted affibodies as alternatives to monoclonal antibody therapeutics. Assessment of multivalent affibodies on a variety of cancer cell lines revealed their broad ability to improve inhibition of Neuregulin (NRG)-induced HER3 and Akt phosphorylation compared to monovalent analogues. Engineered multivalency also promoted enhanced cancer cell growth inhibition by affibodies as single agents and as part of combination therapy approaches. Mechanistic investigations revealed that engineered multivalency enhanced affibody-mediated HER3 downregulation in multiple cancer cell types. Overall, these results highlight the promise of engineered multivalency as a general strategy for enhanced efficacy of HER3-targeted therapeutics against a variety of cancers.
Keyphrases
- tyrosine kinase
- combination therapy
- monoclonal antibody
- papillary thyroid
- cell proliferation
- small molecule
- signaling pathway
- cancer therapy
- ejection fraction
- prostate cancer
- randomized controlled trial
- epidermal growth factor receptor
- newly diagnosed
- squamous cell
- end stage renal disease
- childhood cancer
- squamous cell carcinoma
- oxidative stress
- molecular docking
- big data
- protein protein
- risk assessment
- climate change
- lymph node metastasis
- deep learning