Computational selection of antibody-drug conjugate targets for breast cancer

Area of Science:

• Oncology
• Computational Biology
• Genomics

Background:

• Antibody-drug conjugate (ADC) development relies heavily on selecting effective therapeutic targets.
• Identifying targets overexpressed in cancer while sparing healthy tissues is crucial for ADC safety and efficacy.

Purpose of the Study:

• To computationally identify and prioritize novel therapeutic targets for antibody-drug conjugates (ADCs) in major breast cancer subtypes.
• To discover targets with differential expression in breast cancer compared to vital organs and tissues.

Main Methods:

• Utilized microarray data from over 8,000 samples for target identification.
• Employed an iterative ensemble approach with six classification algorithms and three feature selection techniques, including a novel kernel density-based method, to classify breast cancer subtypes.
• Integrated differential gene expression and subcellular localization data to assemble a list of candidate targets.

Main Results:

• Identified 50 cell membrane targets, including one with an ADC in clinical use and six with ADCs in clinical trials for breast cancer and other solid tumors.
• Discovered 50 extracellular proteins as potential targets for non-internalizing ADC strategies.
• Identified candidate targets associated with epithelial-to-mesenchymal transition by analyzing gene expression in epithelial and mesenchymal cell lines.

Conclusions:

• Computational analysis of human gene expression data is a powerful tool for selecting and prioritizing breast cancer ADC targets.
• This approach has the potential to facilitate the development of novel and more effective cancer therapeutics.