Oncotarget

Research Papers:

Computational selection of antibody-drug conjugate targets for breast cancer

François Fauteux _, Jennifer J. Hill, Maria L. Jaramillo, Youlian Pan, Sieu Phan, Fazel Famili and Maureen O’Connor-McCourt

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Oncotarget. 2016; 7:2555-2571. https://doi.org/10.18632/oncotarget.6679

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Abstract

François Fauteux1, Jennifer J. Hill2, Maria L. Jaramillo3, Youlian Pan1, Sieu Phan1, Fazel Famili1 and Maureen O’Connor-McCourt3

1Information and Communication Technologies, National Research Council Canada, Ottawa, Ontario, Canada

2Human Health Therapeutics, National Research Council Canada, Ottawa, Ontario, Canada

3Human Health Therapeutics, National Research Council Canada, Montreal, Quebec, Canada

Correspondence to:

François Fauteux, e-mail: francois.fauteux@nrc-cnrc.gc.ca

Keywords: ADC, drug target, microarray, feature selection, ensemble classification

Received: July 08, 2015     Accepted: November 21, 2015     Published: December 19, 2015

ABSTRACT

The selection of therapeutic targets is a critical aspect of antibody-drug conjugate research and development. In this study, we applied computational methods to select candidate targets overexpressed in three major breast cancer subtypes as compared with a range of vital organs and tissues. Microarray data corresponding to over 8,000 tissue samples were collected from the public domain. Breast cancer samples were classified into molecular subtypes using an iterative ensemble approach combining six classification algorithms and three feature selection techniques, including a novel kernel density-based method. This feature selection method was used in conjunction with differential expression and subcellular localization information to assemble a primary list of targets. A total of 50 cell membrane targets were identified, including one target for which an antibody-drug conjugate is in clinical use, and six targets for which antibody-drug conjugates are in clinical trials for the treatment of breast cancer and other solid tumors. In addition, 50 extracellular proteins were identified as potential targets for non-internalizing strategies and alternative modalities. Candidate targets linked with the epithelial-to-mesenchymal transition were identified by analyzing differential gene expression in epithelial and mesenchymal tumor-derived cell lines. Overall, these results show that mining human gene expression data has the power to select and prioritize breast cancer antibody-drug conjugate targets, and the potential to lead to new and more effective cancer therapeutics.


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