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Computational selection of antibody-drug conjugate targets for breast cancer.

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.

SUBMITTER: Fauteux F

PROVIDER: S-EPMC4823055 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Computational selection of antibody-drug conjugate targets for breast cancer.

Fauteux François F Hill Jennifer J JJ Jaramillo Maria L ML Pan Youlian Y Phan Sieu S Famili Fazel F O'Connor-McCourt Maureen M

Oncotarget 20160101 3

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 ...[more]

PMID: 26700623

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Project description:As hematopoietic progenitors supply a large number of blood cells, therapeutic strategies targeting hematopoietic progenitors are potentially beneficial to eliminate unwanted blood cells, such as leukemic cells and immune cells causing diseases. However, due to their pluripotency, targeting those cells may impair the production of multiple cell lineages, leading to serious side effects such as anemia and increased susceptibility to infection. To minimize those side effects, it is important to identify monopotent progenitors that give rise to a particular cell lineage. Monocytes and monocyte-derived macrophages play important roles in the development of inflammatory diseases and tumors. Recently, we identified human monocyte-restricted progenitors, namely, common monocyte progenitors and pre-monocytes, both of which express high levels of CD64, a well-known monocyte marker. Here, we introduce a dimeric pyrrolobenzodiazepine (dPBD)-conjugated anti-CD64 antibody (anti-CD64-dPBD) that selectively induces the apoptosis of proliferating human monocyte-restricted progenitors but not non-proliferating mature monocytes. Treatment with anti-CD64-dPBD did not affect other types of hematopoietic cells including hematopoietic stem and progenitor cells, neutrophils, lymphocytes and platelets, suggesting that its off-target effects are negligible. In line with these findings, treatment with anti-CD64-dPBD directly killed proliferating monocytic leukemia cells and prevented monocytic leukemia cell generation from bone marrow progenitors of chronic myelomonocytic leukemia patients in a patient-derived xenograft model. Furthermore, by depleting the source of monocytes, treatment with anti-CD64-dPBD ultimately eliminated tumor-associated macrophages and significantly reduced tumor size in humanized mice bearing solid tumors. Given the selective action of anti-CD64-dPBD on proliferating monocyte progenitors and monocytic leukemia cells, it should be a promising tool to target cancers and other monocyte-related inflammatory disorders with minimal side effects on other cell lineages.

Dataset Information

Computational selection of antibody-drug conjugate targets for breast cancer.

Publications

Computational selection of antibody-drug conjugate targets for breast cancer.

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