Abstract
Antibody-drug conjugate therapy has attracted considerable attention in recent years. Since the selection of appropriate targets is a critical aspect of antibody-drug conjugate research and development, a big data research for discovery of candidate targets per tumor type is outstanding and of high interest. Thus, the purpose of this study was to identify and prioritize candidate antibody-drug conjugate targets with translational potential across common types of cancer by mining the Human Protein Atlas, as a unique big data resource. To perform a multifaceted screening process, XML and TSV files including immunohistochemistry expression data for 45 normal tissues and 20 tumor types were downloaded from the Human Protein Atlas website. For genes without high protein expression across critical normal tissues, a quasi H-score (range, 0-300) was computed per tumor type. All genes with a quasi H − score ≥ 150 were extracted. Of these, genes with cell surface localization were selected and included in a multilevel validation process. Among 19670 genes that encode proteins, 5520 membrane protein-coding genes were included in this study. During a multistep data mining procedure, 332 potential targets were identified based on the level of the protein expression across critical normal tissues and 20 tumor types. After validation, 23 cell surface proteins were identified and prioritized as candidate antibody-drug conjugate targets of which two have interestingly been approved by the FDA for use in solid tumors, one has been approved for lymphoma, and four have currently been entered in clinical trials. In conclusion, we identified and prioritized several candidate targets with translational potential, which may yield new clinically effective and safe antibody-drug conjugates. This large-scale antibody-based proteomic study allows us to go beyond the RNA-seq studies, facilitates bench-to-clinic research of targeted anticancer therapeutics, and offers valuable insights into the development of new antibody-drug conjugates.
Highlights
Much recent interest has centred around research on antibody-drug conjugate (ADC) therapy as a promising targeted therapy for cancer [1,2,3]
Despite considerable advances in the field, only few ADCs have been currently approved by the FDA owing to the lack of enough tumor response or excessive normal tissue toxicity observed in clinical trials [3]
For further comparison and validation of our quasi H-score, as a new method to discover ADC targets, we have investigated the correlation of target H-scores for different tumor types with corresponding FPKM values of the TCGA (The Cancer Genome Atlas) datasets extracted from the Human Protein Atlas (HPA)
Summary
Much recent interest has centred around research on antibody-drug conjugate (ADC) therapy as a promising targeted therapy for cancer [1,2,3]. There are only few large-scale studies which have identified or prioritized ADC targets. In an mRNA-level study, Fauteux et al [6] aimed to identify and prioritize candidate ADC targets for breast cancer. In a data-driven prioritization study, only clinically relevant ADC targets were prioritized across different tumor (sub) types, using transcript-level evidence [7]. To the best of our knowledge, no big data research based on the protein-level evidence still exists for identification and prioritization of candidate ADC targets across a wide range of tumor types
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