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Data-Driven Discovery of Molecular Targets for Antibody-Drug Conjugates in Cancer Treatment.
Razzaghdoust, Abolfazl; Rahmatizadeh, Shahabedin; Mofid, Bahram; Muhammadnejad, Samad; Parvin, Mahmoud; Torbati, Peyman Mohammadi; Basiri, Abbas.
Afiliação
  • Razzaghdoust A; Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Rahmatizadeh S; Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Mofid B; Department of Oncology, Shohada-e-Tajrish Medical Center, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Muhammadnejad S; Gene Therapy Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
  • Parvin M; Department of Pathology, Labbafinejad Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Torbati PM; Department of Pathology, Labbafinejad Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Basiri A; Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Biomed Res Int ; 2021: 2670573, 2021.
Article em En | MEDLINE | ID: mdl-33490264
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imunoconjugados / Proteômica / Descoberta de Drogas / Neoplasias / Antineoplásicos Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imunoconjugados / Proteômica / Descoberta de Drogas / Neoplasias / Antineoplásicos Idioma: En Ano de publicação: 2021 Tipo de documento: Article