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癌症治疗中抗体药物偶联物分子靶点的数据驱动发现

Data-Driven Discovery of Molecular Targets for Antibody-Drug Conjugates in Cancer Treatment.

作者信息

Razzaghdoust Abolfazl, Rahmatizadeh Shahabedin, Mofid Bahram, Muhammadnejad Samad, Parvin Mahmoud, Torbati Peyman Mohammadi, Basiri Abbas

机构信息

Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

出版信息

Biomed Res Int. 2021 Jan 2;2021:2670573. doi: 10.1155/2021/2670573. eCollection 2021.

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 -score (range, 0-300) was computed per tumor type. All genes with a quasi  - 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.

摘要

近年来,抗体药物偶联物疗法备受关注。由于选择合适的靶点是抗体药物偶联物研发的关键环节,针对每种肿瘤类型发现候选靶点的大数据研究十分突出且备受关注。因此,本研究的目的是通过挖掘人类蛋白质图谱这一独特的大数据资源,识别并优先确定具有转化潜力的跨常见癌症类型的候选抗体药物偶联物靶点。为了进行多方面的筛选过程,从人类蛋白质图谱网站下载了包括45种正常组织和20种肿瘤类型的免疫组织化学表达数据的XML和TSV文件。对于在关键正常组织中无高蛋白表达的基因,计算每种肿瘤类型的准分数(范围为0 - 300)。提取所有准分数≥150的基因。其中,选择具有细胞表面定位的基因并纳入多级验证过程。在19670个编码蛋白质的基因中,本研究纳入了5520个膜蛋白编码基因。在多步骤数据挖掘过程中,基于关键正常组织和20种肿瘤类型的蛋白质表达水平,确定了332个潜在靶点。经过验证,确定并优先列出了23种细胞表面蛋白作为候选抗体药物偶联物靶点,其中两种已被美国食品药品监督管理局批准用于实体瘤,一种已被批准用于淋巴瘤,四种目前已进入临床试验。总之,我们识别并优先确定了几个具有转化潜力的候选靶点,这可能会产生新的临床有效且安全的抗体药物偶联物。这项基于抗体的大规模蛋白质组学研究使我们能够超越RNA测序研究,促进靶向抗癌疗法的从 bench 到 clinic 的研究,并为新抗体药物偶联物的开发提供有价值的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98f4/7801065/d1885452eafa/BMRI2021-2670573.001.jpg

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