Shraim Rawan, Mooney Brian, Conkrite Karina L, Hamilton Amber K, Morin Gregg B, Sorensen Poul H, Maris John M, Diskin Sharon J, Sacan Ahmet
Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States.
School of Biomedical Engineering, Science and Health System, Drexel University, Philadelphia, PA 19104, United States.
Bioinformatics. 2025 Mar 4;41(3). doi: 10.1093/bioinformatics/btaf060.
Cancer remains a leading cause of mortality globally. Recent improvements in survival have been facilitated by the development of targeted and less toxic immunotherapies, such as chimeric antigen receptor (CAR)-T cells and antibody-drug conjugates (ADCs). These therapies, effective in treating both pediatric and adult patients with solid and hematological malignancies, rely on the identification of cancer-specific surface protein targets. While technologies like RNA sequencing and proteomics exist to survey these targets, identifying optimal targets for immunotherapies remains a challenge in the field.
To address this challenge, we developed ImmunoTar, a novel computational tool designed to systematically prioritize candidate immunotherapeutic targets. ImmunoTar integrates user-provided RNA-sequencing or proteomics data with quantitative features from multiple public databases, selected based on predefined criteria, to generate a score representing the gene's suitability as an immunotherapeutic target. We validated ImmunoTar using three distinct cancer datasets, demonstrating its effectiveness in identifying both known and novel targets across various cancer phenotypes. By compiling diverse data into a unified platform, ImmunoTar enables comprehensive evaluation of surface proteins, streamlining target identification and empowering researchers to efficiently allocate resources, thereby accelerating the development of effective cancer immunotherapies.
Code and data to run and test ImmunoTar are available at https://github.com/sacanlab/immunotar.
癌症仍然是全球主要的死亡原因。靶向性更强且毒性更低的免疫疗法的发展，如嵌合抗原受体（CAR）-T细胞和抗体-药物偶联物（ADC），推动了生存率的近期改善。这些疗法在治疗患有实体瘤和血液系统恶性肿瘤的儿科和成年患者方面都很有效，它们依赖于癌症特异性表面蛋白靶点的识别。虽然存在如RNA测序和蛋白质组学等技术来调查这些靶点，但确定免疫疗法的最佳靶点仍然是该领域的一个挑战。
为应对这一挑战，我们开发了ImmunoTar，这是一种新型计算工具，旨在系统地对候选免疫治疗靶点进行优先级排序。ImmunoTar将用户提供的RNA测序或蛋白质组学数据与来自多个公共数据库的定量特征整合在一起，这些数据库是根据预定义标准选择的，以生成一个分数，代表该基因作为免疫治疗靶点的适宜性。我们使用三个不同的癌症数据集对ImmunoTar进行了验证，证明了它在识别各种癌症表型中的已知和新靶点方面的有效性。通过将各种数据整合到一个统一的平台上，ImmunoTar能够对表面蛋白进行全面评估，简化靶点识别，并使研究人员能够有效地分配资源，从而加速有效的癌症免疫疗法的开发。
运行和测试ImmunoTar的代码和数据可在https://github.com/sacanlab/immunotar上获取。
