Journal article
ImmunoTar-integrative prioritization of cell surface targets for cancer immunotherapy
Bioinformatics (Oxford, England), v 41(3), btaf060
Mar 2025
PMID: 39932005
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
Supplementary data are available at Bioinformatics online.
Metrics
17 Record Views
Details
- Title
- ImmunoTar-integrative prioritization of cell surface targets for cancer immunotherapy
- Creators
- Rawan Shraim - Children's Hospital of PhiladelphiaBrian Mooney - Molecular OncologyKarina L Conkrite - Children's Hospital of PhiladelphiaAmber K Hamilton - Children's Hospital of PhiladelphiaGregg B Morin - Canada's Michael Smith Genome Sciences CentrePoul H Sorensen - University of British ColumbiaJohn M Maris - Children's Hospital of PhiladelphiaSharon J Diskin - Children's Hospital of PhiladelphiaAhmet Sacan (Corresponding Author) - Drexel University
- Publication Details
- Bioinformatics (Oxford, England), v 41(3), btaf060
- Publisher
- Oxford University Press
- Number of pages
- 12
- Grant note
- National Institutes of Health (NIH): U54-CA232568, R01-CA237562, R35-CA220500, T32-CA009140 Cancer Research UK: CGCATF-2021/100002 National Cancer Institute: CA278687-01 Mark Foundation for Cancer ResearchMichael Smith Health Research BC: RT-2023-3194
This work was supported by National Institutes of Health (NIH) grants U54-CA232568 (J.M.M.), R01-CA237562 (S.J.D.), R35-CA220500 (J.M.M.), and T32-CA009140 (A.K.H.). This work was delivered, in part, by the NexTGen Cancer Grand Challenges partnership funded by Cancer Research UK (CGCATF-2021/100002) and the National Cancer Institute (CA278687-01) and The Mark Foundation for Cancer Research. B.M. is funded by a trainee award from the Michael Smith Health Research BC (RT-2023-3194).
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:001445111800001
- Scopus ID
- 2-s2.0-105001190341
- Other Identifier
- 991022028237904721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Statistics & Probability