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TAPINTO: A Novel Algorithm for Tumor-Associated Antigen Prediction Based on Information about Target Overexpression.
Cheng-Hsun Chuang1, Hsiao-Hsuan Huang2, Yi-Syuan Wu3
1Institute of Molecular Medicine and Bioengineering, National Yang Ming Chiao Tung University, Hsinchu City 30068, Taiwan, ROC.
Computational and Structural Biotechnology Journal
|May 18, 2026
Summary
A new algorithm, TAPINTO, identifies tumor-associated antigens (TAAs) by analyzing heterogeneous expression in cancer patients. This approach aids in discovering novel TAAs for targeted cancer therapies and improving patient outcomes.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Identifying tumor-associated antigens (TAAs) overexpressed in specific patient subgroups is crucial for effective cancer treatment.
- Current methods often fail to account for the heterogeneous nature of gene expression within tumors, limiting the discovery of novel TAAs.
- There is a need for advanced algorithms that can analyze transcriptome heterogeneity to identify robust TAAs.
Purpose of the Study:
- To develop and validate an objective algorithm, TAPINTO, for predicting overexpressed TAAs with heterogeneous expression patterns in cancer patients.
- To leverage expression dispersion within patient subgroups to identify potential TAAs.
- To assess the performance of TAPINTO against existing approaches and its correlation with clinical outcomes.
Main Methods:
- Developed TAPINTO, an algorithm that quantifies TAAs using specific average expression, frequency, and fold change parameters based on expression dispersion in patient subgroups.
- Applied TAPINTO to identify TAAs in breast cancer, including known targets like HER2 and novel candidates (CXCL9, KCNJ3, SQLE, MMP11, SLC7A2).
- Validated TAPINTO's ability to capture heterogeneous expression patterns across cancer hallmarks, subtypes, and prognosis.
Main Results:
- TAPINTO successfully identified HER2 and several other potential TAAs in breast cancer.
- The quantitative parameters generated by TAPINTO showed strong consistency with clinical outcomes of therapeutic antibodies, including objective response rate and progression-free survival.
- The algorithm demonstrated effectiveness in capturing patient-specific expression heterogeneity.
Conclusions:
- TAPINTO provides a novel computational approach to objectively predict overexpressed TAAs by exploiting transcriptome heterogeneity.
- The identified TAAs and the algorithm's parameters show promise for improving diagnosis, prognostication, and targeted therapy in specific cancer patient subgroups.
- This method offers a valuable tool to overcome challenges posed by tumor heterogeneity in precision oncology.
