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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.
Abstract:
Identifying the tumor-associated antigens (TAAs) overexpressed in a subgroup of tumor patients is a substantial challenge for cancer treatment. Although there are several methods based on the concept of differential expression, there is a lack of proper algorithms based on the heterogeneous transcriptome expression for exploring effective TAAs. Here, we propose an algorithm, TAPINTO, to objectively predict overexpressed TAAs whose expression is heterogeneous in cancer patients. This algorithm exploits the dispersion of expression in a subgroup of patients to create 3 quantitative parameters (the specific average expression, frequency, and fold change) for evaluating potential TAAs and has a good performance compared with other approaches. Based on these parameters, TAPINTO successfully identified HER2, a famous therapeutic target, and other potential TAAs (CXCL9, KCNJ3, SQLE, MMP11, and SLC7A2) in breast cancer; moreover, these parameters were dramatically consistent with the trend of clinical outcomes (objective response rate, progression-free survival, and serious adverse effects) of therapeutic antibodies. The ability of TAPINTO to capture heterogeneous expression patterns among patients was further validated in cancer hallmarks, subtypes, and prognosis. This study suggests that this novel method will enable potential TAAs to facilitate the subgroup of patients for diagnosis, prognostication, and therapy to overcome the tumor heterogeneity.
Insights
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.
