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Immunosignature Screening for Multiple Cancer Subtypes Based on Expression Rule
Lei Chen1,2,3, XiaoYong Pan4,5, Tao Zeng6
1School of Life Sciences, Shanghai University, Shanghai, China.
Insights
This study introduces a novel method for cancer monitoring using machine learning to analyze patient immunosignatures from liquid biopsies. The approach identifies key antigens for improved cancer immune monitoring and diagnosis.
Area of Science:
- Immunology
- Bioinformatics
- Oncology
Background:
- Liquid biopsy offers a non-invasive approach for clinical examination.
- Monitoring cancer immunosignatures is a novel method for tumor-associated liquid biopsy.
- Identifying cancer-related antigens is crucial for effective monitoring.
Purpose of the Study:
- To develop and validate machine learning algorithms for analyzing peptide microarray data to identify cancer-specific antigens.
- To establish a quantitative method for cancer immune monitoring and pathologic diagnosis using immunosignature data.
Main Methods:
- Reused two sets of peptide microarray data from tumor tissues.
- Applied Monte Carlo Feature Selection (MCFS) for initial feature analysis.
- Utilized incremental feature selection with support vector machine or random forest for optimal feature extraction and classifier construction.
- Employed repeated incremental pruning to produce error reduction (RIPPER) for rule learning and quantitative analysis.
Main Results:
- Identified key features and extracted quantitative rules from peptide microarray data.
- Demonstrated the potential of machine learning algorithms in analyzing immunosignature data.
- Established a foundation for accurate cancer immune monitoring and pathologic diagnosis.
Conclusions:
- Machine learning algorithms, including MCFS and RIPPER, are effective tools for analyzing complex immunosignature data.
- The developed methods show promise for advancing liquid biopsy techniques in cancer diagnostics.
- This approach facilitates qualitative and quantitative identification of tumor-specific antigens for improved cancer management.
Abstract:
Liquid biopsy (i.e., fluid biopsy) involves a series of clinical examination approaches. Monitoring of cancer immunological status by the "immunosignature" of patients presents a novel method for tumor-associated liquid biopsy. The major work content and the core technological difficulties for the monitoring of cancer immunosignature are the recognition of cancer-related immune-activating antigens by high-throughput screening approaches. Currently, one key task of immunosignature-based liquid biopsy is the qualitative and quantitative identification of typical tumor-specific antigens. In this study, we reused two sets of peptide microarray data that detected the expression level of potential antigenic peptides derived from tumor tissues to avoid the detection differences induced by chip platforms. Several machine learning algorithms were applied on these two sets. First, the Monte Carlo Feature Selection (MCFS) method was used to analyze features in two sets. A feature list was obtained according to the MCFS results on each set. Second, incremental feature selection method incorporating one classification algorithm (support vector machine or random forest) followed to extract optimal features and construct optimal classifiers. On the other hand, the repeated incremental pruning to produce error reduction, a rule learning algorithm, was applied on key features yielded by the MCFS method to extract quantitative rules for accurate cancer immune monitoring and pathologic diagnosis. Finally, obtained key features and quantitative rules were extensively analyzed.
