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Machine learning extracts oncogenic-specific γ-H2AX foci formation pattern upon genotoxic stress
Kanji Furuya1, Masae Ikura2, Tsuyoshi Ikura2
1Laboratory of Genome Maintenance, Department of Genome Biology, Radiation Biology Center, Graduate School of Biostudies, Kyoto University, Kyoto, Japan.
Abstract:
H2AX is a histone H2A variant that becomes phosphorylated upon genotoxic stress. The phosphorylated H2AX (γ-H2AX) plays an antioncogenic role in the DNA damage response and its foci patterns are highly variable, in terms of intensities and sizes. However, whether characteristic γ-H2AX foci patterns are associated with oncogenesis (oncogenic-specific γ-H2AX foci patterns) remains unknown. We previously reported that a defect in the acetyltransferase activity of TIP60 promotes cancer cell growth in human cell lines. In this study, we compared γ-H2AX foci patterns between TIP60 wild-type cells and TIP60 HAT mutant cells by using machine learning. When focused solely on the intensity and size of γ-H2AX foci, we extracted the TIP60 HAT mutant-like oncogenic-specific γ-H2AX foci pattern among all datasets of γ-H2AX foci patterns. Furthermore, by using the dimensionality reduction method UMAP, we also observed TIP60 HAT mutant-like oncogenic-specific γ-H2AX foci patterns in TIP60 wild-type cells. In summary, we propose the existence of an oncogenic-specific γ-H2AX foci pattern and the importance of a machine learning approach to extract oncogenic signaling among the γ-H2AX foci variations.
Insights
Researchers identified a specific pattern of phosphorylated H2AX (γ-H2AX) foci linked to oncogenesis. Machine learning successfully extracted this cancer-specific γ-H2AX pattern, revealing its presence even in normal cells.
Area of Science:
- Molecular Biology
- Cancer Research
- Genomics
Background:
- Histone H2AX phosphorylation (γ-H2AX) is crucial for DNA damage response.
- γ-H2AX foci patterns vary in intensity and size, but oncogenic-specific patterns remain uncharacterized.
- Defects in TIP60 acetyltransferase activity are linked to cancer cell growth.
Purpose of the Study:
- To investigate if characteristic γ-H2AX foci patterns are associated with oncogenesis.
- To compare γ-H2AX foci patterns between TIP60 wild-type and TIP60 HAT mutant cells using machine learning.
- To identify oncogenic-specific γ-H2AX foci patterns.
Main Methods:
- Utilized machine learning to analyze γ-H2AX foci patterns.
- Focused on foci intensity and size for pattern extraction.
- Employed Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction.
Main Results:
- Successfully extracted a TIP60 HAT mutant-like oncogenic-specific γ-H2AX foci pattern.
- Observed these oncogenic-specific γ-H2AX foci patterns in TIP60 wild-type cells using UMAP.
- Demonstrated the variability of γ-H2AX foci patterns.
Conclusions:
- Propose the existence of an oncogenic-specific γ-H2AX foci pattern.
- Highlight the importance of machine learning in identifying oncogenic signaling within γ-H2AX variations.
- Suggest potential for γ-H2AX pattern analysis in cancer diagnostics.
