Inferring signaling pathway abnormalities from histopathological images via logic-constrained gene-pathway...

Area of Science:

• Computational pathology
• Bioinformatics
• Cancer genomics

Background:

• Histopathological analysis traditionally focuses on single-gene mutations, limiting the understanding of complex cancer mechanisms.
• Pathway-level dysregulation is crucial in cancer initiation and progression but is often missed by conventional methods.

Purpose of the Study:

• To develop a novel computational framework, LCG-HGNN, for analyzing gene-pathway interactions in cancer from whole-slide images.
• To improve the accuracy and interpretability of histopathological analysis by capturing pathway alterations.

Main Methods:

• Proposed LCG-HGNN, a Heterogeneous Graph Neural Network integrating gene-pathway structures.
• Developed the KePathGraph framework with dynamic edge weighting and logical clauses for pathway inference.
• Evaluated performance against single-gene and multi-label baseline methods using whole-slide images.

Main Results:

• LCG-HGNN demonstrated superior prediction accuracy compared to existing methods.
• The framework achieved enhanced clinical interpretability by identifying key gene groups and pathway alterations.
• Successfully inferred signaling pathway alterations from histopathological data.

Conclusions:

• Established a pathway-oriented paradigm for histopathological interpretation, moving beyond single-gene focus.
• LCG-HGNN provides deeper insights into cancer mechanisms by analyzing collaborative gene recognition and pathway dysregulation.
• This approach offers a more comprehensive understanding of cancer initiation and progression.