Computational and Structural Biotechnology Journal (Dec 2024)

Multi-layered knowledge graph neural network reveals pathway-level agreement of three breast cancer multi-gene assays

  • Sangseon Lee,
  • Joonhyeong Park,
  • Yinhua Piao,
  • Dohoon Lee,
  • Danyeong Lee,
  • Sun Kim

Journal volume & issue
Vol. 23
pp. 1715 – 1724

Abstract

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Multi-gene assays have been widely used to predict the recurrence risk for hormone receptor (HR)-positive breast cancer patients. However, these assays lack explanatory power regarding the underlying mechanisms of the recurrence risk. To address this limitation, we proposed a novel multi-layered knowledge graph neural network for the multi-gene assays. Our model elucidated the regulatory pathways of assay genes and utilized an attention-based graph neural network to predict recurrence risk while interpreting transcriptional subpathways relevant to risk prediction. Evaluation on three multi-gene assays—Oncotype DX, Prosigna, and EndoPredict—using SCAN-B dataset demonstrated the efficacy of our method. Through interpretation of attention weights, we found that all three assays are mainly regulated by signaling pathways driving cancer proliferation especially RTK-ERK-ETS-mediated cell proliferation for breast cancer recurrence. In addition, our analysis highlighted that the important regulatory subpathways remain consistent across different knowledgebases used for constructing the multi-level knowledge graph. Furthermore, through attention analysis, we demonstrated the biological significance and clinical relevance of these subpathways in predicting patient outcomes. The source code is available at http://biohealth.snu.ac.kr/software/ExplainableMLKGNN.

Keywords