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

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.

Evaluation and optimization of sequence-based gene regulatory deep learning models.

Neural networks have emerged as immensely powerful tools in predicting functional genomic regions, notably evidenced by recent successes in deciphering gene regulatory logic. However, a systematic evaluation of how model architectures and training strategies impact genomics model performance is lacking. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast, to best capture the relationship between regulatory DNA and gene expression. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. While some benchmarks produced similar results across the top-performing models, others differed substantially. All top-performing models used neural networks, but diverged in architectures and novel training strategies, tailored to genomics sequence data. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide any given model into logically equivalent building blocks. We tested all possible combinations for the top three models and observed performance improvements for each. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets. Overall, we demonstrate that high-quality gold-standard genomics datasets can drive significant progress in model development.