Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data
Open accessAbstract
Background: Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges to applying conventional survival analysis. Results: We propose a novel biologically interpretable pathway-based sparse deep neural network, named Cox-PASNet, which integrates high-dimensional gene expression data and clinical data on a simple neural network architecture for survival analysis. Cox-PASNet is biologically interpretable where nodes in the neural network correspond to biological genes and pathways, while capturing the nonlinear and hierarchical effects of biological pathways associated with cancer patient survival. We also propose a heuristic optimization solution to train Cox-PASNet with HDLSS data. Cox-PASNet was intensively evaluated by comparing the predictive performance of current state-of-the-art methods on glioblastoma multiforme (GBM) and ovarian serous cystadenocarcinoma (OV) cancer. In the experiments, Cox-PASNet showed out-performance, compared to the benchmarking methods. Moreover, the neural network architecture of Cox-PASNet was biologically interpreted, and several significant prognostic factors of genes and biological pathways were identified. Conclusions: Cox-PASNet models biological mechanisms in the neural network by incorporating biological pathway databases and sparse coding. The neural network of Cox-PASNet can identify nonlinear and hierarchical associations of genomic and clinical data to cancer patient survival. The open-source code of Cox-PASNet in PyTorch implemented for training, evaluation, and model interpretation is available at: https://github.com/DataX-JieHao/Cox-PASNet.
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Applications have been seen where prior biological knowledge has informed architectural decisions, such as separate branches according to chromosomal position, regularization terms in the training loss or to encode pathways into the architecture.
“We have seen applications where prior biological knowledge has informed architectural decisions, such as separate branches according to chromosomal position [62], regularization terms in the training loss [49] or to encode pathways into the architecture [19, 67].”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [67]
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- Authors
- Hao, J., Kim, Y., Mallavarapu, T., Oh, J. H., & Kang, M.
- Affiliations
- University of Pennsylvania (US)
- Kennesaw State University (US)
- Memorial Sloan Kettering Cancer Center (US)
- University of Nevada, Las Vegas (US)
- Corresponding author
- Kang, Mingon
- Venue
- BMC Medical Genomics
- Published
- 2019-12
- Volume
- 12
- Issue
- S10
- Pages
- 189-189
- ISSN
- 1755-8794 (ISSN-L)
- PMC
- PMC6927105
- OpenAlex
- W2998556046
- Cited by
- 82
- References
- 48
- Citations by year
- 2026: 7 · 2025: 21 · 2024: 13 · 2023: 10 · 2022: 9 · 2021: 17 · 2020: 5
- FWCI
- 2.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Genomics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Scholarship - UNLV (University of Nevada Reno) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- University of Nevada, Las Vegas
- National Institutes of Health (CA008748)
- Institute for Information and Communications Technology Promotion
- National Cancer Institute (CA008748, P30CA008748)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- cancer survival prediction, Cox proportional hazards model, explainable deep learning, gene expression data, clinical data integration, high-dimensional small-sample data, glioblastoma, ovarian serous cystadenocarcinoma
- MeSH terms
- Deep Learning, Humans, Neoplasms, Proportional Hazards Models, Computational Biology
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