Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients
Summary: A major challenge for treating patients with pancreatic ductal adenocarcinoma (PDAC) is the unpredictability of their prognoses due to high heterogeneity. We present Multi-Omics DEep Learning for Prognosis-correlated subtyping (MODEL-P) to identify PDAC subtypes and to predict prognoses of...
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Elsevier
2021
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oai:doaj.org-article:bac116467aa74791ac6eb847e3be8b382021-11-26T04:37:36ZRobust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients2589-004210.1016/j.isci.2021.103415https://doaj.org/article/bac116467aa74791ac6eb847e3be8b382021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2589004221013869https://doaj.org/toc/2589-0042Summary: A major challenge for treating patients with pancreatic ductal adenocarcinoma (PDAC) is the unpredictability of their prognoses due to high heterogeneity. We present Multi-Omics DEep Learning for Prognosis-correlated subtyping (MODEL-P) to identify PDAC subtypes and to predict prognoses of new patients. MODEL-P was trained on autoencoder integrated multi-omics of 146 patients with PDAC together with their survival outcome. Using MODEL-P, we identified two PDAC subtypes with distinct survival outcomes (median survival 10.1 and 22.7 months, respectively, log rank p = 1 × 10−6), which correspond to DNA damage repair and immune response. We rigorously validated MODEL-P by stratifying patients in five independent datasets into these two survival groups and achieved significant survival difference, which is superior to current practice and other subtyping schemas. We believe the subtype-specific signatures would facilitate PDAC pathogenesis discovery, and MODEL-P can provide clinicians the prognoses information in the treatment decision-making to better gauge the benefits versus the risks.Jie JuLeonoor V. WismansDana A.M. MustafaMarcel J.T. ReindersCasper H.J. van EijckAndrew P. StubbsYunlei LiElsevierarticleBiocomputational methodCancer systems biologyCancerScienceQENiScience, Vol 24, Iss 12, Pp 103415- (2021) |
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DOAJ |
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DOAJ |
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EN |
| topic |
Biocomputational method Cancer systems biology Cancer Science Q |
| spellingShingle |
Biocomputational method Cancer systems biology Cancer Science Q Jie Ju Leonoor V. Wismans Dana A.M. Mustafa Marcel J.T. Reinders Casper H.J. van Eijck Andrew P. Stubbs Yunlei Li Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients |
| description |
Summary: A major challenge for treating patients with pancreatic ductal adenocarcinoma (PDAC) is the unpredictability of their prognoses due to high heterogeneity. We present Multi-Omics DEep Learning for Prognosis-correlated subtyping (MODEL-P) to identify PDAC subtypes and to predict prognoses of new patients. MODEL-P was trained on autoencoder integrated multi-omics of 146 patients with PDAC together with their survival outcome. Using MODEL-P, we identified two PDAC subtypes with distinct survival outcomes (median survival 10.1 and 22.7 months, respectively, log rank p = 1 × 10−6), which correspond to DNA damage repair and immune response. We rigorously validated MODEL-P by stratifying patients in five independent datasets into these two survival groups and achieved significant survival difference, which is superior to current practice and other subtyping schemas. We believe the subtype-specific signatures would facilitate PDAC pathogenesis discovery, and MODEL-P can provide clinicians the prognoses information in the treatment decision-making to better gauge the benefits versus the risks. |
| format |
article |
| author |
Jie Ju Leonoor V. Wismans Dana A.M. Mustafa Marcel J.T. Reinders Casper H.J. van Eijck Andrew P. Stubbs Yunlei Li |
| author_facet |
Jie Ju Leonoor V. Wismans Dana A.M. Mustafa Marcel J.T. Reinders Casper H.J. van Eijck Andrew P. Stubbs Yunlei Li |
| author_sort |
Jie Ju |
| title |
Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients |
| title_short |
Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients |
| title_full |
Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients |
| title_fullStr |
Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients |
| title_full_unstemmed |
Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients |
| title_sort |
robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients |
| publisher |
Elsevier |
| publishDate |
2021 |
| url |
https://doaj.org/article/bac116467aa74791ac6eb847e3be8b38 |
| work_keys_str_mv |
AT jieju robustdeeplearningmodelforprognosticstratificationofpancreaticductaladenocarcinomapatients AT leonoorvwismans robustdeeplearningmodelforprognosticstratificationofpancreaticductaladenocarcinomapatients AT danaammustafa robustdeeplearningmodelforprognosticstratificationofpancreaticductaladenocarcinomapatients AT marceljtreinders robustdeeplearningmodelforprognosticstratificationofpancreaticductaladenocarcinomapatients AT casperhjvaneijck robustdeeplearningmodelforprognosticstratificationofpancreaticductaladenocarcinomapatients AT andrewpstubbs robustdeeplearningmodelforprognosticstratificationofpancreaticductaladenocarcinomapatients AT yunleili robustdeeplearningmodelforprognosticstratificationofpancreaticductaladenocarcinomapatients |
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