Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients
Jie Ju (Erasmus MC)
Leonoor V. Wismans (Erasmus MC)
Dana A.M. Mustafa (Erasmus MC)
Marcel JT Reinders (TU Delft - Pattern Recognition and Bioinformatics)
Casper H.J. van Eijck (Erasmus MC)
Yunlei Li (Erasmus MC)
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Abstract
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.
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