Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients

Jie Ju, Leonoor V. Wismans, Dana A.M. Mustafa, Marcel J.T. Reinders, Casper H.J. van Eijck, Andrew P. Stubbs, Yunlei Li*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
4 Downloads (Pure)

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.

Original languageEnglish
Article number103415
JournaliScience
Volume24
Issue number12
DOIs
Publication statusPublished - 17 Dec 2021

Bibliographical note

Funding Information:
This research was supported by an unrestricted grant of Stichting Hanarth Fonds , the Netherlands. Graphical abstract was created using https://smart.servier.com/ .

Publisher Copyright:
© 2021 The Author(s)

Fingerprint

Dive into the research topics of 'Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients'. Together they form a unique fingerprint.

Cite this