Abstract
SummaryA 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.
Highlights
Pancreatic cancer is the third leading cancer-related cause of death worldwide, with a 5-year survival rate of only 9% (Siegel et al, 2020)
We present Multi-Omics DEep Learning for Prognosis-correlated subtyping (MODEL-P) to identify pancreatic ductal adenocarcinoma (PDAC) subtypes and to predict prognoses of new patients
Using MODEL-P, we identified two PDAC subtypes with distinct survival outcomes, which correspond to DNA damage repair and immune response
Summary
Pancreatic cancer is the third leading cancer-related cause of death worldwide, with a 5-year survival rate of only 9% (Siegel et al, 2020). Current medical and molecular tests only provide limited information on tumor aggressiveness and patient prognosis to make a personalized treatment plan. Computed tomography (CT) scan, one of the most commonly used methods to help tumor diagnosis, only provides information on tumor stage, based on which the clinicians infer the tumor or metastatic lesions and assess the possibility for surgery. Another widely used tumor marker for pancreatic cancer prognosis prediction is carbohydrate antigen (CA) 19-9, which is tested in blood. The changes of its secretion indicate the progress of pancreatic cancer and enable monitoring of treatment response, CA 19-9 is not recommended to be used solely to determine operability or predict recurrence or treatment response owing to the high false-positive and false-negative results (Guillen-Ponce et al, 2017; Locker et al, 2006)
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