Deep learning improves pancreatic cancer diagnosis using RNA-based variants

Al-Fatlawi, A. et al. (2021) Deep learning improves pancreatic cancer diagnosis using RNA-based variants. Cancers, 13(11), 2654. (doi: 10.3390/cancers13112654) (PMID:34071263) (PMCID:PMC8199344)

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Abstract

For optimal pancreatic cancer treatment, early and accurate diagnosis is vital. Blood-derived biomarkers and genetic predispositions can contribute to early diagnosis, but they often have limited accuracy or applicability. Here, we seek to exploit the synergy between them by combining the biomarker CA19-9 with RNA-based variants. We use deep sequencing and deep learning to improve differentiating pancreatic cancer and chronic pancreatitis. We obtained samples of nucleated cells found in peripheral blood from 268 patients suffering from resectable, non-resectable pancreatic cancer, and chronic pancreatitis. We sequenced RNA with high coverage and obtained millions of variants. The high-quality variants served as input together with CA19-9 values to deep learning models. Our model achieved an area under the curve (AUC) of 96% in differentiating resectable cancer from pancreatitis using a test cohort. Moreover, we identified variants to estimate survival in resectable cancer. We show that the blood transcriptome harbours variants, which can substantially improve noninvasive clinical diagnosis.

Item Type:Articles
Additional Information:The work was mainly funded by the TG70 program of the state of Saxony, Germany (grant number is TG100240979). A. R. P. is supported by the Mildred Scheel Early Career Center Dresden P2, which is funded by German Cancer Aid (grant number is 70113306).
Keywords:Pancreatic cancer, chronic pancreatitis, transcriptome-wide association study, deep learning.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Bailey, Dr Peter
Creator Roles:
Bailey, P.Data curation
Authors: Al-Fatlawi, A., Malekian, N., García, S., Henschel, A., Kim, I., Dahl, A., Jahnke, B., Bailey, P., Bolz, S. N., Poetsch, A. R., Mahler, S., Grützmann, R., Pilarsky, C., and Schroeder, M.
College/School:College of Medical Veterinary and Life Sciences > Institute of Cancer Sciences
Journal Name:Cancers
Publisher:MDPI
ISSN:2072-6694
ISSN (Online):2072-6694
Published Online:28 May 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Cancers 13(11): 2654
Publisher Policy:Reproduced under a Creative Commons License

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