Using convolutional neural networks to predict NRG1-fusions in PDAC biopsy images
department or school: Science, Faculty of
data provider: DSpace
degree campus: UBCV
creator: Yang, Jenny
issued: 2020-09-11T20:23:12Z
issued: 2020
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Pancreatic ductal adenocarcinoma (PDAC) is considered the most lethal common cancer, with the highest incidence-to-mortality ratio of any solid tumour. Molecular pathology studies and genomic analyses have improved our understandings of how PDAC develops and progresses, and there has been significant progress in treatment strategies for specific genomic alterations. One of these alterations is the NRG1 gene fusion, which has been found to be a rare, but potentially targetable oncogenic driver. To determine whether PDAC patients have an NRG1 gene fusion, we used convolutional neural networks (CNNs) to analyze digital whole slide images (WSIs) of cancer biopsies. In particular, we used histopathological H&E slides from the Personalized OncoGenomics program to train a deep CNN (VGG-16) framework that automatically classifies normal tissue, NRG1-fusion positive tumour tissue, and NRG1-fusion negative tumour tissue. We implemented the model in two-stages, where the first stage classifies normal from tumour tissue, and the second stage classifies the tumour tissue as being NRG1-fusion positive or negative. The model achieved accuracies of 86.5% and 76.0% for each stage, respectively, and an overall accuracy of 68.8%. Additionally, we found that PDAC cases with high expression of the NRG1 gene (93rd-98th percentile of TCGA PDAC cases) were being classified as NRG1-fusion positive, suggesting a possible correlation between NRG1 gene fusions and high parent gene expression. Finally, we attempted to understand the inner workings and decisions of our CNN model by analyzing internal feature maps. We found activation patterns that matched distinct histological features and compared them with a more traditional image segmentation approach. Overall, our findings demonstrate that deep CNNs have the potential to assist pathologists in detecting therapeutically actionable genomic markers.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/75986
note: USING CONVOLUTIONAL NEURAL NETWORKS TO PREDICTNRG1-FUSIONS IN PDAC BIOPSY IMAGESbyJenny YangB.A.Sc., The University of British Columbia, 2019A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THEREQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Bioinformatics)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)September 2020c© Jenny Yang, 2020The following individuals certify that they have read, and recommend to the Faculty ofGraduate and Postdoctoral Studies for acceptance, a thesis entitled:Using Convolutional Neural Networks to Predict NRG1-Fusions in PDAC BiopsyImagessubmitted by Jenny Yang in partial fulfillment of the requirements for the degree of Masterof Science in Bioinformatics.Examining Committee:Dr. Steven Jones, Professor, Department of Medical Genetics, UBCSupervisorDr. Elodie Portales-Casamar, Assistant Professor, Department of Pediatrics, UBCSupervisory Committee MemberDr. Stephen Yip, Associate Professor, Department of Pathology, UBCSupervisory Committee MemberiiAbstractPancreatic ductal adenocarcinoma (PDAC) is considered the most lethal commoncancer, with the highest incidence-to-mortality ratio of any solid tumour. Molecular pathol-ogy studies and genomic analyses have improved our understandings of how PDAC developsand progresses, and there has been significant progress in treatment strategies for specificgenomic alterations. One of these alterations is the NRG1 gene fusion, which has been foundto be a rare, but potentially targetable oncogenic driver. To determine whether PDAC pa-tients have an NRG1 gene fusion, we used convolutional neural networks (CNNs) to analyzedigital whole slide images (WSIs) of cancer biopsies. In particular, we used histopathologicalH&E slides from the Personalized OncoGenomics program to train a deep CNN (VGG-16)framework that automatically classifies normal tissue, NRG1-fusion positive tumour tissue,and NRG1-fusion negative tumour tissue. We implemented the model in two-stages, wh…
has type: Thesis/Dissertation
date issued: 2020-11
is shown at: 10.14288/1.0394327
language: eng
degree discipline: Bioinformatics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/75986