Automatic Tumour Typing based on Patterns of Somatic Passenger Mutations
type: CreativeWork
description: In cancer, a tumour’s cell of origin is the strongest determinant of its clinical behaviour. While cell of origin is typically clear at the time of diagnosis, 3-5% of cancer patients present with a metastatic tumour and no obvious corresponding primary tumour. Despite advances in molecular testing, imaging, and pathology, the primary tumour site cannot be inferred in the majority of these cases. Recent large- scale analysis of cancer genomes has uncovered strong associations between cancer type and somatic mutations, prompting the use of somatic mutations as a tool for identifying cancer type. While existing approaches have attempted to use cancer-associated mutations, which may be more common in specific cancer types to infer the primary tumour type from the metastatic tissue, these methods have had only limited success. A more promising alternative is to use the association between patterns of somatic passenger mutations and cancer type, by exploiting the relationships between both regional mutation density and cancer type, and mutational processes and cancer type. Somatic point mutations accu- mulate in regions of closed chromatin, and so mutation density provides information about chromatin state, which in turn offers hints about the underlying cell type. As some mutational processes are highly cell-type specific, mutational processes also provide clues about cancer type. In this thesis, I describe a number of deep learning systems for automatic tumour typing based on patterns of somatic passenger mutations. I then address challenges for translating the classifier into clinical scenarios through the use of multiple algorithmic improvements. First, I make use of modern advancements in deep learning to extend the classifier to accurately discriminate between 29 cancer types. I then use a number of sta- tistical methods for assessing the uncertainty in the model’s predictions, and for improving uncertainty quantification. Finally, I make use of information theoreti…
abstract: In cancer, a tumour’s cell of origin is the strongest determinant of its clinical behaviour. While cell of origin is typically clear at the time of diagnosis, 3-5% of cancer patients present with a metastatic tumour and no obvious corresponding primary tumour. Despite advances in molecular testing, imaging, and pathology, the primary tumour site cannot be inferred in the majority of these cases. Recent large- scale analysis of cancer genomes has uncovered strong associations between cancer type and somatic mutations, prompting the use of somatic mutations as a tool for identifying cancer type. While existing approaches have attempted to use cancer-associated mutations, which may be more common in specific cancer types to infer the primary tumour type from the metastatic tissue, these methods have had only limited success. A more promising alternative is to use the association between patterns of somatic passenger mutations and cancer type, by exploiting the relationships between both regional mutation density and cancer type, and mutational processes and cancer type. Somatic point mutations accu- mulate in regions of closed chromatin, and so mutation density provides information about chromatin state, which in turn offers hints about the underlying cell type. As some mutational processes are highly cell-type specific, mutational processes also provide clues about cancer type. In this thesis, I describe a number of deep learning systems for automatic tumour typing based on patterns of somatic passenger mutations. I then address challenges for translating the classifier into clinical scenarios through the use of multiple algorithmic improvements. First, I make use of modern advancements in deep learning to extend the classifier to accurately discriminate between 29 cancer types. I then use a number of sta- tistical methods for assessing the uncertainty in the model’s predictions, and for improving uncertainty quantification. Finally, I make use of information theoreti…
author: Atwal, Gurnit
keywords: Cancer diagnosis, Chromatin, Deep learning, Somatic mutations, Uncertainty estimation
date published: 2021-06
date modified: 2026-07-27T21:28:18Z
provider: University of Toronto
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Toronto

PropertyValue
property ID: Handle
value: http://hdl.handle.net/1807/123114