Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer with Gene Expression and Computational Pathology
type: CreativeWork
description: Many breast cancer patients are treated with chemotherapy before surgery for the removal of the tumour, which is known as neoadjuvant chemotherapy (NAT). It improves outcome for many patients, and their response to the treatment is prognostic for their overall outcome. However, not all patients who receive NAT respond to the treatment, and, as a result, many suffer unnecessarily from side effects and delays to surgery. In this thesis, I designed and evaluated two potentially independent and complementary strategies for predicting an individual patient’s response to treatment based on gene expression and computational pathology. Firstly, I attempted to develop a clinically practical approach for classifying breast tumours using RNA from routine formalin fixed paraffin embedded (FFPE) histopathological samples. Breast cancers can be classified into distinct groups, 'integrative clusters' (IntClusts), with different outcomes and potentially different response to NAT. The published classification is based on RNA expression from fresh frozen tissue, which is impractical in a clinical setting. Initially, I attempted to build an accurate classifier for IntClusts based on RNA expression from widely available FFPE tissue, using a user-friendly NanoString technique. Unfortunately, it was not possible to achieve reliable classification with this method using the gene probe set pre-selected based on fresh frozen tissue. Next, I sought to identify the genes whose expression was not affected by the fixative process by comparing paired FFPE and fresh frozen tissue with RNA sequencing, a method that allows the quantification of all genes. There was poor agreement in measured gene expression between the two types of tissue sample, FFPE versus frozen, and between the assessment methods on the same tissue type, RNA-sequencing versus Illumina microarray, resulting in unreliable classification of tumours into integrative clusters. These findings represent a challenge to the adoption of …
abstract: Many breast cancer patients are treated with chemotherapy before surgery for the removal of the tumour, which is known as neoadjuvant chemotherapy (NAT). It improves outcome for many patients, and their response to the treatment is prognostic for their overall outcome. However, not all patients who receive NAT respond to the treatment, and, as a result, many suffer unnecessarily from side effects and delays to surgery. In this thesis, I designed and evaluated two potentially independent and complementary strategies for predicting an individual patient’s response to treatment based on gene expression and computational pathology. Firstly, I attempted to develop a clinically practical approach for classifying breast tumours using RNA from routine formalin fixed paraffin embedded (FFPE) histopathological samples. Breast cancers can be classified into distinct groups, 'integrative clusters' (IntClusts), with different outcomes and potentially different response to NAT. The published classification is based on RNA expression from fresh frozen tissue, which is impractical in a clinical setting. Initially, I attempted to build an accurate classifier for IntClusts based on RNA expression from widely available FFPE tissue, using a user-friendly NanoString technique. Unfortunately, it was not possible to achieve reliable classification with this method using the gene probe set pre-selected based on fresh frozen tissue. Next, I sought to identify the genes whose expression was not affected by the fixative process by comparing paired FFPE and fresh frozen tissue with RNA sequencing, a method that allows the quantification of all genes. There was poor agreement in measured gene expression between the two types of tissue sample, FFPE versus frozen, and between the assessment methods on the same tissue type, RNA-sequencing versus Illumina microarray, resulting in unreliable classification of tumours into integrative clusters. These findings represent a challenge to the adoption of …
author: Cope, Wei
language: eng
keywords: Breast Cancer, Computational pathology, Digital pathology, Gene Expression, Neoadjuvant chemotherapy
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/4ce1d1cc-de52-4166-bbb4-8f12447f453b/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2021-04-01
date modified: 2026-07-22T22:24:27Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.80238