Discovery and Development of Novel Precision Medicine Tools for Breast Cancer
Access status:
Open Access
Type
ThesisThesis type
Doctor of PhilosophyAuthor/s
Daniel, Twingle MathewAbstract
Breast cancer is a heterogeneous disease that can be classified into clinically different subtypes
based on histopathological and molecular features. Estrogen receptor positive (ER+) breast cancer is
classified into two intrinsic molecular subtypes: luminal A and luminal B. ...
See moreBreast cancer is a heterogeneous disease that can be classified into clinically different subtypes based on histopathological and molecular features. Estrogen receptor positive (ER+) breast cancer is classified into two intrinsic molecular subtypes: luminal A and luminal B. Outcomes are overall poorer and proliferation rates are higher in the luminal B subtype compared to luminal A. Commercial multigene tests exploit this molecular difference to accurately predict outcome in ER+ cases. However, in Australia, their use is limited by high cost. For triple negative breast cancers (TNBC) lacking ER, progesterone receptor or amplification of human epidermal growth factor receptor 2 (HER2), outcomes are overall poorer and there are currently no robust predictive tools. This thesis describes the development and validation of the PROSPER proliferation-based gene signature assay, as an affordable test to accurately predict recurrence in ER+ cases. In addition,novel approaches were used to discover gene expression signatures that stratified TNBC into clinically distinct groups. The PROSPER signature was integrated into a multiplexed reverse transcription quantitative PCR (RT-qPCR) assay, which produced robust results in formalin-fixed paraffin-embedded (FFPE) breast cancer cohorts. PROSPER results significantly correlated with the EndoPredict commercial test and proliferation marker Ki-67, demonstrating its utility for ER+ breast cancers. TNBC can be classified into biologically different subtypes. A sequential subtyping approach was used to stratify TNBC cases, and subtype-specific prognostic gene signatures were identified using elastic net regularisation. While this approach demonstrated significant value in predicting diseasefree outcome in the test dataset, predictive values were lower in the validation cohort suggesting further work is required. 3 This study makes an important contribution towards equitable delivery of precision tests for breast cancer patients.
See less
See moreBreast cancer is a heterogeneous disease that can be classified into clinically different subtypes based on histopathological and molecular features. Estrogen receptor positive (ER+) breast cancer is classified into two intrinsic molecular subtypes: luminal A and luminal B. Outcomes are overall poorer and proliferation rates are higher in the luminal B subtype compared to luminal A. Commercial multigene tests exploit this molecular difference to accurately predict outcome in ER+ cases. However, in Australia, their use is limited by high cost. For triple negative breast cancers (TNBC) lacking ER, progesterone receptor or amplification of human epidermal growth factor receptor 2 (HER2), outcomes are overall poorer and there are currently no robust predictive tools. This thesis describes the development and validation of the PROSPER proliferation-based gene signature assay, as an affordable test to accurately predict recurrence in ER+ cases. In addition,novel approaches were used to discover gene expression signatures that stratified TNBC into clinically distinct groups. The PROSPER signature was integrated into a multiplexed reverse transcription quantitative PCR (RT-qPCR) assay, which produced robust results in formalin-fixed paraffin-embedded (FFPE) breast cancer cohorts. PROSPER results significantly correlated with the EndoPredict commercial test and proliferation marker Ki-67, demonstrating its utility for ER+ breast cancers. TNBC can be classified into biologically different subtypes. A sequential subtyping approach was used to stratify TNBC cases, and subtype-specific prognostic gene signatures were identified using elastic net regularisation. While this approach demonstrated significant value in predicting diseasefree outcome in the test dataset, predictive values were lower in the validation cohort suggesting further work is required. 3 This study makes an important contribution towards equitable delivery of precision tests for breast cancer patients.
See less
Date
2024Licence
The author retains copyright of this thesisRights statement
The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.Faculty/School
Faculty of Medicine and Health, Westmead Clinical SchoolAwarding institution
University of SydneyShare