Leah Brown is an emerging cancer researcher focusing on advancing the detection of ovarian cancer at an earlier and more precise rate. Her work centers on developing cutting-edge diagnostic tools by analyzing blood samples from current patients, with the aim of contributing positively to cancer research. Leah hopes her work at the University of Birmingham will lead to improved survival rates across ovarian cancer patients worldwide.
Ovarian cancer remains a critical health challenge with a 10-year survival rate of only 30%. Early diagnosis is essential for improving outcomes, yet current methods lack sufficient sensitivity and specificity. My research focuses on refining early detection techniques by identifying immune system dysfunction as a potential early indicator of cancer.
My project specifically aims to analyse blood samples from ovarian cancer patients and controls using two key approaches:
Protein and ctDNA Detection: The detection of proteins released during chronic immune activation will be optimised and circulating tumour DNA (ctDNA) and RNA will be analysed using multiplex ELISA panels, mass spectrometry, and sequencing technologies.
Cellular Analysis: Assessment of immune cell profiles, such as myeloid suppressor cells and exhausted T cells, through flow cytometry and CyTOF analysis. DNA methylation profiling will be conducted to identify cancer-specific signatures.
The data collected will be used to develop a comprehensive diagnostic tool by combining serum, cellular, and ctDNA analyses, with statistical and machine learning methods enhancing diagnostic accuracy. Although focused on ovarian cancer, these techniques will also be evaluated for other cancers, with the aim of improving early detection and potentially reducing mortality rates across multiple cancer types
The potential impacts of my research on the early detection of ovarian cancer include:
Enhanced Early Diagnosis: My research aims to improve the sensitivity and specificity of detecting proteins, immune cell profiles, and circulating tumour DNA (ctDNA), leading to earlier and more accurate ovarian cancer diagnosis. This could significantly increase survival rates and reduce mortality rates.
Comprehensive Diagnostic Tool: By integrating serum, cellular, and ctDNA analyses, my research could create a robust, multimodal diagnostic tool. This tool may not only enhance ovarian cancer detection but also be applicable to other cancer types, offering a less invasive alternative to biopsies.
Broader Cancer Applications: The methodologies developed could be adapted for other cancers, leading to earlier detection and better outcomes across various malignancies, particularly those with similar immune profiles. This could also enable screening in high-risk populations.
Advancing Cancer-Immune Understanding: The research will deepen our understanding of immune evasion by tumours, informing new therapeutic strategies and aiding in the discovery of novel biomarkers for targeted therapies and personalised medicine.
Personalised Medicine Contributions: Identifying specific biomarkers and immune profiles could lead to tailored diagnostic and treatment approaches, enhancing the effectiveness of cancer care and guiding more precise treatment decisions.
Economic and Healthcare Impact: Early, accurate detection methods could reduce the need for expensive, invasive diagnostics, lowering healthcare costs and improving resource allocation by focusing on early-stage treatment.
Overall, my research could significantly advance cancer diagnosis, enabling earlier detection, improving survival rates, and contributing to a deeper understanding of cancer biology.