Minimally invasive detection of head and neck squamous cell carcinoma

Author: Nuwan Dharmawardana

Dharmawardana, Nuwan, 2021 Minimally invasive detection of head and neck squamous cell carcinoma, Flinders University, College of Medicine and Public Health

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This PhD thesis investigated extracellular vesicle microRNA and exhaled breath volatile organic compounds as minimally invasive biomarkers for the detection of head and neck squamous cell carcinoma (HNSCC).

HNSCC is a debilitating disease with poor patient outcomes associated with advanced stages of disease. There is a paucity of clinically validated biomarkers capable of early detection of HNSCC. Therefore, an initial systematic review of recent literature was conducted to identify reasons for poor translation of biomarkers into clinical practice. These reviews indicated significant variability in methodology in both circulating microRNA and breath analysis research that contributed to the lack of clinically validated biomarkers for detection of HNSCC.

A key focus of this thesis was the development of a predictive model using a minimally invasive biomarker to detect patients with HNSCC. Standardised protocols were developed to collect blood, breath, and microbiome samples from patients in a clinical setting. Subsequently, extracellular vesicle microRNAs and exhaled breath volatile organic compounds were investigated for their potential as a biomarker for detecting HNSCC. Both modalities were accurate at detecting patients with HNSCC with high sensitivity and specificity.

The breath test described in Chapter five was the first and the largest study to describe the use of raw mass spectra from a selected ion flow tube mass spectrometer to accurately detect HNSCC. This thesis was also the first to describe a HNSCC dependent intestinal dysbiosis that was detected using exhaled breath hydrogen and methane analysis.

A range of complex algorithms available for predictive analysis were explored in this thesis including the assessment of their applicability to certain datasets. Binary logistic regression was the most intuitive algorithm for predictive analysis with minimal assumptions regarding the dataset. However, machine learning algorithms should be utilised for large datasets with complex interactions.

Core relationships between circulating microRNAs, exhaled breath compounds, patient factors, environmental factors and disease risk factors were also explored in this thesis. The importance of measuring ambient volatile gas levels during breath sample collection to recognise potential sample contamination was further highlighted. It also identified a measurable marker (acetonitrile) of smoking status which is important as a key risk factor for the development HNSCC as well as treatment outcomes for HNSCC.

Collectively, this thesis provides evidence for the use of circulating microRNAs and exhaled breath volatile compounds as biomarkers for detecting HNSCC. They are both accurate and potentially able to implement into a clinical setting. However, they require in-depth cost-effectiveness analysis and large-scale clinical trials in a primary care setting prior to effective clinical translation.

Keywords: head and neck cancer, breath analysis, biomarkers, microRNA, microbiome, otorhinolaryngology

Subject: Surgery thesis

Thesis type: Doctor of Philosophy
Completed: 2021
School: College of Medicine and Public Health
Supervisor: Eng Ooi