Diagnosing cancer at an early stage can effectively identify tumors at a time when the patient could benefit from treatments with superior clinical outcomes. Improving early cancer detection relies on discovering highly specific, sensitive, and robust biomarkers that can be obtained non-invasively and developing a rapid and low-cost platform. In this context, epigenetic markers have been shown to be a promising tool in the early detection and classification of cancers.

Recently, cell-free methylated DNA immunoprecipitation (cfMeDIP) method was used to distinguish localized from advanced prostate cancers with high sensitivity. The challenge in prostate cancers is that the methylation levels are notoriously heterogeneous, which may lead to misclassification in a subset of patients. In this thesis, machine learning was coupled with a similar target-enrichment method, cell-free Methylation Binding Domain 2-sequencing (cfMBD2_seq), to differentiate benign, localized, and metastatic prostate cancers (mPCa). By training the machine learning model on patients with higher cell-free tumour DNA fraction, the method was able to identify 900 differentially methylated regions (DMRs) that can discriminate metastatic patients from controls with a sensitivity of 95% at a specificity of 100%. Furthermore, the same DMRs detected localized disease at a sensitivity of 37.5% and a specificity of 100%. Upon functional annotation of these DMRs, it was revealed that they are enriched for binding sites of transcription factors related to cell proliferation (i.e., MAZ, Sp2, TIEG1 and E2F-3). This demonstrates the potential of using cfDNA methylation profiling generated using cfMbd2_seq in combination with a machine learning approach in early diagnosis of prostate cancer.

Despite the advancements in these diagnostic tools, the existing sequencing-based cancer detection methods are expensive, labor-intensive, and usually also require extensive sample processing. These drawbacks hinder them from being used in routine clinical applications. To overcome these challenges, the DMRs in the previous method was used to design a set of oligonucleotide probes for label-free detection using a low-cost, mass-sensitive biosensing device known as Thin Film Bulk Acoustic Resonator (TFBAR).

In-liquid biosensing of these targets with TFBAR sensor was demonstrated based on the ability of these devices to detect mass changes at the surface by tracking the resonance frequency. With optimal surface functionalization, the performance of the TFBAR devices were tested to differentiate metastatic prostate cancer cases from controls based on the mass of cfDNA methylated targets. While this is proof of concept, the data generated here offers fundamental knowledge for future studies to develop a portable, low-cost, biosensing devices for Point of Care Testing setting.