Abstract
Background: Lung cancer accounts for 1.8 million deaths globally every year. Due to lung cancer's non-specific symptoms, which overlap with other respiratory illnesses (e.g., tuberculosis, asthma, pulmonary emphysema, and fibrosis), most patients present at the hospital with an advanced stage of malignancy. This is responsible for poor patient survival and drives inequities in outcomes. We and others have shown that alterations in DNA methylation are not only tissue- and cancer-type specific, but, because of their abundance, can enable earlier tumour detection with high sensitivity and specificity. Although mutational studies involving cell-free DNA (cfDNA) have experienced considerable progress in their technical and clinical applications, some challenges persist. Alternatively, cfDNA methylation analysis is emerging as a promising biomarker for early lung cancer detection and management, especially when tissue biopsies and low-dose computed tomography (LDCT) scans are unsuitable or inconclusive. We aim to use unbiased whole genome-scale analyses to identify blood-based cfDNA methylation markers that can improve early detection and prognosis of lung cancer.
Results: In this study, we analysed the cfDNA methylomes of lung cancer patients to determine the tissue of origin and applied this information to lung cancer diagnosis and prognosis. Using cell-free reduced representation bisulfite sequencing (cfRRBS), we have prepared genome-scale methylation libraries from 6 to 10 ng of plasma-derived cfDNA obtained from lung cancer patients at either early or late stage, and non-malignant controls (patients with respiratory illness, but not cancer). The resulting sequencing data were passed through our in-house bioinformatics pipeline, yielding an average of three million high-quality CpGs per sample. We used deep-learning-powered models for tissue deconvolution and correlated these findings with the clinical presentation of the patients. Differential methylation analysis revealed a subset of regions that were highly discriminatory between (1) lung cancer and non-malignant controls and (2) early- and late-stage lung cancer.
Conclusions: We have successfully demonstrated that blood-based cfDNA methylation biomarkers can accurately detect the presence of lung cancer, even at an early stage. Future work will focus on extending these findings and developing a clinically relevant cfDNA-based methylation test that can be used to complement current screening and treatment monitoring strategies.