Phenotyping the tumour microenvironment in non-small lung cancer using patient derived samples
Item statusRestricted Access
Embargo end date31/07/2021
Titmarsh, Helen F.
Lung cancers accounts for more deaths than other types cancer making it a significant cause of morbidity and mortality. Non-small cell lung cancer (NSCLC) is potentially curable via surgical resection, if diagnosed in the early stages of the disease. However, as clinical signs of NSCLC are often absent until extensive disease is present, most patients are diagnosed at time where curative intent surgery is no longer a treatment option. Improving lung cancer outcomes in an unmet clinical need, which in part can be achieved by earlier diagnosis of patients. One way in which this may be achieved is via cancer screening programmes. Currently the most readily available option for NSCLC screening is the use of low dose computer tomography (CT). However, while CT can identify lesions in the lung it is not possible to definitively diagnose lung cancer with this technology. This thesis describes the approaches taken to use patient derived materials to identify novel biomarkers of Non-Small Cell Lung Cancer (NSCLC), which could be used to develop targeted molecular imaging agents to improve the diagnostic specificity of CT for lung cancer diagnosis. As imaging agents are best able to access targets of the surface of cell membranes and in the extracellular environment these regions were targeted for biomarker discovery. This thesis first discusses attempts to isolate surface proteins on cancer epithelial cells found in malignant pleural fluid and tissue samples. Despite attempts at epithelial cell enrichment, these tissues were not a viable option for biomarker discovery as the samples were largely composed of stromal cells and leukocytes. Therefore, alternative approaches were used to look for NSCLC biomarkers in the tumour microenvironment. These included using liquid chromatography tandem mass spectrometry (LC-MS/MS) to look at matrisome proteins in small tissue samples and isolating leukocytes, the most abundant cell type in tumour samples to identify subtypes of tumour infiltrating myeloid cells using flow cytometry. Key findings from exploration of matrisome proteins included that 161 matrisome proteins were differentially expressed between matched tumour and non-tumour samples collected from patients with early-stage disease. Novel biomarkers differentially expressed between tumour and non-tumour tissues include the enzymes ADAMST16 and peroxidasin. Collagen molecules and many other core extracellular matrix proteins were relatively more abundant in non-tumour tissues. We also detected a relative increase in tumour samples of enzymes such involved in protein hydroxylation and a post-translational difference in the hydroxylation of lysine residues in collagen compared to non-tumour tissues. These changes may suggest that cross-linking of collagen and extracellular matrix stiffness differs between tumour and non tumour tissues. To investigate novel markers targeting cancer associated myeloid cells in tissue samples, flow cytometry was performed for surface markers, which early stage or pre-clinical research suggest may be of immunoregulatory importance. These ligands included PD-L1, PD-L2, c-MET and Lox-1. PD-L1 expressing neutrophils were increased in tumour tissue compared to non-tumour lung tissue and PD-L1 appears to be a marker for potentially immunosuppressive (low density neutrophils) in circulation. These results indicate that neutrophils expressing PD-L1, a ligand with known immunosuppressant actions may identify a subset of tumour associated neutrophils in non-small cell lung cancer patients. The results presented in this thesis indicate there are differences in matrisome proteins and leukocytes found in the tumour stroma compared to non-tumour lung tissue. These proteins are worthy of further investigation to determine their utility as biomarkers which can discriminate between cancerous and benign pulmonary nodules, with the view to developing imaging agents which may improve on existing technologies for lung cancer diagnosis and thus improve patient outcomes by diagnosing patients at an early stage of their disease.