Lung cancer is the most common cause of death from cancer worldwide, estimated to be responsible for nearly one in five (1.59 million deaths, 19.4% of the total). Lung cancer is acknowledged as a complex and heterogeneous disease, not only at the biochemical level (genes, proteins, metabolites) but also at the tissue, organism, and population level. In the past decade, with the advancements in high-throughput profiling technologies, a huge amount of work has been done to derive biomarkers to supplement clinical diagnosis. The levels of a variety of different biomarkers, such as proteins and metabolites, in biological fluid or tissue/cells could potentially detect cancer at an early stage, determine cancer subtype, or monitor the sensitivity/resistance to cancer treatment. The research in this thesis aims to discover new biomarkers, using proteomic techniques, with the potential to supplement current clinical criteria for the management of lung cancer patients. Label-free mass spectrometry of bronchoalveolar lavage fluid (BALF), blood (serum), tissue and cell lines was performed to identify candidate biomarkers and perturbed cellular pathways. Validation of significant results was performed using immunological methods and biochemical assays. These studies have yielded valuable information that has unravelled several key molecular events of lung cancer tumorigenesis, including proteomic signature of lung cancer in BALF, tissue and blood. BALF analysis identified a promising signature distinguish between adenocarcinoma of the lung and squamous cell carcinoma of the lung. Many proteins found to be significant changed in abundance in BALF also displayed similar trends in tissue specimens. Tumour heterogeneity was also evident when examining tumour specimens, reinforcing the need for panels of biomarkers and multiple sampling. At strong metabolic pattern was also evident during proteomics based investigations of clinical material, a result that was confirmed using metabolomics platforms to screen patient samples. Drug resistant protein patterns were also identified using label-free mass spectrometry on cell lines models demonstrating resistance to Apitolisib (GDC-0980), a dual phosphatidylinositol-3-kinase and mammalian target of rapamycin kinase inhibitor. Early in vitro data on resistant mechanisms associated with new lung cancer treatments is crucial to allow resistance to be detected in patients and to understand and potentially target resistant pathways. The molecular analysis of a variety of biospecimens has allowed the discovery of relevant candidate biomarkers and consequently the identification of novel proteins that may have a role in the development of lung cancer and establishment of drug resistance. There is a need for incorporating findings from multiple discovery platforms and multiple sample types into a lung cancer specific data framework that can improve our level of understanding of the disease process.