Early Detection of Lung Cancer : “A Role for Serum Biomarkers?”

Lung cancer has the highest mortality rate among cancer patients in the world, in particular because most patients are only diagnosed at an advanced and non-curable stage. Computed tomography (CT) screening on high-risk individuals has shown that early detection could reduce the mortality rate. However, the still high false-positive rate of CT may harm healthy individuals because of unnecessary follow-up scans and invasive follow-up procedures. On the other hand, false-negative and indeterminate results may harm patients due to the delayed diagnosis and treatment of lung cancer. Non-invasive biomarkers, complementary to CT screening, could lower the false-positive and false-negative rate of CT screening at baseline screening. The aim of this thesis was to identify lung cancer-associated proteins, especially sequences of autoantibodies, as potential non-invasive biomarkers for early detection of lung cancer. We applied immunological and high-performance proteomics techniques to identify and quantify these proteins in serum of high-risk individuals from a well-controlled multicenter population study (NELSON)

Peptides from the variable region of specific antibodies are shared among lung cancer patients

Late diagnosis of lung cancer is still the main reason for high mortality rates in lung cancer. Lung cancer is a heterogeneous disease which induces an immune response to different tumor antigens. Several methods for searching autoantibodies have been described that are based on known purified antigen panels. The aim of our study is to find evidence that parts of the antigen-binding-domain of antibodies are shared among lung cancer patients. This was investigated by a novel approach based on sequencing antigen-binding- fragments (Fab) of immunoglobulins using proteomic techniques without the need of previously known antigen panels. From serum of 93 participants of the NELSON trial IgG was isolated and subsequently digested into Fab and Fc. Fab was purified from the digested mixture by SDS-PAGE. The Fab containing gel-bands were excised, tryptic digested and measured on a nano-LC-Orbitrap-Mass- spectrometry system. Multivariate analysis of the mass spectrometry data by linear canonical discriminant analysis combined with stepwise logistic regression resulted in a 12-antibody-peptide model which was able to distinguish lung cancer patients from controls in a high risk population with a sensitivity of 84% and specificity of 90%. With our Fab-purification combined Orbitrap-mass-spectrometry approach, we found peptides from the variable-parts of antibodies which are shared among lung cancer patients