Abstract
BACKGROUND: Large cell neuroendocrine carcinoma (LCNEC) of the lung, a subtype of large cell carcinoma (LCC), is characterized by neuroendocrine differentiation that small cell lung carcinoma (SCLC) shares. Pre-therapeutic histological distinction between LCNEC and SCLC has so far been problematic, leading to adverse clinical outcome. We started a project establishing protein targets characteristic of LCNEC with a proteomic method using formalin fixed paraffin-embedded (FFPE) tissues, which will help make diagnosis convincing. METHODS: Cancer cells were collected by laser microdissection from cancer foci in FFPE tissues of LCNEC (n = 4), SCLC (n = 5), and LCC (n = 5) with definite histological diagnosis. Proteins were extracted from the harvested sections, trypsin-digested, and subjected to HPLC/mass spectrometry. Proteins identified by database search were semi-quantified by spectral counting and statistically sorted by pair-wise G-statistics. The results were immunohistochemically verified using a total of 10 cases for each group to confirm proteomic results. RESULTS: A total of 1981 proteins identified from the three cancer groups were subjected to pair-wise G-test under p < 0.05 and specificity of a protein's expression to LCNEC was checked using a 3D plot with the coordinates comprising G-statistic values for every two group comparisons. We identified four protein candidates preferentially expressed in LCNEC compared with SCLC with convincingly low p-values: aldehyde dehydrogenase 1 family member A1 (AL1A1) (p = 6.1 × 10-4), aldo-keto reductase family 1 members C1 (AK1C1) (p = 9.6x10-10) and C3 (AK1C3) (p = 3.9x10-10) and CD44 antigen (p = 0.021). These p-values were confirmed by non-parametric exact inference tests. Interestingly, all these candidates would belong to cancer stem cell markers. Immunohistochmistry supported proteomic results. CONCLUSIONS: These results suggest that candidate biomarkers of LCNEC were related to cancer stem cells and this proteomic approach via FFPE samples was effective to detect them.