A Non-small Cell Lung Cancer Tissue Microarray to Analyze Protein Expression

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To date, we have developed a comprehensive lung tissue microarray that included 300 NSCLC cases and 183 neuroendocrine tumors. We have engaged in a systematical evaluation of candidate protein markers for lung cancer diagnosis and prognosis. The development and use of tissue microarray have made it possible to effectively evaluate gene expression status in hundreds of human tissue specimens. However, objective assessment of protein staining on the individual tissue core placed on the array is often time consuming and potentially variable. To overcome this limitation, we explored a computer-aided semiautomated scoring (CASA) method that allows fast and reliable scoring of the immunohsitochemistry staining status of the tissue cores on our lung tissue microarray containing 150 lung adenicarcinoma(AD) and 150 squamous cell carcinoma(SCC) cases. TMA slides were scanned by Aperio ScanScope TM GL System and viewed by Aperio ImageScope soft ware (version 7.3.36). For scoring, the images were transferred to Aperio-TMA lab image analysis software (version 7.0.1.235) and analyzed using Aperio's Positive Pixel Count Algorithm which is based on the distribution and intensity of staining signal in each tissue core sample. We optimized the scoring algorithm which allowed us to quantitatively determine the staining status of the individual tissues cores. To evaluate the robustness of this approach, we first compared the CASA approach to IHC staining data for 12 chromosome remodeling proteins scored by a trained pathologist using clustering analysis. The clustering of protein factors by CASA approach was nearly identical those obtained by manual scoring. This demonstrates that our computer-aided scoring method is comparable to the manually scoring method. Using this approach, we evaluated the expression of nearly 100 proteins commonly known to be associated with cancer development. Statistical analysis allowed us to identify genes whose co-expression is strongly associated with clinical survival of the lung cancer patients. In addition, we have made substantial progress toward using genetic markers for lung cancer diagnosis. In collaboration with clinical researchers at Cornell University, we have successfully developed a method using genetic markers to identify lung cancers in fine needle aspirate (FNA) biopsies. Chromosome copy number gains in 1q32, 3q26, 5p15 and 8q24 were used as markers to determine the neoplastic state of FNA biopsies using fluorescent in situ hybridization (FISH). We used two sets of lung of samples. The training set included six paraffin embedded non-cancer lung tissues and 33 formalin-fixed biopsies of lung tumors to establish optimal fixation and FISH scoring criteria for lung cancer detection. The testing set contained 40 routine paraffin sections of FNA biopsies with mixed tumor subtypes and benign lung diseases for used to evaluate the sensitivity and specificity of the FISH method. Our results showed that using only four markers, the neoplastic state of FNA biopsies can be identified with 100% specificity and 94% sensitivity, respectively, in analyzable samples regardless of tumor subtype, stage, and size. Genetic diagnosis is highly specific for lung identification in FNA biopsy and could potentially complement routine cytology and radiology in lung cancer screening diagnosis and follow up. We have now modified this highly sensitive and specific method to the use of chromogenic substrate so that genetic copy number changes can be determined together with tissue morphology and pathology.