From gene expression patterns to IHC diagnostics: Discovery and validation of novel biomarkers for prediction of recurrence in non-small cell carcinoma of the lung (NSCLC)

Abstract

9509 Background: Gene expression data has revealed that lung cancer cases can be divided into biologically distinct groups with differing outcomes. We generated novel antibodies targeted by gene expression data to develop an IHC-based diagnostic test stratifying early stage lung cancer. Methods: Several hundred novel affinity-purified rabbit anti-peptide antisera were screened by IHC for robust differential staining of NSCLC cases. A selected panel of 43 were tested on tissue arrays which contained 329 paraffin block cores from NSCLC patients diagnosed at CCIH from 1989 to 2002 (mean follow-up of 3.3 years). Univariate analysis was used to identify reagents with an association with outcome. This subset of reagents was then used in Cox proportional hazard and regression tree analysis to build models for prediction of early recurrence. These predictor algorithms were validated by 10-fold cross-validation and on tested on an independent set of 84 Stage III/IV NSCLC patients. Results: 15 individual reagents showed an association with early or delayed recurrence (p<0.1). Algorithms using a panel of eight antibodies identified patients at high risk of recurrence in Stage I/II NSCLC (p<0.01). This algorithm was dependent upon first classifying patients as adenocarcinoma or squamous cell carcinoma which could be performed either by morphology assessment or antibody staining pattern. The predictive power of these models was confirmed on a cohort of Stage III/IV NSCLC patients at the UAB Comprehensive Cancer Center (p<0.05). Conclusions: Novel subtypes of lung cancer associated with poor outcome are distinguished by a panel of IHC antisera. These reagents should prove useful for patient stratification in care of early stage lung cancer patients. Author Disclosure Employment or Leadership Consultant or Advisory Role Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Applied Genomics Applied Genomics