Malignant epithelial lung carcinoma can be subclassified by histology into several tumor types, including adenocarcinoma and squamous cell carcinoma. The need for a uniform method of classifying lung carcinomas is growing as clinical trials reveal treatment and side effect differences associated with histological subtypes. Diagnosis is primarily performed by morphological assessment. However, the increased use of needle biopsy has diminished the amount of tissue available for interpretation. These changes in how lung carcinomas are diagnosed and treated suggest that the development of improved molecular-based classification tools could improve patient management. We used a 551-patient surgical specimen lung carcinoma retrospective cohort from a regional hospital to assess the association of a large number of proteins with histological type by immunohistochemistry. Five of these antibodies, targeting the proteins TRIM29, CEACAM5, SLC7A5, MUC1, and CK5/6, were combined into one test using a weighted algorithm trained to discriminate adenocarcinoma from squamous cell carcinoma. Antibody-based classification on 600 M tissue array cores with the five-antibody test was compared to standard histological evaluation on surgical specimens in three independent lung carcinoma cohorts (combined population of 1111 patients). In addition, the five-antibody test was tested against the two-marker panel thyroid transcription factor-1 (TTF-1) and TP63. Both the five-antibody test and TTF-1/TP63 panel had similarly low misclassification rates on the validation cohorts compared to morphological-based diagnosis (4.1 vs 3.5%). However the percentage of patients remaining unclassifiable by TTF-1/TP63 (22%, 95% CI: 20-25%) was twice that of the five-antibody test (11%, 95% CI: 8-13%). The results of this study suggest the five-antibody test may have an immediate function in the clinic for helping pathologists distinguish lung carcinoma histological types. The results also suggest that if validated in prospectively defined clinical trials this classifier might identify candidates for targeted therapy that are overlooked with current diagnostic approaches. © 2009 USCAP, Inc.