A novel five-antibody immunohistochemical test for subclassification of lung carcinoma.

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 muM 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.

Novel prognostic immunohistochemical biomarker panel for estrogen receptor-positive breast cancer.

PURPOSE: Patients with breast cancer experience progression and respond to treatment in diverse ways, but prognostic and predictive tools for the oncologist are limited. We have used gene expression data to guide the production of hundreds of novel antibody reagents to discover novel diagnostic tools for stratifying carcinoma patients. PATIENTS AND METHODS: One hundred forty novel and 23 commercial antisera, selected on their ability to differentially stain tumor samples, were used to stain paraffin blocks from a retrospective breast cancer cohort. Cox proportional hazards and regression tree analysis identified minimal panels of reagents able to predict risk of recurrence. We tested the prognostic association of these prospectively defined algorithms in two independent cohorts. RESULTS: In both validation cohorts, the Kaplan-Meier estimates of recurrence confirmed that both the Cox model using five reagents (p53, NDRG1, CEACAM5, SLC7A5, and HTF9C) and the regression tree model using six reagents (p53, PR, Ki67, NAT1, SLC7A5, and HTF9C) distinguished estrogen receptor (ER)-positive patients with poor outcomes. The Cox model was superior and distinguished patients with poor outcomes from patients with good or moderate outcomes with a hazard ratio of 2.21 (P = .0008) in validation cohort 1 and 1.88 (P = .004) in cohort 2. In multivariable analysis, the calculated risk of recurrence was independent of stage, grade, and lymph node status. A model proposed for ER-negative patients failed validation in the independent cohorts. CONCLUSION: A panel of five antibodies can significantly improve on traditional prognosticators in predicting outcome for ER-positive breast cancer patients.