Background: Gene-expression arrays have generated molecular predictors of relapse and drug sensitivity in breast cancer. We aimed to identify exons differently expressed in malignant and benign breast lesions and to generate a molecular classifier for breast-cancer diagnosis.
Methods: 165 breast samples were obtained by fine-needle aspiration. Complementary DNA was hybridised on splice array. A nearest centroid prediction rule was developed to classify lesions as malignant or benign on a training set, and its performance was assessed on an independent validation set. A two-way ANOVA model identified probe sets with differential expression in malignant and benign lesions while adjusting for scan dates.
Findings: 120 breast cancers and 45 benign lesions were included in the study. A molecular classifier for breast-cancer diagnosis with 1228 probe sets was generated from the training set (n=94). This signature accurately classified all samples (100% accuracy, 95% CI 96-100%). In the validation set (n=71), the molecular predictor accurately classified 68 of 71 tumours (96%, 88-99%). When the 165 samples were taken into account, 37 858 exon probe sets (5.4%) and 3733 genes (7.0%) were differently expressed in malignant and benign lesions (threshold: adjusted p<0.05). Genes involved in spliceosome assembly were significantly overexpressed in malignant disease (permutation p=0.002). In the same population of 165 samples, 956 exon probe sets presented both higher intensity and higher splice index in breast cancer than in benign lesions, although located on unchanged genes.
Interpretation: Many exons are differently expressed by breast cancer and benign lesions, and alternative transcripts contribute to the molecular characteristics of breast malignancy. Development of molecular classifiers for breast-cancer diagnosis with fine-needle aspiration should be possible.