A gene expression signature that can predict the recurrence of tamoxifen-treated primary breast cancer.

PURPOSE: The identification of a molecular signature predicting the relapse of tamoxifen-treated primary breast cancers should help the therapeutic management of estrogen receptor-positive cancers. EXPERIMENTAL DESIGN: A series of 132 primary tumors from patients who received adjuvant tamoxifen were analyzed for expression profiles at the whole-genome level by 70-mer oligonucleotide microarrays. A supervised analysis was done to identify an expression signature. RESULTS: We defined a 36-gene signature that correctly classified 78% of patients with relapse and 80% of relapse-free patients (79% accuracy). Using 23 independent tumors, we confirmed the accuracy of the signature (78%) whose relevance was further shown by using published microarray data from 60 tamoxifen-treated patients (63% accuracy). Univariate analysis using the validation set of 83 tumors showed that the 36-gene classifier is more efficient in predicting disease-free survival than the traditional histopathologic prognostic factors and is as effective as the Nottingham Prognostic Index or the "Adjuvant!" software. Multivariate analysis showed that the molecular signature is the only independent prognostic factor. A comparison with several already published signatures demonstrated that the 36-gene signature is among the best to classify tumors from both training and validation sets. Kaplan-Meier analyses emphasized its prognostic power both on the whole cohort of patients and on a subgroup with an intermediate risk of recurrence as defined by the St. Gallen criteria. CONCLUSION: This study identifies a molecular signature specifying a subgroup of patients who do not gain benefits from tamoxifen treatment. These patients may therefore be eligible for alternative endocrine therapies and/or chemotherapy.

[Digitalized imaging pathology: application to quality control in cancer diagnosis]. / La numérisation d'images en anatomie-pathologique: application a l'assurance qualité pour le diagnostic de cancer.

OBJECTIVES: To test a double histology reading system based on digitalized imaging for cancer diagnosis. MATERIAL AND METHODS: Pathology images of cancer diagnosis material were produced in real time by a digital imaging system integrated into the laboratory data processing system. Over a 30-day period, second readings were performed using the digitalized images. Cases with second readings were classified according to the aspect on the digitalized images as malignant tumor with histological type, malignant tumor with no other precision, and doubtful malignancy. RESULTS: During the study period, 204 cases of cancer were diagnosed, including 178 with digitalized imaging (87%). Among the digitalized cases, 119 (67%) were classified as malignant tumor with histological type, 53 (30%) as malignant tumor with no other precision, and 6 (3%) as doubtful malignancy. The histology material of these latter cases were reviewed and corresponded to malignant tumors. Approximately 2 hours per week were devoted to the second readings. CONCLUSION: A integrated digitalized imaging system can participate in quality control of cancer diagnosis by allowing rapid efficacious second readings.