Gene Expression Profiling for Diagnosis of Triple-Negative Breast Cancer: A Multicenter, Retrospective Cohort Study.

FRONTIERS IN ONCOLOGY(2019)

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摘要
Background: Triple-negative breast cancer (TNBC) accounts for 12-20% of all breast cancers. Diagnosis of TNBC is sometimes quite difficult based on morphological assessment and immunohistochemistry alone, particularly in the metastatic setting with no prior history of breast cancer. Methods: Molecular profiling is a promising diagnostic approach that has the potential to provide an objective classification of metastatic tumors with unknown primary. In this study, performance of a novel 90-gene expression signature for determination of the site of tumor origin was evaluated in 115 TNBC samples. For each specimen, expression profiles of the 90 tumor-specific genes were analyzed, and similarity scores were obtained for each of the 21 tumor types on the test panel. Predicted tumor type was compared to the reference diagnosis to calculate accuracy. Furthermore, rank product analysis was performed to identify genes that were differentially expressed between TNBC and other tumor types. Results: Analysis of the 90-gene expression signature resulted in an overall 97.4% (112/115, 95% CI: 0.92-0.99) agreement with the reference diagnosis. Among all specimens, the signature correctly classified 97.6% of TNBC fromthe primary site (41/42) and lymph node metastasis (41/42) and 96.8% of distant metastatic tumors (30/31). Furthermore, a list of genes, including AZGP1, KRT19, and PIGR, was identified as differentially expressed between TNBC and other tumor types, suggesting their potential use as discriminatory markers. Conclusion: Our results demonstrate excellent performance of a 90-gene expression signature for identification of tumor origin in a cohort of both primary and metastatic TNBC samples. These findings show promise for use of this novel molecular assay to aid in differential diagnosis of TNBC, particularly in the metastatic setting.
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关键词
triple-negative breast cancer,tissue of origin,gene expression profiling,quantitative real-time PCR,tumor classification
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