4.7 Article

LMO2 protein expression predicts survival in patients with diffuse large B-Cell lymphoma treated with anthracycline-based chemotherapy with and without rituximab

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JOURNAL OF CLINICAL ONCOLOGY
卷 26, 期 3, 页码 447-454

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AMER SOC CLINICAL ONCOLOGY
DOI: 10.1200/JCO.2007.13.0690

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  1. NCI NIH HHS [CA 33399, CA34233, CA122105, CA109335] Funding Source: Medline

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Purpose The heterogeneity of diffuse large B-cell lymphoma (DLBCL) has prompted the search for new markers that can accurately separate prognostic risk groups. We previously showed in a multivariate model that LMO2 mRNA was a strong predictor of superior outcome in DLBCL patients. Here, we tested the prognostic impact of LMO2 protein expression in DLBCL patients treated with anthracycline-based chemotherapy with or without rituximab. Patients and Methods DLBCL patients treated with anthracycline-based chemotherapy alone ( 263 patients) or with the addition of rituximab ( 80 patients) were studied using immunohistochemistry for LMO2 on tissue microarrays of original biopsies. Staining results were correlated with outcome. Results In anthracycline-treated patients, LMO2 protein expression was significantly correlated with improved overall survival ( OS) and progression-free survival (PFS) in univariate analyses ( OS, P =.018; PFS, P =.010) and was a significant predictor independent of the clinical International Prognostic Index (IPI) in multivariate analysis. Similarly, in patients treated with the combination of anthracycline-containing regimens and rituximab, LMO2 protein expression was also significantly correlated with improved OS and PFS ( OS, P =.005; PFS, P =.009) and was a significant predictor independent of the IPI in multivariate analysis. Conclusion We conclude that LMO2 protein expression is a prognostic marker in DLBCL patients treated with anthracycline-based regimens alone or in combination with rituximab. After further validation, immunohistologic analysis of LMO2 protein expression may become a practical assay for newly diagnosed DLBCL patients to optimize their clinical management.

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