3 research outputs found

    Prognostic Significance of MYC Rearrangement and Translocation Partner in Diffuse Large B-Cell Lymphoma : A Study by the Lunenburg Lymphoma Biomarker Consortium

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    PURPOSE: MYC rearrangement (MYC-R) occurs in approximately 10% of diffuse large B-cell lymphomas (DLBCLs) and has been associated with poor prognosis in many studies. The impact of MYC-R on prognosis may be influenced by the MYC partner gene (immunoglobulin [IG] or a non-IG gene). We evaluated a large cohort of patients through the Lunenburg Lymphoma Biomarker Consortium to validate the prognostic significance of MYC-R (single-, double-, and triple-hit status) in DLBCL within the context of the MYC partner gene. METHODS: The study cohort included patients with histologically confirmed DLBCL morphology derived from large prospective trials and patient registries in Europe and North America who were uniformly treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone therapy or the like. Fluorescence in situ hybridization for the MYC, BCL2, BCL6, and IG heavy and light chain loci was used, and results were correlated with clinical outcomes. RESULTS: A total of 5,117 patients were identified of whom 2,383 (47%) had biopsy material available to assess for MYC-R. MYC-R was present in 264 (11%) of 2,383 patients and was associated with a significantly shorter progression-free and overall survival, with a strong time-dependent effect within the first 24 months after diagnosis. The adverse prognostic impact of MYC-R was only evident in patients with a concurrent rearrangement of BCL2 and/or BCL6 and an IG partner (hazard ratio, 2.4; 95% CI, 1.6 to 3.6; P < .001). CONCLUSION: The negative prognostic impact of MYC-R in DLBCL is largely observed in patients with MYC double hit/triple-hit disease in which MYC is translocated to an IG partner, and this effect is restricted to the first 2 years after diagnosis. Our results suggest that diagnostic strategies should be adopted to identify this high-risk cohort, and risk-adjusted therapeutic approaches should be refined further

    Lack of reproducibility of histopathological features in MYC-rearranged large B cell lymphoma using digital whole slide images: a study from the Lunenburg lymphoma biomarker consortium

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    Aims: Subclassification of large B cell lymphoma (LBCL) is challenging due to the overlap in histopathological, immunophenotypical and genetic data. In particular, the criteria to separate diffuse large B cell lymphoma (DLBCL) and high-grade B cell lymphoma (HGBL) are difficult to apply in practice. The Lunenburg Lymphoma Biomarker Consortium previously reported a cohort of over 5000 LBCL that included fluorescence in-situ hybridisation (FISH) data. This cohort contained 209 cases with MYC rearrangement that were available for a validation study by a panel of eight expert haematopathologists of how various histopathological features are used. Methods and results: Digital whole slide images of haematoxylin and eosin-stained sections allowed the pathologists to visually score cases independently as well as participate in virtual joint review conferences. Standardised consensus guidelines were formulated for scoring histopathological features and included overall architecture/growth pattern, presence or absence of a starry-sky pattern, cell size, nuclear pleomorphism, nucleolar prominence and a range of cytological characteristics. Despite the use of consensus guidelines, the results show a high degree of discordance among the eight expert pathologists. Approximately 50% of the cases lacked a majority score, and this discordance spanned all six histopathological features. Moreover, none of the histological variables aided in prediction of MYC single versus double/triple-hit or immunoglobulin-partner FISH-based designations or clinical outcome measures. Conclusions: Our findings indicate that there are no specific conventional morphological parameters that help to subclassify MYC-rearranged LBCL or select cases for FISH analysis, and that incorporation of FISH data is essential for accurate classification and prognostication
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