BACKGROUND Germinal center-derived B cell lymphomas are tumors of the lymphoid tissues representing one of the most heterogeneous malignancies. Here we characterize the variety of transcriptomic phenotypes of this disease based on 873 biopsy specimens collected in the German Cancer Aid MMML (Molecular Mechanisms in Malignant Lymphoma) consortium. They include diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), Burkitt's lymphoma, mixed FL/DLBCL lymphomas, primary mediastinal large B cell lymphoma, multiple myeloma, IRF4-rearranged large cell lymphoma, MYC-negative Burkitt-like lymphoma with chr. 11q aberration and mantle cell lymphoma. METHODS We apply self-organizing map (SOM) machine learning to microarray-derived expression data to generate a holistic view on the transcriptome landscape of lymphomas, to describe the multidimensional nature of gene regulation and to pursue a modular view on co-expression. Expression data were complemented by pathological, genetic and clinical characteristics. RESULTS We present a transcriptome map of B cell lymphomas that allows visual comparison between the SOM portraits of different lymphoma strata and individual cases. It decomposes into one dozen modules of co-expressed genes related to different functional categories, to genetic defects and to the pathogenesis of lymphomas. On a molecular level, this disease rather forms a continuum of expression states than clearly separated phenotypes. We introduced the concept of combinatorial pattern types (PATs) that stratifies the lymphomas into nine PAT groups and, on a coarser level, into five prominent cancer hallmark types with proliferation, inflammation and stroma signatures. Inflammation signatures in combination with healthy B cell and tonsil characteristics associate with better overall survival rates, while proliferation in combination with inflammation and plasma cell characteristics worsens it. A phenotypic similarity tree is presented that reveals possible progression paths along the transcriptional dimensions. Our analysis provided a novel look on the transition range between FL and DLBCL, on DLBCL with poor prognosis showing expression patterns resembling that of Burkitt's lymphoma and particularly on 'double-hit' MYC and BCL2 transformed lymphomas. CONCLUSIONS The transcriptome map provides a tool that aggregates, refines and visualizes the data collected in the MMML study and interprets them in the light of previous knowledge to provide orientation and support in current and future studies on lymphomas and on other cancer entities.

中文翻译：

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成熟 B 细胞淋巴瘤的模块化转录组图谱。


背景技术生发中心来源的B细胞淋巴瘤是代表最异质性恶性肿瘤之一的淋巴组织肿瘤。在此，我们根据德国癌症援助 MMML（恶性淋巴瘤分子机制）联盟收集的 873 份活检标本，描述了该疾病的各种转录组表型。它们包括弥漫性大 B 细胞淋巴瘤 (DLBCL)、滤泡性淋巴瘤 (FL)、伯基特淋巴瘤、混合 FL/DLBCL 淋巴瘤、原发性纵隔大 B 细胞淋巴瘤、多发性骨髓瘤、IRF4 重排大细胞淋巴瘤、MYC 阴性伯基特样淋巴瘤与chr。 11q 畸变和套细胞淋巴瘤。方法我们将自组织图谱 (SOM) 机器学习应用于微阵列衍生的表达数据，以生成淋巴瘤转录组景观的整体视图，描述基因调控的多维性质，并追求共表达的模块化视图。表达数据由病理、遗传和临床特征补充。结果我们提出了 B 细胞淋巴瘤的转录组图谱，可以对不同淋巴瘤层和个体病例的 SOM 图像进行视觉比较。它分解成十几个共表达基因的模块，这些模块与不同的功能类别、遗传缺陷和淋巴瘤的发病机制相关。在分子水平上，这种疾病形成了连续的表达状态，而不是清晰分离的表型。我们引入了组合模式类型 (PAT) 的概念，将淋巴瘤分为九个 PAT 组，并在更粗的层面上分为五种突出的癌症标志类型，包括增殖、炎症和基质特征。 炎症特征与健康 B 细胞和扁桃体特征相结合，与更好的总体存活率相关，而增殖与炎症和浆细胞特征相结合，则使总体存活率恶化。提出了表型相似性树，揭示了沿着转录维度的可能的进展路径。我们的分析为 FL 和 DLBCL 之间的转变范围提供了新的视角，对于预后不良的 DLBCL，显示出类似于伯基特淋巴瘤的表达模式，特别是在“双重打击”MYC 和 BCL2 转化淋巴瘤上。结论 转录组图谱提供了一种工具，可以聚合、细化和可视化 MMML 研究中收集的数据，并根据先前的知识对其进行解释，从而为当前和未来的淋巴瘤和其他癌症实体的研究提供方向和支持。