Loeffler-Wirth Henry, Kreuz Markus, Schmidt Maria, Ott German, Siebert Reiner, Binder Hans
Interdisciplinary Centre for Bioinformatics, University Leipzig (IZBI), 04107 Leipzig, Germany.
Fraunhofer Institute for Cell Therapy and Immunology (IZI), 04103 Leipzig, Germany.
Cancers (Basel). 2022 Jul 14;14(14):3434. doi: 10.3390/cancers14143434.
Classification of lymphoid neoplasms is based mainly on histologic, immunologic, and (rarer) genetic features. It has been supplemented by gene expression profiling (GEP) in the last decade. Despite the considerable success, particularly in associating lymphoma subtypes with specific transcriptional programs and classifier signatures of up- or downregulated genes, competing molecular classifiers were often proposed in the literature by different groups for the same classification tasks to distinguish, e.g., BL versus DLBCL or different DLBCL subtypes. Moreover, rarer sub-entities such as MYC and BCL2 "double hit lymphomas" (DHL), IRF4-rearranged large cell lymphoma (IRF4-LCL), and Burkitt-like lymphomas with 11q aberration pattern (mnBLL-11q) attracted interest while their relatedness regarding the major classes is still unclear in many respects. We explored the transcriptional landscape of 873 lymphomas referring to a wide spectrum of subtypes by applying self-organizing maps (SOM) machine learning. The landscape reveals a continuum of transcriptional states activated in the different subtypes without clear-cut borderlines between them and preventing their unambiguous classification. These states show striking parallels with single cell gene expression of the active germinal center (GC), which is characterized by the cyclic progression of B-cells. The expression patterns along the GC trajectory are discriminative for distinguishing different lymphoma subtypes. We show that the rare subtypes take intermediate positions between BL, DLBCL, and FL as considered by the 5th edition of the WHO classification of haemato-lymphoid tumors in 2022. Classifier gene signatures extracted from these states as modules of coregulated genes are competitive with literature classifiers. They provide functional-defined classifiers with the option of consenting redundant classifiers from the literature. We discuss alternative classification schemes of different granularity and functional impact as possible avenues toward personalization and improved diagnostics of GC-derived lymphomas.
淋巴样肿瘤的分类主要基于组织学、免疫学以及（较为罕见的）遗传学特征。在过去十年中，基因表达谱分析（GEP）对其进行了补充。尽管取得了相当大的成功，尤其是在将淋巴瘤亚型与特定转录程序以及上调或下调基因的分类特征相关联方面，但不同研究小组在文献中经常针对相同的分类任务提出相互竞争的分子分类器，以区分例如伯基特淋巴瘤（BL）与弥漫性大B细胞淋巴瘤（DLBCL）或不同的DLBCL亚型。此外，诸如MYC和BCL2“双打击淋巴瘤”（DHL）、IRF4重排大细胞淋巴瘤（IRF4-LCL）以及具有11q畸变模式的伯基特样淋巴瘤（mnBLL-11q）等罕见亚实体引起了关注，而它们在主要类别方面的相关性在许多方面仍不明确。我们通过应用自组织映射（SOM）机器学习探索了873例涵盖广泛亚型的淋巴瘤的转录图谱。该图谱揭示了在不同亚型中激活的连续转录状态，它们之间没有明确的界限，难以进行明确分类。这些状态与活跃生发中心（GC）的单细胞基因表达具有显著的相似性，活跃生发中心的特征是B细胞的循环进展。沿着GC轨迹的表达模式对于区分不同的淋巴瘤亚型具有鉴别性。我们表明，按照2022年世界卫生组织血液淋巴肿瘤分类第5版的分类，罕见亚型处于BL、DLBCL和滤泡性淋巴瘤（FL）之间的中间位置。从这些状态中提取的作为共调控基因模块的分类基因特征与文献中的分类器具有竞争力。它们为功能定义的分类器提供了认可文献中冗余分类器的选项。我们讨论了不同粒度和功能影响的替代分类方案，作为实现GC来源淋巴瘤个性化和改进诊断的可能途径。
