Loeffler-Wirth, Henry
[VerfasserIn];
Kreuz, Markus
[VerfasserIn];
Schmidt, Maria
[VerfasserIn];
Ott, German
[VerfasserIn];
Siebert, Reiner
[VerfasserIn];
Binder, Hans
[VerfasserIn]
Classifying Germinal Center Derived Lymphomas
: Navigate a Complex Transcriptional Landscape
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Medientyp:
E-Artikel
Titel:
Classifying Germinal Center Derived Lymphomas
:
Navigate a Complex Transcriptional Landscape
Beteiligte:
Loeffler-Wirth, Henry
[VerfasserIn];
Kreuz, Markus
[VerfasserIn];
Schmidt, Maria
[VerfasserIn];
Ott, German
[VerfasserIn];
Siebert, Reiner
[VerfasserIn];
Binder, Hans
[VerfasserIn]
Anmerkungen:
Hinweis: Link zur Erstveröffentlichung URL: https://doi.org/10.3390/cancers14143434
Beschreibung:
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