Martin-Leo Hansmann

Zusammenfassung Lymphome werden grundlegend in Hodgkin-Lymphome und B‑ und T‑Zell-Lymphome eingeteilt. Ihre Diagnostik beruht auf morphologischen, immunhistochemischen und zunehmend molekularen Methoden. Das klassische Hodgkin-Lymphom ist durch Hodgkin-Reed-Sternberg-Zellen gekennzeichnet, die ein typisches Markerprofil (CD30+, CD15+, Pax5+) aufweisen und trotz ihrer geringen Anzahl diagnostisch entscheidend sind. Molekular konnten diese Tumoren als B‑Zell-Lymphome identifiziert werden, wobei die Tumorzellen häufig funktionslose Immunglobulingene besitzen. Bei B‑ und T‑Zell-Lymphomen stehen die Tumorzellen zahlenmäßig meist im Vordergrund. Ihre Einteilung erfolgt vor allem nach ihrer zellulären Herkunft. In seltenen Fällen treten sog. Kombinationslymphome auf, die Hodgkin- und andere Lymphom-Komponenten vereinen, z. B. B‑ und T‑Zell-Lymphome. Eine besondere Rolle spielen Grauzonenlymphome. Sie weisen sowohl Merkmale eines klassischen Hodgkin-Lymphoms als auch eines primär mediastinalen großzelligen B‑Zell-Lymphoms auf. Grauzonenlymphome werden in eine klassisches-Hodgkin-Lymphom(cHL)-ähnliche und eine primär-mediastinales-großzelliges-B-Zell-Lymphom(PMBCL)-ähnliche Form unterteilt, zeigen komplexe Diagnostik und sprechen teilweise schlecht auf Therapien an. Die Zukunft der Lymphomdiagnostik liegt in der Kombination klassischer histologischer Methoden mit digitalen Verfahren und molekularen Analysen. Moderne Bildgebung und Einzelzelluntersuchungen ermöglichen eine präzisere Bestimmung sowohl der Tumorzellen als auch ihres Mikromilieus. Darüber hinaus ermöglichen neuartige räumliche Transkriptomikverfahren Kombinationen verschiedener Techniken. 4D-Techniken erlauben Bewegungsanalysen lebender Gewebeschnitte und damit direkte Beobachtungen von Zellinteraktionen. So lassen sich im Gewebeverband örtlich definierte Einzelzellen mit Hybridisierungs- oder Sequenzierungstechniken charakterisieren und mithilfe der Bioinformatik Aussagen über Zell-Zell-Interaktionen und folgende molekulare Veränderungen treffen. Die technischen Entwicklungen eröffnen neue Perspektiven für die personalisierte Medizin und könnten helfen, den Erfolg innovativer Zelltherapien besser vorherzusagen. Dieser Beitrag wurde begleitend zum 62. IAP-Symposium „Lymphknoten und Lymphompathologie – Das Wesentliche“ erstellt.

Background Hematopathology is rapidly advancing. Combining structural, molecular, and dynamic analyses enables a deeper understanding of immune reactions and malignant lymphomas. Materials and methods Three-dimensional (3D) imaging uses fluorescence-stained tissue sections with confocal microscopy. Four-dimensional (4D) live tissue slices allow real-time visualization of immune cell movement. Drugs can be tested directly on fresh samples. Results Three-dimensional images show key immune cell types; 4D imaging reveals cell–cell interactions. Nivolumab, for example, prolonged T cell contact with Hodgkin cells. Conclusion Four-dimensional technology improves early disease detection, treatment monitoring, and drug development, bridging between molecular findings and clinical applications.

Nodal T-follicular helper cell lymphoma, angioimmunoblastic-type (AITL), is characterized by constitutional symptoms, advanced-stage disease, and generalized lymphadenopathy. A genetic hallmark of this lymphoma is the frequent occurrence of the RHOA mutation G17V in neoplastic cells, which is observed in around 60% of patients. Because RHOA is involved in both T-cell receptor downstream signalling and cell migration, we hypothesized that the characteristic presentation of AITL could be the result of enhanced tumor cell migration. Therefore, this study aimed to elucidate the impact of the RHOA variant G17V on the migration of neoplastic T cells. We transfected the T-cell lymphoma cell lines HH and HuT78 to stably express the RHOA-G17V variant. RHOA-G17V-expressing T cells did not exhibit enhanced motility compared to empty-vector-transfected cells in microchannels, a 3D collagen gel, or primary human lymphatic tissue. Cells of the HH cell line expressing RHOA-G17V had an increased number of cells with cleaved collagen compared with the empty-vector-transfected cells. Therefore, we hypothesized that the early spread of AITL tumor cells may be related to remodelling of the extracellular matrix. Accordingly, we observed a significant negative correlation between the relative area of collagen in histological sections from 18 primary AITL and the allele frequency of the RHOA-G17V mutation. In conclusion, our results suggest that the characteristic presentation of AITL with early, widespread dissemination of lymphoma cells is not the result of an enhanced migration capacity due to the RHOA-G17V mutation; instead, this feature may rather be related to extracellular matrix remodelling.

The simulation of immune response is a challenging task because quantitative data are scarce. Quantitative theoretical models either focus on specific cell–cell interactions or have to make assumptions about parameters. The broad variation of, e.g., the dimensions and abundance between lymph nodes as well as between individual patients hampers conclusive quantitative modeling. No theoretical model has been established representing a consensus on the set of major cellular processes involved in the immune response. In this paper, we apply the Petri net formalism to construct a semi-quantitative mathematical model of the lymph nodes. The model covers the major cellular processes of immune response and fulfills the formal requirements of Petri net models. The intention is to develop a model taking into account the viewpoints of experienced pathologists and computer scientists in the field of systems biology. In order to verify formal requirements, we discuss invariant properties and apply the asynchronous firing rule of a place/transition net. Twenty-five transition invariants cover the model, and each is assigned to a functional mode of the immune response. In simulations, the Petri net model describes the dynamic modes of the immune response, its adaption to antigens, and its loss of memory.

Histological sections of the lymphatic system are usually the basis of static (2D) morphological investigations. Here, we performed a dynamic (4D) analysis of human reactive lymphoid tissue using confocal fluorescent laser microscopy in combination with machine learning. Based on tracks for T-cells (CD3), B-cells (CD20), follicular T-helper cells (PD1) and optical flow of follicular dendritic cells (CD35), we put forward the first quantitative analysis of movement-related and morphological parameters within human lymphoid tissue. We identified correlations of follicular dendritic cell movement and the behavior of lymphocytes in the microenvironment. In addition, we investigated the value of movement and/or morphological parameters for a precise definition of cell types (CD clusters). CD-clusters could be determined based on movement and/or morphology. Differentiating between CD3- and CD20 positive cells is most challenging and long term-movement characteristics are indispensable. We propose morphological and movement-related prototypes of cell entities applying machine learning models. Finally, we define beyond CD clusters new subgroups within lymphocyte entities based on long term movement characteristics. In conclusion, we showed that the combination of 4D imaging and machine learning is able to define characteristics of lymphocytes not visible in 2D histology.

T-cell lymphomas are highly heterogeneous and their prognosis is poor under the currently available therapies. Enhancers of zeste homologue 1 and 2 (EZH1/2) are histone H3 lysine-27 trimethyltransferases (H3K27me3). Despite the rapid development of new drugs inhibiting EZH2 and/or EZH1, the molecular interplay of these proteins and the impact on disease progression and prognosis of patients with T-cell lymphomas remains insufficiently understood. In this study, EZH1/2 mutation status was evaluated in 33 monomorphic epitheliotropic intestinal T-cell lymphomas by next generation sequencing and EZH1/2 and H3K27me3 protein expression levels were detected by immunohistochemistry in 46 T-cell lymphomas. Correlations with clinicopathologic features were analyzed and survival curves generated. No EZH1 mutations and one (3%) EZH2 missense mutation were identified. In univariable analysis, high EZH1 expression was associated with an improved overall survival (OS) and progression-free survival (PFS) whereas high EZH2 and H3K27me3 expression were associated with poorer OS and PFS. Multivariable analysis revealed EZH1 (hazard ratio (HR) = 0.183; 95% confidence interval (CI): 0.044–0.767; p = 0.020;) and EZH2 (HR = 8.245; 95% CI: 1.898–35.826; p = 0.005) to be independent, divergent prognostic markers for OS. In conclusion, EZH1/2 protein expression had opposing effects on the prognosis of T-cell lymphoma patients.