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The hallmark of Philadelphia chromosome positive (Ph+) leukemia is the BCR/ABL kinase, which is successfully targeted by selective ATP competitors. However, inhibition of BCR/ABL alone is unable to eradicate Ph+ leukemia. The t(9;22) is a reciprocal translocation which encodes not only for the der22 (Philadelphia chromosome) related BCR/ABL, but also for der9 related ABL/BCR fusion proteins, which can be detected in 65% of patients with chronic myeloid leukemia (CML) and 100% of patients with Ph+ acute lymphatic leukemia (ALL). ABL/BCRs are oncogenes able to influence the lineage commitment of hematopoietic progenitors. Aim of this study was to further disclose the role of p96ABL/BCR for the pathogenesis of Ph+ ALL. The co-expression of p96ABL/BCR enhanced the kinase activity and as a consequence, the transformation potential of p185BCR/ABL. Targeting p96ABL/BCR by RNAi inhibited growth of Ph+ ALL cell lines and Ph+ ALL patient-derived long-term cultures (PD-LTCs). Our in vitro and in vivo stem cell studies further revealed a functional hierarchy of p96ABL/BCR and p185BCR/ABL in hematopoietic stem cells. Co-expression of p96ABL/BCR abolished the capacity of p185BCR/ABL to induce a CML-like disease and led to the induction of ALL. Taken together our here presented data reveal an important role of p96ABL/BCR for the pathogenesis of Ph+ ALL.
The pathogenesis of nodular lymphocyte–predominant Hodgkin lymphoma (NLPHL) and its relationship to other lymphomas are largely unknown. This is partly because of the technical challenge of analyzing its rare neoplastic lymphocytic and histiocytic (L&H) cells, which are dispersed in an abundant nonneoplastic cellular microenvironment. We performed a genome-wide expression study of microdissected L&H lymphoma cells in comparison to normal and other malignant B cells that indicated a relationship of L&H cells to and/or that they originate from germinal center B cells at the transition to memory B cells. L&H cells show a surprisingly high similarity to the tumor cells of T cell–rich B cell lymphoma and classical Hodgkin lymphoma, a partial loss of their B cell phenotype, and deregulation of many apoptosis regulators and putative oncogenes. Importantly, L&H cells are characterized by constitutive nuclear factor {kappa}B activity and aberrant extracellular signal-regulated kinase signaling. Thus, these findings shed new light on the nature of L&H cells, reveal several novel pathogenetic mechanisms in NLPHL, and may help in differential diagnosis and lead to novel therapeutic strategies.
Bioinformatics analysis quantifies neighborhood preferences of cancer cells in Hodgkin lymphoma
(2017)
Motivation Hodgkin lymphoma is a tumor of the lymphatic system and represents one of the most frequent lymphoma in the Western world. It is characterized by Hodgkin cells and Reed-Sternberg cells, which exhibit a broad morphological spectrum. The cells are visualized by immunohistochemical staining of tissue sections. In pathology, tissue images are mainly manually evaluated, relying on the expertise and experience of pathologists. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of cancer cells is of great interest.
Results Here, we systematically quantified and investigated cancer cell properties and their spatial neighborhood relations by applying statistical analyses to whole slide images of Hodgkin lymphoma and lymphadenitis, which describes a non-cancerous inflammation of the lymph node. We differentiated cells by their morphology and studied the spatial neighborhood relation of more than 400,000 immunohistochemically stained cells. We found that, according to their morphological features, the cells exhibited significant preferences for and aversions to cells of specific profiles as nearest neighbor. We quantified differences between Hodgkin lymphoma and lymphadenitis concerning the neighborhood relations of cells and the sizes of cells. The approach can easily be applied to other cancer types.
Background: Nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) usually presents in middle aged men and shows an indolent clinical behavior. However, up to 30% of the patients present a secondary transformation into aggressive diffuse large B cell lymphoma (DLBCL). The aim of the present study was to characterize morphology and immunophenotype of this kind of DLBCL in detail and compare it with conventional DLBCL.
Methods: Morphology and immunophenotype of 33 cases of NLPHL with simultaneous or sequential transformation into DLBCL were investigated. These cases were compared with 41 de novo DLBCL in Finnish men.
Results: The majority of cases exhibited different immunophenotypes in the NLPHL and the DLBCL components. The immunophenotype of the DLBCL secondary to NLPHL was heterogeneous. However, BCL6, EMA, CD75 and J-chain were usually expressed in both components (≥73% positive). Overall, the NLPHL component was more frequently positive for EMA, CD75 and J-chain than the DLBCL component. In contrast, B cell markers, CD10 and BCL2, were more frequently expressed and were expressed at higher levels in the DLBCL component than in the NLPHL component. In the independent series of de novo DLBCL 4 cases could be identified with a growth pattern and immunophenotype that suggested that they had arisen secondarily from NLPHL.
Conclusions: The morphology and immunophenotype of DLBCL arisen from NLPHL is heterogeneous. Further characterization of the particular molecular features of this subgroup is warranted to be able to better identify these cases among conventional DLBCL.
Background: Increased glycolytic activity is a hallmark of cancer, allowing staging and restaging with 18F-fluorodeoxyglucose-positron-emission-tomography (PET). Since interim-PET is an important prognostic tool in Hodgkin lymphoma (HL), the aim of this study was to investigate the expression of proteins involved in the regulation of glucose metabolism in the different HL subtypes and their impact on clinical outcome.
Methods: Lymph node biopsies from 54 HL cases and reactive lymphoid tissue were stained for glucose transporter 1 (GLUT1), lactate dehydrogenase A (LDHA) and lactate exporter proteins MCT1 and MCT4. In a second series, samples from additional 153 HL cases with available clinical data were stained for GLUT1 and LDHA.
Results: Membrane bound GLUT1 expression was frequently observed in the tumor cells of HL (49% of all cases) but showed a broad variety between the different Hodgkin lymphoma subtypes: Nodular sclerosing HL subtype displayed a membrane bound GLUT1 expression in the Hodgkin-and Reed-Sternberg cells in 56% of the cases. However, membrane bound GLUT1 expression was more rarely observed in tumor cells of lymphocyte rich classical HL subtype (30%) or nodular lymphocyte predominant HL subtype (15%). Interestingly, in both of these lymphocyte rich HL subtypes as well as in progressively transformed germinal centers, reactive B cells displayed strong expression of GLUT1. LDHA, acting downstream of glycolysis, was also expressed in 44% of all cases. We evaluated the prognostic value of different GLUT1 and LDHA expression patterns; however, no significant differences in progression free or overall survival were found between patients exhibiting different GLUT1 or LDHA expression patterns. There was no correlation between GLUT1 expression in HRS cells and PET standard uptake values.
Conclusions: In a large number of cases, HRS cells in classical HL express high levels of GLUT1 and LDHA indicating glycolytic activity in the tumor cells. Although interim-PET is an important prognostic tool, a predictive value of GLUT1 or LDHA staining of the primary diagnostic biopsy could not be demonstrated. However, we observed GLUT1 expression in progressively transformed germinal centers and hyperplastic follicles, explaining false positive results in PET. Therefore, PET findings suggestive of HL relapse should always be confirmed by histology.
In contrast to the commonly indolent clinical behavior of nodular lymphocyte predominant Hodgkin lymphoma (NLPHL), T cell/histiocyte rich large B cell lymphoma (THRLBCL) is frequently diagnosed in advanced clinical stages and has a poor prognosis. Besides the different clinical presentations of these lymphoma entities, there are variants of NLPHL with considerable histopathologic overlap compared to THRLBCL. Especially THRLBCL-like NLPHL, a diffuse form of NLPHL, often presents a histopathologic pattern similar to THRLBCL, suggesting a close relationship between both lymphoma entities. To corroborate this hypothesis, we performed gene expression profiling of microdissected tumor cells of NLPHL, THRLBCL-like NLPHL and THRLBCL. In unsupervised analyses, the lymphomas did not cluster according to their entity. Moreover, even in supervised analyses, very few consistently differentially expressed transcripts were found, and for these genes the extent of differential expression was only moderate. Hence, there are no clear and consistent differences in the gene expression of the tumor cells of NLPHL, THRLBCL-like NLPHL and THRLBCL. Based on the gene expression studies, we identified BAT3/BAG6, HIGD1A, and FAT10/UBD as immunohistochemical markers expressed in the tumor cells of all three lymphomas. Characterization of the tumor microenvironment for infiltrating T cells and histiocytes revealed significant differences in the cellular composition between typical NLPHL and THRLBCL cases. However, THRLBCL-like NLPHL presented a histopathologic pattern more related to THRLBCL than NLPHL. In conclusion, NLPHL and THRLBCL may represent a spectrum of the same disease. The different clinical behavior of these lymphomas may be strongly influenced by differences in the lymphoma microenvironment, possibly related to the immune status of the patient at the timepoint of diagnosis.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
Zur genomweiten Genexpressionsanalyse werden Microarray-Experimente verwendet. Ziel dieser Arbeit ist es, Methoden zur Präprozessierung von Microarrays der Firma Affymetrix zu evaluieren und die VSN-Methode für Experimente mit weniger als 1000 Zellen zu verbessern. Bei dieser Technologie wird die Expression jedes Gens durch mehrere Probessets gemessen. Jedes Probeset besteht aus einem Perfect-Match (PM) und einem dazugehörigen Mismatch (MM). Der Expressionswert pro Gen wird durch ein vierstufiges Verfahren aus den einzelnen Probe-Werten berechnet: Hintergrundkorrektur, Normalisierung, PM-Adjustierung und Aggregation. Für jeden dieser Schritte existieren mehrere Algorithmen. Dazu dienten die im affy-Paket des Bioconductor implementierten Methoden MAS5, RMA, VSN und die Methode sRMA von Cope et al. [Cope et al., 2006] in Kombination mit der Methode VSN von Huber et al. [Huber et al., 2002]. Den ersten Teil dieser Arbeit bildet die Reanalyse der Datensätze von Küppers et al. [Küppers et al., 2003] und Piccaluga et al. [Piccaluga et al., 2007] mit der VSN-Methode. Dabei konnte gezeigt werden, dass die VSN-Methode gegenüber Klein et al. [Klein et al., 2001] Vorteile zeigt. Bei beiden Datensätzen wurden zusätzliche Gene gefunden, die für die Pathogenese der jeweiligen Tumorarten wichtig sein können. Einige der zusätzlich gefunden Gene wurden durch andere wissenschaftliche Arbeiten bestätigt. Die Gene, die bisher in keinem Zusammenhang mit der untersuchten Tumorart stehen, sind eine Möglichkeit für die weitere Forschung. Vor allem der Zytokine/Zytokine Signalweg wurde bei beiden Reanalysen als überrepräsentiert erkannt. Da für einige Microarray-Experimente die Anzahl der Zellen und damit die Menge an mRNA nur begrenzt zur Verfügung stehen, müssen die Laborarbeit und die statistischen Analysen angepasst werden. Hierzu werden fünf Methoden für die Präprozessierung untersucht, um zu evaluieren, welche Methode geeignet ist, derartige Expressionsdaten zu verrechnen. Auf Basis eines Testdatensatzes der bereits zur Etablierung des Laborprozesses diente werden Expressionswerte durch empirische Verteilung, Gammaverteilung und ein linear gemischtes Modell simuliert. Die Simulation lässt sich in vier Schritte einteilen: Wahl der Verteilung, Simulation der Expressionsmatrix, Simulation der differentiellen Expression, Sortierung der Probes innerhalb des Probesets. Anschließend werden die fünf Präprozessierungsmethoden mit diesen simulierten Expressionsdaten auf ihre Sensitivität und Spezifität untersucht. Während sich bei den empirisch und gammaverteilt simulierten Expressionsdaten kein eindeutiges Ergebnis abzeichnet, hat sVSN bei den Daten aus dem linear gemischten Modell die größte Sensitivität und die größte Spezifität. Der in dieser Arbeit entwickelte sVSN-Algorithmus wurde zum ersten Mal angewendet und bewertet. Abschließend wird ein Teildatensatz von Brune et al. verwendet und hinsichtlich der fünf Präprozessierungsmethoden untersucht. Die Ergebnisse der sVSN-Methode wird im Detail weiter verfolgt. Die zusätzlich gefunden Gene können durch bereits veröffentlichte Arbeiten bestätigt werden. Letztendlich zeigt sich, dass neuere statistische Methoden (wie das im Rahmen dieser Arbeit entwickelte sVSN) bei der Analyse von Affymetrix Microarrays einen Vorteil bringen. Die sVSN und sRMA Methoden zeigen Vorteile, da die Probes nach der Normalisierung gewichtet werden, bevor diese aggregiert werden. Die MAS5-Methode schneidet am schlechtesten ab und sollte bei geringen Zellmengen nicht eingesetzt werden. Für die Analyse mit geringer Menge an mRNA müssen weitere Untersuchungen vorgenommen werden, um eine geeignete statistische Methode für die Analyse der Expressionsdaten zu finden.
The hallmark of classical Hodgkin lymphoma (cHL) is the presence of giant, mostly multinucleated Hodgkin-Reed-Sternberg (HRS) cells. Whereas it has recently been shown that giant HRS cells evolve from small Hodgkin cells by incomplete cytokinesis and re-fusion of tethered sister cells, it remains unsolved why this phenomenon particularly takes place in this lymphoma and what the differences between these cell types of variable sizes are. The aim of the present study was to characterize microdissected small and giant HRS cells by gene expression profiling and to assess differences of clonal growth behavior as well as susceptibility toward cytotoxic intervention between these different cell types to provide more insight into their distinct cellular potential. Applying stringent filter criteria, only two differentially expressed genes between small and giant HRS cells, SHFM1 and LDHB, were identified. With looser filter criteria, 13 genes were identified to be differentially overexpressed in small compared to giant HRS cells. These were mainly related to energy metabolism and protein synthesis, further suggesting that small Hodgkin cells resemble the proliferative compartment of cHL. SHFM1, which is known to be involved in the generation of giant cells, was downregulated in giant RS cells at the RNA level. However, reduced mRNA levels of SHFM1, LDHB and HSPA8 did not translate into decreased protein levels in giant HRS cells. In cell culture experiments it was observed that the fraction of small and big HRS cells was adjusted to the basic level several days after enrichment of these populations via cell sorting, indicating that small and big HRS cells can reconstitute the full spectrum of cells usually observed in the culture. However, assessment of clonal growth of HRS cells indicated a significantly reduced potential of big HRS cells to form single cell colonies. Taken together, our findings pinpoint to strong similarities but also some differences between small and big HRS cells.
The mechanisms involved in malignant transformation of mature B and T lymphocytes are still poorly understood. In a previous study, we compared gene expression profiles of the tumor cells of Hodgkin lymphoma (HL) and anaplastic large cell lymphoma (ALCL) to their normal cellular counterparts and found the basic leucine zipper protein ATF-like 3 (BATF3) to be significantly upregulated in the tumor cells of both entities. To assess the oncogenic potential of BATF3 in lymphomagenesis and to dissect the molecular interactions of BATF3 in lymphoma cells, we retrovirally transduced murine mature T and B cells with a BATF3-encoding viral vector and transplanted each population into Rag1-deficient recipients. Intriguingly, BATF3-expressing B lymphocytes readily induced B-cell lymphomas after characteristic latencies, whereas T-cell transplanted animals remained healthy throughout the observation time. Further analyses revealed a germinal center B-cell-like phenotype of most BATF3-initiated lymphomas. In a multiple myeloma cell line, BATF3 inhibited BLIMP1 expression, potentially illuminating an oncogenic action of BATF3 in B-cell lymphomagenesis. In conclusion, BATF3 overexpression induces malignant transformation of mature B cells and might serve as a potential target in B-cell lymphoma treatment.