Refine
Has Fulltext
- yes (2)
Is part of the Bibliography
- yes (2)
Document Type
- Journal article (2)
Language
- English (2) (remove)
Keywords
- B cell malignancies (1)
- MTCH2 (1)
- apoptosis (1)
- gene expression (1)
- gene regulation (1)
- machine learning (1)
- molecular subtypes (1)
- myasthenia gravis (1)
- targeted (1)
- thymic carcinoma (1)
- thymoma (1)
- thymus (1)
- tumor heterogeneity (1)
Institute
The molecular pathogenesis of thymomas and thymic arcinomas (TCs) is poorly understood and results of adjuvant therapy are unsatisfactory in case of metastatic disease and tumor recurrence. For these clinical settings, novel therapeutic strategies are urgently needed. Recently, limited sequencing efforts revealed that a broad spectrum of genes that play key roles in various common cancers are rarely affected in thymomas and TCs, suggesting that other oncogenic principles might be important.This made us re-analyze historic expression data obtained in a spectrumof thymomas and thymic squamous cell carcinomas (TSCCs) with a custom-made cDNA microarray. By cluster analysis, different anti-apoptotic signatures were detected in type B3 thymoma and TSCC, including overexpression of BIRC3 in TSCCs. This was confirmed by qRT-PCR in the original and an independent validation set of tumors. In contrast to several other cancer cell lines, the BIRC3-positive TSCC cell line, 1889c showed spontaneous apoptosis after BIRC3 knock-down. Targeting apoptosis genes is worth testing as therapeutic principle in TSCC.
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.