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Tumor Engraftment in a Xenograft Mouse Model of Human Mantle Cell Lymphoma
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A modular transcriptome map of mature B cell lymphomas.

Henry Loeffler-Wirth1, Markus Kreuz2, Lydia Hopp3

  • 1Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, 04107, Leipzig, Germany. wirth@izbi.uni-leipzig.de.

Genome Medicine
|May 2, 2019
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Summary

This study maps lymphoma transcriptomes, revealing a continuum of molecular phenotypes rather than distinct subtypes. Inflammation signatures correlate with better survival, while proliferation signatures indicate poorer outcomes in these B cell lymphomas.

Keywords:
B cell malignanciesGene regulationMachine learningMolecular subtypesTumor heterogeneity

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Area of Science:

  • Hematology
  • Oncology
  • Bioinformatics

Background:

  • Germinal center-derived B cell lymphomas are highly heterogeneous malignancies.
  • Characterized transcriptomic phenotypes across 873 biopsy specimens from the German Cancer Aid MMML consortium.
  • Includes various lymphoma types: DLBCL, FL, Burkitt's, primary mediastinal, multiple myeloma, and others.

Purpose of the Study:

  • To create a holistic transcriptome map of lymphomas using machine learning.
  • To describe gene regulation and co-expression patterns.
  • To stratify lymphomas based on molecular phenotypes and survival associations.

Main Methods:

  • Applied self-organizing map (SOM) machine learning to microarray expression data.
  • Integrated expression data with pathological, genetic, and clinical characteristics.
  • Developed combinatorial pattern types (PATs) for lymphoma stratification.

Main Results:

  • Generated a transcriptome map visualizing lymphoma strata and individual cases.
  • Identified a dozen co-expression modules linked to lymphoma pathogenesis.
  • Revealed a molecular continuum rather than discrete phenotypes, with distinct PAT groups and cancer hallmark types.
  • Found inflammation signatures associated with better survival, while proliferation signatures worsened it.
  • Detailed transitions between FL and DLBCL, and DLBCL subtypes resembling Burkitt's lymphoma.

Conclusions:

  • The transcriptome map serves as a valuable tool for lymphoma research.
  • It aids in visualizing, refining, and interpreting complex lymphoma data.
  • Provides orientation and support for current and future lymphoma studies and other cancer entities.