Dataset Information

Challenges and implications of genomics for T-cell lymphomas.

ABSTRACT: Treatment outcomes for patients with peripheral T-cell lymphomas (PTCLs) and advanced-stage cutaneous T-cell lymphomas (CTCLs) remain poor. The past few years have witnessed an explosion in our understanding of the genetics of these diverse malignancies. Many subtypes harbor highly recurrent mutations, including single-nucleotide variants, insertions/deletions, and chromosomal rearrangements, that affect T-cell receptor signaling, costimulatory molecules, JAK/STAT and phosphatidylinositol 3-kinase pathways, transcription factors, and epigenetic modifiers. An important subset of these mutations is included within commercially available, multigene panels and, in rare circumstances, indicate therapeutic targets. However, current preclinical and clinical evidence suggests that only a minority of mutations identified in TCLs indicate biologic dependence. With a few exceptions that we highlight, mutations identified in TCLs should not be routinely used to select targeted therapies outside of a clinical trial. Participation in trials and publication of both positive and negative results remain the most important mechanisms for improving patient outcomes.

SUBMITTER: Jacobsen ED

PROVIDER: S-EPMC6246015 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Challenges and implications of genomics for T-cell lymphomas.

Jacobsen Eric D ED Weinstock David M DM

Hematology. American Society of Hematology. Education Program 20181101 1

Treatment outcomes for patients with peripheral T-cell lymphomas (PTCLs) and advanced-stage cutaneous T-cell lymphomas (CTCLs) remain poor. The past few years have witnessed an explosion in our understanding of the genetics of these diverse malignancies. Many subtypes harbor highly recurrent mutations, including single-nucleotide variants, insertions/deletions, and chromosomal rearrangements, that affect T-cell receptor signaling, costimulatory molecules, JAK/STAT and phosphatidylinositol 3-kina ...[more]

PMID: 30504292

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Project description:Classifiers based on molecular criteria such as gene expression signatures have been developed to distinguish Burkitt lymphoma and diffuse large B cell lymphoma, which help to explore the intermediate cases where traditional diagnosis is difficult. Transfer of these research classifiers into a clinical setting is challenging because there are competing classifiers in the literature based on different methodology and gene sets with no clear best choice; classifiers based on one expression measurement platform may not transfer effectively to another; and, classifiers developed using fresh frozen samples may not work effectively with the commonly used and more convenient formalin fixed paraffin-embedded samples used in routine diagnosis.Here we thoroughly compared two published high profile classifiers developed on data from different Affymetrix array platforms and fresh-frozen tissue, examining their transferability and concordance. Based on this analysis, a new Burkitt and diffuse large B cell lymphoma classifier (BDC) was developed and employed on Illumina DASL data from our own paraffin-embedded samples, allowing comparison with the diagnosis made in a central haematopathology laboratory and evaluation of clinical relevance.We show that both previous classifiers can be recapitulated using very much smaller gene sets than originally employed, and that the classification result is closely dependent on the Burkitt lymphoma criteria applied in the training set. The BDC classification on our data exhibits high agreement (~95 %) with the original diagnosis. A simple outcome comparison in the patients presenting intermediate features on conventional criteria suggests that the cases classified as Burkitt lymphoma by BDC have worse response to standard diffuse large B cell lymphoma treatment than those classified as diffuse large B cell lymphoma.In this study, we comprehensively investigate two previous Burkitt lymphoma molecular classifiers, and implement a new gene expression classifier, BDC, that works effectively on paraffin-embedded samples and provides useful information for treatment decisions. The classifier is available as a free software package under the GNU public licence within the R statistical software environment through the link http://www.bioinformatics.leeds.ac.uk/labpages/softwares/ or on github https://github.com/Sharlene/BDC.

Dataset Information

Challenges and implications of genomics for T-cell lymphomas.

Publications

Challenges and implications of genomics for T-cell lymphomas.

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