title: Transferring genomics to the clinic: distinguishing Burkitt and diffuse large B cell lymphomas.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: School of Molecular and Cellular Biology, Garstang Building, University of Leeds, Leeds, LS2 9JT UK.
authors.forename: Chulin
authors.initials: C
authors.lastname: Sha
authors.affiliation: Haematological, Malignancy Diagnostic Service, St James's University Hospital, Leeds, UK.
authors.forename: Sharon
authors.initials: S
authors.lastname: Barrans
authors.affiliation: Section of Experimental Haematology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK.
authors.forename: Matthew A
authors.initials: MA
authors.lastname: Care
authors.affiliation: Royal Marsden Hospital, Fulham Road, London, SW3 6JJ UK.
authors.forename: David
authors.initials: D
authors.lastname: Cunningham
authors.affiliation: Haematological, Malignancy Diagnostic Service, St James's University Hospital, Leeds, UK ; Section of Experimental Haematology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK.
authors.forename: Reuben M
authors.initials: RM
authors.lastname: Tooze
authors.affiliation: Haematological, Malignancy Diagnostic Service, St James's University Hospital, Leeds, UK.
authors.forename: Andrew
authors.initials: A
authors.lastname: Jack
authors.affiliation: School of Molecular and Cellular Biology, Garstang Building, University of Leeds, Leeds, LS2 9JT UK.
authors.forename: David R
authors.initials: DR
authors.lastname: Westhead