Sha Chulin, Barrans Sharon, Care Matthew A, Cunningham David, Tooze Reuben M, Jack Andrew, Westhead David R
School of Molecular and Cellular Biology, Garstang Building, University of Leeds, Leeds, LS2 9JT UK.
Haematological, Malignancy Diagnostic Service, St James's University Hospital, Leeds, UK.
Genome Med. 2015 Jul 1;7(1):64. doi: 10.1186/s13073-015-0187-6. eCollection 2015.
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.
基于分子标准（如基因表达特征）的分类器已被开发出来，用于区分伯基特淋巴瘤和弥漫性大B细胞淋巴瘤，这有助于探索传统诊断困难的中间病例。将这些研究用的分类器应用于临床具有挑战性，因为文献中有基于不同方法和基因集的相互竞争的分类器，没有明确的最佳选择；基于一个表达测量平台的分类器可能无法有效地转移到另一个平台；而且，使用新鲜冷冻样本开发的分类器可能无法有效地应用于常规诊断中常用且更方便的福尔马林固定石蜡包埋样本。
在此，我们全面比较了两个基于不同Affymetrix阵列平台和新鲜冷冻组织数据开发的、已发表的知名分类器，检验它们的可转移性和一致性。基于此分析，开发了一种新的伯基特和弥漫性大B细胞淋巴瘤分类器（BDC），并将其应用于我们自己石蜡包埋样本的Illumina DASL数据，以便与中央血液病理学实验室的诊断结果进行比较，并评估其临床相关性。
我们表明，使用比最初使用的基因集小得多的基因集就能重现之前的两个分类器，并且分类结果紧密依赖于训练集中应用的伯基特淋巴瘤标准。我们数据上的BDC分类与原始诊断结果高度一致（约95%）。对根据传统标准呈现中间特征的患者进行的简单结果比较表明，被BDC分类为伯基特淋巴瘤的病例对标准弥漫性大B细胞淋巴瘤治疗的反应比被分类为弥漫性大B细胞淋巴瘤的病例更差。
在本研究中，我们全面研究了之前的两个伯基特淋巴瘤分子分类器，并实施了一种新的基因表达分类器BDC，它能有效地应用于石蜡包埋样本，并为治疗决策提供有用信息。该分类器可通过链接http://www.bioinformatics.leeds.ac.uk/labpages/softwares/或在github上的https://github.com/Sharlene/BDC以GNU公共许可下的免费软件包形式在R统计软件环境中获取。
