Authors
Daniel Xia; Alberto Jose Leon; Jiong Yan; Anjali Silva; Mehran Bakhtiari; Rosemarie Tremblay-LeMay; Shamini Selvarajah; Peter Sabatini; Phedias Diamandis; Trevor Pugh; Robert Kridel; Jan Delabie
Abstract
Although most small B-cell lymphomas (SBCLs) can be diagnosed using routine methods, challenges exist. For example, marginal zone lymphomas (MZLs) can be difficult to rule-in, in large part because no widely-used, sensitive, and specific biomarker is available for the marginal zone cell of origin. In this study, it was hypothesized that DNA methylation array profiling can assist with the classification of SBCLs, including MZLs. Extramedullary SBCLs, including challenging cases, were reviewed internally for pathology consensus and profiled. By combining the resulting array data set with data sets from other groups, a set of 26 informative probes was selected and used to train machine learning models to classify 4 common SBCLs: chronic lymphocytic leukemia/small lymphocytic lymphoma, follicular lymphoma, mantle cell lymphoma, and MZL. Prediction probability cutoff was used to separate classifiable from unclassifiable cases, and show that the trained model was able to classify 95% of independent test cases (n = 264/279). The concordance between model predictions and pathology diagnoses was 99.6% (n = 262/263) among classifiable test cases. One validation reference test case was reclassified based on model prediction. The model was also used to predict the diagnoses of two challenging SBCLs. Although the differential examined and data on difficult cases are limited, these results support accurate methylation-based classification of SBCLs. Furthermore, high specificities of predictions suggest that methylation signatures can be used to rule-in MZLs.
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