Data from A CpG Methylation Classifier to Predict Relapse in Adults with T-Cell Lymphoblastic Lymphoma

Abstract

<div>AbstractPurpose:<p>Adults with T-cell lymphoblastic lymphoma (T-LBL) generally benefit from treatment with acute lymphoblastic leukemia (ALL)-like regimens, but approximately 40% will relapse after such treatment. We evaluated the value of CpG methylation in predicting relapse for adults with T-LBL treated with ALL-like regimens.</p>Experimental Design:<p>A total of 549 adults with T-LBL from 27 medical centers were included in the analysis. Using the Illumina Methylation 850K Beadchip, 44 relapse-related CpGs were identified from 49 T-LBL samples by two algorithms: least absolute shrinkage and selector operation (LASSO) and support vector machine–recursive feature elimination (SVM-RFE). We built a four-CpG classifier using LASSO Cox regression based on association between the methylation level of CpGs and relapse-free survival in the training cohort (<i>n</i> = 160). The four-CpG classifier was validated in the internal testing cohort (<i>n</i> = 68) and independent validation cohort (<i>n</i> = 321).</p>Results:<p>The four-CpG–based classifier discriminated patients with T-LBL at high risk of relapse in the training cohort from those at low risk (<i>P</i> < 0.001). This classifier also showed good predictive value in the internal testing cohort (<i>P</i> < 0.001) and the independent validation cohort (<i>P</i> < 0.001). A nomogram incorporating five independent prognostic factors including the CpG-based classifier, lactate dehydrogenase levels, Eastern Cooperative Oncology Group performance status, central nervous system involvement, and <i>NOTCH1</i>/<i>FBXW7</i> status showed a significantly higher predictive accuracy than each single variable. Stratification into different subgroups by the nomogram helped identify the subset of patients who most benefited from more intensive chemotherapy and/or sequential hematopoietic stem cell transplantation.</p>Conclusions:<p>Our four-CpG–based classifier could predict disease relapse in patients with T-LBL, and could be used to guide treatment decision.</p></div>