In:
Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 26, No. 14 ( 2020-07-15), p. 3760-3770
Kurzfassung:
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. Experimental Design: 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 (n = 160). The four-CpG classifier was validated in the internal testing cohort (n = 68) and independent validation cohort (n = 321). Results: The four-CpG–based classifier discriminated patients with T-LBL at high risk of relapse in the training cohort from those at low risk (P & lt; 0.001). This classifier also showed good predictive value in the internal testing cohort (P & lt; 0.001) and the independent validation cohort (P & lt; 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 NOTCH1/FBXW7 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. Conclusions: Our four-CpG–based classifier could predict disease relapse in patients with T-LBL, and could be used to guide treatment decision.
Materialart:
Online-Ressource
ISSN:
1078-0432
,
1557-3265
DOI:
10.1158/1078-0432.CCR-19-4207
Sprache:
Englisch
Verlag:
American Association for Cancer Research (AACR)
Publikationsdatum:
2020
ZDB Id:
1225457-5
ZDB Id:
2036787-9
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