Gene expression profiles for the prediction of progression-free survival in diffuse large B cell lymphoma: results of a DASL assay

被引:23
|
作者
Kim, Seok Jin [1 ]
Sohn, Insuk [2 ]
Do, In-Gu [2 ]
Jung, Sin Ho [2 ,3 ]
Ko, Young Hyeh
Yoo, Hae Yong
Paik, Soonmyung [2 ]
Kim, Won Seog [1 ]
机构
[1] Sungkyunkwan Univ, Sch Med, Samsung Med Ctr, Dept Med,Div Hematol Oncol, Seoul 135710, South Korea
[2] Samsung Canc Res Inst, Seoul, South Korea
[3] Duke Univ, Dept Biostat & Bioinformat, Durham, NC USA
关键词
Diffuse large B cell lymphoma; Gene; Prognosis; CHEMOTHERAPY PLUS RITUXIMAB; PROGNOSTIC IMPACT; INDUCED APOPTOSIS; ELDERLY-PATIENTS; LUNG CANCERS; R-CHOP; PROTEIN; ERA; CLASSIFICATION; IDENTIFICATION;
D O I
10.1007/s00277-013-1884-0
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
We performed the whole genome cDNA-mediated annealing, selection and ligation assay with 164 formalin-fixed paraffin-embedded (FFPE) tumor samples to develop robust prognostic gene expression profiles in patients with diffuse large B cell lymphoma. The prognostic gene expression profiles were developed and validated by a gradient lasso and leave-one-out cross-validation process. We identified a set of genes whose expression provided prognostic indicators from whole data set (PRKCDBP, CASP10, FAM3C, KCNK12, MAN1A2, PRND, RAB1A, TMEM39B, SLC6A6, MMP12, FEM1B, C3orh37, RBP1, HK1, LOC400464, KIAA0746, and SLC25A23). This gene expression profile-based risk model could classify patients into two cross-validated risk groups with a significant difference in 5-year progression-free survival rates (71.1 vs. 45.5 %) and with a hazard ratio for recurrence of 2.45 (95 % CI, 1.44-4.16, P = 0.001). This model provided prognostic information independent of the International Prognostic Index (IPI), and discriminated high-risk group from patients belong to high/high-intermediate risk of IPI and activated B cell-like type. Thus, gene expression profiling from FFPE could provide additional prognostic information for diffuse large B cell lymphoma and our data underscore the need for development of risk-adapted treatment strategies based on gene expression profiles.
引用
收藏
页码:437 / 447
页数:11
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