Published online 23 November 2008
Haematologica, Vol 94, Issue 1, 61-69 doi:10.3324/haematol.12986
Copyright © 2009 by Ferrata Storti Foundation
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Malignant Lymphomas

The potential of copy number gains and losses, detected by array-based comparative genomic hybridization, for computational differential diagnosis of B-cell lymphomas and genetic regions involved in lymphomagenesis

Ichiro Takeuchi1, Hiroyuki Tagawa2, Akira Tsujikawa3, Masao Nakagawa2, Miyuki Katayama-Suguro3,2, Ying Guo2,4, Masao Seto2,5

1 Department of Scientific and Engineering Simulation, Nagoya Institute of Technology, Nagoya, Japan
2 Division of Molecular Medicine, Aichi Cancer Center Research Institute, Nagoya, Japan
3 Division of Information Engineering, Graduate School of Engineering, Mie University, Tsu, Japan
4 Department of Pathology, State Key Laboratory of Cancer Biology, Xijing Hospital, Fourth Military Medical University, Shaanxi, P.R.China
5 Department of Cancer Genetics, Nagoya University Graduate School of Medicine at Aichi Cancer Center, Nagoya, Japan

Correspondence: Ichiro Takeuchi, Ph.D. Department of Scientific and Engineering Simulation, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan. E-mail:takeuchi.ichiro{at}nitech.ac.jp

Background: The differentiation of biologically and clinically different malignant lymphoma diseases or subtypes is crucial because it leads to better prognostication and therapeutic decision-making. Attempts have been made at subtype classification for diagnosing lymphomas on the basis of gene-expression profiling. Although array-based comparative genomic hybridization (array CGH) has identified a characteristic genomic alteration pattern for each disease entity, it has not been clear whether each patient with certain genomic alterations can be classified by array CGH data.

Design and Methods: Data on copy number gains and losses for 46 diffuse large B-cell lymphomas and 29 mantle cell lymphomas were used. The gene expressions of the diffuse large B-cell lymphomas cases were profiled and hierarchical clustering revealed that 28 of them were of the activated B-cell type and 18 were of the germinal center-B-cell type. Using these data, we developed a computer algorithm to classify lymphoma diseases or subtypes on the basis of copy number gains and losses.

Results: The method correctly classified 88% of the diffuse large B-cell lymphomas and mantle cell lymphomas, and 83% of the activated B-cell and germinal center-B-cell subtypes. These results demonstrate that copy number gains and losses detected by array CGH can be used for classifying lymphomas into biologically and clinically distinct diseases or subtypes.

Conclusions: Our computer algorithm based on array CGH data successfully classified diffuse large B-cell lymphomas and mantle cell lymphomas and activated B-cell and germinal center-B-cell subtypes with high accuracy. An important finding is that the regions automatically identified by the computer algorithm were located in the critical regions that are likely to be involved in the development of lymphoma.

Key words: diffuse large B-cell lymphoma, mantle cell lymphoma, array CGH, genome profile, lymphoma classification.