Genome-wide MicroRNA profiling of mantle cell lymphoma reveal a distinct subgroup with poor prognosis

Authors

Javeed Iqbal, University of Nebraska Medical Center, Omaha, NE
Yulei Shen, University of Nebraska Medical Center, Omaha, NE
Yanyan Liu, University of Nebraska Medical Center, Omaha, NE
Kai Fu, University of Nebraska Medical Center, Omaha, NE
Elaine Jaffe, Center for Cancer Research, NCI, NIH, Bethesda, MDFollow
Cuiling Liu, University of Nebraska Medical Center, Omaha, NE
Zhongfeng Liu, University of Nebraska Medical Center, Omaha, NE
Cynthia Lachel, University of Nebraska Medical Center, Omaha, NE
Karen Deffenbacher, University of Nebraska Medical Center, Omaha, NE
Timothy Greiner, University of Nebraska Medical Center, Omaha, NE
Julie Vose, University of Nebraska Medical Center, Omaha, NE
Sharathkumar Bhagavathi, University of Nebraska Medical Center, Omaha, NE
Louis Staudt, Center for Cancer Research, NCI, NIH, Bethesda, MD
Lisa Rimsza, University of Arizona, Tucson
Andreas Rosenwald, University of Würzburg, Würzburg, Germany
German Ott, Institute of Clinical Pharmacology, Robert-Bosch-Hospital, Stuttgart, Germany
Jan Delabie, University of Oslo, Oslo, Norway
Elias Campo, University of Barcelona, Barcelona, Spain
Rita Braziel, Clinical Pathology, Oregon Health, OR
James Cook, Cleveland Clinic, Cleveland, OH
Raymond Tubbs, Cleveland Clinic, Cleveland, OH
Randy Gascoyne, Center for Lymphoid Cancer, British Columbia Cancer Agency (BCCA), Vancouver, British Columbia
James Armitage, University of Nebraska Medical Center, Omaha, NE
Dennis Weisenburger, University of Nebraska Medical Center, Omaha, NE
Timothy McKeithan, University of Nebraska Medical Center, Omaha, NE
Wing Chan, University of Nebraska Medical Center, Omaha, NE

Date of this Version

4-5-2012

Citation

Blood First Edition Paper, prepublished online April 5, 2012; DOI 10.1182/blood-2011-07-370122

Abstract

MicroRNA (miRNA) deregulation has been implicated in the pathogenesis of mantle cell lymphoma (MCL). Using a high-throughput quantitative real-time PCR platform, we performed miRNA profiling on cyclin D1- positive MCL (n=30) and cyclin D1-negative MCL (n=7) and compared them with small lymphocytic leukemia/lymphoma (SLL, n=12), aggressive B-cell lymphomas (n=138), normal B-cell subsets and stromal cells. We identified a 19-miRNA classifier which included six upregulated miRNAs (miR-135a, miR-708, miR-150, miR-363, miR-184, miR-342-5p) and 13 downregulated miRNAs, that was able to distinguish MCL from other aggressive lymphomas with >90% probability. Some of these upregulated miRNAs are highly expressed in naïve B-cells. MicroRNA classifier showed consistent results in FFPE tissues and was able to distinguish cyclin D1-negative MCL from other lymphomas. A 26-miRNA classifier could distinguish MCL from SLL, dominated by 23 upregulated miRNAs in MCL. Unsupervised hierarchical clustering of MCL cases demonstrated a cluster characterized by high expression of miRNAs from polycistronic miR17~92 cluster and its paralogs miR-106a-363 and miR-106b-25, which was distinct from the other clusters showing enrichment of stroma associated miRNAs. The corresponding gene-expressionprofiling (GEP) data showed that the former cluster of MCL had significantly higher proliferation genesignature (PS), while the other subsets had higher expression of stroma associated genes. Clinical outcome analysis suggests that miRNAs can serve as prognosticators.