Literature DB >> 17105813

A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1.

John D Shaughnessy1, Fenghuang Zhan, Bart E Burington, Yongsheng Huang, Simona Colla, Ichiro Hanamura, James P Stewart, Bob Kordsmeier, Christopher Randolph, David R Williams, Yan Xiao, Hongwei Xu, Joshua Epstein, Elias Anaissie, Somashekar G Krishna, Michele Cottler-Fox, Klaus Hollmig, Abid Mohiuddin, Mauricio Pineda-Roman, Guido Tricot, Frits van Rhee, Jeffrey Sawyer, Yazan Alsayed, Ronald Walker, Maurizio Zangari, John Crowley, Bart Barlogie.   

Abstract

To molecularly define high-risk disease, we performed microarray analysis on tumor cells from 532 newly diagnosed patients with multiple myeloma (MM) treated on 2 separate protocols. Using log-rank tests of expression quartiles, 70 genes, 30% mapping to chromosome 1 (P < .001), were linked to early disease-related death. Importantly, most up-regulated genes mapped to chromosome 1q, and down-regulated genes mapped to chromosome 1p. The ratio of mean expression levels of up-regulated to down-regulated genes defined a high-risk score present in 13% of patients with shorter durations of complete remission, event-free survival, and overall survival (training set: hazard ratio [HR], 5.16; P < .001; test cohort: HR, 4.75; P < .001). The high-risk score also was an independent predictor of outcome endpoints in multivariate analysis (P < .001) that included the International Staging System and high-risk translocations. In a comparison of paired baseline and relapse samples, the high-risk score frequency rose to 76% at relapse and predicted short postrelapse survival (P < .05). Multivariate discriminant analysis revealed that a 17-gene subset could predict outcome as well as the 70-gene model. Our data suggest that altered transcriptional regulation of genes mapping to chromosome 1 may contribute to disease progression, and that expression profiling can be used to identify high-risk disease and guide therapeutic interventions.

Entities:  

Mesh:

Year:  2006        PMID: 17105813     DOI: 10.1182/blood-2006-07-038430

Source DB:  PubMed          Journal:  Blood        ISSN: 0006-4971            Impact factor:   22.113


  357 in total

Review 1.  Many multiple myelomas: making more of the molecular mayhem.

Authors:  Marta Chesi; P Leif Bergsagel
Journal:  Hematology Am Soc Hematol Educ Program       Date:  2011

2.  An intermediate-risk multiple myeloma subgroup is defined by sIL-6r: levels synergistically increase with incidence of SNP rs2228145 and 1q21 amplification.

Authors:  Owen W Stephens; Qing Zhang; Pingping Qu; Yiming Zhou; Shweta Chavan; Erming Tian; David R Williams; Joshua Epstein; Bart Barlogie; John D Shaughnessy
Journal:  Blood       Date:  2011-11-09       Impact factor: 22.113

3.  Prediction of cytogenetic abnormalities with gene expression profiles.

Authors:  Yiming Zhou; Qing Zhang; Owen Stephens; Christoph J Heuck; Erming Tian; Jeffrey R Sawyer; Marie-Astrid Cartron-Mizeracki; Pingping Qu; Jason Keller; Joshua Epstein; Bart Barlogie; John D Shaughnessy
Journal:  Blood       Date:  2012-04-10       Impact factor: 22.113

4.  Epigenetic modulation of MAGE-A3 antigen expression in multiple myeloma following treatment with the demethylation agent 5-azacitidine and the histone deacetlyase inhibitor MGCD0103.

Authors:  Amberly Moreno-Bost; Susann Szmania; Katie Stone; Tarun Garg; Antje Hoerring; Jackie Szymonifka; John Shaughnessy; Bart Barlogie; H Grant Prentice; Frits van Rhee
Journal:  Cytotherapy       Date:  2010-12-20       Impact factor: 5.414

5.  A case of aggressive myeloma recognized shortly after the remission following high-dose chemotherapy with autologous peripheral blood stem cell transplantation.

Authors:  Kaname Ueda; Katsuhiro Miura; Yoshihiro Hatta; Sumiko Kobayashi; Toshitake Tanaka; Atsuko Hojo; Hikaru Ishizuka; Umihiko Sawada; Yoshimasa Kura; Jin Takeuchi
Journal:  Int J Hematol       Date:  2010-08-20       Impact factor: 2.490

6.  Combining fluorescent in situ hybridization data with ISS staging improves risk assessment in myeloma: an International Myeloma Working Group collaborative project.

Authors:  H Avet-Loiseau; B G M Durie; M Cavo; M Attal; N Gutierrez; J Haessler; H Goldschmidt; R Hajek; J H Lee; O Sezer; B Barlogie; J Crowley; R Fonseca; N Testoni; F Ross; S V Rajkumar; P Sonneveld; J Lahuerta; P Moreau; G Morgan
Journal:  Leukemia       Date:  2012-10-03       Impact factor: 11.528

7.  Current approaches to the initial treatment of symptomatic multiple myeloma.

Authors:  Jagoda K Jasielec; Andrzej J Jakubowiak
Journal:  Int J Hematol Oncol       Date:  2013-02

8.  NEK2 induces drug resistance mainly through activation of efflux drug pumps and is associated with poor prognosis in myeloma and other cancers.

Authors:  Wen Zhou; Ye Yang; Jiliang Xia; He Wang; Mohamed E Salama; Wei Xiong; Hongwei Xu; Shashirekha Shetty; Tiehua Chen; Zhaoyang Zeng; Lei Shi; Maurizio Zangari; Rodney Miles; David Bearss; Guido Tricot; Fenghuang Zhan
Journal:  Cancer Cell       Date:  2013-01-14       Impact factor: 31.743

9.  Hyperhaploidy is a novel high-risk cytogenetic subgroup in multiple myeloma.

Authors:  J R Sawyer; E Tian; J D Shaughnessy; J Epstein; C M Swanson; C Stangeby; C L Hale; L Parr; M Lynn; G Sammartino; J L Lukacs; C Stein; C Bailey; M Zangari; F E Davies; F Van Rhee; B Barlogie; G J Morgan
Journal:  Leukemia       Date:  2016-10-03       Impact factor: 11.528

10.  Infusion of haplo-identical killer immunoglobulin-like receptor ligand mismatched NK cells for relapsed myeloma in the setting of autologous stem cell transplantation.

Authors:  Jumei Shi; Guido Tricot; Susann Szmania; Nancy Rosen; Tarun K Garg; Priyangi A Malaviarachchi; Amberly Moreno; Bo Dupont; Katharine C Hsu; Lee Ann Baxter-Lowe; Michele Cottler-Fox; John D Shaughnessy; Bart Barlogie; Frits van Rhee
Journal:  Br J Haematol       Date:  2008-10-16       Impact factor: 6.998

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.