Literature DB >> 18591550

Prediction of survival in multiple myeloma based on gene expression profiles reveals cell cycle and chromosomal instability signatures in high-risk patients and hyperdiploid signatures in low-risk patients: a study of the Intergroupe Francophone du Myélome.

Olivier Decaux1, Laurence Lodé, Florence Magrangeas, Catherine Charbonnel, Wilfried Gouraud, Pascal Jézéquel, Michel Attal, Jean-Luc Harousseau, Philippe Moreau, Régis Bataille, Loïc Campion, Hervé Avet-Loiseau, Stéphane Minvielle.   

Abstract

PURPOSE: Survival of patients with multiple myeloma is highly heterogeneous, from periods of a few weeks to more than 10 years. We used gene expression profiles of myeloma cells obtained at diagnosis to identify broadly applicable prognostic markers. PATIENTS AND METHODS: In a training set of 182 patients, we used supervised methods to identify individual genes associated with length of survival. A survival model was built from these genes. The validity of our model was assessed in our test set of 68 patients and in three independent cohorts comprising 853 patients with multiple myeloma.
RESULTS: The 15 strongest genes associated with the length of survival were used to calculate a risk score and to stratify patients into low-risk and high-risk groups. The survival-predictor score was significantly associated with survival in both the training and test sets and in the external validation cohorts. The Kaplan-Meier estimates of rates of survival at 3 years were 90.5% (95% CI, 85.6% to 95.3%) and 47.4% (95% CI, 33.5% to 60.1%), respectively, in our patients having a low risk or high risk independently of traditional prognostic factors. High-risk patients constituted a homogeneous biologic entity characterized by the overexpression of genes involved in cell cycle progression and its surveillance, whereas low-risk patients were heterogeneous and displayed hyperdiploid signatures.
CONCLUSION: Gene expression-based survival prediction and molecular features associated with high-risk patients may be useful for developing prognostic markers and may provide basis to treat these patients with new targeted antimitotics.

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Year:  2008        PMID: 18591550     DOI: 10.1200/JCO.2007.13.8545

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  140 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.  Principled sure independence screening for Cox models with ultra-high-dimensional covariates.

Authors:  Sihai Dave Zhao; Yi Li
Journal:  J Multivar Anal       Date:  2012-02-01       Impact factor: 1.473

Review 3.  Update on risk stratification and treatment of newly diagnosed multiple myeloma.

Authors:  Prashant Kapoor; S Vincent Rajkumar
Journal:  Int J Hematol       Date:  2011-10-18       Impact factor: 2.490

Review 4.  The use of molecular-based risk stratification and pharmacogenomics for outcome prediction and personalized therapeutic management of multiple myeloma.

Authors:  Sarah K Johnson; Christoph J Heuck; Anthony P Albino; Pingping Qu; Qing Zhang; Bart Barlogie; John D Shaughnessy
Journal:  Int J Hematol       Date:  2011-10-15       Impact factor: 2.490

5.  Niche-Based Screening in Multiple Myeloma Identifies a Kinesin-5 Inhibitor with Improved Selectivity over Hematopoietic Progenitors.

Authors:  Shrikanta Chattopadhyay; Alison L Stewart; Siddhartha Mukherjee; Cherrie Huang; Kimberly A Hartwell; Peter G Miller; Radhika Subramanian; Leigh C Carmody; Rushdia Z Yusuf; David B Sykes; Joshiawa Paulk; Amedeo Vetere; Sonia Vallet; Loredana Santo; Diana D Cirstea; Teru Hideshima; Vlado Dančík; Max M Majireck; Mahmud M Hussain; Shambhavi Singh; Ryan Quiroz; Jonathan Iaconelli; Rakesh Karmacharya; Nicola J Tolliday; Paul A Clemons; Malcolm A S Moore; Andrew M Stern; Alykhan F Shamji; Benjamin L Ebert; Todd R Golub; Noopur S Raje; David T Scadden; Stuart L Schreiber
Journal:  Cell Rep       Date:  2015-02-05       Impact factor: 9.423

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.  A transcriptional and metabolic signature of primary aneuploidy is present in chromosomally unstable cancer cells and informs clinical prognosis.

Authors:  Jason M Sheltzer
Journal:  Cancer Res       Date:  2013-09-16       Impact factor: 12.701

Review 9.  Molecular pathogenesis of multiple myeloma: basic and clinical updates.

Authors:  Marta Chesi; P Leif Bergsagel
Journal:  Int J Hematol       Date:  2013-02-28       Impact factor: 2.490

10.  Uncovering the biology of multiple myeloma among African Americans: a comprehensive genomics approach.

Authors:  Angela Baker; Esteban Braggio; Susanna Jacobus; Sungwon Jung; Dirk Larson; Terry Therneau; Angela Dispenzieri; Scott A Van Wier; Gregory Ahmann; Joan Levy; Louise Perkins; Seungchan Kim; Kimberly Henderson; David Vesole; S Vincent Rajkumar; Diane F Jelinek; John Carpten; Rafael Fonseca
Journal:  Blood       Date:  2013-02-19       Impact factor: 22.113

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