Literature DB >> 22002477

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

Sarah K Johnson1, Christoph J Heuck2, Anthony P Albino3, Pingping Qu4, Qing Zhang2, Bart Barlogie2, John D Shaughnessy5,6,7.   

Abstract

Despite improvement in therapeutic efficacy, multiple myeloma (MM) remains incurable with a median survival of approximately 10 years. Gene-expression profiling (GEP) can be used to elucidate the molecular basis for resistance to chemotherapy through global assessment of molecular alterations that exist at diagnosis, after therapeutic treatment and that evolve during tumor progression. Unique GEP signatures associated with recurrent chromosomal translocations and ploidy changes have defined molecular classes with differing clinical features and outcomes. When compared to other stratification systems the GEP70 test remained a significant predictor of outcome, reduced the number of patients classified with a poor prognosis, and identified patients at increased risk of relapse despite their standard clinico-pathologic and genetic findings. GEP studies of serial samples showed that risk increases over time, with relapsed disease showing GEP shifts toward a signature of poor outcomes. GEP signatures of myeloma cells after therapy were prognostic for event-free and overall survival and thus may be used to identify novel strategies for overcoming drug resistance. This brief review will focus on the use of GEP of MM to define high-risk myeloma, and elucidate underlying mechanisms that are beginning to change clinical decision-making and inform drug design.

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Year:  2011        PMID: 22002477      PMCID: PMC4433024          DOI: 10.1007/s12185-011-0948-y

Source DB:  PubMed          Journal:  Int J Hematol        ISSN: 0925-5710            Impact factor:   2.490


  76 in total

1.  Targeting endoplasmic reticulum protein transport: a novel strategy to kill malignant B cells and overcome fludarabine resistance in CLL.

Authors:  Jennifer S Carew; Steffan T Nawrocki; Yelena V Krupnik; Kenneth Dunner; David J McConkey; Michael J Keating; Peng Huang
Journal:  Blood       Date:  2005-09-06       Impact factor: 22.113

Review 2.  Multiple myeloma.

Authors:  Antonio Palumbo; Kenneth Anderson
Journal:  N Engl J Med       Date:  2011-03-17       Impact factor: 91.245

Review 3.  Genomics in multiple myeloma.

Authors:  Nikhil C Munshi; Hervé Avet-Loiseau
Journal:  Clin Cancer Res       Date:  2011-03-15       Impact factor: 12.531

4.  Proliferation is a central independent prognostic factor and target for personalized and risk-adapted treatment in multiple myeloma.

Authors:  Dirk Hose; Thierry Rème; Thomas Hielscher; Jérôme Moreaux; Tobias Messner; Anja Seckinger; Axel Benner; John D Shaughnessy; Bart Barlogie; Yiming Zhou; Jens Hillengass; Uta Bertsch; Kai Neben; Thomas Möhler; Jean François Rossi; Anna Jauch; Bernard Klein; Hartmut Goldschmidt
Journal:  Haematologica       Date:  2010-09-30       Impact factor: 9.941

5.  International staging system and metaphase cytogenetic abnormalities in the era of gene expression profiling data in multiple myeloma treated with total therapy 2 and 3 protocols.

Authors:  Sarah Waheed; John D Shaughnessy; Frits van Rhee; Yazan Alsayed; Bijay Nair; Elias Anaissie; Jackie Szymonifka; Antje Hoering; John Crowley; Bart Barlogie
Journal:  Cancer       Date:  2010-10-13       Impact factor: 6.860

6.  F18-fluorodeoxyglucose positron emission tomography in the context of other imaging techniques and prognostic factors in multiple myeloma.

Authors:  Twyla B Bartel; Jeff Haessler; Tracy L Y Brown; John D Shaughnessy; Frits van Rhee; Elias Anaissie; Terri Alpe; Edgardo Angtuaco; Ronald Walker; Joshua Epstein; John Crowley; Bart Barlogie
Journal:  Blood       Date:  2009-05-14       Impact factor: 22.113

7.  Expression of PAX5 in CD20-positive multiple myeloma assessed by immunohistochemistry and oligonucleotide microarray.

Authors:  Pei Lin; Mustafa Mahdavy; Fenghuang Zhan; Hua-Zhong Zhang; Ruth L Katz; John D Shaughnessy
Journal:  Mod Pathol       Date:  2004-10       Impact factor: 7.842

8.  In multiple myeloma, t(4;14)(p16;q32) is an adverse prognostic factor irrespective of FGFR3 expression.

Authors:  Jonathan J Keats; Tony Reiman; Christopher A Maxwell; Brian J Taylor; Loree M Larratt; Michael J Mant; Andrew R Belch; Linda M Pilarski
Journal:  Blood       Date:  2002-10-03       Impact factor: 22.113

9.  Genetics and cytogenetics of multiple myeloma: a workshop report.

Authors:  Rafael Fonseca; Bart Barlogie; Regis Bataille; Christian Bastard; P Leif Bergsagel; Marta Chesi; Faith E Davies; Johannes Drach; Philip R Greipp; Ilan R Kirsch; W Michael Kuehl; Jesus M Hernandez; Stephane Minvielle; Linda M Pilarski; John D Shaughnessy; A Keith Stewart; Herve Avet-Loiseau
Journal:  Cancer Res       Date:  2004-02-15       Impact factor: 12.701

Review 10.  Future directions of next-generation novel therapies, combination approaches, and the development of personalized medicine in myeloma.

Authors:  Constantine S Mitsiades; Faith E Davies; Jacob P Laubach; Douglas Joshua; Jesus San Miguel; Kenneth C Anderson; Paul G Richardson
Journal:  J Clin Oncol       Date:  2011-04-11       Impact factor: 44.544

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  8 in total

Review 1.  European perspective on multiple myeloma treatment strategies in 2014.

Authors:  Heinz Ludwig; Pieter Sonneveld; Faith Davies; Joan Bladé; Mario Boccadoro; Michele Cavo; Gareth Morgan; Javier de la Rubia; Michel Delforge; Meletios Dimopoulos; Hermann Einsele; Thierry Facon; Hartmut Goldschmidt; Philippe Moreau; Hareth Nahi; Torben Plesner; Jesús San-Miguel; Roman Hajek; Pia Sondergeld; Antonio Palumbo
Journal:  Oncologist       Date:  2014-07-25

Review 2.  Current approaches for the treatment of multiple myeloma.

Authors:  Reiko Watanabe; Michihide Tokuhira; Masahiro Kizaki
Journal:  Int J Hematol       Date:  2013-03-10       Impact factor: 2.490

3.  Enhancing cytokine-induced killer cell therapy of multiple myeloma.

Authors:  Chunsheng Liu; Lukkana Suksanpaisan; Yun-Wen Chen; Stephen J Russell; Kah-Whye Peng
Journal:  Exp Hematol       Date:  2013-02-08       Impact factor: 3.084

Review 4.  Risk Stratification in Multiple Myeloma.

Authors:  Melissa Gaik-Ming Ooi; Sanjay de Mel; Wee Joo Chng
Journal:  Curr Hematol Malig Rep       Date:  2016-04       Impact factor: 3.952

5.  Influence of pre-hydration and pharmacogenetics on plasma methotrexate concentration and renal dysfunction following high-dose methotrexate therapy.

Authors:  Masakatsu Yanagimachi; Hiroaki Goto; Tetsuji Kaneko; Takuya Naruto; Koji Sasaki; Masanobu Takeuchi; Reo Tanoshima; Hiromi Kato; Tomoko Yokosuka; Ryosuke Kajiwara; Hisaki Fujii; Fumiko Tanaka; Shoko Goto; Hiroyuki Takahashi; Masaaki Mori; Sumio Kai; Shumpei Yokota
Journal:  Int J Hematol       Date:  2013-11-16       Impact factor: 2.490

6.  Curcumin induces cell death of the main molecular myeloma subtypes, particularly the poor prognosis subgroups.

Authors:  Patricia Gomez-Bougie; Maxime Halliez; Sophie Maïga; Catherine Godon; Charlotte Kervoëlen; Catherine Pellat-Deceunynck; Philippe Moreau; Martine Amiot
Journal:  Cancer Biol Ther       Date:  2015       Impact factor: 4.742

7.  Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients.

Authors:  Angelica Macauda; Chiara Piredda; Alyssa I Clay-Gilmour; Juan Sainz; Gabriele Buda; Miroslaw Markiewicz; Torben Barington; Elad Ziv; Michelle A T Hildebrandt; Alem A Belachew; Judit Varkonyi; Witold Prejzner; Agnieszka Druzd-Sitek; John Spinelli; Niels Frost Andersen; Jonathan N Hofmann; Marek Dudziński; Joaquin Martinez-Lopez; Elzbieta Iskierka-Jazdzewska; Roger L Milne; Grzegorz Mazur; Graham G Giles; Lene Hyldahl Ebbesen; Marcin Rymko; Krzysztof Jamroziak; Edyta Subocz; Rui Manuel Reis; Ramon Garcia-Sanz; Anna Suska; Eva Kannik Haastrup; Daria Zawirska; Norbert Grzasko; Annette Juul Vangsted; Charles Dumontet; Marcin Kruszewski; Magdalena Dutka; Nicola J Camp; Rosalie G Waller; Waldemar Tomczak; Matteo Pelosini; Małgorzata Raźny; Herlander Marques; Niels Abildgaard; Marzena Wątek; Artur Jurczyszyn; Elizabeth E Brown; Sonja Berndt; Aleksandra Butrym; Celine M Vachon; Aaron D Norman; Susan L Slager; Federica Gemignani; Federico Canzian; Daniele Campa
Journal:  Int J Cancer       Date:  2021-03-30       Impact factor: 7.396

8.  Five gene probes carry most of the discriminatory power of the 70-gene risk model in multiple myeloma.

Authors:  C J Heuck; P Qu; F van Rhee; S Waheed; S Z Usmani; J Epstein; Q Zhang; R Edmondson; A Hoering; J Crowley; B Barlogie
Journal:  Leukemia       Date:  2014-07-31       Impact factor: 11.528

  8 in total

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