Literature DB >> 30181174

RNA-seq of newly diagnosed patients in the PADIMAC study leads to a bortezomib/lenalidomide decision signature.

Michael A Chapman1,2, Jonathan Sive3, John Ambrose4, Claire Roddie5, Nicholas Counsell6, Anna Lach7, Mahnaz Abbasian7, Rakesh Popat5, Jamie D Cavenagh3, Heather Oakervee3, Matthew J Streetly8, Stephen Schey9, Mickey Koh10, Fenella Willis10, Andres E Virchis11, Josephine Crowe12, Michael F Quinn13, Gordon Cook14, Charles R Crawley2, Guy Pratt15, Mark Cook15, Nivette Braganza6, Toyin Adedayo6, Paul Smith6, Laura Clifton-Hadley6, Roger G Owen16, Pieter Sonneveld17, Jonathan J Keats18, Javier Herrero4, Kwee Yong7.   

Abstract

Improving outcomes in multiple myeloma will involve not only development of new therapies but also better use of existing treatments. We performed RNA sequencing on samples from newly diagnosed patients enrolled in the phase 2 PADIMAC (Bortezomib, Adriamycin, and Dexamethasone Therapy for Previously Untreated Patients with Multiple Myeloma: Impact of Minimal Residual Disease in Patients with Deferred ASCT) study. Using synthetic annealing and the large margin nearest neighbor algorithm, we developed and trained a 7-gene signature to predict treatment outcome. We tested the signature in independent cohorts treated with bortezomib- and lenalidomide-based therapies. The signature was capable of distinguishing which patients would respond better to which regimen. In the CoMMpass data set, patients who were treated correctly according to the signature had a better progression-free survival (median, 20.1 months vs not reached; hazard ratio [HR], 0.40; confidence interval [CI], 0.23-0.72; P = .0012) and overall survival (median, 30.7 months vs not reached; HR, 0.41; CI, 0.21-0.80; P = .0049) than those who were not. Indeed, the outcome for these correctly treated patients was noninferior to that for those treated with combined bortezomib, lenalidomide, and dexamethasone, arguably the standard of care in the United States but not widely available elsewhere. The small size of the signature will facilitate clinical translation, thus enabling more targeted drug regimens to be delivered in myeloma.
© 2018 by The American Society of Hematology.

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Year:  2018        PMID: 30181174      PMCID: PMC6310235          DOI: 10.1182/blood-2018-05-849893

Source DB:  PubMed          Journal:  Blood        ISSN: 0006-4971            Impact factor:   25.476


  65 in total

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Authors:  P Leif Bergsagel; W Michael Kuehl
Journal:  J Clin Oncol       Date:  2005-09-10       Impact factor: 44.544

2.  featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

Authors:  Yang Liao; Gordon K Smyth; Wei Shi
Journal:  Bioinformatics       Date:  2013-11-13       Impact factor: 6.937

3.  A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.

Authors:  Soonmyung Paik; Steven Shak; Gong Tang; Chungyeul Kim; Joffre Baker; Maureen Cronin; Frederick L Baehner; Michael G Walker; Drew Watson; Taesung Park; William Hiller; Edwin R Fisher; D Lawrence Wickerham; John Bryant; Norman Wolmark
Journal:  N Engl J Med       Date:  2004-12-10       Impact factor: 91.245

4.  Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients.

Authors:  Annemiek Broyl; Dirk Hose; Henk Lokhorst; Yvonne de Knegt; Justine Peeters; Anna Jauch; Uta Bertsch; Arjan Buijs; Marian Stevens-Kroef; H Berna Beverloo; Edo Vellenga; Sonja Zweegman; Marie-Josée Kersten; Bronno van der Holt; Laila el Jarari; George Mulligan; Hartmut Goldschmidt; Mark van Duin; Pieter Sonneveld
Journal:  Blood       Date:  2010-06-23       Impact factor: 22.113

5.  limma powers differential expression analyses for RNA-sequencing and microarray studies.

Authors:  Matthew E Ritchie; Belinda Phipson; Di Wu; Yifang Hu; Charity W Law; Wei Shi; Gordon K Smyth
Journal:  Nucleic Acids Res       Date:  2015-01-20       Impact factor: 16.971

6.  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.

Authors:  Olivier Decaux; 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
Journal:  J Clin Oncol       Date:  2008-06-30       Impact factor: 44.544

7.  Gene signature combinations improve prognostic stratification of multiple myeloma patients.

Authors:  W J Chng; T-H Chung; S Kumar; S Usmani; N Munshi; H Avet-Loiseau; H Goldschmidt; B Durie; P Sonneveld
Journal:  Leukemia       Date:  2015-12-16       Impact factor: 11.528

8.  A novel measure of chromosome instability can account for prognostic difference in multiple myeloma.

Authors:  Tae-Hoon Chung; George Mulligan; Rafael Fonseca; Wee Joo Chng
Journal:  PLoS One       Date:  2013-06-20       Impact factor: 3.240

9.  Heterogeneity of genomic evolution and mutational profiles in multiple myeloma.

Authors:  Niccolo Bolli; Hervé Avet-Loiseau; David C Wedge; Peter Van Loo; Ludmil B Alexandrov; Inigo Martincorena; Kevin J Dawson; Francesco Iorio; Serena Nik-Zainal; Graham R Bignell; Jonathan W Hinton; Yilong Li; Jose M C Tubio; Stuart McLaren; Sarah O' Meara; Adam P Butler; Jon W Teague; Laura Mudie; Elizabeth Anderson; Naim Rashid; Yu-Tzu Tai; Masood A Shammas; Adam S Sperling; Mariateresa Fulciniti; Paul G Richardson; Giovanni Parmigiani; Florence Magrangeas; Stephane Minvielle; Philippe Moreau; Michel Attal; Thierry Facon; P Andrew Futreal; Kenneth C Anderson; Peter J Campbell; Nikhil C Munshi
Journal:  Nat Commun       Date:  2014       Impact factor: 14.919

10.  TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions.

Authors:  Daehwan Kim; Geo Pertea; Cole Trapnell; Harold Pimentel; Ryan Kelley; Steven L Salzberg
Journal:  Genome Biol       Date:  2013-04-25       Impact factor: 13.583

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  4 in total

1.  Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data.

Authors:  Adrián Mosquera Orgueira; Marta Sonia González Pérez; José Ángel Díaz Arias; Beatriz Antelo Rodríguez; Natalia Alonso Vence; Ángeles Bendaña López; Aitor Abuín Blanco; Laura Bao Pérez; Andrés Peleteiro Raíndo; Miguel Cid López; Manuel Mateo Pérez Encinas; José Luis Bello López; Maria Victoria Mateos Manteca
Journal:  Leukemia       Date:  2021-05-18       Impact factor: 11.528

2.  A Network Analysis of Multiple Myeloma Related Gene Signatures.

Authors:  Yu Liu; Haocheng Yu; Seungyeul Yoo; Eunjee Lee; Alessandro Laganà; Samir Parekh; Eric E Schadt; Li Wang; Jun Zhu
Journal:  Cancers (Basel)       Date:  2019-09-27       Impact factor: 6.639

3.  Transcriptional profiles define drug refractory disease in myeloma.

Authors:  Yuan Xiao Zhu; Laura A Bruins; Xianfeng Chen; Chang-Xin Shi; Cecilia Bonolo De Campos; Nathalie Meurice; Xuewei Wang; Greg J Ahmann; Colleen A Ramsower; Esteban Braggio; Lisa M Rimsza; A Keith Stewart
Journal:  EJHaem       Date:  2022-05-09

4.  Highly expressed genes in multiple myeloma cells - what can they tell us about the disease?

Authors:  Magne Børset; Samah Elsaadi; Esten N Vandsemb; Eli Svorkdal Hess; Ida J Steiro; Miguel Cocera Fernandez; Anne-Marit Sponaas; Pegah Abdollahi
Journal:  Eur J Haematol       Date:  2022-03-20       Impact factor: 3.674

  4 in total

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