Literature DB >> 31091136

Development and Validation of a Cytogenetic Prognostic Index Predicting Survival in Multiple Myeloma.

Aurore Perrot1, Valérie Lauwers-Cances2, Elodie Tournay2, Cyrille Hulin3, Marie-Lorraine Chretien4, Bruno Royer5, Mamoun Dib6, Olivier Decaux7, Arnaud Jaccard8, Karim Belhadj9, Sabine Brechignac10, Jean Fontan11, Laurent Voillat12, Hélène Demarquette13, Philippe Collet14, Philippe Rodon15, Claudine Sohn16, François Lifermann17, Frédérique Orsini-Piocelle18, Valentine Richez19, Mohamad Mohty20, Margaret Macro21, Stéphane Minvielle22, Philippe Moreau22, Xavier Leleu23, Thierry Facon24, Michel Attal25, Hervé Avet-Loiseau25, Jill Corre25.   

Abstract

PURPOSE: The wide heterogeneity in multiple myeloma (MM) outcome is driven mainly by cytogenetic abnormalities. The current definition of high-risk profile is restrictive and oversimplified. To adapt MM treatment to risk, we need to better define a cytogenetic risk classification. To address this issue, we simultaneously examined the prognostic impact of del(17p); t(4;14); del(1p32); 1q21 gain; and trisomies 3, 5, and 21 in a cohort of newly diagnosed patients with MM.
METHODS: Data were obtained from 1,635 patients enrolled in four trials implemented by the Intergroupe Francophone du Myélome. The oldest collection of data were used for model development and internal validation. For external validation, one of the two independent data sets was used to assess the performance of the model in patients treated with more current regimens. Six cytogenetic abnormalities were identified as clinically relevant, and a prognostic index (PI) that was based on the parameter estimates of the multivariable Cox model was computed for all patients.
RESULTS: In all data sets, a higher PI was consistently associated with a poor survival outcome. Dependent on the validation cohorts used, hazard ratios for patients in the high-risk category for death were between six and 15 times higher than those of patients in the low-risk category. Among patients with t(4;14) or del(17p), we observed a worse survival in those classified in the high-risk category than in those in the intermediate-risk category. The PI showed good performance for discriminating between patients who died and those who survived (Harrell's concordance index greater than 70%).
CONCLUSION: The cytogenetic PI improves the classification of newly diagnosed patients with MM in the high-risk group compared with current classifications. These findings may facilitate the development of risk-adapted treatment strategies.

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Year:  2019        PMID: 31091136      PMCID: PMC6804890          DOI: 10.1200/JCO.18.00776

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


  30 in total

1.  Clinical implications of t(11;14)(q13;q32), t(4;14)(p16.3;q32), and -17p13 in myeloma patients treated with high-dose therapy.

Authors:  Morie A Gertz; Martha Q Lacy; Angela Dispenzieri; Philip R Greipp; Mark R Litzow; Kimberly J Henderson; Scott A Van Wier; Greg J Ahmann; Rafael Fonseca
Journal:  Blood       Date:  2005-06-23       Impact factor: 22.113

2.  Understanding the role of hyperdiploidy in myeloma prognosis: which trisomies really matter?

Authors:  Marie-Lorraine Chretien; Jill Corre; Valerie Lauwers-Cances; Florence Magrangeas; Alice Cleynen; Edwige Yon; Cyrille Hulin; Xavier Leleu; Frederique Orsini-Piocelle; Jean-Sebastien Blade; Claudine Sohn; Lionel Karlin; Xavier Delbrel; Benjamin Hebraud; Murielle Roussel; Gerald Marit; Laurent Garderet; Mohamad Mohty; Philippe Rodon; Laurent Voillat; Bruno Royer; Arnaud Jaccard; Karim Belhadj; Jean Fontan; Denis Caillot; Anne-Marie Stoppa; Michel Attal; Thierry Facon; Philippe Moreau; Stephane Minvielle; Hervé Avet-Loiseau
Journal:  Blood       Date:  2015-10-29       Impact factor: 22.113

3.  Prognostic value of chromosome 1q21 gain by fluorescent in situ hybridization and increase CKS1B expression in myeloma.

Authors:  R Fonseca; S A Van Wier; W J Chng; R Ketterling; M Q Lacy; A Dispenzieri; P L Bergsagel; S V Rajkumar; P R Greipp; M R Litzow; T Price-Troska; K J Henderson; G J Ahmann; M A Gertz
Journal:  Leukemia       Date:  2006-10-05       Impact factor: 11.528

4.  Trisomies in multiple myeloma: impact on survival in patients with high-risk cytogenetics.

Authors:  Shaji Kumar; Rafael Fonseca; Rhett P Ketterling; Angela Dispenzieri; Martha Q Lacy; Morie A Gertz; Suzanne R Hayman; Francis K Buadi; David Dingli; Ryan A Knudson; Alexandra Greenberg; Stephen J Russell; Steven R Zeldenrust; John A Lust; Robert A Kyle; Leif Bergsagel; S Vincent Rajkumar
Journal:  Blood       Date:  2012-01-10       Impact factor: 22.113

5.  Achievement of at least very good partial response is a simple and robust prognostic factor in patients with multiple myeloma treated with high-dose therapy: long-term analysis of the IFM 99-02 and 99-04 Trials.

Authors:  Jean-Luc Harousseau; Herve Avet-Loiseau; Michel Attal; Catherine Charbonnel; Frederic Garban; Cyrille Hulin; Mauricette Michallet; Thierry Facon; Laurent Garderet; Gerald Marit; Nicolas Ketterer; Thierry Lamy; Laurent Voillat; Francois Guilhot; Chantal Doyen; Claire Mathiot; Philippe Moreau
Journal:  J Clin Oncol       Date:  2009-10-13       Impact factor: 44.544

6.  Prognostic factors for hyperdiploid-myeloma: effects of chromosome 13 deletions and IgH translocations.

Authors:  W J Chng; R Santana-Dávila; S A Van Wier; G J Ahmann; S M Jalal; P L Bergsagel; M Chesi; M C Trendle; S Jacobus; E Blood; M M Oken; K Henderson; R A Kyle; M A Gertz; M Q Lacy; A Dispenzieri; P R Greipp; R Fonseca
Journal:  Leukemia       Date:  2006-05       Impact factor: 11.528

Review 7.  IMWG consensus on risk stratification in multiple myeloma.

Authors:  W J Chng; A Dispenzieri; C-S Chim; R Fonseca; H Goldschmidt; S Lentzsch; N Munshi; A Palumbo; J S Miguel; P Sonneveld; M Cavo; S Usmani; B G M Durie; H Avet-Loiseau
Journal:  Leukemia       Date:  2013-08-26       Impact factor: 11.528

8.  Racial differences in primary cytogenetic abnormalities in multiple myeloma: a multi-center study.

Authors:  A J Greenberg; S Philip; A Paner; S Velinova; A Badros; R Catchatourian; R Ketterling; R A Kyle; S Kumar; C M Vachon; S V Rajkumar
Journal:  Blood Cancer J       Date:  2015-02-13       Impact factor: 11.037

9.  A novel prognostic model in myeloma based on co-segregating adverse FISH lesions and the ISS: analysis of patients treated in the MRC Myeloma IX trial.

Authors:  K D Boyd; F M Ross; L Chiecchio; G P Dagrada; Z J Konn; W J Tapper; B A Walker; C P Wardell; W M Gregory; A J Szubert; S E Bell; J A Child; G H Jackson; F E Davies; G J Morgan
Journal:  Leukemia       Date:  2011-08-12       Impact factor: 11.528

10.  Combination of t(4;14), del(17p13), del(1p32) and 1q21 gain FISH probes identifies clonal heterogeneity and enhances the detection of adverse cytogenetic profiles in 233 newly diagnosed multiple myeloma.

Authors:  Thomas Smol; Annika Dufour; Sabine Tricot; Mathieu Wemeau; Laure Stalnikiewicz; Franck Bernardi; Christine Terré; Benoît Ducourneau; Hervé Bisiau; Agnès Daudignon
Journal:  Mol Cytogenet       Date:  2017-07-01       Impact factor: 2.009

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  27 in total

Review 1.  Monitoring minimal residual disease in the bone marrow using next generation sequencing.

Authors:  Even H Rustad; Eileen M Boyle
Journal:  Best Pract Res Clin Haematol       Date:  2020-01-17       Impact factor: 3.020

2.  Pleural effusion-based nomogram to predict outcomes in unselected patients with multiple myeloma: a large single center experience.

Authors:  Zi-Liang Hou; Yu Kang; Guang-Zhong Yang; Zhen Wang; Feng Wang; Yan-Xia Yu; Wen-Ming Chen; Huan-Zhong Shi
Journal:  Ann Hematol       Date:  2021-03-14       Impact factor: 3.673

3.  Early relapse after autologous transplant for myeloma is associated with poor survival regardless of cytogenetic risk.

Authors:  Jill Corre; Lydia Montes; Elodie Martin; Aurore Perrot; Denis Caillot; Xavier Leleu; Karim Belhadj; Thierry Facon; Cyrille Hulin; Mohamad Mohty; Jean Fontan; Margaret Macro; Sabine Brechignac; Arnaud Jaccard; Anne-Marie Stoppa; Frederique Orsini-Piocelle; Didier Adiko; Laurent Voillat; Faiza Keddar; Marly Barry; Helene Demarquette; Marie-Noelle Certain; Isabelle Plantier; Murielle Roussel; Benjamin Hébraud; Thomas Filleron; Michel Attal; Hervé Avet-Loiseau
Journal:  Haematologica       Date:  2019-12-19       Impact factor: 9.941

4.  Meeting report of the 7th Heidelberg Myeloma Workshop: today and tomorrow.

Authors:  M A Baertsch; R Lutz; M S Raab; N Weinhold; H Goldschmidt
Journal:  J Cancer Res Clin Oncol       Date:  2019-08-12       Impact factor: 4.553

5.  Comparison between tumour metabolism derived from 18F-FDG PET/CT and accurate cytogenetic stratification in newly diagnosed multiple myeloma patients.

Authors:  Yannick Silva; Jean-Marc Riedinger; Marie-Lorraine Chrétien; Denis Caillot; Jill Corre; Kévin Guillen; Alexandre Cochet; Claire Tabouret-Viaud; Romaric Loffroy
Journal:  Quant Imaging Med Surg       Date:  2021-10

Review 6.  High-risk disease in newly diagnosed multiple myeloma: beyond the R-ISS and IMWG definitions.

Authors:  Patrick Hagen; Jiwang Zhang; Kevin Barton
Journal:  Blood Cancer J       Date:  2022-05-30       Impact factor: 9.812

7.  Novel prognostic scoring system for autologous hematopoietic cell transplantation in multiple myeloma.

Authors:  Binod Dhakal; Anita D'Souza; Natalie Callander; Saurabh Chhabra; Raphael Fraser; Omar Davila; Kenneth Anderson; Amer Assal; Sherif M Badawy; Jesus Berdeja; Jan Cerny; Raymond Comenzo; Rajshekhar Chakraborty; Robert Peter Gale; Rammurti Kamble; Mohamed A Kharfan-Dabaja; Maxwell Krem; Siddhartha Ganguly; Murali Janakiram; Ankit Kansagra; Reinhold Munker; Hemant Murthy; Sagar Patel; Shaji Kumar; Nina Shah; Muzaffar Qazilbash; Parameswaran Hari
Journal:  Br J Haematol       Date:  2020-10-23       Impact factor: 6.998

8.  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

9.  Copy number evolution and its relationship with patient outcome-an analysis of 178 matched presentation-relapse tumor pairs from the Myeloma XI trial.

Authors:  James Croft; Sidra Ellis; Amy L Sherborne; Kim Sharp; Amy Price; Matthew W Jenner; Mark T Drayson; Roger G Owen; Sally Chown; Jindriska Lindsay; Kamaraj Karunanithi; Hannah Hunter; Walter M Gregory; Faith E Davies; Gareth J Morgan; Gordon Cook; Lilit Atanesyan; Suvi Savola; David A Cairns; Graham Jackson; Richard S Houlston; Martin F Kaiser
Journal:  Leukemia       Date:  2020-12-01       Impact factor: 11.528

10.  The DNA methylation landscape of multiple myeloma shows extensive inter- and intrapatient heterogeneity that fuels transcriptomic variability.

Authors:  Jennifer Derrien; Catherine Guérin-Charbonnel; Victor Gaborit; Loïc Campion; Magali Devic; Elise Douillard; Nathalie Roi; Hervé Avet-Loiseau; Olivier Decaux; Thierry Facon; Jan-Philipp Mallm; Roland Eils; Nikhil C Munshi; Philippe Moreau; Carl Herrmann; Florence Magrangeas; Stéphane Minvielle
Journal:  Genome Med       Date:  2021-08-09       Impact factor: 11.117

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