Improved risk stratification in myeloma using a microRNA-based classifier

Ping Wu; Luca Agnelli; Brian A Walker; Katia Todoerti; Marta Lionetti; David C Johnson; Martin Kaiser; Fabio Mirabella; Christopher Wardell; Walter M Gregory; Faith E Davies; Daniel Brewer; Antonino Neri; Gareth J Morgan

doi:10.1111/bjh.12394

Improved risk stratification in myeloma using a microRNA-based classifier

Ping Wu, Luca Agnelli, Brian A Walker, Katia Todoerti, Marta Lionetti, David C Johnson, Martin Kaiser, Fabio Mirabella, Christopher Wardell, Walter M Gregory, Faith E Davies, Daniel Brewer, Antonino Neri, Gareth J Morgan

Research output: Contribution to journal › Article › peer-review

49 Citations (SciVal)

Abstract

Multiple myeloma (MM) is a heterogeneous disease. International Staging System/fluorescence hybridization (ISS/FISH)-based model and gene expression profiles (GEP) are effective approaches to define clinical outcome, although yet to be improved. The discovery of a class of small non-coding RNAs (micro RNAs, miRNAs) has revealed a new level of biological complexity underlying the regulation of gene expression. In this work, 163 presenting samples from MM patients were analysed by global miRNA profiling, and distinct miRNA expression characteristics in molecular subgroups with prognostic relevance (4p16, MAF and 11q13 translocations) were identified. Furthermore we developed an "outcome classifier", based on the expression of two miRNAs (MIR17 and MIR886-5p), which is able to stratify patients into three risk groups (median OS 19.4, 40.6 and 65.3 months, P = 0.001). The miRNA-based classifier significantly improved the predictive power of the ISS/FISH approach (P = 0.0004), and was independent of GEP-derived prognostic signatures (P

Original language	English
Pages (from-to)	348-59
Number of pages	12
Journal	British Journal of Haematology
Volume	162
Issue number	3
DOIs	https://doi.org/10.1111/bjh.12394
Publication status	Published - Aug 2013

Keywords

Aged
Chromosome Aberrations
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Humans
In Situ Hybridization, Fluorescence
Kaplan-Meier Estimate
MicroRNAs
Middle Aged
Multiple Myeloma
Prognosis
RNA, Neoplasm
Risk Assessment
Tumor Markers, Biological

Access to Document

10.1111/bjh.12394

Cite this

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title = "Improved risk stratification in myeloma using a microRNA-based classifier",

abstract = "Multiple myeloma (MM) is a heterogeneous disease. International Staging System/fluorescence hybridization (ISS/FISH)-based model and gene expression profiles (GEP) are effective approaches to define clinical outcome, although yet to be improved. The discovery of a class of small non-coding RNAs (micro RNAs, miRNAs) has revealed a new level of biological complexity underlying the regulation of gene expression. In this work, 163 presenting samples from MM patients were analysed by global miRNA profiling, and distinct miRNA expression characteristics in molecular subgroups with prognostic relevance (4p16, MAF and 11q13 translocations) were identified. Furthermore we developed an {"}outcome classifier{"}, based on the expression of two miRNAs (MIR17 and MIR886-5p), which is able to stratify patients into three risk groups (median OS 19.4, 40.6 and 65.3 months, P = 0.001). The miRNA-based classifier significantly improved the predictive power of the ISS/FISH approach (P = 0.0004), and was independent of GEP-derived prognostic signatures (P ",

keywords = "Aged, Chromosome Aberrations, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, In Situ Hybridization, Fluorescence, Kaplan-Meier Estimate, MicroRNAs, Middle Aged, Multiple Myeloma, Prognosis, RNA, Neoplasm, Risk Assessment, Tumor Markers, Biological",

author = "Ping Wu and Luca Agnelli and Walker, {Brian A} and Katia Todoerti and Marta Lionetti and Johnson, {David C} and Martin Kaiser and Fabio Mirabella and Christopher Wardell and Gregory, {Walter M} and Davies, {Faith E} and Daniel Brewer and Antonino Neri and Morgan, {Gareth J}",

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T1 - Improved risk stratification in myeloma using a microRNA-based classifier

AU - Wu, Ping

AU - Agnelli, Luca

AU - Walker, Brian A

AU - Todoerti, Katia

AU - Lionetti, Marta

AU - Johnson, David C

AU - Kaiser, Martin

AU - Mirabella, Fabio

AU - Wardell, Christopher

AU - Gregory, Walter M

AU - Davies, Faith E

AU - Brewer, Daniel

AU - Neri, Antonino

AU - Morgan, Gareth J

PY - 2013/8

Y1 - 2013/8

N2 - Multiple myeloma (MM) is a heterogeneous disease. International Staging System/fluorescence hybridization (ISS/FISH)-based model and gene expression profiles (GEP) are effective approaches to define clinical outcome, although yet to be improved. The discovery of a class of small non-coding RNAs (micro RNAs, miRNAs) has revealed a new level of biological complexity underlying the regulation of gene expression. In this work, 163 presenting samples from MM patients were analysed by global miRNA profiling, and distinct miRNA expression characteristics in molecular subgroups with prognostic relevance (4p16, MAF and 11q13 translocations) were identified. Furthermore we developed an "outcome classifier", based on the expression of two miRNAs (MIR17 and MIR886-5p), which is able to stratify patients into three risk groups (median OS 19.4, 40.6 and 65.3 months, P = 0.001). The miRNA-based classifier significantly improved the predictive power of the ISS/FISH approach (P = 0.0004), and was independent of GEP-derived prognostic signatures (P

AB - Multiple myeloma (MM) is a heterogeneous disease. International Staging System/fluorescence hybridization (ISS/FISH)-based model and gene expression profiles (GEP) are effective approaches to define clinical outcome, although yet to be improved. The discovery of a class of small non-coding RNAs (micro RNAs, miRNAs) has revealed a new level of biological complexity underlying the regulation of gene expression. In this work, 163 presenting samples from MM patients were analysed by global miRNA profiling, and distinct miRNA expression characteristics in molecular subgroups with prognostic relevance (4p16, MAF and 11q13 translocations) were identified. Furthermore we developed an "outcome classifier", based on the expression of two miRNAs (MIR17 and MIR886-5p), which is able to stratify patients into three risk groups (median OS 19.4, 40.6 and 65.3 months, P = 0.001). The miRNA-based classifier significantly improved the predictive power of the ISS/FISH approach (P = 0.0004), and was independent of GEP-derived prognostic signatures (P

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KW - Chromosome Aberrations

KW - Gene Expression Profiling

KW - Gene Expression Regulation, Neoplastic

KW - Humans

KW - In Situ Hybridization, Fluorescence

KW - Kaplan-Meier Estimate

KW - MicroRNAs

KW - Middle Aged

KW - Multiple Myeloma

KW - Prognosis

KW - RNA, Neoplasm

KW - Risk Assessment

KW - Tumor Markers, Biological

U2 - 10.1111/bjh.12394

DO - 10.1111/bjh.12394

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SN - 0007-1048

VL - 162

SP - 348

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JO - British Journal of Haematology

JF - British Journal of Haematology

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