TY - JOUR
T1 - A high-risk, Double-Hit, group of newly diagnosed myeloma identified by genomic analysis
AU - Walker, Brian A.
AU - Mavrommatis, Konstantinos
AU - Wardell, Christopher P.
AU - Ashby, T. Cody
AU - Bauer, Michael
AU - Davies, Faith
AU - Rosenthal, Adam
AU - Wang, Hongwei
AU - Qu, Pingping
AU - Hoering, Antje
AU - Samur, Mehmet
AU - Towfic, Fadi
AU - Ortiz, Maria
AU - Flynt, Erin
AU - Yu, Zhinuan
AU - Yang, Zhihong
AU - Rozelle, Dan
AU - Obenauer, John
AU - Trotter, Matthew
AU - Auclair, Daniel
AU - Keats, Jonathan
AU - Bolli, Niccolo
AU - Fulciniti, Mariateresa
AU - Szalat, Raphael
AU - Moreau, Phillipe
AU - Durie, Brian
AU - Stewart, A. Keith
AU - Goldschmidt, Hartmut
AU - Raab, Marc S.
AU - Einsele, Hermann
AU - Sonneveld, Pieter
AU - San Miguel, Jesus
AU - Lonial, Sagar
AU - Jackson, Graham H.
AU - Anderson, Kenneth C.
AU - Avet-Loiseau, Herve
AU - Munshi, Nikhil
AU - Thakurta, Anjan
AU - Morgan, Gareth
N1 - Publisher Copyright:
© 2018, The Author(s).
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Patients with newly diagnosed multiple myeloma (NDMM) with high-risk disease are in need of new treatment strategies to improve the outcomes. Multiple clinical, cytogenetic, or gene expression features have been used to identify high-risk patients, each of which has significant weaknesses. Inclusion of molecular features into risk stratification could resolve the current challenges. In a genome-wide analysis of the largest set of molecular and clinical data established to date from NDMM, as part of the Myeloma Genome Project, we have defined DNA drivers of aggressive clinical behavior. Whole-genome and exome data from 1273 NDMM patients identified genetic factors that contribute significantly to progression free survival (PFS) and overall survival (OS) (cumulative R 2 = 18.4% and 25.2%, respectively). Integrating DNA drivers and clinical data into a Cox model using 784 patients with ISS, age, PFS, OS, and genomic data, the model has a cumlative R 2 of 34.3% for PFS and 46.5% for OS. A high-risk subgroup was defined by recursive partitioning using either a) bi-allelic TP53 inactivation or b) amplification (≥4 copies) of CKS1B (1q21) on the background of International Staging System III, comprising 6.1% of the population (median PFS = 15.4 months; OS = 20.7 months) that was validated in an independent dataset. Double-Hit patients have a dire prognosis despite modern therapies and should be considered for novel therapeutic approaches.
AB - Patients with newly diagnosed multiple myeloma (NDMM) with high-risk disease are in need of new treatment strategies to improve the outcomes. Multiple clinical, cytogenetic, or gene expression features have been used to identify high-risk patients, each of which has significant weaknesses. Inclusion of molecular features into risk stratification could resolve the current challenges. In a genome-wide analysis of the largest set of molecular and clinical data established to date from NDMM, as part of the Myeloma Genome Project, we have defined DNA drivers of aggressive clinical behavior. Whole-genome and exome data from 1273 NDMM patients identified genetic factors that contribute significantly to progression free survival (PFS) and overall survival (OS) (cumulative R 2 = 18.4% and 25.2%, respectively). Integrating DNA drivers and clinical data into a Cox model using 784 patients with ISS, age, PFS, OS, and genomic data, the model has a cumlative R 2 of 34.3% for PFS and 46.5% for OS. A high-risk subgroup was defined by recursive partitioning using either a) bi-allelic TP53 inactivation or b) amplification (≥4 copies) of CKS1B (1q21) on the background of International Staging System III, comprising 6.1% of the population (median PFS = 15.4 months; OS = 20.7 months) that was validated in an independent dataset. Double-Hit patients have a dire prognosis despite modern therapies and should be considered for novel therapeutic approaches.
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U2 - 10.1038/s41375-018-0196-8
DO - 10.1038/s41375-018-0196-8
M3 - Article
C2 - 29967379
AN - SCOPUS:85049511313
SN - 0887-6924
VL - 33
SP - 159
EP - 170
JO - Leukemia
JF - Leukemia
IS - 1
ER -