In:
Blood, American Society of Hematology, Vol. 134, No. Supplement_1 ( 2019-11-13), p. 366-366
Abstract:
Cytogenetics is an important prognostic marker in multiple myeloma (MM). Patients with t(4;14) (~15% of newly diagnosed MM patients) are known to have short progression free survival (PFS) and overall survival (OS). This feature, measured by FISH, is used in combination with ISS=3 as a selection marker for patients with high risk (HR) of progression. Only a subset of patients grouped by t(4;14) and ISS=3 display genuinely poor survival, however, with ~25% dying within 24 months after diagnosis (similar to the Double Hit subgroup defined by Walker et al1). To elucidate this observation, we created the largest dataset of MM t(4;14) patients to date by combining data from the Myeloma Genome Project (MGP, n=73) and data from TOUL (n=100, patients analyzed in routine practice) to identify transcriptomic and/or genomic markers associated with HR t(4;14). Gene expression (GE), copy number aberration (CNA), single nucleotide variant (SNV) and translocations were derived from RNAseq and WGS/WES profiling of biopsies from patients aged less than 75 years who received transplant, and integrated with clinical information (including Age, PFS and OS). Demographics: MGP median age=61; 30% female; median PFS (mPFS)=26.2months (m) and median OS (mOS) not reached. TOUL median age=60; 35% female, mPFS=23.7m and mOS = 86.1m. Our previous work (Ortiz ASH 2018, Ortiz EHA 2018) identified a molecularly-defined HR MM patient subgroup (MDMS8, mPFS & lt;20m, m0S & lt;35m) defined by GE patterns related to cell cycle dysregulation. In that analysis, 24% of t(4;14) patients were identified as MDMS8 (mPFS & lt;13m, mOS & lt;30m), the rest (76%) were grouped in other lower risk molecular segments (mPFS & lt;30m, mOS NR). A GE classifier for t(4;14) in MDMS8 vs the rest of t(4;14) patients was created on the MGP dataset and applied to identify similar patients in the TOUL data, obtaining a significant difference between MDMS8-like t(4;14) patients (20% prevalence, mPFS & lt;15m, mOS & lt;26m) in the TOUL dataset and non-HR t(4;14) (mPFS & lt;26m, mOS & lt;103m) in both PFS (p.value & lt;1e-3) and OS (p.value & lt;1e-5). Although there are some conventional t(4;14) gene expression surrogates, they do not identify the HR t(4:14) subgroup. Comparison of known t(4;14) gene expression markers MMSET and FGFR3 in HR t(4;14) (OS & lt; 24ms & not_alive, N=34) versus non-HR t(4;14) patients (N=94) across both datasets combined did not yield significant differential expression of either gene (p.value & gt;0.10). MMSET was over-expressed in all t(4;14) patients, while FGFR3 displayed a binomial distribution (two groups of patients with high (N=37, median value=10 log2CPM) and low (N=91, median value=2 log2CPM) FGFR3 expression) within t(4;14) patients (p.value & lt;0.05) without association with outcome (p.value & gt;0.10). GE analysis of HR t(4;14) vs non-HR t(4;14) patients aligned with MDMS8 biology, but identified new pathways also including DNA repair, MYC targets and Oxidative Phosphorylation being up-regulated in the HR t(4;14) group. A gene-set variation analysis based on the MSigDb C1 gene-set, wherein genes are grouped based on their genomic location, was performed to identify GE changes of potentially epigenomic origin. Results highlighted chr9q22, chr9q33, and chr13q13 as down-regulated in the HR t(4;14) group, while genes in 16q24 were significantly up-regulated. CNA analysis identified amplifications in chromosomes 3 and 19 and deletions in chr12p as significantly associated with the HR t(4;14) population (p.value & lt; 0.05); while deletions in chr14q (preceding the translocated region) occurred more frequently in the non-HR t(4;14) group. Our results provide new insights into identification of these patients and underlying biology that could drive poor prognosis in t(4;14) patients. Molecular identification of HR t(4;14) patients would enable proper risk classification for this MM patient group and understanding differences in HR t(4;14) biology could provide the basis for identification of a specific therapeutic target for this HR subpopulation. An ongoing aim of this work is development of a clinically applicable classifier that accurately identifies this subpopulation of MM patients and the biological drivers of their high-risk disease. Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Towfic:Celgene Corporation: Employment, Equity Ownership. Flynt:Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Sampath:Celgene Corporation: Employment, Equity Ownership. Rozelle:Celgene Corporation: Other: Contractor for Celgene. Trotter:Celgene Corporation: Employment, Equity Ownership. Thakurta:Celgene: Employment, Equity Ownership.
Type of Medium:
Online Resource
ISSN:
0006-4971
,
1528-0020
DOI:
10.1182/blood-2019-128907
Language:
English
Publisher:
American Society of Hematology
Publication Date:
2019
detail.hit.zdb_id:
1468538-3
detail.hit.zdb_id:
80069-7
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