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3953 Gene Expression Profiling (GEP) of Whole Bone Marrow Biopsies (BMB) in Multiple Myeloma (MM) – Relationship to Plasma-Cell (PC)-Based GEP-Defined Risk and Molecular Subgroups

Program: Oral and Poster Abstracts
Session: 651. Myeloma - Biology and Pathophysiology, excluding Therapy: Poster III
Monday, December 10, 2012, 6:00 PM-8:00 PM
Hall B1-B2, Level 1, Building B (Georgia World Congress Center)

Donald Johann, MD, MS1*, Pingping Qu, PhD2*, Rachael Sexton2*, Antje Hoering2*, Joshua Epstein, DSc1, Shmuel Yaccoby, PhD1, Frits van Rhee, MD, PhD1, Saad Z Usmani, MD FACP1, John Crowley, PhD2 and Bart Barlogie, MD, PhD1

1Myeloma Institute for Research and Therapy, University of Arkansas for Medical Sciences, Little Rock, AR
2Research and Biostatistics, Cancer Research and Biostatistics, Seattle, WA

            PC-GEP provides great insight into MM heterogeneity as defined by 7 distinct molecular subgroups, along with clinical correlates and prognosis, as identified by the GEP-70 model. As is now increasingly appreciated in MM, a close interaction exists between PC and the bone marrow micro-environment (ME), which results not only in MM bone disease but also contributes to MM progression and drug resistance. We postulated that such interaction could be revealed by performing GEP both on PC and the bone marrow biopsy (BMB) proper. In addition to having demonstrated that BMB-GEP can attain a normal (NL)-like status in comparison with NL donors with favorable prognostic implications, we are now reporting on paired comparisons of BMB-GEP and PC-GEP toward elucidating whether different molecular subgroups (MOLS) (namely, CD-1, CD-2, MAF, MAF-B, MS, HY, PR) and GEP-70-defined prognostic risk groups (RISK) engage the ME differentially. Toward this end, a recently defined BMB-GEP model of 65 genes, distinguishing NL, MGUS/AMM, MM and MM-CR, was applied to determine whether linkage existed to MOLS and RISK. Indeed, based on mean-based statistics, BMB-65 scores varied among MOLS and RISK (both p=0.013). A negative correlation (R=-0.204) existed between BMB-65 and PC-70 scores (p=0.0014). Further  analysis within MOLS revealed significant negative correlations within MF (R=-0.57, p=0.0087) and MS subtypes (R=-0.33, p=0.037). None of the other MOLS showed any significant correlations. This is the first demonstration in patient MM samples that MOLS may engage the ME differentially. The more pronounced inverse correlations of MF and MS subtypes reveal a counter-relationship between RISK and BMB-65 score. This implies that with greater aggressiveness of PC, the BMB appears less NL-like, and is thus more modified to support the malignant PC processes. More detailed analyses will be presented in the context of other GEP and clinical parameters, specifically concerning the prognostic implications of BMB scores at baseline.

Description: Scatter plot of GEP70 risk and 65-gene score

Correlation between GEP70 score and BMBX 65-gene score within each molecular subgroup

Molecular Subgroup

N

Correlation of GEP70 risk and BMBX 65-gene score

P value for testing no correlation

CD-1

15

-0.39

0.1494

CD-2

39

-0.22

0.1747

HY

71

-0.15

0.1931

LB

33

0.12

0.4927

MF

20

-0.57

0.0087

MS

40

-0.33

0.0368

PR

25

-0.13

0.55

Overall

243

-0.204

0.001373

Disclosures: No relevant conflicts of interest to declare.

*signifies non-member of ASH