Gene Expression Profiling (GEP) Analysis of Plasma Cells (PC) Obtained From MRI-Defined Focal Lesions (FL) Under CT-Guided Fine-Needle Aspiration Provides Better Risk Stratification in Patients with Multiple Myeloma
Authors: Zhang Qing , Qu Pingping , Hansen Emily , Heuck Christoph , Usmani Saad , Nair Bijay , Waheed Sarah , Alsayed Yazan , Hoering Antje , Crowley John , Shaughnessy John D , Barlogie Bart 
Published: October 2019

Abstract
Abstract 2896

Background:
Focal lesions (FL) are a well-recognized consequence of active multiple myeloma (MM), and distinguish it from its antecedent monoclonal gammopathy of undetermined significance (MGUS). We have recently reported on the superior performance of gene expression profiling (GEP) based on random trephine bone marrow aspirate sampling (RS) in distinguishing between 85% of patients with low-risk (LO) MM and 15% with high-risk (HI) MM compared to conventional prognostic variables. MM occasionally presents as macro-focal disease, in which cases RS may be inconclusive because of paucity of malignant cells in the sample. Here we report the comparison of GEP data from paired FL and RS samples from 106 untreated patients.

Methods:
We identified 106 newly diagnosed patients with paired samples, who were treated on Total Therapy (TT) protocols (3 in TT2, 19 in TT3, 75 in TT4, and 9 in TT5) in our multiple myeloma database. GEP risk scores, molecular subgroup classifications, overall survival (OS), and event-free survival (EFS) were compared and tested with the RS-derived 70-gene risk prediction and molecular subgroup classification models.

Results:
GEP defined molecular subgroups were correlated in 90 of 106 patients (85%). Looking at GEP-defined risk designation, we found a high degree of correlation between RS and FL samples with 95 of 106 samples showing the same designation (90%). For the 11 patients with divergent GEP designations, 8 (73%) were located at the boundary of the RS GEP risk score cutoff of +0.66 (range +0.43 to +0.84). For these risk designation-divergent patients, FL- but not RS-defined risk determined clinical outcome.

Conclusion:
Both the 70-gene risk prediction and molecular subgroup classification models can be used in FL-GEP samples. But, more importantly, for patients with RS-GEP risk score close to cutoff boundary (about 14% in our current data sets), FL-GEP provides better risk stratification, suggesting that the FL signal more adequately reflects to disease biology, progression and treatment response in MM. We therefore recommend that, for patients with borderline RS-based GEP risk scores, FL-GEP be used for staging and prognosis assessment in myeloma. Studies are in progress to determine, among multiple FL samples from the same patient, the variability in risk score compared to multiple RS samples.

Disclosures:
Shaughnessy: Myeloma Health, Celgene, Genzyme, Novartis: Consultancy, Employment, Equity Ownership, Honoraria, Patents & Royalties. Barlogie:Celgene: Consultancy, Honoraria, Research Funding; IMF: Consultancy, Honoraria; MMRF: Consultancy; Millennium: Consultancy, Honoraria, Research Funding; Genzyme: Consultancy; Novartis: Research Funding; NCI: Research Funding; Johnson & Johnson: Research Funding; Centocor: Research Funding; Onyx: Research Funding; Icon: Research Funding.