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4457 Global Expression Changes of Malignant Plasma Cells over Time Reveals the Evolutionary Development of Signatures of Aggressive Clinical Behavior

Program: Oral and Poster Abstracts
Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster III
Hematology Disease Topics & Pathways:
multiple myeloma, Diseases, Technology and Procedures, Plasma Cell Disorders, Clinically relevant, Lymphoid Malignancies
Monday, December 3, 2018, 6:00 PM-8:00 PM
Hall GH (San Diego Convention Center)

Eileen M Boyle, MD, MSc1,2*, Adam Rosenthal, MS3*, Yan Wang2*, Phil Farmer2*, Michael W Rutherford2*, Cody Ashby, PhD4*, Michael A Bauer, PhD2, Sarah K Johnson, PhD2, Christopher P. Wardell, PhD, MSc, BSc2*, Niels Weinhold, PhD2, Antje Hoering, PhD3*, Charles Dumontet1*, Thierry Facon5*, Carolina D. Schinke, MD2, Sharmilan Thanendrarajan, MD2, Frits van Rhee, MD, PhD6, Maurizio Zangari, MD2, Bart Barlogie, MD, PhD7, Faith E. Davies, MD2, Brian A Walker, PhD2 and Gareth Morgan, MD6

1Centre de Recherche en Cancérologie de Lyon (CRCL), Lyon, France
2Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, AR
3Cancer Research and Biostatistics, Seattle, WA
4Myeloma Institute, University for Medical Sciences Arkansas, Little Rock, AR
5Hôpital Claude Huriez, CHRU de Lille, Lille, France
6University of Arkansas for Medical Sciences, Little Rock, AR
7Department of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY

Introduction: Clustering of gene expression signatures at diagnosis has identified a number of distinct disease groups that correlate with outcome in multiple myeloma (MM). Some of these are defined by an etiologic genetic event whereas others, such as the proliferation cluster (PR) and GEP70 risk relate to acquired clinical behaviors regardless of the underlying background. The PR cluster has a number of important features, including markers of proliferation, and has been associated with an adverse outcome. This logic led us to study how gene expression patterns change over time with the aim of gaining insight into acquired features that could be targeted therapeutically or be used to predict outcome.

Methods: We followed 784 newly diagnosed MM patients from the Total Therapy trials over a median of 9.5 years for whom repeated GEP of CD138+ plasma cells using Affymetrix U133 Plus 2.0 plus arrays were obtained. Raw data were MAS5 normalized and GEP70-based high-risk (HR) scores, translocation classification (TC) and molecular cluster classification were derived, as previously reported.

Results: At diagnosis, 85.9% percent of patients (666/784) were identified as low-risk (LR). Among them, 23.1% (154/666) went on to develop HR status (defined by a GEP70 score > 0.66) at least once after initial diagnosis. Among the non-PR cases, 28.5% (193/677) were seen to develop a PR phenotype at some point during follow-up. Similarly, among the PR patients (n=107), we observed that 43.1% (25/58) identified as LR by GEP70 at presentation eventually develop HR status at least once during follow-up.

We further analyzed 147 patients with paired diagnosis and relapse samples. Seventeen percent of patients (25/147) were PR at diagnosis. Most patients were from favorable TC prognostic groups [80% D1-D2, 8% t(11;14), 8% t(4;14) and 4% t(14;20)]. Seventy-six percent of PR patients remained PR at relapse (19/25) whereas 23% switched cluster in accordance to their translocation group. Fifteen percent of patients (22/147) became PR at relapse. They originated from four clusters and three TC groups [77% from the D1-D2, 14% t(4;14) and 9% from the t(11;14)]. Overall-survival from the time of relapse was inferior for patients categorized as PR at relapse compared to other subgroups (p< 0.0001); among PR patients at relapse, there was no difference in outcome between patients classified as PR or non-PR at diagnosis (p= 0.74). When looking at GEP70 defined risk scores, the incidence of HR status rose from 23% to 39% between diagnosis and relapse with a significant increase in mean GEP70 scores using paired t-test (p<0.0001). Patients identified as HR by GEP70 at relapse had an inferior post-relapse outcome compared to patients identified as LR (p< 0.0001); there was no difference in the outcome of patients identified as HR at relapse depending on their risk status at diagnosis (p = 0.10).

Discussion: Following the introduction of therapeutic regimens aimed at maximizing response, long term survival in MM has improved. This also led to an apparent increase in the development of more aggressive disease patterns at relapse including extra-medullary disease and plasma cell leukemia. Here we show, that HR features both in terms of PR and GEP70 risk status, develop as a variable over time. At relapse, most acquired HR cases originate from standard-risk presentation cases, suggesting selective pressure for HR features. Moreover, we show that the detection of such behaviors is associated with an adverse outcome from the time of relapse. These data also suggest that repeating GEP during follow-up adds precision to better comprehend individual risk and may help identify patient specific therapeutic strategies. Indeed, understanding how these patterns develop, which genes are implicated, and their impact on the immune microenvironment should allow us to effectively utilize a wide array of treatment approaches ranging from immune-therapies to novel cell-cycle targeting agents to specifically address this type of aggressive behavior.

Conclusion: The acquisition of high risk patterns captured by GEP70 risk and PR status is an ongoing process from initial diagnosis. Such high risk prognostic features have an adverse outcome from the time of development. Repeating GEP during follow-up may therefore help better predict outcome and identify patient specific therapeutic strategies.

Disclosures: Boyle: Janssen: Honoraria, Other: travel grants; Takeda: Consultancy, Honoraria; Gilead: Honoraria, Other: travel grants; Abbvie: Honoraria; Celgene: Honoraria, Other: travel grants; La Fondation de Frace: Research Funding; Amgen: Honoraria, Other: travel grants. Dumontet: Janssen: Honoraria; Roche: Research Funding; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria. Facon: Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Barlogie: Celgene: Consultancy, Research Funding; Multiple Myeloma Research Foundation: Other: travel stipend; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; Dana Farber Cancer Institute: Other: travel stipend; Millenium: Consultancy, Research Funding; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend. Davies: TRM Oncology: Honoraria; ASH: Honoraria; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; MMRF: Honoraria; Janssen: Consultancy, Honoraria; Abbvie: Consultancy; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees. Morgan: Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Janssen: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria.

*signifies non-member of ASH