Bone marrow microenvironments that contribute to patient outcomes in newly diagnosed multiple myeloma: A cohort study of patients in the Total Therapy clinical trials

Samuel A. Danziger ,Mark McConnell, Jake Gockley, Mary H. Young, Adam Rosenthal, Frank Schmitz, David J. Reiss, Phil Farmer, Daisy V. Alapat, Amrit Singh, Cody Ashby, Michael Bauer, Yan Ren, Kelsie Smith, Suzana S. Couto, Frits van Rhee, Faith Davies, Maurizio Zangari, Nathan Petty, Robert Z. Orlowski, Madhav V. Dhodapkar, Wilbert B. Copeland, Brian Fox, Antje Hoering, Alison Fitch, Katie Newhall, Bart Barlogie, Matthew W. B. Trotter, Robert M. Hershberg, Brian A. Walker, Andrew P. Dervan, Alexander V. Ratushny, Gareth J. Morgan

Abstract

Background

The tumor microenvironment (TME) is increasingly appreciated as an important determinant of cancer outcome, including in multiple myeloma (MM). However, most myeloma microenvironment studies have been based on bone marrow (BM) aspirates, which often do not fully reflect the cellular content of BM tissue itself. To address this limitation in myeloma research, we systematically characterized the whole bone marrow (WBM) microenvironment during premalignant, baseline, on treatment, and post-treatment phases.

Introduction

A major aim of modern cancer therapy is to develop precision immuno-oncology in which treatment is targeted to the biology of distinct disease segments. Multiple myeloma (MM) is a bone marrow (BM) cancer for which modern therapy has increased 5-year patient survival from approximately 35% in 2000 to nearly 70% in 2017; however, more than 10% of patients do not respond well, necessitating novel treatment modalities. To date, work on disease stratification in MM has focused on the clonal plasma cells (the tumor) rather than the environment (the BM) in which the tumor proliferates. This work has led directly to the translocation cyclin D classification; however, tumor-mutation–based stratification does not fully explain patient outcomes. Furthermore, these approaches have failed to identify a consistent mutational pattern driving disease progression from abnormal, but premalignant, plasma cells to cancer.

Methods

Patient data

Patients considered for analysis were enrolled into the Total Therapy (TT) 3–5 trial series; details of these protocols have been described in previous publications . These patients were enrolled between February 25, 2004 and January 11, 2014 with 25–5,241 days of follow-up (mean 2,628 days, standard deviation 1,387 days, S1–S3 Tables). They included 266 males and 170 females aged 32.4–75.2 years at baseline (mean = 58.48, median = 59.85); 387 identified as White, 34 as Black, 5 as Hispanic, 5 as Asian, 3 as Native American, and 2 refused to identify a race. TT 3–5 generally includes treatment with thalidomide, dexamethasone, and autologous BM transplant with personalized treatment regimens informed by tumor gene expression and cytogenetics.

Results

Validation of MGSM27 cell-type predictions

The MGSM27 signature matrix correctly identified that, post-CD138+ selection, the samples were almost entirely pure CD138+ cells (N = 423; Wilcoxon p < 0.001; S1C Fig). In WBM samples in which the global RNA contribution of the CD138+ cells to the admixture was heterogeneous (S1D Fig), the correlation coefficient was 0.63 with a root mean-square error (RMSE) of 9.99% (N = 247; Fisher’s Z p < 0.001). The predictions also mirrored pathologist estimates of eosinophil and neutrophil numbers. Eosinophils had a correlation coefficient of 0.27 with an RMSE of 3.05% (N = 345; Fisher’s Z p < 0.001; S1E Fig), and neutrophils had a correlation coefficient of 0.32 with an RMSE of 9.55% (N = 356; Fisher’s Z p ≤ 0.001; S1F Fig

Discussion

Here, we present an analysis of 867 WBM samples taken during the course of treatment of 436 patients with MM and a follow-up study using samples from predisease patients (55 with MGUS and 76 with SMM, S1–S3 Tables). Purified tumor samples were used to isolate the nontumor portion of WBM gene-expression data, which clustered patients by their microenvironment signatures. One of the clusters (Cluster 5) stood out both in terms of patient survival and cellular composition.

Conclusion

Taken together, the observations in this study provide evidence that the microenvironment is a significant contributor to tumorbehavior and treatment response. Further investigation of the microenvironment cellular content may have significant impacts on outcome after immune therapies and, in addition, the role of atypical myeloid cells including granulocytes, myeloid derived suppressor cells, myeloid precursors, and other myeloid lineage cell types should be explored further in this context. To summarize, this article should encourage myeloma clinicians and researchers to study the TME and pay particular attention to granulocytes; their presence (or absence) may contribute to patient survival.

Acknowledgments

We thank FadiTowfic, AnjanThakurta, PallavurSivakumar, and Isaac Boss for their constructive comments on the manuscript.

Citation: Danziger SA, McConnell M, Gockley J, Young MH, Rosenthal A, Schmitz F, et al. (2020) Bone marrow microenvironments that contribute to patient outcomes in newly diagnosed multiple myeloma: A cohort study of patients in the Total Therapy clinical trials. PLoS Med 17(11): e1003323. https://doi.org/10.1371/journal.pmed.1003323

Editor: Yawara Kawano, Kumamoto University Hospital, JAPAN

Received: January 23, 2020; Accepted: September 18, 2020; Published: November 4, 2020

Copyright: © 2020 Danziger et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All gene expression, clinical characteristics, and cytogenetic data files are available from the Gene Expression Omnibus database (accession number GSE136400 (GEO; https://www.ncbi.nlm.nih.gov/geo/). The R Code for deconvolution and processed data files are available through the Comprehensive R Archive Network (CRAN) in the ADAPTS package (https://cran.r-project.org/web/packages/ADAPTS/index.html).

Funding: This research was funded by Celgene Corporation, a Bristol Myers Squibb company (https://www.celgene.com/). The author(s) received no specific funding for this work.

Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: SAD, MM, MHY, FS, DJR, YR, KS, WBC, BF, AF, KN, APD, and AVR declare employment and equity ownership for Bristol Myers Squibb. JG declares previous employment at Celgene Corporation. SSC declares previous employment and equity ownership at Celgene Corporation. AR, PF, DVA, AS, CA, MB, FVR, MZ, NP, AH, and GJM declare no competing financial interests.

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