
Bone marrow microenvironments that contribute to patient outcomes in newly diagnosed multiple myeloma: A cohort study of patients in the Total Therapy clinical trials
Author(s) -
Samuel A. Danziger,
Mark McConnell,
Jake Gockley,
Mary Young,
Adam Rosenthal,
Frank Schmitz,
David J Reiss,
Phillip Farmer,
Daisy Alapat,
Amrit Pal Singh,
Cody Ashby,
Michael Bauer,
Yan Ren,
Kelsie M. Smith,
Suzana Couto,
Frits van Rhee,
Faith E. Davies,
Maurizio Zangari,
Nathan Petty,
Robert Z. Orłowski,
Madhav V. Dhodapkar,
Wilbert B. Copeland,
Brian A. Fox,
Antje Hoering,
Alison Fitch,
Katie Newhall,
Bart Barlogie,
Matthew Trotter,
Robert M. Hershberg,
Brian A. Walker,
Andrew Dervan,
Alexander V. Ratushny,
Gareth J. Morgan
Publication year - 2020
Publication title -
plos medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.847
H-Index - 228
eISSN - 1549-1676
pISSN - 1549-1277
DOI - 10.1371/journal.pmed.1003323
Subject(s) - multiple myeloma , medicine , bone marrow , tumor microenvironment , oncology , cancer , adverse effect
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. Methods and findings Between 2004 and 2019, 998 BM samples were taken from 436 patients with newly diagnosed MM (NDMM) at the University of Arkansas for Medical Sciences in Little Rock, Arkansas, United States of America. These patients were 61% male and 39% female, 89% White, 8% Black, and 3% other/refused, with a mean age of 58 years. Using WBM and matched cluster of differentiation (CD)138-selected tumor gene expression to control for tumor burden, we identified a subgroup of patients with an adverse TME associated with 17 fewer months of progression-free survival (PFS) (95% confidence interval [CI] 5–29, 49–69 versus 70–82 months, χ 2 p = 0.001) and 15 fewer months of overall survival (OS; 95% CI –1 to 31, 92–120 versus 113–129 months, χ 2 p = 0.036). Using immunohistochemistry-validated computational tools that identify distinct cell types from bulk gene expression, we showed that the adverse outcome was correlated with elevated CD8 + T cell and reduced granulocytic cell proportions. This microenvironment develops during the progression of premalignant to malignant disease and becomes less prevalent after therapy, in which it is associated with improved outcomes. In patients with quantified International Staging System (ISS) stage and 70-gene Prognostic Risk Score (GEP-70) scores, taking the microenvironment into consideration would have identified an additional 40 out of 290 patients (14%, premutation p = 0.001) with significantly worse outcomes (PFS, 95% CI 6–36, 49–73 versus 74–90 months) who were not identified by existing clinical (ISS stage III) and tumor (GEP-70) criteria as high risk. The main limitations of this study are that it relies on computationally identified cell types and that patients were treated with thalidomide rather than current therapies. Conclusions In this study, we observe that granulocyte signatures in the MM TME contribute to a more accurate prognosis. This implies that future researchers and clinicians treating patients should quantify TME components, in particular monocytes and granulocytes, which are often ignored in microenvironment studies.