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

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. 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.

CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer

“…10 a) with a 62.9% survival rate compared to a 53.9% survival rate in the high vs. low group, respectively, whereas the TCGA 7 year survival rates were 96.4% and 71.9%, respectively. Moreover, since the start of this study, additional deconvolution methods have been developed 81 , 82 , some of which have identified possible “spillover” between different cell types, such that there may be limitations in CIBERSORT’s ability to differentiate between similar cell types such as CD8 T cells and gamma delta T cells 83 . While most gamma delta T cells do not express CD8 or CD4, up to 30% can express CD8 84 .…”

Whole-genome sequencing reveals progressive versus stable myeloma precursor conditions as two distinct entities

“…Recent microenvironment investigations revealed distinct immune changes associated with progressive and stable myeloma precursor conditions 21 , 22 , however, it is still unclear if these immune patterns are responsible for the pre-myeloma clonal selection and progression, or rather represent the consequence of the pre-myeloma clonal expansion.…”

Increased Expression of SETDB1 Predicts Poor Prognosis in Multiple Myeloma

“…GEO databases were retrieved and obtained from the Gene Expression Omnibus ( https://www.ncbi.nlm.nih.gov/geo/ ) in the National Center for Biotechnology Information (NCBI). In this present study, clinical information and gene expression profiling microarray were from GSE39754 [ 20 ], GSE5900 [ 21 ], GSE2658 [ 22 ], GSE124435 [ 23 ], GSE31161 [ 24 ], GSE9782 [ 25 ], GSE24080 [ 26 ], GSE136337 [ 27 ], and GSE136324 [ 27 ]. Copy number analysis was conducted to identify genomic regions that are repeated from aCGH data (GSE33685 [ 28 ]and GSE26863 [ 29 ]).…”

“…These data were produced from whole bone marrow (WBM) biopsies and CD138 +-selected myeloma plasma cells using the Affymetrix Human Genome U133 Plus 2.0 Array (Thermo Fisher Scientific) chips. The expression scores of 27 cell types (22-leukocyte signature matrix and five myeloma-specific cell types) [ 27 ] per patient were determined using mRNA expression data of myeloma WBM biopsy samples from GSE156326 dataset through the CIBERSORT [ 33 ] algorithm in R (version 3.6.3). We classified MM samples into two groups on the basis of the SETDB1 optimal cut-off value of myeloma cells and then performed immunoinfiltration analysis on these corresponding WBM biopsy samples from GSE156326.…”