Immunophenotypic changes in the tumor and tumor microenvironment during progression to multiple myeloma
Bibliographic record
Abstract
Investigation of the cellular and molecular mechanisms of disease progression from precursor plasma cell disorders to active disease increases our understanding of multiple myeloma (MM) pathogenesis and supports the development of novel therapeutic strategies. In this analysis, single-cell RNA sequencing, surface protein profiling, and B lymphocyte antigen receptor profiling of unsorted, whole bone marrow (BM) mononuclear cell samples was used to study molecular changes in tumor cells and the tumor microenvironment (TME). A cell atlas of the BM microenvironment was generated from 123 subjects including healthy volunteers and patients with monoclonal gammopathy of unknown significance (MGUS), smoldering MM (SMM), and MM. These analyses revealed commonalities in molecular pathways, including MYC signaling, E2F targets and interferon alpha response, that were altered during disease progression. Evidence of early dysregulation of the immune system in MGUS and SMM, which increases and impacts many cell types as the disease progresses, was found. In parallel with disease progression, population shifts in CD8 + T cells, macrophages, and classical dendritic cells were observed, and the resulting differences in CD8 + T cells and macrophages were associated with poor overall survival outcomes. Potential ligand-receptor interactions that may play a role during the transition from precursor stages to MM were identified, along with potential biomarkers of disease progression, some of which may represent novel therapeutic targets. MIF, IL15, CD320, HGF and FAM3C were detected as potential regulators of the TME by plasma cells, while SERPINA1 and BAFF (TNFSF13B) were found to have the highest potential to contribute to the downstream changes observed between precursor stage and MM cells. These findings demonstrate that myeloma tumorigenesis is associated with dysregulation of molecular pathways driven by gradually occurring immunophenotypic changes in the tumor and TME. Trial registration: This project has been registered at EudraCT (European Union Drug Regulating Authorities Clinical Trials Database) with protocol number NOPRODMMY0001 and EudraCT Number 2018-004443-23 on 12 December 2018.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".