Treating Multiple Myeloma in the Context of the Bone Marrow Microenvironment
Bibliographic record
Abstract
The treatment landscape of multiple myeloma (MM) has evolved considerably with the FDA-approval of at least 15 drugs over the past two decades. Together with the use of autologous stem cell transplantation, these novel therapies have resulted in significant survival benefit for patients with MM. In particular, our improved understanding of the BM and immune microenvironment has led to the development of highly effective immunotherapies that have demonstrated unprecedented response rates even in the multiple refractory disease setting. However, MM remains challenging to treat especially in a high-risk setting. A key mediator of therapeutic resistance in MM is the bone marrow (BM) microenvironment; a deeper understanding is necessary to facilitate the development of therapies that target MM in the context of the BM milieu to elicit deeper and more durable responses with the ultimate goal of long-term control or a cure of MM. In this review, we discuss our current understanding of the role the BM microenvironment plays in MM pathogenesis, with a focus on its immunosuppressive nature. We also review FDA-approved immunotherapies currently in clinical use and highlight promising immunotherapeutic approaches on the horizon.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".