Tumor-Associated Macrophages in Multiple Myeloma: Key Role in Disease Biology and Potential Therapeutic Implications
Bibliographic record
Abstract
Multiple myeloma (MM) is characterized by multiple relapse and, despite the introduction of novel therapies, the disease becomes ultimately drug-resistant. The tumor microenvironment (TME) within the bone marrow niche includes dendritic cells, T-cytotoxic, T-helper, reactive B-lymphoid cells and macrophages, with a complex cross-talk between these cells and the MM tumor cells. Tumor-associated macrophages (TAM) have an important role in the MM pathogenesis, since they could promote plasma cells proliferation and angiogenesis, further supporting MM immune evasion and progression. TAM are polarized towards M1 (classically activated, antitumor activity) and M2 (alternatively activated, pro-tumor activity) subtypes. Many studies demonstrated a correlation between TAM, disease progression, drug-resistance and reduced survival in lymphoproliferative neoplasms, including MM. MM plasma cells in vitro could favor an M2 TAM polarization. Moreover, a possible correlation between the pro-tumor effect of M2 TAM and a reduced sensitivity to proteasome inhibitors and immunomodulatory drugs was hypothesized. Several clinical studies confirmed CD68/CD163 double-positive M2 TAM were associated with increased microvessel density, chemoresistance and reduced survival, independently of the MM stage. This review provided an overview of the biology and clinical relevance of TAM in MM, as well as a comprehensive evaluation of a potential TAM-targeted immunotherapy.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".