New Strategies in Multiple Myeloma: Immunotherapy as a Novel Approach to Treat Patients with Multiple Myeloma
Bibliographic record
Abstract
Multiple myeloma is a B-cell malignancy characterized by proliferation of monoclonal plasma cells in the bone marrow. Although new therapeutic options introduced in recent years have resulted in improved survival outcomes, multiple myeloma remains incurable for a large number of patients, and new treatment options are urgently needed. Over the last 5 years, there has been a renewed interest in the clinical potential of immunotherapy for the treatment of multiple myeloma. Clinical progression of myeloma is known to be associated with progressive immune dysregulation and loss of immune surveillance that contribute to disease progression in association with progressive genetic complexity, rendering signaling-based treatments less effective. A variety of strategies to reverse the multiple myeloma-induced immunosuppression has been developed either in the form of immunomodulatory drugs, checkpoint inhibitors, mAbs, engineered T cells, and vaccines. They have shown encouraging results in patients with relapsed refractory multiple myeloma and hold great promise in further improving patient outcomes in multiple myeloma. This review will summarize the major approaches in multiple myeloma immunotherapies and discuss the mechanisms of action and clinical activity of these strategies. Clin Cancer Res; 22(24); 5959-65. ©2016 AACR.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".