Updates on CAR T cell therapy in multiple myeloma
Bibliographic record
Abstract
Multiple myeloma (MM) is a hematological cancer characterized by the abnormal proliferation of plasma cells. Initial treatments often include immunomodulatory drugs (IMiDs), proteasome inhibitors (PIs), and monoclonal antibodies (mAbs). Despite salient progress in diagnosis and treatment, most MM patients typically have a median life expectancy of only four to five years after starting treatment. In recent developments, the success of chimeric antigen receptor (CAR) T-cells in treating B-cell malignancies exemplifies a new paradigm shift in advanced immunotherapy techniques with promising therapeutic outcomes. Ide-cel and cilta-cel stand as the only two FDA-approved BCMA-targeted CAR T-cells for MM patients, a recognition achieved despite extensive preclinical and clinical research efforts in this domain. Challenges remain regarding certain aspects of CAR T-cell manufacturing and administration processes, including the lack of accessibility and durability due to T-cell characteristics, along with expensive and time-consuming processes limiting health plan coverage. Moreover, MM features, such as tumor antigen heterogeneity, antigen presentation alterations, complex tumor microenvironments, and challenges in CAR-T trafficking, contribute to CAR T-cell exhaustion and subsequent therapy relapse or refractory status. Additionally, the occurrence of adverse events such as cytokine release syndrome, neurotoxicity, and on-target, off-tumor toxicities present obstacles to CAR T-cell therapies. Consequently, ongoing CAR T-cell trials are diligently addressing these challenges and barriers. In this review, we provide an overview of the effectiveness of currently available CAR T-cell treatments for MM, explore the primary resistance mechanisms to these treatments, suggest strategies for improving long-lasting remissions, and investigate the potential for combination therapies involving CAR T-cells.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".