Modulating PD-L1 expression in multiple myeloma: an alternative strategy to target the PD-1/PD-L1 pathway
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Even with recent advances in therapy regimen, multiple myeloma patients commonly develop drug resistance and relapse. The relevance of targeting the PD-1/PD-L1 axis has been demonstrated in pre-clinical models. Monotherapy with PD-1 inhibitors produced disappointing results, but combinations with other drugs used in the treatment of multiple myeloma seemed promising, and clinical trials are ongoing. However, there have recently been concerns about the safety of PD-1 and PD-L1 inhibitors combined with immunomodulators in the treatment of multiple myeloma, and several trials have been suspended. There is therefore a need for alternative combinations of drugs or different approaches to target this pathway. Protein expression of PD-L1 on cancer cells, including in multiple myeloma, has been associated with intrinsic aggressive features independent of immune evasion mechanisms, thereby providing a rationale for the adoption of new strategies directly targeting PD-L1 protein expression. Drugs modulating the transcriptional and post-transcriptional regulation of PD-L1 could represent new therapeutic strategies for the treatment of multiple myeloma, help potentiate the action of other drugs or be combined to PD-1/PD-L1 inhibitors in order to avoid the potentially problematic combination with immunomodulators. This review will focus on the pathophysiology of PD-L1 expression in multiple myeloma and drugs that have been shown to modulate this expression.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it