Identification and Targeting of Mutant Neoantigens in Multiple Myeloma Treatment
Bibliographic record
Abstract
Multiple myeloma (MM) is malignant disease characterized by the clonal proliferation of plasma cells in the bone marrow, leading to anemia, immunosuppression, and other symptoms, that is generally hard to treat. In MM, the immune system is likely exposed to neoplasia-associated neoantigens for several years before the tumor onset. Different types of neoantigens have been identified. Public or shared neoantigens derive from tumor-specific modifications often reported in several patients or across diverse tumors. They are intriguing therapeutic targets because they are frequently observed, and they have an oncogenic effect. Only a small number of public neoantigens have been recognized. Most of the neoantigens that have been identified are patient-specific or "private", necessitating a personalized approach for adaptive cell treatment. It was demonstrated that the targeting of a single greatly immunogenic neoantigen may be appropriate for tumor control. The purpose of this review was to analyze the neoantigens present in patients with MM, and to evaluate the possibility of using their presence as a prognostic factor or as a therapeutic target. We reviewed the most recent literature on neoantigen treatment strategies and on the use of bispecific, trispecific, and conjugated antibodies for the treatment of MM. Finally, a section was dedicated to the use of CAR-T in relapsed and refractory patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".