Timing antigenic escape in multiple myeloma treated with T-cell redirecting immunotherapies
Bibliographic record
Abstract
Recent data highlight genomic events driving antigen escape as a recurring cause of chimeric antigen receptor T-cell (CAR-T) and bispecific T-cell engager (TCE) resistance in multiple myeloma (MM). Yet, it remains unclear if these events, leading to clonal dominance at progression, result from acquisition under treatment selection or selection of pre-existing undetectable clones. This differentiation gains importance as these immunotherapies progress to earlier lines of treatment, prompting the need for innovative diagnostic testing to detect these events early on. By reconstructing phylogenetic trees and exploring chemotherapy mutational signatures as temporal barcodes in 11 relapsed refractory MM patients with available whole genome sequencing data before and after CART/TCE treatment, we demonstrated that somatic antigen escape mechanisms for BCMA- and GPRC5D-targeting therapies are acquired post-diagnosis, likely during CART/TCE treatment. Longitudinal tracking of these mutations using digital PCR in 4 patients consistently showed that genomic events promoting antigen escape were not detectable during the initial months of therapy but began to emerge nearly 1 year post therapy initiation. This finding reduces the necessity for a diagnostic panel to identify these events before CART/TCE. Instead, it underscores the importance of surveillance and identifying patients at higher risk of acquiring these events.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".