Timing Genomic Antigen Loss in Multiple Myeloma Treated with T Cell–Redirecting Immunotherapies
Bibliographic record
Abstract
Genomic antigen loss is a recurring mechanism of resistance to chimeric antigen receptor T-cell (CAR-T) and T-cell engagers (TCE) in relapsed/refractory multiple myeloma (RRMM). Yet, it remains unclear whether these events are acquired under treatment or merely selected from preexisting, undetectable clones. By leveraging chemotherapy mutational signatures as temporal barcodes within whole-genome sequencing data, we could time genomic antigen escape in 4 of 11 patients with RRMM. In all cases, the biallelic loss was driven by genomic events acquired after exposure to BCMA- and GPCR5D-targeted CAR-T/TCE and not present at baseline. Longitudinal digital PCR analysis corroborated that resistance mutations were undetectable at therapy initiation but emerged preceding relapse. Among 752 newly diagnosed patients, only 2.7% and 9% had monoallelic inactivation of TNFRSF17 and GPCR5D, respectively, with no biallelic loss. Our findings suggest limited utility of mutational screening prior to CAR-T/TCE while underscoring the importance of dynamic surveillance during therapy. SIGNIFICANCE: Multiple myeloma has been demonstrated to recurrently develop resistance to T-cell redirection via genomic antigen escape. By leveraging chemotherapy mutational signatures, we demonstrate that somatic antigen-escape mechanisms are uniformly acquired following treatment initiation and not selected from among preexisting clones, emphasizing the importance of dynamic longitudinal surveillance for their emergence. See related commentary by Kauer et al., p. 532.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".