Low-Frequency PPM1D Gene Mutations Affect Treatment Response to CD19-Targeted CAR T-Cell Therapy in Large B-Cell Lymphoma
Bibliographic record
Abstract
Chimeric antigen receptor T (CAR T)-cell therapy has become a standard treatment option for patients with relapsed or refractory diffuse large B-cell lymphoma (r/r DLBCL). Mutations in the PPM1D gene, a frequent driver alteration in clonal hematopoiesis (CH), lead to a gain of function of PPM1D/Wip1 phosphatase, impairing p53-dependent G1 checkpoint and promoting cell proliferation. The presence of PPM1D mutations has been correlated with reduced response to standard chemotherapy in lymphoma patients. In this study, we analyzed the impact of low-frequency PPM1D mutations on the safety and efficacy of CD19-targeted CAR T-cell therapy in a cohort of 85 r/r DLBCL patients. In this cohort, the prevalence of PPM1D gene mutations was 20% with a mean variant allele frequency (VAF) of 0.052 and a median VAF of 0.036. CAR T-induced cytokine release syndrome (CRS) and immune effector cell-associated neuro-toxicities (ICANS) occurred at similar frequencies in patients with and without PPM1D mutations. Clinical outcomes were globally worse in the PPM1D mutated (PPM1Dmut) vs. PPM1D wild type (PPM1Dwt) subset. While the prevalent treatment outcome within the PPM1Dwt subgroup was complete remission (56%), the majority of patients within the PPM1Dmut subgroup had only partial remission (60%). Median progression-free survival (PFS) was 3 vs. 12 months (p = 0.07) and median overall survival (OS) was 5 vs. 37 months (p = 0.004) for the PPM1Dmut and PPM1Dwt cohort, respectively. Our data suggest that the occurrence of PPM1D mutations in the context of CH may predict worse outcomes after CD19-targeted CAR T-cell therapy in patients with r/r DLBCL.
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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 | 0.001 |
| 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".