Sex differences in the pre-treatment cytokine signatures associated with response and survival in B cell lymphoma patients treated with anti-CD19 CAR T-cell therapy
Bibliographic record
Abstract
Abstract Many autoimmune diseases, and in some instances, immune response to infections exhibit gender bias. However, it is not known if gender influences the response of lymphoma patients to anti-CD19 CAR T-cell therapy. To address this, we profiled 51 pre-treatment serum cytokines and chemokines to identify those that were differentially associated with response and survival in male vs. female relapsed/refractory large B-cell lymphoma (R/R LBCL) patients treated with Axicabtagene ciloleucel. Male non-responders (stable/progressive disease) had significantly higher baseline levels of IL-6, IL-8, IL-1RA, MIP-1α, GM-CSF and CRP compared to responders (complete/partial response). Higher levels of IL-6, IL-8, IL-27, and CRP were significantly associated with poor overall survival (OS), and IL-6, IL-8, MIP-1β, and CRP with poor progression-free survival (PFS) in male but not female patients. Baseline metabolic tumor volume (MTV) and lactate dehydrogenase (LDH) levels were also significantly higher in male patients with poor OS and PFS. Finally, baseline IL-8 and CRP were significantly correlated with baseline MTV and LDH levels in male but not female patients. Taken together, elevated baseline levels of IL-8, IL-6, CRP with high pretreatment tumor burden in male non-responder R/R LBCL patients point to an immunosuppressive tumor microenvironment that can potentially hinder anti-tumor activity prior to treatment and CAR T-cell expansion upon treatment in these patients, and result in poor outcomes.
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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) | 0.002 | 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".