Routine Pre-Apheresis Markers As Predictors for Efficacy and Safety Outcomes Following CD19 CAR-T Cell Therapy - a Single Center Experience
Bibliographic record
Abstract
Background : CD19 CAR-T cell therapy is an effective therapy in the setting of relapsed or refractory large B cell lymphoma (LBCL). However, up to 50-60% of patients do not achieve a durable remission. Baseline biomarkers such as circulating CD3 count, total lymphocyte (ALC) and monocyte (AMC) count have recently emerged as potential predictors of response to CD19 CAR-T therapy in LBCL. However, the use of these markers in the real-world setting has not been validated. Our aim was to evaluate if routine baseline pre-apheresis data predict 100 day response and early immune mediated toxicities. Methods : Retrospective chart review was performed. All patients who received anti-CD19 CAR-T cell therapy (Axi-cel or Tisa-cel) for the indication of LBCL at the Princess Margaret Cancer Center, in Toronto, Canada from June 2020 to April 2024 were screened. Patients who had baseline peripheral values drawn at the time of apheresis, successful CAR-T infusion, and day 100 disease assessment were included in the final analysis. 17 patients, who experienced treatment or disease related deaths prior to day 100 assessment, were excluded. Statistical analysis using Wilcoxon rank-sum and Kruskal-Wallis tests were applied. Responders were defined as achieving complete metabolic response (CMR) or partial metabolic response (PMR), and non-responders as progressive metabolic disease (PMD) or stable metabolic disease (SMD) based on the Lugano Criteria by PET scan. Results : A total of 159 patients were included in the analysis. 121(38%) patients received Axi-cel and 38(24%) Tisa-cel. The median age was 61 years (range: 19-83), 58% had refractory and 42% relapsed disease. 30% had received a prior autologous stem cell transplant and 73% required bridging therapy (steroids 9%, chemo alone 25%, radiotherapy 23%, and combination 16%). Peripherally drawn pre-apheresis markers, including median total white blood cell count (WBC), absolute neutrophil count (ANC), absolute monocyte count (AMC), absolute lymphocyte count (ALC), relative percent of ANC, AMC and ALC to total WBC, and median absolute CD3 count were evaluated. There was no statistically significant difference between these median baseline pre-apheresis values in responders (CMR/PMR, N=108) compared to non-responders (PMD/SMD, N=51). Regarding safety outcomes, 11 patients (7%) had grade 0 cytokine release syndrome (CRS), 77(52%) grade 1 and 61(41%) grade ≥2 events. A total of 101 patients had documented highest grade immune mediated neurotoxicity (ICANS), with 64 patients (63%) grade 0, 23(23%) grade 1 and 14(14%) grade ≥2 events. Difference in median AMC percent of total WBC was a significant predictor of developing CRS, with Gr 0: 14.8%(12.2-600), Gr 1: 11.5%(0-37.5) vs Gr ≥2: 12.0%(1.4-35.7) (p=0.004). There was no other statistically significant difference between median baseline pre-apheresis values and highest grade CRS or ICANS. Conclusion : Our real-world analysis did not identify peripheral CD3, WBC, ANC, ALC or AMC as predictors of response to CD19 CAR-T cell therapy in LBCL. We found that in this population, higher AMC to WBC ratio was associated with more frequent occurrence of CRS. Further studies are required to ascertain practical predictors for response and toxicity with CAR-T cell therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| 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 | 0.001 | 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".