Efficacy and Safety of CD22 Chimeric Antigen Receptor (CAR) T-cell Therapy in Patients With B-cell Malignancies: A Protocol for a Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background: Chimeric antigen receptor (CAR) T-cell therapy has had great success in treating patients with relapsed or refractory B-cell malignancies, with CD19-targeting therapies now approved in many countries. However, a subset of patients fail to respond or relapse after CD19 CAR T-cell therapy, in part due to antigen loss, which has prompted the search for alternative antigen targets. CD22 is another antigen found on the surface of B-cells. CARs targeting CD22 alone or in combination with other antigens have been investigated in several pre-clinical and clinical trials.Given the heterogeneity and small size of CAR T-cell therapy clinical trials, systematic reviews are needed to evaluate their efficacy and safety. Here, we propose a systematic review of CAR T-cell therapies targeting CD22, alone or in combination with other antigen targets, in B-cell malignancies.Methods:We will perform a systematic search of EMBASE, MEDLINE, Web of Science, Cochrane Register of Controlled Trials, clinicaltrials.gov, and the International Clinical Trials Registry Platform. Ongoing and completed clinical trials will be identified and catalogued. Interventional studies investigating CD22 CAR T-cells, including various multi-antigen targeting approaches, in patients with relapsed or refractory B-cell malignancies will be eligible for inclusion. Only full-text articles, conferences abstracts, letters and case reports will be considered. Our primary outcome will be a complete response, defined as absence of detectable cancer. Secondary outcomes will include adverse events, overall response, minimal residual disease, and relapse, among others. Quality assessment will be performed using a modified Institute of Health Economics tool designed for interventional single-arm studies. We will report a narrative synthesis of clinical studies, presented in tabular format. If appropriate a meta-analysis will be performed using a random effects model to synthesize results.Discussion:The results of the proposed review will help inform clinicians, patients and other stakeholders of the risks and benefits of CD22 CAR T-cell therapies. It will identify gaps or inconsistencies in outcome reporting and help to guide future clinical trials investigating CAR T-cells.PROSPERO Registration Number: CRD42020193027.
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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 | 0.034 | 0.054 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.027 | 0.038 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".