A Systematic Review of Evidence Supporting the Use of Autologous Cell Vaccines in the Treatment of Hematological Malignancies
Bibliographic record
Abstract
By presenting a patient's own tumor antigens to their immune system, autologous cancer cell vaccines can drive a robust polyclonal adaptive response. In this systematic review and meta-analysis, we investigated the safety and efficacy of these vaccines administered to patients with hematologic malignancies. Our primary outcomes of interest were safety and clinical response, with secondary outcomes including overall, disease-free and progression-free survival, relapse rate, correlative immune assays and health-quality related metrics. We identified 14 studies with 332 patients enrolled, of which 200 were ultimately treated with at least one dose of the vaccine. While we identified both patient-related and technical issues that might limit the feasibility of these trials, very few serious adverse events (AEs) were reported overall, with only 31.5% of patients suffering any AEs. Grade II or lower AE was observed in 10 (71.4%) of the 14 studies. Of the 3 (21.4%) reporting grade IV AEs, two observed the AE in one patient only, and one reported a 20% incidence of grade II-IV AEs. Of 58 evaluable patients, the complete response rate was 21% [95% CI, 10%-38%)] and overall response rate was 36% [95% CI, 24%-49%]. Analysis of individual patient level data (n=50) revealed a 5-year overall survival of 68.7% (SE 7.1%) and disease-free survival of 67.4% (SE 8.1%). Despite the clear safety and a signal towards efficacy, our review has identified several factors limiting administration of these vaccines, which should be considered when developing future clinical trials. PROSPERO registration number CRD42019140187 Disclosures Diallo: Virica Biotech: Other: Owner and Executive. Auer:Imugene: Other: Scientific Advisory Board. Kekre:Gilead: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Celgene: Consultancy, Honoraria.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".