Safety and efficacy of autologous whole cell vaccines in hematologic malignancies: A systematic review and meta‐analysis
Bibliographic record
Abstract
Autologous cell vaccines use a patient's tumor cells to stimulate a broad antitumor response in vivo. This approach shows promise for treating hematologic cancers in early phase clinical trials, but overall safety and efficacy remain poorly described. We conducted a systematic review assessing the use of autologous cell vaccination in treating hematologic cancers. Primary outcomes of interest were safety and clinical response, with secondary outcomes including survival, relapse rate, correlative immune assays and health-quality related metrics. We performed a search of MEDLINE, Embase and the Cochrane Register of Controlled Trials including any interventional trial employing an autologous, whole cell product in any hematologic malignancy. Risk of bias was assessed using a modified Institute of Health Economics tool. Across 20 single arm studies, only 341 of 592 enrolled participants received one or more vaccinations. Primary reasons for not receiving vaccination included rapid disease progression/death and manufacturing challenges. Overall, few high-grade adverse events were observed. One death was reported and attributed to a GM-CSF producing allogeneic cell line co-administered with the autologous vaccine. Of 58 evaluable patients, the complete response rate was 21.0% [95% CI, 10.4%-37.8%)] and overall response rate was 35.8% (95% CI, 24.4%-49.0%). Of 97 evaluable patients for survival, the 5-years overall survival rate was 64.9% (95% CI, 52.6%-77.2%) and disease-free survival was 59.7% (95% CI, 47.7%-71.7%). We conclude that, in hematologic malignancies, based on limited available data, autologous cell vaccines are safe and display a trend towards efficacy but that challenges exist in vaccine manufacture and administration.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.006 | 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 | 0.002 |
| 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".