Safety and efficacy of autologous tumour cell vaccines as a cancer therapeutic to treat solid tumours and haematological malignancies: a meta-analysis protocol for two systematic reviews
Bibliographic record
Abstract
Introduction Autologous cancer cell vaccines are promising personalised immunotherapeutic options for solid and haematological malignancies that uses the patient’s own cells to arm an immune response. Evidence suggests that among patients receiving these vaccines, those who mount an immune response against their own tumour cells have better prognosis, and a myriad of preclinical studies have demonstrated the same. Recently, two autologous cell vaccines Vigil and OncoVAX have made it to phase III clinical trials. Here, we outline a protocol to be used for two separate systematic reviews using a parallel approach for inclusion criteria, data extraction and analysis for autologous cell vaccines in (1) solid and (2) haematological malignancies. We aim to review evidence from controlled and uncontrolled interventional studies of autologous cell vaccines administered to patients with cancer to determine their historical efficacy (with or without associated adjuvants or modifications) with clinical response rates and safety outcomes being of particular importance. Methods and analysis We will search MEDLINE (OVID interface, including In-Process and Epub Ahead of Print), Embase (OVID interface) and the Cochrane Central Register of Controlled Trials (Wiley interface) for articles published from 1947 until 30 July 2018 (date search was performed). Studies will be screened first by title and abstract, then by full-text in duplicate. Interventional trials that report the use of an autologous cell vaccine to patients with cancer of any age will be included. The primary outcomes of interest in this review are clinical response (complete or overall/objective response) and safety outcomes (adverse events). Secondary outcomes include immune response, disease-free survival and overall survival. The risk of bias within studies will be assessed using the appropriate Cochrane Risk of Bias tool. If appropriate, a random effects meta-analysis will be performed to synthesise the data and report summary estimates of effect. Statistical heterogeneity will be assessed using the I 2 statistic. Ethics and dissemination Ethics approval is not required for this systematic review protocol as the review will solely use published literature. Results will be submitted to peer-reviewed journals for publication and presented to relevant stakeholders and scientific meetings. PROSPERO registration number CRD42019140187.
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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 | 0.054 | 0.098 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.038 | 0.046 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".