A Neoantigen-Based Peptide Vaccine for Patients With Advanced Pancreatic Cancer Refractory to Standard Treatment
Bibliographic record
Abstract
Background: Neoantigens are critical targets to elicit robust antitumor T-cell responses. Personalized cancer vaccines developed based on neoantigens have shown promising results by prolonging cancer patients' overall survival (OS) for several cancer types. However, the safety and efficacy of these vaccine modalities remains unclear in pancreatic cancer patients. Methods: This retrospective study enrolled 7 advanced pancreatic cancer patients. Up to 20 neoantigen peptides per patient identified by our in-house pipeline iNeo-Suite were selected, manufactured and administered to these patients with low tumor mutation burden (TMB) (less than 10 mutations/Mb). Each patient received multiple doses of vaccine depending on the progression of the disease. Peripheral blood samples of each patient were collected pre- and post-vaccination for the analysis of the immunogenicity of iNeo-Vac-P01 through ELISpot assay and flow cytometry. Results: effector memory T cells count post vaccination were found in patients with relatively long overall survival. Remarkably, for patient P01 who had a 21-month OS associated with vaccine treatment, the abundance of antigen-specific TCR clone drastically increased from 0% to nearly 100%, indicating the potential of iNeo-Vac-P01 in inducing the activation of a specific subset of T cells to kill cancer cells. Conclusions: Neoantigen identification and selection were successfully applied to advanced pancreatic cancer patients with low TMB. As one of the earliest studies that addressed an issue in treating pancreatic cancer with personalized vaccines, it has been demonstrated that iNeo-Vac-P01, a personalized neoantigen-based peptide vaccine, could improve the currently limited clinical efficacy of pancreatic cancer. Clinical Trial Registration: ClinicalTrials.gov, identifier (NCT03645148).Registered August 24, 2018 - Retrospectively registered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".