A novel immunopeptidomic-based pipeline for the generation of personalized oncolytic cancer vaccines
- University of Helsinki, Finland
- University Vita e Salute San Raffaele, Italy
- University of Helsinki, Finland
- Valo Therapeutics Oy, Finland
- Karolinska Institutet, Sweden
- Karolinska Institute, Sweden
Abstract
Beside the isolation and identification of MHC-I restricted peptides from the surface of cancer cells, one of the challenges is eliciting an effective anti-tumor CD8+ T cell mediated response as part of therapeutic cancer vaccine. Therefore, the establishment of a solid pipeline for the downstream selection of clinically relevant peptides and the subsequent creation of therapeutic cancer vaccines are of utmost importance. Indeed, the use of peptides for eliciting specific anti-tumor adaptive immunity is hindered by two main limitations: the efficient selection of the most optimal candidate peptides and the use of a highly immunogenic platform to combine with the peptides to induce effective tumor-specific adaptive immune responses. Here, we describe for the first time a streamlined pipeline for the generation of personalized cancer vaccines starting from the isolation and selection of the most immunogenic peptide candidates expressed on the tumor cells and ending in the generation of efficient therapeutic oncolytic cancer vaccines. This immunopeptidomics-based pipeline was carefully validated in a murine colon tumor model CT26. Specifically, we used state-of-the-art immunoprecipitation and mass spectrometric methodologies to isolate >8000 peptide targets from the CT26 tumor cell line. The selection of the target candidates was then based on two separate approaches: RNAseq analysis and the HEX software. The latter is a tool previously developed by Chiaro et al. (1), able to identify tumor antigens similar to pathogen antigens, in order to exploit molecular mimicry and tumor pathogen cross-reactive T-cells in cancer vaccine development. The generated list of candidates (twenty-six in total) was further tested in a functional characterization assay using interferon-g ELISpot (Enzyme-Linked Immunospot), reducing the number of candidates to six. These peptides were then tested in our previously described oncolytic cancer vaccine platform PeptiCRAd, a vaccine platform that combines an immunogenic oncolytic adenovirus (OAd) coated with tumor antigen peptides. In our work, PeptiCRAd was successfully used for the treatment of mice bearing CT26, controlling the primary malignant lesion and most importantly a secondary, non-treated, cancer lesion. These results confirmed the feasibility of applying the described pipeline for the selection of peptide candidates and generation of therapeutic oncolytic cancer vaccine, filling a gap in the field of cancer immunotherapy, and paving the way to translate our pipeline into human therapeutic approach.
Data availability
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD026463. The ligandome dataset is currently hidden but will be made public upon eventual acceptance of the current manuscript.Reviewer Account details for ligandome data accessing: https://www.ebi.ac.uk/pride/login PXD026463Username: reviewer_pxd026463@ebi.ac.ukPassword: oMUWIAw3
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Noncoding regions are the main source of targetable tumor-specific antigensNCBI Gene Expression Omnibus, GSE111092.
Article and author information
Author details
Michaela Feodoroff
Funding
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All animal experiments were reviewed and approved by the Experimental Animal Committee of the University of Helsinki and the Provincial Government of Southern Finland (license number ESAVI/11895/2019).4-6 weeks old female Balb/cOlaHsd mice were obtained from Envigo (Laboratory, Bar Harbor, Maine UK).
Copyright
© 2022, Feola et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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A novel immunopeptidomic-based pipeline for the generation of personalized oncolytic cancer vaccines
eLife 11:e71156.
https://doi.org/10.7554/eLife.71156
Further reading
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- Medicine
Background:
Post-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) is a severe and deadly adverse event following ERCP. The ideal method for predicting PEP risk before ERCP has yet to be identified. We aimed to establish a simple PEP risk score model (SuPER model: Support for PEP Reduction) that can be applied before ERCP.
Methods:
This multicenter study enrolled 2074 patients who underwent ERCP. Among them, 1037 patients each were randomly assigned to the development and validation cohorts. In the development cohort, the risk score model for predicting PEP was established via logistic regression analysis. In the validation cohort, the performance of the model was assessed.
Results:
In the development cohort, five PEP risk factors that could be identified before ERCP were extracted and assigned weights according to their respective regression coefficients: –2 points for pancreatic calcification, 1 point for female sex, and 2 points for intraductal papillary mucinous neoplasm, a native papilla of Vater, or the pancreatic duct procedures (treated as ‘planned pancreatic duct procedures’ for calculating the score before ERCP). The PEP occurrence rate was 0% among low-risk patients (≤0 points), 5.5% among moderate-risk patients (1–3 points), and 20.2% among high-risk patients (4–7 points). In the validation cohort, the C statistic of the risk score model was 0.71 (95% CI 0.64–0.78), which was considered acceptable. The PEP risk classification (low, moderate, and high) was a significant predictive factor for PEP that was independent of intraprocedural PEP risk factors (precut sphincterotomy and inadvertent pancreatic duct cannulation) (OR 4.2, 95% CI 2.8–6.3; p<0.01).
Conclusions:
The PEP risk score allows an estimation of the risk of PEP prior to ERCP, regardless of whether the patient has undergone pancreatic duct procedures. This simple risk model, consisting of only five items, may aid in predicting and explaining the risk of PEP before ERCP and in preventing PEP by allowing selection of the appropriate expert endoscopist and useful PEP prophylaxes.
Funding:
No external funding was received for this work.
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- Medicine
Estrogen significantly impacts women’s health, and postmenopausal hypertension is a common issue characterized by blood pressure fluctuations. Current control strategies for this condition are limited in efficacy, necessitating further research into the underlying mechanisms. Although metabolomics has been applied to study various diseases, its use in understanding postmenopausal hypertension is scarce. Therefore, an ovariectomized rat model was used to simulate postmenopausal conditions. Estrogen levels, blood pressure, and aortic tissue metabolomics were analyzed. Animal models were divided into Sham, OVX, and OVX +E groups. Serum estrogen levels, blood pressure measurements, and aortic tissue metabolomics analyses were performed using radioimmunoassay, UHPLC-Q-TOF, and bioinformatics techniques. Based on the above research content, we successfully established a correlation between low estrogen levels and postmenopausal hypertension in rats. Notable differences in blood pressure parameters and aortic tissue metabolites were observed across the experimental groups. Specifically, metabolites that were differentially expressed, particularly L-alpha-aminobutyric acid (L-AABA), showed potential as a biomarker for postmenopausal hypertension, potentially exerting a protective function through macrophage activation and vascular remodeling. Enrichment analysis revealed alterations in sugar metabolism pathways, such as the Warburg effect and glycolysis, indicating their involvement in postmenopausal hypertension. Overall, this current research provides insights into the metabolic changes associated with postmenopausal hypertension, highlighting the role of L-AABA and sugar metabolism reprogramming in aortic tissue. The findings suggest a potential link between low estrogen levels, macrophage function, and vascular remodeling in the pathogenesis of postmenopausal hypertension. Further investigations are needed to validate these findings and explore their clinical implications for postmenopausal women.
