Description

Human leukocyte antigen proteins expose the health status of cells to CD8+ cytotoxic T lymphocytes (CTLs) by presenting peptides at the cell surface (with varying copy numbers) processed from the cellular protein content. Analysis of the HLA ligandome (often called immuno peptidome) can provide important insights in the antigenic signature of human diseases. In the context of cancer: mapping the immuno peptidome is an approach to identify new leads for specific peptide centered immunotherapies. Although prediction algorithms have been developed that can predict what peptides are presented by specific HLA allotypes based on the protein expression profile of a cell, biochemical proof is still required if the peptides are the starting point of therapies or vaccines. In addition, rare allotypes are often not sufficiently characterized to accurately predict the presentation of peptides at the cell surface.

 

Mass spectrometry based analysis of the immuno peptidome is in principle an unbiased method to look at the HLA ligandome either for the identification of new neoantigens or the validation/confirmation of antigens predicted by algorithms. However, although serious progress has been made in terms of sensitivity of mass spectrometry techniques and of identification algorithms, the current immuno peptidomics approach is still limited by the technical sensitivity of the MS analysis and the limitations of the interpretation of the mass spectra. Sensitivity in particular becomes an important issue when moving from analysis HLA ligands from bulk tumor tissue to sorted cell populations where sample quantities become a limiting factor.

 

In this project, we want to tackle some of these limitations and set-up a more sensitive immuno peptidomics platform for application in the field of (lung) oncology.

 

Recent developments in mass spectrometry (in particular trapped ion mobility mass spectrometry) and in the field of peptidomics (the none immune counterpart) have the potential to significantly improve the limit of detection and the number of successful peptide identifications in immuno peptidomics. In addition, new (and existing) methodologies for peptide identification will be developed and applied to immuno peptidomics. The pipeline will be applied this to lung cancer in the context of immunotherapy response.

 

Advanced non-small cell lung cancer (NSCLC) is generally linked with a poor prognosis and is one of the leading causes of cancer-related deaths worldwide. Since only a minority of the patients respond to chemotherapy and targeted therapies, immunotherapy might be a valid alternative in the lung cancer treatment field, as immunotherapy-based options did demonstrate promising results in some other malignancies such as melanoma. Results from early phase clinical trials, however, demonstrate that both whole-cell vaccines and specific antigen-based vaccinations were unable to prove improvements in survival. Therefore, methods that allow to study the molecular mechanisms underlying the maintenance of the local immune response in regions where tumor cells and immune cells co-reside are crucial. Recently, through the use of mass spectrometry imaging analyses, a number of factors with predictive value for the success of immune therapy were identified (patent filed). The underlying mechanisms, however are not well understood. Since immunotherapy unblocks the activity of T-cells in the tumors we believe that the key may lay in understanding the antigen presenting signatures in responders to immunotherapy.


Outcomes in the analyses will lead to a better understanding of immune response in NSCLC and will likely provide leads for therapy and stratification of patients based on biomarkers.

 

Collaboration with University of Antwerp

Registration deadline: 6/03/2020

 


Qualification
  • You hold a master's degree in biochemistry, biology, biomedical sciences, pharmacy, ...
  • You are fluent in English, both oral and written

 

Offer

The PhD student receives a PhD grant from the University of Antwerp. VITO concludes a financing agreement with the University of Antwerp, with VITO undertaking to provide an annual allowance matching the net remuneration of an assistant, plus management costs. The University of Antwerp will pay the selected PhD student a PhD grant matching the before mentioned net amount. 

Requisition

Location: 
Mol
Jobfield: 
PhD
ID: 
28400