Immunotherapy and Immunoprevention - Molecular Vaccine Design

Maria Bonsack, PhD

Postdoctoral Scientist

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Phone: +49 6221 42 3822

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Scientific CV

Since Jan 2019    
Postdoctoral scientist, Immunotherapy and Immunoprevention / Molecular Vaccine Design, DKFZ & DZIF, Heidelberg

2016 - 2019
PhD student, Junior Research Group Immunotherapy and Immunoprevention, DKFZ, Heidelberg

2013 - 2015      
MSc Regenerative Biology and Medicine, Center for Regenerative Therapies of the Dresden University of Technology, Dresden

2010 - 2013          
BSc Molecular Biotechnology, Dresden University of Technology, Dresden

Research Project

Identification of naturally processed and presented HPV epitopes on cancer cells for therapeutic vaccine design

Several malignancies, most importantly cervical cancer, are caused by persistent infections with high-risk human papillomaviruses (HPV). For clearance of HPV infections, effective T cell-mediated responses are needed. These can be triggered in a targeted manner by vaccination with T cell epitopes derived from HPV proteins. Due to viral evasion mechanisms, only some viral epitopes are presented to the immune system on the surface of HPV-infected or -transformed cells.
The main focus of this project is to validate HPV-16-derived T cell epitopes for the major HLA supertypes. Epitopes are predicted in silico, HLA binding is verified experimentally, and immunogenicity and functional assays are applied in order to identify suitable vaccine candidates. Further, samples of HPV-16-transformed cells are analyzed for epitope presence by our highly specific mass spectrometry approach.

Resulting Publications

The shared frameshift mutation landscape of MSI cancers suggests immunoediting during tumor evolution.
Ballhausen A*, Przybilla MJ*, Jendrusch M*, Haupt S, Pfaffendorf E, Seidler F, Witt J, Hernandez-Sanchez A, Urban K, Draxlbauer M, Krausert S, Ahadova A, Kalteis MS, Pfuderer P, Heid D, Stichel D, Gebert J, Bonsack M, Schott S, Bläker H, Seppälä T, Mecklin J-P, Ten Broeke S, Nielsen M, Heuveline V, Krzykalla J, Benner A, Riemer AB, von Knebel Doeberitz M, Kloor M. * Equal contributors.
Nature Communications 2020, in press.

High-throughput prediction of MHC class I and class II neoantigens with MHCnuggets.
Shao XM, Bhattacharya R, Huang J, Sivakumar IKA, Tokheim C, Zheng L, Hirsch D, Kaminow B, Omdahl A, Bonsack M, Riemer AB, Velculescu VE, Anagnostou V, Pagel KA, Karchin R.
Cancer Immunology Research 2020, 8(3): 396-408.

Performance evaluation of MHC class-I binding prediction tools based on an experimentally validated MHC-peptide binding data set.

Bonsack M*, Hoppe S*, Winter J, Tichy D, Zeller C, Küpper MD, Schitter EC, Blatnik R, Riemer AB. * Equal contributors.
Cancer Immunology Research 2019, 7(5): 719-736.

MHCflurry: open-source class I MHC binding affinity prediction.
O’Donnell TJ,  Rubinsteyn A, Bonsack M, Riemer AB, Laserson U, Hammerbacher J.
Cell Systems 2018, 7(1): 129-132.e4.

A targeted LC-MS strategy for low-abundant HLA class-I-presented peptide detection identifies novel human papillomavirus T-cell epitopes.
Blatnik R*, Mohan N*, Bonsack M*, Falkenby LG, Hoppe S, Josef K, Steinbach A, Becker S, Nadler WM, Rucevic M, Larsen MR, Salek M, Riemer AB. * Equal contributors.
Proteomics 2018, 18(11): e1700390.

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