Protein landscape on cancer cells mapped with new technology

2022-03-03

In recent years, great advances have been made in the development of new, successful immunotherapies to treat cancer. Two types of targeted immunotherapies that have revolutionised areas of cancer care are CAR T-cell therapy and antibody treatments. However, there are still significant challenges in the identification of cancer cell surface proteins that function as targets for immunotherapies. Mattias Belting, professor at Lund University and senior consultant at Skåne University Hospital, and guest professor at IGP, is well on the way and his group’s findings are now published in the journal PNAS.

Immunotherapies have revolutionised the treatment of cancer and, in some cases, are able to cure patients with advanced disease. Immunotherapies with CAR T-cells and antibodies share a focus on specific target proteins expressed on the surface of tumour cells, known as cell surface tumour antigens.

“The great challenge is that the structure of cell surface tumour antigens differs between patients and between primary tumours and metastases. There is a great need both for new strategies that can identify accessible, treatable cell surface tumour antigens with high precision at a personalised level. We have worked for many years to establish new methods that provide knowledge about antigens on the surface of cancer cells as a target for immunotherapies”, says Mattias Belting.

Now, he and his research group have developed a new precision medicine technology called ‘Tumour Surfaceome Mapping, TS-MAP’, which makes it possible to carry out a direct analysis of all accessible cell surface tumour antigens in tumour tissue from patients. In a close collaboration between neurosurgery, oncology and advanced proteomics in Lund, the researchers were able to identify several cell surface tumour antigens in fresh tissue from patients with aggressive brain tumours, for which there is currently no effective treatment.

“Our new findings with patient cells and tissues point to the fact that tumour cells fundamentally change their surface landscape when they are removed from their natural, three-dimensional environment, which is important information for future research in the area. The methods previously developed to identify cell surface antigens or to produce antibodies targeting tumour cells use two-dimensional models, which, according to our findings, misrepresent the situation in patient tumours,” says Mattias Belting.

The study was in parts performed in collaboration with researchers at IGP, where Mattias Belting is guest professor. To develop and validate the technology they used brain tumour cells established in Uppsala from glioblastoma patients that have donated tumour tissue for research.

“Glioblastoma cells function as a testbed to understand how the surface antigens vary between when cells are in 2D or 3D. The project is part of a close collaboration between Uppsala and Lund that aims to find new treatment strategies for malignant brain tumours,” says Karin Forsberg Nilsson, professor at IGP and co-author of the study.

Mattias Belting says the results of the study clearly highlight the possibilities and need for personalised strategies based on the great repertoire and variation of tumour antigens in patient tumours. It is also significant that the analysis is carried out on intact tissue.

“Precision medicine in immunotherapy for the treatment of cancer is promising, but also very challenging. In addition to the variation of tumour antigen expressions between and within tumours, we still have insufficient knowledge about the interaction between cancer cells and immune cells in the tumour microenvironment. Currently, we talk about each individual patient needing to be matched to a drug. Perhaps it is the other way around, that we must design a specific drug to match the individual patient, however impossible that may sound”, says Mattias Belting.

More information:
Article in PNAS
Karin Forsberg Nilsson’s research
Mattias Belting's research at Lund University

Last modified: 2022-01-26