Principal Investigator:
Maria Sibilia Personal webpage
Institution:
Medical University of Vienna Webpage
Project title:
Therapeutic targeting of EGFR in colorectal cancer as a novel approach to predict and enhance tumor antigenicity and response to checkpoint inhibitors
Collaborators:
Martin Filipits (Medical University of Vienna) (Co-Principal Investigator) Personal webpage
Status:
Ongoing (01.01.2017 – 31.12.2020) 48 months

 
Abstract:

Despite the big success of checkpoint inhibitors for cancer treatment, many patients like microsatellite stable metastatic colorectal cancer (mCRC) patients fail to respond for reasons that are poorly understood. One standard therapy for mCRC with wildtype RAS is EGFR inhibition combined with chemotherapy. However, for unknown reasons, many patients do not profit from this therapy. Using genetically engineered mouse models (GEMM) we identified a tumor-promoting role of EGFR-expressing myeloid cells in CRC and could demonstrate that EGFR positive myeloid cells are a bad prognostic factor for mCRC patients. We thus hypothesize that EGFR-expressing myeloid cells adopt a pro-tumorigenic phenotype by creating an immunosuppressive environment. EGFR blockade could therefore revert this by increasing immunity against tumors and enhancing the effectiveness of checkpoint inhibitors. We have therefore assembled a group of experts in oncology, bioinformatics and molecular biology to test this hypothesis by employing GEMM of CRC lacking EGFR in different cells combined with mouse and human next generation sequencing of tumor, stromal, and immune cell populations. The complex interplay between mutations in tumor cells and stroma will be tackled by investigations on large patient cohorts combined with mechanistic studies in GEMM. We expect to identify factors conferring resistance to immunotherapy in microsatellite stable CRC patients with the aim to improve precision oncology in mCRC.

 
Scientific disciplines: 301904 - Cancer research (40%) | 301902 - Immunology (30%) | 101004 - Biomathematics (30%)

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