Life Sciences 2020 - Precision MedicineLS20-042

Innate immune reprogramming to overcome therapy resistance in high-risk colorectal cancer


Principal Investigator:
Project title:
Innate immune reprogramming to overcome therapy resistance in high-risk colorectal cancer
Co-Principal Investigator(s):
Michael Bergmann (Medical University of Vienna)
Matthias Farlik-Födinger (Medical University of Vienna)
Status:
Ongoing (01.12.2021 – 30.11.2024)
GrantID:
10.47379/LS20042
Funding volume:
€ 800,000

Despite advances in the treatment of metastatic colorectal cancer (mCRC), 5-year survival is only 14%. Cancer immunotherapy has provided significant clinical benefit but the use of currently available checkpoint inhibitors is limited to only 5% of mCRC patients. 95% of mCRC patients who have a pMMR/MSI-L phenotype show low or no T cell infiltration but are frequently infiltrated by myeloid cells, which drive immunosuppression and therapy resistance. Here we propose to identify and validate new immunomodulatory approaches for high-risk (HR) pMMR/MSI-L stage IV mCRC patients by reprogramming tumor-infiltrating myeloid cells using epigenetic modifiers and myeloid-specific checkpoint inhibitors. We propose to (1) define immunosuppressive myeloid cell landscapes for HR mCRC patient stratification using single-cell multi-omics profiling, (2) identify and validate epigenetic modifiers that reprogram immunosuppressive myeloid cells using a CRISPR screen with single cell phenotypic read-out, (3) generate patient-derived CRC xenografts and reconstruct the tumor-immune microenvironment in a novel MISTRG+ humanized mouse model, and (4) validate different myeloid-reprogramming therapies and combination strategies in CRC-PDX humanized MISTRG+ mice using a multi-omics approach defining the responsive subgroup. Altogether this strategy will provide clinicians with a framework for precision diagnostics and myeloid therapy including effective combination strategies for HR mCRC patients.

 
 
Scientific disciplines: Immunology (50%) | Cancer research (30%) | Bioinformatics (20%)

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