Machine learning based integration of high-dimensional cytometry and radiomics data to identify treatment responses in cancer-immunotherapy

01.04.2019 bis 31.03.2023

Immunotherapy has revolutionized the way cancer patients are treated today and has revived efforts to seek therapeutic targets to raise anti-tumor immunity across all malignant entities. The margins between the different biological approaches to treat cancer are blurring. Check-point inhibition (CPI), CAR-T cells, and cytokines are combined with conventional approaches to treat cancer and to elicit anti-tumor immunity. Measuring the successes of clinical trials often requires monitoring through general clinical parameters, and the outcome measurement is often the rate of progression based on radiological imaging and survival. The downside of this conventional monitoring approach is that patient responses are often detected long after treatment initiation and therapy-resistant patients incur the adverse effects while delaying the administration of potentially better-tailored therapies. Biomarkers that predict and monitor patient responses to immunotherapy are only now emerging for some immunotherapies; whereas new therapeutic approaches often still rely on conventional clinical monitoring.
While biased analysis of blood parameters, and even targeted cytometry and bulk-omics have yielded some insights into potential biomarkers, high-dimensional single-cell cytometry, combined with unbiased algorithm-based pattern recognition have revolutionized biomarker identification and personalized medicine. Similarly, advances in radiological imaging and in particular quantitative radiological image analysis (radiomics) now allow for a more precise determination of tumor burden, cancer characterization and assessment of treatment response.
This project will address malignant melanoma patients undergoing immune checkpoint inhibition (aPD-1 or aPD1/aCTLA-4): we will seek to i) identify biomarkers to predict the therapeutic response to combination cancer immunotherapy, ii) monitor therapy responses during combination therapy and iii) analyze polymorphonuclear granulocytes as indicators of anti-tumor immunity. The project combines cutting-edge clinical oncology and computational radiological image analysis and innovative computational biology to identify prognostic and predictive signatures to establish clinically applicable tools to guide precision medicine decisions and to identify new therapeutic targets for the treatment of cancer.