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Immunotherapy has revolutionised survival outcomes for many patients diagnosed with cancer. However, biomarkers that can reliably distinguish treatment responders from nonresponders, predict potential life-threatening and life-changing drug-induced toxicities, or rationalise treatment choices are still lacking. In response to this unmet clinical need, we introduce Multiomic ANalysis of Immunotherapy Features Evidencing Success and Toxicity, a tumour type-agnostic platform to provide deep profiling of patients receiving immunotherapy that will enable integrative identification of biomarkers and discovery of novel targets using artificial intelligence and machine learning.
Immunotherapy has revolutionised survival outcomes for many patients diagnosed with cancer. However, biomarkers that can reliably distinguish treatment responders from nonresponders, predict potential life-threatening and life-changing drug-induced toxicities, or rationalise treatment choices are still lacking. In response to this unmet clinical need, we introduce Multiomic ANalysis of Immunotherapy Features Evidencing Success and Toxicity, a tumour type-agnostic platform to provide deep profiling of patients receiving immunotherapy that will enable integrative identification of biomarkers and discovery of novel targets using artificial intelligence and machine learning.
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