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Abstract:
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Cancer immunotherapy, such as immune checkpoint therapy or adoptive cell therapy, prompts the immune system to identify and kill cancer cells, and phenomenal successes have been reported. However, durable clinical response of immune checkpoint therapy is only observed in a subset of patients. For example, approximately 20% of melanoma and lung cancer patients show response to immune checkpoint inhibitors. To improve the efficacy of immunotherapy (e.g., to identify the patients who can benefit from immunotherapy or to develop new treatment strategy), it is crucial to have a mechanistic understanding of immunotherapy failure. We develop a new statistical method to use omic data collected from tumor samples to study immune cell composition and associate such cell composition with clinical or molecular outcomes.
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