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Using the immune system to fight cancer was first tested by William Coley in 1893. After considerable efforts, especially in the last 20 years or so, cancer immunotherapy is now mainstream and is one arm for treating cancer along with surgery, radiation and chemotherapy. Because immune-based therapies are designed to stimulate the immune system to fight cancer, it has become increasingly clear that the success of immune-based therapies cannot be predicted using the models used to predict the success of traditional cytotoxic therapies. Essentially one has to document that 1] immune-based therapies elicit the desired immunological change in the patient and 2] these immune changes can potentially translate to efficacy or survival. Another critical question is how to predict whether a patient will respond to immunotherapy, or rather, is a particular baseline immunological profile required for a successful outcome. Therefore, it is critical to develop reliable biomarkers that can predict response to immunotherapy and also the efficacy of immunotherapy. Although such analysis can be easily conducted in the setting of pre-clinical inbred mouse models, translating these methods have proven to be more cumbersome in clinical settings in patients with cancer. This presentation will focus on understanding how the immune system works and on the concept of immune-based therapies. I am hoping this will lead to a dialog on how the statistics community can contribute to clinical trial design by identifying and selecting appropriate endpoints to measure efficacy and safety.