Abstract: Over the past decade, the treatment of cancer has been revolutionized by the emergence of immunotherapies. However, many patients experience low response rates and adverse effects, highlighting the need for better approaches to identify more accurate predictors of response. To address this need, BostonGene combines molecular and immune profiling techniques to provide a comprehensive portrait of a cancer patient’s tumor and immune system. Using artificial intelligence and machine learning systems, BostonGene integrates data from CLIA-certified DNA and RNA sequencing (RNA-seq), flow cytometry, and multiplex imaging to characterize the patient’s tumor and its microenvironment, which plays a role in tumor progression and response. Further analysis of the distribution of cell populations in the peripheral blood are performed with a machine learning-based, clinical immunoprofiling platform using multiparameter flow cytometry, providing an overview of a patient’s overall immune health and potential for response to immunotherapy. Integration of these technologies that analyze the tumor tissue and blood show the potential for accurately predicting immunotherapy responses, ultimately providing patients with more effective treatments that lead to improved clinical outcomes.