Recent clinical trials using immunotherapy have demonstrated its potential to control cancer by disinhibiting the immune system. Immune checkpoint blocking (ICB) antibodies against cytotoxic-T-lymphocyte-associated protein 4 or programmed cell death protein 1/programmed death-ligand 1 have displayed durable clinical responses in various cancers1. Although these new immunotherapies have had a notable effect on cancer treatment, multiple mechanisms of immune resistance exist in tumours. Among the key mechanisms, myeloid cells have a major role in limiting effective tumour immunity2,3,4. Growing evidence suggests that high infiltration of immune-suppressive myeloid cells correlates with poor prognosis and ICB resistance5,6. These observations suggest a need for a precision medicine approach in which the design of the immunotherapeutic combination is modified on the basis of the tumour immune landscape to overcome such resistance mechanisms. Here we employ a pre-clinical mouse model system and show that resistance to ICB is directly mediated by the suppressive activity of infiltrating myeloid cells in various tumours. Furthermore, selective pharmacologic targeting of the gamma isoform of phosphoinositide 3-kinase (PI3Kγ), highly expressed in myeloid cells, restores sensitivity to ICB. We demonstrate that targeting PI3Kγ with a selective inhibitor, currently being evaluated in a phase 1 clinical trial (NCT02637531), can reshape the tumour immune microenvironment and promote cytotoxic-T-cell-mediated tumour regression without targeting cancer cells directly. Our results introduce opportunities for new combination strategies using a selective small molecule PI3Kγ inhibitor, such as IPI-549, to overcome resistance to ICB in patients with high levels of suppressive myeloid cell infiltration in tumours.
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We would like to thank the Flow Cytometry and Integrated Genomics Operation Core Facilities at MSKCC. Swim Across America, Ludwig Institute for Cancer Research, Parker Institute for Cancer Immunotherapy, Center for Experimental Therapeutics at MSKCC (ETC), and the Breast Cancer Research Foundation supported this work. The work was also supported in part by the MSKCC Core Grant (P30 CA008748). O.D.H. was supported by J. Houtard foundation, Nuovo Soldati Foundation and Wallonie-Bruxelles International. We would also like to thank Y. Senbabaoglu for his help in bioinformatics data analysis, A. Bossert for his contribution as part of the GME program as well as J. Gladstone and K. Walsh for their contributions while working as co-op students in the laboratory.
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Nature Reviews Clinical Oncology (2018)