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Abstract
Response to immunotherapies can be variable and unpredictable. Pathology-based phenotyping of tumors into ‘hot’ and ‘cold’ is static, relying solely on T-cell infiltration in single-time single-site biopsies, resulting in suboptimal treatment response prediction. Dynamic vascular events (tumor angiogenesis, leukocyte trafficking) within tumor immune microenvironment (TiME) also influence anti-tumor immunity and treatment response. Here, we report dynamic cellular-level TiME phenotyping in vivo that combines inflammation profiles with vascular features through non-invasive reflectance confocal microscopic imaging. In skin cancer patients, we demonstrate three main TiME phenotypes that correlate with gene and protein expression, and response to toll-like receptor agonist immune-therapy. Notably, phenotypes with high inflammation associate with immunostimulatory signatures and those with high vasculature with angiogenic and endothelial anergy signatures. Moreover, phenotypes with high inflammation and low vasculature demonstrate the best treatment response. This non-invasive in vivo phenotyping approach integrating dynamic vasculature with inflammation serves as a reliable predictor of response to topical immune-therapy in patients.
Standard assessment of immune infiltration of biopsies is not sufficient to accurately predict response to immunotherapy. Here, the authors show that reflectance confocal microscopy can be used to quantify dynamic vasculature and inflammatory features to better predict treatment response in skin cancers.
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1 Memorial Sloan Kettering Cancer Center, New York, USA (GRID:grid.51462.34) (ISNI:0000 0001 2171 9952)
2 Memorial Sloan Kettering Cancer Center, Parker Institute for Cancer Immunotherapy, Ludwig Collaborative and Swim Across America Laboratory, Human Oncology and Pathogenesis Program, New York, USA (GRID:grid.51462.34) (ISNI:0000 0001 2171 9952)
3 Northeastern University, Roux Institute, Portland, USA (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359); Northeastern University, Department of Electrical and Computer Engineering, Boston, USA (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359)
4 Memorial Sloan Kettering Cancer Center, New York, USA (GRID:grid.51462.34) (ISNI:0000 0001 2171 9952); SUNY Downstate Health Sciences University, Brooklyn, USA (GRID:grid.262863.b) (ISNI:0000 0001 0693 2202)
5 SUNY Downstate Health Sciences University, Brooklyn, USA (GRID:grid.262863.b) (ISNI:0000 0001 0693 2202); Karolinska University Hospital Solna, Department of Clinical Pathology and Cancer Diagnostics, Stockholm, Sweden (GRID:grid.24381.3c) (ISNI:0000 0000 9241 5705); University of Alcala, Faculty of Medicine and Health Sciences, Madrid, Spain (GRID:grid.7159.a) (ISNI:0000 0004 1937 0239)
6 Caliber Imaging and Diagnostics, Rochester, USA (GRID:grid.435995.7)
7 University of Alcala, Faculty of Medicine and Health Sciences, Madrid, Spain (GRID:grid.7159.a) (ISNI:0000 0004 1937 0239)
8 Icahn School of Medicine at Mount Sinai, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351)
9 ORIC Pharmaceuticals, San Francisco, USA (GRID:grid.59734.3c)
10 Tata Memorial Hospital, Mumbai, India (GRID:grid.410871.b) (ISNI:0000 0004 1769 5793)
11 Memorial Sloan Kettering Cancer Center, New York, USA (GRID:grid.51462.34) (ISNI:0000 0001 2171 9952); Pontificia Universidad Católica de Chile, Santiago, Chile (GRID:grid.7870.8) (ISNI:0000 0001 2157 0406)
12 Sydney Melanoma Diagnostic Center, Sydney, Australia (GRID:grid.51462.34); Melanoma Institute Australia, Wollstonecraft, Australia (GRID:grid.419690.3) (ISNI:0000 0004 0491 6278)
13 Memorial Sloan Kettering Cancer Center, New York, USA (GRID:grid.51462.34) (ISNI:0000 0001 2171 9952); Weill Cornell Medicine, New York, USA (GRID:grid.5386.8) (ISNI:000000041936877X)
14 Memorial Sloan Kettering Cancer Center, New York, USA (GRID:grid.51462.34) (ISNI:0000 0001 2171 9952); Memorial Sloan Kettering Cancer Center, Parker Institute for Cancer Immunotherapy, Ludwig Collaborative and Swim Across America Laboratory, Human Oncology and Pathogenesis Program, New York, USA (GRID:grid.51462.34) (ISNI:0000 0001 2171 9952); Weill Cornell Medicine, New York, USA (GRID:grid.5386.8) (ISNI:000000041936877X)