Abstract 2761: CODEX highly multiplex image mapping to CITEseq datasets reveal the spatial dynamics of the TME during the development of acquired resistant in immunotherapy treated melanoma
Authors: Quek Camelia , Silva Ines P. , Al-Eryani Ghamdan , Mayer Aaron , Bartonicek Nenad , Harvey Kate , Buckley Chi , Braubach Oliver , Thompson John F. , Stretch Jonathan R. , Saw Robyn P M , Shannon Kerwin F. , Menzies Alexander M. , Scolyer Richard A. , Long Georgina V. , Swarbrick Alexander , Wilmott James S. 
Published: July 2021

Abstract
Primary and acquired resistance to anti-PD-1 and CTLA-4 immunotherapies remains a major obstacle to improving the outcomes of cancer patients. This study utilized a combination of scRNAseq, CITE-seq, a method for CODEX, 40+ protein marker tissue imaging to characterize the molecular and spatial features of cells within melanoma biopsies of patients receiving anti-PD-1 and CTLA-4 immunotherapies. Analysis was conducted on melanoma biopsies taken pre-treatment, early during treatment (7-14 days) and after disease progression. Five patients underwent all analyses; three were responding, two had primary resistance and one had acquired resistance. Tissue dissociates underwent ssRNAseq along with CITE-seq, which incorporated 153+ antibodies to extensively define tumor and immune cell phenotypes. A separate portion of the same lesion underwent formalin fixation and paraffin embedding and was then analyzed using the CODEX high-multiplex imaging system to spatially localize 40+ markers in the tumor. The CODEX imaging system and processor (Akoya Biosciences) were used to generate multiplex images. HALO AI (Indica Labs) was used to perform cell segmentation and quantify single-cell marker expression of the 40+ markers. Tumor areas were classified into vasculature (CD34+/CD31+), melanoma (SOX10+) and stroma regions using the DenseNet deep learning classifier within HALO AI. The subsequent output contained single-cell expression, tissue classification and x and y location data, which were used to map to the CITE-seq data using the CamaraLab STvEA (Govek et al, biorix 2019) to identify CITE-seq defined populations within tissue sections. CODEX analysis of 2.2M cells revealed distinct immune phenotypes of tumor infiltrated cells compared to their peritumor counterparts, with a gradient in the upregulation of immunosuppressive and exhaustive signaling upon entering the tumor region. Changes in cellular populations during treatment were significantly related to the spatial location of the cells within the tumor. Treatment with immunotherapies causes a dynamic change in these profiles which relates to the development of tumor resistance.
Citation Format: Camelia Quek, Ines P. Silva, Ghamdan Al-Eryani, Aaron Mayer, Nenad Bartonicek, Kate Harvey, Chi Buckley, Oliver Braubach, John F. Thompson, Jonathan R. Stretch, Robyn P M Saw, Kerwin F. Shannon, Alexander M. Menzies, Richard A. Scolyer, Georgina V. Long, Alexander Swarbrick, James S. Wilmott. CODEX highly multiplex image mapping to CITEseq datasets reveal the spatial dynamics of the TME during the development of acquired resistant in immunotherapy treated melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2761.