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TABLE OF CONTENTS GENERAL INFORMATION TITLE AUTHORS AND BEST CONTACT LICENSES USEFUL LINKS DATE OF DATA COLLECTION FILE ORGANIZATION FUNDING ---- Release Notes:This is a pre-publication placeholder for primary data release for the publication listed below. In common with GEO, PRIDE, and other data repositories, this will be populated with final data following peer review. ---- GENERAL INFORMATION Publication Title:Immune Profiling of Dermatologic Adverse Events from Checkpoint Blockade using Tissue Cyclic Immunofluorescence Authors:Zoltan Maliga, Daniel Y. Kim, Ai-Tram N. Bui, Jia-Ren Lin, Anna K. Dewan, George G. Murphy, Ajit J. Nirmal, Christine G. Lian, Peter K. Sorger, Nicole R. LeBoeuf Contact Information Nicole LeBoeuf, MD, MPH Vice Chair of Dermatology,Dana-FarberCancer Institute,; Chief, Division of Oncodermatology, Brigham and Women's Hospital; Clinical Director, Center for Cutaneous Oncology, Dana-Farber/Brigham Cancer Center, HarvardMedical School nleboeuf@partners.org Publications that cite or use the data:** Maliga, M, Kim, DY, Tram, B, Lin, JR, Dewan, AK, Jadeja, S, Murphy, G, Nirmal, AJ, Lian, C, Sorger, PK, LeBoeuf, NR. Immune Profiling of Dermatologic Adverse Events from Checkpoint Blockade using Tissue Cyclic Immunofluorescence.https://doi.org/10.1101/2023.04.03.535435 Licenses/restrictions placed on the data:CC BYhttps://creativecommons.org/licenses/by/4.0/ ---- FILE ORGANIZATION FILE TYPES/SUMMARY:Each folder corresponds to a patient sample (N). The following files are available for each patient and are located on [Synapse/AWS/Etc]. File Type Description Location N.ome.tif Stitched multiplex CyCIF image pyramid in ome.tif format AWS N_IHC.ome.tiff Immunohistochemistry stained image of serial FFPE tissue section in .ome.tiff format AWS N_HE.ome.tiff Hematoxylin and eosin stained image of FFPE tissue section in .ome.tiff format AWS markers.csv List of all markers in ome.tif image Synapse N_Quantification.csv Single-cell feature table, including intensity data for all channels Synapse segmentation/ Folder of segmentation maps for tissue image in .tif format AWS Synapse Library:Edit this to link directly to your public Synapse Library Free account registration is required to download files from Synapse. Images and metadata are available in the bucket at the following AWS location:[ADD BUCKET DETAILS] To browse and download the data use either a graphical file transfer application that supports S3 such asCyberDuck, or theAWS CLI tools. A graphical tool may be more convenient but the CLI tools will likely offer higher download speeds. FILE LIST N.ome.tif LSP Slide ID File Name File Size LSP10925 LSP10925_CyCIF_Image.ome.tif 9.3 GB LSP10929 LSP10929_CyCIF_Image.ome.tif 6.0 GB LSP10931 LSP10931_CyCIF_Image.ome.tif 8.7 GB LSP10933 LSP10933_CyCIF_Image.ome.tif 6.7 GB LSP10951 LSP10951_CyCIF_Image.ome.tif 11 GB LSP10980 LSP10980_CyCIF_Image.ome.tif 9.3 GB 51 GB markers.csv File name File Size Synapse ID markers.csv 0.7 KB N_Quantification.csv LSP Slide ID File Name File Size Synapse ID LSP10925 LSP10925_CyCIF_Quantification.csv 20 MB LSP10929 LSP10929_CyCIF_Quantification.csv 12 MB LSP10931 LSP10931_CyCIF_Quantification.csv 5.9 MB LSP10933 LSP10933_CyCIF_Quantification.csv 7.5 MB LSP10951 LSP10951_CyCIF_Quantification.csv 8.3 MB LSP10980 LSP10980_CyCIF_Quantification.csv 12 MB segmentation/ LSP Slide ID File Name File Size LSP10925 LSP10925_cellRingMask.tif 20 MB LSP10929 LSP10929_cellRingMask.tif 12 MB LSP10931 LSP10931_cellRingMask.tif 5.9 MB LSP10933 LSP10933_cellRingMask.tif 7.5 MB LSP10951 LSP10951_cellRingMask.tif 8.3 MB LSP10980 LSP10980_cellRingMask.tif 12 MB 66 MB N_IHC.ome.tiff LSP Slide ID Description File name File Size LSP16237 CD103 Image LSP16237_IHC_CD103.ome.tiff 2.8 GB LSP16240 PD1 Image LSP16240_IHC_PD1.ome.tiff 3.1 GB LSP16241 MART1 Image LSP16241_IHC_MART1.ome.tiff 2.9 GB LSP16242 FOXP3 Image LSP16242_IHC_FOXP3.ome.tiff 3.5 GB LSP16243 CD3 Image LSP16243_IHC_CD3.ome.tiff 2.9 GB LSP16244 CD4 Image LSP16244_IHC_CD4.ome.tiff 3.2 GB LSP16245 CD8 Image LSP16245_IHC_CD8.ome.tiff 3.2 GB LSP16246 CD20 Image LSP16246_IHC_CD20.ome.tiff 3.2 GB LSP16247 CD19 Image LSP16247_IHC_CD19.ome.tiff 3.7 GB LSP16248 CD163 Image LSP16248_IHC_CD163.ome.tiff 4.0 GB LSP16249 PDL1 Image LSP16249_IHC_PDL1.ome.tiff 3.7 GB LSP16250 CD69 Image LSP16250_IHC_CD69.ome.tiff 3.2 GB 39 GB N_HE.ome.tiff LSP Slide ID Description File name File Size LSP16237 H&E Image LSP16234_HE.ome.tiff 14 GB 14 GB ---- FUNDING R50-CA252138, U2C-CA233262 and Ludwig Cancer Research

Supplementary Table from The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution

Supplementary Figure from The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution

Cutaneous melanoma is a highly immunogenic malignancy that is surgically curable at early stages but life-threatening when metastatic. Here we integrate high-plex imaging, 3D high-resolution microscopy, and spatially resolved microregion transcriptomics to study immune evasion and immunoediting in primary melanoma. We find that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis involving precursor states, melanoma in situ, and invasive tumor. Hallmarks of immunosuppression are already detectable in precursor regions. When tumors become locally invasive, a consolidated and spatially restricted suppressive environment forms along the tumor–stromal boundary. This environment is established by cytokine gradients that promote expression of MHC-II and IDO1, and by PD1–PDL1-mediated cell contacts involving macrophages, dendritic cells, and T cells. A few millimeters away, cytotoxic T cells synapse with melanoma cells in fields of tumor regression. Thus, invasion and immunoediting can coexist within a few millimeters of each other in a single specimen.Significance:The reorganization of the tumor ecosystem in primary melanoma is an excellent setting in which to study immunoediting and immune evasion. Guided by classic histopathology, spatial profiling of proteins and mRNA reveals recurrent morphologic and molecular features of tumor evolution that involve localized paracrine cytokine signaling and direct cell–cell contact.This article is highlighted in the In This Issue feature, p. 1397

Supplementary Data from The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution

PDF file - 82K, Response kinetics in treated patients. Baseline tumor measurements are standardized to zero. A. Entire treatment population (cohorts 1-4). B. Cohort 2 patients (MTD). Horizontal line PD = progressive disease representing 20% increase. Horizontal line PR represents 30% decrease from baseline. Filled in circle depicts radiographic development of new sites of disease.

PDF file - 68K, A. Axial CT section of the chest at baseline demonstrates two nodules in the right lung, the dominant one in the lower lobe (white arrows). B. Follow-up CT 3 months after start of treatment demonstrates resolution of one of the nodules and no change in the other nodule. C. Axial CT section 8 months after start of treatment demonstrates complete resolution of the dominant lung nodule.

PDF file - 74K, High-power H&E and CD31 stained post treatment biopsy of intratumoral vessels. The endothelial cells appear morphologically columnar. CD31 staining is concentrated at the interendothelial junctions (arrows).

PDF file 96K, Pre-treatment of donor T-cells with a c-Rel antagonist promotes IL-2 secretion in vivo

PDF file - 59K

PDF file - 64K, Illustrations of the variation in pathologic responses to treatment. Examples of intermediate and no response.

Effects of anti-Nodal antibody treatment on SK-MEL-28 cells.

DTIC and anti-Nodal antibody combination treatment promotes cell death in SK-MEL-28 cells.

PDF file - 76K, CONSORT Diagram.

PDF file - 125K, Individual response of CCR7+CD45RO+ and CCD7-CD45RO+ of CD4+ and CD8+ T cells in melanoma patients treated with ipilimumab (3mg/kg), ipilimumab (3mg/kg) plus bevacizumab, or ipilimumab (10mg/kg) plus bevacizumab.

Nodal in SK-MEL-28 cells surviving treatment with the B-RAF inhibitor, RG7204.

PDF file - 39K, Summary of grade 3 and grade 4 events incidence at each dose level for all events and those events possibly, probably, or definitively related.

PDF file - 111K, Patient Demographics.

PDF file - 59K, Kaplan-Meier estimates for time-to-progression in the entire treated population (A) and by treatment cohort (B). Kaplan-Meier estimates for overall survival in the entire treated population (C) and by treatment cohort (D).

PDF file 128KB, Treg depletion from c-Rel deficient donor T-cells results in exacerbated GVHD

Nodal expression in DTIC-treated melanoma cell lines.

PDF file 92K, Table 1. Characteristics of c-Rel inhibitor compounds; Table 2. Roles of c-Rel in T-cell responses and transplantation immunology

Effects on DTIC-treated melanoma cell lines.

PDF file 135KB, Non-specific negative effect of DMSO on the GVT activity of T-cells

PDF file - 139K, Worst grade adverse event for individual patients that occurred within the first twelve weeks of treatment (induction dose-limiting toxicity period). Adverse events recorded multiple times for any patient are reported only once according to the worst grade.

PDF file 128KB, c-Rel-deficient T-cells are polarized towards Th2 responses during GVHD

Additional methods for supplementary data

PDF file - 43K, Best overall response in ipilimumab plus bevacizumab treated patients per RECIST criteria by treatment cohort.

Figure legends for supplementary figures 1-5

PDF file 191K, Recipients of c-Rel-deficient T-cells exhibit reduced damage of GVHD target organs

PDF file 121K, IL-2 secretion is enhanced in irradiated recipients transplanted with allogeneic T-cells

Cutaneous melanoma is a highly immunogenic malignancy that is surgically curable at early stages but life-threatening when metastatic. Here we integrate high-plex imaging, 3D high-resolution microscopy, and spatially resolved microregion transcriptomics to study immune evasion and immunoediting in primary melanoma. We find that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis involving precursor states, melanoma in situ, and invasive tumor. Hallmarks of immunosuppression are already detectable in precursor regions. When tumors become locally invasive, a consolidated and spatially restricted suppressive environment forms along the tumor–stromal boundary. This environment is established by cytokine gradients that promote expression of MHC-II and IDO1, and by PD1–PDL1-mediated cell contacts involving macrophages, dendritic cells, and T cells. A few millimeters away, cytotoxic T cells synapse with melanoma cells in fields of tumor regression. Thus, invasion and immunoediting can coexist within a few millimeters of each other in a single specimen.Significance:The reorganization of the tumor ecosystem in primary melanoma is an excellent setting in which to study immunoediting and immune evasion. Guided by classic histopathology, spatial profiling of proteins and mRNA reveals recurrent morphologic and molecular features of tumor evolution that involve localized paracrine cytokine signaling and direct cell–cell contact.This article is highlighted in the In This Issue feature, p. 1397

PDF file - 19K, Melanoma antigen expression is decreased at time of progression.

Characteristics of HGSOC cases with platinum relapse data included in the BWH TMA

PDF file - 349K, Inhibition of BRAFV600E is not associated with a significant increase in HLA expression in patients with metastatic melanoma.

Purpose: To evaluate the effects of BRAF inhibition on the tumor microenvironment in patients with metastatic melanoma.Experimental Design: Thirty-five biopsies were collected from 16 patients with metastatic melanoma pretreatment (day 0) and at 10 to 14 days after initiation of treatment with either BRAF inhibitor alone (vemurafenib) or BRAF + MEK inhibition (dabrafenib + trametinib) and were also taken at time of progression. Biopsies were analyzed for melanoma antigens, T-cell markers, and immunomodulatory cytokines.Results: Treatment with either BRAF inhibitor alone or BRAF + MEK inhibitor was associated with an increased expression of melanoma antigens and an increase in CD8+ T-cell infiltrate. This was also associated with a decrease in immunosuppressive cytokines [interleukin (IL)-6 and IL-8] and an increase in markers of T-cell cytotoxicity. Interestingly, expression of exhaustion markers TIM-3 and PD1 and the immunosuppressive ligand PDL1 was increased on treatment. A decrease in melanoma antigen expression and CD8 T-cell infiltrate was noted at time of progression on BRAF inhibitor alone and was reversed with combined BRAF and MEK inhibition.Conclusions: Together, these data suggest that treatment with BRAF inhibition enhances melanoma antigen expression and facilitates T-cell cytotoxicity and a more favorable tumor microenvironment, providing support for potential synergy of BRAF-targeted therapy and immunotherapy. Interestingly, markers of T-cell exhaustion and the immunosuppressive ligand PDL1 are also increased with BRAF inhibition, further implying that immune checkpoint blockade may be critical in augmenting responses to BRAF-targeted therapy in patients with melanoma. Clin Cancer Res; 19(5); 1225–31. ©2013 AACR.

Additional Materials and Methods

Western blot analysis of A2780, IF quantification, immunoblot quantification in human and murine cell lines

Immunofluorescence studies on patient tumor ascites, quantification of 5-hmC from patient ascites by dot blot, 5-hmC measured with mass spectrometry