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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

BARX1-/HAND1- GIST

Purpose:Gastrointestinal stromal tumor (GIST) arises from interstitial cells of Cajal (ICC) or their precursors, which are present throughout the gastrointestinal tract. Although gastric GIST is commonly indolent and small intestine GIST more aggressive, a molecular understanding of disease behavior would inform therapy decisions in GIST. Although a core transcription factor (TF) network is conserved across GIST, accessory TFs HAND1 and BARX1 are expressed in a disease state-specific pattern. Here, we characterize two divergent transcriptional programs maintained by HAND1 and BARX1, and evaluate their association with clinical outcomes.Experimental Design:We evaluated RNA sequencing and TF chromatin immunoprecipitation with sequencing in GIST samples and cultured cells for transcriptional programs associated with HAND1 and BARX1. Multiplexed tissue-based cyclic immunofluorescence and IHC evaluated tissue- and cell-level expression of TFs and their association with clinical factors.Results:We show that HAND1 is expressed in aggressive GIST, modulating KIT and core TF expression and supporting proliferative cellular programs. In contrast, BARX1 is expressed in indolent and micro-GISTs. HAND1 and BARX1 expression were superior predictors of relapse-free survival, as compared with standard risk stratification, and they predict progression-free survival on imatinib. Reflecting the developmental origins of accessory TF programs, HAND1 was expressed solely in small intestine ICCs, whereas BARX1 expression was restricted to gastric ICCs.Conclusions:Our results define anatomic and transcriptional determinants of GIST and molecular origins of clinical phenotypes. Assessment of HAND1 and BARX1 expression in GIST may provide prognostic information and improve clinical decisions on the administration of adjuvant therapy.

Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells characterized by high intratumoral variation. We use highly multiplexed tissue imaging, 3D reconstruction, spatial statistics, and machine learning to identify cell types and states underlying morphological features of known diagnostic and prognostic significance in colorectal cancer. Quantitation of these features in high-plex marker space reveals recurrent transitions from one tumor morphology to the next, some of which are coincident with long-range gradients in the expression of oncogenes and epigenetic regulators. At the tumor invasive margin, where tumor, normal, and immune cells compete, T-cell suppression involves multiple cell types and 3D imaging shows that seemingly localized 2D features such as tertiary lymphoid structures are commonly interconnected and have graded molecular properties. Thus, while cancer genetics emphasizes the importance of discrete changes in tumor state, whole-specimen imaging reveals large-scale morphological and molecular gradients analogous to those in developing tissues.

Background: TOPACIO was a phase I/II study evaluating the PARP inhibitor (PARPi) niraparib in combination with the anti-PD-1 antibody pembrolizumab in patients with locally advanced and metastatic triple-negative breast cancer (TNBC, n=55) and ovarian cancer irrespective of BRCA mutation status. In the efficacy-evaluable population (n=47) the objective response rate (ORR) was 21% and disease control rate (DCR) 49%. Although activity was greater in patients with BRCA mutations (7/15, ORR=47% and 12/15, DCR=80%), durable clinical benefit was seen in patients with wild-type BRCA tumors (3/27, ORR=11% and 9/27, DCR=33%). In a limited cohort of 20 patients with durable clinical benefit, there were 8 BRCA wildtype patients, four of whom had mutations in genes associated with the homologous recombination repair and other DNA damage repair pathways. Pre-treatment tissues were collected and evaluated for tumor PD-L1 status. Patients with PD-L1 positive tumors (28/47, 60%) had a higher response rate (9/28, ORR=32%) than those with PD-L1 negative tumors (1/13, ORR=8%; 6 tumors had unknown PD-L1 status). It remains unstudied whether the tumor’s gene expression profile or immune status in baseline biospecimens is predictive of treatment response. In this study we conducted exploratory biomarker analyses to test the hypothesis that gene expression patterns and immune status are associated with treatment response. Methods: Transcriptional profiling of baseline samples was performed using the BC360 (n=41) and PanCancer IO360 (n=42) panels (Nanostring) and multigene signatures were used to measure tumor and immune activities as well as relative immune cell abundance. Transcriptional analysis was paired with high-dimensional, single-cell cyclic immunofluorescence (CyCIF) of samples that had adequate tissue for analysis (n=19) to characterize the composition and topology of the immune microenvironment at single-cell resolution. Results: Nanostring transcriptional analysis revealed that PAM50 genes stratified tumor samples into 4 subgroups with distinct histology as determined by CyCIF. Each subgroup was capable of responding to niraparib plus pembrolizumab. Multiple genes involved in WNT signaling (WNT5B, TANKS1, TANKS2, PARP4, and NET02) were associated with favorable clinical responses. Low neuropilin and tolloid-like protein 2 (NETO2) gene expression was strongly correlated with favorable progression free survival (PFS; R=-0.61, p=0.0008, Spearman’s correlation), suggesting it may be a predictive biomarker of therapeutic response. Nanostring gene expression signatures for tumor inflammation, apoptosis, and inflammatory chemokines also distinguished responders from non-responders (p Citation Format: Jennifer L Guerriero, Gregory J Baker, Jia-Ren Lin, Yu-An Chen, Ricardo Pastorello, Tuulia Vallius, Janae Davis, Clarence Yapp, Sarah E Church, Eric Miller, Anniina Färkkilä, Shaveta Vinayak, Melinda L Telli, Giulia Fulci, Alan D'Andrea, Geoffrey I Shapiro, Sara M Tolaney, Sandro Santagata, Peter K Sorger, Elizabeth A Mittendorf. High-dimensional, single-cell analysis and transcriptional profiling reveal novel correlatives of response to PARP inhibition plus PD-1 blockade in triple-negative breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-07-13.

Tissue-based sampling and diagnosis are defined as the extraction of information from certain limited spaces and its diagnostic significance of a certain object. Pathologists deal with issues related to tumor heterogeneity since analyzing a single sample does not necessarily capture a representative depiction of cancer, and a tissue biopsy usually only presents a small fraction of the tumor. Many multiplex tissue imaging platforms (MTIs) make the assumption that tissue microarrays (TMAs) containing small core samples of 2-dimensional (2D) tissue sections are a good approximation of bulk tumors although tumors are not 2D. However, emerging whole slide imaging (WSI) or 3D tumor atlases that employ MTIs like cyclic immunofluorescence (CyCIF) strongly challenge this assumption. In spite of the additional insight gathered by measuring the tumor microenvironment in WSI or 3D, it can be prohibitively expensive and time-consuming to process tens or hundreds of tissue sections with CyCIF. Even when resources are not limited, the criteria for region-of-interest (ROI) selection in tissues for downstream analysis remain largely qualitative and subjective as stratified sampling requires the knowledge of objects and evaluates their features. Despite the fact TMAs fail to adequately approximate whole tissue features, a theoretical subsampling of tissue exists that can best represent the tumor in the whole slide image. To address these challenges, we propose deep learning approaches to learn multi-modal image translation tasks from two aspects: 1) generative modeling approach to reconstruct 3D CyCIF representation and 2) co-embedding CyCIF image and Hematoxylin and Eosin (H&E) section to learn multi-modal mappings by a cross-domain translation for minimum representative ROI selection. We demonstrate that generative modeling enables a 3D virtual CyCIF reconstruction of a colorectal cancer specimen given a small subset of the imaging data at training time. By co-embedding histology and MTI features, we propose a simple convex optimization for objective ROI selection. We demonstrate the potential application of ROI selection and the efficiency of its performance with respect to cellular heterogeneity.

The architecture of normal and diseased tissues strongly influences the development and progression of disease as well as responsiveness and resistance to therapy. We describe a tissue-based cyclic immunofluorescence (t-CyCIF) method for highly multiplexed immunofluorescence imaging of specimens mounted on glass slides. t-CyCIF generates up to 60-plex images using an iterative process (a cycle) in which conventional low-plex fluorescence images are repeatedly collected from the same sample and then assembled into a high dimensional representation. t-CyCIF requires no specialized instruments or reagents and is compatible with super-resolution imaging; we demonstrate its application to quantifying signal transduction cascades, tumor antigens and immune markers in diverse tissues and tumors. The simplicity and adaptability of t-CyCIF makes it an effective method for pre-clinical and clinical research and a natural complement to single-cell genomics. Key resources, reagents, and software are listed at the HMS LINCS Center Publication Page http://lincs.hms.harvard.edu/lin-elife-2018/ (RRID:SCR_016370).This page provides links to an OMERO image database from which individual images can be obtained; stitched and registered image panels can be obtained at www.cycif.org (RRID:SCR_016267) and a video illustrating the t-CyCIF method can be found at https://vimeo.com/269885646. This protocol is used in the following manuscripts: Lin J-R, Izar B, Wang S, Yapp C, Mei S, Shah P, Santagata S, Sorger PK. (2018). Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. eLife. PMID: 29993362 Du Z, Lin JR, Rashid R, Maliga Z, Wang S, Aster J, Izar B, Sorger PK, Santagata S. (2019). Qualifying antibodies for image-based immune profiling and multiplexed tissue imaging. Nature Protocols. PMID: 31534232 The original t-CyCIF protocol can be found at dx.doi.org/10.17504/protocols.io.rpxd5pn.

Precision medicine is critically dependent on better methods for diagnosing and staging disease and predicting drug response. Histopathology using Hematoxylin and Eosin (H&E) stained tissue - not genomics – remains the primary diagnostic method in cancer. Recently developed highly-multiplexed tissue imaging methods promise to enhance research studies and clinical practice with precise, spatially-resolved, single-cell data. Here we describe the “Orion” platform for collecting and analyzing H&E and high-plex immunofluorescence (IF) images from the same cells in a whole-slide format suitable for diagnosis. Using a retrospective cohort of 74 colorectal cancer resections, we show that IF and H&E images provide human experts and machine learning algorithms with complementary information that can be used to generate interpretable, multiplexed image-based models predictive of progression-free survival. Combining models of immune infiltration and tumor-intrinsic features achieves a hazard ratio of ∼0.05, demonstrating the ability of multi-modal Orion imaging to generate high-performance biomarkers.

Gain-of-function mutations in isocitrate dehydrogenase (IDH) in human cancers result in the production of d -2-hydroxyglutarate ( d -2HG), an oncometabolite that promotes tumorigenesis through epigenetic alterations. The cancer cell–intrinsic effects of d -2HG are well understood, but its tumor cell–nonautonomous roles remain poorly explored. We compared the oncometabolite d -2HG with its enantiomer, l -2HG, and found that tumor-derived d -2HG was taken up by CD8 + T cells and altered their metabolism and antitumor functions in an acute and reversible fashion. We identified the glycolytic enzyme lactate dehydrogenase (LDH) as a molecular target of d -2HG. d -2HG and inhibition of LDH drive a metabolic program and immune CD8 + T cell signature marked by decreased cytotoxicity and impaired interferon-γ signaling that was recapitulated in clinical samples from human patients with IDH1 mutant gliomas.

Lymphocytes play a key role in immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. Here, we used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of tumor-bearing lung tissues from a Kras/p53 (KP) mouse model and human resections. Networks of directly interacting lymphocytes (‘lymphonets’) emerge as a distinctive feature of the anti-cancer immune response. Lymphonets nucleate from small T-cell clusters and incorporate B cells with increasing size. CXCR3-mediated trafficking modulates lymphonet size and number, but neoantigen expression directs intratumoral localization. Lymphonets preferentially harbor TCF1+/PD1+ progenitor CD8 T cells involved in responses to immune checkpoint blockade (ICB). Upon treatment of mice with ICB therapy or a neoantigen-targeted vaccine, lymphonets retain progenitor and gain cytotoxic CD8 T-cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of CD8 T-cell anti-tumor responses.

Cancer immunotherapies that activate cytotoxic T cell responses against tumors have been remarkably effective in some patients, but the determinants of response remain unclear. To investigate how spatial organization of T cells might influence immunotherapy response, we developed a tissue-based cyclic immunofluorescence (t-CyCIF) platform to characterize preclinical genetically engineered mouse models (GEMMs) of cancer. We optimized a 21-antibody panel delineating major myeloid and lymphoid cell subsets, that included key markers for characterizing T cell phenotypes and function. The panel was used to characterize the Kras/p53-mutant GEMM of lung adenocarcinoma, treated with anti-PD-1/anti-CTLA-4 immune checkpoint blockade (ICB) therapy or a neoantigen-targeted long-peptide vaccine. Spatial profiling by t-CyCIF identified intratumoral lymphocyte networks (“lymphonets”) as a key feature associated with a productive immune response against lung tumors. Lymphonets were predominantly composed of B cells and conventional CD4 T cells, but harbored tumor-reactive CD8 T cell populations. TCF1+ PD1+ progenitor CD8 T cells, a subset previously correlated with patient response to ICB, were almost exclusively localized to lymphonets inside tumors. Lymphonets of similar composition harboring TCF1+ PD1+ cells were also found in early-stage human lung adenocarcinomas. In response to ICB and vaccine therapies in mice, cytotoxic CD8 T cells (Granzyme B+/Perforin+) colocalized with TCF1+ PD1+ cells in lymphonets, likely having differentiated from this progenitor pool. Notably, vaccination further induced a highly proliferative (Ki67hi) and cytotoxic (GranzymeBhiPerforinhi) population of CD8 T cells localized outside of tumors that we posit enter tumors without passing through the intratumoral progenitor cell state. Upon becoming dysfunctional, marked by loss of Granzyme B/Perforin expression and upregulation of inhibitory receptors, CD8 T cells were excluded from lymphonets and localized predominantly to the tumor margin. Altogether, these results shed light on the spatial dynamics associated with a productive response to immunotherapies and open avenues for exploration into the function of lymphonets in supporting cytotoxic CD8 T cell responses in tumors. Citation Format: Megan L. Burger, Giorgio Gaglia, Claire C. Ritch, Danae Rammos, Yang Dai, Grace E. Crossland, Sara Z. Tavana, Simon Warchol, Alex M. Jaeger, Santiago Naranjo, Shannon Coy, Ajit J. Nirmal, Robert Krueger, Jia-Ren Lin, Hanspeter Pfister, Peter K. Sorger, Tyler Jacks, Sandro Santagata. Intratumoral lymphocyte networks harbor TCF1+ PD1+ progenitor CD8 T cells in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5877.

Significance Factors stemming from the tumor microenvironment can modulate drug resistance. Therefore, rational approaches to design efficacious targeted therapies are needed. Here, we show that fibroblasts reduce lapatinib sensitivity in a subset of HER2+ breast cancer cells via paracrine signaling that can be reversed by targeting downstream survival programs such as MTOR and antiapoptotic proteins (BCL-XL and MCL-1). Our finding that fibroblasts derived from either primary tumor or metastatic sites limit lapatinib response has important implications for developing treatment strategies that can restore sensitivity in multiple drug-resistant microenvironments. Due to the diversity of microenvironmental factors between these sites, targeting downstream survival programs instead of individual factors released by fibroblasts represents a promising strategy to combat microenvironment complexity., Despite the implementation of multiple HER2-targeted therapies, patients with advanced HER2+ breast cancer ultimately develop drug resistance. Stromal fibroblasts represent an abundant cell type in the tumor microenvironment and have been linked to poor outcomes and drug resistance. Here, we show that fibroblasts counteract the cytotoxic effects of HER2 kinase-targeted therapy in a subset of HER2+ breast cancer cell lines and allow cancer cells to proliferate in the presence of the HER2 kinase inhibitor lapatinib. Fibroblasts from primary breast tumors, normal breast tissue, and lung tissue have similar protective effects on tumor cells via paracrine factors. This fibroblast-mediated reduction in drug sensitivity involves increased expression of antiapoptotic proteins and sustained activation of the PI3K/AKT/MTOR pathway, despite inhibition of the HER2 and the RAS-ERK pathways in tumor cells. HER2 therapy sensitivity is restored in the fibroblast cocultures by combination treatment with inhibitors of MTOR or the antiapoptotic proteins BCL-XL and MCL-1. Expression of activated AKT in tumor cells recapitulates the effects of fibroblasts resulting in sustained MTOR signaling and poor lapatinib response. Lapatinib sensitivity was not altered by fibroblasts in tumor cells that exhibited sustained MTOR signaling due to a strong gain-of-function PI3KCA mutation. These findings indicate that in addition to tumor cell-intrinsic mechanisms that cause constitutive PI3K/AKT/MTOR pathway activation, secreted factors from fibroblasts can maintain this pathway in the context of HER2 inhibition. Our integrated proteomic–phenotypic approach presents a strategy for the discovery of protective mechanisms in fibroblast-rich tumors and the design of rational combination therapies to restore drug sensitivity.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

Multiplexed tissue imaging facilitates the diagnosis and understanding of complex disease traits. However, the analysis of such digital images heavily relies on the experience of anatomical pathologists for the review, annotation, and description of tissue features. In addition, the wider use of data from tissue atlases in basic and translational research and in classrooms would benefit from software that facilitates the easy visualization and sharing of the images and the results of their analyses. In this Perspective, we describe the ecosystem of software available for the analysis of tissue images and discuss the need for interactive online guides that help histopathologists make complex images comprehensible to non-specialists. We illustrate this idea via a software interface (Minerva), accessible via web browsers, that integrates multi-omic and tissue-atlas features. We argue that such interactive narrative guides can effectively disseminate digital histology data and aid their interpretation.

Inspection of tissues using a light microscope is the primary method of diagnosing many diseases, notably cancer. Highly multiplexed tissue imaging builds on this foundation, enabling the collection of up to 60 channels of molecular information plus cell and tissue morphology using antibody staining. This provides unique insight into disease biology and promises to help with the design of patient-specific therapies. However, a substantial gap remains with respect to visualizing the resulting multivariate image data and effectively supporting pathology workflows in digital environments on screen. We, therefore, developed Scope2Screen, a scalable software system for focus+context exploration and annotation of whole-slide, high-plex, tissue images. Our approach scales to analyzing 100GB images of 10(9) or more pixels per channel, containing millions of individual cells. A multidisciplinary team of visualization experts, microscopists, and pathologists identified key image exploration and annotation tasks involving finding, magnifying, quantifying, and organizing regions of interest (ROIs) in an intuitive and cohesive manner. Building on a scope-to-screen metaphor, we present interactive lensing techniques that operate at single-cell and tissue levels. Lenses are equipped with task-specific functionality and descriptive statistics, making it possible to analyze image features, cell types, and spatial arrangements (neighborhoods) across image channels and scales. A fast sliding-window search guides users to regions similar to those under the lens; these regions can be analyzed and considered either separately or as part of a larger image collection. A novel snapshot method enables linked lens configurations and image statistics to be saved, restored, and shared with these regions. We validate our designs with domain experts and apply Scope2Screen in two case studies involving lung and colorectal cancers to discover cancer-relevant image features.

Histopathology using Hematoxylin and Eosin (H&E) stained tissue sections plays a central role in the diagnosis and staging of diseases. The transition to digital H&E pathology affords an opportunity for integration with recently developed, highly multiplexed tissue imaging methods. Here we describe an approach (and instrument) for collecting and analyzing H&E and high-plex immunofluorescence (IF) images from the same cells at subcellular-resolution in a whole-slide format suitable for translational and clinical research and eventual deployment in a diagnostic setting. IF and H&E images provide highly complementary information for analysis by human experts and machine learning algorithms. Using images of 40 human colorectal cancer resections, we demonstrate the automated generation and ranking of computational models, based either on immune infiltration or tumor-intrinsic features, that are highly predictive of progression-free survival. When these models are combined, a hazard ratio of ~0.045 can be achieved, suggesting the potential of integrated H&E and high-plex imaging in the generation of high performance prognostic biomarkers. Citation Format: Jia-Ren Lin, Yu-An Chen, Daniel Campton, Jeremy Cooper, Shannon Coy, Clarence Yapp, Erin McCarty, Keith L. Ligon, Steven Reese, Tad George, Sandro Santagata, Peter Sorger. Multi-modal digital pathology by sequential acquisition and joint analysis of highly multiplexed immunofluorescence and hematoxylin and eosin images [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer; 2022 Oct 1-4; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_1):Abstract nr A028.

How the glioma immune microenvironment fosters tumorigenesis remains incompletely defined. Here, we use single-cell RNA-sequencing and multiplexed tissue-imaging to characterize the composition, spatial organization, and clinical significance of extracellular purinergic signaling in glioma. We show that microglia are the predominant source of CD39, while tumor cells principally express CD73. In glioblastoma, CD73 is associated with EGFR amplification, astrocyte-like differentiation, and increased adenosine, and is linked to hypoxia. Glioblastomas enriched for CD73 exhibit inflammatory microenvironments, suggesting that purinergic signaling regulates immune adaptation. Spatially-resolved single-cell analyses demonstrate a strong spatial correlation between tumor-CD73 and microglial-CD39, with proximity associated with poor outcomes. Similar spatial organization is present in pediatric high-grade gliomas including H3K27M-mutant diffuse midline glioma. These data reveal that purinergic signaling in gliomas is shaped by genotype, lineage, and functional state, and that core enzymes expressed by tumor and myeloid cells are organized to promote adenosine-rich microenvironments potentially amenable to therapeutic targeting.

Proliferation is a fundamental trait of cancer cells, but its properties and spatial organization in tumours are poorly characterized. Here we use highly multiplexed tissue imaging to perform single-cell quantification of cell cycle regulators and then develop robust, multivariate, proliferation metrics. Across diverse cancers, proliferative architecture is organized at two spatial scales: large domains, and smaller niches enriched for specific immune lineages. Some tumour cells express cell cycle regulators in the (canonical) patterns expected of freely growing cells, a phenomenon we refer to as 'cell cycle coherence'. By contrast, the cell cycles of other tumour cell populations are skewed towards specific phases or exhibit non-canonical (incoherent) marker combinations. Coherence varies across space, with changes in oncogene activity and therapeutic intervention, and is associated with aggressive tumour behaviour. Thus, multivariate measures from high-plex tissue images capture clinically significant features of cancer proliferation, a fundamental step in enabling more precise use of anti-cancer therapies.

SUMMARYProliferation is a fundamental trait of cancer cells but is poorly characterized in tumors by classical histologic methods. We use multiplexed tissue imaging to quantify the abundance of multiple cell cycle regulating proteins at single-cell level and develop robust multivariate proliferation metrics. Across cancers, the proliferative architecture is organized at two distinct spatial scales: large domains, and local niches enriched for specific immune lineages. A subset of tumor cells express cell cycle regulators in canonical patterns consistent with unrestrained proliferation, a phenomenon we refer to as “cell cycle coherence”. By contrast, the cell cycles of other tumor cell populations are skewed toward a specific phase or characterized by non-canonical (incoherent) marker combinations. Coherence varies across space, with changes in oncogene activity, and with therapeutic intervention, and is associated with aggressive behavior. Multivariate measures capture clinically significant features of cancer proliferation, a fundamental step in enabling more precise use of anti-cancer therapies.

SUMMARY Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells that invade adjacent tissue and spread to distant sites. Here we use highly multiplexed tissue imaging, spatial statistics, and machine learning to identify cell types and states underlying morphological features of known diagnostic and prognostic significance in colorectal cancer. We find that a thorough spatial analysis requires imaging the entire tumor region, not small fields of view (e.g. those found in tissue microarrays). When this condition is met, the data reveal frequent transitions between histological archetypes (tumor grades and morphologies) correlated with molecular gradients. At the tumor invasive margin, where tumor, normal, and immune cells compete, localized features in 2D such as tumor buds and mucin pools are seen in 3D to be large connected structures having continuously varying molecular properties. Immunosuppressive cell-cell interactions also exhibit graded variation in type and frequency. Thus, whereas scRNA-Seq emphasizes discrete changes in tumor state, whole-specimen imaging reveals the presence of large- and small-scale spatial gradients analogous to those in developing tissues.

Multi-parameter tissue imaging allows for a contextual understanding of tumor cells in relation to the tumor microenvironment. Pick-Seq is a novel workflow uniquely enabled by the RareCyte CyteFinder® Instrument that combines interrogation of multiple protein markers with investigation of gene expression from selected micro-regions on tissue slides. In this study, formalin-fixed, paraffin-embedded tonsil sections and OCT-embedded, frozen breast cancer sections were stained with epithelial and lymphocyte markers by multi-parameter immunofluorescence (IF) and scanned with the CyteFinder Instrument to identify regions of interest. Based on IF staining patterns, 40 μm diameter micro-regions were retrieved with the integrated CytePicker® Retrieval Module. Micro-region RNA was isolated for whole transcriptome amplification, library preparation, and sequencing. Resulting data were analyzed for differential gene expression, cell composition, and T cell receptor (TCR) sequences. Transcriptomic analysis of tonsil micro-regions confirmed increased expression of B cell markers in follicles and T cell markers in the T cell zone. CIBERSORT revealed distinct cellular compositions between T cell zones and the B cell follicles. Principle component analysis of gene expression found that micro-regions retrieved from the two follicles clustered independently from each other, and from the T cell zone micro-regions. Fragments per kilobase million (FPKM) analysis revealed differing expression of genes, particularly CD19 and CD21, between the adjacent follicles. Subsequent staining confirmed differential protein expression, indicating that only one follicle contained a germinal center. In breast carcinoma, regions were identified for CytePicker retrieval that included (1) tumor cells, (2) tumor cells with interspersed tumor infiltrating lymphocytes (TIL), or (3) adjacent lymphoid aggregates. CIBERSORT analysis demonstrated the presence of T cell-associated transcriptomic profiles in lymphoid aggregates and in TIL-containing micro-regions that were proportional to the number of T cells retrieved. Aligned RNA-seq reads were further analyzed to identify TCR α and β chain sequences from retrieved TILs. These data establish the potential of combining multi-parameter IF microscopy with deep RNA sequencing as a powerful tool for investigation and biomarker discovery in the immuno-oncology of breast cancer. Citation Format: Nolan Ericson, Rebecca Podyminogin, Jennifer Chow, Jia-Ren Lin, Yu-An Chen, Zoltan Maliga, Peter Sorger, Kyla Teplitz, Eric Kaldjian, Tad George. Pick-Seq®: Multi-parameter imaging combined with RNA sequencing of tissue section micro-regions for spatial analysis in breast carcinoma [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-06-09.

Multiplexed tissue imaging enables precise, spatially resolved enumeration and characterization of cell types and states in human resection specimens. A growing number of methods applicable to formalin-fixed, paraffin-embedded (FFPE) tissue sections have been described, the majority of which rely on antibodies for antigen detection and mapping. This protocol provides step-by-step procedures for confirming the selectivity and specificity of antibodies used in fluorescence-based tissue imaging and for the construction and validation of antibody panels. Although the protocol is implemented using tissue-based cyclic immunofluorescence (t-CyCIF) as an imaging platform, these antibody-testing methods are broadly applicable. We demonstrate assembly of a 16-antibody panel for enumerating and localizing T cells and B cells, macrophages, and cells expressing immune checkpoint regulators. The protocol is accessible to individuals with experience in microscopy and immunofluorescence; some experience in computation is required for data analysis. A typical 30-antibody dataset for 20 FFPE slides can be generated within 2 weeks.

BET-bromodomain inhibition (BETi) has shown pre-clinical promise for MYC-amplified medulloblastoma. However, the mechanisms for its action, and ultimately for resistance, have not been fully defined. Here, using a combination of expression profiling, genome-scale CRISPR/Cas9-mediated loss of function and ORF/cDNA driven rescue screens, and cell-based models of spontaneous resistance, we identify bHLH/homeobox transcription factors and cell-cycle regulators as key genes mediating BETi’s response and resistance. Cells that acquire drug tolerance exhibit a more neuronally differentiated cell-state and expression of lineage-specific bHLH/homeobox transcription factors. However, they do not terminally differentiate, maintain expression of CCND2, and continue to cycle through S-phase. Moreover, CDK4/CDK6 inhibition delays acquisition of resistance. Therefore, our data provide insights about the mechanisms underlying BETi effects and the appearance of resistance and support the therapeutic use of combined cell-cycle inhibitors with BETi in MYC-amplified medulloblastoma., BET-bromodomain inhibitors could be used to treat medulloblastoma tumors with Myc amplifications. Here, the authors show that both the response and resistance to BET inhibitors in mice is mediated by bHLH/homeobox transcription factors.

The breast is a dynamic organ whose response to physiological and pathophysiological conditions alters its disease susceptibility, yet the specific effects of these clinical variables on cell state remain poorly annotated. We present a unified, high-resolution breast atlas by integrating single-cell RNA-seq, mass cytometry, and cyclic immunofluorescence, encompassing a myriad of states. We define cell subtypes within the alveolar, hormone-sensing, and basal epithelial lineages, delineating associations of several subtypes with cancer risk factors, including age, parity, and BRCA2 germline mutation. Of particular interest is a subset of alveolar cells, termed basal-luminal (BL) cells, which exhibits poor transcriptional lineage fidelity, accumulates with age, and carries a gene signature associated with basal-like breast cancer. We further utilize a medium-depletion approach to identify molecular factors regulating cell subtype proportion in organoids. Together, these data are a rich resource to elucidate diverse mammary cell states.

Meningiomas are the most common primary tumor of the central nervous system, with ~28,000 new diagnoses annually in the United States1. Currently, there are no approved systemic therapies for meningiomas that recur following local treatment: chemotherapy and hormonal agents have demonstrated minimal benefit in numerous clinical trials2–4. Meningioma comprises a heterogeneous group of neoplasms driven by mutations in a wide array of tumor suppressor genes and oncogenes5–17. Characterization of these mutations has revealed opportunities for rational therapy18–20. For example, a durable therapeutic response has been reported for a metastatic AKT1(E17K)-mutant meningioma treated with a pan-AKT inhibitor.21 Studies also suggest the potential for treating meningioma with immune checkpoint modulators22–24: programmed death receptor 1 ligand (PD-L1) is expressed in a subset of meningiomas and the tumor microenvironment is immunosuppressive22–28. Higher-grade meningiomas also harbor mutations predicted to generate neoantigens, which may foster susceptibility to immunotherapies29. Based on these data, we initiated a phase II study of nivolumab, a humanized IgG4 PD-1 blocking monoclonal antibody, in patients with higher-grade meningiomas that recurred following surgery and radiotherapy ({"type":"clinical-trial","attrs":{"text":"NCT02648997","term_id":"NCT02648997"}}NCT02648997). We report here a patient with an atypical meningioma that was not controlled by repeated surgery and radiation but which was highly response to nivolumab.

Highly multiplexed tissue imaging makes molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of the underlying data poses a substantial computational challenge. Here we describe a modular and open-source computational pipeline (MCMICRO) for performing the sequential steps needed to transform large, multi-channel whole slide images into single-cell data. We demonstrate use of MCMICRO on images of different tissues and tumors acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.

Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of large multichannel images poses a substantial computational challenge. Here, we describe a modular and open-source computational pipeline, MCMICRO, for performing the sequential steps needed to transform whole-slide images into single-cell data. We demonstrate the use of MCMICRO on tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software. MCMICRO is a modular and open-source computational pipeline for transforming highly multiplexed whole-slide images of tissues into single-cell data. MCMICRO is versatile and can be used with CODEX, mxIF, CyCIF, mIHC and H&E staining data.

The hematoxylin and eosin (H&E) stain is the standard used for microscopic examination of fixed tissue specimens. This procedure establishes a consistent process for the manual preparation of H&E-stained slides from FFPE tissue specimens. H&E staining is compatible with, and frequently performed after tissue-based cyclic immunofluorescence (t-CyCIF), enabling conventional histopathology review to be combined with fluorescence imaging.

Purpose: Gastrointestinal stromal tumor (GIST) arises from interstitial cells of Cajal (ICC) or their precursors, which are present throughout the gastrointestinal tract. Although gastric GIST is commonly indolent and small intestine GIST more aggressive, a molecular understanding of disease behavior would inform therapy decisions in GIST. Although a core transcription factor (TF) network is conserved across GIST, accessory TFs HAND1 and BARX1 are expressed in a disease state-specific pattern. Here, we characterize two divergent transcriptional programs maintained by HAND1 and BARX1, and evaluate their association with clinical outcomes. Experimental Design: We evaluated RNA sequencing and TF chromatin immunoprecipitation with sequencing in GIST samples and cultured cells for transcriptional programs associated with HAND1 and BARX1. Multiplexed tissue-based cyclic immunofluorescence and IHC evaluated tissue- and cell-level expression of TFs and their association with clinical factors. Results: We show that HAND1 is expressed in aggressive GIST, modulating KIT and core TF expression and supporting proliferative cellular programs. In contrast, BARX1 is expressed in indolent and micro-GISTs. HAND1 and BARX1 expression were superior predictors of relapse-free survival, as compared with standard risk stratification, and they predict progression-free survival on imatinib. Reflecting the developmental origins of accessory TF programs, HAND1 was expressed solely in small intestine ICCs, whereas BARX1 expression was restricted to gastric ICCs. Conclusions: Our results define anatomic and transcriptional determinants of GIST and molecular origins of clinical phenotypes. Assessment of HAND1 and BARX1 expression in GIST may provide prognostic information and improve clinical decisions on the administration of adjuvant therapy.

Background Despite objective responses to PARP inhibition and improvements in progression-free survival compared to standard chemotherapy in patients with BRCA-associated triple-negative breast cancer (TNBC), benefits are transitory. Methods Using high dimensional single-cell profiling of human TNBC, here we demonstrate that macrophages are the predominant infiltrating immune cell type in BRCA-associated TNBC. Through multi-omics profiling we show that PARP inhibitors enhance both anti- and pro-tumor features of macrophages through glucose and lipid metabolic reprogramming driven by the sterol regulatory element-binding protein 1 (SREBP-1) pathway. Results Combined PARP inhibitor therapy with CSF-1R blocking antibodies significantly enhanced innate and adaptive anti-tumor immunity and extends survival in BRCA-deficient tumors in vivo and is mediated by CD8+ T-cells. Conclusions Collectively, our results uncover macrophage-mediated immune suppression as a liability of PARP inhibitor treatment and demonstrate combined PARP inhibition and macrophage targeting therapy induces a durable reprogramming of the tumor microenvironment, thus constituting a promising therapeutic strategy for TNBC.

The architecture of normal and diseased tissues strongly influences the development and progression of disease as well as responsiveness and resistance to therapy. We describe a tissue-based cyclic immunofluorescence (t-CyCIF) method for highly multiplexed immunofluorescence imaging of specimens mounted on glass slides. t-CyCIF generates up to 60-plex images using an iterative process (a cycle) in which conventional low-plex fluorescence images are repeatedly collected from the same sample and then assembled into a high dimensional representation. t-CyCIF requires no specialized instruments or reagents and is compatible with super-resolution imaging; we demonstrate its application to quantifying signal transduction cascades, tumor antigens and immune markers in diverse tissues and tumors. The simplicity and adaptability of t-CyCIF makes it an effective method for pre-clinical and clinical research and a natural complement to single-cell genomics. Key resources, reagents, and software are listed at the HMS LINCS Center Publication Page http://lincs.hms.harvard.edu/lin-elife-2018/ (RRID:SCR_016370).This page provides links to an OMERO image database from which individual images can be obtained; stitched and registered image panels can be obtained at www.cycif.org (RRID:SCR_016267) and a video illustrating the t-CyCIF method can be found at https://vimeo.com/269885646. This protocol is used in the following manuscripts: Lin J-R, Izar B, Wang S, Yapp C, Mei S, Shah P, Santagata S, Sorger PK. (2018). Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. eLife. PMID: 29993362 Du Z, Lin JR, Rashid R, Maliga Z, Wang S, Aster J, Izar B, Sorger PK, Santagata S. (2019). Qualifying antibodies for image-based immune profiling and multiplexed tissue imaging. Nature Protocols. PMID: 31534232 The original t-CyCIF protocol can be found at dx.doi.org/10.17504/protocols.io.rpxd5pn.

Tissue-based cyclic immunofluorescence (t-CyCIF) is optimized for FFPE specimens mounted on glass slides. Dewaxing and antigen retrieval are important steps to remove wax and expose antigenic sites. This protocol describes dewaxing and antigen retrieval on a Leica Bond RX automated slide processor; similar instruments are manufactured by Ventana or Dako and are commonly found in histopathology core facilities. t-CyCIF can also be performed following manual de-waxing and antigen retrieval (e.g. microwaving slides in citrate buffer or using a pressure cooker).

Malignant abdominal fluid (ascites) frequently develops in women with advanced high-grade serous ovarian cancer (HGSOC) and is associated with drug resistance and a poor prognosis1. To comprehensively characterize the HGSOC ascites ecosystem, we used single-cell RNA sequencing to profile ~11,000 cells from 22 ascites specimens from 11 patients with HGSOC. We found significant inter-patient variability in the composition and functional programs of ascites cells, including immunomodulatory fibroblast sub-populations and dichotomous macrophage populations. We found that the previously described immunoreactive and mesenchymal subtypes of HGSOC, which have prognostic implications, reflect the abundance of immune infiltrates and fibroblasts rather than distinct subsets of malignant cells2. Malignant cell variability was partly explained by heterogeneous copy number alteration patterns or expression of a stemness program. Malignant cells shared expression of inflammatory programs that were largely recapitulated in single-cell RNA sequencing of ~35,000 cells from additionally collected samples, including three ascites, two primary HGSOC tumors and three patient ascites-derived xenograft models. Inhibition of the JAK/STAT pathway, which was expressed in both malignant cells and cancer-associated fibroblasts, had potent anti-tumor activity in primary short-term cultures and patient-derived xenograft models. Our work contributes to resolving the HSGOC landscape3-5 and provides a resource for the development of novel therapeutic approaches.

The recent development of highly multiplexed tissue imaging promises to substantially accelerate research into basic biology and human disease. Concurrently, histopathology in a clinical setting is undergoing a rapid transition to digital methods. Online tissue atlases involving highly multiplexed images of research and clinical specimens will soon join genomics as a systematic source of information on the molecular basis of disease and therapeutic response. However, even with recent advances in machine learning, experience with anatomic pathology shows that there is no immediate substitute for expert visual review, annotation, and description of tissue images. In this perspective we review the ecosystem of software available for analysis of tissue images and identify a need for interactive guides or “digital docents” that allow experts to help make complex images intelligible. We illustrate this idea usingMinervasoftware and discuss how interactive image guides are being integrated into multi-omic browsers for effective dissemination of atlas data.