Discovery and generalization of tissue structures from spatial omics data.

Tissues are organized into anatomical and functional units at different scales. New technologies for high-dimensional molecular profiling in situ have enabled the characterization of structure-function relationships in increasing molecular detail. However, it remains a challenge to consistently identify key functional units across experiments, tissues, and disease contexts, a task that demands extensive manual annotation. Here, we present spatial cellular graph partitioning (SCGP), a flexible method for the unsupervised annotation of tissue structures. We further present a reference-query extension pipeline, SCGP-Extension, that generalizes reference tissue structure labels to previously unseen samples, performing data integration and tissue structure discovery. Our experiments demonstrate reliable, robust partitioning of spatial data in a wide variety of contexts and best-in-class accuracy in identifying expertly annotated structures. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease, skin disorder, and neoplastic diseases, underscoring its potential to drive biological insight and discovery from spatial datasets.

Spatial proteomics of human diabetic kidney disease, from health to class III.

AIMS/HYPOTHESIS: Diabetic kidney disease (DKD) is the leading cause of chronic and end-stage kidney disease in the USA and worldwide. Animal models have taught us much about DKD mechanisms, but translation of this knowledge into treatments for human disease has been slowed by the lag in our molecular understanding of human DKD. METHODS: Using our Spatial TissuE Proteomics (STEP) pipeline (comprising curated human kidney tissues, multiplexed immunofluorescence and powerful analysis tools), we imaged and analysed the expression of 21 proteins in 23 tissue sections from individuals with diabetes and healthy kidneys (n=5), compared to those with DKDIIA, IIA-B and IIB (n=2 each) and DKDIII (n=1). RESULTS: These analyses revealed the existence of 11 cellular clusters (kidney compartments/cell types): podocytes, glomerular endothelial cells, proximal tubules, distal nephron, peritubular capillaries, blood vessels (endothelial cells and vascular smooth muscle cells), macrophages, myeloid cells, other CD45+ inflammatory cells, basement membrane and the interstitium. DKD progression was associated with co-localised increases in inflammatory cells and collagen IV deposition, with concomitant loss of native proteins of each nephron segment. Cell-type frequency and neighbourhood analyses highlighted a significant increase in inflammatory cells and their adjacency to tubular and αSMA+ (α-smooth muscle actin-positive) cells in DKD. Finally, DKD progression showed marked regional variability within single tissue sections, as well as inter-individual variability within each DKD class. CONCLUSIONS/INTERPRETATION: Using the STEP pipeline, we found alterations in protein expression, cellular phenotypic composition and microenvironment structure with DKD progression, demonstrating the power of this pipeline to reveal the pathophysiology of human DKD.

Unveiling Spatial Immune Cell Profile in Kidney Allograft Rejections Using 36-plex Immunofluorescence Imaging.

BACKGROUND: Kidney allograft rejections are orchestrated by a variety of immune cells. Because of the complex histopathologic features, accurate pathological diagnosis poses challenges even for expert pathologists. The objective of this study was to unveil novel spatial indices associated with transplant rejection by using a spatial bioinformatic approach using 36-plex immunofluorescence image data. METHODS: The image obtained from 11 T cell-mediated rejection (TCMR) and 12 antibody-mediated rejection (AMR) samples were segmented into 753 737 single cells using DeepCell's Mesmer algorithm. These cells were categorized into 13 distinct cell types through unsupervised clustering based on their biomarker expression profiles. Cell neighborhood analysis allowed us to stratify kidney tissue into 8 distinct neighborhood components consisting of unique cell type enrichment profiles. RESULTS: In contrast to TCMR samples, AMR samples exhibited a higher frequency of neighborhood components that were characterized by an enrichment of CD31+ endothelial cells. Although the overall frequency of CD68+ macrophages in AMR samples was not significantly high, CD68+ macrophages within endothelial cell-rich lesions exhibited a significantly higher frequency in AMR samples than TCMR samples. Furthermore, the frequency of interactions between CD31+ cells and CD68+ cells was significantly increased in AMR samples, implying the pivotal role of macrophages in AMR pathogenesis. Importantly, patients demonstrating a high frequency of CD31:CD68 interactions experienced significantly poorer outcomes in terms of chronic AMR progression. CONCLUSIONS: Collectively, these data indicate the potential of spatial bioinformatic as a valuable tool for aiding in pathological diagnosis and for uncovering new insights into the mechanisms underlying transplant rejection.

PEPSI: Polarity measurements from spatial proteomics imaging suggest immune cell engagement.

Subcellular protein localization is important for understanding functional states of cells, but measuring and quantifying this information can be difficult and typically requires high-resolution microscopy. In this work, we develop a metric to define surface protein polarity from immunofluorescence (IF) imaging data and use it to identify distinct immune cell states within tumor microenvironments. We apply this metric to characterize over two million cells across 600 patient samples and find that cells identified as having polar expression exhibit characteristics relating to tumor-immune cell engagement. Additionally, we show that incorporating these polarity-defined cell subtypes improves the performance of deep learning models trained to predict patient survival outcomes. This method provides a first look at using subcellular protein expression patterns to phenotype immune cell functional states with applications to precision medicine.

Microenvironmental network of clonal CXCL13+CD4+ T cells and Tregs in pemphigus chronic blisters.

BACKGROUNDPemphigus, a rare autoimmune bullous disease mediated by antidesmoglein autoantibodies, can be controlled with systemic medication like rituximab and high-dose systemic corticosteroids combined with immunosuppressants. However, some patients continue to experience chronically recurrent blisters in a specific area and require long-term maintenance systemic therapy.METHODSSkin with chronic blisters was obtained from patients with pemphigus. Immunologic properties of the skin were analyzed by immunofluorescence staining, bulk and single-cell RNA and TCR sequencing, and a highly multiplex imaging technique known as CO-Detection by indEXing (CODEX). Functional analyses were performed by flow cytometry and bulk RNA-Seq using peripheral blood from healthy donors. Intralesional corticosteroid was injected into patient skin, and changes in chronically recurrent blisters were observed.RESULTSWe demonstrated the presence of skin tertiary lymphoid structures (TLSs) with desmoglein-specific B cells in chronic blisters from patients with pemphigus. In the skin TLSs, CD4+ T cells predominantly produced CXCL13. These clonally expanded CXCL13+CD4+ T cells exhibited features of activated Th1-like cells and downregulated genes associated with T cell receptor-mediated signaling. Tregs are in direct contact with CXCL13+CD4+ memory T cells and increased CXCL13 production of CD4+ T cells through IL-2 consumption and TGF-ß stimulation. Finally, intralesional corticosteroid injection improved chronic blisters and reduced skin TLSs in patients with pemphigus.CONCLUSIONThrough this study we conclude that skin TLSs are associated with the persistence of chronically recurrent blisters in patients with pemphigus, and the microenvironmental network involving CXCL13+CD4+ T cells and Tregs within these structures plays an important role in CXCL13 production.TRIAL REGISTRATIONClinicalTrials.gov NCT04509570.FUNDINGThis work was supported by National Research Foundation of South Korea (NRF-2021R1C1C1007179) and Korea Drug Development Fund, which is funded by Ministry of Science and ICT; Ministry of Trade, Industry, and Energy; and Ministry of Health and Welfare (grant RS-2022-00165917).

Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis.

MOTIVATION: Spatial proteomics data have been used to map cell states and improve our understanding of tissue organization. More recently, these methods have been extended to study the impact of such organization on disease progression and patient survival. However, to date, the majority of supervised learning methods utilizing these data types did not take full advantage of the spatial information, impacting their performance and utilization. RESULTS: Taking inspiration from ecology and epidemiology, we developed novel spatial feature extraction methods for use with spatial proteomics data. We used these features to learn prediction models for cancer patient survival. As we show, using the spatial features led to consistent improvement over prior methods that used the spatial proteomics data for the same task. In addition, feature importance analysis revealed new insights about the cell interactions that contribute to patient survival. AVAILABILITY AND IMPLEMENTATION: The code for this work can be found at gitlab.com/enable-medicine-public/spatsurv.

7-UP: Generating in silico CODEX from a small set of immunofluorescence markers.

Multiplex immunofluorescence (mIF) assays multiple protein biomarkers on a single tissue section. Recently, high-plex CODEX (co-detection by indexing) systems enable simultaneous imaging of 40+ protein biomarkers, unlocking more detailed molecular phenotyping, leading to richer insights into cellular interactions and disease. However, high-plex data can be slower and more costly to collect, limiting its applications, especially in clinical settings. We propose a machine learning framework, 7-UP, that can computationally generate in silico 40-plex CODEX at single-cell resolution from a standard 7-plex mIF panel by leveraging cellular morphology. We demonstrate the usefulness of the imputed biomarkers in accurately classifying cell types and predicting patient survival outcomes. Furthermore, 7-UP's imputations generalize well across samples from different clinical sites and cancer types. 7-UP opens the possibility of in silico CODEX, making insights from high-plex mIF more widely available.

A tissue atlas of ulcerative colitis revealing evidence of sex-dependent differences in disease-driving inflammatory cell types and resistance to TNF inhibitor therapy.

Although literature suggests that resistance to TNF inhibitor (TNFi) therapy in patients with ulcerative colitis (UC) is partially linked to immune cell populations in the inflamed region, there is still substantial uncertainty underlying the relevant spatial context. Here, we used the highly multiplexed immunofluorescence imaging technology CODEX to create a publicly browsable tissue atlas of inflammation in 42 tissue regions from 29 patients with UC and 5 healthy individuals. We analyzed 52 biomarkers on 1,710,973 spatially resolved single cells to determine cell types, cell-cell contacts, and cellular neighborhoods. We observed that cellular functional states are associated with cellular neighborhoods. We further observed that a subset of inflammatory cell types and cellular neighborhoods are present in patients with UC with TNFi treatment, potentially indicating resistant niches. Last, we explored applying convolutional neural networks (CNNs) to our dataset with respect to patient clinical variables. We note concerns and offer guidelines for reporting CNN-based predictions in similar datasets.

Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens.

Multiplexed immunofluorescence imaging allows the multidimensional molecular profiling of cellular environments at subcellular resolution. However, identifying and characterizing disease-relevant microenvironments from these rich datasets is challenging. Here we show that a graph neural network that leverages spatial protein profiles in tissue specimens to model tumour microenvironments as local subgraphs captures distinctive cellular interactions associated with differential clinical outcomes. We applied this spatial cellular-graph strategy to specimens of human head-and-neck and colorectal cancers assayed with 40-plex immunofluorescence imaging to identify spatial motifs associated with cancer recurrence and with patient survival after treatment. The graph deep learning model was substantially more accurate in predicting patient outcomes than deep learning approaches that model spatial data on the basis of the local composition of cell types, and it generated insights into the effect of the spatial compartmentalization of tumour cells and granulocytes on patient prognosis. Local graphs may also aid in the analysis of disease-relevant motifs in histology samples characterized via spatial transcriptomics and other -omics techniques.

Injectable Nanoparticle-Based Hydrogels Enable the Safe and Effective Deployment of Immunostimulatory CD40 Agonist Antibodies.

When properly deployed, the immune system can eliminate deadly pathogens, eradicate metastatic cancers, and provide long-lasting protection from diverse diseases. Unfortunately, realizing these remarkable capabilities is inherently risky as disruption to immune homeostasis can elicit dangerous complications or autoimmune disorders. While current research is continuously expanding the arsenal of potent immunotherapeutics, there is a technological gap when it comes to controlling when, where, and how long these drugs act on the body. Here, this study explored the ability of a slow-releasing injectable hydrogel depot to reduce dose-limiting toxicities of immunostimulatory CD40 agonist (CD40a) while maintaining its potent anticancer efficacy. A previously described polymer-nanoparticle (PNP) hydrogel system is leveraged that exhibits shear-thinning and yield-stress properties that are hypothesized to improve locoregional delivery of CD40a immunotherapy. Using positron emission tomography, it is demonstrated that prolonged hydrogel-based delivery redistributes CD40a exposure to the tumor and the tumor draining lymph node (TdLN), thereby reducing weight loss, hepatotoxicity, and cytokine storm associated with standard treatment. Moreover, CD40a-loaded hydrogels mediate improved local cytokine induction in the TdLN and improve treatment efficacy in the B16F10 melanoma model. PNP hydrogels, therefore, represent a facile, drug-agnostic method to ameliorate immune-related adverse effects and explore locoregional delivery of immunostimulatory drugs.

Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.

Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.

Single-Cell Transcriptomic Census of Endothelial Changes Induced by Matrix Stiffness and the Association with Atherosclerosis.

Vascular endothelial cell (EC) plasticity plays a critical role in the progression of atherosclerosis by giving rise to mesenchymal phenotypes in the plaque lesion. Despite the evidence for arterial stiffening as a major contributor to atherosclerosis, the complex interplay among atherogenic stimuli in vivo has hindered attempts to determine the effects of extracellular matrix (ECM) stiffness on endothelial-mesenchymal transition (EndMT). To study the regulatory effects of ECM stiffness on EndMT, an in vitro model is developed in which human coronary artery ECs are cultured on physiological or pathological stiffness substrates. Leveraging single-cell RNA sequencing, cell clusters with mesenchymal transcriptional features are identified to be more prevalent on pathological substrates than physiological substrates. Trajectory inference analyses reveal a novel mesenchymal-to-endothelial reverse transition, which is blocked by pathological stiffness substrates, in addition to the expected EndMT trajectory. ECs pushed to a mesenchymal character by pathological stiffness substrates are enriched in transcriptional signatures of atherosclerotic ECs from human and murine plaques. This study characterizes at single-cell resolution the transcriptional programs that underpin EC plasticity in both physiological or pathological milieus, and thus serves as a valuable resource for more precisely defining EndMT and the transcriptional programs contributing to atherosclerosis.

Activating Immune Recognition in Pancreatic Ductal Adenocarcinoma via Autophagy Inhibition, MEK Blockade, and CD40 Agonism.

BACKGROUND AND AIMS: Patients with pancreatic ductal adenocarcinoma (PDA) have not yet benefitted from the revolution in cancer immunotherapy due in large part to a dominantly immunosuppressive tumor microenvironment. MEK inhibition combined with autophagy inhibition leads to transient tumor responses in some patients with PDA. We examined the functional effects of combined MEK and autophagy inhibition on the PDA immune microenvironment and the synergy of combined inhibition of MEK and autophagy with CD40 agonism (aCD40) against PDA using immunocompetent model systems. METHODS: We implanted immunologically "cold" murine PDA cells orthotopically in wide type C57BL/6J mice. We administered combinations of inhibitors of MEK1/2, inhibitors of autophagy, and aCD40 and measured anticancer efficacy and immune sequelae using mass cytometry and multiplexed immunofluorescence imaging analysis to characterize the tumor microenvironment. We also used human and mouse PDA cell lines and human macrophages in vitro to perform functional assays to elucidate the cellular effects induced by the treatments. RESULTS: We find that coinhibition of MEK (using cobimetinib) and autophagy (using mefloquine), but not either treatment alone, activates the STING/type I interferon pathway in tumor cells that in turn activates paracrine tumor associated macrophages toward an immunogenic M1-like phenotype. This switch is further augmented by aCD40. Triple therapy (cobimetinib + mefloquine + aCD40) achieved cytotoxic T-cell activation in an immunologically "cold" mouse PDA model, leading to enhanced antitumor immunity. CONCLUSIONS: MEK and autophagy coinhibition coupled with aCD40 invokes immune repolarization and is an attractive therapeutic approach for PDA immunotherapy development.

Whole-body PET Imaging of T-cell Response to Glioblastoma.

PURPOSE: Immunotherapy is a promising approach for many oncological malignancies, including glioblastoma, however, there are currently no available tools or biomarkers to accurately assess whole-body immune responses in patients with glioblastoma treated with immunotherapy. Here, the utility of OX40, a costimulatory molecule mainly expressed on activated effector T cells known to play an important role in eliminating cancer cells, was evaluated as a PET imaging biomarker to quantify and track response to immunotherapy. EXPERIMENTAL DESIGN: A subcutaneous vaccination approach of CpG oligodeoxynucleotide, OX40 mAb, and tumor lysate at a remote site in a murine orthotopic glioma model was developed to induce activation of T cells distantly while monitoring their distribution in stimulated lymphoid organs with respect to observed therapeutic effects. To detect OX40-positive T cells, we utilized our in-house-developed 89Zr-DFO-OX40 mAb and in vivo PET/CT imaging. RESULTS: ImmunoPET with 89Zr-DFO-OX40 mAb revealed strong OX40-positive responses with high specificity, not only in the nearest lymph node from vaccinated area (mean, 20.8%ID/cc) but also in the spleen (16.7%ID/cc) and the tumor draining lymph node (11.4%ID/cc). When the tumor was small (<106 p/sec/cm2/sr in bioluminescence imaging), a high number of responders and percentage shrinkage in tumor signal was indicated after only a single cycle of vaccination. CONCLUSIONS: The results highlight the promise of clinically translating cancer vaccination as a potential glioma therapy, as well as the benefits of monitoring efficacy of these treatments using immunoPET imaging of T-cell activation.

Imaging alloreactive T cells provides early warning of organ transplant rejection.

Diagnosis of organ transplant rejection relies upon biopsy approaches to confirm alloreactive T cell infiltration in the graft. Immune molecular monitoring is under investigation to screen for rejection, though these techniques have suffered from low specificity and lack of spatial information. ImmunoPET utilizing antibodies conjugated to radioisotopes has the potential to improve early and accurate detection of graft rejection. ImmunoPET is capable of noninvasively visualizing the dynamic distribution of cells expressing specific immune markers in the entire body over time. In this work, we identify and characterize OX40 as a surrogate biomarker for alloreactive T cells in organ transplant rejection and monitor its expression by utilizing immunoPET. In a dual murine heart transplant model that has both syngeneic and allogeneic hearts engrafted in bilateral ear pinna on the recipients, OX40 immunoPET clearly depicted alloreactive T cells in the allograft and draining lymph node that were not observed in their respective isograft counterparts. OX40 immunoPET signals also reflected the subject's immunosuppression level with tacrolimus in this study. OX40 immunoPET is a promising approach that may bridge molecular monitoring and morphological assessment for improved transplant rejection diagnosis.

Visualization of Activated T Cells by OX40-ImmunoPET as a Strategy for Diagnosis of Acute Graft-versus-Host Disease.

Graft-versus-host disease (GvHD) is a major complication of allogeneic hematopoietic cell transplantation (HCT), mediated primarily by donor T cells that become activated and attack host tissues. Noninvasive strategies detecting T-cell activation would allow for early diagnosis and possibly more effective management of HCT recipients. PET imaging is a sensitive and clinically relevant modality ideal for GvHD diagnosis, and there is a strong rationale for the use of PET tracers that can monitor T-cell activation and expansion with high specificity. The TNF receptor superfamily member OX40 (CD134) is a cell surface marker that is highly specific for activated T cells, is upregulated during GvHD, and mediates disease pathogenesis. We recently reported the development of an antibody-based activated T-cell imaging agent targeting OX40. In the present study, we visualize the dynamics of OX40 expression in an MHC-mismatch mouse model of acute GvHD using OX40-immunoPET. This approach enabled visualization of T-cell activation at early stages of disease, prior to overt clinical symptoms with high sensitivity and specificity. This study highlights the potential utility of the OX40 PET imaging as a new strategy for GvHD diagnosis and therapy monitoring. SIGNIFICANCE: OX40-immunoPET imaging is a promising noninvasive strategy for early detection of GvHD, capable of detecting signs of GvHD pathology even prior to the development of overt clinical symptoms.

ICOS Is an Indicator of T-cell-Mediated Response to Cancer Immunotherapy.

Immunotherapy is innovating clinical cancer management. Nevertheless, only a small fraction of patient's benefit from current immunotherapies. To improve clinical management of cancer immunotherapy, it is critical to develop strategies for response monitoring and prediction. In this study, we describe inducible T-cell costimulator (ICOS) as a conserved mediator of immune response across multiple therapy strategies. ICOS expression was evaluated by flow cytometry, 89Zr-DFO-ICOS mAb PET/CT imaging was performed on Lewis lung cancer models treated with different immunotherapy strategies, and the change in tumor volume was used as a read-out for therapeutic response. ImmunoPET imaging of ICOS enabled sensitive and specific detection of activated T cells and early benchmarking of immune response. A STING (stimulator of interferon genes) agonist was identified as a promising therapeutic approach in this manner. The STING agonist generated significantly stronger immune responses as measured by ICOS ImmunoPET and delayed tumor growth compared with programmed death-1 checkpoint blockade. More importantly, ICOS ImmunoPET enabled early and robust prediction of therapeutic response across multiple treatment regimens. These data show that ICOS is an indicator of T-cell-mediated immune response and suggests ICOS ImmunoPET as a promising strategy for monitoring, comparing, and predicting immunotherapy success in cancer. SIGNIFICANCE: ICOS ImmunoPET is a promising strategy to noninvasively predict and monitor immunotherapy response.See related commentary by Choyke, p. 2975.

Biodegradable fluorescent nanoparticles for endoscopic detection of colorectal carcinogenesis.

Early and comprehensive endoscopic detection of colonic dysplasia - the most clinically significant precursor lesion to colorectal adenocarcinoma - provides an opportunity for timely, minimally-invasive intervention to prevent malignant transformation. Here, the development and evaluation of biodegradable near-infrared fluorescent silica nanoparticles (FSN) is described that have the potential to improve adenoma detection during fluorescence-assisted white-light colonoscopic surveillance in rodent and human-scale models of colorectal carcinogenesis. FSNs are biodegradable (t1/2 of 2.7 weeks), well-tolerated, and enable detection and delineation of adenomas as small as 0.5 mm2 with high tumor-to-background ratios. Furthermore, in the human-scale, APC 1311/+ porcine model, the clinical feasibility and benefit of using FSN-guided detection of colorectal adenomas using video-rate fluorescence-assisted white-light endoscopy is demonstrated. Since nanoparticles of similar size (e.g., 100-150-nm) or composition (i.e., silica, silica/gold hybrid) have already been successfully translated to the clinic, and, clinical fluorescent/white light endoscopy systems are becoming more readily available, there is a viable path towards clinical translation of the proposed strategy for early colorectal cancer detection and prevention in high-risk patients.

The Immunoimaging Toolbox.

The recent clinical success of cancer immunotherapy has renewed interest in the development of tools to image the immune system. In general, immunotherapies attempt to enable the body's own immune cells to seek out and destroy malignant disease. Molecular imaging of the cells and molecules that regulate immunity could provide unique insight into the mechanisms of action, and failure, of immunotherapies. In this article, we will provide a comprehensive overview of the current state-of-the-art immunoimaging toolbox with a focus on imaging strategies and their applications toward immunotherapy.

Imaging activated T cells predicts response to cancer vaccines.

In situ cancer vaccines are under active clinical investigation, given their reported ability to eradicate both local and disseminated malignancies. Intratumoral vaccine administration is thought to activate a T cell-mediated immune response, which begins in the treated tumor and cascades systemically. In this study, we describe a PET tracer (64Cu-DOTA-AbOX40) that enabled noninvasive and longitudinal imaging of OX40, a cell-surface marker of T cell activation. We report the spatiotemporal dynamics of T cell activation following in situ vaccination with CpG oligodeoxynucleotide in a dual tumor-bearing mouse model. We demonstrate that OX40 imaging was able to predict tumor responses on day 9 after treatment on the basis of tumor tracer uptake on day 2, with greater accuracy than both anatomical and blood-based measurements. These studies provide key insights into global T cell activation following local CpG treatment and indicate that 64Cu-DOTA-AbOX40 is a promising candidate for monitoring clinical cancer immunotherapy strategies.