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Cutaneous T cell lymphomas (CTCL) are rare but aggressive cancers without effective treatments. While a subset of patients derive benefit from PD-1 blockade, there is a critically unmet need for predictive biomarkers of response. Herein, we perform CODEX multiplexed tissue imaging and RNA sequencing on 70 tumor regions from 14 advanced CTCL patients enrolled in a pembrolizumab clinical trial (NCT02243579). We find no differences in the frequencies of immune or tumor cells between responders and non-responders. Instead, we identify topographical differences between effector PD-1+ CD4+ T cells, tumor cells, and immunosuppressive Tregs, from which we derive a spatial biomarker, termed the SpatialScore, that correlates strongly with pembrolizumab response in CTCL. The SpatialScore coincides with differences in the functional immune state of the tumor microenvironment, T cell function, and tumor cell-specific chemokine recruitment and is validated using a simplified, clinically accessible tissue imaging platform. Collectively, these results provide a paradigm for investigating the spatial balance of effector and suppressive T cell activity and broadly leveraging this biomarker approach to inform the clinical use of immunotherapies., PD-1 blockade is effective for only a subset of patients with cutaneous T cell lymphomas. Here, the authors report a spatial biomarker that uses immune and cancer cell topography to predict response to PD-1 blockade in this disease.

Ulcerative colitis is a chronic-relapsing inflammatory disease of the large intestine with a complex, multifactorial pathogenesis. TNF inhibitors are widely used to suppress immune-mediated tissue damage in ulcerative colitis patients; however, therapy failures are common. Predicting TNF inhibitor response requires an understanding of the architectural features that underlie mucosal inflammation and those responsible for resistance. Here, we used highly multiplexed immunofluorescence to uncover the spatially resolved tissue architectures underlying disease progression and treatment response in 42 tissue regions from 34 individuals. We created a tissue atlas and performed spatial analysis to identify cell-cell contacts and cellular neighborhoods. We observed that cellular functional states depend on cellular neighborhood and that a subset of inflammatory cell types and cellular neighborhoods in ulcerative colitis patients persisted even during treatment with TNF inhibitor, indicating resistant niches. A computer vision model, with no a priori assumptions regarding cellular architectural features, was able to predict TNF inhibitor resistance. This spatial model significantly outperformed classification models based on single-cell data alone. Our results demonstrate the value of a spatial tissue atlas as a precision medicine tool to guide treatment of patients suffering from autoimmune diseases.

Background Immunotherapies have induced long-lasting remissions in countless advanced-stage cancer patients, but many more patients have not benefitted. Therefore, novel predictive markers are needed to stratify patients before treatment and select those who will most likely benefit from immunotherapy, while avoiding potentially devastating adverse effects and high treatment costs for those who will not. We reasoned that thoroughly characterizing the architecture of the tumor microenvironment (TME) at the single-cell level by highly multiplexed tissue imaging should reveal novel spatial biomarkers of immunotherapy response. Materials and Methods We used CODEX (CO-Detection by indEXing) highly multiplexed fluorescence microscopy to investigate the TME of cutaneous T cell lymphoma (CTCL) in samples from patients treated with pembrolizumab. 55 protein markers were visualized simultaneously using a tissue microarray of matched pre- and post-treatment skin biopsies from 7 pembrolizumab responders and 7 non-responders. After computational image processing and extraction of single-cell information, cell types were identified by unsupervised clustering followed by supervised curation, and cell-cell distances and ‘cellular neighborhoods’ were computed. We also performed RNA sequencing on laser-capture microdissected tissue microarray cores to extract cell-type specific gene expression profiles by CIBERSORTx analysis. Results CODEX enabled the identification and characterization of malignant CD4+ tumor cells and reactive immune cells in the CTCL TME at the single-cell level, resulting in 21 different cell type clusters with spatial information. Cluster frequencies were not significantly different between responders and non-responders pre- and post-treatment. However, advanced computational analysis of the tumor architecture revealed cellular neighborhoods (CNs) that dynamically changed during pembrolizumab therapy and were correlated with response. Effector-type CNs enriched in tumor-infiltrating CD4+ T cells and dendritic cells were significantly increased after treatment in responders. In contrast, a regulatory T cell-enriched CN was significantly increased in non-responders before and after therapy. Furthermore, a spatial signature of cell-cell distances between tumor cells and effector/regulatory immune cells predicted therapy outcome. In addition, CIBERSORTx analysis revealed that tumor cells in responders, but not in non-responders, increased their expression of immune-activating genes. Conclusions High-dimensional spatial analysis of CTCL tumors revealed a pre-existing immunosuppressive state in pembrolizumab non-responders. Thorough analysis of the TME therefore enables the discovery of novel spatial biomarkers in a concept that accounts for both cell type information and higher-order tumor architecture. Combining highly multiplexed microscopy with CIBERSORTx allows for the discovery of novel, predictive spatial biomarkers of immunotherapy response and will pave the way for future studies that functionally address these cell types and their interactions. Disclosure Information C.M. Schurch: F. Consultant/Advisory Board; Modest; Enable Medicine, LLC. D.J. Phillips: None. M. Matusiak: None. B. Rivero Gutierrez: None. S.S. Bhate: None. G.L. Barlow: None. M.S. Khodadoust: B. Research Grant (principal investigator, collaborator or consultant and pending grants as well as grants already received); Significant; Corvus Pharmaceuticals. R. West: None. Y.H. Kim: B. Research Grant (principal investigator, collaborator or consultant and pending grants as well as grants already received); Significant; Merck, Horizon, Soligenix, miRagen, Forty Seven Inc., Neumedicine, Trillium, Galderma, Elorac. D. Speakers Bureau/Honoraria (speakers bureau, symposia, and expert witness); Significant; Innate Pharma, Eisai, Kyowa Hakko Kirin, Takeda, Seattle Genetics, Medivir, Portola Pharmaceuticals, Corvus Pharmaceuticals. G.P. Nolan: E. Ownership Interest (stock, stock options, patent or other intellectual property); Significant; Akoya Biosciences. F. Consultant/Advisory Board; Significant; Akoya Biosciences.

COVID-19 respiratory infections are associated with copious, adherent respiratory secretions that prolong chronic ventilation and contribute to the morbidity and mortality caused by the disease. We hypothesized that hyaluronan, an extracellular matrix glycosaminoglycan produced at sites of active inflammation that promotes edema in other settings, might be a component of these secretions. To interrogate this, we examined the respiratory secretions collected from eight intubated patients with COVID-19, six control patients with cystic fibrosis (CF), a different respiratory disease also associated with thick adherent secretions, and eight healthy controls. In this sample set we found that hyaluronan content is increased approximately 20-fold in both CF and COVID-19 patients compared to healthy controls. The hyaluronan in COVID-19 samples was comprised of low-molecular weight fragments, the hyaluronan form most strongly linked with pro-inflammatory functions. Hyaluronan is similarly abundant in histologic sections from cadaveric lung tissue from COVID-19 patients. These findings implicate hyaluronan in the thick respiratory secretions characteristic of COVID-19 infection. Therapeutic strategies targeting hyaluronan should be investigated further for potential use in patients with COVID-19.

A coat of pericellular hyaluronan surrounds mature dendritic cells (DC) and contributes to cell-cell interactions. We asked whether 4-methylumbelliferone (4MU), an oral inhibitor of HA synthesis, could inhibit antigen presentation. We find that 4MU treatment reduces pericellular hyaluronan, destabilizes interactions between DC and T-cells, and prevents T-cell proliferation in vitro and in vivo. These effects were observed only when 4MU was added prior to initial antigen presentation but not later, consistent with 4MU-mediated inhibition of de novo antigenic responses. Building on these findings, we find that 4MU delays rejection of allogeneic pancreatic islet transplant and allogeneic cardiac transplants in mice and suppresses allogeneic T-cell activation in human mixed lymphocyte reactions. We conclude that 4MU, an approved drug, may have benefit as an adjunctive agent to delay transplantation rejection.

Cutaneous T-cell lymphoma (CTCL) is a rare, incurable CD4+ T cell malignancy of the skin with a 5-year survival rate of less than 30% in advanced stages. Immune checkpoint inhibitors, such as anti-PD-1 antibodies, have shown dramatic clinical efficacy in multiple advanced cancers, but the majority of cancer patients do not respond to these treatments. The clinical use of immunotherapies will increase considerably in the near future; therefore, predictive biomarkers of response to stratify patients for treatment are needed to limit potentially devastating adverse effects and reduce costs for healthcare systems. A clinical trial of the anti-PD-1 antibody pembrolizumab in CTCL showed that 38% of patients have a durable clinical response. However, standard tests, including comprehensive immunohistochemistry and single-cell quantification of PD-1 expression, have so far failed to identify a predictive biomarker for pembrolizumab response in this cohort. We reasoned that deep profiling of the CTCL tumor microenvironment (TME) using CODEX–a novel technology that allows for highly multiplexed tissue microscopy with >50 simultaneous parameters–could provide insight into the mechanisms of pembrolizumab response and enable prediction. We analyzed the CTCL TME using a tissue microarray of matched biopsies taken before and after pembrolizumab therapy in 7 responders and 7 non-responders. Imaging of 55 markers allowed discriminating malignant CD4+ tumor cells from reactive CD4+ T cells based on nuclear size and differential expression of CD7, CD25 and Ki-67. Unsupervised machine learning followed by supervised curation identified 21 different cell type clusters with spatial information. Integrating these data using advanced computational analysis revealed 10 distinct, conserved cellular neighborhoods (CNs) in the CTCL TME that changed in frequency and distribution during pembrolizumab therapy. In responders, effector-type CNs, including a tumor/dendritic cell CN and a tumor/CD4+ T cell CN, were significantly increased after treatment. In contrast, in non-responders, an immunosuppressive-type CN enriched in regulatory T cells was significantly increased before and after therapy. Importantly, the global frequencies in the tissues of the cell types defining these CNs were not different between patient groups. In addition, RNA sequencing of matched tissue sections revealed higher expression of effector-type cytokines and chemokines in responders. In sum, highly multiplexed analysis of the CTCL TME architecture in combination with RNA sequencing allows discovering novel, predictive spatial biomarkers of immunotherapy response and will pave the way for future studies that functionally address the identified cell types and cellular interactions. Citation Format: Christian M. Schürch, Darci J. Phillips, Belén Rivero Gutierrez, Magdalena Matusiak, Salil S. Bhate, Graham L. Barlow, Steven P. Fling, Nirasha Ramchurren, Robert H. Pierce, Martin A. Cheever, Michael S. Khodadoust, Robert West, Youn H. Kim, Garry P. Nolan. Cellular neighborhoods predict pembrolizumab response in cutaneous T cell lymphoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6669.

Cutaneous T cell lymphoma (CTCL) is a CD4+ T cell malignancy of the skin with heterogeneous outcomes and limited treatment options. Monoclonal antibodies directed against PD-1, such as pembrolizumab, have shown impressive efficacy in multiple advanced malignancies, and are currently tested in clinical trials in patients with CTCL. Initial data indicate that about half of the patients experience treatment response, whereas the other half are non-responders. Non-responders can be further divided into patients with stable disease versus rapid progressors. It is currently unknown why some CTCL patients respond to pembrolizumab while others rapidly progress, and no predictive biomarkers are available. Single-cell analysis approaches to identify biomarkers of response, for example quantifying the expression of PD-1 on tumor cells vs. reactive immune cells, have not enabled stratification of patients. We therefore hypothesized that more complex spatial cellular interactions within the immune tumor microenvironment (iTME) of CTCL could provide insight into the mechanisms of pembrolizumab response and enable prediction. We applied CODEX (CO-Detection by indEXing) highly multiplexed tissue imaging to study the CTCL iTME in matched biopsies before and after pembrolizumab therapy in 7 responders and 7 non-responders (see the Figure). Using 54 markers simultaneously allowed discriminating malignant CD4+ tumor cells from reactive CD4+ T cells and identified 30 different cell clusters with spatial information, including an M2 macrophage cluster that was enriched in non-responders before therapy. Unexpectedly, in pembrolizumab responders compared to non-responders, PD-1 expression levels were higher in multiple clusters of tumor cells and reactive T cells. Computational spatial analysis revealed ten distinct, conserved cellular neighborhoods in the CTCL iTME that changed in composition and frequency during therapy. Interestingly, one cellular neighborhood to be presented dramatically increased after therapy only in responders. Therefore, highly multiplexed spatial analysis of the CTCL iTME allows discovering novel, predictive biomarkers of immunotherapy response and will pave the way for future studies that functionally address the identified cell types and cellular interactions. Disclosures Khodadoust: Corvus Pharmaceuticals: Research Funding. Kim:miRagen: Research Funding; Merck: Research Funding; Medivir: Honoraria, Membership on an entity's Board of Directors or advisory committees; Innate Pharma: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Soligenix: Research Funding; Forty Seven Inc: Research Funding; Neumedicine: Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Trillium: Research Funding; Elorac: Research Funding; Galderma: Research Funding; Corvus: Honoraria, Membership on an entity's Board of Directors or advisory committees; Portola Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Kyowa Hakko Kirin: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Eisai: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Horizon: Research Funding. Nolan:Akoya Biosciences Inc.: Consultancy, Equity Ownership, Patents & Royalties.