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Supplementary Tables 1 and 2. Supplementary Table 1: Fold de-repression of miR-21 target genes after anti-miR-21 treatment. Supplementary Table 2: Taqman Primer and Probes

Supplementary Figure Legends. Figure legends for Supplementary Figures 1, 2, 3, and 4.

Supplementary Data 1. Excel sheet with fold change in gene expression following anti-miR-21 treatment.

Supplementary Figure 1. miR-21 mimic suppresses expression of ANKRD46, DDAH1 and RECK. SKHep1 cells were transfected with miR-21 or negative mimic. RNA was isolated and expression of ANKRD46, DDAH1 and RECK was assessed by qPCR. (Mean, {plus minus} SD, n=3).

Supplementary Figure 3. HMBG1 and LDH are induced upon miR-21 inhibition. SKHep1, HepG2 and Hep3B cells were treated with anti-miR-21 or MM control and extracellular HMBG1 and LDH activity was quantified. (Mean, {plus minus} SEM, n=3)

Supplementary Figure 4. Pathway analysis of gene expression changes following anti-miR-21 treatment. SKHep1 cells treated with anti-miR-21 were subjected to microarray gene expression analysis. Changes in cellular processes (top) and pathways (bottom) upon anti-miR-21 treatment are shown.

Supplementary Figure 2. Anti-miR-21 treatment causes limited caspase 3/7 activation in non-transformed cell lines. Caspase 3/7 activation after treatment of SKHep1, IMR-90, and WI-38 cells with MM control or anti-miR-21 for 72 hours. (Mean, {plus minus} SEM, n=3).

Surprisingly little clarity exists concerning effects of biomaterial properties on spatially localized protein expression, which drives implant success. Wound healing and tissue regeneration must be optimally supported by the implant, adsorbed proteins, immune cells, and fibroblasts; cells determine repair and functional recovery through protein production and regulation. However, not yet fully understood is how implants differentially drive spatial quantities of individual proteins both within the implant interior and the tissue surrounding it. We apply GeoMx® digital spatial profiling to site-specifically investigate protein production in porous implants. Data is collected on the location and quantity of 40+ proteins from formalin-fixed, paraffin-embedded tissue slides of anisotropic tissue scaffolds (n=18) with differing pore sizes (35 µm, 53 µm) and implantation durations (2, 14, 28 days); matching bulk gene expression data (700+ genes) was measured for the identical implants. Notably, we discover fundamental spatial relationships in protein localization that in both implant interior and exterior are uniquely independent or dependent of implant microstructure: dendritic cell marker CD11c and fibronectin significantly dominate the scaffold interior, while cell-to-cell adhesion marker CD34 and anti-inflammatory M2 polarization marker CD163 localize in the exterior. Lastly, collating spatial and bulk information, unique spatiotemporal expression patterns are identified for markers such as fibronectin, which are only uncoverable through spatial profiling and were otherwise hidden in bulk expression results. Together, these discoveries illustrate the critical importance of quantifying spatial expression patterns for implants, facilitating a paradigm shift in the iterative design, mechanistic understanding, and rapid assessment of biomaterials. Statement of Significance : Spatial localization and expression of proteins, which determine implant success, are not fully understood because quantitative high-plex profiling is challenging. Applying GeoMx® digital spatial profiling to site-specifically investigate protein production in porous implants, data is collected on the location and quantity of 40+ protein targets from tissue scaffolds with differing pore sizes (35 µm, 53 µm) and implantation durations (2, 14, 28 days). Collecting in parallel matched bulk gene expression data (700+ genes) for identical implants, we discover significant spatiotemporal expression patterns that remain otherwise hidden in differential bulk results. This new approach for the rapid assessment of biomaterials offers an enhanced mechanistic understanding and enables the tailoring of implants for superior regenerative outcomes.

The molecular underpinnings of organ dysfunction in acute COVID-19 and its potential long-term sequelae are under intense investigation. To shed light on these in the context of liver function, we performed single-nucleus RNA-seq and spatial transcriptomic profiling of livers from 17 COVID-19 decedents. We identified hepatocytes positive for SARS-CoV-2 RNA with an expression phenotype resembling infected lung epithelial cells. Integrated analysis and comparisons with healthy controls revealed extensive changes in the cellular composition and expression states in COVID-19 liver, reflecting hepatocellular injury, ductular reaction, pathologic vascular expansion, and fibrogenesis. We also observed Kupffer cell proliferation and erythrocyte progenitors for the first time in a human liver single-cell atlas, resembling similar responses in liver injury in mice and in sepsis, respectively. Despite the absence of a clinical acute liver injury phenotype, endothelial cell composition was dramatically impacted in COVID-19, concomitantly with extensive alterations and profibrogenic activation of reactive cholangiocytes and mesenchymal cells. Our atlas provides novel insights into liver physiology and pathology in COVID-19 and forms a foundational resource for its investigation and understanding.

Immunotherapy has reshaped the field of cancer therapeutics but the population that benefits are small in many tumor types, warranting a companion diagnostic test. While immunohistochemistry (IHC) for programmed death-ligand 1 (PD-L1) or mismatch repair (MMR) and polymerase chain reaction (PCR) for microsatellite instability (MSI) are the only approved companion diagnostics others are under consideration. An optimal companion diagnostic test might combine the spatial information of IHC with the quantitative information from RNA expression profiling. Here, we show proof of concept for combination of spatially resolved protein information acquired by the NanoString GeoMx® Digital Spatial Profiler (DSP) with transcriptomic information from bulk mRNA gene expression acquired using NanoString nCounter® PanCancer IO 360™ panel on the same cohort of immunotherapy treated melanoma patients to create predictive models associated with clinical outcomes. We show that the combination of mRNA and spatially defined protein information can predict clinical outcomes more accurately (AUC 0.97) than either of these factors alone.

The power of spatial biology lies in the integration of multiple scales of information from subcellular to tissue scale. Until recently, spatial analysis of protein biomarkers in tissues was limited to a few markers at a time using traditional IHC colorimetric or fluorescent readout. Here, we demonstrate the ability of NanoString’s CosMx™ Spatial Molecular Imager (SMI) platform to quantify more than 70 proteins encompassing key targets in immuno-oncology and tumor biology, localize the proteins and analyze protein expression at a single cell level. Key to the technology is the use of fully automated fluidics and imaging systems, short turnaround time, and high sensitivity. The CosMx protein assay has been optimized for FFPE samples, which represent the largest collection of biospecimens available for clinical investigation. To that end, we explored the utility of CosMx spatial proteomics on a series of clinical samples from cancer patients, including the Serial Measurements of Molecular and Architectural Response to Therapy (SMMART) trial, across multiple cancer types. CosMx SMI’s high-level protein multiplexing capabilities enabled spatial analysis of metastatic tumors in response to personalized treatment for a single patient over time. Combining spatial data obtained from both CosMx RNA and protein assays on the same sample reveals distinct regions of Tumor Associated Macrophages and their interactions with tumor cells in some samples. Detection of phosphoproteins also allows for analysis of the impact of kinase inhibitor treatment on the spatial environment in longitudinal biopsies. The highly-multiplexed spatial analysis of proteins in longitudinal metastatic breast cancer biopsies under therapeutic pressure provides a unique opportunity to understand evolution of tumors and develop and implement therapeutic approaches that can directly target mutations arising in the tumor cells while effectively engaging the immune system. FOR RESEARCH USE ONLY. Not for use in diagnostic procedures. Citation Format: Zachary R. Lewis, Brian Birditt, Emily Brown, Kan Chantranuvatana, Brian Filanoski, Chris Corless, Tiên Phan-Everson, Gary Geiss, Tyler Hether, Evie Hobbs, Brett Johnson, Taylor Kelley, Charles Lopez, Rhonda Meredith, Anastasiya Olson, Giang Ong, Mithra Korukonda, Erin Piazza, Jason Reeves, Alyssa Rosenbloom, Kiara Siex, Hye Son Yi, Edward Zhao, Joseph M. Beechem, Gordon Mills. Single cell spatial molecular imaging of 76-plex proteins in clinical cancer samples in response to personalized treatment. [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 5641.

Spatial distribution of T cells is key in understanding the escape of tumors from immune surveillance via the adaptive immune response, including interactions between immune cells and the surrounding tumor microenvironment. T cells are critical to the adaptive immune response to pathogens and cancers, mediating an antigen-specific response through both specificity and diversity of T cell receptor (TCR) clonotypes. Many methods exist to determine specific clonotypes and overall TCR diversity present from bulk tissues or sorted cell populations; however, nearly all fail to capture spatial orientation and arrangement of T cells engaging with their microenvironment, and most require large amounts of starting material from precious samples. Here, we present a TCR expression profiling panel for the GeoMx® Digital Spatial Profiler that can be combined with the GeoMx Cancer Transcriptome Atlas (CTA) or Human Whole Transcriptome Atlas (WTA) on archival formalin-fixed paraffin embedded (FFPE) tissue specimens. This represents the first commercial spatial expression profiling assay for the simultaneous quantification of TCR constant, variable, and joining segments in situ. We show reliable sensitivity and specificity (>90%) with respect to orthogonal sequencing and robust detection of TCR chains with evidence of clonal expansion and CD8 infiltration across tumor regions in colorectal cancer tissue. These events also corresponded to increased signatures of exhaustion from the T cells and suggest that the T cells resident in or near the tumor are tumor-specific and poised for activation via checkpoint blockade. Signaling pathways and tumor-specific signatures were also evaluated to look for mechanisms through which tumor cells respond to T cell infiltration. We further validated the performance of the TCR probe pool in cell pellet arrays with orthogonal TCR sequencing, tonsil and colorectal cancer tissues. Together, the combination of our TCR add-on panel with the CTA or WTA illuminates T cell phenotypes, signaling pathways, population dynamics, and transcriptomic changes, yielding an unparalleled view of the T cell response in any context. FOR RESEARCH USE ONLY. Not for use in diagnostic procedures. Citation Format: Katrina van Raay, Michelle Kriner, Jason Reeves, Erin Piazza, Hargita Kaplan, John Vivian, Francis Fernandez, Margaret Hoang, Joseph Beechem. Spatially resolved expression of T cell receptors elucidates spatial relationships between T cells, immune infiltration, and cancer-associated pathways [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 615.

Detecting and analyzing large numbers of proteins using whole-slide imaging is critical for a comprehensive picture of immune response to cancer. Many existing approaches for high-plex proteomics face issues around simplicity, speed, scalability, and big data analysis. Here, we present an integrated workflow from sample preparation through downstream analysis that addresses many key concerns around high plex proteomics. The CosMx Spatial Molecular Imager (SMI) and AtoMx Spatial Informatics Platform (SIP) comprise of a turnkey, end-to-end workflow that efficiently handles highly multiplex protein analysis at plex sizes exceeding 110 targets. We demonstrate an extension of our commercially available 64-plex human immuno-oncology panel to higher numbers of targets and show how the cloud computing-enabled AtoMx SIP allows flexible construction of analytic pipelines for cell typing and spatial analyses. The CosMx protein assay uses antibodies conjugated with oligonucleotides, which are detected using universal, multi-analyte CosMx readout reagents. The CosMx Human Immuno-oncology panel was optimized to comprehensively profile lymphoid and stromal lineages within the tumor microenvironment as well as markers of cancer signaling and progression. Each CosMx SMI antibody was validated on multi-organ FFPE tissue microarrays covering prevalent solid tumor types with matched controls, and 52 human FFPE cell lines, including overexpression lines for key targets such as GITR, CD278, PD-L1, and PD-1. CosMx SMI uses a deep learning algorithm to segment whole cells and a semi-supervised algorithm to classify cell types. The AtoMx SIP provides full analysis support, including a whole-slide image viewer, and methods for performing built-in or fully customizable analyses for cell typing, ligand-receptor analysis, neighborhood analysis and spatial differential expression. Within the cancer sample profiled, we performed in-depth single-cell proteomic profiling across different cell populations. We detected TLS, characterized TLS maturation, and identified immune interactions with the tumor microenvironment. The CosMx SMI assay profiled the composition and spatial organization of infiltrating immune cells within and around the tumor microenvironment. We found that markers of T cell activation and exhaustion varied across the tumor landscape. CosMx SMI is a high-plex spatial multi-omics platform that enables detection of more than 110 proteins at subcellular resolution in real-world FFPE tissues. The extensibility of the CosMx protein assay to large numbers of protein targets and our flexible, scalable bioinformatic platform provides a straightforward and robust solution for comprehensive immune phenotyping with full spatial context. FOR RESEARCH USE ONLY. Not for use in diagnostic procedures. Citation Format: Tien Phan-Everson, Zachary Lewis, Giang Ong, Yan Liang, Emily Brown, Liuliu Pan, Aster Wardhani, Mithra Korukonda, Carl Brown, Dwayne Dunaway, Edward Zhao, Dan McGuire, Sangsoon Woo, Alyssa Rosenbloom, Brian Filanoski, Rhonda Meredith, Kan Chantranuvatana, Brian Birditt, Hye Son Yi, Erin Piazza, Jason Reeves, John Lyssand, Vik Devgan, Michael Rhodes, Gary Geiss, Joseph Beechem. A complete pipeline for high-plex spatial proteomic profiling and analysis on the cosmxtm spatial molecular imager and atomtm spatial informatics platform. [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 4617.

Background: While introduction of HER2-targeted therapies has dramatically improved outcomes for patients with HER2-positive disease, even with the addition of HER2-targeted agents, 40-50% of patients do not achieve a pCR (pathologic complete response) following neoadjuvant therapy implying that clinical or molecular differences may be present in responders versus non-responders. While recent bulk expression studies have identified several biomarkers associated with response to HER2-targeted therapies in the neoadjuvant setting, these studies are limited in their ability to assign observed changes to specific geographic or phenotypic cell populations, such as the malignant tumor core or the surrounding microenvironment. Methods: Here we used the Digital Spatial Profiler (DSP, NanoString Technologies, Inc.) to profile regions-of-interest containing pancytokeratin (panCK)+ tumor cells and infiltrated immune cells that are co-localized with the tumor cells. Using this technology, we assayed archival tissue from 28 patients with HER2-positive breast cancer from the TRIO-B07 (NCT00769470) clinical trial, who were treated with trastuzumab, lapatinib, or both, followed by standard chemotherapy plus HER2-targeted therapy. Tissue specimens were collected from the pre-treatment diagnostic biopsy (Baseline) and after one cycle of targeted therapy (Runin). To study regional heterogeneity, we selected an average of four panCK-enriched tissue regions from each sample. Using DSP, we performed multiplexed quantification of 38 tumor and immune protein markers and 96 RNA markers on the selected tissue regions and compared our findings to bulk mRNA expression data from the same cohort. Results: Within the panCK-enriched regions, DSP revealed significant treatment-associated decreases in HER2 protein levels and the downstream PI3K-Akt signaling pathway in Runin compared to Baseline samples. In tandem, we observed a significant increase in infiltrating leukocytes, with CD45, a pan-leukocyte marker, and CD8, a marker for T cells that mediate tumor cell killing, showing the most dramatic changes. These changes in Runin compared to Baseline were more significant in the subset of cases that achieved a pCR versus those that do not, independent of ER status. Comparison of Runin samples to matched Baseline samples from the same patient enabled improved prediction of patient outcome (pCR) compared with analysis of a single timepoint alone. We also found that the DSP panCK enrichment strategy captures additional signal not observed in bulk expression data. For instance, using bulk expression, a decrease in HER2 RNA levels between Baseline and Runin was evident but there was no difference in the degree of decrease in HER2 mRNA between pCR and no pCR cases. Using DSP, we observed that the significant decrease in HER2 levels at Runin is more pronounced in cases that achieved a pCR. Across both tumor and immune markers, regional heterogeneity increased at Runin compared to Baseline. Conclusions: In this study, we used DSP and a panCK enrichment strategy to retrospectively delineate the changes that occurred in tumor cells and co-localized immune cells during HER2-targeted therapy. In comparison to traditional or multiplexed IHC, DSP allows for simultaneous profiling of a large number of markers, enabling the characterization of multiple cancer signaling pathways and immune markers on a single tissue specimen. This study demonstrates the utility of pancytokeratin-enriched spatial proteomic profiling to characterize treatment-associated changes and identify predictive biomarkers. NanoString’s Digital Spatial Profiler is for Research Use Only. Not to be used for diagnostic procedures. Citation Format: Katherine Lee McNamara, Jennifer L. Caswell-Jin, Zhicheng Ma, Jason J. Zoeller, Michelle Kriner, Zoey Zhou, Jason Reeves, Margaret Hoang, Joseph Beechem, Dennis J. Slamon, Michael F. Press, Joan Brugge, Sara A. Hurvitz, Christina Curtis. Characterizing the tumor and immune microenvironment through treatment to predict response to neoadjuvant HER2-targeted therapy using the Digital Spatial Profiler [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 P4-10-12.

Simple Summary In breast cancer, there is a high degree of variability in tumors and the surrounding tissue called the tumor microenvironment (TME). To better understand tumor biology and metastasis, as well as to predict response to cancer treatments or the course of the disease, it is important to characterize molecular diversity in the breast TME. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially analyze proteins and RNA transcripts in tumors and surrounding tissues from patients or preclinical models. Using the GeoMx DSP, protein expression and RNA transcripts in the distinct regions of a tumor can be quantified up to and including the whole transcriptome level. Herein, the GeoMx Breast Cancer Consortium presents best practices for GeoMx spatial profiling of tumors to promote the collection of high-quality data, optimization of data analysis and integration of datasets to accelerate biomarker discovery. These best practices can also be applied to any tumor type to provide information about the tumor and the TME. Abstract Breast cancer is a heterogenous disease with variability in tumor cells and in the surrounding tumor microenvironment (TME). Understanding the molecular diversity in breast cancer is critical for improving prediction of therapeutic response and prognostication. High-plex spatial profiling of tumors enables characterization of heterogeneity in the breast TME, which can holistically illuminate the biology of tumor growth, dissemination and, ultimately, response to therapy. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially resolve and quantify proteins and RNA transcripts from tissue sections. The platform is compatible with both formalin-fixed paraffin-embedded and frozen tissues. RNA profiling was developed at the whole transcriptome level for human and mouse samples and protein profiling of 100-plex for human samples. Tissue can be optically segmented for analysis of regions of interest or cell populations to study biology-directed tissue characterization. The GeoMx Breast Cancer Consortium (GBCC) is composed of breast cancer researchers who are developing innovative approaches for spatial profiling to accelerate biomarker discovery. Here, the GBCC presents best practices for GeoMx profiling to promote the collection of high-quality data, optimization of data analysis and integration of datasets to advance collaboration and meta-analyses. Although the capabilities of the platform are presented in the context of breast cancer research, they can be generalized to a variety of other tumor types that are characterized by high heterogeneity.

Summary The CD155/TIGIT axis can be co-opted during immune evasion in chronic viral infections and cancer. Pancreatic adenocarcinoma (PDAC) is a highly lethal malignancy, and immune-based strategies to combat this disease have been largely unsuccessful to date. We corroborate prior reports that a substantial portion of PDAC harbors predicted high-affinity MHC class I-restricted neoepitopes and extend these findings to advanced/metastatic disease. Using multiple preclinical models of neoantigen-expressing PDAC, we demonstrate that intratumoral neoantigen-specific CD8+ T cells adopt multiple states of dysfunction, resembling those in tumor-infiltrating lymphocytes of PDAC patients. Mechanistically, genetic and/or pharmacologic modulation of the CD155/TIGIT axis was sufficient to promote immune evasion in autochthonous neoantigen-expressing PDAC. Finally, we demonstrate that the CD155/TIGIT axis is critical in maintaining immune evasion in PDAC and uncover a combination immunotherapy (TIGIT/PD-1 co-blockade plus CD40 agonism) that elicits profound anti-tumor responses in preclinical models, now poised for clinical evaluation.

The molecular mechanisms underlying the clinical manifestations of COVID-19 and what distinguishes them from common seasonal influenza virus and other lung injury states such as Acute Respiratory Distress Syndrome, remains poorly understood. To address these challenges, we combine transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues to define body-wide transcriptome changes in response to COVID-19. We then match this data with spatial protein and expression profiling across 357 tissue sections from 16 representative patient lung samples and identify tissue compartment-specific damage wrought by SARS-CoV-2 infection, evident as a function of varying viral loads during the clinical course of infection and tissue type specific expression states. Overall, our findings reveal a systemic disruption of canonical cellular and transcriptional pathways across all tissues, which can inform subsequent studies to combat the mortality of COVID-19 and to better understand the molecular dynamics of lethal SARS-CoV-2 and other respiratory infections., Graphical Abstract, Park et al. report system-wide transcriptome damage and tissue identity loss wrought by SARS-CoV-2, influenza, and bacterial infection across multiple organs (heart, liver, lung, kidney, and lymph nodes) and provide spatio-temporal landscape of COVID-19 in the lung.

Anosmia, the loss of smell, is a common and often the sole symptom of COVID-19. The onset of the sequence of pathobiological events leading to olfactory dysfunction remains obscure. Here, we have developed a postmortem bedside surgical procedure to harvest endoscopically samples of respiratory and olfactory mucosae and whole olfactory bulbs. Our cohort of 85 cases included COVID-19 patients who died a few days after infection with SARS-CoV-2, enabling us to catch the virus while it was still replicating. We found that sustentacular cells are the major target cell type in the olfactory mucosa. We failed to find evidence for infection of olfactory sensory neurons, and the parenchyma of the olfactory bulb is spared as well. Thus, SARS-CoV-2 does not appear to be a neurotropic virus. We postulate that transient insufficient support from sustentacular cells triggers transient olfactory dysfunction in COVID-19. Olfactory sensory neurons would become affected without getting infected., Postmortem samples of respiratory and olfactory mucosa and whole olfactory bulbs are harvested immediately after the death of COVID-19 patients revealing ciliated cells and sustentacular cells but not olfactory sensory neurons as the main target cell types for SARS-CoV-2 infection and replication.

The single-sideband phase noise power spectral density and Allan deviation of a portable microwave oscillator system based on an optical frequency comb stabilized to an ultra-low expansion Fabry-Perot reference cavity is characterized at carrier frequencies of 4.0, 8.0, and 16.0 GHz. Compared to industry standard sapphire loaded cavity oscillators, the photonic microwave oscillator shows superior phase noise at offset frequencies $3.0 \times 10^{-14}$ obtains at an averaging time of 400 ms.

Multiplexed spatial profiling can enable biological insights by characterizing gene expression within discrete physical locations of a tissue. However, these advanced techniques can be confounded by variability of sample collection, storage, or profiling protocols, making it difficult to accurately compare data generated by different laboratories. Further, as more spatial profiling datasets become publicly available, having methods to enable meta-analysis of samples collected on different studies will maximize the learnings to support the advancement of treatments or identifying patient segments. To quantify the variability of spatial profiling data at different laboratories and to advance data normalization methodologies, 4 independent laboratories used the NanoString® GeoMx® Digital Spatial Profiling (DSP) to profile serial 5 µm sections of tissue and cell pellet arrays (CPAs). GeoMx DSP enables high throughput, spatially resolved analysis of gene or protein expression from fresh or archival human tissues. In this study, the GeoMx Cancer Transcriptome Atlas was used to profile >1800 genes simultaneously. We examined the concordance of GeoMx data generated in the different laboratories when controlling for methodical variation (e.g., reagents, tissue source) and experimentally varying region of interest (ROI) size, collection site, and sample preservation methods. Sections of tonsil, colon, and 2 CPAs were profiled separately at the 4 laboratories. Each analyzed fresh cut (FC) tissues and two sites examined sample stability by analyzing the impact of storing slides at -80°C for 1 month prior to spatial profiling. Concordance analysis was performed using the Horn-Morisita Index on raw data comparing paired and unpaired ROIs across each set of slides. In CPA samples where each pellet was a different tumor type (e.g., NSCLC, melanoma), we observed strong clustering by cell line. While data initially showed varying degrees of clustering by slide, factoring out this variable removed the association of slide, allowing integration of the data across profiling locations without affecting concordance within slides. In tonsil, ROIs with increasing area were profiled. Comparing expression between pairs of samples for a given area, concordance increased with ROI size (R = -0.40, p In this study, we quantify slide-specific variation observed in high-plex RNA profiling by the DSP platform and detail methods for accounting for this variation. We note that many downstream analyses (e.g., differential expression) already model slide effects during the analysis, but modeling it explicitly allows for direct comparison of concordance with other approaches (e.g., clustering, PCA). These methods support the use of multi-institution studies leveraging the GeoMx platform. Citation Format: Tyler Hether, Tim Howes, David Scoville, Charlie Glaser, Yanyun Li, Rami Vanguri, Neeman Mohibullah, Wan-Jung Chang, Todd Yoder, Minnal Gupta, Kathy Ton, Yan Liang, Ying Huang, Zach Herbert, Jason Reeves, Elizabeth Mittendorf, Simon Lacey, Travis Hollmann, Sarah Warren, Theresa LaVallee. A multi-institution examination of concordance in spatial transcriptomics using the GeoMx Cancer Transcriptome Atlas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 66.

In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin–angiotensin–aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.

The SARS-CoV-2 pandemic has caused over 1 million deaths globally, mostly due to acute lung injury and acute respiratory distress syndrome, or direct complications resulting in multiple-organ failures. Little is known about the host tissue immune and cellular responses associated with COVID-19 infection, symptoms, and lethality. To address this, we collected tissues from 11 organs during the clinical autopsy of 17 individuals who succumbed to COVID-19, resulting in a tissue bank of approximately 420 specimens. We generated comprehensive cellular maps capturing COVID-19 biology related to patients’ demise through single-cell and single-nucleus RNA-Seq of lung, kidney, liver and heart tissues, and further contextualized our findings through spatial RNA profiling of distinct lung regions. We developed a computational framework that incorporates removal of ambient RNA and automated cell type annotation to facilitate comparison with other healthy and diseased tissue atlases. In the lung, we uncovered significantly altered transcriptional programs within the epithelial, immune, and stromal compartments and cell intrinsic changes in multiple cell types relative to lung tissue from healthy controls. We observed evidence of: alveolar type 2 (AT2) differentiation replacing depleted alveolar type 1 (AT1) lung epithelial cells, as previously seen in fibrosis; a concomitant increase in myofibroblasts reflective of defective tissue repair; and, putative TP63+ intrapulmonary basal-like progenitor (IPBLP) cells, similar to cells identified in H1N1 influenza, that may serve as an emergency cellular reserve for severely damaged alveoli. Together, these findings suggest the activation and failure of multiple avenues for regeneration of the epithelium in these terminal lungs. SARS-CoV-2 RNA reads were enriched in lung mononuclear phagocytic cells and endothelial cells, and these cells expressed distinct host response transcriptional programs. We corroborated the compositional and transcriptional changes in lung tissue through spatial analysis of RNA profiles in situ and distinguished unique tissue host responses between regions with and without viral RNA, and in COVID-19 donor tissues relative to healthy lung. Finally, we analyzed genetic regions implicated in COVID-19 GWAS with transcriptomic data to implicate specific cell types and genes associated with disease severity. Overall, our COVID-19 cell atlas is a foundational dataset to better understand the biological impact of SARS-CoV-2 infection across the human body and empowers the identification of new therapeutic interventions and prevention strategies.

Recent studies have provided insights into the pathology of and immune response to COVID-191–8. However, a thorough investigation of the interplay between infected cells and the immune system at sites of infection has been lacking. Here we use high-parameter imaging mass cytometry9 that targets the expression of 36 proteins to investigate the cellular composition and spatial architecture of acute lung injury in humans (including injuries derived from SARS-CoV-2 infection) at single-cell resolution. These spatially resolved single-cell data unravel the disordered structure of the infected and injured lung, alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyperinflammatory cell state that is associated with lung damage. We leverage the temporal range of fatal outcomes of COVID-19 in relation to the onset of symptoms, which reveals increased macrophage extravasation and increased numbers of mesenchymal cells and fibroblasts concomitant with increased proximity between these cell types as the disease progresses—possibly as a result of attempts to repair the damaged lung tissue. Our data enable us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. We use this landscape to characterize the pathophysiology of the human lung from its macroscopic presentation to the single-cell level, which provides an important basis for understanding COVID-19 and lung pathology in general. Imaging mass cytometry of the human lung reveals the cellular composition and spatial architecture during COVID-19 and other acute injuries, enabling the characterization of lung pathophysiology from structural, immunological and clinical perspectives.

Introduction In colorectal cancer (CRC) there have been many recent advances in immune related biomarkers that are both prognostic and predictive of response to immunotherapy. Microsatellite instability (MSI)/mismatch repair deficiency dMMR is present in 15–20% of CRCs and correlates with increased immunogenic mutations that often augment lymphocyte infiltration into the tumor microenvironment (TME). Additionally, location of tumor infiltrating T cells in two areas of the TME, the tumor center (CT) and invasive margin (IM) has also been shown to be prognostic and predictive of response to immunotherapy. Here we use multiplexed protein and RNA digital spatial profiling to elicit the immune landscape of MSI-MSS characterized CRC tumors. Methods Forty-eight CRC tumors were analyzed for gene expression using the NanoString® nCounter® PanCancer IO 360™ Research Use Only (RUO) Gene Expression Panel and assessed for 48 cell typing and biological signatures, including MMR loss/MSI predictor and the Tumor Inflammation Signature (TIS). A subset of 18 CRC tumors (6 MSI-TIS-hi, 6 MSS-TIS-hi, 6 MSS-TIS-lo) was selected for analysis with the RUO GeoMx™ Digital Spatial Profiler (DSP) using 40 antibodies (human IO protein panel), or 84 RNA probes (human IO RNA panel). Selection of regions of interest (ROIs) in two locations, CT and IM were guided by staining with fluorescent markers (CD45, CD3, pan-CK, DNA). 300–600 µM diameter circle ROIs were selected, and in some cases segmented by pan-CK+/pan-CK-. For 2 immune hot samples contour profiling at the IM into stromal and tumor regions was performed using 1400+ RNA probes with NGS readout. Summary Using whole tissue gene expression analysis, we determined the TIS and IO 360 signature scores for 48 CRC tumors using PanCancer IO 360 assay. 18 tumors within this cohort were selected based on TIS status to further dissect the location-dependent immune contexture of the TME. Protein DSP confirmed loss of dMMR markers (MSH2/MLH1) and identified an increased amount of potentially suppressive macrophages (CD163+PD-L1+) in MSI-TIS-hi versus MSS-TIS-hi tumors. Segmentation of ROIs based on tumor versus stroma (pan-CK±) identified samples with high proportions of tumor-invading TILs. Two MSI-TIS-hi profiled using probes against 1400+ mRNA targets confirmed protein results (CD163 in IM) and identified tumor-related signatures corresponding to the inside of the tumor (Cytokeratins, HER2/ERBB2, MET). Conclusions Here we show the use of novel high-plex spatial profiling to profile location and pathways in the TME of MSI and MSS CRC tumors. These findings elicit unique biology related to the location and signaling of immune cells, which have the potential to unveil targets for therapeutic combinations. Disclosure Information S.E. Church: A. Employment (full or part-time); Significant; NanoString Technologies. J. Reeves: A. Employment (full or part-time); Significant; NanoString Technologies. D.R. Zollinger: A. Employment (full or part-time); Significant; NanoString Technologies. J. McKay-Fleisch: A. Employment (full or part-time); Significant; NanoString Technologies. A.J. Bahrami: A. Employment (full or part-time); Significant; NanoString Technologies. M. Holpert: A. Employment (full or part-time); Significant; NanoString Technologies. A.M. White: A. Employment (full or part-time); Significant; NanoString Technologies. M.D. Bailey: A. Employment (full or part-time); Significant; NanoString Technologies. C.R. Merritt: A. Employment (full or part-time); Significant; NanoString Technologies. M. Hoang: A. Employment (full or part-time); Significant; NanoString Technologies. S. Warren: A. Employment (full or part-time); Significant; NanoString Technologies. J.M. Beechem: A. Employment (full or part-time); Significant; NanoString Technologies.

Pancreatic ductal adenocarcinoma (PDAC) remains a treatment-refractory disease. Characterizing PDAC by mRNA profiling remains particularly challenging. Previously identified bulk expression subtypes were influenced by contaminating stroma and have not yet informed clinical management, whereas single cell RNA-seq (scRNA-seq) of fresh tumors under-represented key cell types. Here, we developed a robust single-nucleus RNA-seq (snRNA-seq) technique for frozen archival PDAC specimens and used it to study both untreated tumors and those that received neoadjuvant chemotherapy and radiotherapy (CRT). Gene expression programs learned across untreated malignant cell and fibroblast profiles uncovered a clinically relevant molecular taxonomy with improved prognostic stratification compared to prior classifications. Moreover, in the increasingly-adopted neoadjuvant treatment context, there was a depletion of classical-like phenotypes in malignant cells in favor of basal-like phenotypes associated with TNF-NFkB and interferon signaling as well as the presence of novel acinar and neuroendocrine classical-like states, which may be more resilient to cytotoxic treatment. Spatially-resolved transcriptomics revealed an association between malignant cells expressing these basal-like programs and higher immune infiltration with increased lymphocytic content, whereas those exhibiting classical-like programs were linked to sparser macrophage-predominant microniches, perhaps pointing to susceptibility to distinct therapeutic strategies. Our refined molecular taxonomy and spatial resolution can help advance precision oncology in PDAC through informative stratification in clinical trials and insights into differential therapeutic targeting leveraging the immune system.

Approximately, 50% of patients with uveal melanoma develop distant metastasis for which no standard therapy is established. In contrast to cutaneous melanoma, the anti-CTLA-4 antibody ipilimumab showed no clinical activity in uveal melanoma. Liver directed therapies improve local control, but fail to show overall survival (OS) benefit. Preclinical experiments demonstrated that radiofrequency ablation (RFA) induced durable responses in combination with anti-CTLA-4. The aim of this phase Ib/II study was to assess safety and efficacy of RFA plus ipilimumab in uveal melanoma. Patients underwent RFA of one liver lesion and subsequently received four courses ipilimumab 0.3, 3 or 10 mg/kg every 3 weeks in a 3 + 3 design. Primary endpoints were safety in terms of dose limiting toxicities per cohort to define the recommended phase II dose (RP2D) in the phase Ib part and confirmed the objective response rate and disease control rate (DCR) of non-RFA lesions in the phase II part. Secondary endpoints were progression-free survival (PFS) and OS. Ipilimumab 10 mg/kg + RFA was initially defined as the RP2D. However, after 19 patients, the study was amended to adjust the RP2D to ipilimumab 3 mg/kg + RFA, because 47% of patients treated with 10 mg/kg had developed grade 3 colitis. In the 3 mg/kg cohort, also 19 patients have been treated. Immunotherapy-related grade >= 3 adverse events were observed in 53% of patients in the 10 mg/kg cohort versus 32% in the 3 mg/kg cohort. No confirmed objective responses were observed; the confirmed DCR was 5% in the 10 mg/kg cohort and 11% in the 3 mg/kg cohort. Median PFS was 3 months and comparable for both cohorts, median OS was 14.2 months for the 10 mg/kg cohort versus 9.7 months for the 3 mg/kg cohort. Combining RFA with ipilimumab 3 mg/kg was well tolerated, but showed very limited clinical activity in uveal melanoma.

BackgroundThe profound disparity in response to immune checkpoint blockade (ICB) by cutaneous melanoma (CM) and uveal melanoma (UM) patients is not well understood. Therefore, we characterized metastases of CM and UM from the same metastatic site (liver), in order to dissect the potential underlying mechanism in differential response on ICB.MethodsTumor liver samples from CM (n=38) and UM (n=28) patients were analyzed at the genomic (whole exome sequencing), transcriptional (RNA sequencing) and protein (immunohistochemistry and GeoMx Digital Spatial Profiling) level.ResultsComparison of CM and UM metastases from the same metastatic site revealed that, although originating from the same melanocyte lineage, CM and UM differed in somatic mutation profile, copy number profile, tumor mutational burden (TMB) and consequently predicted neoantigens. A higher melanin content and higher expression of the melanoma differentiation antigen MelanA was observed in liver metastases of UM patients. No difference in B2M and human leukocyte antigen-DR (HLA-DR) expression was observed. A higher expression of programmed cell death ligand 1 (PD-L1) was found in CM compared with UM liver metastases, although the majority of CM and UM liver metastases lacked PD-L1 expression. There was no difference in the extent of immune infiltration observed between CM and UM metastases, with the exception of a higher expression of CD163 (pConclusionsWhile TMB was different between CM and UM metastases, tumor immune infiltration was similar. The greater dependency on PD-L1 as an immune checkpoint in CM and the identification of higher exhaustion ratios in UM may both serve as explanations for the difference in response to ICB. Consequently, in order to improve current treatment for metastatic UM, reversal of T cell exhaustion beyond programmed cell death 1 blockade should be considered.

610 Background: Pancreatic ductal adenocarcinoma (PDAC) is highly lethal and resistance to chemotherapy and radiotherapy is a major obstacle to improving clinical outcomes. Hence, there is an urgent need to elucidate the gene expression programs, spatial context, and interactions among different cell types in residual disease after neoadjuvant treatment. Methods: We optimized and applied single-nucleus RNA-seq (snRNA-seq) to 43 frozen primary PDAC tumors. Eighteen were treatment-naïve, 14 received FOLFIRINOX followed by radiotherapy with 5-FU or capecitabine (CRT), and 5 were subjected to CRT combined with losartan on protocol (CRTL). We performed unsupervised clustering of single nucleus profiles and then annotated and quantified cell subsets. Malignant and fibroblast gene expression programs were identified by consensus non-negative matrix factorization (cNMF). We mapped our cell type signatures and expression programs onto the tumor architecture using whole-transcriptome digital spatial profiling (DSP) to uncover distinct multicellular spatial neighborhoods and intercellular interactions that compose PDAC and are remodeled by neoadjuvant treatment. Results: Consistent with treatment effect, the proportion of malignant cells was significantly lower in tumors treated with neoadjuvant therapy. Within the immune compartment, CRTL was associated with a higher fraction of CD8+ T cells and Tregs compared to untreated and CRT tumors. Differential expression analysis of CD8+ T cells revealed greater effector function (e.g., IL2, CCL4, CCL5) and reduced quiescence/dysfunction markers (e.g., TIGIT, TCF7, KLF2, LEF1) associated with CRTL. We discovered expression programs across malignant and fibroblast profiles that formed a refined molecular taxonomy, including a novel neuronal-like malignant program enriched in the neoadjuvant groups and associated with the worst prognosis in independent cohorts. Ex vivo treatment of organoids derived from an untreated PDAC with FOLFIRINOX chemotherapy and radiotherapy recapitulated enrichment of the neuronal-like program. Whole-transcriptome DSP revealed three distinct multicellular neighborhoods: classical, squamoid-basaloid, and treatment-enriched. The observed enrichment in post-treatment residual disease of multiple spatially-defined receptor-ligand interactions and a neighborhood featuring colocalization of the neuronal-like malignant program, neurotropic CAF program, and CD8+ T cells may open new opportunities for therapeutic targeting in PDAC. Conclusions: Our work provides a high-resolution molecular framework for understanding the inter- and intra-tumoral heterogeneity of pancreatic cancer, spatial organization into discrete multicellular communities, and treatment-associated reprogramming as a blueprint for exploring novel therapeutic strategies tailored to residual disease.

BackgroundMerkel cell carcinoma (MCC) is a rare and aggressive skin cancer with neuroendocrine features, and it is associated with elevated mortality. The pathogenesis is associated with presence of clonally integrated Merkel cell polyomavirus (MCPyV) or ultraviolet light (UV) exposure.1 The MCPyV causes up to 80% of MCC tumors in North America and Europe.2–4 Recently immunotherapy is having good results,5 the phase 2 trial JAVELIN Merkel 200 indicated that treatment with Avelumab (PDL1 inhibitor) in patients with metastatic MCC pre-treated have a meaningful long-term survival outcomes respect chemotherapy. Moreover, ORRs were highest in patients with high TMB that were also MCPyV−, PD-L1+ or had a greater CD8+ T cell density at the invasive margin.6 In this study, we investigated the biological signatures in patients with MCPyV or not.MethodsFrom April 2011 to June 2018, we collected retrospectively 50 FFPE (Formalin-Fixed Paraffin-Embed) from 37 patients with metastatic MCC and 13 tissues from a secondary metastatic site. All patients have appropriately signed informed consent. We performed an immunohistochemistry assays (IHC) for MCPyV and PDL1. In addition, through the NanoString GeoMx DSP (Digital Spatial Profiling), we analysed 11 patients (6 MCPyV+; 5 MCPyV-) with cutaneous metastasis using a 44-plex antibody cocktail. For each slide we selected three different areas: Intratumoral, extratumoral and tumour border, in each area we selected CD4+ and CD8+ cells in 4 different ROIs (Region of Interest). Statistical analysis was performed via Bonferroni correction, P< 0.05 was considered statistically significant for median stratification.ResultsThe DSP analysis showed that the tumour border cells have an overexpression of IDO respect intratumoral area (adj. pConclusionsIn this retrospective study, our preliminary data shown that tumour edge have an important role in the modulations of immune infiltrate and patients with Merkel cell polyomavirus could have a different pathway of immunosuppression compared to patients with non-virus related etiology. Further investigations are needed to get additional information.AcknowledgementsThe study was supported by the Institutional Project ”Ricerca Corrente” of Istituto Nazionale Tumori IRCCS Fondazione ”G. Pascale” of Napoli, Italy.ReferencesKaae J, Hansen AV, Biggar RJ, et al. Merkel cell carcinoma: incidence, mortality, and risk of other cancers. J Natl Cancer Inst 2010 June 2;102(11):793–801.Feng H, Shuda M, Chang Y, et al. Clonal integration of a polyomavirus in human Merkel cell carcinoma. Science 2008 February 22;319(5866):1096–100.Garneski KM, Warcola AH, Feng Q, et al. Merkel cell polyomavirus is more frequently present in North American than Australian Merkel cell carcinoma tumors. J Invest Dermatol 2009 January;129(1):246–8.Goh G, Walradt T, Markarov V, et al. Mutational landscape of MCPyV-positive and MCPyV-negative Merkel cell carcinomas with implications for immunotherapy. Oncotarget 2016 January 19;7(3):3403–15.Bichakjian CK, Olencki T, Aasi SZ, et al. Merkel cell carcinoma, version 1.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2018 June;16(6):742–774.D’Angelo SP, Bhatia S, Brohl AS, et al. Avelumab in patients with previously treated metastatic Merkel cell carcinoma: long-term data and biomarker analyses from the single-arm phase 2 JAVELIN Merkel 200 trial. J Immunother Cancer 2020 May;8(1):e000674.Ethics ApprovalThe study was approved by internal ethics board of the Istituto Nazionale Tumori IRCCS Fondazione ”G. Pascale” of Napoli Italy, approval number of registry 33/17 OSS.ConsentWritten informed consent was obtained from the patient for publication of this abstract and any accompanying images. A copy of the written consent is available for review by the Editor of this journal.

Immune responses to cancer are highly variable, with mismatch repair-deficient (MMRd) tumors exhibiting more anti-tumor immunity than mismatch repair-proficient (MMRp) tumors. To understand the rules governing these varied responses, we transcriptionally profiled 371,223 cells from colorectal tumors and adjacent normal tissues of 28 MMRp and 34 MMRd individuals. Analysis of 88 cell subsets and their 204 associated gene expression programs revealed extensive transcriptional and spatial remodeling across tumors. To discover hubs of interacting malignant and immune cells, we identified expression programs in different cell types that co-varied across tumors from affected individuals and used spatial profiling to localize coordinated programs. We discovered a myeloid cell-attracting hub at the tumor-luminal interface associated with tissue damage and an MMRd-enriched immune hub within the tumor, with activated T cells together with malignant and myeloid cells expressing T cell-attracting chemokines. By identifying interacting cellular programs, we reveal the logic underlying spatially organized immune-malignant cell networks.

A molecular classification of pancreatic ductal adenocarcinoma (PDAC) that informs clinical management remains elusive. Previously identified bulk expression subtypes in the untreated setting were influenced by contaminating stroma whereas single cell RNA-seq (scRNA-seq) of fresh tumors under-represented key cell types. Two consensus subtypes have arisen from these prior efforts: (1) classical-like, and (2) basal-like. Basal-like tumors were associated with worse survival in the metastatic setting but attempts to refine this binary classification have failed to further stratify patient survival. Here, we developed a robust single-nucleus RNA-seq (snRNA-seq) technique for banked frozen PDAC specimens and studied a cohort of untreated resected primary tumors (n ~ 20). Gene expression programs learned across malignant cell and cancer-associated fibroblast (CAF) profiles uncovered a clinically-relevant molecular taxonomy with improved prognostic stratification compared to prior classifications. Digital spatial profiling revealed an association between malignant cells expressing basal-like programs and greater immune infiltration with relatively fewer macrophages, whereas those exhibiting classical-like programs were linked to inflammatory CAFs and macrophage-predominant microniches. Recent clinical trials have supported the increasing adoption of neoadjuvant therapy to aggressively address the risk of micro-metastatic spread and to circumvent concerns of treatment tolerance in the postoperative setting. There is an urgent need to understand how preoperative treatment impacts residual tumor cells and their interactions with other cell types in the tumor microenvironment to identify additional therapeutic vulnerabilities that can be exploited. Towards this end, we performed snRNA-seq on an unmatched cohort of neoadjuvant-treated resected primary tumors (n ~ 25) with most cases involving FOLFIRINOX chemotherapy followed by chemoradiation. Remarkably, the quality of single-nucleus mRNA profiles was comparable between heavily pre-treated and untreated specimens. We identified differentially expressed genes between treated and untreated samples to infer cell-type specific reprogramming in the residual tumor. This analysis revealed that in the neoadjuvant treatment context, there was lower expression of classical-like phenotypes in malignant cells in favor of basal-like phenotypes associated with TNF-NFkB and interferon signaling as well as the presence of novel acinar and neuroendocrine classical-like states. Our refined molecular taxonomy and spatial resolution may help advance precision oncology in PDAC through informative stratification in clinical trials and insights into compartment-specific therapies. Citation Format: William L. Hwang, Karthik A. Jagadeesh, Jimmy A. Guo, Hannah I. Hoffman, Payman Yadollahpour, Jason Reeves, Eugene Drokhlyansky, Nicholas Van Wittenberghe, Samouil Farhi, Denis Schapiro, George Eng, Jason M. Schenkel, William A. Freed-Pastor, Orr Ashenberg, Clifton Rodrigues, Domenic Abbondanza, Toni Delorey, Devan Phillips, Jorge Roldan, Debora Ciprani, Marina Kern, Jaimie L. Barth, Daniel R. Zollinger, Kit Fuhrman, Robin Fropf, Joseph Beechem, Colin Weekes, Cristina R. Ferrone, Jennifer Y. Wo, Theodore S. Hong, Orit Rozenblatt-Rosen, Andrew J. Aguirre, Mari Mino-Kenudson, Carlos Fernandez-del- Castillo, Andrew S. Liss, David T. Ting, Tyler Jacks, Aviv Regev. Multi-compartment reprogramming and spatially-resolved interactions in frozen pancreatic cancer with and without neoadjuvant chemotherapy and radiotherapy at single-cell resolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 94.

Muscle-invasive bladder cancer (MIBC) is an aggressive disease with limited therapeutic options. PD-1 pathway targeting immunotherapies have been approved to treat advanced bladder cancer, but most patients exhibit primary resistance, suggesting that immune evasion mechanisms exist. The PPARγ pathway has been identified as a potential therapeutic target in MIBC that is associated with reduced CD8+ T-cell infiltration and increased resistance to immunotherapies. We comprehensively profiled the tumor microenvironment (TME) in formalin-fixed, paraffin-embedded (FFPE) tissues from a cohort of PPARγhigh (n=13) and PPRARγlow (n=12) MIBC, integrating bulk gene expression, targeted mutation sequencing, immunohistochemistry and multiplex spatial profiling of RNA and protein expression on the GeoMx™ Digital Spatial Profiling (DSP) platform. Molecular subtyping was consistent between traditional methods and GeoMx profiling, and, in this cohort, we observed little evidence of spatial heterogeneity in tumor subtyping. The previously characterized T-cell exclusion phenotype of PPARγhigh MIBC was recapitulated on the GeoMx platform and was further extended to show that this is a general phenomenon across immune cell types, supporting potential combination of PPARγ inhibition with ICIs. Furthermore, we found that while immune cells were excluded from PPARγhigh tumors, the stromal compartment from these tumors was not significantly different than those PPARγlow tumors. By preserving spatial relationships during the GeoMx analysis, we also identify a novel association between lower immune cell expression in the tumors and higher expression of β-catenin in the stroma, and differential expression of other WNT pathway members associated with PPARγ activity.One Sentence SummaryA new method for capturing tumor-immune signaling in FFPE tissues explores how the PPARG signaling axis is associated with immune cell exclusion in bladder cancer.

Molecular subtyping of pancreatic ductal adenocarcinoma (PDAC) remains in its nascent stages and does not currently inform clinical management or therapeutic development. Previously identified bulk expression subtypes in the untreated setting were influenced by contaminating stroma whereas single cell RNA-seq (scRNA-seq) of fresh tumors under-represented key cell types. Two consensus subtypes have arisen from these prior efforts: (1) classical-pancreatic, encompassing a spectrum of pancreatic lineage precursors, and (2) basal-like/squamous/quasi-mesenchymal, characterized by loss of endodermal identity and aberrations in chromatin modifiers. Basal-like tumors were associated with poorer responses to chemotherapy and worse survival in the metastatic setting but attempts to refine this binary classification have failed to further stratify patient survival. Recent clinical trials have supported the increasing adoption of neoadjuvant therapy to aggressively address the risk of micro-metastatic spread and to circumvent concerns of treatment tolerance in the postoperative setting. There is an urgent need to understand how preoperative treatment reprograms residual tumor cells to identify additional therapeutic vulnerabilities that can be exploited in combination with neoadjuvant CRT. Here, we developed a robust single-nucleus RNA-seq (snRNA-seq) technique for frozen archival PDAC specimens and used it to study both untreated tumors (n = 15) and those that received neoadjuvant CRT (n = 11). Gene expression programs learned across malignant cell and fibroblast profiles uncovered a clinically relevant molecular taxonomy with improved prognostic stratification (median survival: 11.2 months in highest risk group to 44.7 months in lowest risk group) compared to prior classifications. Moreover, in the neoadjuvant treatment context, there was lower expression of classical-like phenotypes in malignant cells in favor of basal-like phenotypes associated with TNF-NFkB and interferon signaling as well as the presence of novel acinar and neuroendocrine classical-like states, which may be more resilient to cytotoxic treatment. These results suggest that differentiated endodermal phenotypes are only prevalent enough to be detected under treatment selection pressure and when observed in treatment-naïve bulk studies, may reflect normal cell contamination. Spatially-resolved transcriptomics revealed an association between malignant cells expressing basal-like programs and higher immune infiltration with increased lymphocytic content, whereas those exhibiting classical-like programs were linked to sparser macrophage-predominant microniches, perhaps pointing to distinct therapeutic susceptibilities. Our refined molecular taxonomy and spatial resolution may help advance precision oncology in PDAC through informative stratification in clinical trials and insights into differential therapeutic targeting leveraging the immune system. Citation Format: William L. Hwang, Karthik A. Jagadeesh, Jimmy A. Guo, Hannah I. Hoffman, Eugene Drokhlyansky, Nicholas Van Wittenberghe, Samouil Farhi, Denis Schapiro, Jason Reeves, Daniel R. Zollinger, George Eng, Jason M. Schenkel, William A. Freed-Pastor, Clifton Rodrigues, Domenic Abbondanza, Debora Ciprani, Jennifer Y. Wo, Theodore S. Hong, Andrew J. Aguirre, Orit Rozenblatt-Rosen, Mari Mino-Kenudson, Carlos Fernandez-del Castillo, Andrew S. Liss, Tyler E. Jacks, Aviv Regev. Single-nucleus and spatial transcriptomics of archival pancreatic ductal adenocarcinoma reveals multi-compartment reprogramming after neoadjuvant treatment [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2020 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2020;80(22 Suppl):Abstract nr PR-007.

The GeoMx Digital Spatial Profiling (DSP) Platform enables robust detection of high-plex protein and RNA expression from user-defined sections within FFPE samples. As the number of targets detected within such tissues increases, it becomes important to apply systems biology strategies in order to better interpret the complex biology of the tumor microenvironment. In this study, we investigated the expression profiles of more than 1600 genes by utilizing more than 10,000 DSP-specific in situ hybridized (ISH) probes on FFPE samples. The bioinformatics tools we have development are enabling us to move beyond single-gene profiling to a better understanding of pathway-based expression. Our study includes colorectal cancer patient samples from which we have matching bulk RNA sequencing and NanoString analysis to compare. The panel of genes profiled have a strong focus around capturing biological signaling along canonical signaling pathways and cell-intrinsic signaling from immune cells and other cell types. We demonstrate the ability to leverage foundational pathway interrogation tools, including Reactome, with the data to capture spatially-resolved pathway interactions and signaling within FFPE tissues. As we look towards the future of the GeoMx platform and high-plex RNA profiling of tissue samples, these experiments highlight not only the need but the capacity for this platform to derive deep understanding of the biology within and across a single slide of tissue. These experiments are being used to drive development of the software features within the GeoMx ecosystem, which will provide further support for pathway-level exploration of expression when working with highly multiplexed reagents in future platform offerings. GeoMx™ DSP technology is for Research Use Only and not for use in diagnostic procedures. Citation Format: Tressa R. Hood, Jason Reeves, Zach Norgaard, Margaret Hoang, Sarah Warren, Erin Piazza, Rich Boykin, Joseph Beechem. Pathway enrichment analysis of gene expression data from formalin-fixed paraffin embedded (FFPE) samples using the GeoMx™ DSP Platform [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 840.

Introduction Renal Cell Carcinoma (RCC) is the deadliest urological malignancy. Profiling its complex microenvironment (TME) in situ is crucial to understand the mechanisms of progression and immune evasion that lead to metastasis and death. NanoString® GeoMx™ Digitial Spatial Profiling (DSP) platform facilitates these studies by enabling highly multiplexed, spatially resolved characterisation of proteins and RNA from FFPE tissue. DSP visualises and quantifies targets from areas of interest (AOI) using oligonucleotide-conjugated antibodies. Here, DSP is combined with automated image analysis (IA). When coupled with multiplexed immunofluorescence (IF), IA is able to automatically segment tumor from stroma and profile marker co-expression at single cell level. We present the advantages of using a combinatorial strategy, applied to clear cell RCC (ccRCC) tissue sections, in order to predict patient outcome. Methods 165 patients, grouped into 11 tumour microarray (TMA) slides were labelled with multiplex IF and scanned with a Zeiss Axioscan.z1. Scans were imported into Definiens Tissue Studio® IA software. Multiple TMA cores were sampled from matched non-cancerous kidney, primary, and venous thrombus (VTT) ccRCC. Tumor regions (labelled with Pan-cadherin and CA9) and stroma were segmented prior to automated immune quantification, where CD3, CD163, PD-1 and PD-L1 antibodies were used to profile the immune contexture. DSP was performed on the corresponding serial sections, where a 60-plex antibody panel was applied to each TMA core. Statistical analysis was performed on R Studio, where cox-proportional hazard ratios and Kaplan-Meier curves were used to correlate marker densities to risk of metastasis and cancer-related death. Results Both IA and DSP associated M2 macrophages (CD163) and T cells (CD3) to increased risk of metastasis and poor survival. IA demonstrated that tumor/stroma segmentation and single cell marker co-registration complements DSP analysis by allowing a more detailed profiling of the TME. In particular, a high density of PD-L1 positive tumor cells and PD-1 positive T-cells were correlated to poor survival in VTT and non-cancerous cores, respectively. DSP's high-plex ability is useful to investigate the relationship among the proteins of interest. It confirmed the T-cell exhaustion marker TIM-3 as a poor prognostic factor, thus demonstrating that quantifying only CD3 positive T cells may be insufficient to predict a precise prognosis. Conclusions This data demonstrates that both co-registration of cellular protein expression and highly plexed analysis can add value to the prediction of patient outcome and the risk of metastasis. We further report the prognostic significance of analysing the molecular signature of the immune contexture in both ccRCC tumorous and its adjacent non-cancerous tissue. Citation Format: Raffaele De Filippis, Sarah Warren, Youngmi Kim, Andrew White, Jason Reeves, Grant D. Stewart, David J. Harrison, Joe M. Beechem, Peter D. Caie. Combining automated image analysis and digital spatial profiling to investigate prognostic immune signatures in clear cell renal cell carcinoma [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 2670.