Clinically relevant orthotopic pancreatic cancer models for adoptive T cell transfer therapy.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive tumor. Prognosis is poor and survival is low in patients diagnosed with this disease, with a survival rate of ~12% at 5 years. Immunotherapy, including adoptive T cell transfer therapy, has not impacted the outcomes in patients with PDAC, due in part to the hostile tumor microenvironment (TME) which limits T cell trafficking and persistence. We posit that murine models serve as useful tools to study the fate of T cell therapy. Currently, genetically engineered mouse models (GEMMs) for PDAC are considered a "gold-standard" as they recapitulate many aspects of human disease. However, these models have limitations, including marked tumor variability across individual mice and the cost of colony maintenance. METHODS: Using flow cytometry and immunohistochemistry, we characterized the immunological features and trafficking patterns of adoptively transferred T cells in orthotopic PDAC (C57BL/6) models using two mouse cell lines, KPC-Luc and MT-5, isolated from C57BL/6 KPC-GEMM (KrasLSL-G12D/+p53-/- and KrasLSL-G12D/+p53LSL-R172H/+, respectively). RESULTS: The MT-5 orthotopic model best recapitulates the cellular and stromal features of the TME in the PDAC GEMM. In contrast, far more host immune cells infiltrate the KPC-Luc tumors, which have less stroma, although CD4+ and CD8+ T cells were similarly detected in the MT-5 tumors compared with KPC-GEMM in mice. Interestingly, we found that chimeric antigen receptor (CAR) T cells redirected to recognize mesothelin on these tumors that signal via CD3ζ and 41BB (Meso-41BBζ-CAR T cells) infiltrated the tumors of mice bearing stroma-devoid KPC-Luc orthotopic tumors, but not MT-5 tumors. CONCLUSIONS: Our data establish for the first time a reproducible and realistic clinical system useful for modeling stroma-rich and stroma-devoid PDAC tumors. These models shall serve an indepth study of how to overcome barriers that limit antitumor activity of adoptively transferred T cells.

Coordinated single-cell tumor microenvironment dynamics reinforce pancreatic cancer subtype.

Bulk analyses of pancreatic ductal adenocarcinoma (PDAC) samples are complicated by the tumor microenvironment (TME), i.e. signals from fibroblasts, endocrine, exocrine, and immune cells. Despite this, we and others have established tumor and stroma subtypes with prognostic significance. However, understanding of underlying signals driving distinct immune and stromal landscapes is still incomplete. Here we integrate 92 single cell RNA-seq samples from seven independent studies to build a reproducible PDAC atlas with a focus on tumor-TME interdependence. Patients with activated stroma are synonymous with higher myofibroblastic and immunogenic fibroblasts, and furthermore show increased M2-like macrophages and regulatory T-cells. Contrastingly, patients with 'normal' stroma show M1-like recruitment, elevated effector and exhausted T-cells. To aid interoperability of future studies, we provide a pretrained cell type classifier and an atlas of subtype-based signaling factors that we also validate in mouse data. Ultimately, this work leverages the heterogeneity among single-cell studies to create a comprehensive view of the orchestra of signaling interactions governing PDAC.

FaStaNMF: a Fast and Stable Non-negative Matrix Factorization for Gene Expression.

Gene expression analysis of samples with mixed cell types only provides limited insight to the characteristics of specific tissues. In silico deconvolution can be applied to extract cell type specific expression, thus avoiding prohibitively expensive techniques such as cell sorting or single-cell sequencing. Non-negative matrix factorization (NMF) is a deconvolution method shown to be useful for gene expression data, in part due to its constraint of non-negativity. Unlike other methods, NMF provides the capability to deconvolve without prior knowledge of the components of the model. However, NMF is not guaranteed to provide a globally unique solution. In this work, we present FaStaNMF, a method that balances achieving global stability of the NMF results, which is essential for inter-experiment and inter-lab reproducibility, with accuracy and speed. Results: FaStaNMF was applied to four datasets with known ground truth, created based on publicly available data or by using our simulation infrastructure, RNAGinesis. We assessed FaStaNMF on three criteria - speed, accuracy, and stability, and it favorably compared to the standard approach of achieving reproduceable results with NMF. We expect that FaStaNMF can be applied successfully to a wide array of biological data, such as different tumor/immune and other disease microenvironments.

Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease for which potent therapies have limited efficacy. Several studies have described the transcriptomic landscape of PDAC tumors to provide insight into potentially actionable gene expression signatures to improve patient outcomes. Despite centralization efforts from multiple organizations and increased transparency requirements from funding agencies and publishers, analysis of public PDAC data remains difficult. Bioinformatic pitfalls litter public transcriptomic data, such as subtle inclusion of low-purity and non-adenocarcinoma cases. These pitfalls can introduce non-specificity to gene signatures without appropriate data curation, which can negatively impact findings. To reduce barriers to analysis, we have created pdacR ( http://pdacR.bmi.stonybrook.edu , github.com/rmoffitt/pdacR), an open-source software package and web-tool with annotated datasets from landmark studies and an interface for user-friendly analysis in clustering, differential expression, survival, and dimensionality reduction. Using this tool, we present a multi-dataset analysis of PDAC transcriptomics that confirms the basal-like/classical model over alternatives.

PLCOjs, a FAIR GWAS web SDK for the NCI Prostate, Lung, Colorectal and Ovarian Cancer Genetic Atlas project.

MOTIVATION: The Division of Cancer Epidemiology and Genetics (DCEG) and the Division of Cancer Prevention (DCP) at the National Cancer Institute (NCI) have recently generated genome-wide association study (GWAS) data for multiple traits in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Genomic Atlas project. The GWAS included 110 000 participants. The dissemination of the genetic association data through a data portal called GWAS Explorer, in a manner that addresses the modern expectations of FAIR reusability by data scientists and engineers, is the main motivation for the development of the open-source JavaScript software development kit (SDK) reported here. RESULTS: The PLCO GWAS Explorer resource relies on a public stateless HTTP application programming interface (API) deployed as the sole backend service for both the landing page's web application and third-party analytical workflows. The core PLCOjs SDK is mapped to each of the API methods, and also to each of the reference graphic visualizations in the GWAS Explorer. A few additional visualization methods extend it. As is the norm with web SDKs, no download or installation is needed and modularization supports targeted code injection for web applications, reactive notebooks (Observable) and node-based web services. AVAILABILITY AND IMPLEMENTATION: code at https://github.com/episphere/plco; project page at https://episphere.github.io/plco.

Spatial Characterization of Tumor-Infiltrating Lymphocytes and Breast Cancer Progression.

Tumor-infiltrating lymphocytes (TILs) have been established as a robust prognostic biomarker in breast cancer, with emerging utility in predicting treatment response in the adjuvant and neoadjuvant settings. In this study, the role of TILs in predicting overall survival and progression-free interval was evaluated in two independent cohorts of breast cancer from the Cancer Genome Atlas (TCGA BRCA) and the Carolina Breast Cancer Study (UNC CBCS). We utilized machine learning and computer vision algorithms to characterize TIL infiltrates in digital whole-slide images (WSIs) of breast cancer stained with hematoxylin and eosin (H&E). Multiple parameters were used to characterize the global abundance and spatial features of TIL infiltrates. Univariate and multivariate analyses show that large aggregates of peritumoral and intratumoral TILs (forests) were associated with longer survival, whereas the absence of intratumoral TILs (deserts) is associated with increased risk of recurrence. Patients with two or more high-risk spatial features were associated with significantly shorter progression-free interval (PFI). This study demonstrates the practical utility of Pathomics in evaluating the clinical significance of the abundance and spatial patterns of distribution of TIL infiltrates as important biomarkers in breast cancer.

Use of Hydroxychloroquine, Remdesivir, and Dexamethasone Among Adults Hospitalized With COVID-19 in the United States : A Retrospective Cohort Study.

BACKGROUND: Relatively little is known about the use patterns of potential pharmacologic treatments of COVID-19 in the United States. OBJECTIVE: To use the National COVID Cohort Collaborative (N3C), a large, multicenter, longitudinal cohort, to characterize the use of hydroxychloroquine, remdesivir, and dexamethasone, overall as well as across individuals, health systems, and time. DESIGN: Retrospective cohort study. SETTING: 43 health systems in the United States. PARTICIPANTS: 137 870 adults hospitalized with COVID-19 between 1 February 2020 and 28 February 2021. MEASUREMENTS: Inpatient use of hydroxychloroquine, remdesivir, or dexamethasone. RESULTS: Among 137 870 persons hospitalized with confirmed or suspected COVID-19, 8754 (6.3%) received hydroxychloroquine, 29 272 (21.2%) remdesivir, and 53 909 (39.1%) dexamethasone during the study period. Since the release of results from the RECOVERY (Randomised Evaluation of COVID-19 Therapy) trial in mid-June, approximately 78% to 84% of people who have had invasive mechanical ventilation have received dexamethasone or other glucocorticoids. The use of hydroxychloroquine increased during March 2020, peaking at 42%, and started declining by April 2020. By contrast, remdesivir and dexamethasone use gradually increased over the study period. Dexamethasone and remdesivir use varied substantially across health centers (intraclass correlation coefficient, 14.2% for dexamethasone and 84.6% for remdesivir). LIMITATION: Because most N3C data contributors are academic medical centers, findings may not reflect the experience of community hospitals. CONCLUSION: Dexamethasone, an evidence-based treatment of COVID-19, may be underused among persons who are mechanically ventilated. The use of remdesivir and dexamethasone varied across health systems, suggesting variation in patient case mix, drug access, treatment protocols, and quality of care. PRIMARY FUNDING SOURCE: National Center for Advancing Translational Sciences; National Heart, Lung, and Blood Institute; and National Institute on Aging.

A distinct microglial subset at the tumor-stroma interface of glioma.

The characterization of the tumor microenvironment (TME) in high grade gliomas (HGG) has generated significant interest in an effort to understand how neoplastic lesions in the central nervous system (CNS) are supported and to devise novel therapeutic targets. The TME of the CNS contains unique and specialized cells, including the resident myeloid cells, microglia. Myeloid involvement in HGG, such as glioblastoma, is associated with poor outcomes. Glioma-associated microglia and infiltrating monocytes/macrophages (GAM) accumulate within the neoplastic lesion where they facilitate tumor growth and drive immunosuppression. However, it has been difficult to differentiate whether microglia and macrophages have similar or distinct roles in pathology, and if the spatial organization of these cells informs outcomes. Here, we characterize the tumor-stroma border and identify peritumoral GAM (PGAM) as a unique subpopulation of GAM. Using data mining and analyses of samples derived from both murine and human sources we show that PGAM exhibit a pro-inflammatory and chemotactic phenotype that is associated with peripheral monocyte recruitment, and decreased overall survival. PGAM act as a unique subset of GAM at the tumor-stroma interface. We define a novel gene signature to identify these cells and suggest that PGAM constitute a cellular target of the TME.

Reciprocal regulation of pancreatic ductal adenocarcinoma growth and molecular subtype by HNF4α and SIX1/4.

OBJECTIVE: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a 5-year survival of less than 5%. Transcriptomic analysis has identified two clinically relevant molecular subtypes of PDAC: classical and basal-like. The classical subtype is characterised by a more favourable prognosis and better response to chemotherapy than the basal-like subtype. The classical subtype also expresses higher levels of lineage specifiers that regulate endodermal differentiation, including the nuclear receptor hepatocyte nuclear factor 4 α (HNF4α). The objective of this study is to evaluate the role of HNF4α, SIX4 and SIX1 in regulating the growth and molecular subtype of PDAC. DESIGN: We manipulate the expression of HNF4α, SIX4 and SIX1 in multiple in vitro and in vivo PDAC models. We determine the consequences of manipulating these genes on PDAC growth, differentiation and molecular subtype using functional assays, gene expression analysis and cross-species comparisons with human datasets. RESULTS: We show that HNF4α restrains tumour growth and drives tumour cells toward an epithelial identity. Gene expression analysis of murine models and human tumours shows that HNF4α activates expression of genes associated with the classical subtype. HNF4α also directly represses SIX4 and SIX1, two mesodermal/neuronal lineage specifiers expressed in the basal-like subtype. Finally, SIX4 and SIX1 drive proliferation and regulate differentiation in HNF4α-negative PDAC. CONCLUSION: Our data show that HNF4α regulates the growth and molecular subtype of PDAC by multiple mechanisms, including activation of the classical gene expression programme and repression of SIX4 and SIX1, which may represent novel dependencies of the basal-like subtype.

Cholesterol Pathway Inhibition Induces TGF-ß Signaling to Promote Basal Differentiation in Pancreatic Cancer.

Oncogenic transformation alters lipid metabolism to sustain tumor growth. We define a mechanism by which cholesterol metabolism controls the development and differentiation of pancreatic ductal adenocarcinoma (PDAC). Disruption of distal cholesterol biosynthesis by conditional inactivation of the rate-limiting enzyme Nsdhl or treatment with cholesterol-lowering statins switches glandular pancreatic carcinomas to a basal (mesenchymal) phenotype in mouse models driven by KrasG12D expression and homozygous Trp53 loss. Consistently, PDACs in patients receiving statins show enhanced mesenchymal features. Mechanistically, statins and NSDHL loss induce SREBP1 activation, which promotes the expression of Tgfb1, enabling epithelial-mesenchymal transition. Evidence from patient samples in this study suggests that activation of transforming growth factor ß signaling and epithelial-mesenchymal transition by cholesterol-lowering statins may promote the basal type of PDAC, conferring poor outcomes in patients.

Correction: Direct therapeutic targeting of immune checkpoint PD-1 in pancreatic cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer.

Quantitative assessment of spatial relations between tumor and tumor-infiltrating lymphocytes (TIL) is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and evaluated convolutional neural network analysis pipelines to generate combined maps of cancer regions and TILs in routine diagnostic breast cancer whole slide tissue images. The combined maps provide insight about the structural patterns and spatial distribution of lymphocytic infiltrates and facilitate improved quantification of TILs. Both tumor and TIL analyses were evaluated by using three convolutional neural network networks (34-layer ResNet, 16-layer VGG, and Inception v4); the results compared favorably with those obtained by using the best published methods. We have produced open-source tools and a public data set consisting of tumor/TIL maps for 1090 invasive breast cancer images from The Cancer Genome Atlas. The maps can be downloaded for further downstream analyses.

Irreversible JNK1-JUN inhibition by JNK-IN-8 sensitizes pancreatic cancer to 5-FU/FOLFOX chemotherapy.

Over 55,000 people in the United States are diagnosed with pancreatic ductal adenocarcinoma (PDAC) yearly, and fewer than 20% of these patients survive a year beyond diagnosis. Chemotherapies are considered or used in nearly every PDAC case, but there is limited understanding of the complex signaling responses underlying resistance to these common treatments. Here, we take an unbiased approach to study protein kinase network changes following chemotherapies in patient-derived xenograft (PDX) models of PDAC to facilitate design of rational drug combinations. Proteomics profiling following chemotherapy regimens reveals that activation of JNK-JUN signaling occurs after 5-fluorouracil plus leucovorin (5-FU + LEU) and FOLFOX (5-FU + LEU plus oxaliplatin [OX]), but not after OX alone or gemcitabine. Cell and tumor growth assays with the irreversible inhibitor JNK-IN-8 and genetic manipulations demonstrate that JNK and JUN each contribute to chemoresistance and cancer cell survival after FOLFOX. Active JNK1 and JUN are specifically implicated in these effects, and synergy with JNK-IN-8 is linked to FOLFOX-mediated JUN activation, cell cycle dysregulation, and DNA damage response. This study highlights the potential for JNK-IN-8 as a biological tool and potential combination therapy with FOLFOX in PDAC and reinforces the need to tailor treatment to functional characteristics of individual tumors.

Comparative single-cell RNA sequencing (scRNA-seq) reveals liver metastasis-specific targets in a patient with small intestinal neuroendocrine cancer.

Genomic analysis of a patient's tumor is the cornerstone of precision oncology, but it does not address whether metastases should be treated differently. Here we tested whether comparative single-cell RNA sequencing (scRNA-seq) of a primary small intestinal neuroendocrine tumor to a matched liver metastasis could guide the treatment of a patient's metastatic disease. Following surgery, the patient was put on maintenance treatment with a somatostatin analog. However, the scRNA-seq analysis revealed that the neuroendocrine epithelial cells in the liver metastasis were less differentiated and expressed relatively little SSTR2, the predominant somatostatin receptor. There were also differences in the tumor microenvironments. RNA expression of vascular endothelial growth factors was higher in the primary tumor cells, reflected by an increased number of endothelial cells. Interestingly, vascular expression of the major VEGF receptors was considerably higher in the liver metastasis, indicating that the metastatic vasculature may be primed for expansion and susceptible to treatment with angiogenesis inhibitors. The patient eventually progressed on Sandostatin, and although consideration was given to adding an angiogenesis inhibitor to her regimen, her disease progression involved non-liver metastases that had not been characterized. Although in this specific case comparative scRNA-seq did not alter treatment, its potential to help guide therapy of metastatic disease was clearly demonstrated.

Purity Independent Subtyping of Tumors (PurIST), A Clinically Robust, Single-sample Classifier for Tumor Subtyping in Pancreatic Cancer.

PURPOSE: Molecular subtyping for pancreatic cancer has made substantial progress in recent years, facilitating the optimization of existing therapeutic approaches to improve clinical outcomes in pancreatic cancer. With advances in treatment combinations and choices, it is becoming increasingly important to determine ways to place patients on the best therapies upfront. Although various molecular subtyping systems for pancreatic cancer have been proposed, consensus regarding proposed subtypes, as well as their relative clinical utility, remains largely unknown and presents a natural barrier to wider clinical adoption. EXPERIMENTAL DESIGN: We assess three major subtype classification schemas in the context of results from two clinical trials and by meta-analysis of publicly available expression data to assess statistical criteria of subtype robustness and overall clinical relevance. We then developed a single-sample classifier (SSC) using penalized logistic regression based on the most robust and replicable schema. RESULTS: We demonstrate that a tumor-intrinsic two-subtype schema is most robust, replicable, and clinically relevant. We developed Purity Independent Subtyping of Tumors (PurIST), a SSC with robust and highly replicable performance on a wide range of platforms and sample types. We show that PurIST subtypes have meaningful associations with patient prognosis and have significant implications for treatment response to FOLIFIRNOX. CONCLUSIONS: The flexibility and utility of PurIST on low-input samples such as tumor biopsies allows it to be used at the time of diagnosis to facilitate the choice of effective therapies for patients with pancreatic ductal adenocarcinoma and should be considered in the context of future clinical trials.

De novo compartment deconvolution and weight estimation of tumor samples using DECODER.

Tumors are mixtures of different compartments. While global gene expression analysis profiles the average expression of all compartments in a sample, identifying the specific contribution of each compartment remains a challenge. With the increasing recognition of the importance of non-neoplastic components, the ability to breakdown the gene expression contribution of each is critical. Here, we develop DECODER, an integrated framework which performs de novo deconvolution and single-sample compartment weight estimation. We use DECODER to deconvolve 33 TCGA tumor RNA-seq data sets and show that it may be applied to other data types including ATAC-seq. We demonstrate that it can be utilized to reproducibly estimate cellular compartment weights in pancreatic cancer that are clinically meaningful. Application of DECODER across cancer types advances the capability of identifying cellular compartments in an unknown sample and may have implications for identifying the tumor of origin for cancers of unknown primary.

Keratin 17 identifies the most lethal molecular subtype of pancreatic cancer.

Although the overall five-year survival of patients with pancreatic ductal adenocarcinoma (PDAC) is dismal, there are survival differences between cases with clinically and pathologically indistinguishable characteristics, suggesting that there are uncharacterized properties that drive tumor progression. Recent mRNA sequencing studies reported gene-expression signatures that define PDAC molecular subtypes that correlate with differences in survival. We previously identified Keratin 17 (K17) as a negative prognostic biomarker in other cancer types. Here, we set out to determine if K17 is as accurate as molecular subtyping of PDAC to identify patients with the shortest survival. K17 mRNA was analyzed in two independent PDAC cohorts for discovery (n = 124) and validation (n = 145). Immunohistochemical localization and scoring of K17 immunohistochemistry (IHC) was performed in a third independent cohort (n = 74). Kaplan-Meier and Cox proportional-hazard regression models were analyzed to determine cancer specific survival differences in low vs. high mRNA K17 expressing cases. We established that K17 expression in PDACs defines the most aggressive form of the disease. By using Cox proportional hazard ratio, we found that increased expression of K17 at the IHC level is also associated with decreased survival of PDAC patients. Additionally, within PDACs of advanced stage and negative surgical margins, K17 at both mRNA and IHC level is sufficient to identify the subgroup with the shortest survival. These results identify K17 as a novel negative prognostic biomarker that could inform patient management decisions.

Keratin 17 is a sensitive and specific biomarker of urothelial neoplasia.

There is a clinical need to identify novel biomarkers to improve diagnostic accuracy for the detection of urothelial tumors. The current study aimed to evaluate keratin 17 (K17), an oncoprotein that drives cell cycle progression in cancers of multiple anatomic sites, as a diagnostic biomarker of urothelial neoplasia in bladder biopsies and in urine cytology specimens. We evaluated K17 expression by immunohistochemistry in formalin-fixed, paraffin embedded tissue specimens of non-papillary invasive urothelial carcinoma (UC) (classical histological cases), high grade papillary UC (PUC-LG), low grade papillary UC (PUC-HG), papillary urothelial neoplasia of low malignant potential (PUNLMP), and normal bladder mucosa. A threshold was established to dichotomize K17 status in tissue specimens as positive vs. negative, based on the proportion of cells that showed strong staining. In addition, K17 immunocytochemistry was performed on urine cytology slides, scoring positive test results based on the detection of K17 in any urothelial cells. Mann-Whitney and receiver operating characteristic analyses were used to compare K17 expression between histologic diagnostic categories. The median proportion of K17 positive tumor cells was 70% (range 20-90%) in PUNLMP, 30% (range 5-100%) in PUC-LG, 20% (range 1-100%), in PUC-HG, 35% (range 5-100%) in UC but staining was rarely detected (range 0-10%) in normal urothelial mucosa. Defining cases in which K17 was detected in ≥10% of cells were considered positive, the sensitivity of K17 in biopsies was 89% (95% CI: 80-96%) and the specificity was 88% (95% CI: 70-95%) to distinguish malignant lesions (PUC-LG, PUC-HG, and UC) from normal urothelial mucosa. Furthermore, K17 immunocytochemistry had a sensitivity of 100% and a specificity of 96% for urothelial carcinoma in 112 selected urine specimens. Thus, K17 is a sensitive and specific biomarker of urothelial neoplasia in tissue specimens and should be further explored as a novel biomarker for the cytologic diagnosis of urine specimens.

Direct therapeutic targeting of immune checkpoint PD-1 in pancreatic cancer.

BACKGROUND: Pancreatic cancer (PC) hijacks innate cellular processes to promote cancer growth. We hypothesized that PC exploits PD-1/PD-L1 not only to avoid immune responses, but to directly enhance growth. We also hypothesized that immune checkpoint inhibitors (ICIs) have direct cytotoxicity in PC. We sought to elucidate therapeutic targeting of PD-1/PD-L1. METHODS: PD-1 was assessed in PC cells, patient-derived organoids (PDOs), and clinical tissues. Then, PC cells were exposed to PD-L1 to evaluate proliferation. To test PD-1/PD-L1 signaling, cells were exposed to PD-L1 and MAPK was examined. Radio-immunoconjugates with anti-PD-1 drugs were developed to test uptake in patient-derived tumor xenografts (PDTXs). Next, PD-1 function was assessed by xenografting PD-1-knockdown cells. Finally, PC models were exposed to ICIs. RESULTS: PD-1 expression was demonstrated in PCs. PD-L1 exposure increased proliferation and activated MAPK. Imaging PDTXs revealed uptake of radio-immunoconjugates. PD-1 knockdown in vivo revealed 67% smaller volumes than controls. Finally, ICI treatment of both PDOs/PDTXs demonstrated cytotoxicity and anti-MEK1/2 combined with anti-PD-1 drugs produced highest cytotoxicity in PDOs/PDTXs. CONCLUSIONS: Our data reveal PCs innately express PD-1 and activate druggable oncogenic pathways supporting PDAC growth. Strategies directly targeting PC with novel ICI regimens may work with adaptive immune responses for optimal cytotoxicity.

Expression of neuropilin-1 is linked to glioma associated microglia and macrophages and correlates with unfavorable prognosis in high grade gliomas.

High grade gliomas, including glioblastoma (GB), are devastating malignancies with very poor prognosis. Over the course of the last decade, there has been a failure to develop new treatments for GB. Reasons for this failure include the lack of validation of novel molecular targets, which are often characterized in animal models and directly transposed to human trials. Here we build on our previous findings, which describe how the multi-functional co-receptor Neuropilin-1 (NRP1) signals through glioma associated microglia/macrophages (GAMS) to promote murine glioma, and investigate NRP1 expression in human glioma. Clinical and gene expression data were obtained via The Cancer Genome Atlas (TCGA), and analyzed using R statistical software. Additionally, CIBERSORT in silico deconvolution was used to determine fractions of immune cell sub-populations within the gene expression datasets. We find that NRP1 expression is correlated with poor prognosis, glioma grade, and associates with the mesenchymal GB subtype. In human GB, NRP1 expression is highly correlated with markers of monocytes/macrophages, as well as genes that contribute to the pro-tumorigenic phenotype of these cells.