Kinome state is predictive of cell viability in pancreatic cancer tumor and cancer-associated fibroblast cell lines.

Numerous aspects of cellular signaling are regulated by the kinome-the network of over 500 protein kinases that guides and modulates information transfer throughout the cell. The key role played by both individual kinases and assemblies of kinases organized into functional subnetworks leads to kinome dysregulation driving many diseases, particularly cancer. In the case of pancreatic ductal adenocarcinoma (PDAC), a variety of kinases and associated signaling pathways have been identified for their key role in the establishment of disease as well as its progression. However, the identification of additional relevant therapeutic targets has been slow and is further confounded by interactions between the tumor and the surrounding tumor microenvironment. In this work, we attempt to link the state of the human kinome, or kinotype, with cell viability in treated, patient-derived PDAC tumor and cancer-associated fibroblast cell lines. We applied classification models to independent kinome perturbation and kinase inhibitor cell screen data, and found that the inferred kinotype of a cell has a significant and predictive relationship with cell viability. We further find that models are able to identify a set of kinases whose behavior in response to perturbation drive the majority of viability responses in these cell lines, including the understudied kinases CSNK2A1/3, CAMKK2, and PIP4K2C. We next utilized these models to predict the response of new, clinical kinase inhibitors that were not present in the initial dataset for model devlopment and conducted a validation screen that confirmed the accuracy of the models. These results suggest that characterizing the perturbed state of the human protein kinome provides significant opportunity for better understanding of signaling behavior and downstream cell phenotypes, as well as providing insight into the broader design of potential therapeutic strategies for PDAC.

Updates in Molecular Profiling of Pancreatic Ductal Adenocarcinoma.

Outcomes from pancreatic ductal adenocarcinoma (PDAC) remain poor and better methods of prognostication and therapeutic approaches are needed. Recent advances in cancer genomics have led to the development of molecular subtypes of PDAC associated with clinical outcomes. Current evidence also suggests that the subtypes have differential response to first-line chemotherapy regimens. PDAC is also characterized by different stroma and immune environments. Further work is needed to confirm the utility of these subtypes to predicting response to different systemic therapies.

Gastrointestinal Malignancy: Genetic Implications to Clinical Applications.

Advances in molecular genetics have revolutionized our understanding of the pathogenesis, progression, and therapeutic options for treating gastrointestinal (GI) cancers. This chapter provides a comprehensive overview of the molecular landscape of GI cancers, focusing on key genetic alterations implicated in tumorigenesis across various anatomical sites including GIST, colon and rectum, and pancreas. Emphasis is placed on critical oncogenic pathways, such as mutations in tumor suppressor genes, oncogenes, chromosomal instability, microsatellite instability, and epigenetic modifications. The role of molecular biomarkers in predicting prognosis, guiding treatment decisions, and monitoring therapeutic response is discussed, highlighting the integration of genomic profiling into clinical practice. Finally, we address the evolving landscape of precision oncology in GI cancers, considering targeted therapies and immunotherapies.

Purity Independent Subtyping of Tumors (PurIST) Pancreatic Cancer Classifier: Analytic Validation of a 16-RNA Expression Signature Distinguishing Basal and Classical Subtypes.

The two major molecular subtypes of pancreatic adenocarcinoma reportedly have differential response to FOLFIRINOX-based therapy. To promote rapid assignment of basal versus classical subtypes, an array-based single-sample classifier assay was developed and applied to 74 formalin-fixed, paraffin-embedded biopsy or resection specimens of known subtype based on transcriptomics. The Purity Independent Subtyping of Tumors (PurIST) algorithm assigns subtype based on relative expression of 16 RNAs counted by RNA sequencing (RNAseq) versus more practical array-based NanoString nCounter Elements XT technology. Subtype calls were largely concordant between RNAseq and array methods (72/74, 97% agreement). Compared with the lengthy RNAseq protocol, the array-based assay takes just 3 working days to analyze, permitting rapid reporting of tumor subtype. In conclusion, the PurIST pancreatic cancer classifier has robust performance to classify pancreatic adenocarcinoma into basal versus classical subtypes. Clinical validation studies are underway to evaluate outcome in patients whose standard-of-care chemotherapy regimen is selected on the basis of rapid subtype assignment (NCT04683315).

Pan-lysyl oxidase inhibition disrupts fibroinflammatory tumor stroma, rendering cholangiocarcinoma susceptible to chemotherapy.

BACKGROUND: Cholangiocarcinoma (CCA) features highly desmoplastic stroma that promotes structural and functional resistance to therapy. Lysyl oxidases (LOX, LOXL1-4) catalyze collagen cross-linking, thereby increasing stromal rigidity and facilitating therapeutic resistance. Here, we evaluate the role of lysyl oxidases in stromal desmoplasia and the effects of pan-lysyl oxidase (pan-LOX) inhibition in CCA. METHODS: Resected CCA and normal liver specimens were analyzed from archival tissues. Spontaneous and orthotopic murine models of intrahepatic CCA (iCCA) were used to assess the impact of the pan-LOX inhibitor PXS-5505 in treatment and correlative studies. The functional role of pan-LOX inhibition was interrogated through in vivo and ex vivo assays. RESULTS: All 5 lysyl oxidases are upregulated in CCA and reduced lysyl oxidase expression is correlated with an improved prognosis in resected patients with CCA. Spontaneous and orthotopic murine models of intrahepatic cholangiocarcinoma upregulate all 5 lysyl oxidase isoforms. Pan-LOX inhibition reversed mechanical compression of tumor vasculature, resulting in improved chemotherapeutic penetrance and cytotoxic efficacy. The combination of chemotherapy with pan-LOX inhibition increased damage-associated molecular pattern release, which was associated with improved antitumor T-cell responses. Pan-LOX inhibition downregulated macrophage invasive signatures in vitro, rendering tumor-associated macrophages more susceptible to chemotherapy. Mice bearing orthotopic and spontaneously occurring intrahepatic cholangiocarcinoma tumors exhibited delayed tumor growth and improved survival following a combination of pan-LOX inhibition with chemotherapy. CONCLUSIONS: CCA upregulates all 5 lysyl oxidase isoforms, and pan-LOX inhibition reverses tumor-induced mechanical forces associated with chemotherapy resistance to improve chemotherapeutic efficacy and reprogram antitumor immune responses. Thus, combination therapy with pan-LOX inhibition represents an innovative therapeutic strategy in CCA.

Tumor-intrinsic and Cancer-associated Fibroblast Subtypes Independently Predict Outcomes in Pancreatic Cancer.

OBJECTIVE: To assess the utility of tumor-intrinsic and cancer-associated fibroblast (CAF) subtypes of pancreatic ductal adenocarcinoma (PDAC) in predicting response to neoadjuvant therapy (NAT) and overall survival (OS). BACKGROUND: PDAC remains a deadly disease with limited treatment options, and both the tumor as well as the microenvironment play an important role in pathogenesis. Gene expression-based tumor-intrinsic subtypes (classical and basal-like) have been shown to predict outcomes, but tumor microenvironment subtypes are still evolving. METHODS: RNA-sequencing was performed on 114 deidentified resected PDAC tumors. Clinical data were collected by retrospective chart review. Single sample classifiers were used to determine classical and basal-like subtypes as well as tumor-permissive permCAF and tumor-restraining restCAF subtypes. Survival was analyzed using the log-rank test. RESULTS: Patients who received NAT had an increase in OS, with a median survival of 27.9 months compared with 20.1 months for those who did not receive NAT, but the difference did not reach statistical significance (hazard ratio: 0.64, P =0.076). Either tumor-intrinsic or CAF subtypes alone were associated with OS regardless of NAT or no NAT, and patients with classical or restCAF subtypes had the best outcomes. When evaluated together, patients with the classical-restCAF subtype had the best OS and basal-permCAF the worst OS ( P <0.0001). Patients undergoing NAT with the classical-restCAF subtype demonstrated the longest OS compared with the other groups ( P =0.00041). CONCLUSIONS: CAF subtypes have an additive effect over tumor-intrinsic subtypes in predicting survival with or without neoadjuvant FOLFIRINOX in PDAC. Molecular subtyping of both tumor and CAF compartments of PDAC may be important steps in selecting first-line systemic therapy.

Determination of permissive and restraining cancer-associated fibroblast (DeCAF) subtypes.

Cancer-associated fibroblast (CAF) subpopulations in pancreatic ductal adenocarcinoma (PDAC) have been identified using single-cell RNA sequencing (scRNAseq) with divergent characteristics, but their clinical relevance remains unclear. We translate scRNAseq-derived CAF cell-subpopulation-specific marker genes to bulk RNAseq data, and develop a single- sample classifier, DeCAF, for the classification of clinically rest raining and perm issive CAF subtypes. We validate DeCAF in 19 independent bulk transcriptomic datasets across four tumor types (PDAC, mesothelioma, bladder and renal cell carcinoma). DeCAF subtypes have distinct histology features, immune landscapes, and are prognostic and predict response to therapy across cancer types. We demonstrate that DeCAF is clinically replicable and robust for the classification of CAF subtypes in patients for multiple tumor types, providing a better framework for the future development and translation of therapies against permissive CAF subtypes and preservation of restraining CAF subtypes. Significance: We introduce a replicable and robust classifier, DeCAF, that delineates the significance of the role of permissive and restraining CAF subtypes in cancer patients. DeCAF is clinically tractable, prognostic and predictive of treatment response in multiple cancer types and lays the translational groundwork for the preclinical and clinical development of CAF subtype specific therapies.

First-in-Class Humanized Antibody against Alternatively Spliced Tissue Factor Augments Anti-Metastatic Efficacy of Chemotherapy in a Preclinical Model of Pancreatic Ductal Adenocarcinoma.

Alternatively spliced tissue factor (asTF) promotes the progression of pancreatic ductal adenocarcinoma (PDAC) by activating ß1-integrins on PDAC cell surfaces. hRabMab1, a first-in-class humanized inhibitory anti-asTF antibody we recently developed, can suppress PDAC primary tumor growth as a single agent. Whether hRabMab1 has the potential to suppress metastases in PDAC is unknown. Following in vivo screening of three asTF-proficient human PDAC cell lines, we chose to make use of KRAS G12V-mutant human PDAC cell line PaCa-44, which yields aggressive primary orthotopic tumors with spontaneous spread to PDAC-relevant anatomical sites, along with concomitant severe leukocytosis. The experimental design featured orthotopic tumors formed by luciferase labeled PaCa-44 cells; administration of hRabMab1 alone or in combination with gemcitabine/paclitaxel (gem/PTX); and the assessment of the treatment outcomes on the primary tumor tissue as well as systemic spread. When administered alone, hRabMab1 exhibited poor penetration of tumor tissue; however, hRabMab1 was abundant in tumor tissue when co-administered with gem/PTX, which resulted in a significant decrease in tumor cell proliferation; leukocyte infiltration; and neovascularization. Gem/PTX alone reduced primary tumor volume, but not metastatic spread; only the combination of hRabMab1 and gem/PTX significantly reduced metastatic spread. RNA-seq analysis of primary tumors showed that the addition of hRabMab1 to gem/PTX enhanced the downregulation of tubulin binding and microtubule motor activity. In the liver, hRabMab1 reduced liver metastasis as a single agent. Only the combination of hRabMab1 and gem/PTX eliminated tumor cell-induced leukocytosis. We here demonstrate for the first time that hRabMab1 may help suppress metastasis in PDAC. hRabMab1's ability to improve the efficacy of chemotherapy is significant and warrants further investigation.

Efficient computation of high-dimensional penalized generalized linear mixed models by latent factor modeling of the random effects.

Modern biomedical datasets are increasingly high-dimensional and exhibit complex correlation structures. Generalized linear mixed models (GLMMs) have long been employed to account for such dependencies. However, proper specification of the fixed and random effects in GLMMs is increasingly difficult in high dimensions, and computational complexity grows with increasing dimension of the random effects. We present a novel reformulation of the GLMM using a factor model decomposition of the random effects, enabling scalable computation of GLMMs in high dimensions by reducing the latent space from a large number of random effects to a smaller set of latent factors. We also extend our prior work to estimate model parameters using a modified Monte Carlo Expectation Conditional Minimization algorithm, allowing us to perform variable selection on both the fixed and random effects simultaneously. We show through simulation that through this factor model decomposition, our method can fit high-dimensional penalized GLMMs faster than comparable methods and more easily scale to larger dimensions not previously seen in existing approaches.

Myofibroblastic cancer-associated fibroblast subtype heterogeneity in pancreatic cancer.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has a fibrotic stroma that has both tumor-promoting and tumor-restraining properties. Different types of cancer-associated fibroblasts (CAFs) have been described. Here, we investigated whether CAFs within the same subtype exhibit heterogeneous functions. METHODS: We evaluated the gene and protein expression differences in two myofibroblastic CAF (myCAF) lines using single-cell and bulk RNA-sequencing. We utilized proliferation and migration assays to determine the effect of different CAF lines on a tumor cell line. RESULTS: We found that myCAF lines express an activated stroma subtype gene signature, which is associated with a shorter survival in patients. Although both myCAF lines expressed α-smooth muscle actin (α-SMA), platelet-derived growth factor-α (PDGFR-α), fibroblast-activated protein (FAP), and vimentin, we observed heterogeneity between the two lines. Similarly, despite being consistent with myCAF gene expression overall, heterogeneity within specific genes was observed. We found that these differences extended to the functional level where the two myCAF lines had different effects on the same tumor cell line. The myCAF216 line, which had slightly increased inflammatory CAF-like gene expression and higher protein expression of α-SMA, PDGFR-α, and FAP was found to restrain migration of tumor cells. CONCLUSIONS: We found that two myCAF lines with globally similar expression characteristics had different effects on the same tumor cell line, one promoting and the other restraining migration. Our study highlights that there may be unappreciated heterogeneity within CAF subtypes. Further investigation and attention to specific genes or proteins that may drive this heterogeneity will be important.

Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors.

Protein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Their central role in signaling leads to kinome dysfunction being a common driver of disease, and in particular cancer, where numerous kinases have been identified as having a causal or modulating role in tumor development and progression. As a result, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Understanding the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is thus of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC50). We then built computational models on this data set that achieve a high degree of prediction accuracy (R2 of 0.7 and RMSE of 0.9) and were able to identify a set of well-characterized and understudied kinases that significantly affect cell responses. We further validated these models experimentally by testing predicted effects in breast cancer cell lines and extended the model scope by performing additional validation in patient-derived pancreatic cancer cell lines. Overall, these results demonstrate that broad quantification of kinome inhibition state is highly predictive of downstream cellular phenotypes.

Sub-Cluster Identification through Semi-Supervised Optimization of Rare-Cell Silhouettes (SCISSORS) in single-cell RNA-sequencing.

MOTIVATION: Single-cell RNA-sequencing (scRNA-seq) has enabled the molecular profiling of thousands to millions of cells simultaneously in biologically heterogenous samples. Currently, the common practice in scRNA-seq is to determine cell type labels through unsupervised clustering and the examination of cluster-specific genes. However, even small differences in analysis and parameter choosing can greatly alter clustering results and thus impose great influence on which cell types are identified. Existing methods largely focus on determining the optimal number of robust clusters, which can be problematic for identifying cells of extremely low abundance due to their subtle contributions toward overall patterns of gene expression. RESULTS: Here, we present a carefully designed framework, SCISSORS, which accurately profiles subclusters within broad cluster(s) for the identification of rare cell types in scRNA-seq data. SCISSORS employs silhouette scoring for the estimation of heterogeneity of clusters and reveals rare cells in heterogenous clusters by a multi-step semi-supervised reclustering process. Additionally, SCISSORS provides a method for the identification of marker genes of high specificity to the cell type. SCISSORS is wrapped around the popular Seurat R package and can be easily integrated into existing Seurat pipelines. AVAILABILITY AND IMPLEMENTATION: SCISSORS, including source code and vignettes, are freely available at https://github.com/jr-leary7/SCISSORS.

Identifying patient profiles of disparate care in resectable pancreas cancer using latent class analysis.

BACKGROUND AND OBJECTIVES: Disparities in pancreas cancer care are multifactorial, but factors are often examined in isolation. Research that integrates these factors in a single conceptual framework is lacking. We use latent class analysis (LCA) to evaluate the association between intersectionality and patterns of care and survival in patients with resectable pancreas cancer. METHODS: LCA was used to identify demographic profiles in resectable pancreas cancer (n = 140 344) diagnosed from 2004 to 2019 in the National Cancer Database (NCDB). LCA-derived patient profiles were used to identify differences in receipt of minimum expected treatment (definitive surgery), optimal treatment (definitive surgery and chemotherapy), time to treatment, and overall survival. RESULTS: Minimum expected treatment (hazard ratio [HR] 0.69, 95% confidence interval [CI]: 0.65, 0.75) and optimal treatment (HR 0.58, 95% CI: 0.55, 0.62) were associated with improved overall survival. Seven latent classes were identified based on age, race/ethnicity, and socioeconomic status (SES) attributes (zip code-linked education and income, insurance, geography). Compared to the referent group (≥65 years + White + med/high SES), the ≥65 years + Black profile had the longest time-to-treatment (24 days vs. 28 days) and lowest odds of receiving minimum (odds ratio [OR] 0.67, 95% CI: 0.64, 0.71) or optimal treatment (OR 0.76, 95% CI: 0.72, 0.81). The Hispanic patient profile had the lowest median overall survival-55.3 months versus 67.5 months. CONCLUSIONS: Accounting for intersectionality in the NCDB resectable pancreatic cancer patient cohort identifies subgroups at higher risk for inequities in care. LCA demonstrates that older Black patients and Hispanic patients are at particular risk for being underserved and should be prioritiz for directed interventions.

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.

Balance between immunoregulatory B cells and plasma cells drives pancreatic tumor immunity.

Plasma cell responses are associated with anti-tumor immunity and favorable response to immunotherapy. B cells can amplify anti-tumor immune responses through antibody production; yet B cells in patients and tumor-bearing mice often fail to support this effector function. We identify dysregulated transcriptional program in B cells that disrupts differentiation of naive B cells into anti-tumor plasma cells. The signaling network contributing to this dysfunction is driven by interleukin (IL) 35 stimulation of a STAT3-PAX5 complex that upregulates the transcriptional regulator BCL6 in naive B cells. Transient inhibition of BCL6 in tumor-educated naive B cells is sufficient to reverse the dysfunction in B cell differentiation, stimulating the intra-tumoral accumulation of plasma cells and effector T cells and rendering pancreatic tumors sensitive to anti-programmed cell death protein 1 (PD-1) blockade. Our findings argue that B cell effector dysfunction in cancer can be due to an active systemic suppression program that can be targeted to synergize with T cell-directed immunotherapy.

Truncated O-Glycan-Bearing MUC16 Enhances Pancreatic Cancer Cells Aggressiveness via α4ß1 Integrin Complexes and FAK Signaling.

Elevated levels of Mucin-16 (MUC16) in conjunction with a high expression of truncated O-glycans is implicated in playing crucial roles in the malignancy of pancreatic ductal adenocarcinoma (PDAC). However, the mechanisms by which such aberrant glycoforms present on MUC16 itself promote an increased disease burden in PDAC are yet to be elucidated. This study demonstrates that the CRISPR/Cas9-mediated genetic deletion of MUC16 in PDAC cells decreases tumor cell migration. We found that MUC16 enhances tumor malignancy by activating the integrin-linked kinase and focal adhesion kinase (ILK/FAK)-signaling axis. These findings are especially noteworthy in truncated O-glycan (Tn and STn antigen)-expressing PDAC cells. Activation of these oncogenic-signaling pathways resulted in part from interactions between MUC16 and integrin complexes (α4ß1), which showed a stronger association with aberrant glycoforms of MUC16. Using a monoclonal antibody to functionally hinder MUC16 significantly reduced the migratory cascades in our model. Together, these findings suggest that truncated O-glycan containing MUC16 exacerbates malignancy in PDAC by activating FAK signaling through specific interactions with α4 and ß1 integrin complexes on cancer cell membranes. Targeting these aberrant glycoforms of MUC16 can aid in the development of a novel platform to study and treat metastatic pancreatic cancer.

Aberrant Expression and Subcellular Localization of ECT2 Drives Colorectal Cancer Progression and Growth.

ECT2 is an activator of RHO GTPases that is essential for cytokinesis. In addition, ECT2 was identified as an oncoprotein when expressed ectopically in NIH/3T3 fibroblasts. However, oncogenic activation of ECT2 resulted from N-terminal truncation, and such truncated ECT2 proteins have not been found in patients with cancer. In this study, we observed elevated expression of full-length ECT2 protein in preneoplastic colon adenomas, driven by increased ECT2 mRNA abundance and associated with APC tumor-suppressor loss. Elevated ECT2 levels were detected in the cytoplasm and nucleus of colorectal cancer tissue, suggesting cytoplasmic mislocalization as one mechanism of early oncogenic ECT2 activation. Importantly, elevated nuclear ECT2 correlated with poorly differentiated tumors, and a low cytoplasmic:nuclear ratio of ECT2 protein correlated with poor patient survival, suggesting that nuclear and cytoplasmic ECT2 play distinct roles in colorectal cancer. Depletion of ECT2 reduced anchorage-independent cancer cell growth and invasion independent of its function in cytokinesis, and loss of Ect2 extended survival in a Kras G12D Apc-null colon cancer mouse model. Expression of ECT2 variants with impaired nuclear localization or guanine nucleotide exchange catalytic activity failed to restore cancer cell growth or invasion, indicating that active, nuclear ECT2 is required to support tumor progression. Nuclear ECT2 promoted ribosomal DNA transcription and ribosome biogenesis in colorectal cancer. These results support a driver role for both cytoplasmic and nuclear ECT2 overexpression in colorectal cancer and emphasize the critical role of precise subcellular localization in dictating ECT2 function in neoplastic cells. SIGNIFICANCE: ECT2 overexpression and mislocalization support its role as a driver in colon cancer that is independent from its function in normal cell cytokinesis.

GM-CSF drives myelopoiesis, recruitment and polarisation of tumour-associated macrophages in cholangiocarcinoma and systemic blockade facilitates antitumour immunity.

OBJECTIVE: Intrahepatic cholangiocarcinoma (iCCA) is rising in incidence, and at present, there are limited effective systemic therapies. iCCA tumours are infiltrated by stromal cells, with high prevalence of suppressive myeloid populations including tumour-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs). Here, we show that tumour-derived granulocyte-macrophage colony-stimulating factor (GM-CSF) and the host bone marrow is central for monopoiesis and potentiation of TAMs, and abrogation of this signalling axis facilitates antitumour immunity in a novel model of iCCA. METHODS: Blood and tumours were analysed from iCCA patients and controls. Treatment and correlative studies were performed in mice with autochthonous and established orthotopic iCCA tumours treated with anti-GM-CSF monoclonal antibody. RESULTS: Systemic elevation in circulating myeloid cells correlates with poor prognosis in patients with iCCA, and patients who undergo resection have a worse overall survival if tumours are more infiltrated with CD68+ TAMs. Mice with spontaneous iCCA demonstrate significant elevation of monocytic myeloid cells in the tumour microenvironment and immune compartments, and tumours overexpress GM-CSF. Blockade of GM-CSF with a monoclonal antibody decreased tumour growth and spread. Mice bearing orthotopic tumours treated with anti-GM-CSF demonstrate repolarisation of immunosuppressive TAMs and MDSCs, facilitating T cell response and tumour regression. GM-CSF blockade dampened inflammatory gene networks in tumours and TAMs. Human tumours with decreased GM-CSF expression exhibit improved overall survival after resection. CONCLUSIONS: iCCA uses the GM-CSF-bone marrow axis to establish an immunosuppressive tumour microenvironment. Blockade of the GM-CSF axis promotes antitumour T cell immunity.