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. While 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. Methods: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.

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ObjectiveIntrahepatic 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.MethodsBlood 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.ResultsSystemic 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.ConclusionsiCCA uses the GM-CSF-bone marrow axis to establish an immunosuppressive tumour microenvironment. Blockade of the GM-CSF axis promotes antitumour T cell immunity.

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Single-cell RNA-sequencing (scRNA-seq) has enabled the molecular profiling of thousands to millions of cells simultaneously in biologically heterogenous samples. Currently, 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 choice can greatly alter clustering solutions and thus impose great influence on which cell types are identified. Existing methods largely focus on determining the optimal number of robust clusters, which is not favorable for identifying cells of extremely low abundance due to their subtle contributions towards overall patterns of gene expression. Here we present a carefully designed framework, SCISSORS, which accurately profiles subclusters within major 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 implementing a multi-step, semi-supervised reclustering process. Additionally, SCISSORS provides a method for the identification of marker genes of rare cells, which may be used for further study. SCISSORS is wrapped around the popular Seurat R package and can be easily integrated into existing Seurat pipelines. SCISSORS, including source code and vignettes for two example datasets, is freely available at https://github.com/jrleary/SCISSORS.