Integrated genomic analysis of 456 pancreatic ductal adenocarcinomas identified 32 recurrently mutated genes that aggregate into 10 pathways: KRAS, TGF-β, WNT, NOTCH, ROBO/SLIT signalling, G1/S transition, SWI-SNF, chromatin modification, DNA repair and RNA processing. Expression analysis defined 4 subtypes: (1) squamous; (2) pancreatic progenitor; (3) immunogenic; and (4) aberrantly differentiated endocrine exocrine (ADEX) that correlate with histopathological characteristics. Squamous tumours are enriched for TP53 and KDM6A mutations, upregulation of the TP63∆N transcriptional network, hypermethylation of pancreatic endodermal cell-fate determining genes and have a poor prognosis. Pancreatic progenitor tumours preferentially express genes involved in early pancreatic development (FOXA2/3, PDX1 and MNX1). ADEX tumours displayed upregulation of genes that regulate networks involved in KRAS activation, exocrine (NR5A2 and RBPJL), and endocrine differentiation (NEUROD1 and NKX2-2). Immunogenic tumours contained upregulated immune networks including pathways involved in acquired immune suppression. These data infer differences in the molecular evolution of pancreatic cancer subtypes and identify opportunities for therapeutic development.

Genomic analyses identify molecular subtypes of pancreatic cancer

Intestinal epithelial cells absorb nutrients, respond to microbes, function as a barrier and help to coordinate immune responses. Here we report profiling of 53,193 individual epithelial cells from the small intestine and organoids of mice, which enabled the identification and characterization of previously unknown subtypes of intestinal epithelial cell and their gene signatures. We found unexpected diversity in hormone-secreting enteroendocrine cells and constructed the taxonomy of newly identified subtypes, and distinguished between two subtypes of tuft cell, one of which expresses the epithelial cytokine Tslp and the pan-immune marker CD45, which was not previously associated with non-haematopoietic cells. We also characterized the ways in which cell-intrinsic states and the proportions of different cell types respond to bacterial and helminth infections: Salmonella infection caused an increase in the abundance of Paneth cells and enterocytes, and broad activation of an antimicrobial program; Heligmosomoides polygyrus caused an increase in the abundance of goblet and tuft cells. Our survey highlights previously unidentified markers and programs, associates sensory molecules with cell types, and uncovers principles of gut homeostasis and response to pathogens.

/pdf/a-single-cell-survey-of-the-small-intestinal-epithelium-232otkp0dj.pdf

A single-cell survey of the small intestinal epithelium

The airways of the lung are the primary sites of disease in asthma and cystic fibrosis. Here we study the cellular composition and hierarchy of the mouse tracheal epithelium by single-cell RNA-sequencing (scRNA-seq) and in vivo lineage tracing. We identify a rare cell type, the Foxi1+ pulmonary ionocyte; functional variations in club cells based on their location; a distinct cell type in high turnover squamous epithelial structures that we term ‘hillocks’; and disease-relevant subsets of tuft and goblet cells. We developed ‘pulse-seq’, combining scRNA-seq and lineage tracing, to show that tuft, neuroendocrine and ionocyte cells are continually and directly replenished by basal progenitor cells. Ionocytes are the major source of transcripts of the cystic fibrosis transmembrane conductance regulator in both mouse (Cftr) and human (CFTR). Knockout of Foxi1 in mouse ionocytes causes loss of Cftr expression and disrupts airway fluid and mucus physiology, phenotypes that are characteristic of cystic fibrosis. By associating cell-type-specific expression programs with key disease genes, we establish a new cellular narrative for airways disease.

/pdf/a-revised-airway-epithelial-hierarchy-includes-cftr-3z0fduyq1s.pdf

A revised airway epithelial hierarchy includes CFTR-expressing ionocytes

The implementation of targeted therapies for acute myeloid leukaemia (AML) has been challenging because of the complex mutational patterns within and across patients as well as a dearth of pharmacologic agents for most mutational events. Here we report initial findings from the Beat AML programme on a cohort of 672 tumour specimens collected from 562 patients. We assessed these specimens using whole-exome sequencing, RNA sequencing and analyses of ex vivo drug sensitivity. Our data reveal mutational events that have not previously been detected in AML. We show that the response to drugs is associated with mutational status, including instances of drug sensitivity that are specific to combinatorial mutational events. Integration with RNA sequencing also revealed gene expression signatures, which predict a role for specific gene networks in the drug response. Collectively, we have generated a dataset—accessible through the Beat AML data viewer (Vizome)—that can be leveraged to address clinical, genomic, transcriptomic and functional analyses of the biology of AML. Analyses of samples from patients with acute myeloid leukaemia reveal that drug response is associated with mutational status and gene expression; the generated dataset provides a basis for future clinical and functional studies of this disease.

/pdf/functional-genomic-landscape-of-acute-myeloid-leukaemia-302m84i6kq.pdf

Functional genomic landscape of acute myeloid leukaemia

Ageing is a major risk factor for many neurological pathologies, but its mechanisms remain unclear. Unlike other tissues, the parenchyma of the central nervous system (CNS) lacks lymphatic vasculature and waste products are removed partly through a paravascular route. (Re)discovery and characterization of meningeal lymphatic vessels has prompted an assessment of their role in waste clearance from the CNS. Here we show that meningeal lymphatic vessels drain macromolecules from the CNS (cerebrospinal and interstitial fluids) into the cervical lymph nodes in mice. Impairment of meningeal lymphatic function slows paravascular influx of macromolecules into the brain and efflux of macromolecules from the interstitial fluid, and induces cognitive impairment in mice. Treatment of aged mice with vascular endothelial growth factor C enhances meningeal lymphatic drainage of macromolecules from the cerebrospinal fluid, improving brain perfusion and learning and memory performance. Disruption of meningeal lymphatic vessels in transgenic mouse models of Alzheimer’s disease promotes amyloid-β deposition in the meninges, which resembles human meningeal pathology, and aggravates parenchymal amyloid-β accumulation. Meningeal lymphatic dysfunction may be an aggravating factor in Alzheimer’s disease pathology and in age-associated cognitive decline. Thus, augmentation of meningeal lymphatic function might be a promising therapeutic target for preventing or delaying age-associated neurological diseases.

/pdf/functional-aspects-of-meningeal-lymphatics-in-ageing-and-2zv11ccfip.pdf

Functional aspects of meningeal lymphatics in ageing and Alzheimer’s disease

Summary: Heterogeneity and latent variables are now widely recognized as major sources of bias and variability in high-throughput experiments. The most well-known source of latent variation in genomic experiments are batch effects—when samples are processed on different days, in different groups or by different people. However, there are also a large number of other variables that may have a major impact on high-throughput measurements. Here we describe the sva package for identifying, estimating and removing unwanted sources of variation in high-throughput experiments. The sva package supports surrogate variable estimation with the sva function, direct adjustment for known batch effects with the ComBat function and adjustment for batch and latent variables in prediction problems with the fsva function.

Availability: The R package sva is freely available from http://www.bioconductor.org.

Contact: jleek@jhsph.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

/pdf/the-sva-package-for-removing-batch-effects-and-other-273ierrc1y.pdf

The sva package for removing batch effects and other unwanted variation in high-throughput experiments

Motivation: Sample source, procurement process and other technical variations introduce batch effects into genomics data. Algorithms to remove these artifacts enhance differences between known biological covariates, but also carry potential concern of removing intragroup biological heterogeneity and thus any personalized genomic signatures. As a result, accurate identification of novel subtypes from batch-corrected genomics data is challenging using standard algorithms designed to remove batch effects for class comparison analyses. Nor can batch effects be corrected reliably in future applications of genomics-based clinical tests, in which the biological groups are by definition unknown a priori.

Results: Therefore, we assess the extent to which various batch correction algorithms remove true biological heterogeneity. We also introduce an algorithm, permuted-SVA (pSVA), using a new statistical model that is blind to biological covariates to correct for technical artifacts while retaining biological heterogeneity in genomic data. This algorithm facilitated accurate subtype identification in head and neck cancer from gene expression data in both formalin-fixed and frozen samples. When applied to predict Human Papillomavirus (HPV) status, pSVA improved cross-study validation even if the sample batches were highly confounded with HPV status in the training set.

Availability and implementation: All analyses were performed using R version 2.15.0. The code and data used to generate the results of this manuscript is available from https://sourceforge.net/projects/psva.

Contact: ude.imhj@gitrefje

Supplementary information: Supplementary data are available at Bioinformatics online.

/pdf/preserving-biological-heterogeneity-with-a-permuted-5frl4odlq8.pdf

Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction.

Batch effects are responsible for the failure of promising genomic prognostic signatures, major ambiguities in published genomic results, and retractions of widely-publicized findings. Batch effect corrections have been developed to remove these artifacts, but they are designed to be used in population studies. But genomic technologies are beginning to be used in clinical applications where samples are analyzed one at a time for diagnostic, prognostic, and predictive applications. There are currently no batch correction methods that have been developed specifically for prediction. In this paper, we propose an new method called frozen surrogate variable analysis (fSVA) that borrows strength from a training set for individual sample batch correction. We show that fSVA improves prediction accuracy in simulations and in public genomic studies. fSVA is available as part of the sva Bioconductor package.

Removing batch effects for prediction problems with frozen surrogate variable analysis.

Batch effects are responsible for the failure of promising genomic prognos- tic signatures, major ambiguities in published genomic results, and retractions of widely-publicized findings. Batch effect corrections have been developed to re- move these artifacts, but they are designed to be used in population studies. But genomic technologies are beginning to be used in clinical applications where sam- ples are analyzed one at a time for diagnostic, prognostic, and predictive applica- tions. There are currently no batch correction methods that have been developed specifically for prediction. In this paper, we propose an new method called frozen surrogate variable analysis (fSVA) that borrows strength from a training set for individual sample batch correction. We show that fSVA improves prediction ac- curacy in simulations and in public genomic studies. fSVA is available as part of the sva Bioconductor package.

Removing batch effects for prediction problems with frozen surrogate variable analysis

Measurements from microarrays and other high-throughput technologies are susceptible to non-biological artifacts like batch effects. It is known that batch effects can alter or obscure the set of significant results and biological conclusions in high-throughput studies. Here we examine the impact of batch effects on predictors built from genomic technologies. To investigate batch effects, we collected publicly available gene expression measurements with known outcomes, and estimated batches using date. Using these data we show (1) the impact of batch effects on prediction depends on the correlation between outcome and batch in the training data, and (2) removing expression measurements most affected by batch before building predictors may improve the accuracy of those predictors. These results suggest that (1) training sets should be designed to minimize correlation between batches and outcome, and (2) methods for identifying batch-affected probes should be developed to improve prediction results for studies with high correlation between batches and outcome.

/pdf/the-practical-effect-of-batch-on-genomic-prediction-148agzji6h.pdf

Hilary S. Parker

Papers

The sva package for removing batch effects and other unwanted variation in high-throughput experiments

Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction.

Removing batch effects for prediction problems with frozen surrogate variable analysis.

Removing batch effects for prediction problems with frozen surrogate variable analysis

The practical effect of batch on genomic prediction