Dynamic evolution of fibroblasts revealed by single cell RNA sequencing of human pancreatic cancer.

Cancer progression and response to therapy are inextricably reliant on the co-evolution of a supportive tissue microenvironment. This is particularly evident in pancreatic ductal adenocarcinoma (PDAC), a tumor type characterized by expansive and heterogeneous stroma. Herein, we employed single cell RNAseq and spatial transcriptomics of normal, inflamed, and malignant pancreatic tissues to contextualize stromal dynamics associated with disease and treatment status, identifying temporal and spatial trajectories of fibroblast differentiation. Using analytical tools to infer cellular communication, together with a newly developed assay to annotate genomic alterations in cancer cells, we additionally explored the complex intercellular networks underlying tissue circuitry, highlighting a fibroblast-centric interactome that grows in strength and complexity in the context of malignant transformation. Our study yields new insights on the stromal remodeling events favoring the development of a tumor-supportive microenvironment and provides a powerful resource for the exploration of novel points of therapeutic intervention in PDAC.

Single-cell sequencing reveals suppressive transcriptional programs regulated by MIS/AMH in neonatal ovaries.

Müllerian inhibiting substance (MIS/AMH), produced by granulosa cells of growing follicles, is an important regulator of folliculogenesis and follicle development. Treatment with exogenous MIS in mice suppresses follicle development and prevents ovulation. To investigate the mechanisms by which MIS inhibits follicle development, we performed single-cell RNA sequencing of whole neonatal ovaries treated with MIS at birth and analyzed at postnatal day 6, coinciding with the first wave of follicle growth. We identified distinct transcriptional signatures associated with MIS responses in the ovarian cell types. MIS treatment inhibited proliferation in granulosa, surface epithelial, and stromal cell types of the ovary and elicited a unique signature of quiescence in granulosa cells. In addition to decreasing the number of growing preantral follicles, we found that MIS treatment uncoupled the maturation of germ cells and granulosa cells. In conclusion, MIS suppressed neonatal follicle development by inhibiting proliferation, imposing a quiescent cell state, and preventing granulosa cell differentiation.

Genetically Defined Syngeneic Mouse Models of Ovarian Cancer as Tools for the Discovery of Combination Immunotherapy.

Despite advances in immuno-oncology, the relationship between tumor genotypes and response to immunotherapy remains poorly understood, particularly in high-grade serous tubo-ovarian carcinomas (HGSC). We developed a series of mouse models that carry genotypes of human HGSCs and grow in syngeneic immunocompetent hosts to address this gap. We transformed murine-fallopian tube epithelial cells to phenocopy homologous recombination-deficient tumors through a combined loss of Trp53, Brca1, Pten, and Nf1 and overexpression of Myc and Trp53 R172H, which was contrasted with an identical model carrying wild-type Brca1. For homologous recombination-proficient tumors, we constructed genotypes combining loss of Trp53 and overexpression of Ccne1, Akt2, and Trp53 R172H, and driven by KRAS G12V or Brd4 or Smarca4 overexpression. These lines form tumors recapitulating human disease, including genotype-driven responses to treatment, and enabled us to identify follistatin as a driver of resistance to checkpoint inhibitors. These data provide proof of concept that our models can identify new immunotherapy targets in HGSC. SIGNIFICANCE: We engineered a panel of murine fallopian tube epithelial cells bearing mutations typical of HGSC and capable of forming tumors in syngeneic immunocompetent hosts. These models recapitulate tumor microenvironments and drug responses characteristic of human disease. In a Ccne1-overexpressing model, immune-checkpoint resistance was driven by follistatin.This article is highlighted in the In This Issue feature, p. 211.

Prediction of cancer driver genes through network-based moment propagation of mutation scores.

MOTIVATION: Gaining a comprehensive understanding of the genetics underlying cancer development and progression is a central goal of biomedical research. Its accomplishment promises key mechanistic, diagnostic and therapeutic insights. One major step in this direction is the identification of genes that drive the emergence of tumors upon mutation. Recent advances in the field of computational biology have shown the potential of combining genetic summary statistics that represent the mutational burden in genes with biological networks, such as protein-protein interaction networks, to identify cancer driver genes. Those approaches superimpose the summary statistics on the nodes in the network, followed by an unsupervised propagation of the node scores through the network. However, this unsupervised setting does not leverage any knowledge on well-established cancer genes, a potentially valuable resource to improve the identification of novel cancer drivers. RESULTS: We develop a novel node embedding that enables classification of cancer driver genes in a supervised setting. The embedding combines a representation of the mutation score distribution in a node's local neighborhood with network propagation. We leverage the knowledge of well-established cancer driver genes to define a positive class, resulting in a partially labeled dataset, and develop a cross-validation scheme to enable supervised prediction. The proposed node embedding followed by a supervised classification improves the predictive performance compared with baseline methods and yields a set of promising genes that constitute candidates for further biological validation. AVAILABILITY AND IMPLEMENTATION: Code available at https://github.com/BorgwardtLab/MoProEmbeddings. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology.

Population-based biobanks with genomic and dense phenotype data provide opportunities for generating effective therapeutic hypotheses and understanding the genomic role in disease predisposition. To characterize latent components of genetic associations, we apply truncated singular value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in 337,199 White British individuals in the UK Biobank study. We systematically identify key components of genetic associations and the contributions of variants, genes, and phenotypes to each component. As an illustration of the utility of the approach to inform downstream experiments, we report putative loss of function variants, rs114285050 (GPR151) and rs150090666 (PDE3B), that substantially contribute to obesity-related traits and experimentally demonstrate the role of these genes in adipocyte biology. Our approach to dissect components of genetic associations across the human phenome will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.

Single-cell sequencing of neonatal uterus reveals an Misr2+ endometrial progenitor indispensable for fertility.

The Mullerian ducts are the anlagen of the female reproductive tract, which regress in the male fetus in response to MIS. This process is driven by subluminal mesenchymal cells expressing Misr2, which trigger the regression of the adjacent Mullerian ductal epithelium. In females, these Misr2+ cells are retained, yet their contribution to the development of the uterus remains unknown. Here, we report that subluminal Misr2+ cells persist postnatally in the uterus of rodents, but recede by week 37 of gestation in humans. Using single-cell RNA sequencing, we demonstrate that ectopic postnatal MIS administration inhibits these cells and prevents the formation of endometrial stroma in rodents, suggesting a progenitor function. Exposure to MIS during the first six days of life, by inhibiting specification of the stroma, dysregulates paracrine signals necessary for uterine development, eventually resulting in apoptosis of the Misr2+ cells, uterine hypoplasia, and complete infertility in the adult female.

GeNets: a unified web platform for network-based genomic analyses.

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to compare the signal-to-noise ratios of different networks and to identify the optimal network with which to interpret a particular genetic dataset. We present GeNets, a platform in which users can train a machine-learning model (Quack) to carry out these comparisons and execute, store, and share analyses of genetic and RNA-sequencing datasets.

NetSig: network-based discovery from cancer genomes.

Methods that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes; but such approaches are challenging to validate at scale, and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks with data from 4,742 tumor exomes. NetSig can accurately classify known driver genes in 60% of tested tumor types and predicts 62 new driver candidates. Using a quantitative experimental framework to determine in vivo tumorigenic potential in mice, we found that NetSig candidates induce tumors at rates that are comparable to those of known oncogenes and are ten-fold higher than those of random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Our study presents a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.

Heterogeneous contribution of microdeletions in the development of common generalised and focal epilepsies.

BACKGROUND: Microdeletions are known to confer risk to epilepsy, particularly at genomic rearrangement 'hotspot' loci. However, microdeletion burden not overlapping these regions or within different epilepsy subtypes has not been ascertained. OBJECTIVE: To decipher the role of microdeletions outside hotspots loci and risk assessment by epilepsy subtype. METHODS: We assessed the burden, frequency and genomic content of rare, large microdeletions found in a previously published cohort of 1366 patients with genetic generalised epilepsy (GGE) in addition to two sets of additional unpublished genome-wide microdeletions found in 281 patients with rolandic epilepsy (RE) and 807 patients with adult focal epilepsy (AFE), totalling 2454 cases. Microdeletions were assessed in a combined and subtype-specific approaches against 6746 controls. RESULTS: When hotspots are considered, we detected an enrichment of microdeletions in the combined epilepsy analysis (adjusted p=1.06×10-6,OR 1.89, 95% CI 1.51 to 2.35). Epilepsy subtype-specific analyses showed that hotspot microdeletions in the GGE subgroup contribute most of the overall signal (adjusted p=9.79×10-12, OR 7.45, 95% CI 4.20-13.5). Outside hotspots , microdeletions were enriched in the GGE cohort for neurodevelopmental genes (adjusted p=9.13×10-3,OR 2.85, 95% CI 1.62-4.94). No additional signal was observed for RE and AFE. Still, gene-content analysis identified known (NRXN1, RBFOX1 and PCDH7) and novel (LOC102723362) candidate genes across epilepsy subtypes that were not deleted in controls. CONCLUSIONS: Our results show a heterogeneous effect of recurrent and non-recurrent microdeletions as part of the genetic architecture of GGE and a minor contribution in the aetiology of RE and AFE.

A scored human protein-protein interaction network to catalyze genomic interpretation.

Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap, or InWeb_IM) with severalfold more interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism.

Genetic and Proteomic Interrogation of Lower Confidence Candidate Genes Reveals Signaling Networks in ß-Catenin-Active Cancers.

Genome-scale expression studies and comprehensive loss-of-function genetic screens have focused almost exclusively on the highest confidence candidate genes. Here, we describe a strategy for characterizing the lower confidence candidates identified by such approaches. We interrogated 177 genes that we classified as essential for the proliferation of cancer cells exhibiting constitutive ß-catenin activity and integrated data for each of the candidates, derived from orthogonal short hairpin RNA (shRNA) knockdown and clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9-mediated gene editing knockout screens, to yield 69 validated genes. We then characterized the relationships between sets of these genes using complementary assays: medium-throughput stable isotope labeling by amino acids in cell culture (SILAC)-based mass spectrometry, yielding 3,639 protein-protein interactions, and a CRISPR-mediated pairwise double knockout screen, yielding 375 combinations exhibiting greater- or lesser-than-additive phenotypic effects indicating genetic interactions. These studies identify previously unreported regulators of ß-catenin, define functional networks required for the survival of ß-catenin-active cancers, and provide an experimental strategy that may be applied to define other signaling networks.

CD44 Splice Variant v8-10 as a Marker of Serous Ovarian Cancer Prognosis.

CD44 is a transmembrane hyaluronic acid receptor gene that encodes over 100 different tissue-specific protein isoforms. The most ubiquitous, CD44 standard, has been used as a cancer stem cell marker in ovarian and other cancers. Expression of the epithelial CD44 variant containing exons v8-10 (CD44v8-10) has been associated with more chemoresistant and metastatic tumors in gastrointestinal and breast cancers, but its role in ovarian cancer is unknown; we therefore investigated its use as a prognostic marker in this disease. The gene expression profiles of 254 tumor samples from The Cancer Genome Atlas RNAseqV2 were analyzed for the presence of CD44 isoforms. A trend for longer survival was observed in patients with high expression of CD44 isoforms that include exons v8-10. Immunohistochemical (IHC) analysis of tumors for presence of CD44v8-10 was performed on an independent cohort of 210 patients with high-grade serous ovarian cancer using a tumor tissue microarray. Patient stratification based on software analysis of staining revealed a statistically significant increase in survival in patients with the highest levels of transmembrane protein expression (top 10 or 20%) compared to those with the lowest expression (bottom 10 and 20%) (p = 0.0181, p = 0.0262 respectively). Expression of CD44v8-10 in primary ovarian cancer cell lines was correlated with a predominantly epithelial phenotype characterized by high expression of epithelial markers and low expression of mesenchymal markers by qPCR, Western blot, and IHC. Conversely, detection of proteolytically cleaved and soluble extracellular domain of CD44v8-10 in patient ascites samples was correlated with significantly worse prognosis (p<0.05). Therefore, presence of transmembrane CD44v8-10 on the surface of primary tumor cells may be a marker of a highly epithelial tumor with better prognosis while enzymatic cleavage of CD44v8-10, as detected by presence of the soluble extracellular domain in ascites fluid, may be indicative of a more metastatic disease and worse prognosis.

Systematic Functional Interrogation of Rare Cancer Variants Identifies Oncogenic Alleles.

UNLABELLED: Cancer genome characterization efforts now provide an initial view of the somatic alterations in primary tumors. However, most point mutations occur at low frequency, and the function of these alleles remains undefined. We have developed a scalable systematic approach to interrogate the function of cancer-associated gene variants. We subjected 474 mutant alleles curated from 5,338 tumors to pooled in vivo tumor formation assays and gene expression profiling. We identified 12 transforming alleles, including two in genes (PIK3CB, POT1) that have not been shown to be tumorigenic. One rare KRAS allele, D33E, displayed tumorigenicity and constitutive activation of known RAS effector pathways. By comparing gene expression changes induced upon expression of wild-type and mutant alleles, we inferred the activity of specific alleles. Because alleles found to be mutated only once in 5,338 tumors rendered cells tumorigenic, these observations underscore the value of integrating genomic information with functional studies. SIGNIFICANCE: Experimentally inferring the functional status of cancer-associated mutations facilitates the interpretation of genomic information in cancer. Pooled in vivo screen and gene expression profiling identified functional variants and demonstrated that expression of rare variants induced tumorigenesis. Variant phenotyping through functional studies will facilitate defining key somatic events in cancer. Cancer Discov; 6(7); 714-26. ©2016 AACR.See related commentary by Cho and Collisson, p. 694This article is highlighted in the In This Issue feature, p. 681.

Temporal proteomics of NGF-TrkA signaling identifies an inhibitory role for the E3 ligase Cbl-b in neuroblastoma cell differentiation.

SH-SY5Y neuroblastoma cells respond to nerve growth factor (NGF)-mediated activation of the tropomyosin-related kinase A (TrkA) with neurite outgrowth, thereby providing a model to study neuronal differentiation. We performed a time-resolved analysis of NGF-TrkA signaling in neuroblastoma cells using mass spectrometry-based quantitative proteomics. The combination of interactome, phosphoproteome, and proteome data provided temporal insights into the molecular events downstream of NGF binding to TrkA. We showed that upon NGF stimulation, TrkA recruits the E3 ubiquitin ligase Cbl-b, which then becomes phosphorylated and ubiquitylated and decreases in abundance. We also found that recruitment of Cbl-b promotes TrkA ubiquitylation and degradation. Furthermore, the amount of phosphorylation of the kinase ERK and neurite outgrowth increased upon Cbl-b depletion in several neuroblastoma cell lines. Our findings suggest that Cbl-b limits NGF-TrkA signaling to control the length of neurites.

Atg16L1 T300A variant decreases selective autophagy resulting in altered cytokine signaling and decreased antibacterial defense.

A coding polymorphism (Thr300Ala) in the essential autophagy gene, autophagy related 16-like 1 (ATG16L1), confers increased risk for the development of Crohn disease, although the mechanisms by which single disease-associated polymorphisms contribute to pathogenesis have been difficult to dissect given that environmental factors likely influence disease initiation in these patients. Here we introduce a knock-in mouse model expressing the Atg16L1 T300A variant. Consistent with the human polymorphism, T300A knock-in mice do not develop spontaneous intestinal inflammation, but exhibit morphological defects in Paneth and goblet cells. Selective autophagy is reduced in multiple cell types from T300A knock-in mice compared with WT mice. The T300A polymorphism significantly increases caspase 3- and caspase 7-mediated cleavage of Atg16L1, resulting in lower levels of full-length Atg16Ll T300A protein. Moreover, Atg16L1 T300A is associated with decreased antibacterial autophagy and increased IL-1ß production in primary cells and in vivo. Quantitative proteomics for protein interactors of ATG16L1 identified previously unknown nonoverlapping sets of proteins involved in ATG16L1-dependent antibacterial autophagy or IL-1ß production. These findings demonstrate how the T300A polymorphism leads to cell type- and pathway-specific disruptions of selective autophagy and suggest a mechanism by which this polymorphism contributes to disease.

DoReMi: context-based prioritization of linear motif matches.

Many protein domains bind to short peptide sequences, called linear motifs. Data on their sequence specificities is sparse, which is why biologists usually resort to basic pattern searches to identify new putative binding sites for experimental follow-up. Most motifs have poor specificity and prioritization of the matches is thus crucial when scanning a full proteome with a pattern. Here we present a generic method to prioritize motif occurrence predictions by using cellular contextual information. We take 2 parameters as input: the motif occurrences and one or more of the interacting domains. The potential hits are ranked based on how strongly the context network associates them with a protein containing one of the specified domains, which leads to an increased predictive performance. The method is available through a web interface at doremi.jensenlab.org, which allows for an easy application of the method. We show that this approach leads to improved predictions of binding partners for PDZ domains and the SUMO binding domain. This is consistent with the earlier observation that coupling sequence motifs with network information improves kinase-specific substrate predictions.

Predicting kinase activity in angiotensin receptor phosphoproteomes based on sequence-motifs and interactions.

Recent progress in the understanding of seven-transmembrane receptor (7TMR) signalling has promoted the development of a new generation of pathway selective ligands. The angiotensin II type I receptor (AT1aR) is one of the most studied 7TMRs with respect to selective activation of the ß-arrestin dependent signalling. Two complimentary global phosphoproteomics studies have analyzed the complex signalling induced by the AT1aR. Here we integrate the data sets from these studies and perform a joint analysis using a novel method for prediction of differential kinase activity from phosphoproteomics data. The method builds upon NetworKIN, which applies sophisticated linear motif analysis in combination with contextual network modelling to predict kinase-substrate associations with high accuracy and sensitivity. These predictions form the basis for subsequently nonparametric statistical analysis to identify likely activated kinases. This suggested that AT1aR-dependent signalling activates 48 of the 285 kinases detected in HEK293 cells. Of these, Aurora B, CLK3 and PKG1 have not previously been described in the pathway whereas others, such as PKA, PKB and PKC, are well known. In summary, we have developed a new method for kinase-centric analysis of phosphoproteomes to pinpoint differential kinase activity in large-scale data sets.

Proteomic analysis of arginine methylation sites in human cells reveals dynamic regulation during transcriptional arrest.

The covalent attachment of methyl groups to the side-chain of arginine residues is known to play essential roles in regulation of transcription, protein function, and RNA metabolism. The specific N-methylation of arginine residues is catalyzed by a small family of gene products known as protein arginine methyltransferases; however, very little is known about which arginine residues become methylated on target substrates. Here we describe a proteomics methodology that combines single-step immunoenrichment of methylated peptides with high-resolution mass spectrometry to identify endogenous arginine mono-methylation (MMA) sites. We thereby identify 1027 site-specific MMA sites on 494 human proteins, discovering numerous novel mono-methylation targets and confirming the majority of currently known MMA substrates. Nuclear RNA-binding proteins involved in RNA processing, RNA localization, transcription, and chromatin remodeling are predominantly found modified with MMA. Despite this, MMA sites prominently are located outside RNA-binding domains as compared with the proteome-wide distribution of arginine residues. Quantification of arginine methylation in cells treated with Actinomycin D uncovers strong site-specific regulation of MMA sites during transcriptional arrest. Interestingly, several MMA sites are down-regulated after a few hours of transcriptional arrest. In contrast, the corresponding di-methylation or protein expression levels are not altered, confirming that MMA sites contain regulated functions on their own. Collectively, we present a site-specific MMA data set in human cells and demonstrate for the first time that MMA is a dynamic post-translational modification regulated during transcriptional arrest by a hitherto uncharacterized arginine demethylase.

TIMP-1 increases expression and phosphorylation of proteins associated with drug resistance in breast cancer cells.

Tissue inhibitor of metalloproteinase 1 (TIMP-1) is a protein with a potential biological role in drug resistance. To elucidate the unknown molecular mechanisms underlying the association between high TIMP-1 levels and increased chemotherapy resistance, we employed SILAC-based quantitative mass spectrometry to analyze global proteome and phosphoproteome differences of MCF-7 breast cancer cells expressing high or low levels of TIMP-1. In TIMP-1 high expressing cells, 312 proteins and 452 phosphorylation sites were up-regulated. Among these were the cancer drug targets topoisomerase 1, 2A, and 2B, which may explain the resistance phenotype to topoisomerase inhibitors that was observed in cells with high TIMP-1 levels. Pathway analysis showed an enrichment of proteins from functional categories such as apoptosis, cell cycle, DNA repair, transcription factors, drug targets and proteins associated with drug resistance or sensitivity, and drug transportation. The NetworKIN algorithm predicted the protein kinases CK2a, CDK1, PLK1, and ATM as likely candidates involved in the hyperphosphorylation of the topoisomerases. Up-regulation of protein and/or phosphorylation levels of topoisomerases in TIMP-1 high expressing cells may be part of the mechanisms by which TIMP-1 confers resistance to treatment with the widely used topoisomerase inhibitors in breast and colorectal cancer.