A mucus production programme promotes classical pancreatic ductal adenocarcinoma.

OBJECTIVE: The optimal therapeutic response in cancer patients is highly dependent upon the differentiation state of their tumours. Pancreatic ductal adenocarcinoma (PDA) is a lethal cancer that harbours distinct phenotypic subtypes with preferential sensitivities to standard therapies. This study aimed to investigate intratumour heterogeneity and plasticity of cancer cell states in PDA in order to reveal cell state-specific regulators. DESIGN: We analysed single-cell expression profiling of mouse PDAs, revealing intratumour heterogeneity and cell plasticity and identified pathways activated in the different cell states. We performed comparative analysis of murine and human expression states and confirmed their phenotypic diversity in specimens by immunolabeling. We assessed the function of phenotypic regulators using mouse models of PDA, organoids, cell lines and orthotopically grafted tumour models. RESULTS: Our expression analysis and immunolabeling analysis show that a mucus production programme regulated by the transcription factor SPDEF is highly active in precancerous lesions and the classical subtype of PDA - the most common differentiation state. SPDEF maintains the classical differentiation and supports PDA transformation in vivo. The SPDEF tumour-promoting function is mediated by its target genes AGR2 and ERN2/IRE1ß that regulate mucus production, and inactivation of the SPDEF programme impairs tumour growth and facilitates subtype interconversion from classical towards basal-like differentiation. CONCLUSIONS: Our findings expand our understanding of the transcriptional programmes active in precancerous lesions and PDAs of classical differentiation, determine the regulators of mucus production as specific vulnerabilities in these cell states and reveal phenotype switching as a response mechanism to inactivation of differentiation states determinants.

Inferring and modeling inheritance of differentially methylated changes across multiple generations.

High-throughput methylation sequencing enables genome-wide detection of differentially methylated sites (DMS) or regions (DMR). Increasing evidence suggests that treatment-induced DMS can be transmitted across generations, but the analysis of induced methylation changes across multiple generations is complicated by the lack of sound statistical methods to evaluate significance levels. Due to software design, DMS detection was usually made on each generation separately, thus disregarding stochastic effects expected when a large number of DMS is detected in each generation. Here, we present a novel method based on Monte Carlo sampling, methylInheritance, to evaluate that the number of conserved DMS between several generations is associated to an effect inherited from a treatment and not randomness. Moreover, we developed an inheritance simulation package, methInheritSim, to demonstrate the performance of the methylInheritance method and to evaluate the power of different experimental designs. Finally, we applied methylInheritance to a DNA methylation dataset obtained from early-life persistent organic pollutants (POPs) exposed Sprague-Dawley female rats and their descendants through a paternal transmission. The results show that metylInheritance can efficiently identify treatment-induced inherited methylation changes. Specifically, we identified two intergenerationally conserved DMS at transcription start site (TSS); one of those persisted transgenerationally. Three transgenerationally conserved DMR were found at intra or integenic regions.

metagene Profiles Analyses Reveal Regulatory Element's Factor-Specific Recruitment Patterns.

ChIP-Sequencing (ChIP-Seq) provides a vast amount of information regarding the localization of proteins across the genome. The aggregation of ChIP-Seq enrichment signal in a metagene plot is an approach commonly used to summarize data complexity and to obtain a high level visual representation of the general occupancy pattern of a protein. Here we present the R package metagene, the graphical interface Imetagene and the companion package similaRpeak. Together, they provide a framework to integrate, summarize and compare the ChIP-Seq enrichment signal from complex experimental designs. Those packages identify and quantify similarities or dissimilarities in patterns between large numbers of ChIP-Seq profiles. We used metagene to investigate the differential occupancy of regulatory factors at noncoding regulatory regions (promoters and enhancers) in relation to transcriptional activity in GM12878 B-lymphocytes. The relationships between occupancy patterns and transcriptional activity suggest two different mechanisms of action for transcriptional control: i) a "gradient effect" where the regulatory factor occupancy levels follow transcription and ii) a "threshold effect" where the regulatory factor occupancy levels max out prior to reaching maximal transcription. metagene, Imetagene and similaRpeak are implemented in R under the Artistic license 2.0 and are available on Bioconductor.

Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling.

Genome-wide mapping of nucleosomes has revealed a great deal about the relationships between chromatin structure and control of gene expression. Recent next generation CHIP-chip and CHIP-Seq technologies have accelerated our understanding of basic principles of chromatin organization. These technologies have taught us that nucleosomes play a crucial role in gene regulation by allowing physical access to transcription factors. Recent methods and experimental advancements allow the determination of nucleosome positions for a given genome area. However, most of these methods estimate the number of nucleosomes either by an EM algorithm using a BIC criterion or an effective heuristic strategy. Here, we introduce a Bayesian method for identifying nucleosome positions. The proposed model is based on a Multinomial-Dirichlet classification and a hierarchical mixture distributions. The number and the positions of nucleosomes are estimated using a reversible jump Markov chain Monte Carlo simulation technique. We compare the performance of our method on simulated data and MNase-Seq data from Saccharomyces cerevisiae against PING and NOrMAL methods.

Analysis of ZNF350/ZBRK1 promoter variants and breast cancer susceptibility in non-BRCA1/2 French Canadian breast cancer families.

ZNF350/ZBRK1 is a transcription factor, which associates with BRCA1 to co-repress GADD45A to regulate DNA damage repair, and the expression of ZNF350 is altered in different human carcinomas. In a previous study, we identified ZNF350 genomic variants potentially involved in breast cancer susceptibility in high-risk non-BRCA1/2 breast cancer individuals, which pointed toward a potential association for variants in the 5'-UTR and promoter regions. Therefore, direct sequencing was undertaken and identified 12 promoter variants, whereas haplotype analyses put in evidence four common haplotypes with a frequency>2%. However, based on their frequency observed in breast cancer and unrelated healthy individuals, these are not statistically associated with breast cancer risk. Luciferase promoter assays in two breast cancer cell lines identified two haplotypes (H11 and H12) stimulating significantly the expression of ZNF350 transcript compared with the common haplotype H8. The high expression of the H11 allele was associated with the variant c.-874A. Using MatInspector and Transcription Element Search softwares, in silico analyses predicted that the variant c.-874A created a binding site for the factors c-Myc and myogenin. This study represents the first characterization step of the ZNF350 promoter. Additional studies in larger cohorts and other populations will be needed to further evaluate whether common and/or rare ZNF350 promoter variants and haplotypes could be associated with a modest risk of breast cancer.

Analysis of germline gene copy number variants of patients with sporadic pancreatic adenocarcinoma reveals specific variations.

OBJECTIVES: The rapid fatality of pancreatic cancer is, in large part, the result of diagnosis at an advanced stage in the majority of patients. Identification of individuals at risk of developing pancreatic adenocarcinoma would be useful to improve the prognosis of this disease. There is presently no biological or genetic indicator allowing the detection of patients at risk. Our main goal was to identify copy number variants (CNVs) common to all patients with sporadic pancreatic cancer. METHODS: We analyzed gene CNVs in leukocyte DNA from 31 patients with sporadic pancreatic adenocarcinoma and from 93 matched controls. Genotyping was performed with the use of the GeneChip Human Mapping 500K Array Set (Affymetrix). RESULTS: We identified 431 single nucleotide polymorphism (SNP) probes with abnormal hybridization signal present in the DNA of all 31 patients. Of these SNP probes, 284 corresponded to 3 or more copies and 147 corresponded to 1 or 0 copies. Several cancer-associated genes were amplified in all patients. Conversely, several genes supposed to oppose cancer development were present as single copy. CONCLUSIONS: These data suggest that a set of 431 CNVs could be associated with the disease. This set could be useful for early diagnosis.

Genetic Ancestry Inference from Cancer-Derived Molecular Data across Genomic and Transcriptomic Platforms.

Genetic ancestry-oriented cancer research requires the ability to perform accurate and robust genetic ancestry inference from existing cancer-derived data, including whole-exome sequencing, transcriptome sequencing, and targeted gene panels, very often in the absence of matching cancer-free genomic data. Here we examined the feasibility and accuracy of computational inference of genetic ancestry relying exclusively on cancer-derived data. A data synthesis framework was developed to optimize and assess the performance of the ancestry inference for any given input cancer-derived molecular profile. In its core procedure, the ancestral background of the profiled patient is replaced with one of any number of individuals with known ancestry. The data synthesis framework is applicable to multiple profiling platforms, making it possible to assess the performance of inference specifically for a given molecular profile and separately for each continental-level ancestry; this ability extends to all ancestries, including those without statistically sufficient representation in the existing cancer data. The inference procedure was demonstrated to be accurate and robust in a wide range of sequencing depths. Testing of the approach in four representative cancer types and across three molecular profiling modalities showed that continental-level ancestry of patients can be inferred with high accuracy, as quantified by its agreement with the gold standard of deriving ancestry from matching cancer-free molecular data. This study demonstrates that vast amounts of existing cancer-derived molecular data are potentially amenable to ancestry-oriented studies of the disease without requiring matching cancer-free genomes or patient self-reported ancestry. SIGNIFICANCE: The development of a computational approach that enables accurate and robust ancestry inference from cancer-derived molecular profiles without matching cancer-free data provides a valuable methodology for genetic ancestry-oriented cancer research.

Specific transcriptional response of four blockers of estrogen receptors on estradiol-modulated genes in the mouse mammary gland.

Novel agents for the endocrine therapy of breast cancer are needed, especially in order to take advantage of the multiple consecutive responses observed in metastatic progressing breast cancer following previous hormone therapy, thus delaying the use of cytotoxic chemotherapy with its frequent poor tolerance and serious side effects. Acolbifene (ACOL) is a novel and unique antiestrogen which represents a unique opportunity to achieve the most potent and specific blockade of estrogen action in the mammary gland and uterus while exerting estrogen-like beneficial effects in other tissues, especially the bones. To better understand the specificity of action of ACOL, we have used Affymetrix GeneChips containing 45,000 probe sets to analyze 34,000 genes to determine the specificity of this compound compared to the pure antiestrogen fulvestrant, as well as to the mixed antagonists/agonists tamoxifen and raloxifene to block the effect of estradiol (E(2)) and to induce effects of their own on the genomic profile in the mouse mammary gland. The genes modulated by E(2) were those identified in two separate experiments and validated by quantitative real-time PCR (qPCR). Three hours after the single subcutaneous injection of E(2) (0.05 µg), the simultaneous administration of ACOL, fulvestrant, tamoxifen, and raloxifene blocked by 98, 61, 43, and 92 % the number of E(2)-upregulated genes, respectively. On the other hand, 70, 10, 25, and 55 % of the genes down-regulated by E(2) were blocked by the same compounds. Of the 128 genes modulated by E(2), 49 are associated with tumorigenesis while 22 are known to be associated with breast cancer. When used alone, ACOL modulated the smallest number of genes also influenced by E(2), namely 4 %, thus possibly explaining potential utilities of this compound in breast cancer prevention and therapy.

Gene interactions in depression: pathways out of darkness.

Mood disorders, including major depressive disorder and bipolar disorder, are influenced by both genetic and environmental factors. Hypotheses about the neurobiology of mood disorders have been postulated and putatively associated genes identified. Recently, the immune-related gene encoding purinergic receptor P2X, ligand-gated ion channel, 7 (P2RX7) has been genetically associated with major depressive disorder and bipolar disorder. New candidate genes and emerging gene networks and pathways involved in the aetiology of mood disorders point to a major role for neuronal survival and the adaptive immune systems.