Smoking-associated gene expression alterations in nasal epithelium reveal immune impairment linked to lung cancer risk.

BACKGROUND: Lung cancer is the leading cause of cancer-related death in the world. In contrast to many other cancers, a direct connection to modifiable lifestyle risk in the form of tobacco smoke has long been established. More than 50% of all smoking-related lung cancers occur in former smokers, 40% of which occur more than 15 years after smoking cessation. Despite extensive research, the molecular processes for persistent lung cancer risk remain unclear. We thus set out to examine whether risk stratification in the clinic and in the general population can be improved upon by the addition of genetic data and to explore the mechanisms of the persisting risk in former smokers. METHODS: We analysed transcriptomic data from accessible airway tissues of 487 subjects, including healthy volunteers and clinic patients of different smoking statuses. We developed a computational model to assess smoking-associated gene expression changes and their reversibility after smoking is stopped, comparing healthy subjects to clinic patients with and without lung cancer. RESULTS: We find persistent smoking-associated immune alterations to be a hallmark of the clinic patients. Integrating previous GWAS data using a transcriptional network approach, we demonstrate that the same immune- and interferon-related pathways are strongly enriched for genes linked to known genetic risk factors, demonstrating a causal relationship between immune alteration and lung cancer risk. Finally, we used accessible airway transcriptomic data to derive a non-invasive lung cancer risk classifier. CONCLUSIONS: Our results provide initial evidence for germline-mediated personalized smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality.

Data-Driven Medicine in the Diagnosis and Treatment of Infertility.

Infertility, although not a life-threatening condition, affects around 15% of couples trying for a pregnancy. The increasing availability of large datasets from various sources, together with advances in machine learning (ML) and artificial intelligence (AI), are enabling a transformational change in infertility care. However, real-world applications of data-driven medicine in infertility care are still relatively limited. At present, very little can prevent infertility from arising; more work is required to learn about ways to improve natural conception and the detection and diagnosis of infertility, improve assisted reproduction treatments (ART) and ultimately develop useful clinical-decision support systems to assure the successful outcome of either fertility preservation or infertility treatment. In this opinion article, we discuss recent influential work on the application of big data and AI in the prevention, diagnosis and treatment of infertility. We evaluate the challenges of the sector and present an interpretation of the different innovation forces that are driving the emergence of a systems approach to infertility care. Efforts including the integration of multi-omics information, collection of well-curated biological samples in specialised biobanks, and stimulation of the active participation of patients are considered. In the era of Big Data and AI, there is now an exciting opportunity to leverage the progress in genomics and digital technologies and develop more sophisticated approaches to diagnose and treat infertility disorders.

Clonal somatic copy number altered driver events inform drug sensitivity in high-grade serous ovarian cancer.

Chromosomal instability is a major challenge to patient stratification and targeted drug development for high-grade serous ovarian carcinoma (HGSOC). Here we show that somatic copy number alterations (SCNAs) in frequently amplified HGSOC cancer genes significantly correlate with gene expression and methylation status. We identify five prevalent clonal driver SCNAs (chromosomal amplifications encompassing MYC, PIK3CA, CCNE1, KRAS and TERT) from multi-regional HGSOC data and reason that their strong selection should prioritise them as key biomarkers for targeted therapies. We use primary HGSOC spheroid models to test interactions between in vitro targeted therapy and SCNAs. MYC chromosomal copy number is associated with in-vitro and clinical response to paclitaxel and in-vitro response to mTORC1/2 inhibition. Activation of the mTOR survival pathway in the context of MYC-amplified HGSOC is statistically associated with increased prevalence of SCNAs in genes from the PI3K pathway. Co-occurrence of amplifications in MYC and genes from the PI3K pathway is independently observed in squamous lung cancer and triple negative breast cancer. In this work, we show that identifying co-occurrence of clonal driver SCNA genes could be used to tailor therapeutics for precision medicine.

Immunophenotypes of pancreatic ductal adenocarcinoma: Meta-analysis of transcriptional subtypes.

Pancreatic ductal adenocarcinoma (PDAC) is the most common malignancy of the pancreas and has one of the highest mortality rates of any cancer type with a 5-year survival rate of <5%. Recent studies of PDAC have provided several transcriptomic classifications based on separate analyses of individual patient cohorts. There is a need to provide a unified transcriptomic PDAC classification driven by therapeutically relevant biologic rationale to inform future treatment strategies. Here, we used an integrative meta-analysis of 353 patients from four different studies to derive a PDAC classification based on immunologic parameters. This consensus clustering approach indicated transcriptomic signatures based on immune infiltrate classified as adaptive, innate and immune-exclusion subtypes. This reveals the existence of microenvironmental interpatient heterogeneity within PDAC and could serve to drive novel therapeutic strategies in PDAC including immune modulation approaches to treating this disease.

Analysis of ChIP-seq Data in R/Bioconductor.

The development of novel high-throughput sequencing methods for ChIP (chromatin immunoprecipitation) has provided a very powerful tool to study gene regulation in multiple conditions at unprecedented resolution and scale. Proactive quality-control and appropriate data analysis techniques are of critical importance to extract the most meaningful results from the data. Over the last years, an array of R/Bioconductor tools has been developed allowing researchers to process and analyze ChIP-seq data. This chapter provides an overview of the methods available to analyze ChIP-seq data based primarily on software packages from the open-source Bioconductor project. Protocols described in this chapter cover basic steps including data alignment, peak calling, quality control and data visualization, as well as more complex methods such as the identification of differentially bound regions and functional analyses to annotate regulatory regions. The steps in the data analysis process were demonstrated on publicly available data sets and will serve as a demonstration of the computational procedures routinely used for the analysis of ChIP-seq data in R/Bioconductor, from which readers can construct their own analysis pipelines.

RNA polymerase II primes Polycomb-repressed developmental genes throughout terminal neuronal differentiation.

Polycomb repression in mouse embryonic stem cells (ESCs) is tightly associated with promoter co-occupancy of RNA polymerase II (RNAPII) which is thought to prime genes for activation during early development. However, it is unknown whether RNAPII poising is a general feature of Polycomb repression, or is lost during differentiation. Here, we map the genome-wide occupancy of RNAPII and Polycomb from pluripotent ESCs to non-dividing functional dopaminergic neurons. We find that poised RNAPII complexes are ubiquitously present at Polycomb-repressed genes at all stages of neuronal differentiation. We observe both loss and acquisition of RNAPII and Polycomb at specific groups of genes reflecting their silencing or activation. Strikingly, RNAPII remains poised at transcription factor genes which are silenced in neurons through Polycomb repression, and have major roles in specifying other, non-neuronal lineages. We conclude that RNAPII poising is intrinsically associated with Polycomb repression throughout differentiation. Our work suggests that the tight interplay between RNAPII poising and Polycomb repression not only instructs promoter state transitions, but also may enable promoter plasticity in differentiated cells.

Active and poised promoter states drive folding of the extended HoxB locus in mouse embryonic stem cells.

Gene expression states influence the 3D conformation of the genome through poorly understood mechanisms. Here, we investigate the conformation of the murine HoxB locus, a gene-dense genomic region containing closely spaced genes with distinct activation states in mouse embryonic stem (ES) cells. To predict possible folding scenarios, we performed computer simulations of polymer models informed with different chromatin occupancy features that define promoter activation states or binding sites for the transcription factor CTCF. Single-cell imaging of the locus folding was performed to test model predictions. While CTCF occupancy alone fails to predict the in vivo folding at genomic length scale of 10 kb, we found that homotypic interactions between active and Polycomb-repressed promoters co-occurring in the same DNA fiber fully explain the HoxB folding patterns imaged in single cells. We identify state-dependent promoter interactions as major drivers of chromatin folding in gene-dense regions.

Complex multi-enhancer contacts captured by genome architecture mapping.

The organization of the genome in the nucleus and the interactions of genes with their regulatory elements are key features of transcriptional control and their disruption can cause disease. Here we report a genome-wide method, genome architecture mapping (GAM), for measuring chromatin contacts and other features of three-dimensional chromatin topology on the basis of sequencing DNA from a large collection of thin nuclear sections. We apply GAM to mouse embryonic stem cells and identify enrichment for specific interactions between active genes and enhancers across very large genomic distances using a mathematical model termed SLICE (statistical inference of co-segregation). GAM also reveals an abundance of three-way contacts across the genome, especially between regions that are highly transcribed or contain super-enhancers, providing a level of insight into genome architecture that, owing to the technical limitations of current technologies, has previously remained unattainable. Furthermore, GAM highlights a role for gene-expression-specific contacts in organizing the genome in mammalian nuclei.

BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes.

Allele-specific measurements of transcription factor binding from ChIP-seq data are key to dissecting the allelic effects of non-coding variants and their contribution to phenotypic diversity. However, most methods of detecting an allelic imbalance assume diploid genomes. This assumption severely limits their applicability to cancer samples with frequent DNA copy-number changes. Here we present a Bayesian statistical approach called BaalChIP to correct for the effect of background allele frequency on the observed ChIP-seq read counts. BaalChIP allows the joint analysis of multiple ChIP-seq samples across a single variant and outperforms competing approaches in simulations. Using 548 ENCODE ChIP-seq and six targeted FAIRE-seq samples, we show that BaalChIP effectively corrects allele-specific analysis for copy-number variation and increases the power to detect putative cis-acting regulatory variants in cancer genomes.

Master Regulators of Oncogenic KRAS Response in Pancreatic Cancer: An Integrative Network Biology Analysis.

BACKGROUND: KRAS is the most frequently mutated gene in pancreatic ductal adenocarcinoma (PDAC), but the mechanisms underlying the transcriptional response to oncogenic KRAS are still not fully understood. We aimed to uncover transcription factors that regulate the transcriptional response of oncogenic KRAS in pancreatic cancer and to understand their clinical relevance. METHODS AND FINDINGS: We applied a well-established network biology approach (master regulator analysis) to combine a transcriptional signature for oncogenic KRAS derived from a murine isogenic cell line with a coexpression network derived by integrating 560 human pancreatic cancer cases across seven studies. The datasets included the ICGC cohort (n = 242), the TCGA cohort (n = 178), and five smaller studies (n = 17, 25, 26, 36, and 36). 55 transcription factors were coexpressed with a significant number of genes in the transcriptional signature (gene set enrichment analysis [GSEA] p < 0.01). Community detection in the coexpression network identified 27 of the 55 transcription factors contributing to three major biological processes: Notch pathway, down-regulated Hedgehog/Wnt pathway, and cell cycle. The activities of these processes define three distinct subtypes of PDAC, which demonstrate differences in survival and mutational load as well as stromal and immune cell composition. The Hedgehog subgroup showed worst survival (hazard ratio 1.73, 95% CI 1.1 to 2.72, coxPH test p = 0.018) and the Notch subgroup the best (hazard ratio 0.62, 95% CI 0.42 to 0.93, coxPH test p = 0.019). The cell cycle subtype showed highest mutational burden (ANOVA p < 0.01) and the smallest amount of stromal admixture (ANOVA p < 2.2e-16). This study is limited by the information provided in published datasets, not all of which provide mutational profiles, survival data, or the specifics of treatment history. CONCLUSIONS: Our results characterize the regulatory mechanisms underlying the transcriptional response to oncogenic KRAS and provide a framework to develop strategies for specific subtypes of this disease using current therapeutics and by identifying targets for new groups.

FGFR2 risk SNPs confer breast cancer risk by augmenting oestrogen responsiveness.

The fibroblast growth factor receptor 2 (FGFR2) locus is consistently the top hit in genome-wide association studies for oestrogen receptor-positive (ER(+)) breast cancer. Yet, its mode of action continues to be controversial. Here, we employ a systems biology approach to demonstrate that signalling via FGFR2 counteracts cell activation by oestrogen. In the presence of oestrogen, the oestrogen receptor (ESR1) regulon (set of ESR1 target genes) is in an active state. However, signalling by FGFR2 is able to reverse the activity of the ESR1 regulon. This effect is seen in multiple distinct FGFR2 signalling model systems, across multiple cells lines and is dependent on the presence of FGFR2. Increased oestrogen exposure has long been associated with an increased risk of breast cancer. We therefore hypothesized that risk variants should reduce FGFR2 expression and subsequent signalling. Indeed, transient transfection experiments assaying the three independent variants of the FGFR2 risk locus (rs2981578, rs35054928 and rs45631563) in their normal chromosomal context show that these single-nucleotide polymorphisms (SNPs) map to transcriptional silencer elements and that, compared with wild type, the risk alleles augment silencer activity. The presence of risk variants results in lower FGFR2 expression and increased oestrogen responsiveness. We thus propose a molecular mechanism by which FGFR2 can confer increased breast cancer risk that is consistent with oestrogen exposure as a major driver of breast cancer risk. Our findings may have implications for the clinical use of FGFR2 inhibitors.

Accumulated Metabolites of Hydroxybutyric Acid Serve as Diagnostic and Prognostic Biomarkers of Ovarian High-Grade Serous Carcinomas.

Ovarian cancer is a heterogeneous disease of low prevalence, but poor survival. Early diagnosis is critical for survival, but it is often challenging because the symptoms of ovarian cancer are subtle and become apparent only during advanced stages of the disease. Therefore, the identification of robust biomarkers of early disease is a clinical priority. Metabolomic profiling is an emerging diagnostic tool enabling the detection of biomarkers reflecting alterations in tumor metabolism, a hallmark of cancer. In this study, we performed metabolomic profiling of serum and tumor tissue from 158 patients with high-grade serous ovarian cancer (HGSOC) and 100 control patients with benign or non-neoplastic lesions. We report metabolites of hydroxybutyric acid (HBA) as novel diagnostic and prognostic biomarkers associated with tumor burden and patient survival. The accumulation of HBA metabolites caused by HGSOC was also associated with reduced expression of succinic semialdehyde dehydrogenase (encoded by ALDH5A1), and with the presence of an epithelial-to-mesenchymal transition gene signature, implying a role for these metabolic alterations in cancer cell migration and invasion. In conclusion, our findings represent the first comprehensive metabolomics analysis in HGSOC and propose a new set of metabolites as biomarkers of disease with diagnostic and prognostic capabilities.

Regulators of genetic risk of breast cancer identified by integrative network analysis.

Genetic risk for breast cancer is conferred by a combination of multiple variants of small effect. To better understand how risk loci might combine, we examined whether risk-associated genes share regulatory mechanisms. We created a breast cancer gene regulatory network comprising transcription factors and groups of putative target genes (regulons) and asked whether specific regulons are enriched for genes associated with risk loci via expression quantitative trait loci (eQTLs). We identified 36 overlapping regulons that were enriched for risk loci and formed a distinct cluster within the network, suggesting shared biology. The risk transcription factors driving these regulons are frequently mutated in cancer and lie in two opposing subgroups, which relate to estrogen receptor (ER)(+) luminal A or luminal B and ER(-) basal-like cancers and to different luminal epithelial cell populations in the adult mammary gland. Our network approach provides a foundation for determining the regulatory circuits governing breast cancer, to identify targets for intervention, and is transferable to other disease settings.

Impact of artifact removal on ChIP quality metrics in ChIP-seq and ChIP-exo data.

With the advent of ChIP-seq multiplexing technologies and the subsequent increase in ChIP-seq throughput, the development of working standards for the quality assessment of ChIP-seq studies has received significant attention. The ENCODE consortium's large scale analysis of transcription factor binding and epigenetic marks as well as concordant work on ChIP-seq by other laboratories has established a new generation of ChIP-seq quality control measures. The use of these metrics alongside common processing steps has however not been evaluated. In this study, we investigate the effects of blacklisting and removal of duplicated reads on established metrics of ChIP-seq quality and show that the interpretation of these metrics is highly dependent on the ChIP-seq preprocessing steps applied. Further to this we perform the first investigation of the use of these metrics for ChIP-exo data and make recommendations for the adaptation of the NSC statistic to allow for the assessment of ChIP-exo efficiency.

Fine-scale mapping of the FGFR2 breast cancer risk locus: putative functional variants differentially bind FOXA1 and E2F1.

The 10q26 locus in the second intron of FGFR2 is the locus most strongly associated with estrogen-receptor-positive breast cancer in genome-wide association studies. We conducted fine-scale mapping in case-control studies genotyped with a custom chip (iCOGS), comprising 41 studies (n = 89,050) of European ancestry, 9 Asian ancestry studies (n = 13,983), and 2 African ancestry studies (n = 2,028) from the Breast Cancer Association Consortium. We identified three statistically independent risk signals within the locus. Within risk signals 1 and 3, genetic analysis identified five and two variants, respectively, highly correlated with the most strongly associated SNPs. By using a combination of genetic fine mapping, data on DNase hypersensitivity, and electrophoretic mobility shift assays to study protein-DNA binding, we identified rs35054928, rs2981578, and rs45631563 as putative functional SNPs. Chromatin immunoprecipitation showed that FOXA1 preferentially bound to the risk-associated allele (C) of rs2981578 and was able to recruit ERα to this site in an allele-specific manner, whereas E2F1 preferentially bound the risk variant of rs35054928. The risk alleles were preferentially found in open chromatin and bound by Ser5 phosphorylated RNA polymerase II, suggesting that the risk alleles are associated with changes in transcription. Chromatin conformation capture demonstrated that the risk region was able to interact with the promoter of FGFR2, the likely target gene of this risk region. A role for FOXA1 in mediating breast cancer susceptibility at this locus is consistent with the finding that the FGFR2 risk locus primarily predisposes to estrogen-receptor-positive disease.

Master regulators of FGFR2 signalling and breast cancer risk.

The fibroblast growth factor receptor 2 (FGFR2) locus has been consistently identified as a breast cancer risk locus in independent genome-wide association studies. However, the molecular mechanisms underlying FGFR2-mediated risk are still unknown. Using model systems we show that FGFR2-regulated genes are preferentially linked to breast cancer risk loci in expression quantitative trait loci analysis, supporting the concept that risk genes cluster in pathways. Using a network derived from 2,000 transcriptional profiles we identify SPDEF, ERα, FOXA1, GATA3 and PTTG1 as master regulators of fibroblast growth factor receptor 2 signalling, and show that ERα occupancy responds to fibroblast growth factor receptor 2 signalling. Our results indicate that ERα, FOXA1 and GATA3 contribute to the regulation of breast cancer susceptibility genes, which is consistent with the effects of anti-oestrogen treatment in breast cancer prevention, and suggest that fibroblast growth factor receptor 2 signalling has an important role in mediating breast cancer risk.

Polycomb associates genome-wide with a specific RNA polymerase II variant, and regulates metabolic genes in ESCs.

Polycomb repressor complexes (PRCs) are important chromatin modifiers fundamentally implicated in pluripotency and cancer. Polycomb silencing in embryonic stem cells (ESCs) can be accompanied by active chromatin and primed RNA polymerase II (RNAPII), but the relationship between PRCs and RNAPII remains unclear genome-wide. We mapped PRC repression markers and four RNAPII states in ESCs using ChIP-seq, and found that PRC targets exhibit a range of RNAPII variants. First, developmental PRC targets are bound by unproductive RNAPII (S5p(+)S7p(-)S2p(-)) genome-wide. Sequential ChIP, Ring1B depletion, and genome-wide correlations show that PRCs and RNAPII-S5p physically bind to the same chromatin and functionally synergize. Second, we identify a cohort of genes marked by PRC and elongating RNAPII (S5p(+)S7p(+)S2p(+)); they produce mRNA and protein, and their expression increases upon PRC1 knockdown. We show that this group of PRC targets switches between active and PRC-repressed states within the ESC population, and that many have roles in metabolism.

Eukaryotic gene regulation in three dimensions and its impact on genome evolution.

Recent advances in molecular techniques and high-resolution imaging are beginning to provide exciting insights into the higher order chromatin organization within the cell nucleus and its influence on eukaryotic gene regulation. This improved understanding of gene regulation also raises fundamental questions about how spatial features might have constrained the organization of genes on eukaryotic chromosomes and how mutations that affect these processes might contribute to disease conditions. In this review, we discuss recent studies that highlight the role of spatial components in gene regulation and their impact on genome evolution. We then address implications for human diseases and outline new directions for future research.