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.

TreeAndLeaf: an R/Bioconductor package for graphs and trees with focus on the leaves.

MOTIVATION: Dendrogram is a classical diagram for visualizing binary trees. Although efficient to represent hierarchical relations, it provides limited space for displaying information on the leaf elements, especially for large trees. RESULTS: Here, we present TreeAndLeaf, an R/Bioconductor package that implements a hybrid layout strategy to represent tree diagrams with focus on the leaves. The TreeAndLeaf package combines force-directed graph and tree layout algorithms using a single visualization system, allowing projection of multiple layers of information onto a graph-tree diagram. The Supplementary Information provides two case studies that use breast cancer data from epidemiological and experimental studies. AVAILABILITY AND IMPLEMENTATION: TreeAndLeaf is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/TreeAndLeaf/ (version≥1.4.2). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A prospective study of tea drinking temperature and risk of esophageal squamous cell carcinoma.

Previous studies have reported an association between hot tea drinking and risk of esophageal cancer, but no study has examined this association using prospectively and objectively measured tea drinking temperature. We examined the association of tea drinking temperature, measured both objectively and subjectively at study baseline, with future risk of esophageal squamous cell carcinoma (ESCC) in a prospective study. We measured tea drinking temperature using validated methods and collected data on several other tea drinking habits and potential confounders of interest at baseline in the Golestan Cohort Study, a population-based prospective study of 50,045 individuals aged 40-75 years, established in 2004-2008 in northeastern Iran. Study participants were followed-up for a median duration of 10.1 years (505,865 person-years). During 2004-2017, 317 new cases of ESCC were identified. The objectively measured tea temperature (HR 1.41, 95% CI 1.10-1.81; for ≥60°C vs. <60°C), reported preference for very hot tea drinking (HR 2.41, 95% CI 1.27-4.56; for "very hot" vs. "cold/lukewarm"), and reported shorter time from pouring tea to drinking (HR 1.51, 95% CI 1.01-2.26; for <2 vs. ≥6 min) were all associated with ESCC risk. In analysis of the combined effects of measured temperature and amount, compared to those who drank less than 700 ml of tea/day at <60°C, drinking 700 mL/day or more at a higher-temperature (≥60°C) was consistently associated with an about 90% increase in ESCC risk. Our results substantially strengthen the existing evidence supporting an association between hot beverage drinking and ESCC.

RTNduals: an R/Bioconductor package for analysis of co-regulation and inference of dual regulons.

MOTIVATION: Transcription factors (TFs) are key regulators of gene expression, and can activate or repress multiple target genes, forming regulatory units, or regulons. Understanding downstream effects of these regulators includes evaluating how TFs cooperate or compete within regulatory networks. Here we present RTNduals, an R/Bioconductor package that implements a general method for analyzing pairs of regulons. RESULTS: RTNduals identifies a dual regulon when the number of targets shared between a pair of regulators is statistically significant. The package extends the RTN (Reconstruction of Transcriptional Networks) package, and uses RTN transcriptional networks to identify significant co-regulatory associations between regulons. The Supplementary Information reports two case studies for TFs using the METABRIC and TCGA breast cancer cohorts. AVAILABILITY AND IMPLEMENTATION: RTNduals is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/RTNduals/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RTNsurvival: an R/Bioconductor package for regulatory network survival analysis.

MOTIVATION: Transcriptional networks are models that allow the biological state of cells or tumours to be described. Such networks consist of connected regulatory units known as regulons, each comprised of a regulator and its targets. Inferring a transcriptional network can be a helpful initial step in characterizing the different phenotypes within a cohort. While the network itself provides no information on molecular differences between samples, the per-sample state of each regulon, i.e. the regulon activity, can be used for describing subtypes in a cohort. Integrating regulon activities with clinical data and outcomes would extend this characterization of differences between subtypes. RESULTS: We describe RTNsurvival, an R/Bioconductor package that calculates regulon activity profiles using transcriptional networks reconstructed by the RTN package, gene expression data, and a two-tailed Gene Set Enrichment Analysis. Given regulon activity profiles across a cohort, RTNsurvival can perform Kaplan-Meier analyses and Cox Proportional Hazards regressions, while also considering confounding variables. The Supplementary Information provides two case studies that use data from breast and liver cancer cohorts and features uni- and multivariate regulon survival analysis. AVAILABILITY AND IMPLEMENTATION: RTNsurvival is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/RTNsurvival/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Raman micro-spectroscopy for accurate identification of primary human bronchial epithelial cells.

Live cell Raman micro-spectroscopy is emerging as a promising bioanalytical technique for label-free discrimination of a range of different cell types (e.g. cancer cells and fibroblasts) and behaviors (e.g. apoptosis). The aim of this study was to determine whether confocal Raman micro-spectroscopy shows sufficient sensitivity and specificity for identification of primary human bronchial epithelial cells (HBECs) to be used for live cell biological studies in vitro. We first compared cell preparation substrates and media, considering their influence on lung cell proliferation and Raman spectra, as well as methods for data acquisition, using different wavelengths (488 nm, 785 nm) and scan protocols (line, area). Evaluating these parameters using human lung cancer (A549) and fibroblast (MRC5) cell lines confirmed that line-scan data acquisition at 785 nm using complete cell media on a quartz substrate gave optimal performance. We then applied our protocol to acquisition of data from primary human bronchial epithelial cells (HBEC) derived from three independent sources, revealing an average sensitivity for different cell types of 96.3% and specificity of 95.2%. These results suggest that Raman micro-spectroscopy is suitable for delineating primary HBEC cell cultures, which in future could be used for identifying different lung cell types within co-cultures and studying the process of early carcinogenesis in lung cell culture.

ERα Binding by Transcription Factors NFIB and YBX1 Enables FGFR2 Signaling to Modulate Estrogen Responsiveness in Breast Cancer.

Two opposing clusters of transcription factors (TF) have been associated with the differential risks of estrogen receptor positive or negative breast cancers, but the mechanisms underlying the opposing functions of the two clusters are undefined. In this study, we identified NFIB and YBX1 as novel interactors of the estrogen receptor (ESR1). NFIB and YBX1 are both risk TF associated with progression of ESR1-negative disease. Notably, they both interacted with the ESR1-FOXA1 complex and inhibited the transactivational potential of ESR1. Moreover, signaling through FGFR2, a known risk factor in breast cancer development, augmented these interactions and further repressed ESR1 target gene expression. We therefore show that members of two opposing clusters of risk TFs associated with ESR1-positive and -negative breast cancer can physically interact. We postulate that this interaction forms a toggle between two developmental pathways affected by FGFR2 signaling, possibly offering a junction to exploit therapeutically.Significance: Binding of the transcription factors NFIB and YBX1 to the estrogen receptor can promote an estrogen-independent phenotype that can be reverted by inhibiting FGFR2 signaling. Cancer Res; 78(2); 410-21. ©2017 AACR.

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.

Identification of Post-Transcriptional Modulators of Breast Cancer Transcription Factor Activity Using MINDy.

We have recently identified transcription factors (TFs) that are key drivers of breast cancer risk. To better understand the pathways or sub-networks in which these TFs mediate their function we sought to identify upstream modulators of their activity. We applied the MINDy (Modulator Inference by Network Dynamics) algorithm to four TFs (ESR1, FOXA1, GATA3 and SPDEF) that are key drivers of estrogen receptor-positive (ER+) breast cancer risk, as well as cancer progression. Our computational analysis identified over 500 potential modulators. We assayed 189 of these and identified 55 genes with functional characteristics that were consistent with a role as TF modulators. In the future, the identified modulators may be tested as potential therapeutic targets, able to alter the activity of TFs that are critical in the development of breast cancer.

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.

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.

TOX3 mutations in breast cancer.

TOX3 maps to 16q12, a region commonly lost in breast cancers and recently implicated in the risk of developing breast cancer. However, not much is known of the role of TOX3 itself in breast cancer biology. This is the first study to determine the importance of TOX3 mutations in breast cancers. We screened TOX3 for mutations in 133 breast tumours and identified four mutations (three missense, one in-frame deletion of 30 base pairs) in six primary tumours, corresponding to an overall mutation frequency of 4.5%. One potentially deleterious missense mutation in exon 3 (Leu129Phe) was identified in one tumour (genomic DNA and cDNA). Whilst copy number changes of 16q12 are common in breast cancer, our data show that mutations of TOX3 are present at low frequency in tumours. Our results support that TOX3 should be further investigated to elucidate its role in breast cancer biology.

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.

The histone methyltransferase Wolf-Hirschhorn syndrome candidate 1-like 1 (WHSC1L1) is involved in human carcinogenesis.

Histone lysine methylation plays a fundamental role in chromatin organization. Although a set of histone methyltransferases have been identified and biochemically characterized, the pathological roles of their dysfunction in human cancers are still not well understood. In this study, we demonstrate important roles of WHSC1L1 in human carcinogenesis. Expression levels of WHSC1L1 transcript were significantly elevated in various human cancers including bladder carcinoma. Immunohistochemical analysis of bladder, lung, and liver cancers confirmed overexpression of WHSC1L1. WHSC1L1-specific small interfering RNAs significantly knocked down its expression and resulted in suppression of proliferation of bladder and lung cancer cell lines. WHSC1L1 knockdown induced cell cycle arrest at the G(2)/M phase followed by multinucleation of cancer cells. Expression profile analysis using Affymetrix GeneChip(®) showed that WHSC1L1 affected the expression of a number of genes including CCNG1 and NEK7, which are known to play crucial roles in the cell cycle progression at mitosis. As WHSC1L1 expression is significantly low in various normal tissues including vital organs, WHSC1L1 could be a good candidate molecule for development of novel treatment for various types of cancer.

Enhanced HSP70 lysine methylation promotes proliferation of cancer cells through activation of Aurora kinase B.

Although heat-shock protein 70 (HSP70), an evolutionarily highly conserved molecular chaperone, is known to be post-translationally modified in various ways such as phosphorylation, ubiquitination and glycosylation, physiological significance of lysine methylation has never been elucidated. Here we identify dimethylation of HSP70 at Lys-561 by SETD1A. Enhanced HSP70 methylation was detected in various types of human cancer by immunohistochemical analysis, although the methylation was barely detectable in corresponding non-neoplastic tissues. Interestingly, methylated HSP70 predominantly localizes to the nucleus of cancer cells, whereas most of the HSP70 protein locates to the cytoplasm. Nuclear HSP70 directly interacts with Aurora kinase B (AURKB) in a methylation-dependent manner and promotes AURKB activity in vitro and in vivo. We also find that methylated HSP70 has a growth-promoting effect in cancer cells. Our findings demonstrate a crucial role of HSP70 methylation in human carcinogenesis.

RB1 methylation by SMYD2 enhances cell cycle progression through an increase of RB1 phosphorylation.

It is well known that RB functions are regulated by posttranslational modifications such as phosphorylation and acetylation, but the significance of lysine methylation on RB has not been fully elucidated. Our expression analysis of SMYD2 by quantitative real-time polymerase chain reaction showed that expression levels of SMYD2 are significantly elevated in human bladder carcinomas compared with nonneoplastic bladder tissues (P < .0001), and its expression levels in tumor tissues were much higher than those of any other normal tissues. SMYD2 knockdown resulted in the suppression of cancer cell growth, and cell cycle analysis indicated that SMYD2 might play a crucial role in the G(1)/S transition. According to an in vitro methyltransferase assay, we found that SMYD2 methylates RB1 protein, and liquid chromatography-tandem mass spectrometry analysis revealed lysine 810 of RB1 to be methylated by SMYD2. Importantly, this methylation enhanced Ser 807/811 phosphorylation of RB1 both in vitro and in vivo. Furthermore, we demonstrated that methylated RB1 accelerates E2F transcriptional activity and promotes cell cycle progression. SMYD2 is an important oncoprotein in various types of cancer, and SMYD2-dependent RB1 methylation at lysine 810 promotes cell cycle progression of cancer cells. Further study may explore SMYD2-dependent RB1 methylation as a potential therapeutic target in human cancer.

Histone lysine methyltransferase SETD8 promotes carcinogenesis by deregulating PCNA expression.

Although the physiologic significance of lysine methylation of histones is well known, whether lysine methylation plays a role in the regulation of nonhistone proteins has not yet been examined. The histone lysine methyltransferase SETD8 is overexpressed in various types of cancer and seems to play a crucial role in S-phase progression. Here, we show that SETD8 regulates the function of proliferating cell nuclear antigen (PCNA) protein through lysine methylation. We found that SETD8 methylated PCNA on lysine 248, and either depletion of SETD8 or substitution of lysine 248 destabilized PCNA expression. Mechanistically, lysine methylation significantly enhanced the interaction between PCNA and the flap endonuclease FEN1. Loss of PCNA methylation retarded the maturation of Okazaki fragments, slowed DNA replication, and induced DNA damage, and cells expressing a methylation-inactive PCNA mutant were more susceptible to DNA damage. An increase of methylated PCNA was found in cancer cells, and the expression levels of SETD8 and PCNA were correlated in cancer tissue samples. Together, our findings reveal a function for lysine methylation on a nonhistone protein and suggest that aberrant lysine methylation of PCNA may play a role in human carcinogenesis.

Effects of BRCA2 cis-regulation in normal breast and cancer risk amongst BRCA2 mutation carriers.

INTRODUCTION: Cis-acting regulatory single nucleotide polymorphisms (SNPs) at specific loci may modulate penetrance of germline mutations at the same loci by introducing different levels of expression of the wild-type allele. We have previously reported that BRCA2 shows differential allelic expression and we hypothesize that the known variable penetrance of BRCA2 mutations might be associated with this mechanism. METHODS: We combined haplotype analysis and differential allelic expression of BRCA2 in breast tissue to identify expression haplotypes and candidate cis-regulatory variants. These candidate variants underwent selection based on in silico predictions for regulatory potential and disruption of transcription factor binding, and were functionally analyzed in vitro and in vivo in normal and breast cancer cell lines. SNPs tagging the expression haplotypes were correlated with the total expression of several genes in breast tissue measured by Taqman and microarray technologies. The effect of the expression haplotypes on breast cancer risk in BRCA2 mutation carriers was investigated in 2,754 carriers. RESULTS: We identified common haplotypes associated with differences in the levels of BRCA2 expression in human breast cells. We characterized three cis-regulatory SNPs located at the promoter and two intronic regulatory elements which affect the binding of the transcription factors C/EBPα, HMGA1, D-binding protein (DBP) and ZF5. We showed that the expression haplotypes also correlated with changes in the expression of other genes in normal breast. Furthermore, there was suggestive evidence that the minor allele of SNP rs4942440, which is associated with higher BRCA2 expression, is also associated with a reduced risk of breast cancer (per-allele hazard ratio (HR) = 0.85, 95% confidence interval (CI) = 0.72 to 1.00, P-trend = 0.048). CONCLUSIONS: Our work provides further insights into the role of cis-regulatory variation in the penetrance of disease-causing mutations. We identified small-effect genetic variants associated with allelic expression differences in BRCA2 which could possibly affect the risk in mutation carriers through altering expression levels of the wild-type allele.

Allele-specific expression analysis methods for high-density SNP microarray data.

MOTIVATION: In the past decade, a number of technologies to quantify allele-specific expression (ASE) in a genome-wide manner have become available to researchers. We investigate the application of single-nucleotide polymorphism (SNP) microarrays to this task, exploring data obtained from both cell lines and primary tissue for which both RNA and DNA profiles are available. RESULTS: We analyze data from two experiments that make use of high-density Illumina Infinium II genotyping arrays to measure ASE. We first preprocess each data set, which involves removal of outlier samples, careful normalization and a two-step filtering procedure to remove SNPs that show no evidence of expression in the samples being analyzed and calls that are clear genotyping errors. We then compare three different tests for detecting ASE, one of which has been previously published and two novel approaches. These tests vary at the level at which they operate (per SNP per individual or per SNP) and in the input data they require. Using SNPs from imprinted genes as true positives for ASE, we observe varying sensitivity for the different testing procedures that improves with increasing sample size. Methods that rely on RNA signal alone were found to perform best across a range of metrics. The top ranked SNPs recovered by all methods appear to be reasonable candidates for ASE. AVAILABILITY AND IMPLEMENTATION: Analysis was carried out in R (http://www.R-project.org/) using existing functions.