Multi-cancer computational analysis reveals invasion-associated variant of desmoplastic reaction involving INHBA, THBS2 and COL11A1

<p>Abstract</p> <p>Background</p> <p>Despite extensive research, the details of the biological mechanisms by which cancer cells acquire motility and invasiveness are largely unknown. This study identifies an invasion associated gene signature shedding light on these mechanisms.</p> <p>Methods</p> <p>We analyze data from multiple cancers using a novel computational method identifying sets of genes whose coordinated overexpression indicates the presence of a particular phenotype, in this case high-stage cancer.</p> <p>Results</p> <p>We conclude that there is one shared "core" metastasis-associated gene expression signature corresponding to a specific variant of stromal desmoplastic reaction, present in a large subset of samples that have exceeded a threshold of invasive transition specific to each cancer, indicating that the corresponding biological mechanism is triggered at that point. For example this threshold is reached at stage IIIc in ovarian cancer and at stage II in colorectal cancer. Therefore, its presence indicates that the corresponding stage has been reached. It has several features, such as coordinated overexpression of particular collagens, mainly <it>COL11A1 </it>and other genes, mainly <it>THBS2 </it>and <it>INHBA</it>. The composition of the overexpressed genes indicates invasion-facilitating altered proteolysis in the extracellular matrix. The prominent presence in the signature of INHBA in all cancers strongly suggests a biological mechanism centered on activin A induced TGF-β signaling, because activin A is a member of the TGF-β superfamily consisting of an INHBA homodimer. Furthermore, we establish that the signature is predictive of neoadjuvant therapy response in at least one breast cancer data set.</p> <p>Conclusions</p> <p>Therefore, these results can be used for developing high specificity biomarkers sensing cancer invasion and predicting response to neoadjuvant therapy, as well as potential multi-cancer metastasis inhibiting therapeutics targeting the corresponding biological mechanism.</p

Elevated PTTG and PBF predicts poor patient outcome and modulates DNA damage response genes in thyroid cancer

The proto-oncogene PTTG and its binding partner PBF have been widely studied in multiple cancer types, particularly thyroid and colorectal, but their combined role in tumourigenesis is uncharacterised. Here, we show for the first time that together PTTG and PBF significantly modulate DNA damage response (DDR) genes, including p53 target genes, required to maintain genomic integrity in thyroid cells. Critically, DDR genes were extensively repressed in primary thyrocytes from a bitransgenic murine model (Bi-Tg) of thyroid-specific PBF and PTTG overexpression. Irradiation exposure to amplify p53 levels further induced significant repression of DDR genes in Bi-Tg thyrocytes (P=2.4 × 10−4) compared with either PBF- (P=1.5 × 10−3) or PTTG-expressing thyrocytes (P=NS). Consistent with this, genetic instability was greatest in Bi-Tg thyrocytes with a mean genetic instability (GI) index of 35.8±2.6%, as well as significant induction of gross chromosomal aberrations in thyroidal TPC-1 cells following overexpression of PBF and PTTG. We extended our findings to human thyroid cancer using TCGA data sets (n=322) and found striking correlations with PBF and PTTG expression in well-characterised DDR gene panel RNA-seq data. In addition, genetic associations and transient transfection identified PBF as a downstream target of the receptor tyrosine kinase-BRAF signalling pathway, emphasising a role for PBF as a novel component in a pathway well described to drive neoplastic growth. We also showed that overall survival (P=1.91 × 10−5) and disease-free survival (P=4.9 × 10−5) was poorer for TCGA patients with elevated tumoural PBF/PTTG expression and mutationally activated BRAF. Together our findings indicate that PBF and PTTG have a critical role in promoting thyroid cancer that is predictive of poorer patient outcome

Influence of Statistical Estimators of Mutual Information and Data Heterogeneity on the Inference of Gene Regulatory Networks

The inference of gene regulatory networks from gene expression data is a difficult problem because the performance of the inference algorithms depends on a multitude of different factors. In this paper we study two of these. First, we investigate the influence of discrete mutual information (MI) estimators on the global and local network inference performance of the C3NET algorithm. More precisely, we study different MI estimators (Empirical, Miller-Madow, Shrink and Schürmann-Grassberger) in combination with discretization methods (equal frequency, equal width and global equal width discretization). We observe the best global and local inference performance of C3NET for the Miller-Madow estimator with an equal width discretization. Second, our numerical analysis can be considered as a systems approach because we simulate gene expression data from an underlying gene regulatory network, instead of making a distributional assumption to sample thereof. We demonstrate that despite the popularity of the latter approach, which is the traditional way of studying MI estimators, this is in fact not supported by simulated and biological expression data because of their heterogeneity. Hence, our study provides guidance for an efficient design of a simulation study in the context of network inference, supporting a systems approach

Casual Compressive Sensing for Gene Network Inference

We propose a novel framework for studying causal inference of gene interactions using a combination of compressive sensing and Granger causality techniques. The gist of the approach is to discover sparse linear dependencies between time series of gene expressions via a Granger-type elimination method. The method is tested on the Gardner dataset for the SOS network in E. coli, for which both known and unknown causal relationships are discovered

Immunohistochemical estimation of cell cycle phase in laryngeal neoplasia

We previously developed an immunohistochemical method for estimating cell cycle state and phase in tissue samples, including biopsies that are too small for flow cytometry. We have used our technique to examine whether primary abnormalities of the cell cycle exist in laryngeal neoplasia. Antibodies against the markers of cell cycle entry, minichromosome maintenance protein-2 (Mcm-2) and Ki67, and putative markers of cell cycle phase, cyclin D1 (G1-phase), cyclin A (S-phase), cyclin B1 (G2-phase) and phosphohistone H3 (Mitosis) were applied to paraffin-embedded sections of normal larynx (n=8), laryngeal dysplasia (n=10) and laryngeal squamous cell carcinoma (n=10). Cells expressing each marker were determined as a percentage of total cells, termed the labelling index (LI), and as a percentage of Mcm-2-positive cells, termed the labelling fraction (LF). The frequency of coexpression of each putative phase marker was investigated by confocal microscopy. There was a correlation between Mcm-2 and Ki67 LIs (ρ=0.93) but Mcm-2 LIs were consistently higher. All cells expressing a phase marker coexpressed Mcm-2, whereas Ki67 was not expressed in a proportion of these cells. The putative phase markers showed little coexpression. Labelling index values increased on progression from normal larynx through laryngeal dysplasia to squamous cell carcinoma for Mcm-2 (P=0.001), Ki67 (P=0.0002), cyclin D1 (P=0.015), cyclin A (P=0.0001) and cyclin B1 (P=0.0004). There was no evidence of an increase in the LF for any phase marker. Immunohistochemistry can be used to estimate cell cycle state and phase in laryngeal biopsies. Our data argues against primary cell cycle phase abnormalities in laryngeal neoplasia

Temperature Control of Fimbriation Circuit Switch in Uropathogenic Escherichia coli: Quantitative Analysis via Automated Model Abstraction

Uropathogenic Escherichia coli (UPEC) represent the predominant cause of urinary tract infections (UTIs). A key UPEC molecular virulence mechanism is type 1 fimbriae, whose expression is controlled by the orientation of an invertible chromosomal DNA element—the fim switch. Temperature has been shown to act as a major regulator of fim switching behavior and is overall an important indicator as well as functional feature of many urologic diseases, including UPEC host-pathogen interaction dynamics. Given this panoptic physiological role of temperature during UTI progression and notable empirical challenges to its direct in vivo studies, in silico modeling of corresponding biochemical and biophysical mechanisms essential to UPEC pathogenicity may significantly aid our understanding of the underlying disease processes. However, rigorous computational analysis of biological systems, such as fim switch temperature control circuit, has hereto presented a notoriously demanding problem due to both the substantial complexity of the gene regulatory networks involved as well as their often characteristically discrete and stochastic dynamics. To address these issues, we have developed an approach that enables automated multiscale abstraction of biological system descriptions based on reaction kinetics. Implemented as a computational tool, this method has allowed us to efficiently analyze the modular organization and behavior of the E. coli fimbriation switch circuit at different temperature settings, thus facilitating new insights into this mode of UPEC molecular virulence regulation. In particular, our results suggest that, with respect to its role in shutting down fimbriae expression, the primary function of FimB recombinase may be to effect a controlled down-regulation (rather than increase) of the ON-to-OFF fim switching rate via temperature-dependent suppression of competing dynamics mediated by recombinase FimE. Our computational analysis further implies that this down-regulation mechanism could be particularly significant inside the host environment, thus potentially contributing further understanding toward the development of novel therapeutic approaches to UPEC-caused UTIs

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Limited dissemination of the wastewater treatment plant core resistome

Horizontal gene transfer is a major contributor to the evolution of bacterial genomes and can facilitate the dissemination of antibiotic resistance genes between environmental reservoirs and potential pathogens. Wastewater treatment plants (WWTPs) are believed to play a central role in the dissemination of antibiotic resistance genes. However, the contribution of the dominant members of the WWTP resistome to resistance in human pathogens remains poorly understood. Here we use a combination of metagenomic functional selections and comprehensive metagenomic sequencing to uncover the dominant genes of the WWTP resistome. We find that this core resistome is unique to the WWTP environment, with <10% of the resistance genes found outside the WWTP environment. Our data highlight that, despite an abundance of functional resistance genes within WWTPs, only few genes are found in other environments, suggesting that the overall dissemination of the WWTP resistome is comparable to that of the soil resistome