Features associated with melanoma metastasis in Latvia.

Cutaneous melanoma (CM) is the most aggressive form of skin cancer, exhibits an increasing incidence worldwide and has a high potential to develop metastasis. The current study aimed to identify a set of parameters that may aid in predicting the probability and timing of the onset of CM metastasis. A retrospective analysis was performed using the archive data of 2,026 patients with CM that were treated at the Riga East University Hospital Latvian Oncology Centre, which is the largest oncological hospital in the country, between 1998 and 2015. A case-control study design was employed, where patients with metastasis (n=278) were used as the cases and patients without metastasis were used as the controls. The present study examined the associations between metastasis and potential risk factors and constructed multivariate models of features that predicted metastasis using stepwise regression. Time until metastasis was analyzed using negative binomial regression models. The results of the present study indicated an increase in the number of melanomas that developed metastases during 1998-2015. Tumor Breslow thickness was demonstrated to be significantly larger in patients with metastasis compared with those without (P=0.012). The presence of ulceration significantly increased the risk of metastases [odds ratio (OR), 1.66; 95% CI, 1.07-2.59; P=0.033]. The absence of pigment in melanoma tissues was indicated to lead to a greater likelihood of metastasis (OR, 2.14; 95% CI, 1.10-4.19; P=0.035). Shorter times from diagnosis until the onset of metastases were observed in older patients (incident rate ratio (IRR), 6.85; 95% CI, 2.43-19.30; P=2.78×10-4), and a borderline significant association was observed in those with ulcerated tumors (IRR, 1.33; 95% CI, 0.98-1.80; P=0.064). Therefore, the main features associated with the development of melanoma metastasis in Latvia were indicated to be tumor ulceration, absence of pigment and Breslow thickness.

The complementary effect of rs1042522 in TP53 and rs1805007 in MC1R is associated with an elevated risk of cutaneous melanoma in Latvian population.

Genetic factors serve important roles in melanoma susceptibility. Although much genetic variation has been associated with cutaneous melanoma (CM), little is known about the interactions between genetic variants. The current study investigated the joint effect of rs1042522 in the tumour protein 53 (TP53) gene, rs2279744 in the murine double minute-2 (MDM2) gene and several single nucleotide polymorphisms (SNPs) in the melanocortin 1 receptor (MC1R) gene. All of these genes are interconnected in a single signalling pathway that regulates pigmentation. The current study included 479 individuals, of which, 255 were patients with CM and 224 were controls from the Latvian population. Multifaceted analyses of potential interactions between SNPs were performed, whilst taking into account the pigmentation phenotypes of individuals and tumour characteristics (Breslow thickness and ulceration). Univariate analyses revealed a borderline significant association between rs1042522 in the TP53 gene and CM risk. The results also confirmed a known association with rs1805007 in the MC1R gene. The rs1042522 was also selected as a CM risk factor in multivariate models, suggesting an effect that is independent from and complementary to that of rs1805007. The results indicated that these SNPs need to be taken into account when determining melanoma risk. A strong association between CM and red hair was identified for rs1805007, and rs1805008 in the MC1R gene was mainly associated with red hair. An association was also determined between rs2279744 in the MDM2 gene and brown eye colour. No convincing associations were identified between the analysed SNPs and Breslow thickness of tumours or ulcerations.

Dynamics of gene regulatory networks and their dependence on network topology and quantitative parameters - the case of phage λ.

BACKGROUND: Gene regulatory networks can be modelled in various ways depending on the level of detail required and biological questions addressed. One of the earliest formalisms used for modeling is a Boolean network, although these models cannot describe most temporal aspects of a biological system. Differential equation models have also been used to model gene regulatory networks, but these frameworks tend to be too detailed for large models and many quantitative parameters might not be deducible in practice. Hybrid models bridge the gap between these two model classes - these are useful when concentration changes are important while the information about precise concentrations and binding site affinities is partial. RESULTS: In this paper we study the stable behaviours of phage λ via a hybrid system based model. We identify wild type and mutant behaviours that arise for various orderings of binding site affinities. We propose experiments for detecting these behaviours: we suggest several ways of altering binding affinities with either mutations or genome rearrangements to achieve modified behaviours. The feasibility of these experiments is assessed. The interplay between the qualitative aspects of a network, e.g. network topology, and quantitative parameters, e.g. growth and degradation rates of proteins, is demonstrated. We also provide a software for exploring all feasible states of a hybrid system model and identifying all attractors. CONCLUSIONS: The behaviours of phage λ are determined mainly by the topology of this network and by the mutual order of binding affinities. Exact affinities and growth and degradation rates of proteins fine tune the system. We show that only two stable behaviours are possible for phage λ if the main constraints of λ switch are preserved - these behaviours correspond to lysis and lysogeny. We identify several variants of both lysis and lysogeny - one wild type and one modified behaviour for each. We elucidate the necessary constraints for binding site affinities to achieve both wild type lysis and lysogeny. Our software is applicable to a wide range of biological models described as a hybrid system.

Platelet function is modified by common sequence variation in megakaryocyte super enhancers.

Linking non-coding genetic variants associated with the risk of diseases or disease-relevant traits to target genes is a crucial step to realize GWAS potential in the introduction of precision medicine. Here we set out to determine the mechanisms underpinning variant association with platelet quantitative traits using cell type-matched epigenomic data and promoter long-range interactions. We identify potential regulatory functions for 423 of 565 (75%) non-coding variants associated with platelet traits and we demonstrate, through ex vivo and proof of principle genome editing validation, that variants in super enhancers play an important role in controlling archetypical platelet functions.

The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease.

Many common variants have been associated with hematological traits, but identification of causal genes and pathways has proven challenging. We performed a genome-wide association analysis in the UK Biobank and INTERVAL studies, testing 29.5 million genetic variants for association with 36 red cell, white cell, and platelet properties in 173,480 European-ancestry participants. This effort yielded hundreds of low frequency (<5%) and rare (<1%) variants with a strong impact on blood cell phenotypes. Our data highlight general properties of the allelic architecture of complex traits, including the proportion of the heritable component of each blood trait explained by the polygenic signal across different genome regulatory domains. Finally, through Mendelian randomization, we provide evidence of shared genetic pathways linking blood cell indices with complex pathologies, including autoimmune diseases, schizophrenia, and coronary heart disease and evidence suggesting previously reported population associations between blood cell indices and cardiovascular disease may be non-causal.

Bayesian models for syndrome- and gene-specific probabilities of novel variant pathogenicity.

BACKGROUND: With the advent of affordable and comprehensive sequencing technologies, access to molecular genetics for clinical diagnostics and research applications is increasing. However, variant interpretation remains challenging, and tools that close the gap between data generation and data interpretation are urgently required. Here we present a transferable approach to help address the limitations in variant annotation. METHODS: We develop a network of Bayesian logistic regression models that integrate multiple lines of evidence to evaluate the probability that a rare variant is the cause of an individual's disease. We present models for genes causing inherited cardiac conditions, though the framework is transferable to other genes and syndromes. RESULTS: Our models report a probability of pathogenicity, rather than a categorisation into pathogenic or benign, which captures the inherent uncertainty of the prediction. We find that gene- and syndrome-specific models outperform genome-wide approaches, and that the integration of multiple lines of evidence performs better than individual predictors. The models are adaptable to incorporate new lines of evidence, and results can be combined with familial segregation data in a transparent and quantitative manner to further enhance predictions. Though the probability scale is continuous, and innately interpretable, performance summaries based on thresholds are useful for comparisons. Using a threshold probability of pathogenicity of 0.9, we obtain a positive predictive value of 0.999 and sensitivity of 0.76 for the classification of variants known to cause long QT syndrome over the three most important genes, which represents sufficient accuracy to inform clinical decision-making. A web tool APPRAISE [http://www.cardiodb.org/APPRAISE] provides access to these models and predictions. CONCLUSIONS: Our Bayesian framework provides a transparent, flexible and robust framework for the analysis and interpretation of rare genetic variants. Models tailored to specific genes outperform genome-wide approaches, and can be sufficiently accurate to inform clinical decision-making.

HSM - a hybrid system based approach for modelling intracellular networks.

The paper proposes a hybrid system based approach for modelling of intracellular networks and introduces a restricted subclass of hybrid systems - HSM - with an objective of still being able to provide sufficient power for the modelling of biological systems, while imposing some restrictions that facilitate analysis of systems described by such models. The use of hybrid system based models has become increasingly popular, likely due to the facts that: 1) they provide sufficiently powerful mathematical formalism to describe biological processes of interest and do it in a 'natural way' from the biological perspective; 2) there are well established mathematical techniques as well as supporting software tools for analysing such models. However often these models are very dependent on the quantitative parameters of the system (concentrations of proteins, their growth functions etc.) that are seldom exactly known, instead of more limited information of the system that can be observed in practice (directions of change in concentrations, but not the exact values etc.). As a result these models may work well for simulation of the system (prediction of its state starting from some initial conditions), but are too complicated for prediction of all possible qualitatively different behaviours a modelled system might have. With HSM we try to propose a hybrid system based formalism that is still sufficiently powerful for description of biological systems, while being as restricted as possible to facilitate the analysis of the systems described. We separate between the quantitative system parameters and their qualitative values that can be observed in practice. For HSM we provide an algorithm that analyses the system without the need to know the exact parameter values. We apply our model and analysis methods to a well-studied gene network of λ-phage. The phage has two well-known qualitatively different behaviours - lysis and lysogeny. We show that our model has an attractor structure that corresponds well to these two behaviours and that these are the only stable behaviours that can be exhibited by the system. The algorithm also generates (in principle biologically verifiable) hypotheses about the mutations of λ-phage that should change its observable behaviour.

Reconstruction of gene regulatory networks under the finite state linear model.

We study the Finite State Linear Model (FSLM) for modelling gene regulatory networks proposed by A. Brazma and T. Schlitt in [4]. The model incorporates biologically intuitive gene regulatory mechanism similar to that in Boolean networks, and can describe also the continuous changes in protein levels. We consider several theoretical properties of this model; in particular we show that the problem whether a particular gene will reach an active state is algorithmically unsolvable. This imposes some practical difficulties in simulation and reverse engineering of FSLM networks. Nevertheless, our simulation experiments show that sufficiently many of FSLM networks exhibit a regular behaviour and that the model is still quite adequate to describe biological reality. We also propose a comparatively efficient O(2(K)n(K+1)M(2K)m log m) time algorithm for reconstruction of FSLM networks from experimental data. Experiments on reconstruction of random networks are performed to estimate the running time of the algorithm in practice, as well as the number of measurements needed for successful network reconstruction.