Differential kynurenine pathway metabolism in highly metastatic aggressive breast cancer subtypes: beyond IDO1-induced immunosuppression.

BACKGROUND: Immunotherapy has recently been proposed as a promising treatment to stop breast cancer (BrCa) progression and metastasis. However, there has been limited success in the treatment of BrCa with immune checkpoint inhibitors. This implies that BrCa tumors have other mechanisms to escape immune surveillance. While the kynurenine pathway (KP) is known to be a key player mediating tumor immune evasion and while there are several studies on the roles of the KP in cancer, little is known about KP involvement in BrCa. METHODS: To understand how KP is regulated in BrCa, we examined the KP profile in BrCa cell lines and clinical samples (n = 1997) that represent major subtypes of BrCa (luminal, HER2-enriched, and triple-negative (TN)). We carried out qPCR, western blot/immunohistochemistry, and ultra-high pressure liquid chromatography on these samples to quantify the KP enzyme gene, protein, and activity, respectively. RESULTS: We revealed that the KP is highly dysregulated in the HER2-enriched and TN BrCa subtype. Gene, protein expression, and KP metabolomic profiling have shown that the downstream KP enzymes KMO and KYNU are highly upregulated in the HER2-enriched and TN BrCa subtypes, leading to increased production of the potent immunosuppressive metabolites anthranilic acid (AA) and 3-hydroxylanthranilic acid (3HAA). CONCLUSIONS: Our findings suggest that KMO and KYNU inhibitors may represent new promising therapeutic targets for BrCa. We also showed that KP metabolite profiling can be used as an accurate biomarker for BrCa subtyping, as we successfully discriminated TN BrCa from other BrCa subtypes.

Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals.

INTRODUCTION: Recently, quantitative metabolomics identified a panel of 10 plasma lipids that were highly predictive of conversion to Alzheimer's disease (AD) in cognitively normal older individuals (n = 28, area under the curve [AUC] = 0.92, sensitivity/specificity of 90%/90%). METHODS: Quantitative targeted metabolomics in serum using an identical method as in the index study. RESULTS: We failed to replicate these findings in a substantially larger study from two independent cohorts-the Baltimore Longitudinal Study of Aging ([BLSA], n = 93, AUC = 0.642, sensitivity/specificity of 51.6%/65.7%) and the Age, Gene/Environment Susceptibility-Reykjavik Study ([AGES-RS], n = 100, AUC = 0.395, sensitivity/specificity of 47.0%/36.0%). In analyses applying machine learning methods to all 187 metabolite concentrations assayed, we find a modest signal in the BLSA with distinct metabolites associated with the preclinical and symptomatic stages of AD, whereas the same methods gave poor classification accuracies in the AGES-RS samples. DISCUSSION: We believe that ours is the largest blood biomarker study of preclinical AD to date. These findings underscore the importance of large-scale independent validation of index findings from biomarker studies with relatively small sample sizes.

Genome-wide analysis of long noncoding RNA stability.

Transcriptomic analyses have identified tens of thousands of intergenic, intronic, and cis-antisense long noncoding RNAs (lncRNAs) that are expressed from mammalian genomes. Despite progress in functional characterization, little is known about the post-transcriptional regulation of lncRNAs and their half-lives. Although many are easily detectable by a variety of techniques, it has been assumed that lncRNAs are generally unstable, but this has not been examined genome-wide. Utilizing a custom noncoding RNA array, we determined the half-lives of ∼800 lncRNAs and ∼12,000 mRNAs in the mouse Neuro-2a cell line. We find only a minority of lncRNAs are unstable. LncRNA half-lives vary over a wide range, comparable to, although on average less than, that of mRNAs, suggestive of complex metabolism and widespread functionality. Combining half-lives with comprehensive lncRNA annotations identified hundreds of unstable (half-life < 2 h) intergenic, cis-antisense, and intronic lncRNAs, as well as lncRNAs showing extreme stability (half-life > 16 h). Analysis of lncRNA features revealed that intergenic and cis-antisense RNAs are more stable than those derived from introns, as are spliced lncRNAs compared to unspliced (single exon) transcripts. Subcellular localization of lncRNAs indicated widespread trafficking to different cellular locations, with nuclear-localized lncRNAs more likely to be unstable. Surprisingly, one of the least stable lncRNAs is the well-characterized paraspeckle RNA Neat1, suggesting Neat1 instability contributes to the dynamic nature of this subnuclear domain. We have created an online interactive resource (http://stability.matticklab.com) that allows easy navigation of lncRNA and mRNA stability profiles and provides a comprehensive annotation of ~7200 mouse lncRNAs.

'Neuroinflammation' differs categorically from inflammation: transcriptomes of Alzheimer's disease, Parkinson's disease, schizophrenia and inflammatory diseases compared.

'Neuroinflammation' has become a widely applied term in the basic and clinical neurosciences but there is no generally accepted neuropathological tissue correlate. Inflammation, which is characterized by the presence of perivascular infiltrates of cells of the adaptive immune system, is indeed seen in the central nervous system (CNS) under certain conditions. Authors who refer to microglial activation as neuroinflammation confuse this issue because autoimmune neuroinflammation serves as a synonym for multiple sclerosis, the prototypical inflammatory disease of the CNS. We have asked the question whether a data-driven, unbiased in silico approach may help to clarify the nomenclatorial confusion. Specifically, we have examined whether unsupervised analysis of microarray data obtained from human cerebral cortex of Alzheimer's, Parkinson's and schizophrenia patients would reveal a degree of relatedness between these diseases and recognized inflammatory conditions including multiple sclerosis. Our results using two different data analysis methods provide strong evidence against this hypothesis demonstrating that very different sets of genes are involved. Consequently, the designations inflammation and neuroinflammation are not interchangeable. They represent different categories not only at the histophenotypic but also at the transcriptomic level. Therefore, non-autoimmune neuroinflammation remains a term in need of definition.

Impact of androgen suppression and zoledronic acid on bone mineral density and fractures in the Trans-Tasman Radiation Oncology Group (TROG) 03.04 Randomised Androgen Deprivation and Radiotherapy (RADAR) randomized controlled trial for locally advanced prostate cancer.

OBJECTIVE: To study the influence of adjuvant androgen suppression and bisphosphonates on incident vertebral and non-spinal fracture rates and bone mineral density (BMD) in men with locally advanced prostate cancer. PATIENTS AND METHODS: Between 2003 and 2007, 1071 men with locally advanced prostate cancer were randomly allocated, using a 2 × 2 trial design, to 6 months i.m. leuprorelin (androgen suppression [AS]) before radiotherapy alone ± 12 months additional leuprorelin ± 18 months zoledronic acid (ZdA), commencing at randomization. The main endpoint was incident thoraco-lumbar vertebral fractures, which were assessed radiographically at randomization and at 3 years, then reassessed by centralized review. Subsidiary endpoints included incident non-spinal fractures, which were documented throughout follow-up, and BMD, which was measured in 222 subjects at baseline, 2 years and 4 years. RESULTS: Incident vertebral fractures at 3 years were observed in 132 subjects. Their occurrence was not increased by 18 months' AS, nor reduced by ZdA. Incident non-spinal fractures occurred in 72 subjects and were significantly related to AS duration but not to ZdA. Osteopenia and osteoporosis prevalence rates at baseline were 23.4 and 1.4%, respectively, at the hip. Treatment for 6 and 18 months with AS caused significant reductions in hip BMD at 2 and 4 years (P < 0.01) and ZdA prevented these losses at both time points. CONCLUSION: In an AS-naïve population, 18 months of ZdA treatment prevented the sustained BMD losses caused by 18 months of AS treatment; however, the study power was insufficient to show that AS duration or ZdA influenced vertebral fracture rates.

Approximating the nuclear binding energy using analytic continued fractions.

Understanding nuclear behaviour is fundamental in nuclear physics. This paper introduces a data-driven approach, Continued Fraction Regression (cf-r), to analyze nuclear binding energy (B(A, Z)). Using a tailored loss function and analytic continued fractions, our method accurately approximates stable and experimentally confirmed unstable nuclides. We identify the best model for nuclides with A ≥ 200 , achieving precise predictions with residuals smaller than 0.15 MeV. Our model's extrapolation capabilities are demonstrated as it converges with upper and lower bounds at the nuclear mass limit, reinforcing its accuracy and robustness. The results offer valuable insights into the current limitations of state-of-the-art data-driven approaches in approximating the nuclear binding energy. This work provides an illustration on the use of analytical continued fraction regression for a wide range of other possible applications.

New alternatives to the Lennard-Jones potential.

We present a new method for approximating two-body interatomic potentials from existing ab initio data based on representing the unknown function as an analytic continued fraction. In this study, our method was first inspired by a representation of the unknown potential as a Dirichlet polynomial, i.e., the partial sum of some terms of a Dirichlet series. Our method allows for a close and computationally efficient approximation of the ab initio data for the noble gases Xenon (Xe), Krypton (Kr), Argon (Ar), and Neon (Ne), which are proportional to r - 6 and to a very simple d e p t h = 1 truncated continued fraction with integer coefficients and depending on n - r only, where n is a natural number (with n = 13 for Xe, n = 16 for Kr, n = 17 for Ar, and n = 27 for Neon). For Helium (He), the data is well approximated with a function having only one variable n - r with n = 31 and a truncated continued fraction with d e p t h = 2 (i.e., the third convergent of the expansion). Also, for He, we have found an interesting d e p t h = 0 result, a Dirichlet polynomial of the form k 1 6 - r + k 2 48 - r + k 3 72 - r (with k 1 , k 2 , k 3 all integers), which provides a surprisingly good fit, not only in the attractive but also in the repulsive region. We also discuss lessons learned while facing the surprisingly challenging non-linear optimisation tasks in fitting these approximations and opportunities for parallelisation.

Continued fractions and the Thomson problem.

We introduce new analytical approximations of the minimum electrostatic energy configuration of n electrons, E(n), when they are constrained to be on the surface of a unit sphere. Using 453 putative optimal configurations, we searched for approximations of the form [Formula: see text] where g(n) was obtained via a memetic algorithm that searched for truncated analytic continued fractions finally obtaining one with Mean Squared Error equal to [Formula: see text] for the model of the normalized energy ([Formula: see text]). Using the Online Encyclopedia of Integer Sequences, we searched over 350,000 sequences and, for small values of n, we identified a strong correlation of the highest residual of our best approximations with the sequence of integers n defined by the condition that [Formula: see text] is a prime. We also observed an interesting correlation with the behavior of the smallest angle [Formula: see text], measured in radians, subtended by the vectors associated with the nearest pair of electrons in the optimal configuration. When using both [Formula: see text] and [Formula: see text] as variables a very simple approximation formula for [Formula: see text] was obtained with MSE= [Formula: see text] and MSE= 73.2349 for E(n). When expanded as a power series in infinity, we observe that an unknown constant of an expansion as a function of [Formula: see text] of E(n) first proposed by Glasser and Every in 1992 as [Formula: see text], and later refined by Morris, Deaven and Ho as [Formula: see text] in 1996, may actually be very close to -1.10462553440167 when the assumed optima for [Formula: see text] are used.

Sifting the wheat from the chaff: prioritizing GWAS results by identifying consistency across analytical methods.

The curse of multiple testing has led to the adoption of a stringent Bonferroni threshold for declaring genome-wide statistical significance for any one SNP as standard practice. Although justified in avoiding false positives, this conservative approach has the potential to miss true associations as most studies are drastically underpowered. As an alternative to increasing sample size, we compare results from a typical SNP-by-SNP analysis with three other methods that incorporate regional information in order to boost or dampen an otherwise noisy signal: the haplotype method (Schaid et al. [2002] Am J Hum Genet 70:425-434), the gene-based method (Liu et al. [2010] Am J Hum Genet 87:139-145), and a new method (interaction count) that uses genome-wide screening of pairwise SNP interactions. Using a modestly sized case-control study, we conduct a genome-wide association studies (GWAS) of age-related macular degeneration, and find striking agreement across all methods in regions of known associated variants. We also find strong evidence of novel associated variants in two regions (Chromosome 2p25 and Chromosome 10p15) in which the individual SNP P-values are only suggestive, but where there are very high levels of agreement between all methods. We propose that consistency between different analysis methods may be an alternative to increasingly larger sample sizes in sifting true signals from noise in GWAS.

Are myocardial infarction--associated single-nucleotide polymorphisms associated with ischemic stroke?

BACKGROUND AND PURPOSE: Ischemic stroke (IS) shares many common risk factors with coronary artery disease (CAD). We hypothesized that genetic variants associated with myocardial infarction (MI) or CAD may be similarly involved in the etiology of IS. To test this hypothesis, we evaluated whether single-nucleotide polymorphisms (SNPs) at 11 different loci recently associated with MI or CAD through genome-wide association studies were associated with IS. METHODS: Meta-analyses of the associations between the 11 MI-associated SNPs and IS were performed using 6865 cases and 11 395 control subjects recruited from 9 studies. SNPs were either genotyped directly or imputed; in a few cases a surrogate SNP in high linkage disequilibrium was chosen. Logistic regression was performed within each study to obtain study-specific ßs and standard errors. Meta-analysis was conducted using an inverse variance weighted approach assuming a random effect model. RESULTS: Despite having power to detect odds ratio of 1.09-1.14 for overall IS and 1.20-1.32 for major stroke subtypes, none of the SNPs were significantly associated with overall IS and/or stroke subtypes after adjusting for multiple comparisons. CONCLUSIONS: Our results suggest that the major common loci associated with MI risk do not have effects of similar magnitude on overall IS but do not preclude moderate associations restricted to specific IS subtypes. Disparate mechanisms may be critical in the development of acute ischemic coronary and cerebrovascular events.

The multiple sclerosis whole blood mRNA transcriptome and genetic associations indicate dysregulation of specific T cell pathways in pathogenesis.

Multiple sclerosis (MS) is an autoimmune disease with a genetic component, caused at least in part by aberrant lymphocyte activity. The whole blood mRNA transcriptome was measured for 99 untreated MS patients: 43 primary progressive MS, 20 secondary progressive MS, 36 relapsing remitting MS and 45 age-matched healthy controls. The ANZgene Multiple Sclerosis Genetics Consortium genotyped more than 300 000 SNPs for 115 of these samples. Transcription from genes on translational regulation, oxidative phosphorylation, immune synapse and antigen presentation pathways was markedly increased in all forms of MS. Expression of genes tagging T cells was also upregulated (P < 10(-12)) in MS. A T cell gene signature predicts disease state with a concordance index of 0.79 with age and gender as co-variables, but the signature is not associated with clinical course or disability. The ANZgene genome wide association screen identified two novel regions with genome wide significance: one encoding the T cell co-stimulatory molecule, CD40; the other a region on chromosome 12q13-14. The CD40 haplotype associated with increased MS susceptibility has decreased gene expression in MS (P < 0.0007). The second MS susceptibility region includes 17 genes on 12q13-14 in tight linkage disequilibrium. Of these, only 13 are expressed in leukocytes, and of these the expression of one, FAM119B, is much lower in the susceptibility haplotype (P < 10(-14)). Overall, these data indicate dysregulation of T cells can be detected in the whole blood of untreated MS patients, and supports targeting of activated T cells in therapy for all forms of MS.

Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci.

OBJECTIVE: To perform a 1-stage meta-analysis of genome-wide association studies (GWAS) of multiple sclerosis (MS) susceptibility and to explore functional consequences of new susceptibility loci. METHODS: We synthesized 7 MS GWAS. Each data set was imputed using HapMap phase II, and a per single nucleotide polymorphism (SNP) meta-analysis was performed across the 7 data sets. We explored RNA expression data using a quantitative trait analysis in peripheral blood mononuclear cells (PBMCs) of 228 subjects with demyelinating disease. RESULTS: We meta-analyzed 2,529,394 unique SNPs in 5,545 cases and 12,153 controls. We identified 3 novel susceptibility alleles: rs170934(T) at 3p24.1 (odds ratio [OR], 1.17; p = 1.6 × 10(-8)) near EOMES, rs2150702(G) in the second intron of MLANA on chromosome 9p24.1 (OR, 1.16; p = 3.3 × 10(-8)), and rs6718520(A) in an intergenic region on chromosome 2p21, with THADA as the nearest flanking gene (OR, 1.17; p = 3.4 × 10(-8)). The 3 new loci do not have a strong cis effect on RNA expression in PBMCs. Ten other susceptibility loci had a suggestive p < 1 × 10(-6) , some of these loci have evidence of association in other inflammatory diseases (ie, IL12B, TAGAP, PLEK, and ZMIZ1). INTERPRETATION: We have performed a meta-analysis of GWAS in MS that more than doubles the size of previous gene discovery efforts and highlights 3 novel MS susceptibility loci. These and additional loci with suggestive evidence of association are excellent candidates for further investigations to refine and validate their role in the genetic architecture of MS.

The Gene Interaction Miner: a new tool for data mining contextual information for protein-protein interaction analysis.

MOTIVATION: This work was motivated by the need for an automated tool for discovery of genetic networks and the availability of extensive contextual protein-protein interaction information in the iHOP repository. At the moment, this information cannot be explored to its full potential due to the lack of software tools to reliably collect, process and display that information in a way that life scientists can quickly analyze genes of interest and search for potential interaction networks. Commercial tools can perform a similar job, but results appear to be less informative than those obtained using contextual information. RESULTS: The Gene Interaction Miner (GIM) could successfully uncover complex network structures of protein-protein interactions for a test dataset composed of genes already related to Alzheimer's disease. That same set, when examined using two other analysis tools, namely STRING and Pathway Studio, resulted in incomplete protein-protein interaction networks, which indicate that the use of curated databases only gives a partial picture of the biological processes behind the disease. AVAILABILITY: The dataset used in this work and a running version of the software tool is available for download from the web site http://www.cs.newcastle.edu.au/~mendes/softwareGIM.html.

Is there more than one proctitis syndrome? A revisitation using data from the TROG 96.01 trial.

PURPOSE: We sought to categorize longitudinal radiation-induced rectal toxicity data obtained from men participating in a randomised controlled trial for locally advanced prostate cancer. MATERIALS AND METHODS: Data from self-assessed questionnaires of rectal symptoms and clinician recorded remedial interventions were collected during the TROG 96.01 trial. In this trial, volunteers were randomised to radiation with or without neoadjuvant androgen deprivation. Characterization of longitudinal variations in symptom intensity was achieved using prevalence data. An integrated visualization and clustering approach based on memetic algorithms was used to define the compositions of symptom clusters occurring before, during and after radiation. The utility of the CTC grading system as a means of identifying specific injury profiles was evaluated using concordance analyses. RESULTS: Seven well-defined clusters of rectal symptoms were present prior to treatment, 25 were seen immediately following radiation and 7 at years 1, 2 and 3 following radiation. CTC grading did not concord with the degree of rectal 'distress' and 'problems' at all time points. Concordance was not improved by adding urgency to the CTC scale. CONCLUSIONS: The CTC scale has serious shortcomings. A powerful new technique for non-hierarchical clustering may contribute to the categorization of rectal toxicity data for genomic profiling studies and detailed patho-physiological studies.

Distinguishing childhood absence epilepsy patients from controls by the analysis of their background brain electrical activity.

Background electroencephalography (EEG), recorded with scalp electrodes, in children with childhood absence epilepsy (CAE) and control individuals has been analyzed. We considered 5 CAE patients, all right-handed females and aged 6-8 years. The 15 control individuals had the same characteristics of the CAE ones, but presented a normal EEG. The EEG was obtained using bipolar connections from a standard 10-20 electrode placement (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1 and O2). Recordings were undertaken in the resting state with eyes closed. EEG hallmarks of absence seizure activity are widely accepted, but there is a recognition that the bulk of interictal EEG in CAE appears normal to visual inspection. The functional activity between electrodes was evaluated using a wavelet decomposition in conjunction with the Wootters distance. Then, pairs of electrodes with differentiated behavior between CAE and controls were identified using a test statistic-based feature selection technique. This approach identified clear differences between CAE and healthy control background EEG in the frontocentral electrodes, as measured by Principal Component Analysis. The findings of this pilot study can have strong implications in future clinical practice.

Combinatorial optimization models for finding genetic signatures from gene expression datasets.

The aim of this chapter is to present combinatorial optimization models and techniques for the analysis of microarray datasets. The chapter illustrates the application of a novel objective function that guides the search for high-quality solutions for sequential ordering of expression profiles. The approach is unsupervised and a metaheuristic method (a memetic algorithm) is used to provide high-quality solutions. For the problem of selecting discriminative groups of genes, we used a supervised method that has provided good results in a variety of datasets. This chapter illustrates the application of these models in an Alzheimer's disease microarray dataset.

Genetic signatures for a rodent model of Parkinson's disease using combinatorial optimization methods.

This chapter illustrates the use of the combinatorial optimization models presented in Chapter 19 for the Feature Set selection and Gene Ordering problems to find genetic signatures for diseases using micro-array data. We demonstrate the quality of this approach by using a microarray dataset from a mouse model of Parkinson's disease. The results are accompanied by a description of the currently known molecular functions and biological processes of the genes in the signatures.

Microarrays--identifying molecular portraits for prostate tumors with different Gleason patterns.

We present in this chapter the combined use of several recently introduced methodologies for the analysis of microarray datasets. These computational techniques are varied in type and very powerful when combined. We have selected a prostate cancer dataset which is available in the public domain to allow for further comparisons with existing methods. The task is to identify biomarkers that correlate with the clinical phenotype of interest, i.e., Gleason patterns 3, 4, and 5. A supervised method, based on the mathematical formalism of (alpha, beta)-k-feature sets (1), is used to select differentially expressed genes. After these "molecular signatures" are identified, we applied an unsupervised method (a memetic algorithm) to order the samples (2). The objective is to maximize a global measure of correlation in the two-dimensional display of gene expression profiles. With the resulting ordering and taxonomy we are able to identify samples that have been assigned a certain Gleason pattern, and have gene expression patterns different from most of the other samples in the group. We reiterate the approach to obtain molecular signatures that produce coherent patterns of gene expression in each of the three Gleason pattern groups, and we analyze the statistically significant patterns of gene expression that seem to be implicated in these different stages of disease.

Using the QAPgrid Visualization Approach for Biomarker Identification of Cell-Specific Transcriptomic Signatures.

In this chapter, we illustrate the use of an integrated mathematical method for joint clustering and visualization of large-scale datasets. In applying these clustering methodologies to biological datasets, we aim to identify differentially expressed genes according to cell type by building molecular signatures supported by statistical scores. In doing so, we also aim to find a global map of highly co-expressed clusters. Variations in these clusters may well indicate other pathological trends and changes.

Basal-like breast cancer: molecular profiles, clinical features and survival outcomes.

BACKGROUND: Basal-like constitutes an important molecular subtype of breast cancer characterised by an aggressive behaviour and a limited therapy response. The outcome of patients within this subtype is, however, divergent. Some individuals show an increased risk of dying in the first five years, and others a long-term survival of over ten years after the diagnosis. In this study, we aim at identifying markers associated with basal-like patients' survival and characterising subgroups with distinct disease outcome. METHODS: We explored the genomic and transcriptomic profiles of 351 basal-like samples from the METABRIC and ROCK data sets. Two selection methods, labelled Differential and Survival filters, were employed to determine genes/probes that are differentially expressed in tumour and control samples, and are associated with overall survival. These probes were further used to define molecular subgroups, which vary at the microRNA level and in DNA copy number. RESULTS: We identified the expression signature of 80 probes that distinguishes between two basal-like subgroups with distinct clinical features and survival outcomes. Genes included in this list have been mainly linked to cancer immune response, epithelial-mesenchymal transition and cell cycle. In particular, high levels of CXCR6, HCST, C3AR1 and FPR3 were found in Basal I; whereas HJURP, RRP12 and DNMT3B appeared over-expressed in Basal II. These genes exhibited the highest betweenness centrality and node degree values and play a key role in the basal-like breast cancer differentiation. Further molecular analysis revealed 17 miRNAs correlated to the subgroups, including hsa-miR-342-5p, -150, -155, -200c and -17. Additionally, increased percentages of gains/amplifications were detected on chromosomes 1q, 3q, 8q, 10p and 17q, and losses/deletions on 4q, 5q, 8p and X, associated with reduced survival. CONCLUSIONS: The proposed signature supports the existence of at least two subgroups of basal-like breast cancers with distinct disease outcome. The identification of patients at a low risk may impact the clinical decisions-making by reducing the prescription of high-dose chemotherapy and, consequently, avoiding adverse effects. The recognition of other aggressive features within this subtype may be also critical for improving individual care and for delineating more effective therapies for patients at high risk.