Mutation analysis of cell-free DNA and single circulating tumor cells in metastatic breast cancer patients with high CTC counts

Purpose: The purpose of this study was to directly compare mutation profiles in multiple single CTCs and cfDNA isolated from the same blood samples taken from patients with metastaic breast cancer (MBC). We aimed to determine whether cell-free DNA would reflect the heterogeneity observed in 40 single CTCs. Experimental design: CTCs were enumerated by Cellsearch. CTC count was compared with the quantity of matched cfDNA and serum CA15-3 and alkaline phosphatase (ALP) in 112 patients with metastatic breast cancer. In 5 patients with {greater than or equal to}100 CTCs, multiple individual EpCAM-positive CTCs were isolated by DEPArray and compared with matched cfDNA and primary tumour tissue by targeted next generation sequencing (NGS) of ~2200 mutations in 50 cancer genes. Results: In the whole cohort, total cfDNA levels and cell counts ({greater than or equal to}5 CTCs) were both significantly associated with overall survival, unlike CA15-3 and ALP. NGS analysis of 40 individual EpCAM-positive CTCs from 5 patients with MBC revealed mutational heterogeneity in PIK3CA, TP53, ESR1 and KRAS genes between individual CTCs. In all 5 patients cfDNA profiles provided an accurate reflection of mutations seen in individual CTCs. ESR1 and KRAS gene mutations were absent from primary tumour tissue and therefore likely reflect either a minor sub-clonal mutation or were acquired with disease progression. Conclusion: Our results demonstrate that cfDNA reflects persisting EpCAM-positive CTCs in patients with high CTC counts and therefore may enable monitoring of the metastatic burden for clinical decision-making. Experimental Design: DNA methylation was investigated in independent tumor cohorts using Illumina HumanMethylation arrays and gene expression by Affymetrix arrays and qRT-PCR. The role of Msh homeobox 1 (MSX1) in drug sensitivity was investigated by gene reintroduction and siRNA knockdown of ovarian cancer cell lines. Results: CpG sites at contiguous genomic locations within the MSX1 gene have significantly lower levels of methylation in independent cohorts of HGSOC patients, which recur by 6 months compared with after 12 months (P < 0.05, q < 0.05, n = 78), have poor RECIST response (P < 0.05, q < 0.05, n = 61), and are associated with PFS in an independent cohort (n = 146). A decrease in methylation at these CpG sites correlates with decreased MSX1 gene expression. MSX1 expression is associated with PFS (HR, 0.92; 95% CI, 0.85–0.99; P = 0.029; n = 309). Cisplatin-resistant ovarian cancer cell lines have reduced MSX1 expression, and MSX1 overexpression leads to cisplatin sensitization, increased apoptosis, and increased cisplatin-induced p21 expression. Conclusions: Hypomethylation of CpG sites within the MSX1 gene is associated with resistant HGSOC disease at presentation and identifies expression of MSX1 as conferring platinum drug sensitivity

Backbone and side-chain 1H, 13C and 15N assignments of the ubiquitin-associated domain of human X-linked inhibitor of apoptosis protein

X-linked inhibitor of apoptosis protein (XIAP), a leading member of the family of inhibitor of apoptosis (IAP) proteins, is considered as the most potent and versatile inhibitor of caspases and apoptosis. It has been reported that XIAP is frequently overexpressed in cancer and its expression level is implicated in contributing to tumorigenesis, disease progression, chemoresistance and poor patient-survival. Therefore, XIAP is one of the leading targets in drug development for cancer therapy. Recently, based on bioinformatics study, a previously unrecognized but evolutionarily conserved ubiquitin-associated (UBA) domain in IAPs was identified. The UBA domain is found to be essential for the oncogenic potential of IAP, to maintain endothelial cell survival and to protect cells from TNF-α-induced apoptosis. Moreover, the UBA domain is required for XIAP to activate NF-κB. In the present study, we report the near complete resonance assignments of the UBA domain-containing region of human XIAP protein. Secondary structure prediction based on chemical shift index (CSI) analysis reveals that the protein is predominately α-helical, which is consistent with the structures of known UBA proteins

Estimation of heritability from limited family data using genome-wide identity-by-descent sharing

<p>Abstract</p> <p>Background</p> <p>In classical pedigree-based analysis, additive genetic variance is estimated from between-family variation, which requires the existence of larger phenotyped and pedigreed populations involving numerous families (parents). However, estimation is often complicated by confounding of genetic and environmental family effects, with the latter typically occurring among full-sibs. For this reason, genetic variance is often inferred based on covariance among more distant relatives, which reduces the power of the analysis. This simulation study shows that genome-wide identity-by-descent sharing among close relatives can be used to quantify additive genetic variance solely from within-family variation using data on extremely small family samples.</p> <p>Methods</p> <p>Identity-by-descent relationships among full-sibs were simulated assuming a genome size similar to that of humans (effective number of loci ~80). Genetic variance was estimated from phenotypic data assuming that genomic identity-by-descent relationships could be accurately re-created using information from genome-wide markers. The results were compared with standard pedigree-based genetic analysis.</p> <p>Results</p> <p>For a polygenic trait and a given number of phenotypes, the most accurate estimates of genetic variance were based on data from a single large full-sib family only. Compared with classical pedigree-based analysis, the proposed method is more robust to selection among parents and for confounding of environmental and genetic effects. Furthermore, in some cases, satisfactory results can be achieved even with less ideal data structures, i.e., for selectively genotyped data and for traits for which the genetic variance is largely under the control of a few major genes.</p> <p>Conclusions</p> <p>Estimation of genetic variance using genomic identity-by-descent relationships is especially useful for studies aiming at estimating additive genetic variance of highly fecund species, using data from small populations with limited pedigree information and/or few available parents, i.e., parents originating from non-pedigreed or even wild populations.</p

Best Linear Unbiased Prediction of Genomic Breeding Values Using a Trait-Specific Marker-Derived Relationship Matrix

With the availability of high density whole-genome single nucleotide polymorphism chips, genomic selection has become a promising method to estimate genetic merit with potentially high accuracy for animal, plant and aquaculture species of economic importance. With markers covering the entire genome, genetic merit of genotyped individuals can be predicted directly within the framework of mixed model equations, by using a matrix of relationships among individuals that is derived from the markers. Here we extend that approach by deriving a marker-based relationship matrix specifically for the trait of interest

Within- and across-breed genomic prediction using whole-genome sequence and single nucleotide polymorphism panels

International audienceBackground Currently, genomic prediction in cattle is largely based on panels of about 54k single nucleotide polymorphisms (SNPs). However with the decreasing costs of and current advances in next-generation sequencing technologies, whole-genome sequence (WGS) data on large numbers of individuals is within reach. Availability of such data provides new opportunities for genomic selection, which need to be explored.MethodsThis simulation study investigated how much predictive ability is gained by using WGS data under scenarios with QTL (quantitative trait loci) densities ranging from 45 to 132 QTL/Morgan and heritabilities ranging from 0.07 to 0.30, compared to different SNP densities, with emphasis on divergent dairy cattle breeds with small populations. The relative performances of best linear unbiased prediction (SNP-BLUP) and of a variable selection method with a mixture of two normal distributions (MixP) were also evaluated. Genomic predictions were based on within-population, across-population, and multi-breed reference populations.ResultsThe use of WGS data for within-population predictions resulted in small to large increases in accuracy for low to moderately heritable traits. Depending on heritability of the trait, and on SNP and QTL densities, accuracy increased by up to 31 %. The advantage of WGS data was more pronounced (7 to 92 % increase in accuracy depending on trait heritability, SNP and QTL densities, and time of divergence between populations) with a combined reference population and when using MixP. While MixP outperformed SNP-BLUP at 45 QTL/Morgan, SNP-BLUP was as good as MixP when QTL density increased to 132 QTL/Morgan.ConclusionsOur results show that, genomic predictions in numerically small cattle populations would benefit from a combination of WGS data, a multi-breed reference population, and a variable selection method

Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits

Background: In the analysis of complex traits, genetic effects can be confounded with non-genetic effects, especially when using full-sib families. Dominance and epistatic effects are typically confounded with additive genetic and non-genetic effects. This confounding may cause the estimated genetic variance components to be inaccurate and biased

Adaptation of gastrointestinal nematode parasites to host genotype: single locus simulation models

Background: Breeding livestock for improved resistance to disease is an increasingly important selection goal. However, the risk of pathogens adapting to livestock bred for improved disease resistance is difficult to quantify. Here, we explore the possibility of gastrointestinal worms adapting to sheep bred for low faecal worm egg count using computer simulation. Our model assumes sheep and worm genotypes interact at a single locus, such that the effect of an A allele in sheep is dependent on worm genotype, and the B allele in worms is favourable for parasitizing the A allele sheep but may increase mortality on pasture. We describe the requirements for adaptation and test if worm adaptation (1) is slowed by non-genetic features of worm infections and (2) can occur with little observable change in faecal worm egg count. Results: Adaptation in worms was found to be primarily influenced by overall worm fitness, viz. the balance between the advantage of the B allele during the parasitic stage in sheep and its disadvantage on pasture. Genetic variation at the interacting locus in worms could be from de novo or segregating mutations, but de novo mutations are rare and segregating mutations are likely constrained to have (near) neutral effects on worm fitness. Most other aspects of the worm infection we modelled did not affect the outcomes. However, the host-controlled mechanism to reduce faecal worm egg count by lowering worm fecundity reduced the selection pressure on worms to adapt compared to other mechanisms, such as increasing worm mortality. Temporal changes in worm egg count were unreliable for detecting adaptation, despite the steady environment assumed in the simulations. Conclusions: Adaptation of worms to sheep selected for low faecal worm egg count requires an allele segregating in worms that is favourable in animals with improved resistance but less favourable in other animals. Obtaining alleles with this specific property seems unlikely. With support from experimental data, we conclude that selection for low faecal worm egg count should be stable over a short time frame (e.g. 20 years). We are further exploring model outcomes with multiple loci and comparing outcomes to other control strategies

Evolution of small putative group I introns in the SSU rRNA gene locus of Phialophora species

<p>Abstract</p> <p>Background</p> <p>Group I introns (specifically subgroup IC1) are common in the nuclear ribosomal RNA genes of fungi. While most range in length from more than 200 to nearly 1800 nucleotides (nt) in length, several small putative (or degenerate) group I introns have been described that are between 56 and 81 nt. Although small, previously we demonstrated that the <it>Pa</it>SSU intron in the rRNA small subunit gene of <it>Phialophora americana </it>isolate Wang 1046 is capable of <it>in vitro </it>splicing using a standard group I intron pathway, thus qualifying it as a functional ribozyme.</p> <p>Findings</p> <p>Here, we describe eight short putative group I introns, ranging in length from 63 to 75 nt, in the rRNA small subunit genes of <it>Phialophora </it>isolates, a fungal genus that ranges from saprobic to pathogenic on plants and animals. All contain putative pairing regions P1, P7, and P10, as well as a pairing region formed between the middle of the intron and part of the 3' exon. The other pairing regions common in the core of standard group I introns are absent. However, parts of the 3' exon may aid in the stabilization of these small introns. Although the eight putative group I introns were from at least three species of <it>Phialophora</it>, phylogenetic analysis indicated that the eight are monophyletic. They are also monophyletic with the small introns of two lichen-forming fungi, <it>Porpidia crustulata </it>and <it>Arthonia lapidicola</it>.</p> <p>Conclusions</p> <p>The small putative group I introns in <it>Phialophora </it>have common features that may represent group I introns at their minima. They appear to have a single origin as indicated by their monophyly in phylogenetic analyses.</p

The GABA transporter 1 (SLC6A1): a novel candidate gene for anxiety disorders

Recent evidence suggests that the GABA transporter 1 (GAT-1; SLC6A1) plays a role in the pathophysiology and treatment of anxiety disorders. In order to understand the impact of genetic variation within SLC6A1 on pathological anxiety, we performed a case–control association study with anxiety disorder patients with and without syndromal panic attacks. Using the method of sequential addition of cases, we found that polymorphisms in the 5′ flanking region of SLC6A1 are highly associated with anxiety disorders when considering the severity of syndromal panic attacks as phenotype covariate. Analysing the effect size of the association, we observed a constant increase in the odds ratio for disease susceptibility with an increase in panic severity (OR ~ 2.5 in severely affected patients). Nominally significant association effects were observed considering the entire patient sample. These data indicate a high load of genetic variance within SLC6A1 on pathological anxiety and highlight GAT-1 as a promising target for treatment of anxiety disorders with panic symptoms