[Show abstract][Hide abstract] ABSTRACT: Cancer cachexia is a complex and multifactorial disease. Evolving definitions highlight the fact that a diverse range of biological processes contribute to cancer cachexia. Part of the variation in who will and who will not develop cancer cachexia may be genetically determined. As new definitions, classifications and biological targets continue to evolve, there is a need for reappraisal of the literature for future candidate association studies. This review summarizes genes identified or implicated as well as putative candidate genes contributing to cachexia, identified through diverse technology platforms and model systems to further guide association studies. A systematic search covering 1986-2012 was performed for potential candidate genes / genetic polymorphisms relating to cancer cachexia. All candidate genes were reviewed for functional polymorphisms or clinically significant polymorphisms associated with cachexia using the OMIM and GeneRIF databases. Pathway analysis software was used to reveal possible network associations between genes. Functionality of SNPs/genes was explored based on published literature, algorithms for detecting putative deleterious SNPs and interrogating the database for expression of quantitative trait loci (eQTLs). A total of 154 genes associated with cancer cachexia were identified and explored for functional polymorphisms. Of these 154 genes, 119 had a combined total of 281 polymorphisms with functional and/or clinical significance in terms of cachexia associated with them. Of these, 80 polymorphisms (in 51 genes) were replicated in more than one study with 24 polymorphisms found to influence two or more hallmarks of cachexia (i.e., inflammation, loss of fat mass and/or lean mass and reduced survival). Selection of candidate genes and polymorphisms is a key element of multigene study design. The present study provides a contemporary basis to select genes and/or polymorphisms for further association studies in cancer cachexia, and to develop their potential as susceptibility biomarkers of cachexia.
[Show abstract][Hide abstract] ABSTRACT: The question of how best to attribute the unit costs of the annotated biospecimen product that is provided to a research user is a common issue for many biobanks. Some of the factors influencing user fees are capital and operating costs, internal and external demand and market competition, and moral standards that dictate that fees must have an ethical basis. It is therefore important to establish a transparent and accurate costing tool that can be utilized by biobanks and aid them in establishing biospecimen user fees. To address this issue, we built a biospecimen user fee calculator tool, accessible online at www.biobanking.org . The tool was built to allow input of: i) annual operating and capital costs; ii) costs categorized by the major core biobanking operations; iii) specimen products requested by a biobank user; and iv) services provided by the biobank beyond core operations (e.g., histology, tissue micro-array); as well as v) several user defined variables to allow the calculator to be adapted to different biobank operational designs. To establish default values for variables within the calculator, we first surveyed the members of the Canadian Tumour Repository Network (CTRNet) management committee. We then enrolled four different participants from CTRNet biobanks to test the hypothesis that the calculator tool could change approaches to user fees. Participants were first asked to estimate user fee pricing for three hypothetical user scenarios based on their biobanking experience (estimated pricing) and then to calculate fees for the same scenarios using the calculator tool (calculated pricing). Results demonstrated significant variation in estimated pricing that was reduced by calculated pricing, and that higher user fees are consistently derived when using the calculator. We conclude that adoption of this online calculator for user fee determination is an important first step towards harmonization and realistic user fees.
Biopreservation and Biobanking 08/2014; 12(4):234-239. · 1.50 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Despite the integral role of biorepositories in fueling translational research and the advancement of medicine, there are significant gaps in harmonization of biobanking practices, resulting in variable biospecimen collection, storage, and processing. This significantly impacts accurate downstream analysis and, in particular, creates a problem for biorepository networks or consortia. The Canadian Tumour Repository Network (CTRNet; www.ctrnet.ca ) is a consortium of Canadian tumor biorepositories that aims to enhance biobanking capacity and quality through standardization. To minimize the issue of variable biobanking practices throughout its network, CTRNet has developed and maintained a comprehensive set of 45 standard operating procedures (SOPs). There were four key elements to the CTRNet SOP development process: 1) an SOP development team was formed from members across CTRNet to co-produce each SOP; 2) a principal author was appointed with responsibility for overall coordination of the SOP development process; 3) the CTRNet Management Committee (composed of principal investigators for each member biorepository) reviewed/revised each SOP completed by the development team; and 4) external expert reviewers provided feedback and recommendations on each SOP. Once final Management Committee approval was obtained, the ratified SOP was published on the CTRNet website for public access. Since the SOPs were first published on the CTRNet website (June 2008), there have been approximately 15,000 downloads of one or more CTRNet SOPs/Policies by users from over 60 countries. In accordance with biobanking best practices, CTRNet performs an exhaustive review of its SOPs at set intervals, to coincide with each granting cycle. The last revision was completed in May 2012.
Biopreservation and Biobanking 12/2013; 11(6):387-96. · 1.50 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Molecular classification of breast cancer is based, in part, on the presence or absence of amplification of the human epidermal growth factor receptor 2 (ERBB2) gene, which leads to HER2 protein overproduction. While the presence of the overexpressed HER2 protein is a necessary precondition for sensitivity to anti-HER2 therapies, many patients develop resistance. Thus, identification of the downstream effectors of this pathway will help in understanding mechanism(s) of chemoresistance and further, the identified molecules themselves may have the potential to be used as therapeutic targets. In this work, we studied the proteomic changes that accompany the HER2 gene amplification to identify potential new therapeutic targets and biomarkers. We analyzed bio-triplicate proteome samples extracted from wild-type MCF-7 human breast cancer cells and their isogenic stably overexpressing HER2 (amplified) transfectants. In total, 2455 unique proteins were quantified with 1278 of them differentially expressed in HER2 normal and HER2 overexpressing MCF-7 cells. Select biomarker candidates of particular interest were validated by western blotting, and evaluated for clinical relevance by immunohistochemical assessment of protein abundance in breast tumor biopsies. HER2 transfection produced marked changes in proteins related to multiple aspects of cancer biology, and the identified expression patterns were recapitulated in the clinical samples. Biological Significances Breast cancer is a major cause of death in women. Molecular classification of breast cancer is based, in part, on the presence or absence of amplification of the human epidermal growth factor receptor 2 (ERBB2) gene, which leads to HER2 protein overproduction that triggers intracellular signaling events that drive proliferation, invasion, metastases, and resistance to apoptosis. While the presence of the overexpressed HER2 gene product, HER2 protein, is a necessary precondition for sensitivity to the therapeutic monoclonal antibody trastuzumab, the downstream effects of HER2 protein overexpression are incompletely understood. In this work, we applied quantitative proteomics to identify proteomic changes accompanying ERBB2 gene amplification. The significances of this work include 1) identification of new biomarkers associated with the HER2 phenotype, 2) measurement of the magnitude of the proteomic changes triggered by amplification of this single gene, and 3) better understanding of the downstream biological changes triggered by HER2 overexpression.
Journal of proteomics 07/2013; · 5.07 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Genome-wide association studies (GWASs) have identified low-penetrance common variants (i.e., single nucleotide polymorphisms, SNPs) associated with breast cancer susceptibility. Although GWASs are primarily focused on single-locus effects, gene-gene interactions (i.e., epistasis) are also assumed to contribute to the genetic risks for complex diseases including breast cancer. While it has been hypothesized that moderately ranked (P value based) weak single-locus effects in GWASs could potentially harbor valuable information for evaluating epistasis, we lack systematic efforts to investigate SNPs showing consistent associations with weak statistical significance across independent discovery and replication stages. The objectives of this study were i) to select SNPs showing single-locus effects with weak statistical significance for breast cancer in a GWAS and/or candidate-gene studies; ii) to replicate these SNPs in an independent set of breast cancer cases and controls; and iii) to explore their potential SNP-SNP interactions contributing to breast cancer susceptibility. A total of 17 SNPs related to DNA repair, modification and metabolism pathway genes were selected since these pathways offer a priori knowledge for potential epistatic interactions and an overall role in breast carcinogenesis. The study design included predominantly Caucasian women (2,795 cases and 4,505 controls) from Alberta, Canada. We observed two two-way SNP-SNP interactions (APEX1-rs1130409 and RPAP1-rs2297381; MLH1-rs1799977 and MDM2-rs769412) in logistic regression that conferred elevated risks for breast cancer (P interaction<7.3×10(-3)). Logic regression identified an interaction involving four SNPs (MBD2-rs4041245, MLH1-rs1799977, MDM2-rs769412, BRCA2-rs1799943) (P permutation = 2.4×10(-3)). SNPs involved in SNP-SNP interactions also showed single-locus effects with weak statistical significance, while BRCA2-rs1799943 showed stronger statistical significance (P correlation/trend = 3.2×10(-4)) than the others. These single-locus effects were independent of body mass index. Our results provide a framework for evaluating SNPs showing statistically weak but reproducible single-locus effects for epistatic effects contributing to disease susceptibility.
PLoS ONE 06/2013; 8(6):e64896. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: More than 40 single nucleotide polymorphisms (SNPs) for breast cancer susceptibility were identified by genome-wide association studies (GWASs). However, additional SNPs likely contribute to breast cancer susceptibility and overall genetic risk, prompting this investigation for additional variants. Six putative breast cancer susceptibility SNPs identified in a two-stage GWAS that we reported earlier were replicated in a follow-up stage 3 study using an independent set of breast cancer cases and controls from Canada, with an overall cumulative sample size of 7,219 subjects across all three stages. The study design also encompassed the 11 variants from GWASs previously reported by various consortia between the years 2007-2009 to (i) enable comparisons of effect sizes, and (ii) identify putative prognostic variants across studies. All SNP associations reported with breast cancer were also adjusted for body mass index (BMI). We report a strong association with 4q31.22-rs1429142 (combined per allele odds ratio and 95% confidence interval = 1.28 [1.17-1.41] and P combined = 1.5×10(-7)), when adjusted for BMI. Ten of the 11 breast cancer susceptibility loci reported by consortia also showed associations in our predominantly Caucasian study population, and the associations were independent of BMI; four FGFR2 SNPs and TNRC9-rs3803662 were among the most notable associations. Since the original report by Garcia-Closas et al. 2008, this is the second study to confirm the association of 8q24.21-rs13281615 with breast cancer outcomes.
PLoS ONE 05/2013; 8(5):e62550. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Human biological specimens are important for translational research programs such as the Canadian Ovarian Experimental Unified Resource (COEUR) funded by the Terry Fox Research Institute. Sample quality is an important consideration, as it directly impacts the quality of ensuing research. The aim of the present study was to determine the quality of tissues collected from different sites contributing to the COEUR cohort. Samples from high-grade serous ovarian tumors (fresh frozen and corresponding paraffin-embedded tissues) were provided by nine participating Canadian biobanks. All samples were shipped to a central site using a Standard Operating Protocol (SOP). DNA and RNA extraction was conducted by the quality control division of the Canadian Tumor Repository Network (CTRNet). DNA quality was determined by ß-globin gene PCR amplification, and RNA quality by the RNA integrity number (RIN), as measured by the Agilent BioAnalyzer. DNA of acceptable quality had at least three bands of ß-globin amplified from DNA (n=115/135), and a RIN number ≥7 was considered very good for RNA (n=80/135). Sample preparation and storage time had little effect on RNA or DNA quality. Protein expression was assessed on tissue microarray by immunohistochemistry with antibodies against p53, WT1, E-cadherin, CK-7, and Ki67 from formalin fixed-paraffin embedded (FFPE) tissues. As seen with a nonhierarchical clustering statistical method, there was no significant difference in immunostaining of paraffin tissues among specimens from different biobanks. Interestingly, patients with worse outcome were highly positive for p53 and weak for WT1. In conclusion, while there was no common SOP for retrospectively collected material across Canadian biobanks, these results indicate that specimens collected at these multiple sites are of comparable quality, and can serve as an adequate resource to create a national cohort for the validation of molecular biomarkers in ovarian cancer.
Biopreservation and Biobanking 04/2013; 11(2):83-93. · 1.50 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: BACKGROUND: Population stratification is a systematic difference in allele frequencies between subpopulations. This can lead to spurious association findings in the case--control genome wide association studies (GWASs) used to identify single nucleotide polymorphisms (SNPs) associated with disease-linked phenotypes. Methods such as self-declared ancestry, ancestry informative markers, genomic control, structured association, and principal component analysis are used to assess and correct population stratification but each has limitations. We provide an alternative technique to address population stratification. RESULTS: We propose a novel machine learning method, ETHNOPRED, which uses the genotype and ethnicity data from the HapMap project to learn ensembles of disjoint decision trees, capable of accurately predicting an individual's continental and sub-continental ancestry. To predict an individual's continental ancestry, ETHNOPRED produced an ensemble of 3 decision trees involving a total of 10 SNPs, with 10-fold cross validation accuracy of 100% using HapMap II dataset. We extended this model to involve 29 disjoint decision trees over 149 SNPs, and showed that this ensemble has an accuracy of >= 99.9%, even if some of those 149 SNP values were missing. On an independent dataset, predominantly of Caucasian origin, our continental classifier showed 96.8% accuracy and improved genomic control's lamda from 1.22 to 1.11. We next used the HapMap III dataset to learn classifiers to distinguish European subpopulations (North-Western vs. Southern), East Asian subpopulations (Chinese vs. Japanese), African subpopulations (Eastern vs. Western), North American subpopulations (European vs. Chinese vs. African vs. Mexican vs. Indian), and Kenyan subpopulations (Luhya vs. Maasai). In these cases, ETHNOPRED produced ensembles of 3, 39, 21, 11, and 25 disjoint decision trees, respectively involving 31, 502, 526, 242 and 271 SNPs, with 10-fold cross validation accuracy of 86.5% +/- 2.4%, 95.6% +/- 3.9%, 95.6% +/- 2.1%, 98.3% +/- 2.0%, and 95.9% +/- 1.5%. However, ETHNOPRED was unable to produce a classifier that can accurately distinguish Chinese in Beijing vs. Chinese in Denver. CONCLUSIONS: ETHNOPRED is a novel technique for producing classifiers that can identify an individual's continental and sub-continental heritage, based on a small number of SNPs. We show that its learned classifiers are simple, cost-efficient, accurate, transparent, flexible, fast, applicable to large scale GWASs, and robust to missing values.
[Show abstract][Hide abstract] ABSTRACT: Breast cancer recurrence (BCR) is a common treatment outcome despite curative-intent primary treatment of non-metastatic breast cancer. Currently used prognostic and predictive factors utilize tumor-based markers, and are not optimal determinants of risk of BCR. Germline-based copy number aberrations (CNAs) have not been evaluated as determinants of predisposition to experience BCR. In this study, we accessed germline DNA from 369 female breast cancer subjects who received curative-intent primary treatment following diagnosis. Of these, 155 experienced BCR and 214 did not, after a median duration of follow up after breast cancer diagnosis of 6.35 years (range = 0.60-21.78) and 8.60 years (range = 3.08-13.57), respectively. Whole genome CNA genotyping was performed on the Affymetrix SNP array 6.0 platform. CNAs were identified using the SNP-Fast Adaptive States Segmentation Technique 2 algorithm implemented in Nexus Copy Number 6.0. Six samples were removed due to poor quality scores, leaving 363 samples for further analysis. We identified 18,561 CNAs with ≥1 kb as a predefined cut-off for observed aberrations. Univariate survival analyses (log-rank tests) identified seven CNAs (two copy number gains and five copy neutral-loss of heterozygosities, CN-LOHs) showing significant differences (P<2.01×10(-5)) in recurrence-free survival (RFS) probabilities with and without CNAs.We also observed three additional but distinct CN-LOHs showing significant differences in RFS probabilities (P<2.86×10(-5)) when analyses were restricted to stratified cases (luminal A, n = 208) only. After adjusting for tumor stage and grade in multivariate analyses (Cox proportional hazards models), all the CNAs remained strongly associated with the phenotype of BCR. Of these, we confirmed three CNAs at 17q11.2, 11q13.1 and 6q24.1 in representative samples using independent genotyping platforms. Our results suggest further investigations on the potential use of germline DNA variations as prognostic markers in cancer-associated phenotypes.
PLoS ONE 01/2013; 8(1):e53850. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Top differentially expressed gene lists are often inconsistent between studies and it has been suggested that small sample sizes contribute to lack of reproducibility and poor prediction accuracy in discriminative models. We considered sex differences (69♂, 65♀) in 134 human skeletal muscle biopsies using DNA microarray. The full dataset and subsamples (n = 10 (5♂, 5♀) to n = 120 (60♂, 60♀)) thereof were used to assess the effect of sample size on the differential expression of single genes, gene rank order and prediction accuracy. Using our full dataset (n = 134), we identified 717 differentially expressed transcripts (p<0.0001) and we were able predict sex with ∼90% accuracy, both within our dataset and on external datasets. Both p-values and rank order of top differentially expressed genes became more variable using smaller subsamples. For example, at n = 10 (5♂, 5♀), no gene was considered differentially expressed at p<0.0001 and prediction accuracy was ∼50% (no better than chance). We found that sample size clearly affects microarray analysis results; small sample sizes result in unstable gene lists and poor prediction accuracy. We anticipate this will apply to other phenotypes, in addition to sex.
PLoS ONE 01/2013; 8(6):e65380. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Two core aspects of the discipline of biobanking are biospecimen quality and good governance. Meeting the demands of both sample quality and governance can be challenging, especially in a resource limited environment. Frequently, differences between biobank processes reduce the ability for cooperative action and specimen sharing with researchers. In the Canadian context, we have made an attempt to identify these gaps and have provided a framework to support excellence, initially for tumor biobanks. The Canadian Tumour Repository Network (CTRNet) was established with funding from the Canadian Institute of Health Sciences (CIHR) Institute of Cancer Research (ICR) to foster translational research through improved access to high quality tumour biospecimens. Consistent with this mandate, CTRNet has focused on the establishment and deployment of common standards to harmonize biospecimen quality and approaches to governance. More recently, CTRNet has implemented a certification program to communicate these standards in conjunction with simultaneous exposure to education focusing on the rationale and foundations underlying these standards. The CTRNet certification program comprises registration and certification steps as two linked phases. In the registration phase, launched in November 2011, biobanks are registered into the system and individuals complete an introductory educational module. In the subsequent certification phase, the type of biobank is classified and assigned relevant educational modules and adoption of relevant standards of practice is confirmed through review of documentation including policies and protocols that address the CTRNet Required Operational Practices (ROPs). An important feature of the program is that it is intended for all types of tumor biobanks, so the scope and extent of assessment is scaled to the type of biobank. This program will provide an easily adoptable and flexible mechanism to communicate common standards through education and address both quality assurance and governance across the broad spectrum of biobanks.
Biopreservation and Biobanking 10/2012; 10(5):426-32. · 1.50 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The cytoskeleton is essential to cell morphology, cargo trafficking, and cell division. As the neuronal cytoskeleton is extremely complex, it is no wonder that a startling number of neurodegenerative disorders (including but not limited to Alzheimer's disease, Parkinson's disease and Huntington's disease) share the common feature of a dysfunctional neuronal cytoskeleton. Recently, concern has been raised about a possible link between anesthesia, post-operative cognitive dysfunction, and the exacerbation of neurodegenerative disorders. Experimental investigations suggest that anesthetics bind to and affect cytoskeletal microtubules, and that anesthesia-related cognitive dysfunction involves microtubule instability, hyper-phosphorylation of the microtubule-associated protein tau, and tau separation from microtubules. However, exact mechanisms are yet to be identified. In this paper the interaction of anesthetics with the microtubule subunit protein tubulin is investigated using computer-modeling methods. Homology modeling, molecular dynamics simulations and surface geometry techniques were used to determine putative binding sites for volatile anesthetics on tubulin. This was followed by free energy based docking calculations for halothane (2-bromo-2-chloro-1,1,1-trifluoroethane) on the tubulin body, and C-terminal regions for specific tubulin isotypes. Locations of the putative binding sites, halothane binding energies and the relation to cytoskeleton function are reported in this paper.
PLoS ONE 06/2012; 7(6):e37251. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The mechanisms by which trastuzumab imparts clinical benefit remain incompletely understood. Antibody-dependent cellular cytotoxicity via interactions with Fcγ receptors (FcγR) on leukocytes may contribute to its antitumor effects. Single-nucleotide polymorphisms (SNP) in FCGR3A and FCGR2A genes lead to amino acid substitutions at positions 158 and 131, respectively, and affect binding of antibodies to FcγR such that 158V/V and 131H/H bind with highest affinity. This study aimed to determine whether high-affinity SNPs are associated with disease-free survival (DFS) among patients with HER2-positive nonmetastatic breast cancer.
Genomic DNA was isolated from 1,286 patients enrolled in a trial of adjuvant trastuzumab-based chemotherapy. Genotyping was conducted using Sanger sequencing and Sequenom mass spectrometry.
Patient samples (N = 1,189) were successfully genotyped for FCGR3A and 1,218 for FCGR2A. Compared with the overall results of the BCIRG006 study, in the subset of patients genotyped in this analysis, a less robust improvement in DFS was observed for the trastuzumab arms than control arm (HR, 0.842; P = 0.1925). When stratified for prognostic features, the HR in favor of trastuzumab was consistent with that of the overall study (HR, 0.74; P = 0.036). No correlation between DFS and FCGR3A/2A genotypes was seen for trastuzumab-treated patients (158V/V vs. V/F vs. F/F, P = 0.98; 131H/H vs. H/R vs. R/R, P = 0.76; 158V/V and/or 131H/H vs. others, P = 0.67).
This analysis evaluating the association between FCGR3A/2A genotypes and trastuzumab efficacy in HER2-positive breast cancer did not show a correlation between FCGR3A-V/F and FCGR2A-H/R SNPs and DFS in patients treated with trastuzumab.
Clinical Cancer Research 04/2012; 18(12):3478-86. · 8.19 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The variable predisposition to cachexia may, in part, be due to the interaction of host genotype. We analyzed 129 single nucleotide polymorphisms (SNPs) in 80 genes for association with cachexia based on degree of weight loss (>5, >10, >15%) as well as weight loss in the presence of systemic inflammation (C-reactive protein, > 10 mg/l). 775 cancer patients were studied with a validation association study performed on an independently recruited cohort (n = 101) of cancer patients. The C allele (minor allele frequency 10.7%) of the rs6136 (SELP) SNP was found to be associated with weight loss >10% both in the discovery study (odds ratio (OR) 0.52; 95% confidence intervals (CI), 0.29-0.93; p = 0.026) and the validation study (OR 0.09, 95% CI 0.01-0.98, p = 0.035). In separate studies, induction of muscle atrophy gene expression was investigated using qPCR following either tumour-induced cachexia in rats or intra-peritoneal injection of lipopolysaccharide in mice. P-selectin was found to be significantly upregulated in muscle in both models. Identification of P-selectin as relevant in both animal models and in cachectic cancer patients supports this as a risk factor/potential mediator in cachexia.
EMBO Molecular Medicine 04/2012; 4(6):462-71. · 7.80 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Primary triple-negative breast cancers (TNBCs), a tumour type defined by lack of oestrogen receptor, progesterone receptor and ERBB2 gene amplification, represent approximately 16% of all breast cancers. Here we show in 104 TNBC cases that at the time of diagnosis these cancers exhibit a wide and continuous spectrum of genomic evolution, with some having only a handful of coding somatic aberrations in a few pathways, whereas others contain hundreds of coding somatic mutations. High-throughput RNA sequencing (RNA-seq) revealed that only approximately 36% of mutations are expressed. Using deep re-sequencing measurements of allelic abundance for 2,414 somatic mutations, we determine for the first time-to our knowledge-in an epithelial tumour subtype, the relative abundance of clonal frequencies among cases representative of the population. We show that TNBCs vary widely in their clonal frequencies at the time of diagnosis, with the basal subtype of TNBC showing more variation than non-basal TNBC. Although p53 (also known as TP53), PIK3CA and PTEN somatic mutations seem to be clonally dominant compared to other genes, in some tumours their clonal frequencies are incompatible with founder status. Mutations in cytoskeletal, cell shape and motility proteins occurred at lower clonal frequencies, suggesting that they occurred later during tumour progression. Taken together, our results show that understanding the biology and therapeutic responses of patients with TNBC will require the determination of individual tumour clonal genotypes.
[Show abstract][Hide abstract] ABSTRACT: Genome-wide association studies for breast cancer have identified over 40 single-nucleotide polymorphisms (SNPs), a subset of which remains statistically significant after genome-wide correction. Improved strategies for mining of genome-wide association data have been suggested to address heritable component of genetic risk in breast cancer. In this study, we attempted a two-stage association design using markers from a genome-wide study (stage 1, Affymetrix Human SNP 6.0 array, cases=302, controls=321). We restricted our analysis to DNA repair/modifications/metabolism pathway related gene polymorphisms for their obvious role in carcinogenesis in general and for their known protein-protein interactions vis-à-vis, potential epistatic effects. We selected 22 SNPs based on linkage disequilibrium patterns and high statistical significance. Genotyping assays in an independent replication study of 1178 cases and 1314 controls were attempted using Sequenom iPLEX Gold platform (stage 2). Six SNPs (rs8094493, rs4041245, rs7614, rs13250873, rs1556459 and rs2297381) showed consistent and statistically significant associations with breast cancer risk in both stages, with allelic odds ratios (and P-values) of 0.85 (0.0021), 0.86 (0.0026), 0.86 (0.0041), 1.17 (0.0043), 1.20 (0.0103) and 1.13 (0.0154), respectively, in combined analysis (N=3115). Of these, three polymorphisms were located in methyl-CpG-binding domain protein 2 gene regions and were in strong linkage disequilibrium. The remaining three SNPs were in proximity to RAD21 homolog (S. pombe), O-6-methylguanine-DNA methyltransferase and RNA polymerase II-associated protein 1. The identified markers may be relevant to breast cancer susceptibility in populations if these findings are confirmed in independent cohorts.
European journal of human genetics: EJHG 01/2012; 20(6):682-9. · 3.56 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Urine and plasma metabolites originate from endogenous metabolic pathways in different organs and exogenous sources (diet). Urine and plasma were obtained from advanced cancer patients and investigated to determine if variations in lean and fat mass, dietary intake, and energy metabolism relate to variation in metabolite profiles. Patients (n = 55) recorded their diets for 3 d and after an overnight fast they were evaluated by DXA and indirect calorimetry. Metabolites were measured by NMR and direct injection MS. Three algorithms were used [partial least squares discriminant-analysis, support vector machines (SVM), and least absolute shrinkage and selection operator] to relate patients' plasma/urine metabolic profile with their dietary/physiological assessments. Leave-one-out cross-validation and permutation testing were conducted to determine statistical validity. None of the algorithms, using 63 urine metabolites, could learn to predict variations in individual's resting energy expenditure, respiratory quotient, or their intake of total energy, fat, sugar, or carbohydrate. Urine metabolites predicted appendicular lean tissue (skeletal muscle) with excellent cross-validation accuracy (98% using SVM). Total lean tissue correlated highly with appendicular muscle (Pearson r = 0.98; P < 0.0001) and gave similar cross-validation accuracies. Fat mass was effectively predicted using the 63 urine metabolites or the 143 plasma metabolites, exclusively. In conclusion, in this population, lean and fat mass variation could be effectively predicted using urinary metabolites, suggesting a potential role for metabolomics in body composition research. Furthermore, variation in lean and fat mass potentially confounds metabolomic studies attempting to characterize diet or disease conditions. Future studies should account or correct for such variation.
Journal of Nutrition 12/2011; 142(1):14-21. · 4.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Skeletal muscle catabolism is a co-morbidity of many chronic diseases and is the result of systemic inflammation. Although direct inflammatory cytokine action on muscle promotes atrophy, nonmuscle sites of action for inflammatory mediators are less well described. We demonstrate that central nervous system (CNS)-delimited interleukin 1β (IL-1β) signaling alone can evoke a catabolic program in muscle, rapidly inducing atrophy. This effect is dependent on hypothalamic-pituitary-adrenal (HPA) axis activation, as CNS IL-1β-induced atrophy is abrogated by adrenalectomy. Furthermore, we identified a glucocorticoid-responsive gene expression pattern conserved in models of acute and chronic inflammatory muscle atrophy. In contrast with studies suggesting that the direct action of inflammatory cytokines on muscle is sufficient to induce catabolism, adrenalectomy also blocks the atrophy program in response to systemic inflammation, demonstrating that glucocorticoids are requisite for this process. Additionally, circulating levels of glucocorticoids equivalent to those produced under inflammatory conditions are sufficient to cause profound muscle wasting. Together, these data suggest that a significant component of inflammation-induced muscle catabolism occurs indirectly via a relay in the CNS.
Journal of Experimental Medicine 11/2011; 208(12):2449-63. · 13.91 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Previous genome-wide association studies (GWAS) have shown several risk alleles to be associated with breast cancer. However, the variants identified so far contribute to only a small proportion of disease risk. The objective of our GWAS was to identify additional novel breast cancer susceptibility variants and to replicate these findings in an independent cohort. We performed a two-stage association study in a cohort of 3,064 women from Alberta, Canada. In Stage I, we interrogated 906,600 single nucleotide polymorphisms (SNPs) on Affymetrix SNP 6.0 arrays using 348 breast cancer cases and 348 controls. We used single-locus association tests to determine statistical significance for the observed differences in allele frequencies between cases and controls. In Stage II, we attempted to replicate 35 significant markers identified in Stage I in an independent study of 1,153 cases and 1,215 controls. Genotyping of Stage II samples was done using Sequenom Mass-ARRAY iPlex platform. Six loci from four different gene regions (chromosomes 4, 5, 16 and 19) showed statistically significant differences between cases and controls in both Stage I and Stage II testing, and also in joint analysis. The identified variants were from EDNRA, ROPN1L, C16orf61 and ZNF577 gene regions. The presented joint analyses from the two-stage study design were not significant after genome-wide correction. The SNPs identified in this study may serve as potential candidate loci for breast cancer risk in a further replication study in Stage III from Alberta population or independent validation in Caucasian cohorts elsewhere.
Human Genetics 03/2011; 130(4):529-37. · 4.52 Impact Factor