[Show abstract][Hide abstract] ABSTRACT: Background
Prognostication of Breast Cancer (BC) relies largely on traditional clinical factors and biomarkers such as hormone or growth factor receptors. Due to their suboptimal specificities, it is challenging to accurately identify the subset of patients who are likely to undergo recurrence and there remains a major need for markers of higher utility to guide therapeutic decisions. MicroRNAs (miRNAs) are small non-coding RNAs that function as post-transcriptional regulators of gene expression and have shown promise as potential prognostic markers in several cancer types including BC.
In our study, we sequenced miRNAs from 104 BC samples and 11 apparently healthy normal (reduction mammoplasty) breast tissues. We used Case–control (CC) and Case-only (CO) statistical paradigm to identify prognostic markers. Cox-proportional hazards regression model was employed and risk score analysis was performed to identify miRNA signature independent of potential confounders. Representative miRNAs were validated using qRT-PCR. Gene targets for prognostic miRNAs were identified using in silico predictions and in-house BC transcriptome dataset. Gene ontology terms were identified using DAVID bioinformatics v6.7. A total of 1,423 miRNAs were captured. In the CC approach, 126 miRNAs were retained with predetermined criteria for good read counts, from which 80 miRNAs were differentially expressed. Of these, four and two miRNAs were significant for Overall Survival (OS) and Recurrence Free Survival (RFS), respectively. In the CO approach, from 147 miRNAs retained after filtering, 11 and 4 miRNAs were significant for OS and RFS, respectively. In both the approaches, the risk scores were significant after adjusting for potential confounders. The miRNAs associated with OS identified in our cohort were validated using an external dataset from The Cancer Genome Atlas (TCGA) project. Targets for the identified miRNAs were enriched for cell proliferation, invasion and migration.
The study identified twelve non-redundant miRNAs associated with OS and/or RFS. These signatures include those that were reported by others in BC or other cancers. Importantly we report for the first time two new candidate miRNAs (miR-574-3p and miR-660-5p) as promising prognostic markers. Independent validation of signatures (for OS) using an external dataset from TCGA further strengthened the study findings.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-015-1899-0) contains supplementary material, which is available to authorized users.
[Show abstract][Hide abstract] ABSTRACT: Background:
Epirubicin is metabolized by uridine glucuronosyltransferase 2B7 (UGT2B7), an enzyme rich in single nucleotide polymorphisms (SNPs). We studied whether the -161 C > T germline SNP in UGT2B7 was related to epirubicin metabolism and whether differences exist in the toxicity and efficacy of epirubicin-based chemotherapy among patients who were TT homozygotes, CT heterozygotes, and CC homozygotes.
Patients and methods:
A total of 132 women with non-metastatic breast cancer receiving FEC (5-fluorouracil 500 mg/m(2), epirubicin 100 mg/m(2), cyclophosphamide 500 mg/m(2)) were prospectively enrolled. Toxicity was assessed in cycle 1 using the National Cancer Institute Common Toxicity Criteria, version 2.0.
The sequence at -161 was studied in 132 subjects; 37 were TT homozygotes, 63 were CT heterozygotes, 26 were CC homozygotes, and 6 could not be genotyped. The CC genotype patients had decreased epirubicin clearance (median, 103.3 L/hr) compared with the CT/TT genotype patients (median, 134.0 L/hr; P = .002). The CC homozygous patients had an increased risk of grade 3 to 4 leukopenia compared with the TT homozygotes or heterozygotes (P = .038 and P = .032, respectively). TT homozygotes or heterozygotes had an increased risk of early recurrence (P = .039; χ(2) test).
The results of the present prospective pharmacogenetic study suggest that the UGT2B7 -161 C > T SNP correlate with drug metabolism, toxicity, and efficacy in patients receiving epirubicin chemotherapy. Further studies of this UGT2B7 SNP as a predictor of epirubicin toxicity and efficacy are warranted.
Clinical Breast Cancer 09/2015; DOI:10.1016/j.clbc.2015.09.006 · 2.11 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Our previous work identified an intermediate binding site for taxanes in the microtubule nanopore. The goal of this study was to test derivatives of paclitaxel designed to bind to this intermediate site differentially depending on the isotype of β-tubulin. Since β-tubulin iso-types have tissue-dependent expression—specifically, the βIII isotype is very abundant in aggressive tumors and much less common in normal tissues—this is expected to lead to tu-bulin targeted drugs that are more efficacious and have less side effects. Seven derivatives of paclitaxel were designed and four of these were amenable for synthesis in sufficient purity and yield for further testing in breast cancer model cell lines. None of the derivatives studied were superior to currently used taxanes, however computer simulations provided insights into the activity of the derivatives. Our results suggest that neither binding to the intermediate binding site nor the final binding site is sufficient to explain the activities of the derivative taxanes studied. These findings highlight the need to iteratively improve on the design of taxanes based on their activity in model systems. Knowledge gained on the ability of the engineered drugs to bind to targets and bring about activity in a predictable manner is a step towards personalizing therapies.
PLoS ONE 06/2015; 10(6):e0129168. DOI:10.1371/journal.pone.0129168 · 3.23 Impact Factor
[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.
Journal of Genetics 12/2014; 93(3):893-916. DOI:10.1007/s12041-014-0405-9 · 1.09 Impact Factor
[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. DOI:10.1089/bio.2014.0008 · 1.34 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. DOI:10.1089/bio.2013.0061 · 1.34 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background
This paper introduces and applies a genome wide predictive study to learn a model that predicts whether a new subject will develop breast cancer or not, based on her SNP profile.
We first genotyped 696 female subjects (348 breast cancer cases and 348 apparently healthy controls), predominantly of Caucasian origin from Alberta, Canada using Affymetrix Human SNP 6.0 arrays. Then, we applied EIGENSTRAT population stratification correction method to remove 73 subjects not belonging to the Caucasian population. Then, we filtered any SNP that had any missing calls, whose genotype frequency was deviated from Hardy-Weinberg equilibrium, or whose minor allele frequency was less than 5%. Finally, we applied a combination of MeanDiff feature selection method and KNN learning method to this filtered dataset to produce a breast cancer prediction model. LOOCV accuracy of this classifier is 59.55%. Random permutation tests show that this result is significantly better than the baseline accuracy of 51.52%. Sensitivity analysis shows that the classifier is fairly robust to the number of MeanDiff-selected SNPs. External validation on the CGEMS breast cancer dataset, the only other publicly available breast cancer dataset, shows that this combination of MeanDiff and KNN leads to a LOOCV accuracy of 60.25%, which is significantly better than its baseline of 50.06%. We then considered a dozen different combinations of feature selection and learning method, but found that none of these combinations produces a better predictive model than our model. We also considered various biological feature selection methods like selecting SNPs reported in recent genome wide association studies to be associated with breast cancer, selecting SNPs in genes associated with KEGG cancer pathways, or selecting SNPs associated with breast cancer in the F-SNP database to produce predictive models, but again found that none of these models achieved accuracy better than baseline.
We anticipate producing more accurate breast cancer prediction models by recruiting more study subjects, providing more accurate labelling of phenotypes (to accommodate the heterogeneity of breast cancer), measuring other genomic alterations such as point mutations and copy number variations, and incorporating non-genetic information about subjects such as environmental and lifestyle factors.
[Show abstract][Hide abstract] ABSTRACT: Unlabelled:
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 the 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.
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 significance of this work includes 1) identification of new biomarkers associated with the HER2 phenotype, 2) measurement of the magnitude of the proteomic changes triggered by the amplification of this single gene, and 3) better understanding of the downstream biological changes triggered by HER2 overexpression.
Journal of proteomics 07/2013; 91. DOI:10.1016/j.jprot.2013.06.034 · 3.89 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 06/2013; 8(6):e65380. DOI:10.1371/journal.pone.0065380 · 3.23 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. DOI:10.1371/journal.pone.0064896 · 3.23 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. DOI:10.1371/journal.pone.0062550 · 3.23 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. DOI:10.1089/bio.2012.0044 · 1.34 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.
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 λ 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.
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. DOI:10.1371/journal.pone.0053850 · 3.23 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. DOI:10.1089/bio.2012.0026 · 1.34 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. DOI:10.1371/journal.pone.0037251 · 3.23 Impact Factor