Saliva samples are a viable alternative to blood samples as a source of DNA for high throughput genotyping.

BACKGROUND: The increasing trend for incorporation of biological sample collection within clinical trials requires sample collection procedures which are convenient and acceptable for both patients and clinicians. This study investigated the feasibility of using saliva-extracted DNA in comparison to blood-derived DNA, across two genotyping platforms: Applied Biosystems Taqman™ and Illumina Beadchip™ genome-wide arrays. METHOD: Patients were recruited from the Pharmacogenetics of Breast Cancer Chemotherapy (PGSNPS) study. Paired blood and saliva samples were collected from 79 study participants. The Oragene DNA Self-Collection kit (DNAgenotek®) was used to collect and extract DNA from saliva. DNA from EDTA blood samples (median volume 8 ml) was extracted by Gen-Probe, Livingstone, UK. DNA yields, standard measures of DNA quality, genotype call rates and genotype concordance between paired, duplicated samples were assessed. RESULTS: Total DNA yields were lower from saliva (mean 24 μg, range 0.2-52 μg) than from blood (mean 210 μg, range 58-577 μg) and a 2-fold difference remained after adjusting for the volume of biological material collected. Protein contamination and DNA fragmentation measures were greater in saliva DNA. 78/79 saliva samples yielded sufficient DNA for use on Illumina Beadchip arrays and using Taqman assays. Four samples were randomly selected for genotyping in duplicate on the Illumina Beadchip arrays. All samples were genotyped using Taqman assays. DNA quality, as assessed by genotype call rates and genotype concordance between matched pairs of DNA was high (>97%) for each measure in both blood and saliva-derived DNA. CONCLUSION: We conclude that DNA from saliva and blood samples is comparable when genotyping using either Taqman assays or genome-wide chip arrays. Saliva sampling has the potential to increase participant recruitment within clinical trials, as well as reducing the resources and organisation required for multicentre sample collection.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

A risk prediction algorithm for ovarian cancer incorporating BRCA1, BRCA2, common alleles and other familial effects.

BACKGROUND: Although BRCA1 and BRCA2 mutations account for only ∼27% of the familial aggregation of ovarian cancer (OvC), no OvC risk prediction model currently exists that considers the effects of BRCA1, BRCA2 and other familial factors. Therefore, a currently unresolved problem in clinical genetics is how to counsel women with family history of OvC but no identifiable BRCA1/2 mutations. METHODS: We used data from 1548 patients with OvC and their relatives from a population-based study, with known BRCA1/2 mutation status, to investigate OvC genetic susceptibility models, using segregation analysis methods. RESULTS: The most parsimonious model included the effects of BRCA1/2 mutations, and the residual familial aggregation was accounted for by a polygenic component (SD 1.43, 95% CI 1.10 to 1.86), reflecting the multiplicative effects of a large number of genes with small contributions to the familial risk. We estimated that 1 in 630 individuals carries a BRCA1 mutation and 1 in 195 carries a BRCA2 mutation. We extended this model to incorporate the explicit effects of 17 common alleles that are associated with OvC risk. Based on our models, assuming all of the susceptibility genes could be identified we estimate that the half of the female population at highest genetic risk will account for 92% of all OvCs. CONCLUSIONS: The resulting model can be used to obtain the risk of developing OvC on the basis of BRCA1/2, explicit family history and common alleles. This is the first model that accounts for all OvC familial aggregation and would be useful in the OvC genetic counselling process.This work has been supported by grants from Cancer Research UK (C1005/A12677, C12292/A11174, C490/A10119, C490/A10124) including the PROMISE research programme, the Eve Appeal and the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge.This is the final version of the article. It first appeared from BMJ Publishing via http://dx.doi.org/10.1136/jmedgenet-2015-10307

Reducing overdiagnosis by polygenic risk-stratified screening: findings from the Finnish section of the ERSPC.

background: We derived estimates of overdiagnosis by polygenic risk groups and examined whether polygenic risk-stratified screening for prostate cancer reduces overdiagnosis. methods: We calculated the polygenic risk score based on genotypes of 66 known prostate cancer loci for 4967 men from the Finnish section of the European Randomised Study of Screening for Prostate Cancer. We stratified the 72 072 men in the trial into those with polygenic risk below and above the median. Using a maximum likelihood method based on interval cancers, we estimated the mean sojourn time (MST) and episode sensitivity. For each polygenic risk group, we estimated the proportion of screen-detected cancers that are likely to be overdiagnosed from the difference between the observed and expected number of screen-detected cancers. results: Of the prostate cancers, 74% occurred among men with polygenic risk above population median. The sensitivity was 0.55 (95% confidence interval (CI) 0.45–0.65) and MST 6.3 (95% CI 4.2–8.3) years. The overall overdiagnosis was 42% (95% CI 37–52) of the screen-detected cancers, with 58% (95% CI 54–65) in men with the lower and 37% (95% CI 31–47) in those with higher polygenic risk. conclusion: Targeting screening to men at higher polygenic risk could reduce the proportion of cancers overdiagnosed

Common variants in the ATM, BRCA1, BRCA2, CHEK2 and TP53 cancer susceptibility genes are unlikely to increase breast cancer risk

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.Abstract Introduction Certain rare, familial mutations in the ATM, BRCA1, BRCA2, CHEK2 or TP53 genes increase susceptibility to breast cancer but it has not, until now, been clear whether common polymorphic variants in the same genes also increase risk. Methods We have attempted a comprehensive, single nucleotide polymorphism (SNP)- and haplotype-tagging association study on each of these five genes in up to 4,474 breast cancer cases from the British, East Anglian SEARCH study and 4,560 controls from the EPIC-Norfolk study, using a two-stage study design. Nine tag SNPs were genotyped in ATM, together with five in BRCA1, sixteen in BRCA2, ten in CHEK2 and five in TP53, with the aim of tagging all other known, common variants. SNPs generating the common amino acid substitutions were specifically forced into the tagging set for each gene. Results No significant breast cancer associations were detected with any individual or combination of tag SNPs. Conclusion It is unlikely that there are any other common variants in these genes conferring measurably increased risks of breast cancer in our study population

Cancer stem cell markers in breast cancer: pathological, clinical and prognostic significance

INTRODUCTION: The cancer stem cell (CSC) hypothesis states that tumours consist of a cellular hierarchy with CSCs at the apex driving tumour recurrence and metastasis. Hence, CSCs are potentially of profound clinical importance. We set out to establish the clinical relevance of breast CSC markers by profiling a large cohort of breast tumours in tissue microarrays (TMAs) using immunohistochemistry (IHC). METHODS: We included 4, 125 patients enrolled in the SEARCH population-based study with tumours represented in TMAs and classified into molecular subtype according to a validated IHC-based five-marker scheme. IHC was used to detect CD44/CD24, ALDH1A1, aldehyde dehydrogenase family 1 member A3 (ALDH1A3) and integrin alpha-6 (ITGA6). A 'Total CSC' score representing expression of all four CSC markers was also investigated. Association with breast cancer specific survival (BCSS) at 10 years was assessed using a Cox proportional-hazards model. This study was complied with REMARK criteria. RESULTS: In ER negative cases, multivariate analysis showed that ITGA6 was an independent prognostic factor with a time-dependent effect restricted to the first two years of follow-up (hazard ratio (HR) for 0 to 2 years follow-up, 2.4; 95% confidence interval (95% CI), 1.2 to 4.8; P = 0.009). The composite 'Total CSC' score carried independent prognostic significance in ER negative cases for the first four years of follow-up (HR for 0 to 4 years follow-up, 1.3; 95% CI, 1.1 to 1.6; P = 0.006). CONCLUSIONS: Breast CSC markers do not identify identical subpopulations in primary tumours. Both ITGA6 and a composite Total CSC score show independent prognostic significance in ER negative disease. The use of multiple markers to identify tumours enriched for CSCs has the greatest prognostic value. In the absence of more specific markers, we propose that the effective translation of the CSC hypothesis into patient benefit will necessitate the use of a panel of markers to robustly identify tumours enriched for CSCs

Mutation analysis and characterization of ATR sequence variants in breast cancer cases from high-risk French Canadian breast/ovarian cancer families

BACKGROUND: Ataxia telangiectasia-mutated and Rad3-related (ATR) is a member of the PIK-related family which plays, along with ATM, a central role in cell-cycle regulation. ATR has been shown to phosphorylate several tumor suppressors like BRCA1, CHEK1 and TP53. ATR appears as a good candidate breast cancer susceptibility gene and the current study was designed to screen for ATR germline mutations potentially involved in breast cancer predisposition. METHODS: ATR direct sequencing was performed using a fluorescent method while widely available programs were used for linkage disequilibrium (LD), haplotype analyses, and tagging SNP (tSNP) identification. Expression analyses were carried out using real-time PCR. RESULTS: The complete sequence of all exons and flanking intronic sequences were analyzed in DNA samples from 54 individuals affected with breast cancer from non-BRCA1/2 high-risk French Canadian breast/ovarian families. Although no germline mutation has been identified in the coding region, we identified 41 sequence variants, including 16 coding variants, 3 of which are not reported in public databases. SNP haplotypes were established and tSNPs were identified in 73 healthy unrelated French Canadians, providing a valuable tool for further association studies involving the ATR gene, using large cohorts. Our analyses led to the identification of two novel alternative splice transcripts. In contrast to the transcript generated by an alternative splicing site in the intron 41, the one resulting from a deletion of 121 nucleotides in exon 33 is widely expressed, at significant but relatively low levels, in both normal and tumoral cells including normal breast and ovarian tissue. CONCLUSION: Although no deleterious mutations were identified in the ATR gene, the current study provides an haplotype analysis of the ATR gene polymorphisms, which allowed the identification of a set of SNPs that could be used as tSNPs for large-scale association studies. In addition, our study led to the characterization of a novel Δ33 splice form, which could generate a putative truncated protein lacking several functional domains. Additional studies in large cohorts and other populations will be needed to further evaluate if common and/or rare ATR sequence variants can be associated with a modest or intermediate breast cancer risk

Implications of polygenic risk-stratified screening for prostate cancer on overdiagnosis.

PURPOSE: This study aimed to quantify the probability of overdiagnosis of prostate cancer by polygenic risk. METHODS: We calculated the polygenic risk score based on 66 known prostate cancer susceptibility variants for 17,012 men aged 50-69 years (9,404 men identified with prostate cancer and 7,608 with no cancer) derived from three UK-based ongoing studies. We derived the probabilities of overdiagnosis by quartiles of polygenic risk considering that the observed prevalence of screen-detected prostate cancer is a combination of underlying incidence, mean sojourn time (MST), test sensitivity, and overdiagnosis. RESULTS: Polygenic risk quartiles 1 to 4 comprised 9, 18, 25, and 48% of the cases, respectively. For a prostate-specific antigen test sensitivity of 80% and MST of 9 years, 43, 30, 25, and 19% of the prevalent screen-detected cancers in quartiles 1 to 4, respectively, were likely to be overdiagnosed cancers. Overdiagnosis decreased with increasing polygenic risk, with 56% decrease between the lowest and the highest polygenic risk quartiles. CONCLUSION: Targeting screening to men at higher polygenic risk could reduce the problem of overdiagnosis and lead to a better benefit-to-harm balance in screening for prostate cancer.N.P. is a Cancer Research UK Clinician Scientist Fellow. The COGS project was funded through a European Commission’s Seventh Framework Programme grant (agreement number: 223175- HEALTH-F2-2009–223175), Cancer Research UK (C490/A10124), the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge. The ProtecT study is supported by the UK National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme, HTA 96/20/99; ISRCTN20141297. The Comparative Arm of ProtecT (CAP) trial is funded by Cancer Research UK and the UK Department of Health (C11043/A4286, C18281/A8145, C18281/A11326, and C18281/A15064). UKGPCS is funded by Cancer Research UK and the National Cancer Research Network. The Biomedical Research Centre at the Institute of Cancer Research and Royal Marsden NHS Foundation Trust receive funding support from NIHR. SEARCH is funded by Cancer Research UK. We thank all the participants in these studies: members of the ProtecT study research group (Anne George, Michael Davis, and Athene Lane), Don Conroy, Craig Luccarini, Caroline Baynes, the SEARCH team, the Eastern Cancer Registration and Information Centre, and the general practitioners who assisted with recruitment. This work was supported by funding from the Cancer Research UK Clinician Scientist Fellowship.This is the final published version. It first appeared at http://www.nature.com/gim/journal/vaop/ncurrent/full/gim2014192a.html