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OBJECTIVE: Clinical genetic testing is commercially available for rs61764370, an inherited variant residing in a KRAS 3' UTR microRNA binding site, based on suggested associations with increased ovarian and breast cancer risk as well as with survival time. However, prior studies, emphasizing particular subgroups, were relatively small. Therefore, we comprehensively evaluated ovarian and breast cancer risks as well as clinical outcome associated with rs61764370. METHODS: Centralized genotyping and analysis were performed for 140,012 women enrolled in the Ovarian Cancer Association Consortium (15,357 ovarian cancer patients; 30,816 controls), the Breast Cancer Association Consortium (33,530 breast cancer patients; 37,640 controls), and the Consortium of Modifiers of BRCA1 and BRCA2 (14,765 BRCA1 and 7904 BRCA2 mutation carriers). RESULTS: We found no association with risk of ovarian cancer (OR=0.99, 95% CI 0.94-1.04, p=0.74) or breast cancer (OR=0.98, 95% CI 0.94-1.01, p=0.19) and results were consistent among mutation carriers (BRCA1, ovarian cancer HR=1.09, 95% CI 0.97-1.23, p=0.14, breast cancer HR=1.04, 95% CI 0.97-1.12, p=0.27; BRCA2, ovarian cancer HR=0.89, 95% CI 0.71-1.13, p=0.34, breast cancer HR=1.06, 95% CI 0.94-1.19, p=0.35). Null results were also obtained for associations with overall survival following ovarian cancer (HR=0.94, 95% CI 0.83-1.07, p=0.38), breast cancer (HR=0.96, 95% CI 0.87-1.06, p=0.38), and all other previously-reported associations. CONCLUSIONS: rs61764370 is not associated with risk of ovarian or breast cancer nor with clinical outcome for patients with these cancers. Therefore, genotyping this variant has no clinical utility related to the prediction or management of these cancers., Other Research Unit

Background: New developments in the search for susceptibility alleles in complex disorders provide support for the possibility of a polygenic approach to the prevention and treatment of common diseases. Methods: We examined the implications, both for individualized disease prevention and for public health policy, of findings concerning the risk of breast cancer that are based on common genetic variation. Results: Our analysis suggests that the risk profile generated by the known, common, moderate-risk alleles does not provide sufficient discrimination to warrant individualized prevention. However, useful risk stratification may be possible in the context of programs for disease prevention in the general population. Conclusions: The clinical use of single, common, low-penetrance genes is limited, but a few susceptibility alleles may distinguish women who are at high risk for breast cancer from those who are at low risk, particularly in the context of population screening. N Engl J Med 2008;358:2796-803. [ABSTRACT FROM AUTHOR]

The knowledge of human genetic variation that will come from the human genome sequence makes feasible a polygenic approach to disease prevention, in which it will be possible to identify individuals as susceptible by their genotype profile and to prevent disease by targeting interventions to those at risk. There is doubt, however, regarding the magnitude of these genetic effects and thus the potential to apply them to either individuals or populations. We have therefore examined the potential for prediction of risk based on common genetic variation using data from a population-based series of individuals with breast cancer. The data are compatible with a log-normal distribution of genetic risk in the population that is sufficiently wide to provide useful discrimination of high- and low-risk groups. Assuming all of the susceptibility genes could be identified, the half of the population at highest risk would account for 88% of all affected individuals. By contrast, if currently identified risk factors for breast cancer were used to stratify the population, the half of the population at highest risk would account for only 62% of all cases. These results suggest that the construction and use of genetic-risk profiles may provide significant improvements in the efficacy of populationbased programs of intervention for cancers and other diseases. [ABSTRACT FROM AUTHOR]

Rare pathogenic variants in known breast cancer-susceptibility genes and known common susceptibility variants do not fully explain the familial aggregation of breast cancer. To investigate plausible genetic models for the residual familial aggregation, we studied 17,425 families ascertained through population-based probands, 86% of whom were screened for pathogenic variants in BRCA1 , BRCA2 , PALB2 , CHEK2 , ATM , and TP53 via gene-panel sequencing. We conducted complex segregation analyses and fitted genetic models in which breast cancer incidence depended on the effects of known susceptibility genes and other unidentified major genes and a normally distributed polygenic component. The proportion of familial variance explained by the six genes was 46% at age 20–29 years and decreased steadily with age thereafter. After allowing for these genes, the best fitting model for the residual familial variance included a recessive risk component with a combined genotype frequency of 1.7% (95% CI: 0.3%–5.4%) and a penetrance to age 80 years of 69% (95% CI: 38%–95%) for homozygotes, which may reflect the combined effects of multiple variants acting in a recessive manner, and a polygenic variance of 1.27 (95% CI: 0.94%–1.65), which did not vary with age. The proportion of the residual familial variance explained by the recessive risk component was 40% at age 20–29 years and decreased with age thereafter. The model predicted age-specific familial relative risks consistent with those observed by large epidemiological studies. The findings have implications for strategies to identify new breast cancer-susceptibility genes and improve disease-risk prediction, especially at a young age. A large population-based family study, with gene-panel sequencing data, investigated the genetic models that explain the residual breast cancer familial aggregation after considering known susceptibility genes. The results may have implications for strategies to identify new breast cancer-susceptibility genes and improve disease-risk prediction, especially at a young age. [ABSTRACT FROM AUTHOR]

Translation of genome-wide association study (GWAS) findings into preventive approaches is challenged by the identification of the causal risk variants and the understanding of the biological mechanisms by which they act. We present using allelic expression (AE) ratios to perform quantitative case–control analysis as a novel approach to identify risk associations, causal regulatory variants, and target genes. Using the breast cancer (BC) risk locus 17q22 to validate this approach, we measured AE ratios in normal breast tissue samples from controls and cases, as well as from unmatched blood samples. Then we used in-silico and in-vitro analysis to map and functionally characterised candidate causal variants. We found a significant shift in the AE patterns of STXBP4 (rs2628315) and COX11 (rs17817901) in the normal breast tissue of cases and healthy controls. Preferential expression of the G-rs2628315 and A-rs17817901 alleles, more often observed in cases, was associated with an increased risk for BC. Analysis of blood samples from cases and controls found a similar association. Furthermore, we identified two putative cis -regulatory variants – rs17817901 and rs8066588 – that affect a miRNA and a transcription factor binding site, respectively. We propose causal variants and target genes for the 17q22 BC risk locus and show that using AE ratios in case–control association studies is helpful in identifying risk and mapping causal variants. • Case-control analysis using AE as a phenotype detects risk associations. • AE of STXBP4 and COX11 in breast tissue and blood associates with BC risk. • rs17817901 and rs8066588 are putative cis-regulatory risk-causing variants. • Biological risk mechanisms likely involve altered miRNA and protein binding. [ABSTRACT FROM AUTHOR]

Purpose: The search for clinical and biologic biomarkers associated with late radiotherapy toxicity is hindered by the use of multiple and different endpoints from a variety of scoring systems, hampering comparisons across studies and pooling of data. We propose a novel metric, the Standardized Total Average Toxicity (STAT) score, to try to overcome these difficulties. Methods and Materials: STAT scores were derived for 1010 patients from the Cambridge breast intensity-modulated radiotherapy trial and 493 women from the University Hospitals of Leicester. The sensitivity of the STAT score to detect differences between patient groups, stratified by factors known to influence late toxicity, was compared with that of individual endpoints. Analysis of residuals was used to quantify the effect of these covariates. Results: In the Cambridge cohort, STAT scores detected differences (p < 0.00005) between patients attributable to breast volume, surgical specimen weight, dosimetry, acute toxicity, radiation boost to tumor bed, postoperative infection, and smoking (p < 0.0002), with no loss of sensitivity over individual toxicity endpoints. Diabetes (p = 0.017), poor postoperative surgical cosmesis (p = 0.0036), use of chemotherapy (p = 0.0054), and increasing age (p = 0.041) were also associated with increased STAT score. When the Cambridge and Leicester datasets were combined, STAT was associated with smoking status (p < 0.00005), diabetes (p = 0.041), chemotherapy (p = 0.0008), and radiotherapy boost (p = 0.0001). STAT was independent of the toxicity scale used and was able to deal with missing data. There were correlations between residuals of the STAT score obtained using different toxicity scales (r > 0.86, p < 0.00005 for both datasets). Conclusions: The STAT score may be used to facilitate the analysis of overall late radiation toxicity, from multiple trials or centers, in studies of possible genetic and nongenetic determinants of radiotherapy toxicity. [ABSTRACT FROM AUTHOR]

Abstract: Background and purpose: Several small studies have reported associations between TGFB1 single nucleotide polymorphisms (SNPs), considered to increase secretion of TGF-β1, and greater than 3-fold increases in incidence of fibrosis – an indicator of late toxicity after radiotherapy in breast cancer patients. Materials and methods: Two SNPs in TGFB1, C-509T (rs1800469) and L10P (rs1800470), were genotyped in 778 breast cancer patients who had received radiotherapy to the breast. Late radiotherapy toxicity was assessed two years after radiotherapy using a validated photographic technique, clinical assessment and patient questionnaires. Results: On photographic assessment, 210 (27%) patients showed some degree of breast shrinkage, whilst 45 (6%) patients showed marked breast shrinkage. There was no significant association of genotype at either of the TGFB1 SNPs with any measure of late radiation toxicity. Conclusion: This adequately powered trial failed to confirm previously reported increases in fibrosis with TGFB1 genotype – any increase greater than 1.36 can be excluded with 95% confidence. Similar frequent failures to replicate associations with candidate genes have been resolved using genome-wide association scans: this methodology detects common, low risk alleles but requires even larger patient numbers for adequate statistical power. [ABSTRACT FROM AUTHOR]