Dataset Information

PIK3CA rs7640662 (C/G) single nucleotide polymorphism lacks association with breast cancer cases in Persians.

ABSTRACT: Phosphatidylinositol-3-kinase (PI3K) is a group of enzymes involved in cellular growth, proliferation, differentiation, cell motility, intracellular trafficking, and survival that play very important roles in developing breast cancer. PIK3CA is a gene that encodes ? catalytic subunit of this enzyme. A common polymorphism of PIK3CA, rs7640662 (C/G), was analyzed, and its association to breast cancer cases was determined. In this study, DNA was extracted from peripheral blood samples of 278 women suffering from breast cancer and 128 healthy women. Tetra-primer amplification refractory mutation system polymerase chain reaction (T-ARMS-PCR) method was performed to genotype rs7640662. P values and ODD ratios were measured using SPSS. P value less than 0.05 and ODD ratios more than 1 were considered as significant. All ODD ratios were less than 1, and P values were more than 0.05 showing that rs7640662 (C/G) and breast cancer are not significantly associated. However, the genotypes observed in the Persian population, as an ancient population living in the Middle East, was significantly different from the genotypes reported by HapMap for Asian populations. As a conclusion, rs7640662 was not associated with the risk of breast cancer in a Persian population; however, it was observed that heterozygote (GC) is the most common genotypes in both case and control samples.

SUBMITTER: Mir A

PROVIDER: S-EPMC4369140 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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PIK3CA rs7640662 (C/G) single nucleotide polymorphism lacks association with breast cancer cases in Persians.

Mir Atefeh A Sadegh Mahdiyeh Harati MH Ahmadinia Zahra Z Kaboli Parham Jabbarzadeh PJ

Interventional medicine & applied science 20150320 1

Phosphatidylinositol-3-kinase (PI3K) is a group of enzymes involved in cellular growth, proliferation, differentiation, cell motility, intracellular trafficking, and survival that play very important roles in developing breast cancer. PIK3CA is a gene that encodes α catalytic subunit of this enzyme. A common polymorphism of PIK3CA, rs7640662 (C/G), was analyzed, and its association to breast cancer cases was determined. In this study, DNA was extracted from peripheral blood samples of 278 women ...[more]

PMID: 25838920

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Project description:The proto-oncogene PIK3CA has been well studied for its activating mutations and genomic amplifications but not single nucleotide polymorphism (SNP) in thyroid cancer. We investigated SNP rs17849071 (minor allele G and major allele T) in PIK3CA in thyroid tumors in 503 subjects by PCR and sequencing of a region of intron 9 carrying this SNP. This SNP was found in both normal and thyroid tumor tissues as well as in different generations of a studied family, confirming it to be a germline genetic event in thyroid tumor patients. In comparison with normal subjects, a dramatically lower prevalence of the heterozygous genotype G/T at rs17849071 was found in patients with follicular thyroid cancer (FTC). Specifically, rs17849071G/T was found in 15% (18/117) normal subjects vs. 1.3% (1/77) FTC patients, with an odds ratio of 0.07 (95% CI 0.01-0.55; P = 0.001). This represents a 93% risk reduction for FTC with this SNP. In contrast, no difference was seen with benign thyroid neoplasms in which the prevalence of rs17849071G/T was 13.1% (17/130), with an odds ratio of 0.83 (95% CI 0.40-1.69; P = 0.72). There was a trend of lower prevalences of rs17849071G/T and odds ratio in other types of thyroid cancer without statistical significance. We also found an interesting inverse relationship of rs17849071G/T with PIK3CA amplification. With copy number ≥4 defined as copy gain, 2.9% (1/34) rs17849071G/T vs. 19.0% (67/352) rs17849071T/T cases displayed PIK3CA amplification (P = 0.01). Conversely, 1.5% (1/68) cases with PIK3CA amplification vs. 10.4% (33/318) cases without PIK3CA amplification harbored rs17849071G/T (P = 0.01). This provides an explanation for the reciprocal relationship of rs17849071G/T with FTC, since PIK3CA amplification is an important oncogenic mechanism in thyroid cancer, particularly FTC. Thus, the present study uncovers an interesting phenomenon that rs17849071G/T is protective against FTC possibly through preventing PIK3CA amplifications.

Project description: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. RESULTS: 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. CONCLUSIONS: 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.

Dataset Information

PIK3CA rs7640662 (C/G) single nucleotide polymorphism lacks association with breast cancer cases in Persians.

Publications

PIK3CA rs7640662 (C/G) single nucleotide polymorphism lacks association with breast cancer cases in Persians.

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