Evidence for Association of the rs17822931-A Allele in ABCC11 with a Decreased Risk of Estrogen Receptor-negative Breast Cancer

The rs17822931 SNP of the human ABCC11 gene determines earwax types, and is also associated with some functions of apocrine glands, including the mammary gland. Nevertheless, whether the ABCC11 polymorphism is correlated with estrogen receptor (ER) status of breast cancer (BC) remains unclear. To investigate the correlation between rs17822931 and BC, we screened the genotypes in a total of 276 and 295 histological BC samples collected from Japanese and Ukrainian BC patients, and 269 and 264 ethnically-matched healthy individuals, respectively, using TaqManTM PCR. Genotype frequencies at rs178229131 in Japanese and Ukrainian BC patients were not significantly different from those in their respective control populations. Consistently, no correlation between rs178229131 and the susceptibility to BC was found. The AA genotype, which corresponds to dry earwax, occurred less frequently in ER -negative BC in Japanese [odds ratio, 0.48; 95% confidential interval, 0.29-0.80] but not in Ukrainian patients although a similar correlation was weakly observed. Our results indicate that the rs178229131-A allele may be important in reducing the risk of ER -negative BC development

VarSight: prioritizing clinically reported variants with binary classification algorithms.

BackgroundWhen applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient's phenotypes. Typically, this is done through annotation, filtering, and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance.MethodsWe tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network.ResultsWe treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20.ConclusionsWe demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets

The Role of STAT3 in Thyroid Cancer

Thyroid cancer is the most common endocrine malignancy and its global incidence rates are rapidly increasing. Although the mortality of thyroid cancer is relatively low, its rate of recurrence or persistence is relatively high, contributing to incurability and morbidity of the disease. Thyroid cancer is mainly treated by surgery and radioiodine remnant ablation, which is effective only for non-metastasized primary tumors. Therefore, better understanding of the molecular targets available in this tumor is necessary. Similarly to many other tumor types, oncogenic molecular alterations in thyroid epithelium include aberrant signal transduction of the mitogen-activated protein kinase, phosphatidylinositol 3-kinase/AKT (also known as protein kinase B), NF-кB, and WNT/β-catenin pathways. However, the role of the Janus kinase (JAK)/signal transducer and activator of transcription (STAT3) pathway, a well-known mediator of tumorigenesis in different tumor types, is relatively less understood in thyroid cancer. Intriguingly, recent studies have demonstrated that, in thyroid cancer, the JAK/STAT3 pathway may function in the context of tumor suppression rather than promoting tumorigenesis. In this review, we provide an update of STAT3 function in thyroid cancer and discuss some of the evidences that support this hypothesis

A Novel Association between the 27-bp Deletion and 538G\u3eA Mutation in the ABCC11 Gene

A single nucleotide polymorphism in the ABCC11 gene, 538G\u3eA (rs17822931), is known to determine human ear wax type. The G/G and G/A genotypes correspond to the wet type, while the A/A genotype corresponds to the dry type. Another earwax determinant, a 27-bp deletion (Δ27) downstream from the rs17822931 site, is a rare variant that leads to the dry phenotype. In a previous report, we found an individual with the G allele who unexpectedly showed the dry type of earwax, leading to the identification of Δ27. We also demonstrated that the Δ27 allele was present in individuals of Japanese, Thai, native North American, Andean, and Bolivian ancestry but absent in those of European and African ancestry. Here, we assessed the Δ27 allele frequency among Japanese and Ukrainian individuals and identified a novel association between the Δ27 and 538G\u3eA mutations. The Δ27 allele frequency was 0.002 (3/1,520; one individual is heterozygous, and another is homozygous) among Japanese individuals and 0 (0/794) among Ukrainians. We also found a previously unreported homozygous genotype for both the Δ27 and A alleles. Our findings suggest that the Δ27 deletion may have occurred in an ABCC11 gene with the 538G\u3eA mutation

Less Frequent NSD1-Intragenic Deletions in Japanese Sotos Syndrome: Analysis of 30 Patients by NSD1-Exon Array CGH, Quantitative Fluorescent Duplex PCR, and Fluorescence In Situ Hybridization

Sotos syndrome (SoS, OMIM #117550) is an autosomal dominant overgrowth syndrome with pre- and postnatal excessive growth, characteristic craniofacial features, and variable degrees of developmental delay. Haploinsufficiency of the nuclear receptor binding SET domain containing protein 1 (NSD1) gene causes SoS, as two thirds of SoS patients had either a whole-gene microdeletion or an intragenic point mutation. However, the etiology of other patients remains undetermined. In the present study, we analyzed 30 Japanese SoS patients on whether they have NSD1 intragenic deletions by NSD1-specific exon microarray comparative genomic hybridization (array CGH). Although the analysis suggested a deletion at the 5\u27 region of NSD1 in 16 of the 30 patients, no such abnormalities were confirmed by subsequent quantitative fluorescent duplex PCR and fluorescence in situ hybridization. As no intragenic deletions have been identified in our series of SoS patients, other genetic aberrations need to be identified

VarSight: prioritizing clinically reported variants with binary classification algorithms.

BackgroundWhen applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient's phenotypes. Typically, this is done through annotation, filtering, and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance.MethodsWe tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network.ResultsWe treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20.ConclusionsWe demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets