Monoallelic expression in melanoma.

BACKGROUND: Monoallelic expression (MAE) is a frequent genomic phenomenon in normal tissues, however its role in cancer is yet to be fully understood. MAE is defined as the expression of a gene that is restricted to one allele in the presence of a diploid heterozygous genome. Constitutive MAE occurs for imprinted genes, odorant receptors and random X inactivation. Several studies in normal tissues have showed MAE in approximately 5-20% of the cases. However, little information exists on the MAE rate in cancer. In this study we assessed the presence and rate of MAE in melanoma. The genetic basis of melanoma has been studied in depth over the past decades, leading to the identification of mutations/genetic alterations responsible for melanoma development. METHODS: To examine the role of MAE in melanoma we used 15 melanoma cell lines and compared their RNA-seq data with genotyping data obtained by the parental TIL (tumor infiltrating lymphocytes). Genotyping was performed using the Illumina HumanOmni1 beadchip. The RNA-seq library preparation and sequencing was performed using the Illumina TruSeq Stranded Total RNA Human Kit and subsequently sequenced using a HiSeq 2500 according to manufacturer's guidelines. By comparing genotyping data with RNA-seq data, we identified SNPs in which DNA genotypes were heterozygous and corresponding RNA genotypes were homozygous. All homozygous DNA genotypes were removed prior to the analysis. To confirm the validity to detect MAE, we examined heterozygous DNA genotypes from X chromosome of female samples as well as for imprinted and olfactory receptor genes and confirmed MAE. RESULTS: MAE was detected in all 15 cell lines although to a different rate. When looking at the B-allele frequencies we found a preferential pattern of complete monoallelic expression rather then differential monoallelic expression across the 15 melanoma cell lines. As some samples showed high differences in the homozygous and heterozygous call rate, we looked at the single chromosomes and showed that MAE may be explained by underlying large copy number imbalances in some instances. Interestingly these regions included genes known to play a role in melanoma initiation and progression. Nevertheless, some chromosome regions showed MAE without CN imbalances suggesting that additional mechanisms (including epigenetic silencing) may explain MAE in melanoma. CONCLUSION: The biological implications of MAE are yet to be realized. Nevertheless, our findings suggest that MAE is a common phenomenon in melanoma cell lines. Further analyses are currently being undertaken to evaluate whether MAE is gene/pathway specific and to understand whether MAE can be employed by cancers to achieve a more aggressive phenotype.

Diagnosis implications of the whole genome sequencing in a large Lebanese family with hyaline fibromatosis syndrome.

BACKGROUND: Hyaline fibromatosis syndrome (HFS) is a recently introduced alternative term for two disorders that were previously known as juvenile hyaline fibromatosis (JHF) and infantile systemic hyalinosis (ISH). These two variants are secondary to mutations in the anthrax toxin receptor 2 gene (ANTXR2) located on chromosome 4q21. The main clinical features of both entities include papular and/or nodular skin lesions, gingival hyperplasia, joint contractures and osteolytic bone lesions that appear in the first few years of life, and the syndrome typically progresses with the appearance of new lesions. METHODS: We describe five Lebanese patients from one family, aged between 28 and 58 years, and presenting with nodular and papular skin lesions, gingival hyperplasia, joint contractures and bone lesions. Because of the particular clinical features and the absence of a clinical diagnosis, Whole Genome Sequencing (WGS) was carried out on DNA samples from the proband and his parents. RESULTS: A mutation in ANTXR2 (p. Gly116Val) that yielded a diagnosis of HFS was noted. CONCLUSIONS: The main goal of this paper is to add to the knowledge related to the clinical and radiographic aspects of HFS in adulthood and to show the importance of Next-Generation Sequencing (NGS) techniques in resolving such puzzling cases.

Temple-Baraitser Syndrome and Zimmermann-Laband Syndrome: one clinical entity?

BACKGROUND: KCNH1 encodes a voltage-gated potassium channel that is predominantly expressed in the central nervous system. Mutations in this gene were recently found to be responsible for Temple-Baraitser Syndrome (TMBTS) and Zimmermann-Laband syndrome (ZLS). METHODS: Here, we report a new case of TMBTS diagnosed in a Lebanese child. Whole genome sequencing was carried out on DNA samples of the proband and his parents to identify mutations associated with this disease. Sanger sequencing was performed to confirm the presence of detected variants. RESULTS: Whole genome sequencing revealed three missense mutations in TMBTS patient: c.1042G > A in KCNH1, c.2131 T > C in STK36, and c.726C > A in ZNF517. According to all predictors, mutation in KCNH1 is damaging de novo mutation that results in substitution of Glycine by Arginine, i.e., p.(Gly348Arg). This mutation was already reported in a patient with ZLS that could affect the connecting loop between helices S4-S5 of KCNH1 with a gain of function effect. CONCLUSIONS: Our findings demonstrate that KCNH1 mutations cause TMBTS and expand the mutational spectrum of KCNH1 in TMBTS. In addition, all cases of TMBTS were reviewed and compared to ZLS. We suggest that the two syndromes are a continuum and that the variability in the phenotypes is the result of the involvement of genetic modifiers.

Model Comparison for Breast Cancer Prognosis Based on Clinical Data.

We compared the performance of several prediction techniques for breast cancer prognosis, based on AU-ROC performance (Area Under ROC) for different prognosis periods. The analyzed dataset contained 1,981 patients and from an initial 25 variables, the 11 most common clinical predictors were retained. We compared eight models from a wide spectrum of predictive models, namely; Generalized Linear Model (GLM), GLM-Net, Partial Least Square (PLS), Support Vector Machines (SVM), Random Forests (RF), Neural Networks, k-Nearest Neighbors (k-NN) and Boosted Trees. In order to compare these models, paired t-test was applied on the model performance differences obtained from data resampling. Random Forests, Boosted Trees, Partial Least Square and GLMNet have superior overall performance, however they are only slightly higher than the other models. The comparative analysis also allowed us to define a relative variable importance as the average of variable importance from the different models. Two sets of variables are identified from this analysis. The first includes number of positive lymph nodes, tumor size, cancer grade and estrogen receptor, all has an important influence on model predictability. The second set incudes variables related to histological parameters and treatment types. The short term vs long term contribution of the clinical variables are also analyzed from the comparative models. From the various cancer treatment plans, the combination of Chemo/Radio therapy leads to the largest impact on cancer prognosis.