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Müller-Nurasyid, M.

Publications and source records attributed to Müller-Nurasyid, M..

2 recordsLinked to original sources

Exploration of oncogenic cooperation between germline variation and somatic mutation in prostate cancer progression

Prostate carcinoma (PCa) is the most common cancer of men, associated with a still unresolved issue of accurate risk-stratification. While recent advances in omics technologies have provided clues as to how molecular changes shape the onset and progression of PCa, it remains largely unclear whether germline variants and somatic mutations cooperate to contribute to PCa progression and outcome. Thus, we explored whether oncogenic cooperation between regulatory germline variants and somatic driver mutations can help explain why some PCa patients develop a more aggressive phenotype, which may have implications for risk-adapted medical treatment. Here, by employing an integrative functional genomics approach, we identified receptor-type protein-tyrosine phosphatase kappa (PTPRK) as a TMPRSS2::ERG (TE)-modulated gene associated with PCa progression whose expression is controlled by cooperation of the TE-fusion with a regulatory single nucleotide polymorphism (SNP). Analysis of available clinically annotated patient cohorts demonstrated that PTPRK is overexpressed in TE-positive PCa tumors and associated with higher Gleason scores and metastatic disease. TE knockdown in PCa cell lines reduced PTPRK expression, while ectopic overexpression of the fusion in TE-negative PCa cell lines and prostatic epithelium cells induced its expression. Functionally, PTPRK silencing inhibited cellular proliferation, cell cycle progression, and clonogenic growth of PCa cells, which was mirrored by dysregulation of corresponding gene and protein signatures in global transcriptomic and phospho-proteomic analyses after PTPRK knockdown. Analysis of TE ChIP-Seq and Hi-C data from PCa cells highlighted a proximal TE-bound DNA element whose TE-dependent enhancer activity was validated in reporter assays and which could be abrogated by a regulatory SNP. Collectively, our results provide evidence of how exploration of oncogenic cooperation may help to identify novel biomarkers and potentially druggable pathways and highlight the role of the regulatory genome in PCa progression.

cancer biology↗

An exact and efficient score test for variance components models

Testing for the existence of variance components in linear mixed models is a fundamental task in many applicative fields. In statistical genetics, the score test has recently become instrumental in the task of testing an association between a set of genetic markers and a phenotype. With few markers, this amounts to set-based variance component tests, which attempt to increase power in association studies by aggregating weak individual effects. When the entire genome is considered, it allows testing for the heritability of a phenotype, defined as the proportion of phenotypic variance explained by genetics. In the popular score-based Sequence Kernel Association Test (SKAT) method, the assumed distribution of the score test statistic is uncalibrated in small samples, with a correction being computationally expensive. This may cause severe inflation or deflation of p-values, even when the null hypothesis is true. Here, we characterize the conditions under which this discrepancy holds, and show it may occur also in large real datasets, such as a dataset from the Wellcome Trust Case Control Consortium 2 (n=13,950) study, and in particular when the individuals in the sample are unrelated. In these cases the SKAT approximation tends to be highly over-conservative and therefore underpowered. To address this limitation, we suggest an efficient method to calculate exact p-values for the score test in the case of a single variance component and a continuous response vector, which can speed up the analysis by orders of magnitude. Our results enable fast and accurate application of the score test in heritability and in set-based association tests. Our method is available in http://github.com/cozygene/RL-SKAT.

genetics↗