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Biomedical subjects

Michael G Schimek

Publications and source records attributed to Michael G Schimek.

3 recordsLinked to original sources

Two transforming C-RAF germ-line mutations identified in patients with therapy-related acute myeloid leukemia.

Mutations leading to activation of the RAF-mitogen-activated protein kinase/extracellular signal-regulated (ERK) kinase (MEK)-ERK pathway are key events in the pathogenesis of human malignancies. In a screen of 82 acute myeloid leukemia (AML) samples, 45 (55%) showed activated ERK and thus were further analyzed for mutations in B-RAF and C-RAF. Two C-RAF germ-line mutations, S427G and I448V, were identified in patients with therapy-related AML in the absence of alterations in RAS and FLT3. Both exchanges were located within the kinase domain of C-RAF. In vitro and in vivo kinase assays revealed significantly increased activity for (S427G)C-RAF but not for (I448V)C-RAF. The involvement of the S427G C-RAF mutation in constitutive activation of ERK was further confirmed through demonstration of activating phosphorylations on C-RAF, MEK, and ERK in neoplastic cells, but not in nonneoplastic cells. Transformation and survival assays showed oncogenic and antiapoptotic properties for both mutations. Screening healthy individuals revealed a <1/400 frequency of these mutations and, in the case of I448V, inheritance was observed over three generations with another mutation carrier suffering from cancer. Taken together, these data are the first to relate C-RAF mutations to human malignancies. As both mutations are of germ-line origin, they might constitute a novel tumor-predisposing factor.

Acute Disease↗

Penalized binary regression for gene expression profiling.

OBJECTIVES: A typical bioinformatics task in microarray analysis is the classification of biological samples into two alternative categories. A procedure is needed which, based on the expression levels measured, allows us to compute the probability that a new sample belongs to a certain class. METHODS: For the purpose of classification the statistical approach of binary regression is considered. Highdimensionality and at the same time small sample sizes make it a challenging task. Standard logit or probit regression fails because of condition problems and poor predictive performance. The concepts of frequentist and of Bayesian penalization for binary regression are introduced. A Bayesian interpretation of the penalized log-likelihood is given. Finally the role of cross-validation for regularization and feature selection is discussed. RESULTS: Penalization makes classical binary regression a suitable tool for microarray analysis. We illustrate penalized logit and Bayesian probit regression on a well-known data set and compare the obtained results, also with respect to published results from decision trees. CONCLUSIONS: The frequentist and the Bayesian penalization concept work equally well on the example data, however some method-specific differences can be made out. Moreover the Bayesian approach yields a quantification (posterior probabilities) of the bias due to the constraining assumptions.

Bayes Theorem↗