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Heuckmann J.M.,Blackfield AG | Thomas R.K.,University of Cologne
Annals of Oncology | Year: 2015

The identification of 'druggable' kinase gene alterations has revolutionized cancer treatment in the last decade by providing new and successfully targetable drug targets. Thus, genotyping tumors for matching the right patients with the right drugs have become a clinical routine. Today, advances in sequencing technology and computational genome analyses enable the discovery of a constantly growing number of genome alterations relevant for clinical decision making. As a consequence, several technological approaches have emerged in order to deal with these rapidly increasing demands for clinical cancer genome analyses. Here, we describe challenges on the path to the broad introduction of diagnostic cancer genome analyses and the technologies that can be applied to overcome them. We define three generations of molecular diagnostics that are in clinical use. The latest generation of these approaches involves deep and thus, highly sensitive sequencing of all therapeutically relevant types of genome alterations-mutations, copy number alterations and rearrangements/fusions-in a single assay. Such approaches therefore have substantial advantages (less time and less tissue required) over PCR-based methods that typically have to be combined with fluorescence in situ hybridization for detection of gene amplifications and fusions. Since these new technologies work reliably on routine diagnostic formalin-fixed, paraffin-embedded specimens, they can help expedite the broad introduction of personalized cancer therapy into the clinic by providing comprehensive, sensitive and accurate cancer genome diagnoses in 'real-time'. © The Author 2015. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved.

Biehl M.,University of Groningen | Hammer B.,Bielefeld University | Schleif F.-M.,University of Birmingham | Schleif F.-M.,Mittweida University of Applied Sciences | And 2 more authors.
Proceedings of the International Joint Conference on Neural Networks | Year: 2015

We present a theoretical analysis of Learning Vector Quantization (LVQ) with adaptive distance measures. Specifically, we consider generalized Euclidean distances which are parameterized in terms of a quadratic matrix of adaptive relevance parameters. Winner-takes-all prescriptions based on the heuristic LVQ1 are in the center of our interest. We derive and study stationarity conditions and show, among other results, that stationary prototypes can be written as linear combinations of the training data apart from irrelevant contributions in the null-space of the relevance matrix. The investigation of the metrics updates reveals that relevance matrices become singular with only one or very few non-zero eigenvalues. Implications of this property are discussed and, furthermore, the effect of preventing singularity by introducing an appropriate penalty term is studied. Theoretical findings are confirmed in terms of illustrative example data sets. © 2015 IEEE.

Malchers F.,University of Cologne | Dietlein F.,University of Cologne | Schottle J.,University of Cologne | Schottle J.,Max Planck Institute for Neurological Research | And 30 more authors.
Cancer Discovery | Year: 2014

The 8p12 locus (containing the FGFR1 tyrosine kinase gene) is frequently amplified in squamous cell lung cancer. However, it is currently unknown which of the 8p12- amplified tumors are also sensitive to fibroblast growth factor receptor (FGFR) inhibition. We found that, in contrast with other recurrent amplifications, the 8p12 region included multiple centers of amplification, suggesting marked genomic heterogeneity. FGFR1 -amplified tumor cells were dependent on FGFR ligands in vitro and in vivo. Furthermore, ectopic expression of FGFR1 was oncogenic, which was enhanced by expression of MYC. We found that MYC was coexpressed in 40% of FGFR1 - amplified tumors. Tumor cells coexpressing MYC were more sensitive to FGFR inhibition, suggesting that patients with FGFR1- amplified and MYC-overexpressing tumors may benefit from FGFR inhibitor therapy. Thus, both cell-autonomous and non-cell-autonomous mechanisms of transformation modulate FGFR dependency in FGFR1 -amplified lung cancer, which may have implications for patient selection for treatment with FGFR inhibitors. © 2014 American Association for Cancer Research.

Fernandez-Cuesta L.,University of Cologne | Fernandez-Cuesta L.,International Agency for Research on Cancer IARC WHO | Sun R.,Max Planck Institute for Molecular Genetics | Sun R.,Columbia University | And 40 more authors.
Genome Biology | Year: 2015

Genomic translocation events frequently underlie cancer development through generation of gene fusions with oncogenic properties. Identification of such fusion transcripts by transcriptome sequencing might help to discover new potential therapeutic targets. We developed TRUP (Tumor-specimen suited RNA-seq Unified Pipeline) (https://github.com/ruping/TRUP), a computational approach that combines split-read and read-pair analysis with de novo assembly for the identification of chimeric transcripts in cancer specimens. We apply TRUP to RNA-seq data of different tumor types, and find it to be more sensitive than alternative tools in detecting chimeric transcripts, such as secondary rearrangements in EML4-ALK-positive lung tumors, or recurrent inactivating rearrangements affecting RASSF8. © 2015 Fernandez-Cuesta et al.; licensee BioMed Central.

Richters A.,TU Dortmund | Ketzer J.,University of Duisburg - Essen | Getlik M.,TU Dortmund | Grutter C.,TU Dortmund | And 12 more authors.
Journal of Medicinal Chemistry | Year: 2013

Mutations in the catalytic domain at the gatekeeper position represent the most prominent drug-resistant variants of kinases and significantly impair the efficacy of targeted cancer therapies. Understanding the mechanisms of drug resistance at the molecular and atomic levels will aid in the design and development of inhibitors that have the potential to overcome these resistance mutations. Herein, by introducing adaptive elements into the inhibitor core structure, we undertake the structure-based development of type II hybrid inhibitors to overcome gatekeeper drug-resistant mutations in cSrc-T338M, as well as clinically relevant tyrosine kinase KIT-T670I and Abl-T315I variants, as essential targets in gastrointestinal stromal tumors (GISTs) and chronic myelogenous leukemia (CML). Using protein X-ray crystallography, we confirm the anticipated binding mode in cSrc, which proved to be essential for overcoming the respective resistances. More importantly, the novel compounds effectively inhibit clinically relevant gatekeeper mutants of KIT and Abl in biochemical and cellular studies. © 2013 American Chemical Society.

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