Helmholtz Gemeinschaft

Search
Browse
Statistics
Feeds

„Deep sequencing“ und praediktive Modellierung als Konzept therapeutischer Entscheidungsfindungen in der Onkologie [Deep sequencing and predictive modeling as a concept for therapeutic decision-making in oncology]

Item Type:Article
Title:„Deep sequencing“ und praediktive Modellierung als Konzept therapeutischer Entscheidungsfindungen in der Onkologie [Deep sequencing and predictive modeling as a concept for therapeutic decision-making in oncology]
Creators Name:Lehrach, H. and Schaefer, R. and Schlag, P. M.
Abstract:Precise knowledge of the complex activation mechanisms of cellular signaling pathways and of the molecular interactions is an essential prerequisite for the design of targeted therapies and prediction of therapy response. The plethora of possible interactions validated in tumor models and patient specimens suggests that the patterns of mutations and activation mechanisms do not only occur in a tumor class-specific manner but also reflect the individual genetic“make-up” of given tumors. Hence, tumor treatment may be based on such individual parameters rather than be directed by general tumor classifications or paradigmatic sets of alterations. Following detailed omics analyses, bioinformatic processing of the resulting complex data sets is essential to predict the impact of an enormous number of alterations of the genome, transcriptome and last but not least the proteome to forecast the influence of patient-specific genetic variation on therapy efficacy and side-effects. In silico modeling of data obtained by detailed analysis of defective biological processes characteristic for every single tumor provides opportunities for selecting effective drugs and therapeutics and for simulating their effects. Our vision for the future is that this fundamental and essential approach based on detailed molecular profiling of each patient’s tumor in conjunction with individual in silico modeling of the biological consequences and the resulting therapeutic targets will become a key feature of data-driven and computation-intensive personalized medicine. The ultimate goal is to improve efficacy and outcome of cancer patient treatment and to eventually reduce medical costs by avoiding ineffective therapies.
Keywords:Tumor, Omics Analysis, Oncogenic Pathway Modeling, Targeted Therapy, Personalized Medicine
Source:Onkologe
ISSN:0947-8965
Publisher:Springer (Germany)
Volume:17
Number:6
Page Range:477-486
Date:June 2011
Official Publication:https://doi.org/10.1007/s00761-011-2025-9

Repository Staff Only: item control page

Open Access
MDC Library