Practical Course HP-F1: Molecular, Genetic, Cellular and Bioinformatic Approaches in Cancer Research

Type: Practical Course with Student Seminars


Date: 2.-20. November 2020

Introduction, delivery of script/literature: date and time t.b.a. (DKFZ Main Building, INF 280, 2. floor, H2.02.073 or elsewhere/online)

Locations: DKFZ Teaching Lab; Various DKFZ and NCT division labs and seminar rooms

Hosts/Supervisors: Karin Müller-Decker (responsible organizer contact:; Friederike Herbst; Maïwen Caudron-Herger, Sven Diederichs



  1. The role of pro-inflammatory COX-2 in cancer: Analysis of genetically engineered mouse tumor models
  2. Analysis of mass spectrometry data on protein-RNA interactions using a major bioinformatic tool
  3. Understanding molecular and functional heterogeneity of patient derived tumor models to improve treatment strategies


  1. Transgenic mice as tumour models. Analysis of oncogene Ki-Ras and COX-2 expression/activities. Protein enrichment from various tissues by immunoprecipitation, pull-down assays, immunoblot analysis, in situ protein detection by multi-colour immunofluorescence; lipid extractions, reverse-phase-chromatography of lipids, enzyme-immuno-assay of lipids.
  2. The analysis of a mass spectrometry dataset linked to protein-RNA interactions will serve as basis to introduce simple programming notions in R ranging from reading a file to the production of graphics. The results of the proteomic analysis and their meaning will be discussed.
  3. Major challenges for the development of mechanism-based strategies in clinical cancer care are the variety of patient specific molecular aberrations like genetic mutations as well as functional heterogeneity within individual tumors. To understand implications of inter- and intratumoral heterogeneity for personalized treatment approaches we use patient-derived tumor models generated from individual samples.
    The aim of this practical course is I) the generation of individual tumor models allowing II) the molecular and functional characterization with the overall aim to predict the clinical outcome and to improve personalized treatment strategies.


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