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Navigating proteomics techniques for molecular profiling of clinical cancer cohorts

RECORD | Already taken place Th, 6.2.2025
The presentation explores the integration of proteomics into clinical practice, particularly in cancer research and emphasizes the use of mass spectrometry-based proteomics.
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Thermo Fisher Scientific: Navigating proteomics techniques for molecular profiling of clinical cancer cohorts
Thermo Fisher Scientific: Navigating proteomics techniques for molecular profiling of clinical cancer cohorts

The presentation explores the integration of proteomics into clinical practice, particularly in cancer research. It emphasizes the use of mass spectrometry-based proteomics for molecular profiling of clinical cancer cohorts, combining genetic and proteomic data to understand disease mechanisms and patient outcomes. The speaker discusses the challenges and successes in translating proteomics discoveries into clinical routines, highlighting the importance of extensive clinical data and longitudinal studies. Case studies demonstrate how proteomics can provide deeper insights into diseases like lung cancer and multiple myeloma, with examples of high-throughput assays achieving significant protein coverage. The presentation also covers the application of machine learning to enhance data analysis and the potential of proteomics to improve diagnostic precision and treatment efficacy. Overall, it underscores the transformative potential of proteomics in personalized medicine and clinical diagnostics.

Learning points :

  • Integration of Proteomics in Clinical Practice: Learn how mass spectrometry-based proteomics can be utilized for molecular profiling and improving patient outcomes in clinical settings. 
  • Challenges and Solutions in Translating Discoveries: Understand the hurdles and strategies for effectively incorporating proteomics discoveries into routine clinical practice, supported by extensive clinical data and longitudinal studies. 
  • Enhanced Data Analysis with Machine Learning: Discover the role of machine learning in analyzing proteomics data to boost diagnostic precision and treatment efficacy in personalized medicine.

Who should attend?

  • Cancer Researchers: Professionals focused on integrating genetic and proteomic data for deeper insights into cancer mechanisms and treatments. 
  • Clinical Data Scientists: Individuals interested in applying machine learning to enhance data analysis and improve diagnostic precision. 
  • Healthcare Practitioners: Those looking to adopt proteomics for personalized medicine and more effective clinical diagnostics.
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Presenter:  Philipp Mertins, PhD (Head of Proteomics Platform, Max Delbrück Center and Berlin Institute of Healt)

Philipp Mertins earned his PhD at the Max Planck Institute in Munich, focusing on ​signal transduction and proteomics research. He then joined the Broad Institute of MIT ​and Harvard, where he worked with Dr. Steve Carr as a staff scientist and group leader, ​significantly contributing to the Clinical Proteomic Tumor Analysis Consortium ​(CPTAC). Since 2017, Dr. Mertins has been heading the Proteomics Platform at the ​Max Delbrück Center and the Berlin Institute of Health in Berlin, where he continues to ​advance proteomic technologies in clinical proteomics and translational research.​

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