Routine Proteome Profiling of Cancer Tissues
Author Information
Author(s): Johanna Tüshaus, Stephan Eckert, Marius Schliemann, Yuxiang Zhou, Pauline Pfeiffer, Christiane Halves, Federico Fusco, Johannes Weigel, Lisa Hönikl, Vicki Butenschön, Rumyana Todorova, Hilka Rauert-Wunderlich, Matthew The, Andreas Rosenwald, Volker Heinemann, Julian Holch, Katja Steiger, Claire Delbridge, Bernhard Meyer, Wilko Weichert, Carolin Mogler, Peer-Hendrik Kuhn, Bernhard Kuster
Primary Institution: Technical University of Munich
Hypothesis
Can a new normalization method improve proteome profiling of FFPE cancer tissues?
Conclusion
The study established a comprehensive pan-cancer proteome resource from 1,220 FFPE tumor samples, revealing significant protein expression differences across cancer types.
Supporting Evidence
- Proteome profiling of FFPE specimens can identify over 4000 proteins on average.
- A new normalization method was introduced to ensure equal sample loading.
- The study provides a publicly accessible resource for cancer proteomics.
- Data analysis revealed tissue-specific and cancer entity-specific protein expression patterns.
- Initial findings suggest significant differences in protein expression across cancer types.
Takeaway
Researchers studied cancer samples to find out how many different proteins they could identify, helping to understand cancer better.
Methodology
The study used quantitative mass spectrometry and bioinformatics to analyze FFPE tumor samples from six cancer types.
Limitations
The study's findings may not be generalizable to all cancer types due to the focus on specific entities.
Participant Demographics
The study included tumor samples from six cancer types: glioblastoma, oral squamous cell carcinoma, diffuse large B-cell lymphoma, pancreatic ductal adenocarcinoma, colorectal cancer, and melanoma.
Digital Object Identifier (DOI)
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