Title |
Spatial Tissue Proteomics Quantifies Inter- and Intratumor Heterogeneity in Hepatocellular Carcinoma (HCC)*
|
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Published in |
Molecular and Cellular Proteomics, January 2018
|
DOI | 10.1074/mcp.ra117.000189 |
Pubmed ID | |
Authors |
Katarzyna Buczak, Alessandro Ori, Joanna M Kirkpatrick, Kerstin Holzer, Daniel Dauch, Stephanie Roessler, Volker Endris, Felix Lasitschka, Luca Parca, Alexander Schmidt, Lars Zender, Peter Schirmacher, Jeroen Krijgsveld, Stephan Singer, Martin Beck |
Abstract |
The inter-patient variability of tumor proteomes has been investigated on a large scale but many tumors display also intra-tumoral heterogeneity regarding morphological and genetic features. It remains largely unknown to what extent the local proteome of tumors intrinsically differs. Here, we used hepatocellular carcinoma as a model system to quantify both inter- and intra-tumor heterogeneity across human patient specimens with spatial resolution. We defined proteomic features that distinguish neoplastic from the directly adjacent non-neoplastic tissue, such as decreased abundance of NADH dehydrogenase complex I. We then demonstrated the existence of intra-tumoral variations in protein abundance that re-occur across different patient samples, and affect clinically relevant proteins, even in the absence of obvious morphological differences or genetic alterations. Our work demonstrates the suitability and the benefits of using mass spectrometry based proteomics to analyze diagnostic tumor specimens with spatial resolution. Data are available via ProteomeXchange with identifier PXD007052. |
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Other | 1 | 3% |
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Demographic breakdown
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---|---|---|
Members of the public | 20 | 57% |
Scientists | 12 | 34% |
Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Unknown | 1 | 3% |
Mendeley readers
Geographical breakdown
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---|---|---|
Unknown | 108 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 23 | 21% |
Researcher | 23 | 21% |
Student > Master | 12 | 11% |
Other | 8 | 7% |
Student > Bachelor | 7 | 6% |
Other | 14 | 13% |
Unknown | 21 | 19% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 14 | 13% |
Medicine and Dentistry | 8 | 7% |
Chemistry | 6 | 6% |
Engineering | 5 | 5% |
Other | 7 | 6% |
Unknown | 25 | 23% |