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pmid:29363612

Spatial Tissue Proteomics Quantifies Inter- and Intratumor Heterogeneity in Hepatocellular Carcinoma (HCC).

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

Molecular & cellular proteomics : MCP · 2018 doi:10.1074/mcp.RA117.000189

Paper

complete

Experiment

complete

Data

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partial

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Spatial Tissue Proteomics Quantifies Inter- and Intratumor Heterogeneity in Hepatocellular Carcinoma (HCC).
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Katarzyna Buczak
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Alessandro Ori
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Joanna M Kirkpatrick
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Kerstin Holzer
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Daniel Dauch
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Stephanie Roessler
[6]
Volker Endris
[7]
Felix Lasitschka
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Luca Parca
[9]
Alexander Schmidt
[10]
Lars Zender
[11]
Peter Schirmacher
[12]
Jeroen Krijgsveld
[13]
Stephan Singer
[14]
Martin Beck
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Molecular & cellular proteomics : MCP
year
2018
abstract
The interpatient variability of tumor proteomes has been investigated on a large scale but many tumors display also intratumoral 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 intratumor heterogeneity across human patient specimens with spatial resolution. We defined proteomic features that distinguish neoplastic from the directly adjacent nonneoplastic tissue, such as decreased abundance of NADH dehydrogenase complex I. We then demonstrated the existence of intratumoral 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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We defined proteomic features that distinguish neoplastic from th", "he benefits of using mass spectrometry-based proteomics to analyze diagnostic tumor specimens with s", "odissected FFPE Specimens With Comprehensive Proteomic Coverage We developed a novel strategy for e"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["expressed between the conditions tested. For murine HCC proteome we used label-free quantificati", "nd Processing for Label-free Quantification (Murine HCC Samples) For normal liver and tumor samp", "les) For normal liver and tumor samples from mouse model, peptides were measured by data-dependent ac", "ng the preprocessCore library (30). 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