A new class of protein biomarkers based on subcellular distribution: application to a mouse liver cancer model

Tatjana Sajic, Rodolfo Ciuffa, Vera Lemos, Pan Xu, Valentina Leone, Chen Li, Evan G. Williams, Georgios Makris, Amir Banaei-Esfahani, Mathias Heikenwalder, Kristina Schoonjans, Ruedi Aebersold

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

To-date, most proteomic studies aimed at discovering tissue-based cancer biomarkers have compared the quantity of selected proteins between case and control groups. However, proteins generally function in association with other proteins to form modules localized in particular subcellular compartments in specialized cell types and tissues. Sub-cellular mislocalization of proteins has in fact been detected as a key feature in a variety of cancer cells. Here, we describe a strategy for tissue-biomarker detection based on a mitochondrial fold enrichment (mtFE) score, which is sensitive to protein abundance changes as well as changes in subcellular distribution between mitochondria and cytosol. The mtFE score integrates protein abundance data from total cellular lysates and mitochondria-enriched fractions, and provides novel information for the classification of cancer samples that is not necessarily apparent from conventional abundance measurements alone. We apply this new strategy to a panel of wild-type and mutant mice with a liver-specific gene deletion of Liver receptor homolog 1 (Lrh-1 hep−/− ), with both lines containing control individuals as well as individuals with liver cancer induced by diethylnitrosamine (DEN). Lrh-1 gene deletion attenuates cancer cell metabolism in hepatocytes through mitochondrial glutamine processing. We show that proteome changes based on mtFE scores outperform protein abundance measurements in discriminating DEN-induced liver cancer from healthy liver tissue, and are uniquely robust against genetic perturbation. We validate the capacity of selected proteins with informative mtFE scores to indicate hepatic malignant changes in two independent mouse models of hepatocellular carcinoma (HCC), thus demonstrating the robustness of this new approach to biomarker research. Overall, the method provides a novel, sensitive approach to cancer biomarker discovery that considers contextual information of tested proteins.

Original languageEnglish
Article number6913
Number of pages20
JournalScientific Reports
Volume9
Issue number1
DOIs
Publication statusPublished - 6 May 2019

Keywords

  • mass spectrometry
  • prognostic markers

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