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PLoS Comput Biol. 2015 May 28;11(5):e1004293. doi: 10.1371/journal.pcbi.1004293. eCollection 2015 May.

Causal Modeling of Cancer-Stromal Communication Identifies PAPPA as a Novel Stroma-Secreted Factor Activating NFκB Signaling in Hepatocellular Carcinoma.

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Department of Statistical Bioinformatics, University of Regensburg, Regensburg, Germany.
Department of Internal Medicine I, University Hospital Regensburg, Regensburg, Germany.
Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
Biobank under the authority of Human Tissue and Cell Research (HTCR) and Center for Liver Cell Research, Department of General, Visceral, Transplantation, Vascular and Thoracic Surgery, Hospital of Ludwig-Maximilians-University of Munich, Munich, Germany.
Department of Molecular Biology, Division of Chemistry and Bioanalytics, University of Salzburg, Salzburg, Austria.


Inter-cellular communication with stromal cells is vital for cancer cells. Molecules involved in the communication are potential drug targets. To identify them systematically, we applied a systems level analysis that combined reverse network engineering with causal effect estimation. Using only observational transcriptome profiles we searched for paracrine factors sending messages from activated hepatic stellate cells (HSC) to hepatocellular carcinoma (HCC) cells. We condensed these messages to predict ten proteins that, acting in concert, cause the majority of the gene expression changes observed in HCC cells. Among the 10 paracrine factors were both known and unknown cancer promoting stromal factors, the former including Placental Growth Factor (PGF) and Periostin (POSTN), while Pregnancy-Associated Plasma Protein A (PAPPA) was among the latter. Further support for the predicted effect of PAPPA on HCC cells came from both in vitro studies that showed PAPPA to contribute to the activation of NFκB signaling, and clinical data, which linked higher expression levels of PAPPA to advanced stage HCC. In summary, this study demonstrates the potential of causal modeling in combination with a condensation step borrowed from gene set analysis [Model-based Gene Set Analysis (MGSA)] in the identification of stromal signaling molecules influencing the cancer phenotype.

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