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

Abstract

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.

Detection of time delays and directional interactions based on time series from complex dynamical systems

“…In the physical world, time delays are intimately related to causation, as signals travel at a finite speed. Successful identification of time delays and causal interactions in complex systems is relevant to significant problems of current interest such as precision medicine [1], effective policy and management recommendations on climate and ecosystems [2,3], energy optimization in urban systems [4], epidemiology [5], control strategy for gene regulation [6], financial regulations [7], human brain functions [8], and human behaviors in online social systems [9]. Traditional methods based on the Granger causality [10,11] and transfer entropy [12] have issues such as difficulty with nonlinearity and stringent data requirement.…”

Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks

“…In this vein, the Dialogue for Reverse Engineering Assessment and Methods (DREAM) project, over the last decade, has also ran several challenges related to network reconstruction [81,90,58]. Lessons were learned about the power of combining complementary reconstruction methods [81,3], and such methods showed great performance in reconstructing real networks from genuine data sets related to cancer [10,36], protein signaling [116,96], or to fitness-related data [45]. When only observational data is available, bounds on causal effects can be retrieved (see the discussion on EoC or CoE below as well) and verified by means of intervention (knock-out) experiments [76,131].…”

Recent Advances in Practical Methods for Liver Cell Biology: A Short Overview

“…Furthermore, numerous studies have shown that HSCs are related to the formation and progression of HCC (Table 1) [17,18]. The treatment of human HCC cells with conditioned media of HSCs from different human donors resulted in significantly different functional effects as well as gene expression changes in the HCC cells [70]. Bioinformatic modeling led to the identification of pregnancy-associated plasma protein A (PAPPA) as novel cancer-promoting stromal factor secreted by HSCs, which is related to advanced-stage HCC [70].…”

“…The treatment of human HCC cells with conditioned media of HSCs from different human donors resulted in significantly different functional effects as well as gene expression changes in the HCC cells [70]. Bioinformatic modeling led to the identification of pregnancy-associated plasma protein A (PAPPA) as novel cancer-promoting stromal factor secreted by HSCs, which is related to advanced-stage HCC [70]. In addition, HSCs can also be used in co-culture models with other cells, such as HSCs treated with conditioned media from different human melanoma cells (Table 1) [19], or conditioned media from steatotic human hepatocytes that produce a more fibrotic phenotype in HSCs compared with normal hepatocytes (Table 1) [20].…”