Exploiting Clinical Trial Data Drastically Narrows the Window of Possible Solutions to the Problem of Clinical Adaptation of a Multiscale Cancer Model

The development of computational models for simulating tumor growth and response to treatment has gained significant momentum during the last few decades. At the dawn of the era of personalized medicine, providing insight into complex mechanisms involved in cancer and contributing to patient-specific therapy optimization constitute particularly inspiring pursuits. The in silico oncology community is facing the great challenge of effectively translating simulation models into clinical practice, which presupposes a thorough sensitivity analysis, adaptation and validation process based on real clinical data. In this paper, the behavior of a clinically-oriented, multiscale model of solid tumor response to chemotherapy is investigated, using the paradigm of nephroblastoma response to preoperative chemotherapy in the context of the SIOP/GPOH clinical trial. A sorting of the model's parameters according to the magnitude of their effect on the output has unveiled the relative importance of the corresponding biological mechanisms; major impact on the result of therapy is credited to the oxygenation and nutrient availability status of the tumor and the balance between the symmetric and asymmetric modes of stem cell division. The effect of a number of parameter combinations on the extent of chemotherapy-induced tumor shrinkage and on the tumor's growth rate are discussed. A real clinical case of nephroblastoma has served as a proof of principle study case, demonstrating the basics of an ongoing clinical adaptation and validation process. By using clinical data in conjunction with plausible values of model parameters, an excellent fit of the model to the available medical data of the selected nephroblastoma case has been achieved, in terms of both volume reduction and histological constitution of the tumor. In this context, the exploitation of multiscale clinical data drastically narrows the window of possible solutions to the clinical adaptation problem

Intronic Cis-Regulatory Modules Mediate Tissue-Specific and Microbial Control of angptl4/fiaf Transcription

The intestinal microbiota enhances dietary energy harvest leading to increased fat storage in adipose tissues. This effect is caused in part by the microbial suppression of intestinal epithelial expression of a circulating inhibitor of lipoprotein lipase called Angiopoietin-like 4 (Angptl4/Fiaf). To define the cis-regulatory mechanisms underlying intestine-specific and microbial control of Angptl4 transcription, we utilized the zebrafish system in which host regulatory DNA can be rapidly analyzed in a live, transparent, and gnotobiotic vertebrate. We found that zebrafish angptl4 is transcribed in multiple tissues including the liver, pancreatic islet, and intestinal epithelium, which is similar to its mammalian homologs. Zebrafish angptl4 is also specifically suppressed in the intestinal epithelium upon colonization with a microbiota. In vivo transgenic reporter assays identified discrete tissue-specific regulatory modules within angptl4 intron 3 sufficient to drive expression in the liver, pancreatic islet β-cells, or intestinal enterocytes. Comparative sequence analyses and heterologous functional assays of angptl4 intron 3 sequences from 12 teleost fish species revealed differential evolution of the islet and intestinal regulatory modules. High-resolution functional mapping and site-directed mutagenesis defined the minimal set of regulatory sequences required for intestinal activity. Strikingly, the microbiota suppressed the transcriptional activity of the intestine-specific regulatory module similar to the endogenous angptl4 gene. These results suggest that the microbiota might regulate host intestinal Angptl4 protein expression and peripheral fat storage by suppressing the activity of an intestine-specific transcriptional enhancer. This study provides a useful paradigm for understanding how microbial signals interact with tissue-specific regulatory networks to control the activity and evolution of host gene transcription