BackgroundCells constantly sense many internal and environmental signals and respond through their complex signaling network, leading to particular biological outcomes. However, a systematic characterization and optimization of multi-signal responses remains a pressing challenge to traditional experimental approaches due to the arising complexity associated with the increasing number of signals and their intensities.ResultsWe established and validated a data-driven mathematical approach to systematically characterize signal-response relationships. Our results demonstrate how mathematical learning algorithms can enable systematic characterization of multi-signal induced biological activities. The proposed approach enables identification of input combinations that can result in desired biological responses. In retrospect, the results show that, unlike a single drug, a properly chosen combination of drugs can lead to a significant difference in the responses of different cell types, increasing the differential targeting of certain combinations. The successful validation of identified combinations demonstrates the power of this approach. Moreover, the approach enables examining the efficacy of all lower order mixtures of the tested signals. The approach also enables identification of system-level signaling interactions between the applied signals. Many of the signaling interactions identified were consistent with the literature, and other unknown interactions emerged.ConclusionsThis approach can facilitate development of systems biology and optimal drug combination therapies for cancer and other diseases and for understanding key interactions within the cellular network upon treatment with multiple signals.

Thousands of putative enhancers are characterized in the human genome, yet few have been shown to have a functional role in cancer progression. Inhibiting oncokinases, such as EGFR, ALK, ERBB2, and BRAF, is a mainstay of current cancer therapy but is hindered by innate drug resistance mediated by up-regulation of the HGF receptor, MET. The mechanisms mediating such genomic responses to targeted therapy are unknown. Here, we identify lineage-specific enhancers at the MET locus for multiple common tumor types, including a melanoma lineage-specific enhancer 63 kb downstream from the MET TSS. This enhancer displays inducible chromatin looping with the MET promoter to up-regulate MET expression upon BRAF inhibition. Epigenomic analysis demonstrated that the melanocyte-specific transcription factor, MITF, mediates this enhancer function. Targeted genomic deletion (<7 bp) of the MITF motif within the MET enhancer suppressed inducible chromatin looping and innate drug resistance, while maintaining MITF-dependent, inhibitor-induced melanoma cell differentiation. Epigenomic analysis can thus guide functional disruption of regulatory DNA to decouple pro-and antioncogenic functions of a dominant transcription factor and block innate resistance to oncokinase therapy.

More than100 human genetic skin diseases, impacting over 20% of the population, are characterized by disrupted epidermal differentiation. A significant proportion of the 90 genes identified in these disorders to date are concentrated within several functional pathways, suggesting the emergence of organizing themes in epidermal differentiation. Among these are the Notch, TGFβ, IKK, Ras/MAPK, Phosphoinositide 3-kinase, p63, and Wnt signaling pathways as well as core biologic processes mediating calcium homeostasis, tissue integrity, cornification, and lipid biogenesis. Here, we review recent results supporting the central role of these pathways in epidermal differentiation, highlighting the integration of genetic information with functional studies to illuminate the biological actions of these pathways in humans as well as guide development of future therapeutics to correct their dysfunction.

The basis for impaired differentiation in TP63 mutant ankyloblepharon-ectodermal dysplasia-clefting (AEC) syndrome is unknown. Human epidermis harboring AEC TP63 mutants recapitulated this impairment, along with downregulation of differentiation activators, including HOPX, GRHL3, KLF4, PRDM1, and ZNF750. Gene-set enrichment analysis indicated that disrupted expression of epidermal differentiation programs under the control of ZNF750 and KLF4 accounted for the majority of disrupted epidermal differentiation resulting from AEC mutant TP63. Chromatin immunoprecipitation (ChIP) analysis and ChIP-sequencing of TP63 binding in differentiated keratinocytes revealed ZNF750 as a direct target of wild-type and AEC mutant TP63. Restoring ZNF750 to AEC model tissue rescued activator expression and differentiation, indicating that AEC TP63-mediated ZNF750 inhibition contributes to differentiation defects in AEC. Incorporating disease-causing mutants into regenerated human tissue can thus dissect pathomechanisms and identify targets that reverse disease features.