Bertram Klinger

Network quantification of EGFR signaling unveils potential for targeted combination therapy

The epidermal growth factor receptor (EGFR) signaling network is activated in most solid tumors, and small‐molecule drugs targeting this network are increasingly available. However, often only specific combinations of inhibitors are effective. Therefore, the prediction of potent combinatorial treatments is a major challenge in targeted cancer therapy. In this study, we demonstrate how a model‐based evaluation of signaling data can assist in finding the most suitable treatment combination. We generated a perturbation data set by monitoring the response of RAS/PI3K signaling to combined stimulations and inhibitions in a panel of colorectal cancer cell lines, which we analyzed using mathematical models. We detected that a negative feedback involving EGFR mediates strong cross talk from ERK to AKT. Consequently, when inhibiting MAPK, AKT activity is increased in an EGFR‐dependent manner. Using the model, we predict that in contrast to single inhibition, combined inactivation of MEK and EGFR could inactivate both endpoints of RAS, ERK and AKT. We further could demonstrate that this combination blocked cell growth in BRAF‐ as well as KRAS‐mutated tumor cells, which we confirmed using a xenograft model.

Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS

RAS mutations are highly relevant for progression and therapy response of human tumours, but the genetic network that ultimately executes the oncogenic effects is poorly understood. Here, we used a reverse‐engineering approach in an ovarian cancer model to reconstruct KRAS oncogene‐dependent cytoplasmic and transcriptional networks from perturbation experiments based on gene silencing and pathway inhibitor treatments. We measured mRNA and protein levels in manipulated cells by microarray, RT–PCR and western blot analysis, respectively. The reconstructed model revealed complex interactions among the transcriptional and cytoplasmic components, some of which were confirmed by double pertubation experiments. Interestingly, the transcription factors decomposed into two hierarchically arranged groups. To validate the model predictions, we analysed growth parameters and transcriptional deregulation in the KRAS‐transformed epithelial cells. As predicted by the model, we found two functional groups among the selected transcription factors. The experiments thus confirmed the predicted hierarchical transcription factor regulation and showed that the hierarchy manifests itself in downstream gene expression patterns and phenotype.

Discovering causal signaling pathways through gene-expression patterns

High-throughput gene-expression studies result in lists of differentially expressed genes. Most current meta-analyses of these gene lists include searching for significant membership of the translated proteins in various signaling pathways. However, such membership enrichment algorithms do not provide insight into which pathways caused the genes to be differentially expressed in the first place. Here, we present an intuitive approach for discovering upstream signaling pathways responsible for regulating these differentially expressed genes. We identify consistently regulated signature genes specific for signal transduction pathways from a panel of single-pathway perturbation experiments. An algorithm that detects overrepresentation of these signature genes in a gene group of interest is used to infer the signaling pathway responsible for regulation. We expose our novel resource and algorithm through a web server called SPEED: Signaling Pathway Enrichment using Experimental Data sets. SPEED can be freely accessed at http://speed.sys-bio.net/.

Drug resistance mechanisms in colorectal cancer dissected with cell type-specific dynamic logic models

Genomic features are used as biomarkers of sensitivity to kinase inhibitors used widely to treat human cancer, but effective patient stratification based on these principles remains limited in impact. Insofar as kinase inhibitors interfere with signaling dynamics, and, in turn, signaling dynamics affects inhibitor responses, we investigated associations in this study between cell-specific dynamic signaling pathways and drug sensitivity. Specifically, we measured 14 phosphoproteins under 43 different perturbed conditions (combinations of 5 stimuli and 7 inhibitors) in 14 colorectal cancer cell lines, building cell line-specific dynamic logic models of underlying signaling networks. Model parameters representing pathway dynamics were used as features to predict sensitivity to a panel of 27 drugs. Specific parameters of signaling dynamics correlated strongly with drug sensitivity for 14 of the drugs, 9 of which had no genomic biomarker. Following one of these associations, we validated a drug combination predicted to overcome resistance to MEK inhibitors by coblockade of GSK3, which was not found based on associations with genomic data. These results suggest that to better understand the cancer resistance and move toward personalized medicine, it is essential to consider signaling network dynamics that cannot be inferred from static genotypes. Cancer Res; 77(12); 3364-75. ©2017 AACR.

Multilevel regulation of HIF-1 signaling by TTP

Phosphorylation of the RNA-binding protein tristetraprolin (TTP) by p38 MAPK/MK2 does not prevent its RNA interaction and switches the mode of TTP action from destabilization to stabilization of the HIF-1α mRNA and subsequent activation of HIF-1 signaling.

Perturbation-response genes reveal signaling footprints in cancer gene expression

Aberrant cell signaling can cause cancer and other diseases and is a focal point of drug research. A common approach is to infer signaling activity of pathways from gene expression. However, mapping gene expression to pathway components disregards the effect of post-translational modifications, and downstream signatures represent very specific experimental conditions. Here we present PROGENy, a method that overcomes both limitations by leveraging a large compendium of publicly available perturbation experiments to yield a common core of Pathway RespOnsive GENes. Unlike pathway mapping methods, PROGENy can (i) recover the effect of known driver mutations, (ii) provide or improve strong markers for drug indications, and (iii) distinguish between oncogenic and tumor suppressor pathways for patient survival. Collectively, these results show that PROGENy accurately infers pathway activity from gene expression in a wide range of conditions.Deregulation of signalling is responsible for many cancer phenotypes. Leveraging available perturbation data, here the authors assess large-scale pathway activity patterns based on consensus downstream readout genes, enabling accurate prediction of the effects of mutations and small molecules.

The role of the cancer stem cell marker CD271 in DNA damage response and drug resistance of melanoma cells

Several lines of evidence have suggested that stemness and acquired resistance to targeted inhibitors or chemotherapeutics are mechanistically linked. Here we observed high cell surface and total levels of nerve growth factor receptor/CD271, a marker of melanoma-initiating cells, in sub-populations of chemoresistant cell lines. CD271 expression was increased in drug-sensitive cells but not resistant cells in response to DNA-damaging chemotherapeutics etoposide, fotemustine and cisplatin. Comparative analysis of melanoma cells engineered to stably express CD271 or a targeting short hairpin RNA by expression profiling provided numerous genes regulated in a CD271-dependent manner. In-depth analysis of CD271-responsive genes uncovered the association of CD271 with regulation of DNA repair components. In addition, gene set enrichment analysis revealed enrichment of CD271-responsive genes in drug-resistant cells, among them DNA repair components. Moreover, our comparative screen identified the fibroblast growth factor 13 (FGF13) as a target of CD271, highly expressed in chemoresistant cells. Further we show that levels of CD271 determine drug response. Knock-down of CD271 in fotemustine-resistant cells decreased expression of FGF13 and at least partly restored sensitivity to fotemustine. Together, we demonstrate that expression of CD271 is responsible for genes associated with DNA repair and drug response. Further, we identified 110 CD271-responsive genes predominantly expressed in melanoma metastases, among them were NEK2, TOP2A and RAD51AP1 as potential drivers of melanoma metastasis. In addition, we provide mechanistic insight in the regulation of CD271 in response to drugs. We found that CD271 is potentially regulated by p53 and in turn is needed for a proper p53-dependent response to DNA-damaging drugs. In summary, we provide for the first time insight in a CD271-associated signaling network connecting CD271 with DNA repair, drug response and metastasis.

Hypoxia-induced gene expression results from selective mRNA partitioning to the endoplasmic reticulum

Protein synthesis is a primary energy-consuming process in the cell. Therefore, under hypoxic conditions, rapid inhibition of global mRNA translation represents a major protective strategy to maintain energy metabolism. How some mRNAs, especially those that encode crucial survival factors, continue to be efficiently translated in hypoxia is not completely understood. By comparing specific transcript levels in ribonucleoprotein complexes, cytoplasmic polysomes and endoplasmic reticulum (ER)-bound ribosomes, we show that the synthesis of proteins encoded by hypoxia marker genes is favoured at the ER in hypoxia. Gene expression profiling revealed that transcripts particularly increased by the HIF-1 transcription factor network show hypoxia-induced enrichment at the ER. We found that mRNAs favourably translated at the ER have higher conservation scores for both the 5′- and 3′-untranslated regions (UTRs) and contain less upstream initiation codons (uAUGs), indicating the significance of these sequence elements for sustained mRNA translation under hypoxic conditions. Furthermore, we found enrichment of specific cis-elements in mRNA 5′- as well as 3′-UTRs that mediate transcript localization to the ER in hypoxia. We conclude that transcriptome partitioning between the cytoplasm and the ER permits selective mRNA translation under conditions of energy shortage.

Consequences of feedback in signal transduction for targeted therapies.

Over the last two decades, many small-molecule inhibitors that target kinase signalling have been developed. More than 20 of these inhibitors are FDA (U.S. Food and Drug Administration)-approved and are now being used in the clinics to treat tumours; even more have entered clinical trials. However, resistance to these inhibitors, either intrinsic to the tumour or acquired during treatment, remains a major problem in targeted therapeutics. One of the mechanisms by which tumours become resistant is the rewiring of the signalling networks via feedback, by which the tumour cells re-activate signalling or activate alternative signalling pathways. In the present article, we review insights from recent quantitative signalling studies combining mathematical modelling and experiments that revealed how feedback rewires MAPK (mitogen-activated protein kinase)/PI3K (phosphoinositide 3-kinase) signalling upon treatment and how that affects drug sensitivity.

Computer-assisted curation of a human regulatory core network from the biological literature

MOTIVATION A highly interlinked network of transcription factors (TFs) orchestrates the context-dependent expression of human genes. ChIP-chip experiments that interrogate the binding of particular TFs to genomic regions are used to reconstruct gene regulatory networks at genome-scale, but are plagued by high false-positive rates. Meanwhile, a large body of knowledge on high-quality regulatory interactions remains largely unexplored, as it is available only in natural language descriptions scattered over millions of scientific publications. Such data are hard to extract and regulatory data currently contain together only 503 regulatory relations between human TFs. RESULTS We developed a text-mining-assisted workflow to systematically extract knowledge about regulatory interactions between human TFs from the biological literature. We applied this workflow to the entire Medline, which helped us to identify more than 45 000 sentences potentially describing such relationships. We ranked these sentences by a machine-learning approach. The top-2500 sentences contained ∼900 sentences that encompass relations already known in databases. By manually curating the remaining 1625 top-ranking sentences, we obtained more than 300 validated regulatory relationships that were not present in a regulatory database before. Full-text curation allowed us to obtain detailed information on the strength of experimental evidences supporting a relationship. CONCLUSIONS We were able to increase curated information about the human core transcriptional network by >60% compared with the current content of regulatory databases. We observed improved performance when using the network for disease gene prioritization compared with the state-of-the-art. AVAILABILITY AND IMPLEMENTATION Web-service is freely accessible at http://fastforward.sys-bio.net/. CONTACT leser@informatik.hu-berlin.de or nils.bluethgen@charite.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.