Reverse engineering signalling networks in cancer cells

Obwohl die Krebstherapie im letzten Jahrhundert große Fortschritte gemacht hat, bleibt die Resistenz gegen medikamentöse Behandlungen ein großes Hindernis im Kampf gegen den Krebs. In dieser Arbeit habe ich ein R-Paket namens STASNet entwickelt, das semi-quantitative Modelle der Signaltransduktion aus Signalisierungs-Störungsantwortdaten unter Verwendung von Least Square Modular Response Analysis-Modellen generiert. Um zu untersuchen, wie gut STASNet die Aktivität von Signalwegen quantifizieren kann, haben wir Perturbationsdaten von einem Paar isogener Darmkrebszelllinien mit und ohne SHP2-Knock-out, einem bekannten Resistenzmechanismus bei dieser Krebsart, verwendet. Ich habe dann untersucht die Resistenz gegen die MEK- und ALK-Hemmung beim Neuroblastom, einem pädiatrischen Krebs mit schlechter Prognose. Ein Wirkstoffscreening zeigte, dass der MEK-Inhibitor Selumetinib ein Panel von Neuroblastom-Zelllinien in drei sensitive und sechs resistente Zelllinien trennte, dass konnte nicht mit einzelnen molekularen Markern erklärt. STASNet-Modelle zeigten, dass die starke Resistenz gegen Selumetinib durch eine starke Rückkopplung von ERK auf MEK oder eine vielschichtige Rückkopplung sowohl auf MEK als auch auf IGF1R getrieben wurde. Aus dem Modell konnte eine kombinatorische Therapie abgeleitet werden, die auf MEK in Kombination mit entweder RAF oder IGF1R abzielt, je nach Art der in der Zelllinie vorhandenen Rückkopplungen. Schließlich ergab die Untersuchung der Wirkung von NF1-KO auf die Signalübertragung, dass der Verlust von NF1 den MAPK-Weg für die Liganden-induzierte Aktivierung hypersensibilisierte, aber das ERK-RAF-Rückkopplung störte. Die Erkenntnisse aus den in dieser Arbeit entwickelten Modellen werden somit dazu beitragen, personalisierte Kombinationen von Inhibitoren zu entwerfen, die als Zweitlinientherapie nach molekularer Untersuchung der Tumorreaktion auf die Erstbehandlung eingesetzt werden könnten.Cancer therapy has seen immense progress over the last century but resistance to drug treatments remains a major obstacle in the war against cancer. I developed an R package named STASNet to generate models of signal transduction from signalling perturbation-response data using Least Square Modular Response Analysis models. I used these models to study how differences in signal transduction relate to drug resistance and can be used to make predictions about resistance mechanisms and optimal treatments. To show how STASNet can accurately quantify the activity of signalling pathways, I used perturbation data from a pair of isogenic colon cancer cell line with and without SHP2 knock-out, a known resistance mechanism in this cancer type, which showed that MAPK signalling is more affected by SHP2 knock-out than PI3K signalling, confirming the role of SHP2 as a primary MAPK component. I investigated resistance to MEK and ALK inhibition in neuroblastoma, a pediatric cancer with a dismal prognosis. The MEK inhibitor Selumetinib separated a panel of neuroblastoma cell lines into three sensitive and six resistant cell lines that could not be explained with individual molecular markers. STASNet models trained on perturbation-response data from these cell lines revealed that the strong resistance to Selumetinib was driven by a strong feedback from ERK to MEK or a multi-layered feedback to both MEK and IGF1R. This was confirmed by phosphoproteomics and suggested a therapy targeting MEK in combination with either RAF or IGF1R depending on the type of feedback present in the cell line that was confirmed experimentally. Finally, studying the effect of NF1-KO on signalling revealed that the loss of NF1 hyper-sensitized the MAPK pathway to ligand-induced activation but disrupted the ERK-RAF feedback. Those insights to design personalized combinations of inhibitors that could be used as second line therapy after molecularly monitoring the tumor response to the initial treatment

a web application to create interactive molecular network portraits using multi-level omics data

Human diseases such as cancer are routinely characterized by high-throughput molecular technologies, and multi-level omics data are accumulated in public databases at increasing rate. Retrieval and visualization of these data in the context of molecular network maps can provide insights into the pattern of regulation of molecular functions reflected by an omics profile. In order to make this task easy, we developed NaviCom, a Python package and web platform for visualization of multi-level omics data on top of biological network maps. NaviCom is bridging the gap between cBioPortal, the most used resource of large-scale cancer omics data and NaviCell, a data visualization web service that contains several molecular network map collections. NaviCom proposes several standardized modes of data display on top of molecular network maps, allowing addressing specific biological questions. We illustrate how users can easily create interactive network-based cancer molecular portraits via NaviCom web interface using the maps of Atlas of Cancer Signalling Network (ACSN) and other maps. Analysis of these molecular portraits can help in formulating a scientific hypothesis on the molecular mechanisms deregulated in the studied disease

Mutations in ALK signaling pathways conferring resistance to ALK inhibitor treatment lead to collateral vulnerabilities in neuroblastoma cells

Background: Development of resistance to targeted therapies has tempered initial optimism that precision oncology would improve poor outcomes for cancer patients. Resistance mechanisms, however, can also confer new resistance-specific vulnerabilities, termed collateral sensitivities. Here we investigated anaplastic lymphoma kinase (ALK) inhibitor resistance in neuroblastoma, a childhood cancer frequently affected by activating ALK alterations. Methods: Genome-wide forward genetic CRISPR-Cas9 based screens were performed to identify genes associated with ALK inhibitor resistance in neuroblastoma cell lines. Furthermore, the neuroblastoma cell line NBLW-R was rendered resistant by continuous exposure to ALK inhibitors. Genes identified to be associated with ALK inhibitor resistance were further investigated by generating suitable cell line models. In addition, tumor and liquid biopsy samples of four patients with ALK-mutated neuroblastomas before ALK inhibitor treatment and during tumor progression under treatment were genomically profiled. Results: Both genome-wide CRISPR-Cas9-based screens and preclinical spontaneous ALKi resistance models identified NF1 loss and activating NRASQ61K mutations to confer resistance to chemically diverse ALKi. Moreover, human neuroblastomas recurrently developed de novo loss of NF1 and activating RAS mutations after ALKi treatment, leading to therapy resistance. Pathway-specific perturbations confirmed that NF1 loss and activating RAS mutations lead to RAS-MAPK signaling even in the presence of ALKi. Intriguingly, NF1 loss rendered neuroblastoma cells hypersensitive to MEK inhibition. Conclusions: Our results provide a clinically relevant mechanistic model of ALKi resistance in neuroblastoma and highlight new clinically actionable collateral sensitivities in resistant cells

Modeling unveils sex differences of signaling networks in mouse embryonic stem cells

Abstract For a short period during early development of mammalian embryos, both X chromosomes in females are active, before dosage compensation is ensured through X‐chromosome inactivation. In female mouse embryonic stem cells (mESCs), which carry two active X chromosomes, increased X‐dosage affects cell signaling and impairs differentiation. The underlying mechanisms, however, remain poorly understood. To dissect X‐dosage effects on the signaling network in mESCs, we combine systematic perturbation experiments with mathematical modeling. We quantify the response to a variety of inhibitors and growth factors for cells with one (XO) or two X chromosomes (XX). We then build models of the signaling networks in XX and XO cells through a semi‐quantitative modeling approach based on modular response analysis. We identify a novel negative feedback in the PI3K/AKT pathway through GSK3. Moreover, the presence of a single active X makes mESCs more sensitive to the differentiation‐promoting Activin A signal and leads to a stronger RAF1‐mediated negative feedback in the FGF‐triggered MAPK pathway. The differential response to these differentiation‐promoting pathways can explain the impaired differentiation propensity of female mESCs

Modelling signalling networks from perturbation data

Motivation: Intracellular signalling is realized by complex signalling networks, which are almost impossible to understand without network models, especially if feedbacks are involved. Modular Response Analysis (MRA) is a convenient modelling method to study signalling networks in various contexts. Results: We developed the software package STASNet (STeady-STate Analysis of Signalling Networks) that provides an augmented and extended version of MRA suited to model signalling networks from incomplete perturbation schemes and multi-perturbation data. Using data from the Dialogue on Reverse Engineering Assessment and Methods challenge, we show that predictions from STASNet models are among the top-performing methods. We applied the method to study the effect of SHP2, a protein that has been implicated in resistance to targeted therapy in colon cancer, using a novel dataset from the colon cancer cell line Widr and a SHP2-depleted derivative. We find that SHP2 is required for mitogen-activated protein kinase signalling, whereas AKT signalling only partially depends on SHP2. Availability and implementation: An R-package is available at https://github.com/molsysbio/STASNet. Supplementary information: Supplementary data are available at Bioinformatics online

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

Isoform-specific Ras signaling is growth factor dependent

From Crossref via Jisc Publications RouterHistory: ppub 2019-04-15, issued 2019-04-15HRAS, NRAS, and KRAS isoforms are almost identical proteins that are ubiquitously expressed and activate a common set of effectors. In vivo studies have revealed that they are not biologically redundant; however, the isoform specificity of Ras signaling remains poorly understood. Using a novel panel of isogenic SW48 cell lines endogenously expressing wild-type or G12V-mutated activated Ras isoforms, we have performed a detailed characterization of endogenous isoform-specific mutant Ras signaling. We find that despite displaying significant Ras activation, the downstream outputs of oncogenic Ras mutants are minimal in the absence of growth factor inputs. The lack of mutant KRAS-induced effector activation observed in SW48 cells appears to be representative of a broad panel of colon cancer cell lines harboring mutant KRAS. For MAP kinase pathway activation in KRAS-mutant cells, the requirement for coincident growth factor stimulation occurs at an early point in the Raf activation cycle. Finally, we find that Ras isoform-specific signaling was highly context dependent and did not conform to the dogma derived from ectopic expression studies

Mutations in ALK signaling pathways conferring resistance to ALK inhibitor treatment lead to collateral vulnerabilities in neuroblastoma cells.

BACKGROUND: Development of resistance to targeted therapies has tempered initial optimism that precision oncology would improve poor outcomes for cancer patients. Resistance mechanisms, however, can also confer new resistance-specific vulnerabilities, termed collateral sensitivities. Here we investigated anaplastic lymphoma kinase (ALK) inhibitor resistance in neuroblastoma, a childhood cancer frequently affected by activating ALK alterations. METHODS: Genome-wide forward genetic CRISPR-Cas9 based screens were performed to identify genes associated with ALK inhibitor resistance in neuroblastoma cell lines. Furthermore, the neuroblastoma cell line NBLW-R was rendered resistant by continuous exposure to ALK inhibitors. Genes identified to be associated with ALK inhibitor resistance were further investigated by generating suitable cell line models. In addition, tumor and liquid biopsy samples of four patients with ALK-mutated neuroblastomas before ALK inhibitor treatment and during tumor progression under treatment were genomically profiled. RESULTS: Both genome-wide CRISPR-Cas9-based screens and preclinical spontaneous ALKi resistance models identified NF1 loss and activating NRASQ61K mutations to confer resistance to chemically diverse ALKi. Moreover, human neuroblastomas recurrently developed de novo loss of NF1 and activating RAS mutations after ALKi treatment, leading to therapy resistance. Pathway-specific perturbations confirmed that NF1 loss and activating RAS mutations lead to RAS-MAPK signaling even in the presence of ALKi. Intriguingly, NF1 loss rendered neuroblastoma cells hypersensitive to MEK inhibition. CONCLUSIONS: Our results provide a clinically relevant mechanistic model of ALKi resistance in neuroblastoma and highlight new clinically actionable collateral sensitivities in resistant cells