Large-scale datasets uncovering cell signalling networks in cancer: context matters

A single cell-based computational platform to identify chemical compounds targeting desired sets of transcription factors for cellular conversion

Cellular conversion can be induced by perturbing a handful of key transcription factors (TFs). Replacement of direct manipulation of key TFs with chemical compounds offers a less laborious and safer strategy to drive cellular conversion for regenerative medicine. Nevertheless, identifying optimal chemical compounds currently requires large-scale screening of chemical libraries, which is resource intensive. Existing computational methods aim at predicting cell conversion TFs, but there are no methods for identifying chemical compounds targeting these TFs. Here, we develop a single cell-based platform (SiPer) to systematically prioritize chemical compounds targeting desired TFs to guide cellular conversions. SiPer integrates a large compendium of chemical perturbations on non-cancer cells with a network model and predicted known and novel chemical compounds in diverse cell conversion examples. Importantly, we applied SiPer to develop a highly efficient protocol for human hepatic maturation. Overall, SiPer provides a valuable resource to efficiently identify chemical compounds for cell conversion.

ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells

Prior knowledge of perturbation data can significantly assist in inferring the relationship between chemical perturbations and their specific transcriptional response. However, current databases mostly contain cancer cell lines, which are unsuitable for the aforementioned inference in non-cancer cells, such as cells related to non-cancer disease, immunology and aging. Here, we present ChemPert (https://chempert.uni.lu/), a database consisting of 82 270 transcriptional signatures in response to 2566 unique perturbagens (drugs, small molecules and protein ligands) across 167 non-cancer cell types, as well as the protein targets of 57 818 perturbagens. In addition, we develop a computational tool that leverages the non-cancer cell datasets, which enables more accurate predictions of perturbation responses and drugs in non-cancer cells compared to those based onto cancer databases. In particular, ChemPert correctly predicted drug effects for treating hepatitis and novel drugs for osteoarthritis. The ChemPert web interface is user-friendly and allows easy access of the entire datasets and the computational tool, providing valuable resources for both experimental researchers who wish to find datasets relevant to their research and computational researchers who need comprehensive non-cancer perturbation transcriptomics datasets for developing novel algorithms. Overall, ChemPert will facilitate future in silico compound screening for non-cancer cells.

Tumor Drug Concentration and Phosphoproteomic Profiles After Two Weeks of Treatment With Sunitinib in Patients with Newly Diagnosed Glioblastoma

PURPOSE

Tyrosine kinase inhibitors (TKI) have poor efficacy in patients with glioblastoma (GBM). Here, we studied whether this is predominantly due to restricted blood-brain barrier penetration or more to biological characteristics of GBM.

PATIENTS AND METHODS

Tumor drug concentrations of the TKI sunitinib after 2 weeks of preoperative treatment was determined in 5 patients with GBM and compared with its in vitro inhibitory concentration (IC50) in GBM cell lines. In addition, phosphotyrosine (pTyr)-directed mass spectrometry (MS)-based proteomics was performed to evaluate sunitinib-treated versus control GBM tumors.

RESULTS

The median tumor sunitinib concentration of 1.9 μmol/L (range 1.0-3.4) was 10-fold higher than in concurrent plasma, but three times lower than sunitinib IC50s in GBM cell lines (median 5.4 μmol/L, 3.0-8.5; P = 0.01). pTyr-phosphoproteomic profiles of tumor samples from 4 sunitinib-treated versus 7 control patients revealed 108 significantly up- and 23 downregulated (P < 0.05) phosphopeptides for sunitinib treatment, resulting in an EGFR-centered signaling network. Outlier analysis of kinase activities as a potential strategy to identify drug targets in individual tumors identified nine kinases, including MAPK10 and INSR/IGF1R.

CONCLUSIONS

Achieved tumor sunitinib concentrations in patients with GBM are higher than in plasma, but lower than reported for other tumor types and insufficient to significantly inhibit tumor cell growth in vitro. Therefore, alternative TKI dosing to increase intratumoral sunitinib concentrations might improve clinical benefit for patients with GBM. In parallel, a complex profile of kinase activity in GBM was found, supporting the potential of (phospho)proteomic analysis for the identification of targets for (combination) treatment.

Identification of phenotype-specific networks from paired gene expression-cell shape imaging data

The morphology of breast cancer cells is often used as an indicator of tumor severity and prognosis. Additionally, morphology can be used to identify more fine-grained, molecular developments within a cancer cell, such as transcriptomic changes and signaling pathway activity. Delineating the interface between morphology and signaling is important to understand the mechanical cues that a cell processes in order to undergo epithelial-to-mesenchymal transition and consequently metastasize. However, the exact regulatory systems that define these changes remain poorly characterized. In this study, we used a network-systems approach to integrate imaging data and RNA-seq expression data. Our workflow allowed the discovery of unbiased and context-specific gene expression signatures and cell signaling subnetworks relevant to the regulation of cell shape, rather than focusing on the identification of previously known, but not always representative, pathways. By constructing a cell-shape signaling network from shape-correlated gene expression modules and their upstream regulators, we found central roles for developmental pathways such as WNT and Notch, as well as evidence for the fine control of NF-kB signaling by numerous kinase and transcriptional regulators. Further analysis of our network implicates a gene expression module enriched in the RAP1 signaling pathway as a mediator between the sensing of mechanical stimuli and regulation of NF-kB activity, with specific relevance to cell shape in breast cancer.