Motivation
Signal transduction networks regulate many essential biological processes and are frequently aberrated in diseases such as cancer. A mechanistic understanding of such networks, and how they differ between cell populations, is essential to design effective treatment strategies. Typically, such networks are computationally reconstructed based on systematic perturbation experiments, followed by quantification of signaling protein activity. Recent technological advances now allow for the quantification of the activity of many (signaling) proteins simultaneously in single cells. This makes it feasible to reconstruct or quantify signaling networks without performing systematic perturbations.

Results
Here, we introduce single-cell modular response analysis (scMRA) and single-cell comparative network reconstruction (scCNR) to derive signal transduction networks by exploiting the heterogeneity of single-cell (phospho-)protein measurements. The methods treat stochastic variation in total protein abundances as natural perturbation experiments, whose effects propagate through the network and hence facilitate the reconstruction and quantification of the underlying signaling network. scCNR reconstructs cell population-specific networks, where cells from different populations have the same underlying topology, but the interaction strengths can differ between populations. We extensively validated scMRA and scCNR on simulated data, and applied it to unpublished data of (phospho-)protein measurements of EGFR-inhibitor-treated keratinocytes to recover signaling differences downstream of EGFR. scCNR will help to unravel the mechanistic signaling differences between cell populations, and will subsequently guide the development of well-informed treatment strategies.

Targeted inhibition of aberrant signaling is an important treatment strategy in cancer, but responses are often short-lived. Multi-drug combinations have the potential to mitigate this, but to avoid toxicity such combinations must be selective and given at low dosages. Here, we present a pipeline to identify promising multi-drug combinations. We perturbed an isogenic PI3K mutant and wild-type cell line pair with a limited set of drugs and recorded their signaling state and cell viability. We then reconstructed their signaling networks and mapped the signaling response to changes in cell viability. The resulting models, which allowed us to predict the effect of unseen combinations, indicated that no combination selectively reduces the viability of the PI3K mutant cells. However, we were able to validate 25 of the 30 combinations that we predicted to be anti-selective. Our pipeline enables efficient prioritization of multi-drug combinations from the enormous search space of possible combinations.

Many organisms have several similar transporters with different affinities for the same substrate. Typically, high-affinity transporters are expressed when substrate is scarce and low-affinity ones when it is abundant. The benefit of using low instead of high-affinity transporters remains unclear, especially when additional nutrient sensors are present. Here, we investigate two hypotheses. It was previously hypothesized that there is a trade-off between the affinity and the catalytic efficiency of transporters, and we find some but no definitive support for it. Additionally, we propose that for uptake by facilitated diffusion, at saturating substrate concentrations, lowering the affinity enhances the net uptake rate by reducing substrate efflux. As a consequence, there exists an optimal, external-substrate-concentration dependent transporter affinity. A computational model of Saccharomyces cerevisiae glycolysis shows that using the low affinity HXT3 transporter instead of the high affinity HXT6 enhances the steady-state flux by 36%. We tried to test this hypothesis with yeast strains expressing a single glucose transporter modified to have either a high or a low affinity. However, due to the intimate link between glucose perception and metabolism, direct experimental proof for this hypothesis remained inconclusive. Still, our theoretical results provide a novel reason for the presence of low-affinity transport systems.

Intrinsic and/or acquired resistance represents one of the great challenges in targeted cancer therapy. A deeper understanding of the molecular biology of cancer has resulted in more efficient strategies, where one or multiple drugs are adopted in novel therapies to tackle resistance. This beneficial effect of using combination treatments has also been observed in colorectal cancer patients harboring the BRAF(V600E) mutation, whereby dual inhibition of BRAF(V600E) and EGFR increases antitumor activity. Notwithstanding this success, it is not clear whether this combination treatment is the only or most effective treatment to block intrinsic resistance to BRAF inhibitors. Here, we investigate molecular responses upon single and multi-target treatments, over time, using BRAF(V600E) mutant colorectal cancer cells as a model system. Through integration of transcriptomic, proteomic and phosphoproteomics data we obtain a comprehensive overview, revealing both known and novel responses. We primarily observe widespread up-regulation of receptor tyrosine kinases and metabolic pathways upon BRAF inhibition. These findings point to mechanisms by which the drug-treated cells switch energy sources and enter a quiescent-like state as a defensive response, while additionally compensating for the MAPK pathway inhibition.

Motivation Signal-transduction networks are often aberrated in cancer cells, and new anti-cancer drugs that specifically target oncogenes involved in signaling show great clinical promise. However, the effectiveness of such targeted treatments is often hampered by innate or acquired resistance due to feedbacks, crosstalks or network adaptations in response to drug treatment. A quantitative understanding of these signaling networks and how they differ between cells with different oncogenic mutations or between sensitive and resistant cells can help in addressing this problem. Results Here, we present Comparative Network Reconstruction (CNR), a computational method to reconstruct signaling networks based on possibly incomplete perturbation data, and to identify which edges differ quantitatively between two or more signaling networks. Prior knowledge about network topology is not required but can straightforwardly be incorporated. We extensively tested our approach using simulated data and applied it to perturbation data from a BRAF mutant, PTPN11 KO cell line that developed resistance to BRAF inhibition. Comparing the reconstructed networks of sensitive and resistant cells suggests that the resistance mechanism involves re-establishing wild-type MAPK signaling, possibly through an alternative RAF-isoform. Availability and implementation CNR is available as a python module at https://github.com/NKI-CCB/cnr. Additionally, code to reproduce all figures is available at https://github.com/NKI-CCB/CNR-analyses. Supplementary information Supplementary data are available at Bioinformatics online.