Pan-Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival

SignalingProfiler 2.0 a network-based approach to bridge multi-omics data to phenotypic hallmarks

Unraveling how cellular signaling is remodeled upon perturbation is crucial for understanding disease mechanisms and identifying potential drug targets. In this pursuit, computational tools generating mechanistic hypotheses from multi-omics data have invaluable potential. Here, we present a newly implemented version (2.0) of SignalingProfiler, a multi-step pipeline to draw mechanistic hypotheses on the signaling events impacting cellular phenotypes. SignalingProfiler 2.0 derives context-specific signaling networks by integrating proteogenomic data with the prior knowledge-causal network. This is a freely accessible and flexible tool that incorporates statistical, footprint-based, and graph algorithms to accelerate the integration and interpretation of multi-omics data. Through a benchmarking process on three proof-of-concept studies, we demonstrate the tool's ability to generate hierarchical mechanistic networks recapitulating novel and known perturbed signaling and phenotypic outcomes, in both human and mice contexts. In summary, SignalingProfiler 2.0 addresses the emergent need to derive biologically relevant information from complex multi-omics data by extracting interpretable networks.

Predicted mechanistic impacts of human protein missense variants

Genome sequencing efforts have led to the discovery of tens of millions of protein missense variants found in the human population with the majority of these having no annotated role and some likely contributing to trait variation and disease. Sequence-based artificial intelligence approaches have become highly accurate at predicting variants that are detrimental to the function of proteins but they do not inform on mechanisms of disruption. Here we combined sequence and structure-based methods to perform proteome-wide prediction of deleterious variants with information on their impact on protein stability, protein-protein interactions and small-molecule binding pockets. AlphaFold2 structures were used to predict approximately 100,000 small-molecule binding pockets and stability changes for over 200 million variants. To inform on protein-protein interfaces we used AlphaFold2 to predict structures for nearly 500,000 protein complexes. We illustrate the value of mechanism-aware variant effect predictions to study the relation between protein stability and abundance and the structural properties of interfaces underlying trans protein quantitative trait loci (pQTLs). We characterised the distribution of mechanistic impacts of protein variants found in patients and experimentally studied example disease linked variants in FGFR1.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

NB compounds are potent and efficacious FOXM1 inhibitors in high-grade serous ovarian cancer cells

BACKGROUND

Genetic studies implicate the oncogenic transcription factor Forkhead Box M1 (FOXM1) as a potential therapeutic target in high-grade serous ovarian cancer (HGSOC). We evaluated the activity of different FOXM1 inhibitors in HGSOC cell models.

RESULTS

We treated HGSOC and fallopian tube epithelial (FTE) cells with a panel of previously reported FOXM1 inhibitors. Based on drug potency, efficacy, and selectivity, determined through cell viability assays, we focused on two compounds, NB-73 and NB-115 (NB compounds), for further investigation. NB compounds potently and selectively inhibited FOXM1 with lesser effects on other FOX family members. NB compounds decreased FOXM1 expression via targeting the FOXM1 protein by promoting its proteasome-mediated degradation, and effectively suppressed FOXM1 gene targets at both the protein and mRNA level. At the cellular level, NB compounds promoted apoptotic cell death. Importantly, while inhibition of apoptosis using a pan-caspase inhibitor rescued HGSOC cells from NB compound-induced cell death, it did not rescue FOXM1 protein degradation, supporting that FOXM1 protein loss from NB compound treatment is specific and not a general consequence of cytotoxicity. Drug washout studies indicated that FOXM1 reduction was retained for at least 72 h post-treatment, suggesting that NB compounds exhibit long-lasting effects in HGSOC cells. NB compounds effectively suppressed both two-dimensional and three-dimensional HGSOC cell colony formation at sub-micromolar concentrations. Finally, NB compounds exhibited synergistic activity with carboplatin in HGSOC cells.

CONCLUSIONS

NB compounds are potent, selective, and efficacious inhibitors of FOXM1 in HGSOC cells and are worthy of further investigation as HGSOC therapeutics.

Cell cycle-dependent gene networks for cell proliferation activated by nuclear CK2α complexes

Nuclear expression of protein kinase CK2α is reportedly elevated in human carcinomas, but mechanisms underlying its variable localization in cells are poorly understood. This study demonstrates a functional connection between nuclear CK2 and gene expression in relation to cell proliferation. Growth stimulation of quiescent human normal fibroblasts and phospho-proteomic analysis identified a pool of CK2α that is highly phosphorylated at serine 7. Phosphorylated CK2α translocates into the nucleus, and this phosphorylation appears essential for nuclear localization and catalytic activity. Protein signatures associated with nuclear CK2 complexes reveal enrichment of apparently unique transcription factors and chromatin remodelers during progression through the G1 phase of the cell cycle. Chromatin immunoprecipitation-sequencing profiling demonstrated recruitment of CK2α to active gene loci, more abundantly in late G1 phase than in early G1, notably at transcriptional start sites of core histone genes, growth stimulus-associated genes, and ribosomal RNAs. Our findings reveal that nuclear CK2α complexes may be essential to facilitate progression of the cell cycle, by activating histone genes and triggering ribosomal biogenesis, specified in association with nuclear and nucleolar transcriptional regulators.

Trellis tree-based analysis reveals stromal regulation of patient-derived organoid drug responses

Patient-derived organoids (PDOs) can model personalized therapy responses; however, current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly multiplexed mass cytometry platform to measure post-translational modification (PTM) signaling, DNA damage, cell-cycle activity, and apoptosis in >2,500 colorectal cancer (CRC) PDOs and cancer-associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell datasets, we developed "Trellis"-a highly scalable, tree-based treatment effect analysis method. Trellis single-cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rarer, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate PDO plasticity-shifting proliferative colonic stem cells (proCSCs) to slow-cycling revival colonic stem cells (revCSCs) to protect cancer cells from chemotherapy.

Gene essentiality in cancer is better predicted by mRNA abundance than by gene regulatory network-inferred activity

Gene regulatory networks (GRNs) are often deregulated in tumor cells, resulting in altered transcriptional programs that facilitate tumor growth. These altered networks may make tumor cells vulnerable to the inhibition of specific regulatory proteins. Consequently, the reconstruction of GRNs in tumors is often proposed as a means to identify therapeutic targets. While there are examples of individual targets identified using GRNs, the extent to which GRNs can be used to predict sensitivity to targeted intervention in general remains unknown. Here we use the results of genome-wide CRISPR screens to systematically assess the ability of GRNs to predict sensitivity to gene inhibition in cancer cell lines. Using GRNs derived from multiple sources, including GRNs reconstructed from tumor transcriptomes and from curated databases, we infer regulatory gene activity in cancer cell lines from ten cancer types. We then ask, in each cancer type, if the inferred regulatory activity of each gene is predictive of sensitivity to CRISPR perturbation of that gene. We observe slight variation in the correlation between gene regulatory activity and gene sensitivity depending on the source of the GRN and the activity estimation method used. However, we find that there is consistently a stronger relationship between mRNA abundance and gene sensitivity than there is between regulatory gene activity and gene sensitivity. This is true both when gene sensitivity is treated as a binary and a quantitative property. Overall, our results suggest that gene sensitivity is better predicted by measured expression than by GRN-inferred activity.

Repurposing Synthetic Congeners of a Natural Product Aurone Unveils a Lead Antitumor Agent Inhibiting Folded P-Loop Conformation of MET Receptor Tyrosine Kinase

A library of 24 congeners of the natural product sulfuretin were evaluated against nine panels representing nine cancer diseases. While sulfuretin elicited very weak activities at 10 µM concentration, congener 1t was identified as a potential compound triggering growth inhibition of diverse cell lines. Mechanistic studies in HCT116 colon cancer cells revealed that congener 1t dose-dependently increased levels of cleaved-caspases 8 and 9 and cleaved-PARP, while it concentration-dependently decreased levels of CDK4, CDK6, Cdc25A, and Cyclin D and E resulting in induction of cell cycle arrest and apoptosis in colon cancer HCT116 cells. Mechanistic study also presented MET receptor tyrosine kinase as the molecular target mediating the anticancer activity of compound 1t in HCT116 cells. In silico study predicted folded p-loop conformation as the form of MET receptor tyrosine kinase responsible for binding of compound 1t. Together, the current study presents compound 1t as an interesting anticancer lead for further development.

Gene regulatory network inference in the era of single-cell multi-omics

The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.

Principles of phosphoproteomics and applications in cancer research

Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.