Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Orbitrap Fusion Lumos
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Andrea Califano
PROVIDER: MSV000091204 | MassIVE | Sat Feb 04 13:19:00 GMT 2023
SECONDARY ACCESSION(S): PXD039859
REPOSITORIES: MassIVE
Similar Datasets
Project description: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. By leveraging progress in proteomic technologies and network-based methodologies over the past decade we developed VESPA—an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations—and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.
2023-03-09 | GSE224396 | GEO
Project description:With the emergence of various drug-based treatment strategies, many drug response prediction models have been developed to understand their effects. However, in order to gain comprehensive understanding of a drug response, a prediction model should reflect the underlying biological mechanisms, but the current models suffer from interpretability and scalability problems. Machine learning-based prediction models base their predictions on inferred features, which usually are not well correlated with biological mechanisms, posing a challenge on interpretability of its response predictions. In this regard, using Boolean modeling schemes may allow interpretations on mechanisms that contribute to a particular response, but optimizing Boolean models is difficult because of their high dimensional search space and discontinuous loss function. Here, we developed a scalable derivative-free optimizer for weighted sum Boolean network through meta-reinforcement learning. By using graph network and coordinate-wise policy, our learned optimizer can optimize high dimensional Boolean networks containing over 100 parameters of arbitrary structure, showing higher sample efficiency compared with other meta-heuristic algorithms. The optimized Boolean networks successfully predict the drug responses congruent with public databases and in-house experimental data. Moreover, mechanistic analysis of optimized networks shows reliable interpretability of the predictions by meaningful suggestions of known basket trial drug response prediction markers.
2024-04-27 | GSE184731 | GEO
Project description:Recent data demonstrate that extracellular signals are transmitted through a network of proteins rather than hierarchical signaling pathways. This network model suggests why inhibition of a single component of a canonical pathway, even when targeting a mutationally activated driver of cancer, has insufficiently dramatic effects on the treatment of cancer. The biological outcome of signals propagated through a network is inherently more robust and resistant to inhibition of a single network component due to compensatory and redundant signaling events. In this study, we performed a functional chemical genetic screen analogous to synthetic lethal screening in yeast genetics to identify novel interactions between signaling inhibitors that would not be predicted based on our current understanding of signaling networks. We screened over 300 drug combinations in nine melanoma cell lines and have identified pairs of compounds that show synergistic cytotoxicity. Among the most robust and surprising results was synergy between sorafenib, a multi-kinase inhibitor with activity against Raf, and diclofenac, a non-steroidal anti-inflammatory drug (NSAID). This synergy did not correlate with the known RAS and BRAF mutational status of the melanoma cell lines. The NSAIDs celecoxib and ibuprofen could qualitatively substitute for diclofenac. Similarly, the MEK inhibitor PD325901 and the Raf inhibitor RAF265 could qualitatively substitute for sorafenib. These drug substitution experiments suggest that inhibition of cyclo-oxygenase and MAP kinase signaling are components of the observed synergistic cytotoxicity. Genome-wide expression profiling demonstrates synergy-specific down-regulation of survival-related genes. This study provides proof of principle that synthetic lethal screening can uncover novel functional drug combinations and suggests that the underlying signaling networks that control responses to targeted agents can vary substantially depending on unexplored components of the cell genotype. RNA from VMM39, DM331, and SLM2 cells with/without mutations in Ras and/or Braf, treated with Sorafenib and/or Diclofenac.
2012-12-06 | E-GEOD-39192 | biostudies-arrayexpress
Project description:Aberrant activation of signaling pathways controlled in normal epithelial cells by the epidermal growth factor receptor (EGFR) has been linked to cetuximab (a monoclonal antibody against EGFR) resistance in head and neck squamous cell carcinoma (HNSCC). To infer relevant and specific pathway activation downstream of EGFR from gene expression in HNSCC, we generated gene expression signatures using immortalized keratinocytes (HaCaT) subjected to either ligand stimulation or pharmacological inhibition of the signaling intermediaries PI-3-Kinase and MEK or transfected with EGFR, RELA/p65, or HRASVal12. The gene expression patterns that distinguished the various HaCaT variants and conditions were inferred using the Markov chain Monte Carlo (MCMC) matrix factorization algorithm Coordinated Gene Activity in Pattern Sets (CoGAPS). This approach inferred gene expression signatures with greater relevance to cell signaling pathway activation than the expression signatures inferred with standard linear models. Furthermore, the pathway signature generated using HaCaT-HRASVal12 further associated with the cetuximab treatment response in isogenic cetuximab-sensitive (UMSCC1) and -resistant (1CC8) cell lines. Our data suggest that the CoGAPS algorithm can generate gene expression signatures that are pertinent to downstream effects of receptor signaling pathway activation and potentially be useful in modeling resistance mechanisms to targeted therapies. 58 total RNA collected from HaCaT cell lines with combinations of the following experimental conditions: forced expression of EGFR, RELA/p65, and HRAS-VAL12D; grown in PBS, serum starve, and media stimulated with TNF or EGF; treated with gefitinib, LY294002, and U1026.
2012-05-22 | E-GEOD-32975 | biostudies-arrayexpress
Project description:Orthologous proteins often harbor numerous substitutions, but whether these differences result from neutral or adaptive processes is usually unclear. To tackle this challenge, we examined the divergent evolution of a model bacterial signaling pathway comprising the kinase PhoR and its cognate substrate PhoB. We show that the specificity-determining residues of these proteins are typically under purifying selection, but have, in α-proteobacteria, undergone a burst of diversification followed by extended stasis. By reversing mutations that accumulated in an α-proteobacterial PhoR, we demonstrate that these substitutions were adaptive, enabling PhoR to avoid cross-talk with a paralogous pathway that arose specifically in α-proteobacteria. Our findings demonstrate that duplication and the subsequent need to avoid cross-talk strongly influence signaling protein evolution. These results provide a concrete example of how system-wide insulation can be achieved post-duplication through a surprisingly limited number of mutations. Our work may help explain the apparent ease with which paralogous protein families expanded in all organisms. Two-condition experiment, mutant vs. WT in given growth media
2012-07-01 | E-GEOD-37545 | biostudies-arrayexpress
Project description:Cellular communication strongly relies on the secretion of soluble mediators that, in general, interact with cell surface receptors and enable biological responses in a proper timescale for signaling processes to occur. Therefore, the study of secreted proteins (the secretome) from normal cells and tumoral counterparts allows a comprehensive portrait of the so-called ‘effectors’ in many biological circuits, including those related to tumor development. Cell homeostasis is disrupted in many ways during oncogenesis; the main biological implications are derived from a wide range of factors, including somatic mutations, epigenetic modifications and post-transcriptional/translational modifications, which ultimately results in the modulation of the gene expression. Collectively, these factors often result in rewiring connections in signaling networks in malignant cells. Since protein-protein interactions may eventually enable transformed cells to grow, proliferate and invade neighboring tissues, a comprehensive view of the imbalance caused by the oncogenic processes requires the understanding of the interplay of interactors in altered biological circuits. Pathway and network analysis are analytic approaches that reduce the data involving thousands of altered genes/proteins in complex biological systems to smaller datasets which, in turn, enable the detection and interpretation of specific biological questions such as cancer-related processes, clinically distinct outcomes and drug targets, for example. In this respect, protein-protein interaction networks (PPINs) are static representations of protein connections in biological systems. Although this simplification lacks the dynamic nature of the real protein world, it allows the identification of specific topologies related to the patterns of connectivity of a given PPIN. Moreover, some additional network features may be observed, such as the identification of subgraphs (communities), which contain proteins that may be functionally related, revealing an additional layer of complexity among protein-protein interactions within the network. Kaushik and coworkers used network analysis and to study melanoma stage progression and found that melanoma-related genes can modulate their activities by rewiring network connections, independent of altered gene expression. A recent work with DNA methylation, gene expression and corresponding clinical data from breast invasive carcinoma, skin cutaneous melanoma and uterine corpus endometrial carcinoma underscored gene co-methylation networks, resulting in the identification of core gene modules which allowed the molecular typing of cancer and the prognosis of patients. Such network-based approach revealed that gene modules were more reliable in cancer prognosis than gene expression profiles. Systems-level analysis of signaling networks, gene expression profiles and cell phenotyping, in combination with mathematical modeling, helped to understand the interface between growth factor and DNA signaling pathways in breast cancer9. According to the authors, the identification of rewiring patterns in apoptotic signaling pathways may have main implications for combined therapies in the treatment of breast cancer. In this context, the main goal of this work was to profile the secretome composition of a murine cellular melanoma model composed by a normal melanocyte cell line, Melan-a, and its tumoral phenotype, Tm1, using the proteomic data to build interactomes for both secretomes. Our data revealed an expressive rewiring in the tumoral interactome of secreted proteins, with important biological roles in malignant transformation including the transforming growth factor beta (TGF-beta) signaling pathway.
2020-12-07 | PXD015114 | Pride
Project description:Aberrant activation of signaling pathways controlled in normal epithelial cells by the epidermal growth factor receptor (EGFR) has been linked to cetuximab (a monoclonal antibody against EGFR) resistance in head and neck squamous cell carcinoma (HNSCC). To infer relevant and specific pathway activation downstream of EGFR from gene expression in HNSCC, we generated gene expression signatures using immortalized keratinocytes (HaCaT) subjected to either ligand stimulation or pharmacological inhibition of the signaling intermediaries PI-3-Kinase and MEK or transfected with EGFR, RELA/p65, or HRASVal12. The gene expression patterns that distinguished the various HaCaT variants and conditions were inferred using the Markov chain Monte Carlo (MCMC) matrix factorization algorithm Coordinated Gene Activity in Pattern Sets (CoGAPS). This approach inferred gene expression signatures with greater relevance to cell signaling pathway activation than the expression signatures inferred with standard linear models. Furthermore, the pathway signature generated using HaCaT-HRASVal12 further associated with the cetuximab treatment response in isogenic cetuximab-sensitive (UMSCC1) and -resistant (1CC8) cell lines. Our data suggest that the CoGAPS algorithm can generate gene expression signatures that are pertinent to downstream effects of receptor signaling pathway activation and potentially be useful in modeling resistance mechanisms to targeted therapies.
2012-05-23 | GSE32975 | GEO
Project description:The heat shock response continues to be layered with additional complexity as interactions and cross-talk among heat shock proteins, the reactive oxygen network and hormonal signaling are discovered. However, comparative analyses exploring variation in each of these processes among species remains relatively unexplored. In controlled environment experiments, photosynthetic response curves were conducted from 22 °C to 42 °C and indicated that temperature optimum of light saturated photosynthesis was greater for Glycine max relative to Arabidopsis thaliana or Populus trichocarpa. Transcript profiles were taken at defined states along the temperature response curves and inferred pathway analysis revealed species-specific variation in the abiotic stress and the minor carbohydrate raffinose/galactinol pathways. A weighted gene co-expression network approach was used to group individual genes into network modules linking biochemical measures of the antioxidant system to leaf-level photosynthesis among P. trichocarpa, G. max and A. thaliana. Network enabled results revealed an expansion in the G. max HSP17 protein family and divergence in the regulation of the antioxidant and heat shock module relative to P. trichocarpa and A. thaliana. These results indicate that although the heat shock response is highly conserved, there is considerable species-specific variation in its regulation. fully expanded leaf samples from 4 randomly collected plants per species were harvested at 4 physiological states as determined from prior gas exchange measurements (growth temperature - baseline, photosynthetic optimum, 20% inhibition of optimum and 30% inhibition of optimum).This resulted in 16 separate samples hybridized to independent microarrays totaling 4 – replicates x 4 physiological states).
2011-04-01 | E-GEOD-26195 | biostudies-arrayexpress
Project description:The heat shock response continues to be layered with additional complexity as interactions and cross-talk among heat shock proteins, the reactive oxygen network and hormonal signaling are discovered. However, comparative analyses exploring variation in each of these processes among species remains relatively unexplored. In controlled environment experiments, photosynthetic response curves were conducted from 22 °C to 42 °C and indicated that temperature optimum of light saturated photosynthesis was greater for Glycine max relative to Arabidopsis thaliana or Populus trichocarpa. Transcript profiles were taken at defined states along the temperature response curves and inferred pathway analysis revealed species-specific variation in the abiotic stress and the minor carbohydrate raffinose/galactinol pathways. A weighted gene co-expression network approach was used to group individual genes into network modules linking biochemical measures of the antioxidant system to leaf-level photosynthesis among P. trichocarpa, G. max and A. thaliana. Network enabled results revealed an expansion in the G. max HSP17 protein family and divergence in the regulation of the antioxidant and heat shock module relative to P. trichocarpa and A. thaliana. These results indicate that although the heat shock response is highly conserved, there is considerable species-specific variation in its regulation. fully expanded leaf samples from 4 randomly collected plants per species were harvested at 4 physiological states as determined from prior gas exchange measurements (growth temperature - baseline, photosynthetic optimum, 20% inhibition of optimum and 30% inhibition of optimum).This resulted in 16 separate samples hybridized to independent microarrays totaling 4 – replicates x 4 physiological states).
2011-04-01 | E-GEOD-26197 | biostudies-arrayexpress