| 1. |
- Vega, Paige N., et al.
(author)
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Cancer-Associated Fibroblasts and Squamous Epithelial Cells Constitute a Unique Microenvironment in a Mouse Model of Inflammation-Induced Colon Cancer
- 2022
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In: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 12
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Journal article (peer-reviewed)abstract
- The tumor microenvironment plays a key role in the pathogenesis of colorectal tumors and contains various cell types including epithelial, immune, and mesenchymal cells. Characterization of the interactions between these cell types is necessary for revealing the complex nature of tumors. In this study, we used single-cell RNA-seq (scRNA-seq) to compare the tumor microenvironments between a mouse model of sporadic colorectal adenoma (Lrig1(CreERT2/+);Apc(2lox14/+)) and a mouse model of inflammation-driven colorectal cancer induced by azoxymethane and dextran sodium sulfate (AOM/DSS). While both models develop tumors in the distal colon, we found that the two tumor types have distinct microenvironments. AOM/DSS tumors have an increased abundance of two populations of cancer-associated fibroblasts (CAFs) compared with APC tumors, and we revealed their divergent spatial association with tumor cells using multiplex immunofluorescence (MxIF) imaging. We also identified a unique squamous cell population in AOM/DSS tumors, whose origins were distinct from anal squamous epithelial cells. These cells were in higher proportions upon administration of a chemotherapy regimen of 5-Fluorouracil/Irinotecan. We used computational inference algorithms to predict cell-cell communication mediated by ligand-receptor interactions and downstream pathway activation, and identified potential mechanistic connections between CAFs and tumor cells, as well as CAFs and squamous epithelial cells. This study provides important preclinical insight into the microenvironment of two distinct models of colorectal tumors and reveals unique roles for CAFs and squamous epithelial cells in the AOM/DSS model of inflammation-driven cancer.
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| 2. |
- Meimetis, Nikolaos, et al.
(author)
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AutoTransOP: translating omics signatures without orthologue requirements using deep learning
- 2024
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In: NPJ systems biology and applications. - 2056-7189. ; 10:1
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Journal article (peer-reviewed)abstract
- The development of therapeutics and vaccines for human diseases requires a systematic understanding of human biology. Although animal and in vitro culture models can elucidate some disease mechanisms, they typically fail to adequately recapitulate human biology as evidenced by the predominant likelihood of clinical trial failure. To address this problem, we developed AutoTransOP, a neural network autoencoder framework, to map omics profiles from designated species or cellular contexts into a global latent space, from which germane information for different contexts can be identified without the typically imposed requirement of matched orthologues. This approach was found in general to perform at least as well as current alternative methods in identifying animal/culture-specific molecular features predictive of other contexts-most importantly without requiring homology matching. For an especially challenging test case, we successfully applied our framework to a set of inter-species vaccine serology studies, where 1-to-1 mapping between human and non-human primate features does not exist.
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| 3. |
- Meimetis, Nikolaos, et al.
(author)
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Inference of drug off-target effects on cellular signaling using interactome-based deep learning
- 2024
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In: iScience. - 2589-0042. ; 27:4
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Journal article (peer-reviewed)abstract
- Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors’ activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.
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| 4. |
- Mentre, France, et al.
(author)
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Pharmacometrics and Systems Pharmacology 2030
- 2020
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In: Clinical Pharmacology and Therapeutics. - : Wiley. - 0009-9236 .- 1532-6535. ; 107:1, s. 76-78
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Journal article (other academic/artistic)abstract
- In 2012, a new journal was launched from the ASCPT family, CPT: Pharmacometrics and Systems Pharmacology (PSP) as both quantitative system pharmacology (QSP) and pharmacometrics were growing fields in pharmacology, drug development, and drug use. In this Perspective, the present editors and associate editors of PSP want to share their strategic vision of where these two fields, separately and together, should, would, or could be 10 years from now.
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| 5. |
- Nilsson, Avlant, 1985, et al.
(author)
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Artificial neural networks enable genome-scale simulations of intracellular signaling
- 2022
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1
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Journal article (peer-reviewed)abstract
- Mammalian cells adapt their functional state in response to external signals in form of ligands that bind receptors on the cell-surface. Mechanistically, this involves signal-processing through a complex network of molecular interactions that govern transcription factor activity patterns. Computer simulations of the information flow through this network could help predict cellular responses in health and disease. Here we develop a recurrent neural network framework constrained by prior knowledge of the signaling network with ligand-concentrations as input and transcription factor-activity as output. Applied to synthetic data, it predicts unseen test-data (Pearson correlation r = 0.98) and the effects of gene knockouts (r = 0.8). We stimulate macrophages with 59 different ligands, with and without the addition of lipopolysaccharide, and collect transcriptomics data. The framework predicts this data under cross-validation (r = 0.8) and knockout simulations suggest a role for RIPK1 in modulating the lipopolysaccharide response. This work demonstrates the feasibility of genome-scale simulations of intracellular signaling. Many diseases are caused by disruptions to the network of biochemical reactions that allow cells to respond to external signals. Here Nilsson et al develop a method to simulate cellular signaling using artificial neural networks to predict cellular responses and activities of signaling molecules.
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