Reconstruction Of Regulatory Networks Research Articles

Abstract PO2-01-08: Differential HOXB13 gene expression and promoter methylation analysis in breast cancer

Abstract Background: The Breast Cancer Index (BCI) is a gene expression-based signature comprising two functional biomarker panels: the Molecular Grade Index (MGI) and BCI (H/I), which is a ratio of the HOXB13 and IL17BR gene expression. Several studies have shown that BCI (H/I) is a predictive biomarker for extended adjuvant endocrine therapy benefit in hormone receptor-positive (HR+) early-stage breast cancer. Molecular mechanisms underlying endocrine responsiveness remain to be fully explored. The objective of this analysis is to evaluate the correlation between gene expression and methylation in breast cancer patients displaying different HOXB13 mRNA expression levels from The Cancer Genome Atlas (TCGA) project. Methods: Data from methylation microarrays and mRNA sequencing were examined in combination with clinical metadata from 1,095 TCGA breast cancer patients. After removing outliers, HR+ samples were ordered based on their HOXB13 expression levels and the top 10% of samples (HOXB13-high; n=95) and bottom 10% (HOXB13-low; n=95) were selected for further analysis. Differentially expressed genes (DEGs) were determined by comparing all protein-coding genes between the HOXB13-high and HOXB13-low sets, based on an absolute log-fold change above 2 and a Benjamini-Hochberg adjusted p-value threshold of 0.01. Among the subset of samples with matching methylation data (n=60 for the HOXB13-high group and n=48 for the HOXB13-low group), hypo-methylated and hyper-methylated differentially methylated probes (DMPs) were identified based on at least 10% change of β (i.e., average promoter methylation) and a Benjamini-Hochberg adjusted p-value threshold of 0.01. Motif and transcription factor (TF) enrichment analyses were identified at a minimum incidence of 10 and a lower boundary of 1.5 for the 95% confidence interval of the odds ratio. Results: Our analysis identified a total of 613 DEGs between the HOXB13-high and HOXB13-low groups. Gene ontology analysis of DEGs revealed a statistically significant enrichment of genes associated with PTK6 regulated cell cycle, oncogene-induced cellular senescence, and regulation of T cell activation. Analysis of protein-protein interactions of differentially expressed genes revealed differences in key biological processes including changes in cell cycle regulation, p53 pathway, and IL-17 signaling. A total of 5,295 hyper-methylated and 580 hypo-methylated DMPs were discovered when comparing HOXB13-high and HOXB13-low samples. Changes in global methylation patterns enabled classification of breast cancer samples with high or low HOXB13 expression with an accuracy of 0.90. Reconstruction of gene regulatory networks from DNA methylation and transcriptome profiles elucidated significant pairs of DMPs and differentially expressed genes. Specifically, a total of 197 differentially expressed genes were attributed to hyper-methylated DMPs, including genes involved in response to estrogen, activation of HOX genes during differentiation, and drug-mediated inhibition of CDK4/6 activity. Additionally, 8 differentially expressed genes were attributed to hypo-methylated DMPs and were associated with proteasomal protein catabolic processes. Lastly, motif analysis of transcriptional factors for the hyper-methylated DMPs revealed enrichment of binding motifs attributed to FOXA1, ESR1, XBR1, and FOXP1. Conclusion: A comprehensive and comparative analysis of mRNA expression levels and methylation patterns between TCGA breast cancer samples with high and low expression of HOXB13 revealed key signaling pathways and biological processes that may provide insights on the molecular mechanism of HOXB13 in the regulation of response to endocrine therapy in HR+ breast cancer. Citation Format: Natalia Siuliukina, Yi Zhang, Kai Treuner, Mark Pegram. Differential HOXB13 gene expression and promoter methylation analysis in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-01-08.

S100a9 might act as a modulator of the Toll-like receptor 4 transduction pathway in chronic rhinosinusitis with nasal polyps

Chronic rhinosinusitis with nasal polyp (CRSwNP) is a highly prevalent disorder characterized by persistent nasal and sinus mucosa inflammation. Despite significant morbidity and decreased quality of life, there are limited effective treatment options for such a disease. Therefore, identifying causal genes and dysregulated pathways paves the way for novel therapeutic interventions. In the current study, a three-way interaction approach was used to detect dynamic co-expression interactions involved in CRSwNP. In this approach, the internal evolution of the co-expression relation between a pair of genes (X, Y) was captured under a change in the expression profile of a third gene (Z), named the switch gene. Subsequently, the biological relevancy of the statistically significant triplets was confirmed using both gene set enrichment analysis and gene regulatory network reconstruction. Finally, the importance of identified switch genes was confirmed using a random forest model. The results suggested four dysregulated pathways in CRSwNP, including “positive regulation of intracellular signal transduction”, “arachidonic acid metabolic process”, “spermatogenesis” and “negative regulation of cellular protein metabolic process”. Additionally, the S100a9 as a switch gene together with the gene pair {Cd14, Tpd52l1} form a biologically relevant triplet. More specifically, we suggested that S100a9 might act as a potential upstream modulator in toll-like receptor 4 transduction pathway in the major CRSwNP pathologies.

Organ-delimited gene regulatory networks provide high accuracy in candidate transcription factor selection across diverse processes

Organ-specific gene expression datasets that include hundreds to thousands of experiments allow the reconstruction of organ-level gene regulatory networks (GRNs). However, creating such datasets is greatly hampered by the requirements of extensive and tedious manual curation. Here, we trained a supervised classification model that can accurately classify the organ-of-origin for a plant transcriptome. This K-Nearest Neighbor-based multiclass classifier was used to create organ-specific gene expression datasets for the leaf, root, shoot, flower, and seed in Arabidopsis thaliana. A GRN inference approach was used to determine the: i. influential transcription factors (TFs) in each organ and, ii. most influential TFs for specific biological processes in that organ. These genome-wide, organ-delimited GRNs (OD-GRNs), recalled many known regulators of organ development and processes operating in those organs. Importantly, many previously unknown TF regulators were uncovered as potential regulators of these processes. As a proof-of-concept, we focused on experimentally validating the predicted TF regulators of lipid biosynthesis in seeds, an important food and biofuel trait. Of the top 20 predicted TFs, eight are known regulators of seed oil content, e.g., WRI1, LEC1, FUS3. Importantly, we validated our prediction of MybS2, TGA4, SPL12, AGL18, and DiV2 as regulators of seed lipid biosynthesis. We elucidated the molecular mechanism of MybS2 and show that it induces purple acid phosphatase family genes and lipid synthesis genes to enhance seed lipid content. This general approach has the potential to be extended to any species with sufficiently large gene expression datasets to find unique regulators of any trait-of-interest.

Abstract 6236: Systems biology modeling identifies SMARCD3 as a master regulator facilitating everolimus resistance in ER+ breast cancer

Abstract Breast cancer is the most common malignancy in women and estrogen receptor positive (ER+) breast cancers represent nearly 75% of all breast tumors. Everolimus, an mTORC1 inhibitor, in combination with exemestane has been approved for patients with metastatic ER+ breast cancer. However, many patients eventually develop resistance to everolimus and leads to poor survival outcomes. Characterizing everolimus-resistant cells can reveal targetable molecular phenotypes that can eventually lead to new therapeutic targets. In this study, we elucidate systems-level phenotypic distinctions between everolimus sensitive and refractory cells. We generated several isogenic ER+ breast cancer cell lines resistant or sensitive to everolimus. We then leveraged transcriptomic profiles from sensitive and resistant cells before or after everolimus treatment to decipher the underlying mechanisms essential to the resistant state. Using linear mixed effect models, we identified several meta-phenotypes that were distinct between sensitive and resistant cell states. These included growth-factor signaling, cell cycle, metabolism, stemness, and apoptosis meta-phenotypes. Our analyses also identified strong activation of growth-factor receptors including IGF1R/INSR and ESR1 in the resistant cells, which were maintained despite everolimus treatment. Next, we adopted a transcriptional regulatory network reconstruction framework to identify the master regulators responsible for the activation of resistance meta-phenotypes. We identified SMARCD3 as the key master regulator controlling expression of genes across diverse resistance meta-phenotypes, including growth-factor signaling. Further, using transcriptomic profiles from a neoadjuvant trial of 23 post-menopausal ER+ breast cancers treated with everolimus, we found that SMARCD3 expression was elevated in resistant tumors. To confirm the role of SMARCD3 in everolimus resistance, we repressed transcription of the SMARCD3 gene using CRISPR interference to re-sensitize resistant cells to everolimus treatment. In conclusion, we used context-specific cell transcriptomic profiles and integrative systems-biology approaches to elucidate mechanisms of resistance in response to everolimus treatment in ER+ breast cancer. Our findings suggest SMARCD3 upregulation promotes resistance to everolimus treatment and may serve as a novel therapeutic target in ER+ breast cancer. Citation Format: Eric F. Medina, Patrick A. Cosgrove, Eleni Farmaki, Vince K. Grolmusz, Feng Chi, Jason I. Griffiths, Andrea H. Bild, Aritro Nath. Systems biology modeling identifies SMARCD3 as a master regulator facilitating everolimus resistance in ER+ breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6236.

Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data.

Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. Here we present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single-cell/nuclei RNA-sequencing data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperformed 12 existing gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPION's scalability to population-level analyses using a single-cell RNA-sequencing atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences between tumor regions detected by SCORPION are consistent across multiple cohorts as well as with our understanding of disease progression, and elucidate phenotypic regulators that may impact patient survival.

SPREd: a simulation-supervised neural network tool for gene regulatory network reconstruction.

Reconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a gene's expression using transcription factors' (TFs') expression as features and designate important features/TFs as regulators of the gene. Here, we present an entirely different paradigm, where GRN edges are directly predicted by the ML model. The new approach, named "SPREd," is a simulation-supervised neural network for GRN inference. Its inputs comprise expression relationships (e.g. correlation, mutual information) between the target gene and each TF and between pairs of TFs. The output includes binary labels indicating whether each TF regulates the target gene. We train the neural network model using synthetic expression data generated by a biophysics-inspired simulation model that incorporates linear as well as non-linear TF-gene relationships and diverse GRN configurations. We show SPREd to outperform state-of-the-art GRN reconstruction tools GENIE3, ENNET, PORTIA, and TIGRESS on synthetic datasets with high co-expression among TFs, similar to that seen in real data. A key advantage of the new approach is its robustness to relatively small numbers of conditions (columns) in the expression matrix, which is a common problem faced by existing methods. Finally, we evaluate SPREd on real data sets in yeast that represent gold-standard benchmarks of GRN reconstruction and show it to perform significantly better than or comparably to existing methods. In addition to its high accuracy and speed, SPREd marks a first step toward incorporating biophysics principles of gene regulation into ML-based approaches to GRN reconstruction. Data and code are available from https://github.com/iiiime/SPREd.

A review on gene regulatory network reconstruction algorithms based on single cell RNA sequencing.

Understanding gene regulatory networks (GRNs) is essential for unraveling the molecular mechanisms governing cellular behavior. With the advent of high-throughput transcriptome measurement technology, researchers have aimed to reverse engineer the biological systems, extracting gene regulatory rules from their outputs, which represented by gene expression data. Bulk RNA sequencing, a widely used method for measuring gene expression, has been employed for GRN reconstruction. However, it falls short in capturing dynamic changes in gene expression at the level of individual cells since it averages gene expression across mixed cell populations. In this review, we provide an overview of 15 GRN reconstruction tools and discuss their respective strengths and limitations, particularly in the context of single cell RNA sequencing (scRNA-seq). Recent advancements in scRNA-seq break new ground of GRN reconstruction. They offer snapshots of the individual cell transcriptomes and capturing dynamic changes. We emphasize how these technological breakthroughs have enhanced GRN reconstruction. GRN reconstructors can be classified based on their requirement for cellular trajectory, which represents a dynamical cellular process including differentiation, aging, or disease progression. Benchmarking studies support the superiority of GRN reconstructors that do not require trajectory analysis in identifying regulator-target relationships. However, methods equipped with trajectory analysis demonstrate better performance in identifying key regulatory factors. In conclusion, researchers should select a suitable GRN reconstructor based on their specific research objectives.

Inference of differential key regulatory networks and mechanistic drug repurposing candidates from scRNA-seq data with SCANet.

The reconstruction of small key regulatory networks that explain the differences in the development of cell (sub)types from single-cell RNA sequencing is a yet unresolved computational problem. To this end, we have developed SCANet, an all-in-one package for single-cell profiling that covers the whole differential mechanotyping workflow, from inference of trait/cell-type-specific gene co-expression modules, driver gene detection, and transcriptional gene regulatory network reconstruction to mechanistic drug repurposing candidate prediction. To illustrate the power of SCANet, we examined data from two studies. First, we identify the drivers of the mechanotype of a cytokine storm associated with increased mortality in patients with acute respiratory illness. Secondly, we find 20 drugs for eight potential pharmacological targets in cellular driver mechanisms in the intestinal stem cells of obese mice. SCANet is a free, open-source, and user-friendly Python package that can be seamlessly integrated into single-cell-based systems medicine research and mechanistic drug discovery.

Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R.

Deep learning has emerged as a powerful tool for solving complex problems, including reconstruction of gene regulatory networks within the realm ofbiology. These networks consist of transcription factors and their associations with genes they regulate. Despite the utility of deep learning methods in studying gene expression and regulation, their accessibility remains limited forbiologists, mainlydue to the prerequisitesofprogramming skills and anuanced grasp of the underlying algorithms. This chapter presents a deep learning protocol thatutilize TensorFlow and the Keras API in R/RStudio, with the aim of making deep learning accessible for individuals without specialized expertise. The protocol focuses on the genome-wideprediction of regulatory interactions between transcription factors and genes, leveraging publicly available gene expression data in conjunction with well-established benchmarks. The protocol encompasses pivotal phases including data preprocessing, conceptualization of neural network architectures, iterative processes ofmodel training and validation, as well asforecasting of novel regulatory associations. Furthermore, it providesinsights into parameter tuning for deep learning models. By adhering to this protocol, researchers are expected to gain a comprehensive understanding of applying deep learning techniques to predict regulatory interactions. This protocol can be readily modifiable to servediverseresearch problems, therebyempowering scientists to effectivelyharness the capabilities of deep learning in their investigations.

OR20-03 Leptin-mediated Regulation Of Gene Regulatory Networks In Gonadotropes

Abstract Disclosure: J. Banik: None. A. Lagasse: None. A. Haney: None. S. Byrum: None. U. Boehm: None. G.V. Childs: None. M.C. MacNicol: None. A.M. MacNicol: None. A.K. Odle: None. The female reproductive system is energetically expensive and requires tight control of the hypothalamic-pituitary-gonadal (HPG) axis to promote gonadal priming and ovulation. Thus, a crosstalk between the metabolic and reproductive systems exists to regulate the rate-limiting steps of reproduction. Leptin is an adipokine hormone that is produced and secreted proportionally from white fat cells, and leptin receptors (Lepr) are present in the hypothalamus, pituitary, and gonads. Leptin binding to hypothalamic LepR functions as a sensor of nutritional status and, thus, orchestrates energy expenditure during reproductive events. We hypothesized that leptin is crucial to influence fertility in females via differential gene regulation during the estrus cycle in pituitary gonadotropes. To test this hypothesis, we utilized Cre-LoxP technology to selectively ablate leptin receptors within female gonadotropes (CreGnRHR+/- Leprfl/fl TdTomato+/+, referred to as “Gon-Lepr-null”) and performed single-cell RNA-sequencing (scRNA-seq) to identify leptin-dependent targets within the gonadotrope transcriptome. We observed a significant decrease in cell percentage within the gonadotrope cluster, and differential analysis reported a significant decrease in Fshb expression in the Lepr-null mutant gonadotropes. Moreover, the lack of leptin signaling in gonadotropes also caused a shift in the lactotrope-specific transcript signature in the Gon-Lepr-null mutants. We employed SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data, to delineate altered upstream regulators contributing to the reduction of Fshb expression in gonadotropes and the shift in the lactotrope transcriptome signature in Gon-Lepr-null mutants versus control animals. Regulons are co-regulated gene sets that share binding sites for a common transcription factor driver, and we identified 381 and 399 regulons specific to controls and mutants, respectively. Examination of the gonadotrope regulons revealed Gata2, Nhlh2, NeuroD1, and Sox11 to be drivers of Fshb expression in control animals only. Differential expression analysis showed significant downregulation of Gata2 and Sox11 in Lepr-null mutant gonadotropes, suggesting Fshb is a leptin-dependent target of Gata2 and Sox11. Moreover, Gata2, Nhlh2, NeuroD1, and Sox11 target 117 genes in controls and 263 genes in mutants, implying a more restricted ability of Gata2, Nhlh2, NeuroD1, and/or Sox11 to target gene expression in the presence of leptin signaling in controls, as an abrogation of leptin signaling leads to an expansion of the regulatory network in Lepr-null mutant gonadotropes. Overall, our analysis reveals how leptin signaling modulates the gonadotrope transcriptome to optimize pituitary reproductive functions at the molecular level. Presentation: Saturday, June 17, 2023

Granzymes expression patterns predict immunotherapy response and identify the heterogeneity of CD8+ T cell subsets.

Recent studies illustrated the effects of granzymes (GZMs) gene alterations on immunotherapy response of cancer patients. Thus, we aimed to systematically analyze the expression and prognostic value of GZMs for immunotherapy in different cancers, and identified heterogeneity of the GZMs expression-based CD8+ T cell subsets. First, we analyzed GZMs expression and prognostic value at pan-cancer level. Meanwhile, we established a GZMs score by using the single-sample gene set enrichment analysis (ssGSEA) algorithm to calculate the enrichment scores (ES) based on a gene set of five GZMs. The potential value of GZMs score for predicting survival and immunotherapy response was evaluated using the tumor immune dysfunction and exclusion (TIDE) and immunophenoscore (IPS) algorithm, and we validated it in immunotherapy cohorts. CellChat, scMetabolism, and SCENIC R packages were used for intercellular communication networks, quantifying metabolism activity, and regulatory network reconstruction, respectively. The GZMs score was significantly associated with IPS, TIDE score. Patients with high GZMs score tended to have higher objective response rates of immunotherapy in melanoma and urothelial carcinoma. GZMs expression-based CD8+ T cell subsets presented heterogeneity in functions, metabolism, intercellular communications, and the tissue-resident memory programs in lung adenocarcinoma (LUAD). The transcription factors RUNX3 and ETS1, which may regulate the expression of GZMs, was found to be positively correlated with the tissue-resident memory T cells-related marker genes. The higher GZMs score may indicate better response and overall survival (OS) outcome for immunotherapy in melanoma and urothelial carcinoma but worse OS in renal cell carcinoma (RCC). The GZMs score is a potential prognostic biomarker of diverse cancers. RUNX3 and ETS1 may be the potential targets to regulate the infiltration of GZMs expression-based CD8+ T cell subsets and affect the tissue-resident memory programs in LUAD, which may affect the prognosis of LUAD patients and the response to immunotherapy.

Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection.

Despite extensive research efforts, reconstruction of gene regulatory networks (GRNs) from transcriptomics data remains a pressing challenge in systems biology. While non-linear approaches for reconstruction of GRNs show improved performance over simpler alternatives, we do not yet have understanding if joint modelling of multiple target genes may improve performance, even under linearity assumptions. To address this problem, we propose two novel approaches that cast the GRN reconstruction problem as a blend between regularized multivariate regression and graphical models that combine the L2,1-norm with classical regularization techniques. We used data and networks from the DREAM5 challenge to show that the proposed models provide consistently good performance in comparison to contenders whose performance varies with data sets from simulation and experiments from model unicellular organisms Escherichia coli and Saccharomyces cerevisiae. Since the models' formulation facilitates the prediction of master regulators, we also used the resulting findings to identify master regulators over all data sets as well as their plasticity across different environments. Our results demonstrate that the identified master regulators are in line with experimental evidence from the model bacterium E. coli. Together, our study demonstrates that simultaneous modelling of several target genes results in improved inference of GRNs and can be used as an alternative in different applications.

Enhancer remodeling drives MLL oncogene-dependent transcriptional dysregulation in leukemia stem cells.

Acute myeloid leukemia (AML) with mixed-lineage leukemia (MLL) gene rearrangement (MLLr) comprises a cellular hierarchy in which a subpopulation of cells serves as functional leukemia stem cells (LSCs). They are maintained by a unique gene expression program and chromatin states, which are thought to reflect the actions of enhancers. Here, we delineate the active enhancer landscape and observe pervasive enhancer malfunction in LSCs. Reconstruction of regulatory networks revealed a master set of hematopoietic transcription factors. We show that EP300 is an essential transcriptional coregulator for maintaining LSC oncogenic potential because it controls essential gene expression through modulation of H3K27 acetylation and assessments of transcription factor dependencies. Moreover, the EP300 inhibitor A-485 affects LSC growth by targeting enhancer activity via histone acetyltransferase domain inhibition. Together, these data implicate a perturbed MLLr-specific enhancer accessibility landscape, suggesting the possibility for disruption of the LSC enhancer regulatory axis as a promising therapeutic strategy in AML.

Landscape and perturbation of enhancer-driven core transcription regulatory circuits in cancer

Landscape and perturbation of enhancer-driven core transcription regulatory circuits in cancer

Inferring Gene Regulatory Networks via Ensemble Path Consistency Algorithm Based on Conditional Mutual Information.

Utilizing gene expression data to infer gene regulatory networks has received great attention because gene regulation networks can reveal complex life phenomena by studying the interaction mechanism among nodes. However, the reconstruction of large-scale gene regulatory networks is often not ideal due to the curse of dimensionality and the impact of external noise. In order to solve this problem, we introduce a novel algorithms called ensemble path consistency algorithm based on conditional mutual information (EPCACMI), whose threshold of mutual information is dynamically self-adjusted. We first use principal component analysis to decompose a large-scale network into several subnetworks. Then, according to the absolute value of coefficient of each principal component, we could remove a large number of unrelated nodes in every subnetwork and infer the relationships among these selected nodes. Finally, all inferred subnetworks are integrated to form the structure of the complete network. Rather than inferring the whole network directly, the influence of a mass of redundant noise could be weakened. Compared with other related algorithms like MRNET, ARACNE, PCAPMI and PCACMI, the results show that EPCACMI is more effective and more robust when inferring gene regulatory networks with more nodes.

BioGraph: Data Model for Linking and Querying Diverse Biological Metadata.

Studying the association of gene function, diseases, and regulatory gene network reconstruction demands data compatibility. Data from different databases follow distinct schemas and are accessible in heterogenic ways. Although the experiments differ, data may still be related to the same biological entities. Some entities may not be strictly biological, such as geolocations of habitats or paper references, but they provide a broader context for other entities. The same entities from different datasets can share similar properties, which may or may not be found within other datasets. Joint, simultaneous data fetching from multiple data sources is complicated for the end-user or, in many cases, unsupported and inefficient due to differences in data structures and ways of accessing the data. We propose BioGraph-a new model that enables connecting and retrieving information from the linked biological data that originated from diverse datasets. We have tested the model on metadata collected from five diverse public datasets and successfully constructed a knowledge graph containing more than 17 million model objects, of which 2.5 million are individual biological entity objects. The model enables the selection of complex patterns and retrieval of matched results that can be discovered only by joining the data from multiple sources.

Abstract 4876: A systems pharmacology approach to discover synergistic targeted therapy combinations

Abstract The discovery of oncogenic signal transduction in cancer has enabled the development and clinical use of dozens of kinase-directed targeted therapies, but patient response is often transient and resistance invariably develops. In the setting of BRAF-mutant melanoma, for instance, a majority of patients relapse within one year of initiating targeted therapy. Combination therapies blocking compensatory signaling pathways provide a strategy for boosting treatment efficacy and delaying resistance. To find synergistic kinase inhibitor combinations, we used a multimodal systems pharmacology approach to quantify the early adaptive signaling and gene regulatory dynamics in patient-derived BRAFV600E-mutant melanoma cells under oncogene inhibition. Cells were treated with vemurafenib or vehicle and sampled longitudinally at timepoints spanning minutes to days. To measure signaling dynamics, thousands of protein phosphorylation sites covering commonly altered signaling axes in cancer were motif-enriched by immunoprecipitation, barcoded with isobaric tandem mass tags (TMT), and quantified by high-resolution mass spectrometry (MS). The resulting time-resolved phosphoproteomics data showed rapid (<15 minutes) and potent (>8-fold) downregulation of ERK1/2, confirming strong BRAF inhibition. Statistical integration of signaling measurements with time-series RNA-seq data collected at the same timepoints enabled reconstruction of multi-scale regulatory networks governing the adaptive response to oncogene inhibition. In particular, we observed early induction of SRC-family kinase (SFK) signaling and a broad cytoskeletal signaling module, implicating a compensatory prosurvival signaling program. The induction intensified over the 3-day treatment window and was fully reversed following a 6-day drug holiday, suggesting a reversible nature of drug adaptation. Immunoblotting confirmed sustained loss of ERK1/2 activity and concomitant elevated SFK phosphorylation following drug treatment. Accumulation of reactive oxygen species, a known activator of SFKs, strongly correlated with SFK activity, and both were ablated following treatment with the antioxidant precursor N-acetylcysteine. This adaptive response predicted a significant degree of synergy between vemurafenib and the pan-SFK inhibitor dasatinib, which was validated in a panel of patient-derived melanoma cell lines and in melanoma xenograft mouse models. Targeted MS of phospho-ERK1/2 and canonical SFK substrates, including CTTN, PXN, and PAG1, validated the mechanism of action of this combination. Complementary data from patient-derived non small-cell lung cancer (NSCLC) cells under tyrosine kinase inhibitor treatment demonstrates the generality of our integrative approach, and supports the notion that SFK activation may be a hallmark response to oncogenic RTK-RAS-ERK pathway inhibition. Citation Format: Cameron T. Flower, Chunmei Liu, James R. Heath, Wei Wei, Forest M. White. A systems pharmacology approach to discover synergistic targeted therapy combinations. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4876.

WpLogicNet: logic gate and structure inference in gene regulatory networks.

The gene regulatory process resembles a logic system in which a target gene is regulated by a logic gate among its regulators. While various computational techniques are developed for a gene regulatory network (GRN) reconstruction, the study of logical relationships has received little attention. Here, we propose a novel tool called wpLogicNet that simultaneously infers both the directed GRN structures and logic gates among genes or transcription factors (TFs) that regulate their target genes, based on continuous steady-state gene expressions. wpLogicNet proposes a framework to infer the logic gates among any number of regulators, with a low time-complexity. This distinguishes wpLogicNet from the existing logic-based models that are limited to inferring the gate between two genes or TFs. Our method applies a Bayesian mixture model to estimate the likelihood of the target gene profile and to infer the logic gate a posteriori. Furthermore, in structure-aware mode, wpLogicNet reconstructs the logic gates in TF-gene or gene-gene interaction networks with known structures. The predicted logic gates are validated on simulated datasets of TF-gene interaction networks from Escherichia coli. For the directed-edge inference, the method is validated on datasets from E.coli and DREAM project. The results show that compared to other well-known methods, wpLogicNet is more precise in reconstructing the network and logical relationships among genes. The datasets and R package of wpLogicNet are available in the github repository, https://github.com/CompBioIPM/wpLogicNet. Supplementary data are available at Bioinformatics online.

A data-driven optimization method for coarse-graining gene regulatory networks

One major challenge in systems biology is to understand how various genes in a gene regulatory network (GRN) collectively perform their functions and control network dynamics. This task becomes extremely hard to tackle in the case of large networks with hundreds of genes and edges, many of which have redundant regulatory roles and functions. The existing methods for model reduction usually require the detailed mathematical description of dynamical systems and their corresponding kinetic parameters, which are often not available. Here, we present a data-driven method for coarse-graining large GRNs, named SacoGraci, using ensemble-based mathematical modeling, dimensionality reduction, and gene circuit optimization by Markov Chain Monte Carlo methods. SacoGraci requires network topology as the only input and is robust against errors in GRNs. We benchmark and demonstrate its usage with synthetic, literature-based, and bioinformatics-derived GRNs. We hope SacoGraci will enhance our ability to model the gene regulation of complex biological systems.

Local causal pathway discovery for single-cell RNA sequencing count data: a benchmark study

Aim: Recent developments in single-cell RNA sequencing (scRNAseq) and analysis have revealed regulatory behaviors not previously described using bulk analysis. scRNAseq features resolution at the level of the individual cell and provides opportunities for identifying cell type-specific gene regulatory networks. The technology promises to discover biomarkers and targeted treatments with enhanced effectiveness and reduced side effects. Pathway reverse engineering and causal algorithms have been validated in bulk sequencing transcriptomic data successfully for gene regulatory network reconstruction. In the current study, we evaluated the performance of local causal discovery algorithms for de novo reconstruction of local gene regulatory networks tailored to scRNAseq count data.