Biology subjects

Tomassoni, L.

Publications and source records attributed to Tomassoni, L..

7 recordsLinked to original sources

bioRxiv2024

Identification and Targeting of Regulators of SARS-CoV-2-Host Interactions in the Airway Epithelium.

Although the impact of SARS-CoV-2 in the lung has been extensively studied, the molecular regulators and targets of the host-cell programs hijacked by the virus in distinct human airway epithelial cell populations remain poorly understood. This is in part ascribed to the use of nonprimary cell systems, overreliance on single-cell gene expression profiling that does not ultimately reflect protein activity, and bias toward the downstream effects rather than their mechanistic determinants. Here we address these issues by network-based analysis of single cell transcriptomic profiles of pathophysiologically relevant human adult basal, ciliated and secretory cells to identify master regulator (MR) protein modules controlling their SARS-CoV-2-mediated reprogramming. This uncovered chromatin remodeling, endosomal sorting, ubiquitin pathways, as well as proviral factors identified by CRISPR analyses as components of the host response collectively or selectively activated in these cells. Large-scale perturbation assays, using a clinically relevant drug library, identified 11 drugs able to invert the entire MR signature activated by SARS-CoV-2 in these cell types. Leveraging MR analysis and perturbational profiles of human primary cells represents a novel mechanism-based approach and resource that can be directly generalized to interrogate signatures of other airway conditions for drug prioritization.

cell biology↗

bioRxiv2024

Elucidation and Pharmacologic Targeting of Master Regulator Dependencies in Coexisting Diffuse Midline Glioma Subpopulations

Diffuse Midline Gliomas (DMGs) are universally fatal, primarily pediatric malignancies affecting the midline structures of the central nervous system. Despite decades of clinical trials, treatment remains limited to palliative radiation therapy. A major challenge is the coexistence of molecularly distinct malignant cell states with potentially orthogonal drug sensitivities. To address this challenge, we leveraged established network-based methodologies to elucidate Master Regulator (MR) proteins representing mechanistic, non-oncogene dependencies of seven coexisting subpopulations identified by single-cell analysis--whose enrichment in essential genes was validated by pooled CRISPR/Cas9 screens. Perturbational profiles of 372 clinically relevant drugs helped identify those able to invert the activity of subpopulation-specific MRs for follow-up in vivo validation. While individual drugs predicted to target individual subpopulations--including avapritinib, larotrectinib, and ruxolitinib--produced only modest tumor growth reduction in orthotopic models, systemic co-administration induced significant survival extension, making this approach a valuable contribution to the rational design of combination therapy.

systems biology↗

bioRxiv2023

Tumor Explants Elucidate a Cascade of Paracrine SHH, WNT, and VEGF Signals Driving Pancreatic Cancer Angiosuppression

The sparse vascularity of Pancreatic Ductal Adenocarcinoma (PDAC) presents a mystery: what prevents this aggressive malignancy from undergoing neoangiogenesis to counteract hypoxia and better support growth? An incidental finding from prior work on paracrine communication between malignant PDAC cells and fibroblasts revealed that inhibition of the Hedgehog (HH) pathway partially relieved angiosuppression, increasing tumor vascularity through unknown mechanisms. Initial efforts to study this phenotype were hindered by difficulties replicating the complex interactions of multiple cell types in vitro. Here we identify a cascade of paracrine signals between multiple cell types that act sequentially to suppress angiogenesis in PDAC. Malignant epithelial cells promote HH signaling in fibroblasts, leading to inhibition of WNT signaling in fibroblasts and epithelial cells, thereby limiting VEGFR2-dependent activation of endothelial hypersprouting. This cascade was elucidated using human and murine PDAC explant models, which effectively retain the complex cellular interactions of native tumor tissues.

cancer biology↗

bioRxiv2022

Case Study of Single-cell Protein Activity Based Drug Prediction for Precision Treatment of Cholangiocarcinoma

Cholangiocarcinoma is a rare, aggressive malignancy with limited treatment options, due to a paucity of actionable mutations and low response to immune checkpoint inhibitors. Furthermore, its extreme heterogeneity prevents identification of actionable dependencies from bulk-tissue profiles. To address these challenges, we introduce a highly generalizable, single-cell framework for the mechanism-based prioritization of drugs to treat rare, highly heterogeneous tumors. Analysis of transformed cells, accounting for only 10% of a cholangiocarcinoma patient biopsy revealed three molecularly-distinct subpopulations, predicted to be sensitive to four drugs by regulatory network-based analysis. Validation in a low-passage, patient-derived xenograft (PDX) from the same patient confirmed tumor growth rate control by two of these drugs (plicamycin and dacinostat) and further validated predicted subpopulation-specific effects, suggesting they may represent promising candidates for follow-up clinical trials, either alone or in combination with current standard-of-care chemotherapies. The proposed approach can be generalized to elucidate complementary dependencies of rare, heterogeneous tumors, at the single cell level.

cancer biology↗

bioRxiv2020

Pancreatic Ductal Adenocarcinoma Comprises Coexisting Regulatory States with both Common and Distinct Dependencies

Despite extensive efforts, reproducible assessment of pancreatic ductal adenocarcinoma (PDA) heterogeneity and plasticity at the single cell level remains elusive. Systematic, network-based analysis of regulatory protein activity in single cells identified three PDA Developmental Lineages (PDLs), coexisting in virtually all tumors, whose transcriptional states are mechanistically driven by aberrant activation of Master Regulator (MR) proteins associated with gastrointestinal lineages (GLS state), morphogen and EMT pathways (MOS state), and acinar-to-ductal metaplasia (ALS state), respectively. Each PDL is further subdivided into sub-states characterized by low vs. high MAPK pathway activity. This taxonomy was remarkably conserved across multiple cohorts, cell lines, and PDX models, and harmonized with bulk profile analyses. Cross-state plasticity and MR essentiality was confirmed by barcode-based lineage tracing and CRISPR/Cas9 assays, respectively, while MR ectopic expression induced PDL transdifferentiation. Together these data provide a mechanistic foundation for PDA heterogeneity and a roadmap for targeting PDA cellular subtypes.

cancer biology↗

bioRxiv2019

A new Bayesian methodology for nonlinear model calibration in Computational Systems Biology

Computational modeling is a common tool to quantitatively describe biological processes. However, most model parameters are usually unknown because they cannot be directly measured. Therefore, a key issue in Systems Biology is model calibration, i.e. estimate parameters from experimental data. Existing methodologies for parameter estimation are divided in two classes: frequentist and Bayesian methods. The first ones optimize a cost function while the second ones estimate the parameter posterior distribution through different sampling techniques. Here, we present an innovative Bayesian method, called Conditional Robust Calibration (CRC), for nonlinear model calibration and robustness analysis using omics data. CRC is an iterative algorithm based on the sampling of a proposal distribution and on the definition of multiple objective functions, one for each observable. CRC estimates the probability density function (pdf) of parameters conditioned to the experimental measures and it performs a robustness analysis, quantifying how much each parameter influences the observables behavior. We apply CRC to three Ordinary Differential Equations (ODE) models to test its performances compared to the other state of the art approaches, namely Profile Likelihood (PL), Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) and Delayed Rejection Adaptive Metropolis (DRAM). Compared with these methods, CRC finds a robust solution with a reduced computational cost. CRC is developed as a set of Matlab functions (version R2018), whose fundamental source code is freely available at https://github.com/fortunatobianconi/CRC.

systems biology↗

bioRxiv2017

Conditional Robust Calibration (CRC): a new computational Bayesian methodology for model parameters estimation and identifiability analysis

Computational modeling is a remarkable and common tool to quantitatively describe a biological process. However, most model parameters, such as kinetics parameters, initial conditions and scale factors, are usually unknown because they cannot be directly measured.\n\nTherefore, key issues in Systems Biology are model calibration and identifiability analysis, i.e. estimate parameters from experimental data and assess how well those parameters are determined by the dimension and quality of the data.\n\nCurrently in the Systems Biology and Computational Biology communities, the existing methodologies for parameter estimation are divided in two classes: frequentist methods and Bayesian methods. The first ones are based on the optimization of a cost function while the second ones estimate the posterior distribution of model parameters through different sampling techniques.\n\nIn this work, we present an innovative Bayesian method, called Conditional Robust Calibration (CRC), for model calibration and identifiability analysis. The algorithm is an iterative procedure based on parameter space sampling and on the definition of multiple objective functions related to each output variables. The method estimates step by step the probability density function (pdf) of parameters conditioned to the experimental measures and it returns as output a subset in the parameter space that best reproduce the dataset.\n\nWe apply CRC to six Ordinary Differential Equations (ODE) models with different characteristics and complexity to test its performances compared with profile likelihood (PL) and Approximate Bayesian Computation Sequential Montecarlo (ABC-SMC) approaches. The datasets selected for calibration are time course measurements of different nature: noisy or noiseless, real or in silico.\n\nCompared with PL, our approach finds a more robust solution because parameter identifiability is inferred by conditional pdfs of estimated parameters. Compared with ABC-SMC, we have found a more precise solution with a reduced computational cost.

systems biology↗