Biology subjects

Eduati, F.

Publications and source records attributed to Eduati, F..

13 recordsLinked to original sources

bioRxiv2026

Combining prior knowledge and transcriptomics data for logic models of patient subgroups

Computational modeling provides a powerful framework for in silico exploration of anti-cancer therapeutic targets and tumor response mechanisms. Oncogenic signaling pathways play a central role in tumor behavior and represent promising targets for personalized combination therapies. However, these pathways are complex, and although logic-based models are well suited for representing signaling dynamics, they are often constrained by model-specific data requirements, limited scalability, and time-consuming manual curation. Here, we introduce Functional Integration of Contextualized Omics for Unraveling regulatory dynamicS (FICUS), a framework that integrates omics-driven network contextualization with dynamic Boolean and logic-ODE modeling. FICUS enables automated, data-driven protein network inference and patient stratification, allowing shared signaling mechanisms to be identified across patient subgroups while preserving patient-specific dynamic responses. We applied FICUS to the SU2C-MARK lung cancer cohort and the The Cancer Genome Atlas kidney cancer cohort, demonstrating its utility for post-hoc analyses and downstream interrogation of dynamic tumor models. Overall, our results highlight the flexibility of FICUS in capturing heterogeneous signaling mechanisms across patient subgroups, addressing a key challenge in precision oncology.

Systems Biology↗

bioRxiv2026

Informing agent-based models with spatial data using convolutional autoencoders

Spatial computational models such as agent-based models (ABMs) offer powerful in silico tools to study tumor dynamics, yet imaging data are still rarely used to inform these models directly. We present an ABM optimization framework that leverages convolutional encoders to compare spatial patterns between experimental imaging data and ABM-generated outputs within a shared latent space. This quantitative comparison was used to estimate ABM parameters across three datasets, ranging from synthetic data to 3D tumoroid-T cell co-culture microscopy and histopathology images from The Cancer Genome Atlas skin cutaneous melanoma samples. Estimated parameters were evaluated using data-derived features and experimental knowledge, including experimental conditions and gene expressions. Simulations using optimized parameters reproduced key spatial features of the training images, such as tumor boundary complexity and tumor-tumor neighborhood structure. Together, these results demonstrate a flexible framework for ABM parameter optimization using spatial data across modalities, enabling systematic investigation of how spatial architecture influences tumor progression and immune interactions.

systems biology↗

bioRxiv2025

Active learning-guided mechanistic modeling of CXCL9 regulation in pancreatic cancer

Cold tumors like pancreatic cancer suffer from poor immune infiltration, limiting effective anti-tumor responses. The chemokine CXCL9 promotes immune cell recruitment, but the signaling mechanisms regulating its expression in tumor cells remain poorly understood and underexplored as targets for modulation. We present a framework that integrates active learning with mechanistic logic-ODE models to guide perturbation screenings and uncover regulators of CXCL9 in pancreatic cancer cells. Using perturbation-response data and curated prior knowledge, we trained interpretable models to identify signaling mechanisms that enhance CXCL9 expression and prioritize drug combinations. Active learning enabled data-efficient model refinement and guided informative experiments under resource constraints. Benchmarking on synthetic data and experimental validation confirmed the performance of different acquisition strategies and revealed cell line-specific regulatory differences. Our results provide insight into tumor cell-intrinsic control of CXCL9 and demonstrate how combining active learning with mechanistic modeling supports rational, targeted experimental design.

systems biology↗

bioRxiv2023

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomic data. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through the analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability that explains the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME, and ultimately to determine its unfolding and fate during immunotherapy with immune checkpoint blockers.

bioinformatics↗

bioRxiv2023

Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties

Inhibiting androgen receptor (AR) signaling through androgen deprivation therapy (ADT) reduces prostate cancer (PCa) growth in virtually all patients, but response is temporary, and resistance inevitably develops, ultimately leading to lethal castration-resistant prostate cancer (CRPC). The tumor microenvironment (TME) plays an important role in the development and progression of PCa. In addition to tumor cells, TME-resident macrophages and fibroblasts express AR and are therefore also affected by ADT. However, the interplay of different TME cell types in the development of CRPC remains largely unexplored. To understand the complex stochastic nature of cell-cell interactions, we created a PCa-specific agent-based model (PCABM) based on in vitro cell proliferation data. PCa cells, fibroblasts, "pro-inflammatory" M1-like and "pro-tumor" M2-like polarized macrophages are modeled as agents from a simple set of validated base assumptions. PCABM allows us to simulate the effect of ADT on the interplay between various prostate TME cell types. The resulting in vitro growth patterns mimic human PCa. Our PCABM can effectively model hormonal perturbations by ADT, in which PCABM suggests that CRPC arises in clusters of resistant cells, as is observed in multifocal PCa. In addition, fibroblasts compete for cellular space in the TME while simultaneously creating niches for tumor cells to proliferate in. Finally, PCABM predicts that ADT has immunomodulatory effects on macrophages that may enhance tumor survival. Taken together, these results suggest that AR plays a critical role in the cellular interplay and stochastic interactions in the TME that influence tumor cell behavior and CRPC development.

systems biology↗

bioRxiv2023

Exploring the onset and progression of prostate cancer through a multicellular agent-based model

Over ten percent of men will be diagnosed with prostate cancer (PCa) during their lifetime. Arising from luminal cells of the prostatic acinus, PCa is influenced by multiple cells in its microenvironment. To expand our knowledge and explore means to prevent and treat the disease, it is important to understand what drives the onset and early stages of PCa. In this study, we developed an agent-based model of a prostatic acinus including its microenvironment, to allow for in silico studying of PCa development. The model was based on prior reports and in-house data of tumor cells co-cultured with Cancer Associated Fibroblasts (CAFs) and pro-tumor and/or anti-tumor macrophages. Growth patterns depicted by the model were pathologically validated on H&E slide images of human PCa specimens. We identified that stochasticity of interactions between macrophages and tumor cells at early stages strongly affect tumor development. Additionally, we discovered that more systematic deviations in tumor development result from a combinatorial effect of the probability of acquiring mutations and the tumor-promoting abilities of CAFs and macrophages. In silico modeled tumors were then compared with 494 cancer patients with matching characteristics, showing strong association between predicted tumor load and patients clinical outcome. Our findings suggest that the likelihood of tumor formation depends on a combination of stochastic events and systematic characteristics. While stochasticity cannot be controlled, information on systematic effects may aid the development of prevention strategies tailored to the molecular characteristics of an individual patient.

systems biology↗

bioRxiv2023

Mathematically mapping the network of cells in the tumor microenvironment

Cell-cell interaction networks are pivotal in cancer development and treatment response. These networks can be inferred from data; however, this process often combines data from multiple patients, and/or creates networks on a cell-types level. It creates a good average overview of cell-cell interaction networks but fails to capture patient heterogeneity and/or masks potentially relevant local network structures. We propose a mathematical model based on random graphs (called RaCInG) to alleviate these issues using prior knowledge on potential cellular interactions and patients bulk RNA-seq data. We have applied RaCInG to extract 444 network features related to the tumor microenvironment, unveiled associations with immune response and subtypes, and identified cancer-type specific differences in inter-cellular signaling. Additionally, we have used RaCInG to explain how immune phenotypes regulated by context-specific intercellular communication affect immunotherapy response. RaCInG is a modular pipeline, and we envision its application for cell-cell interaction reconstruction in different contexts.

systems biology↗

bioRxiv2022

A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

PurposePredictive biomarkers of immune checkpoint inhibitors (ICIs) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The bestperforming models showed improved predictive power over reference variables, including TMB or PD-L1. ConclusionThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICIs clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICIs efficacy. Context summaryO_ST_ABSKey objectiveC_ST_ABSNot all patients with non-small cell lung cancer (NSCLC) eligible for immune checkpoint inhibitor (ICIs) respond to treatment, but accurate predictive biomarkers of ICIs clinical outcomes are currently lacking. This crowdsourced initiative enabled the robust assessment of predictive models using data from two randomized clinical trials of first-line ICI in metastatic NSCLC. Knowledge generatedModels submitted indicate that a combination of programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), and immune gene signatures might be able to identify patients more likely to respond to ICIs. TMB and PD-L1 seemed important to predict progression-free survival and overall survival. Mechanisms including apoptosis, T-cell crosstalk, and adaptive immune resistance appeared essential to predict response. Relevance

cancer biology↗

bioRxiv2021

easier: interpretable predictions of antitumor immune response from bulk RNA-seq data

Immunotherapy with immune checkpoint blockers (ICB) is associated with striking clinical success, but only in a small fraction of patients. Thus, we need computational biomarker-based methods that can anticipate which patients will respond to treatment. Current established biomarkers are imperfect due to their incomplete view of the tumor and its microenvironment. We have recently presented a novel approach that integrates transcriptomics data with biological knowledge to study tumors at a more holistic level. Validated in four different solid cancers, our approach outperformed the state-of-the-art methods to predict response to ICB. Here, we introduce estimate systems immune response (easier), an R/Bioconductor package that applies our approach to quantify biomarkers and assess patients likelihood to respond to immunotherapy, providing just the patients baseline bulk-tumor RNA-sequencing (RNA-seq) data as input.

systems biology↗

bioRxiv2021

Predictive systems biomarkers of response to immune checkpoint inhibitors

Cancer cells can leverage several cell-intrinsic and -extrinsic mechanisms to escape immune system recognition. The inherent complexity of the tumor microenvironment, with its multicellular and dynamic nature, poses great challenges for the extraction of biomarkers of immune response and immunotherapy efficacy. Here, we use RNA-seq data combined with different sources of prior-knowledge to derive system-based signatures of the tumor microenvironment, quantifying immune-cell composition and intra- and inter-cellular communications. We applied multi-task learning to these signatures to predict different hallmarks of immune responses and derive cancer-type-specific models based on interpretable systems biomarkers. By applying our models to independent RNA-seq data from cancer patients treated with PD-1 inhibitors, we demonstrated that our method to Estimate Systems Immune Response (EaSIeR) accurately predicts therapeutic outcome. We anticipate that EaSIeR will be a valuable tool to provide a holistic description of immune responses in complex and dynamic systems such as tumors using available RNA-seq data.

cancer biology↗

bioRxiv2018

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

Mechanistic modeling of signaling pathways mediating patient-specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available, that precludes the generation of large-scale perturbation data. Here, we present an approach that couples ex vivo high-throughput screenings of cancer biopsies using microfluidics with logic-based modeling to generate patient-specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K-Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.

systems biology↗

bioRxiv2016

Dissecting cancer resistance to therapies with cell-type-specific dynamic logic models

Therapies targeting specific molecular processes, in particular kinases, are major strategies to treat cancer. Genomic features are commonly used as biomarkers for drug sensitivity, but our ability to stratify patients based on these features is still limited. As response to kinase inhibitors is a dynamic process affecting largely signal transduction, we investigated the association between cell-specific dynamic signaling pathways and drug sensitivity. We measured 14 phosphoproteins under 43 different perturbed conditions (combination of 5 stimuli and 7 inhibitors) for 14 colorectal cancer cell-lines, and built cell-line-specific dynamic logic models of the underlying signaling network. Model parameters, representing pathway dynamics, were used as features to predict sensitivity to a panel of 27 drugs. This analysis revealed associations between cell-specific signaling pathways and drug sensitivity for 14 of the drugs, 9 of which have no genomic biomarker. Following one of these associations, we validated a drug combination predicted to overcome resistance to MEK inhibitors by co-blockade of GSK3. These results underscore the value of perturbation-based studies to find biomarkers and combination therapies complementing those based on a static genomic characterization.

cancer biology↗

bioRxiv2016

Rapid identification of optimal drug combinations for personalized cancer therapy using microfluidics

Functional screening of live patient cancer cells holds great potential for personalized medicine and allows to overcome the limited translatability of results from existing in-vitro and ex-vivo screening models. Here we present a plug-based microfluidics approach enabling the testing of drug combinations directly on cancer cells from patient biopsies. The entire procedure takes less than 48 hours after surgery and does not require ex vivo cultivation. We screened more than 1100 samples for different primary human tumors (each with 56 conditions and at least 20 replicates), and obtained highly specific sensitivity profiles. This approach allowed us to derive optimal treatment options which we further validated in two different pancreatic cancer cell lines. This workflow should pave the way for rapid determination of optimal personalized cancer therapies at assay costs of less than US$ 150 per patient.

cancer biology↗