Biology subjects

Bekkar, A.

Publications and source records attributed to Bekkar, A..

3 recordsLinked to original sources

bioRxiv2025

A combined computational and experimental approach to successfully predict the behavior of metastatic tumor-associated macrophages in humanbreast cancer

We identified in human breast cancer a scarce population of Tumor-Associated Monocytes (TAMs) endowed with a pro-metastatic activity and associated with reduced distant-metastasis free survival of patients. We developed a novel framework combining computational and experimental methodologies to dampen TAM pro-metastatic activity. Multi-modal experimental data from TAMs exposed in vitro to a series of perturbations were collected to build a Boolean dynamical network of TAMs. This framework successfully identified the biological pathways underlying TAM pro-metastatic activity and predicted potent pharmacological interventions that inhibited the pro-metastatic activity of TAMs isolated from patient tumors. This study showcases the power of integrating computational predictions with experimental validation in identifying new therapeutic avenues that can be extended to other cancer types.

systems biology↗

bioRxiv2024

Integrating knowledge, omics and AI to develop patient-specific virtual avatars

We propose a method for creating personalized regulatory networks, enabling the development of virtual avatars for cancer patients, with patient-derived xenograft (PDX) models as a test case. Starting from a Prior Knowledge Network (PKN) based on the hallmarks of cancer, we constructed gene networks that are contextualized to each sample by integrating sample-specific gene expression data. These networks were optimized using a genetic algorithm to align with individual molecular profiles, focusing on key cancer-related processes. Following network optimization, we employed Graph Convolutional Networks (GCNs) to classify samples based the structures and interactions of their individualized network models and molecular profiles. This personalized approach provides insights into drug responses and helps predict treatment outcomes, offering a path toward more targeted cancer therapies. Author summaryCancer treatment can be more effective when therapies are personalized to each patients unique molecular profile. In this study, we introduce a method to create virtual avatars of cancer patients by personalizing regulatory networks using patient-derived xenograft (PDX) models as a proof of concept. Starting from known cancer hallmarks, we developed individualized gene networks for each sample by leveraging their specific gene expression data. These networks were refined with an optimization process to match the distinct molecular characteristics of each sample. By applying advanced machine learning, specifically Graph Convolutional Networks (GCNs), we classified these personalized models to better understand likely drug responses and predict treatment outcomes. This approach brings us closer to tailoring cancer therapies to individual patients, potentially improving treatment success by targeting key cancer pathways unique to each person.

systems biology↗

bioRxiv2019

PamgeneAnalyzeR open and reproducible pipeline for kinase profiling

Protein phosphorylation - catalyzed by protein kinases - is the most common post-translational modification. It increases the functional diversity of the proteome and influences various aspects of normal physiology and can be altered in disease states. High throughput profiling of kinases is becoming an essential experimental approach to investigate their activity and this can be achieved using technologies such as PamChip(R) arrays provided by PamGene for kinase activity measurement. Here we present \"pamgeneAnalyzeR\", an R package developed as an alternative to the manual steps necessary to extract the data from PamChip(R) peptide microarrays images in a reproducible and robust manner. The extracted data can be directly used for downstream analysis.\n\nAvailability and implementationPamgeneAnalyzeR is implemented in R and can be obtained from https://github.com/amelbek/pamgeneAnalyzeR.

bioinformatics↗