Biology subjects

Ballesta, A.

Publications and source records attributed to Ballesta, A..

4 recordsLinked to original sources

bioRxiv2024

Integrated multiomic profiling reveals SWI/SNF subunit-specific pathway alterations and targetable vulnerabilities

Mutations in subunits of the SWItch Sucrose Non-Fermentable (SWI/SNF) chromatin remodeling complex occur in {approx}20% of cancers and represent a highly unmet medical need. To identify novel therapeutic approaches, we systematically characterized transcriptomic and proteomic changes caused by the loss of SWI/SNF subunits or other epigenetic enzymes in isogenic cell lines, which we subsequently integrated with high-throughput drug screening and independent genetic screens of the DepMap project. Using an optimized bioinformatics pipeline for pathway enrichment, we identified Metabolism of proteins as the most frequently dysregulated Reactome pathway category in SWI/SNF-defective cell lines. Drug screening and multiomic integration revealed multiple chemicals selectively cytotoxic for SWI/SNF-defective models, including CBP/EP300 or mitochondrial respiration inhibitors. A novel algorithm for the analysis of DepMap CRISPR screens independently identified synthetic lethality between SWI/SNF defects and EP300 or mitochondrial respiration genes, which we further revalidated in disease-relevant models. These results unravel novel genetic dependencies for SWI/SNF-defective cancers.

cancer biology↗

bioRxiv2024

Circumventing glioblastoma resistance to temozolomide through optimal drug combinations designed by quantitative systems pharmacology and machine learning

Glioblastoma is currently associated to a dismal prognosis despite intensive treatment involving maximal-safe surgery, radiotherapy and temozolomide (TMZ)-based chemotherapy. Disease progression or relapse is often due to initial or acquired resistance to temozolomide, which may be mediated by the over-expression of the repair enzyme MGMT. To design TMZ-based drug combinations circumventing the initial resistance of MGMT-overexpressing cells, a quantitative systems pharmacology (QSP) model representing TMZ cellular pharmacokinetics-pharmacodynamics and their connection to the most altered pathways in GBM was developed. This digital network representation of TMZ cellular pharmacology successfully integrates, in a mechanistic fashion, multi-type time- and dose-resolved datasets, available in control or MGMT-overexpressing cells. In silico target inhibition screening identified an optimal antitumor strategy consisting in priming cancer cells with inhibitors of the base excision repair and of the homologous recombination pathway prior to TMZ exposure. This drug combination was validated in dedicated experiments, thus allowing to re-sensitize cells which were initially resistant to TMZ. Using machine learning, functional signatures of response to such optimal multiagent therapy were derived to assist decision making about administering it to other cancer cell lines or patients. The developed framework can be extended to account for additional patientspecific altered pathways and may be translated towards the clinics by representing the tumor micro-environment and drug whole-body pharmacokinetics. Overall, we successfully demonstrated the relevance of combined QSP and machine learning to design multi-agent pharmacotherapies circumventing initial tumor resistance. One Sentence SummaryAn integrated in vitro-in silico approach allowed to design optimal drug combinations re-sensitizing temozolomide-resistant glioblastoma cells.

cancer biology↗

bioRxiv2020

NLRP3 controls ATM activation in response to DNA damage.

The DNA damage response (DDR) is essential to preserve genomic integrity and acts as a barrier to cancer. The ATM pathway orchestrates the cellular response to DNA double strand breaks (DSBs), and its attenuation is frequent during tumorigenesis. Here, we show that NLRP3, a Pattern Recognition Receptor known for its role in the inflammasome complex formation, interacts with the ATM kinase to control the early phase of DDR, independently of its inflammasome activity. NLRP3 down-regulation in human bronchial epithelial cells impairs ATM pathway activation as shown by an altered ATM substrate phosphorylation profile, and due to impaired p53 activation, confers resistance to acute genomic stress. Moreover, we found that NLRP3 is down-regulated in Non-Small Cell Lung Cancer (NSCLC) tissues and NLRP3 expression is correlated with patient overall survival. NLRP3 re-expression in NSCLC cells restores appropriate ATM signaling. Our findings identify a non-immune function for NLRP3 in genome integrity surveillance and strengthen the concept of a functional link between innate immunity and DNA damage sensing pathways.

cancer biology↗

bioRxiv2019

Optimizing circadian drug infusion schedules towards personalized cancer chronotherapy

AimsPrecision medicine requires accurate technologies for drug administration and proper systems pharmacology approaches for patient data analysis. Here, plasma pharmacokinetics (PK) data of the OPTILIV trial in which cancer patients received oxaliplatin, 5-fluorouracil and irinotecan via chronomodulated schedules delivered by an infusion pump into the hepatic artery were mathematically investigated.\n\nMethodsA pump-to-patient model was designed in order to accurately represent the drug solution dynamics from the pump to the patient blood. It was connected to semi-mechanistic PK models to analyse inter-patient variability in PK parameters.\n\nResultsLarge time delays of up to 1h41 between the actual pump start and the time of drug detection in patient blood was predicted by the model and confirmed by PK data. Sudden delivery spike in the patient artery due to glucose rinse after drug administration accounted for up to 10.7% of the total drug dose. New model-guided delivery profiles were designed to precisely lead to the drug exposure intended by clinicians. Next, the complete mathematical framework achieved a very good fit to individual time-concentration PK profiles and concluded that inter-subject differences in PK parameters was the lowest for irinotecan, intermediate for oxaliplatin and the largest for 5-fluorouracil. Clustering patients according to their PK parameter values revealed two patient subgroups for each drug in which inter-patient variability was largely decreased compared to that in the total population.\n\nConclusionsThis study provides a complete mathematical framework to optimize drug infusion pumps and inform on inter-patient PK variability, a step towards precise and personalized cancer chronotherapy.\n\nAuthor summaryAccuracy and safety of infusion pumps remain a critical issue in the clinics and the development of accurate mathematical models to optimize drug administration though such devices has a key part to play in the advancement of precision medicine. Here, PK data from cancer patient receiving irinotecan, oxaliplatin and 5-fluorouracil into the hepatic artery via an infusion pump was mathematically investigated. A pump-to-patient model was designed and revealed significant inconsistencies between intended drug profiles and actual plasma concentrations. This mathematical model was then used to suggest improved profiles in order to minimise error and optimise delivery. Physiologically-based PK models of the three drugs were then linked to the pump-to-patient model. The whole framework achieved a very good fit to data and allowed quantifying inter-patient variability in PK parameters and linking them to potential clinical biomarkers via patient clustering. The developed methodology improves our understanding of patient-specific drug pharmacokinetics towards personalized drug administration.

systems biology↗