Biology subjects

Pasqualetti, F.

Publications and source records attributed to Pasqualetti, F..

10 recordsLinked to original sources

bioRxiv2026

From Single-Cell Emergent Behaviors to Clinical Outcome: PTEN-driven Migratory Efficiency as a Potential New Vulnerability in Glioblastoma

BackgroundGlioblastoma (GB) is a highly aggressive brain tumor with a median survival of approximately 14 months, primarily due to its ability to infiltrate healthy brain tissue both as single cells and in collectives. A deeper understanding of GB cell motility, both individual and collective, is crucial for developing patient-specific therapies. We aimed to characterize migration in patient-derived GB cells using advanced modeling to identify stratification markers and therapeutic vulnerabilities. MethodsWe developed Single-Cell Behavior Live Imaging (ScBLI), an approach integrating live imaging with computational analysis, applied to 30 GB primary cell cultures. Trajectories and morphological features were tracked and analyzed. Diffusion Entropy Analysis (DEA) was applied to classify trajectories based on the Delta Scaling parameter ({delta} scaling). We evaluated functional responses correlating all findings with clinical outcomes and transcriptomic profiles. ResultsWe analyzed 4,279 cell trajectories. Based on {delta} scaling (range 0.28-0.837), we defined three distinct motility groups: Low (L, {delta} scaling [≤]0.5), Medium (M, 0.5 < {delta} scaling [≤] 0.7), and High (H, {delta} scaling >0.7). Functional assays demonstrated that Group H cells are more performant in both positive and negative chemotaxis. Clinically, the three groups showed a clear linear progression with patient survival: High {delta} scaling correlated with the shortest survival (poorer prognosis), while Low {delta} correlated with the longest survival, suggesting that structured motility drives invasiveness. Integrative multi-omic analysis, encompassing both exome and transcriptome profiling, demonstrated that these groups are defined by distinct molecular landscapes rather than poor behavioral traits. Moreover, exome data revealed that Group H is significantly enriched in PTEN alterations (75% vs. 8% in Group L), with PTEN gain-of-function (GoF) mutations exclusively restricted to this group (100% vs 0% in Group L). Notably, within our extended cohort (n=51) currently characterized by whole-exome sequencing, we observed that specific PTEN GoF mutations were associated with a significantly shorter survival compared to PTEN wild-type cases (median OS 6.4 vs 16.6 months; p=0.02), which typically harbor the canonical loss of chromosome 10q. A similar clinical trend was observed when comparing directly GoF carriers to patients with truncating (Ter) alterations (median OS 6.4 vs 14.3 months; p=0.09). Conversely, no survival difference was found between truncating (Ter) mutations and wild-type cases. ConclusionOur findings demonstrate for the first time that migratory efficiency, quantified through DEA, represents a powerful predictor of glioblastoma aggressiveness. Tumor cells adopting highly efficient exploration strategies are strongly associated with poor clinical outcomes and are characterized by distinct molecular signatures, notably PTEN gain-of-function alterations. Statement of significanceOur multi-scale computational framework elucidates emergent behavioral phenotypes as pivotal drivers of glioblastoma progression. By demonstrating a correlation between enhanced migratory efficiency, PTEN gain-of-function, and significantly reduced overall survival, we establish a foundational paradigm for deciphering the emergent complexity governing tumor invasiveness.

cancer biology↗

bioRxiv2024

Learning and actioning general principles of cancer cell drug sensitivity

High-throughput screening platforms for the profiling of drug sensitivity of hundreds of cancer cell lines (CCLs) have generated large datasets that hold the potential to unlock targeted, anti-tumor therapies. In this study, we leveraged these datasets to create predictive models of cancer cells drug sensitivity. To this aim we trained explainable machine learning algorithms by employing cell line transcriptomics to predict the growth inhibitory potential of drugs. We used large language models (LLMs) to expand descriptions of the mechanisms of action (MOA) for each drug starting from available annotations, which were matched to the semantically closest pathways from reference knowledge bases. By leveraging this AI-curated resource, and the interpretability of our model, we demonstrated that pathways enriched for genes crucial for prediction often matched known drug-MOAs and essential genes, suggesting that our models learned the molecular determinants of drug response. Furthermore, we demonstrated that by incorporating only LLM-curated genes associated with MOAs, we enhanced the predictive accuracy of our drug models. To enhance translatability to a clinical setting, we employed a pipeline to align bulk RNAseq from CCLs, used for training the models, to those from patient samples, used for inference. We proved the effectiveness of our approach on TCGA samples, where patients best scoring drugs matched those prescribed for their cancer type. We further showed its usefulness by predicting and experimentally validating effective drugs for the patients of two highly lethal solid tumors, i.e. pancreatic cancer and glioblastoma. In summary, our method facilitates the inference and interpretation of cancer cell line drug sensitivity and holds potential to effectively translate them into new cancer therapeutics. Highlights-Interpretable drug-response prediction models were trained on large scale pharmacogenomics data sets (i.e. GDSC and PRISM). -Large language models were used to enhance the curation of biological pathways associated to drugs MOA -Unbiased interpretation of the models demonstrated learning of drug MOAs and gene essentiality -Inference of TCGA cohort samples recovered mono- and combination cancer drug prescriptions and indicate potential repurposing candidates. -Drug candidates predicted from bulk RNAseq samples of pancreatic cancer and glioblastoma were experimentally validated.

bioinformatics↗

bioRxiv2023

BIRC3: A Prognostic Predictor and Novel Therapeutic Target in TMZ-Resistant Glioblastoma Tumors

BackgroundGlioblastoma (GB) is an incurable malignant tumor of the central nervous system, with a poor prognosis. Robust molecular biomarkers associated with therapeutic response or survival are still lacking in GB. Previously, using NADH-fluorescence lifetime imaging (NADH-FLIM), as a new drug screening precision medicine ex-vivo approach, we categorized patient-derived vital tumors into TMZ responder (Resp) and non-responder (Non-Resp) groups, revealing differentially expressed genes. MethodsExpanding on our previous study, we assessed TMZ response in a larger cohort of primary and recurrent ex-vivo live GB tumors (n=33) using NADH-FLIM. Transcriptome analysis was performed to characterize TMZ Resp and Non-Resp cases, and in-silico and functional cellular investigations were conducted to explore the efficacy of potential biomarkers. ResultsGenes dysregulated in the previous study showed consistent expression patterns. BIRC3, a potent apoptosis inhibitor, was significantly upregulated in TMZ-resistant samples. BIRC3 expression complemented MGMT status as a prognostic factor in multiple TCGA cohorts. BIRC3 functioned as a prognostic factor of survival also in separate European private glioblastoma cohorts. The BIRC3 antagonist, AZD5582, in combination with TMZ, effectively reversed TMZ resistance by restoring apoptosis in glioblastoma cell lines and patient-derived organoids. ConclusionsBIRC3 holds promise as a prognostic biomarker and predictor of TMZ response in GB. Assessing BIRC3 expression could aid in stratifying patients for combined TMZ and AZD5582 therapy. Our study highlights the potential of functional precision medicine and BIRC3 assessment as a standard tool in glioblastoma clinical oncology, improving outcomes. KEYPOINTSO_LIBIRC3, previously overlooked, identified through dynamic precision medicine using TMZ perturbation of glioblastoma tissue as a robust prognostic factor. C_LIO_LIThe gene BIRC3 is an independent prognostic factor associated with shorter survival and TMZ resistance, rigorously validated across various case studies and datasets, including two expansive European case studies. C_LIO_LIProposal of anti-BIRC3 drug, AZD5582, shows promise as a novel therapeutic option to overcome TMZ resistance in GB tumors, providing hope for improved outcomes and personalized treatment strategies for patients with limited treatment options C_LI IMPORTANCE OF THE STUDYGlioblastoma (GB), an aggressive cancer type with a bleak prognosis, lacks dependable biomarkers for treatment prediction. Few markers like MGMT promoter methylation, IDH1 mutation, TERT gene mutations, and EGFR amplification are known, but their predictive consistency varies. Temozolomide (TMZ) resistance, seen in over 50% of GB patients, complicates matters. BIRC3, an apoptosis-inhibiting gene, displays heightened expression in TMZ-resistant tumors. Our study examined BIRC3 in GB patient samples, finding it an independent prognostic factor linked to shorter survival and TMZ resistance. Our research builds upon Wang et al.s 2016 and 2017 findings, delving deeper through TCGA data and European case studies. BIRC3s consistent prominence suggests its significance, with functional experiments confirming its role. We assessed AZD5582, targeting BIRC3, which, when combined with TMZ, curtailed cell growth and induced apoptosis. Notably, AZD5582 countered TMZ resistance in patient-derived GB-EPXs, except for low BIRC3 cases. Our precision medicine approach enhances personalized therapies and outcomes, highlighting BIRC3s potential as a prognostic marker and AZD5582 as a new therapy for TMZ-resistant GB.

cancer biology↗

bioRxiv2023

Using network control theory to study the dynamics of the structural connectome

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains dynamics. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter dynamics in a desired way. We have extensively developed and validated the application of NCT to the human structural connectome. Through these efforts, we have studied (i) how different aspects of connectome topology affect neural dynamics, (ii) whether NCT outputs cohere with empirical data on brain function and stimulation, and (iii) how NCT outputs vary across development and correlate with behavior and mental health symptoms. In this protocol, we introduce a framework for applying NCT to structural connectomes following two main pathways. Our primary pathway focuses on computing the control energy associated with transitioning between specific neural activity states. Our second pathway focuses on computing average controllability, which indexes nodes general capacity to control dynamics. We also provide recommendations for comparing NCT outputs against null network models. Finally, we support this protocol with a Python-based software package called network control theory for python (nctpy).

neuroscience↗

bioRxiv2022

Metabolic-imaging of human glioblastoma explants: a new precision-medicine model to predict tumor treatment response early

BackgroundGlioblastoma (GB) is the most severe form of brain cancer, with a 12-15 month median survival. Surgical resection, temozolomide (TMZ) treatment, and radiotherapy (RT) remain the primary therapeutic options for GB, and no new therapies have been introduced in recent years. This therapeutic standstill is primarily due to preclinical approaches that do not fully respect the complexity of GB cell biology and fail to test efficiently anti-cancer treatments. Therefore, better treatment screening approaches are needed. In this study, we have developed a novel functional precision medicine approach to test the response to anticancer treatments in organoids derived from the resected tumors of glioblastoma patients. MethodsGB organoids were grown for a short period of time to prevent any genetic and morphological evolution and divergence from the tumor of origin. We chose metabolic imaging by NAD(P)H fluorescence lifetime imaging microscopy (FLIM) to predict early and non-invasively ex-vivo anti-cancer treatment responses of GB organoids. TMZ was used as the benchmark drug to validate the approach. Whole-transcriptome and whole-exome analyses were then performed to characterize tumor cases stratification. ResultsOur functional precision medicine approach was completed within one week after surgery and two groups of TMZ Responder and Non Responder tumors were identified. FLIM-based metabolic tumor stratification was well-reflected at the molecular level, confirming the validity of our approach, highlighting also new target genes associated with TMZ treatment and identifying a new 17 gene molecular signature associated with survival. The number of promoter methylated tumors for the MGMT gene was higher in the responsive group, as expected, however, some non-methylated tumor cases turned out to be nevertheless responsive to TMZ, suggesting that our procedure could be synergistic with the classical MGMT methylation biomarker. ConclusionsFor the first time, FLIM-based metabolic imaging was used on ex-vivo live glioblastoma organoids. Unlike other approaches, ex-vivo patient-tailored drug response is performed at an early stage of tumor culturing with no animal involvement and with minimal tampering with the original tumor cytoarchitecture. This functional precision medicine approach can be exploited in a range of clinical and laboratory settings to improve the clinical management of GB patients and implemented on other cancers as well.

molecular biology↗

bioRxiv2022

Single-cell molecular characterization to partition the human glioblastoma tumor microenvironment (TME) genetic background

Glioblastoma (GB) is a devastating primary brain malignancy. Recurrence of GB is inevitable despite the standard treatment of surgery, chemotherapy, and radiation, and the median survival is limited to around 15 months. Barriers to treatment include the complex interactions among the different cellular components inhabitant of the tumor microenvironment. These challenges are further compounded by extensive inter- and intra-tumor heterogeneity and by the dynamic plasticity of GB cells. The complex heterogeneous nature of GB cells is helped by the local inflammatory tumor microenvironment, which mostly induces tumor aggressiveness and drug resistance. More effective therapy development heavily depends on higher resolution molecular subtype signatures. All cellular components of a GB tumor are subjected to a continuous pressure inducing proliferation that might favor the accumulation of genetic mutations. Understanding the genetic background of several cellular component s belonging to a GB microenvironment could give insights into tumor behavior and progression. In the present study by using fluorescent multiple labeling and DEPArray cell separator, we recovered several single cells or groups of single cells from populations of different origins from IDH-WT GB samples. From each GB sample, we collected astrocytes (GFAP+), microglia (IBA+), stem cells (CD133+), and endothelial cells (CD105+) and performed Copy Number Aberration (CNA) analysis with a low sequencing depth. The same tumors were subjected to a bulk CNA analysis. The tumor partition in its single components allowed single-cell molecular subtyping which revealed new aspects of GB altered genetic background. Nowadays, single-cell approaches are leading to a new understanding of GB physiology and disease. Moreover, single-cell CNAs resource will permit new insights into genome heterogeneity, mutational processes, and clonal evolution in malignant tissues.

cancer biology↗

bioRxiv2021

Pathological and metabolic underpinnings of energetic inefficiency in temporal lobe epilepsy

The human brain consumes a disproportionate amount of energy to generate neural dynamics. Yet precisely how energetic processes are altered in neurological disorders remains far from understood. Here, we use network control theory to profile the brains energy landscape, describing the rich dynamical repertoire supported by the structural connectome. This approach allows us to estimate the energy required to activate a circuit, and determine which regions most support that activation. Focusing on temporal lobe epilepsy (TLE), we show that patients require more control energy to activate the limbic network than healthy volunteers, especially ipsilateral to the seizure focus. Further, greater energetic costs are largely localized to the ipsilateral temporo-limbic regions. Importantly, the energetic imbalance between ipsilateral and contralateral temporo-limbic regions is tracked by asymmetric metabolic patterns, which in turn are explained by asymmetric gray matter volume loss. In TLE, failure to meet the extra energy demands may lead to suboptimal brain dynamics and inadequate activation. Broadly, our investigation provides a theoretical framework unifying gray matter integrity, local metabolism, and energetic generation of neural dynamics.

neuroscience↗

bioRxiv2019

Brain state stability during working memory is explained by network control theory, modulated by dopamine D1/D2 receptor function, and diminished in schizophrenia

Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory entails brain-wide switching between activity states. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Schizophrenia patients show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations.

neuroscience↗

bioRxiv2018

Optimization of Energy State Transition Trajectory Supports the Development of Executive Function During Youth

Executive function develops rapidly during adolescence, and failures of executive function are associated with both risk-taking behaviors and psychopathology. However, it remains relatively unknown how structural brain networks mature during this critical period to facilitate energetically demanding transitions to activate the frontoparietal system, which is critical for executive function. In a sample of 946 human youths (ages 8-23 yr) who completed diffusion imaging as part of the Philadelphia Neurodevelopment Cohort, we capitalized upon recent advances in network control theory in order to calculate the control energy necessary to activate the frontoparietal system given the existing structural network topology. We found that the control energy required to activate the frontoparietal system declined with development. Moreover, we found that this control energy pattern contains sufficient information to make accurate predictions about individuals brain maturity. Finally, the control energy costs of the cingulate cortex were negatively correlated with executive performance, and partially mediated the development of executive performance with age. These results could not be explained by changes in general network control properties or in network modularity. Taken together, our results reveal a mechanism by which structural networks develop during adolescence to facilitate the instantiation of activation states necessary for executive function.\n\nSIGNIFICANCE STATEMENTExecutive function undergoes protracted development during youth, but it is unknown how structural brain networks mature to facilitate the activation of the frontoparietal cortex that is critical for executive processes. Here, we leverage recent advances in network control theory to establish that structural brain networks evolve in adolescence to lower the energetic cost of activating the frontoparietal system. Our results suggest a new mechanistic framework for understanding how brain network maturation supports cognition, with clear implications for disorders marked by executive dysfunction, such as ADHD and psychosis.

neuroscience↗

bioRxiv2018

White Matter Network Architecture Guides Direct Electrical Stimulation Through Optimal State Transitions

Electrical brain stimulation is currently being investigated as a potential therapy for neurological disease. However, opportunities to optimize and personalize such therapies are challenged by the fact that the beneficial impact (and potential side effects) of focal stimulation on both neighboring and distant regions is not well understood. Here, we use network control theory to build a formal model of brain network function that makes explicit predictions about how stimulation spreads through the brains white matter network and influences large-scale dynamics. We test these predictions using combined electrocorticography (ECoG) and diffusion weighted imaging (DWI) data from patients with medically refractory epilepsy undergoing evaluation for resective surgery, and who volunteered to participate in an extensive stimulation regimen. We posit a specific model-based manner in which white matter tracts constrain stimulation, defining its capacity to drive the brain to new states, including states associated with successful memory encoding. In a first validation of our model, we find that the true pattern of white matter tracts can be used to more accurately predict the state transitions induced by direct electrical stimulation than the artificial patterns of a topological or spatial network null model. We then use a targeted optimal control framework to solve for the optimal energy required to drive the brain to a given state. We show that, intuitively, our model predicts larger energy requirements when starting from states that are farther away from a target memory state. We then suggest testable hypotheses about which structural properties will lead to efficient stimulation for improving memory based on energy requirements. We show that the strength and homogeneity of edges between controlled and uncontrolled nodes, as well as the persistent modal controllability of the stimulated region, predict energy requirements. Our work demonstrates that individual white matter architecture plays a vital role in guiding the dynamics of direct electrical stimulation, more generally offering empirical support for the utility of network control theoretic models of brain response to stimulation.

neuroscience↗