Biology subjects

Venafra, V.

Publications and source records attributed to Venafra, V..

7 recordsLinked to original sources

bioRxiv2025

Targeting Kinases to Reshape the Immunopeptidome landscape in Chronic Myeloid Leukemia

Antileukemia immunity is essential for disease control and maintaining treatment-free remission in chronic myeloid leukemia (CML). Immunotherapeutic approaches hold significant promise for enhancing immune-mediated disease control. However, successful immunotherapy relies on identifying CML-associated antigens and achieving robust antigen presentation. Protein kinases are central regulators of signaling, and protein turnover, influencing which peptides are processed and presented on HLA molecules. Indeed, previous works with CDK4/6 or MEK inhibitors showed that kinases influence immune recognition of cancer cells. However, the molecular mechanisms underlying the modulation of the immunopeptidome by kinases inhibitors treatment remained unclear. Here, we investigated whether pharmacological inhibition of key kinases, including BCR-ABL, JNK and LCK, could be leveraged to reshape the immunopeptidome of CML cells, enhancing the exposure of specific tumor-associated antigens. By employing high-sensitive mass spectrometry (MS)-based immunopeptidomics, we quantified more than 20,000 HLA-I peptides. Pharmacological suppression of LCK, JNK and BCR-ABL drastically remodels the antigen landscape, impacting the exposure of about 4,000 HLA-I ligands. By integrating immunopeptidomics, phospho-immunopeptidomics, and proteomic, our study revealed three complementary molecular mechanisms of kinase-dependent antigen control: direct phosphorylation of HLA-I peptides (i), modulation of source protein stability (ii), and control of transcription factors governing antigen expression (iii). Moreover, comparative HLA ligandome analysis of benign hematological specimens and CML primary samples identified a panel of about 90 frequently presented CML-exclusive peptides, whose presentation can be significantly increased by LCK and JNK inhibition. Our study establishes a framework to rationally combine kinase inhibitors with immunotherapies to enhance antigen visibility and improve antileukemic immunity.

cancer biology↗

bioRxiv2025

PatientProfiler: A network-based approach to personalized medicine

Deciphering the intricate mechanisms underlying reprogramming in cancer cells is a crucial challenge in oncology as it holds the key to advance our ability to diagnose and treat cancer patients. For this reason, comprehensive and patient-specific multi-omic characterization of tumor specimens has become increasingly common in clinical practice. While these efforts have advanced our understanding of the molecular mechanisms underlying breast cancer progression, the identification of personalized therapeutic approaches remains a distant goal. The main shortcoming is the absence of a robust computational framework to integrate and interpret the available multi-dimensional data and to drive translational solutions. To fill this gap, we developed PatientProfiler, a computational pipeline that leverages causal interaction data, annotated in our in-house manually-curated resource, SIGNOR, to address how the genetic and molecular background of single patients contributes to the establishment of a malignant phenotype. PatientProfiler is an open-source, R-based package composed of several functions that allows multi-omic data analysis and standardization, generation of patient-specific mechanistic models of signal transduction, and extraction of network-based prognostic biomarkers. To benchmark the tool, we retrieved genomic, transcriptomic, (phospho)proteomic, and clinical data derived from 122 treatment-naive breast cancer biopsies, available at the CPTAC portal. Thanks to this approach, we identified patient-specific mechanistic models (one patient, one network) that recapitulate dysregulated signaling pathways in breast cancer. This collection of models provides valuable insights into the underlying mechanisms of tumorigenesis and disease progression. Moreover, in-depth topological exploration of these networks has allowed us to define seven communities (subnetworks), each associated with a unique transcriptomic signature and a distinct prognostic value. In summary, our work demonstrates that PatientProfiler is a tool for patient-specific network analysis, advancing personalized medicine towards the identification of actionable biomarkers and tailored therapeutic strategies.

systems biology↗

bioRxiv2025

Phosphoproteomics reveals novel BCR::ABL1-independent mechanisms of resistance in chronic myeloid leukemia

BCR::ABL1 drives chronic myeloid leukemia (CML) disease and treatment, as revealed by the success of tyrosine kinase inhibitor (TKI) therapy. However, additional poorly characterized molecular pathways, acting as BCR::ABL1 independent mechanisms, play crucial roles in CML, contributing to leukemic stem cells (LSCs) persistence, TKI resistance and disease progression. Here, by combining high sensitive mass spectrometry (MS)-based phosphoproteomics with the SignalingProfiler pipeline, we obtained two signaling maps offering a comprehensive description of the BCR::ABL1 dependent and independent pro-survival signalling mechanisms. We leveraged these maps to unbiasedly and systematically discover therapeutic vulnerabilities, by implementing the Druggability Score computational algorithm. By this strategy, and in combination with in vitro and in ex vivo functional assays, we show a crucial role of acquired FLT3-dependency in resistant CML models. In conclusion, we reposition FLT3, one of the most frequently mutated drivers of acute leukemia, as a potential therapeutic target for TKI resistant CML patients.

systems biology↗

bioRxiv2024

NetworkCommons: bridging data, knowledge and methods to build and evaluate context-specific biological networks

SummaryWe present NetworkCommons, a platform for integrating prior knowledge, omics data, and network inference methods, facilitating their usage and evaluation. NetworkCommons aims to be an infrastructure for the network biology community that supports the development of better methods and benchmarks, by enhancing interoperability and integration. Availability and ImplementationNetworkCommons is implemented in Python and offers programmatic access to multiple omics datasets, network inference methods, and benchmarking setups. It is a free software, available at https://github.com/saezlab/networkcommons. Contactsaezlab@ebi.ac.uk, martin.garrido@embl.de Supplementary DataContribution guidelines, additional figures, and descriptions for data, knowledge, methods, evaluation strategies and their implementation are available in the Supplementary Data and in the NetworkCommons documentation at https://networkcommons.readthedocs.io/.

bioinformatics↗

bioRxiv2024

SignalingProfiler 2.0: a network-based approach to bridge multi-omics data to phenotypic hallmarks

Unraveling the cellular signaling remodeling upon a perturbation is a fundamental challenge to understand disease mechanisms and to identify potential drug targets. In this pursuit, computational tools that generate mechanistic hypotheses from multi-omics data have invaluable potential. Here we present SignalingProfiler 2.0, a multi-step pipeline to systematically derive context-specific signaling models by integrating proteogenomic data with prior knowledge-causal networks. This is a freely accessible and flexible tool that incorporates statistical, footprint-based, and graph algorithms to accelerate the integration and interpretation of multi-omics data. Through benchmarking and rigorous parameter selection on a proof-of-concept study, performed in metformin-treated breast cancer cells, we demonstrate the tools ability to generate a hierarchical mechanistic network that recapitulates novel and known drug-perturbed signaling and phenotypic outcomes. In summary, SignalingProfiler 2.0 addresses the emergent need to derive biologically relevant information from complex multi-omics data by extracting interpretable networks.

systems biology↗

bioRxiv2023

Unveiling the signaling network of FLT3-ITD AML improves drug sensitivity prediction

Currently, the identification of patient-specific therapies in cancer is mainly informed by personalized genomic analysis. In the setting of acute myeloid leukemia (AML), patient-drug treatment matching fails in a subset of patients harboring atypical internal tandem duplications (ITDs) in the tyrosine kinase domain of the FLT3 gene. To address this unmet medical need, here we develop a systems-based strategy that integrates multiparametric analysis of crucial signaling pathways, and patient-specific genomic and transcriptomic data with a prior-knowledge signaling network using a Boolean-based formalism. By this approach, we derive personalized predictive models describing the signaling landscape of AML FLT3-ITD positive cell lines and patients. These models enable us to derive mechanistic insight into drug resistance mechanisms and suggest novel opportunities for combinatorial treatments. Interestingly, our analysis reveals that the JNK kinase pathway plays a crucial role in the tyrosine kinase inhibitor response of FLT3-ITD cells through cell cycle regulation. Finally, our work shows that patient-specific logic models have the potential to inform precision medicine approaches.

cancer biology↗

bioRxiv2022

A key role of the WEE1-CDK1 axis in mediating TKI-therapy resistance in FLT3-ITD positive acute myeloid leukemia patients

Internal tandem duplications (ITDs) in the FLT3 gene are frequently identified and confer a poor prognosis in patient affected by acute myeloid leukemia (AML). The insertion site of the ITDs in FLT3 significantly impacts the sensitivity to tyrosine kinase inhibitors (TKIs) therapy, affecting patients clinical outcome. To decipher the molecular mechanisms driving the different sensitivity to TKIs therapy of FLT3-ITD mutation, we used high-sensitive mass spectrometry-based (phospho)proteomics and deep sequencing. Here, we present a novel generally-applicable strategy that supports the integration of unbiased large-scale datasets with literature-derived signaling networks. The approach produced FLT3-ITDs specific predictive models and revealed a crucial and conserved role of the WEE1-CDK1 axis in TKIs resistance. Remarkably, we found that pharmacological inhibition of the WEE1 kinase synergizes and strengthens the pro-apoptotic effect of TKIs therapy in cell lines and patient-derived primary blasts. In conclusion, this work proposes a new molecular mechanism of TKIs resistance in AML and suggests a combination therapy as option to improve therapeutic efficacy.

cancer biology↗