Biology subjects

Sacco, F.

Publications and source records attributed to Sacco, F..

10 recordsLinked to original sources

bioRxiv2026

Organelle proteomics reveals novel metabolic vulnerabilities in FLT3-ITD cells

In acute myeloid leukemia (AML), the insertion site of internal tandem duplications (ITDs) within the FLT3 gene critically determines the sensitivity to tyrosine kinase inhibitors (TKIs). Despite recent advances, patients harboring ITDs in the tyrosine kinase domain (TKD) still lack effective therapeutic options. To elucidate the molecular basis underlying the differential TKI sensitivity of FLT3-ITD cells, we integrated high-resolution mass spectrometry-based (phospho)proteomics with subcellular fractionation. Our analysis revealed that midostaurin induces the subcellular redistribution of approximately 2500 proteins involved in crucial biological processes, including cell cycle control, autophagy, and metabolism. Functional analyses further demonstrated that the ITD insertion site determines the autophagy response to midostaurin and modulates mitochondrial metabolism, influencing organelle architecture and ATP production, even at steady state. Importantly, by integrating subcellular proteomic dataset with functional metabolic assays, we uncovered a lipid-dependent vulnerability of FLT3-ITD cells: lipid restriction enhances FLT3 trafficking to the plasma membrane, and markedly reduces cell viability, restoring midostaurin sensitivity of resistant FLT3-ITD cells. Together, our findings reveal that the FLT3-ITD insertion site orchestrates a coordinated remodeling of subcellular protein organization, autophagy, and metabolism, and identify lipid-mediated control of FLT3 compartmentalization as a therapeutically actionable mechanism to overcome TKI resistance in FLT3-ITD AML.

cancer biology↗

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

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↗

bioRxiv2019

Myo-REG: a portal for signaling interactions in muscle regeneration

Muscle regeneration is a complex process governed by the interplay between several muscle resident mononuclear cell populations. Following acute or chronic damage these cell populations are activated, communicate via cell-cell interactions and/or paracrine signals, influencing fate decisions via the activation or repression of internal signaling cascades. These are highly dynamic processes, occurring with distinct temporal and spatial kinetics. The main challenge toward a system level description of the muscle regeneration process is the integration of this plethora of inter- and intra-cellular interactions.\n\nWe integrated the information on muscle regeneration in a web portal. The scientific content annotated in this portal is organized into two information layers representing relationships between different cell types and intracellular signaling-interactions, respectively. The annotation of the pathways governing the response of each cell type to a variety of stimuli/perturbations occurring during muscle regeneration takes advantage of the information stored in the SIGNOR database. Additional curation efforts have been carried out to increase the coverage of molecular interactions underlying muscle regeneration and to annotate cell-cell interactions.\n\nTo facilitate the access to information on cell and molecular interactions in the context of muscle regeneration, we have developed Myo-REG, a web portal that captures and integrates published information on skeletal muscle regeneration.\n\nThe muscle-centered resource we provide is one of a kind in the myology field. A friendly interface allows users to explore, approximately 100 cell interactions or to analyze intracellular pathways related to muscle regeneration. Finally, we discuss how data can be extracted from this portal to support in silico modeling experiments.

bioinformatics↗

bioRxiv2018

In vivo phosphoproteomics reveals pathogenic signaling changes in diabetic islets

Progressive decline of pancreatic beta cells function is key to the pathogenesis of type 2 diabetes. Protein phosphorylation is the central mechanism controlling glucose-stimulated insulin secretion in beta cells. However, if and how signaling networks are remodeled in diabetic islets in vivo remain unknowns. Here we applied high-sensitivity mass spectrometry-based proteomics and quantified the levels of about 6,500 proteins and 13,000 phosphopeptides in islets of obese diabetic mice and matched controls. This highlighted drastic remodeling of key kinase hubs and signaling pathways. We integrated our phosphoproteomic dataset with a literature-derived signaling network, which revealed a crucial and conserved role of GSK3 kinase in the control of the beta cells-specific transcription factor PDX1 and insulin secretion, which we functionally verified. Our resource will enable the community to investigate potential mechanisms and drug targets in type 2 diabetes.

systems biology↗

bioRxiv2017

Skeletal muscle fibro-adipogenic progenitors of dystrophic mice are insensitive to NOTCH-dependent regulation of adipogenesis

Fibro adipogenic progenitors (FAPs) promote satellite cell differentiation in adult skeletal muscle regeneration. However, in pathological conditions, FAPs are responsible for fibrosis and fat infiltrations. Here we show that the NOTCH pathway negatively modulates FAP differentiation both in vitro and in vivo. However, FAPs isolated from young dystrophin-deficient mdx mice are insensitive to this control mechanism. Nonetheless, factors released by hematopoietic cells restore the sensitivity to NOTCH adipogenic inhibition. An unbiased mass spectrometry-based proteomic analysis of FAPs from muscles of wild type and mdx mice, revealed that the synergistic cooperation between NOTCH and inflammatory signals controls FAP differentiation. These results offer a basis for rationalizing the pathological outcomes of fat infiltrations in skeletal muscle and may suggest new therapeutic strategies to mitigate the detrimental effects of fatty depositions in muscles of dystrophic patients.\n\nHighlightsO_LISingle-cell mass cytometry reveals that wt and mdx FAPs are in different cell states.\nC_LIO_LIActivation of the NOTCH signaling pathway negatively regulates adipogenesis of wt but not mdx FAPs.\nC_LIO_LIDeep proteomics suggests a mechanism explaining the different sensitivity of mdx- FAPs to NOTCH.\nC_LIO_LITNF-a stimulation restores the anti-adipogenic effect of NOTCH in mdx FAPs.\nC_LI

cell biology↗