Publications by Marta Lovino

Down syndrome (DS) represents one of the most common genetic disorders attributable to a partial or complete trisomy of chromosome 21 that affects about 1 in 700 individuals at birth. The diagnosis of Alzheimer's Disease (AD)-correlated cognitive decline in this population requires new approaches and new biomarkers that comprehensively assess health status and early cognitive decline. In this observational study, we explored for the first time the relation of IL-18, a cytokine member of IL-1 family involved in both innate and acquired immune responses, with DS associated cognitive decline. We observed that plasma total IL-18, in subjects with DS over 35 with and without AD-related cognitive decline, and plasma concentrations of its binding protein in subjects with DS (19-35 years) were correlated with lower plasma concentrations of Transforming Growth Factor (TGF-beta 1), which are linked to an increased rate of cognitive decline in adults with DS. In addition, we found a significant association between low baseline concentrations of Free IL-18, the active form of the cytokine, and an increased rate of cognitive decline at 12 months, calculated as delta of the Test for Severe Impairment (dTSI), in individuals with DS (19-35 years). Finally, we demonstrated a reduction of Free IL-18/TNF-alpha ratio, considered as a new possible double biomarker, in both young and older adult DS subjects without AD-related cognitive decline (area under the receiver operating curve (AUC) was 0.82 and 0.71, respectively), suggesting the advantage of the composite biomarkers in the discrimination of patients from healthy people over single biomarkers.

Medulloblastoma, the most common malignant brain tumor of childhood, exhibits significant biological complexity that demands deeper exploration. Here, we present a large multiomics dataset integrating data from 384 primary medulloblastoma patient samples across five omic layers: CpG methylome, transcriptome, proteome, phosphoproteome, and metabolome, paired with associated clinical metadata. Data integration revealed intertumoral heterogeneity of lipid metabolism across proteomic subtypes. Notably, while the MYC-FASN-SCD axis drives lipid biosynthesis, pathway inhibition elicits a compensatory escape mechanism in vivo through exogenous fatty acid uptake. Unexpectedly, we demonstrated that MYC triggers lipid storage, creating a unique dependency on lipid droplet-mitochondria communications to sustain tumor maintenance in vivo. Together, this comprehensive analysis reveals a targetable vulnerability downstream of MYC that constitutes a promising therapeutic approach to treat currently untreatable medulloblastoma subtypes.

Whole Slide Images (WSIs) are a core data modality in computational pathology, yet their gigapixel resolution requires weakly supervised approaches such as Multiple Instance Learning (MIL) for prognostic modeling. While recent advances in representation learning have introduced domain-specific and foundation models for histopathology, it remains unclear how the choice of patch-level embedding influences survival prediction performance when combined with different MIL architectures and applied across heterogeneous cancer cohorts. Systematic evaluations addressing this gap are still limited. In this work, we present a comprehensive benchmark aimed at deriving practical guidelines for MIL-based Overall Survival (OS) prediction from WSIs. We compare four representative tile embedding strategies (ResNet50, ProvGigaPath, UNI, and CONCH) across five The Cancer Genome Atlas (TCGA) cohorts: bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), head and neck squamous cell carcinoma (HNSC), stomach adenocarcinoma (STAD) and one dataset from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), clear cell renal cell carcinoma (ccRCC). Patch-level features are aggregated using three state-of-the-art MIL survival models: Attention-Based MIL (ABMIL), Transformer-based MIL (TransMIL), and Dual-Stream MIL (DSMIL). Furthermore, the analysis is extended to include a comparison with two state-of-the-art slide-level encoders, TITAN and the ProvGigaPath slide encoder, utilizing a Cox Proportional Hazard (CPH) model as the prediction head to benchmark patch-aggregation approaches against end-to-end slide representations. Prognostic performance is assessed using the concordance index (c-index), with interpretability evaluated through risk stratification analysis. Our results show that embedding choice has a substantial and consistent impact on OS prediction accuracy, robustness, and interpretability across cancer types, with domain-specific and foundation models outperforming conventional convolutional baselines. These findings provide evidence-based practical guidelines for designing robust and generalizable WSI-based survival prediction pipelines in computational pathology.

Computational pathology has seen significant advancements through the application of deep learning to Whole-Slide Images (WSIs). However, most existing models operate under a mono-tissue assumption, using only a single WSI to inform the prediction. This limitation is primarily driven by the structure of publicly available datasets, which rarely provide more than one slide per patient. Consequently, multi-tissue data integration remains underexplored in computational pathology, not due to a lack of clinical relevance but rather to a shortage of both public datasets and modeling strategies capable of handling multi-sample input. To address this gap, this study introduces MuGNet, a novel graph-based deep learning framework that enables patient-level prediction from a variable number of WSIs. The MuGNet framework represents each patient as a network of tissue samples, with each WSI encoded as a node in a patient-specific graph. Edges are established to capture structural or biological relationships between tissues. A Graph Neural Network (GNN) with an attention-based message passing mechanism is then employed to process the patient graphs and generate patient-level predictions. MuGNet was evaluated on a private High Grade Serous Carcinoma (HGSC) dataset across three distinct prediction tasks to assess its robustness: one classification task (Homologous Recombination Deficiency status prediction, HRD) and two regression tasks (Overall Survival and Platinum Free Interval prediction, OS and PFI). MuGNet's performance was compared against three state-of-the-art Multiple Instance Learning (MIL) models: Attention-Based MIL (ABMIL), Dual-Stream MIL (DSMIL), and Transformer-based MIL (TransMIL). For the HRD status prediction task, MuGNet achieved an AUC of 0.7220 ± 0.0739, approximately 6% higher than the most performant MIL model. For the OS and PFI tasks, the model demonstrated an improvement in the C-index of approximately 10% over ABMIL and DSMIL and up to 2% over TransMIL, highlighting its superior performance and robustness in handling multi-sample input. Beyond its predictive accuracy, MuGNet provides interpretability. The attention weights learned across the graph nodes allow for the identification of tissue sites that contribute most to the prediction, revealing that the model consistently prioritizes metastasis-rich tissue regions such as the lymph nodes.

Ovarian high-grade serous carcinoma (ovarian HGSC) is a clinically challenging disease with a poor prognosis, particularly for patients receiving neoadjuvant chemotherapy (NACT) before debulking surgery. In this study, we evaluate the progression-free interval (PFI) after NACT based on hematoxylin and eosin-stained whole-slide images (WSIs) of omental tumor tissue. Digital pathology tools are emerging, aiming at assisting pathologists in diagnosis and analysis; however, distinguishing features associated with response to NACT remain elusive. Multiple instance learning (MIL) coupled with attention mechanisms has shown promise in predicting treatment response from WSIs. Additionally, segmentation tools can identify and delineate regions in WSIs. Whereas some efforts have been made to develop explainable models for clinical outcome, there remains a need for genuinely interpretable models for pathologists. This article introduces the PATHOS framework, a novel approach to explaining crucial features of treatment response based on the PFI time in NACT treated patients from WSIs. PATHOS is composed of three blocks: (1) MIL block to identify informative regions, (2) panoptic segmentation and downstream analysis block for feature computation, and (3) classification block to predict the PFI. The results demonstrate that PATHOS enhances the interpretability of response to NACT in ovarian HGSC patients by highlighting pathologically significant features relevant to PFI prediction, such as tumor cell morphology, stromal abundance, and the spatial distribution of stromal regions. Furthermore, PATHOS identifies approximately 10% of the total WSI area as an informative region for clinical outcome.

PURPOSE. Photoreceptors are retinal cells with a high glucose metabolism and retinal degeneration, specifically retinitis pigmentosa (RP), affects glycolysis. We aimed to evaluate changes in the expression of genes related to glucose metabolism in rod photoreceptors at different stages of retinal degeneration in murine models and human retinal organoids. METHODS. RNA sequencing (RNA-seq) analysis was performed on a photoreceptor-like cell line induced to undergo degeneration and validated by real-time qPCR analysis of retinas from two murine models and one human organoid model of RP. Bioinformatic analysis was performed on published RNA-seq datasets from three murine RP models. Real-time qPCR analysis was also performed on retinas treated with an adeno-associated virus type 2 vector carrying the neurotrophic H105A peptide, derived from the pigment epithelium-derived factor. RESULTS. The aerobic glycolysis genes, Hk2, Pkm1, Pkm2, Ldha, and Slc6a6 and other glucose metabolism genes were found downregulated in the in vitro model of photoreceptor degeneration and in the in vivo RhoP23H/+, rd1, and rd10 models at early stages of the disease. The decreased expression of the aerobic glycolysis genes, except for PKM2, was confirmed in human organoids with mutations in the USH2A gene associated with RP. Expression was partially recovered in RhoP23H/+ retinas after treatment with the adeno-associated virus type 2 vector expressing the neurotrophic H105A peptide. CONCLUSIONS. Glucose metabolism gene expression was found altered during the progression of RP in murine and human models of the disease. Expression was partially recovered in a molecular response to the treatment with the neurotrophic factor H105A.

A gene fusion is a chromosomal aberration from juxtaposing separate genes. Since some gene fusions are involved in tumorigenesis, proper gene fusion investigation and analysis are crucial in the literature. After DNA/RNA sample extraction, detecting gene fusions requires first gene fusion detection tools, which usually provide many false positives. Given the high experimental costs in wet lab validation of a single fusion, gene fusion prioritization tools were made available over the years to significantly narrow down candidate gene fusions for validation (e.g., Oncofuse, Pegasus, DEEPrior, ChimerDriver). Although a few reviews about gene fusion detection tools are available, a benchmark on prioritization tools is not available yet in the literature. The aim of this paper is twofold: 1. to provide a curated dataset for a fair gene fusion prioritization tool evaluation. 2. to develop a proper comparison based on time, resources, and tool confidence on selected gene fusions. Based on this benchmark, it can be stated that ChimerDriver is the most reliable tool for prioritizing oncogenic fusions.

The control of soil-borne diseases in hops, such as Verticillium wilt remains challenging due to the limited effectiveness of fungicides, the perennial nature of hop cultivation, and the long-term persistence of the pathogens in the soil. Microbial biocontrol agents (mBCAs) with plant growth-promoting (PGP) and antagonistic effects offer a sustainable ecofriendly alternative for hops protection. Two Pseudomonas spp. strains from the UniMORE microbial collection were selected for this study based on their strong antagonistic activity against Verticillium spp. and multiple plant growth-promoting (PGP) traits. Rhizospheric and endophytic colonization capacities of the strains DLS1929 and DLS2318 were evaluated in hop plants (cv. Cascade) under controlled conditions at seven- and fourteen-days post-inoculation (DPI). Both bacterial strains were rhizosphere and endorhiza competent, with slight differences in their abundances. The highest cell density was observed at 7 DPI for the strain DLS2318, reaching log10 6.39 CFU g−1 root fresh weight in the rhizosphere and log10 4.75 CFU g−1 root fresh weight in the endorhiza; at 14 DPI, colonization results were in line with the previous assessment. Confocal laser scanning microscopy visualization of both eGFP-tagged Pseudomonas spp. strains confirmed their rhizosphere competence in hop. Additionally, root colonization by these bacteria enhanced the photosynthetic capacity in hop leaves, supporting their potential as a PGP agents observed in vitro. Successful root colonization and PGP effects are key prerequisites for an effective biocontrol of soilborne pathogens. Further studies are required to assess the consistent efficacy in the field of these beneficial mBCA candidates. This research was funded by the Italian Ministry of University and Research (MUR), under the European Union funding – Next Generation EU - PRIN- 2022, (prot. 2022M3HR45) project: “IoHOP: Quality valorization of the Italian hop based on a multi-approach strategy”.