Carlo Caltagirone

Background: Mutations in the FBXO7 gene (PARK15) cause an autosomal recessive, early-onset neurodegenerative disorder typically presenting as Parkinsonian-Pyramidal Syndrome (PPS). Despite its recognition, the high phenotypic variability often delays diagnosis. Here, we report a novel Italian family and synthesize data from all published cases to date, offering an updated clinical and molecular overview of the disease. Methods: We performed clinical and molecular characterization of a newly identified family. Furthermore, we conducted a systematic literature review (from 2008 to 2026) to aggregate clinical, genetic, and geographic data of all reported PARK15 cases. Results: Two siblings presented with a complex phenotype including early-onset parkinsonism, cognitive decline, psychiatric symptoms, and aphasia-type speech disorders. Genetic analyses identified two novel likely pathogenic variants: a missense substitution in the UBL domain (p.Ile74Met) and a frameshift indel (p.Val233GlufsTer8). The literature review (incorporating clinical data from Europe, Asia, and South America) confirms a high prevalence of postural instability (87.5%), bradykinesia (83.3%), and pyramidal signs (~60%). We observed a distinct distribution of variants: missense mutations cluster in the N-terminal UBL and F-box domains, while truncating variants are more common in the C-terminal region. Discussion: Our findings expand the FBXO7 mutational landscape and underscore the “atypical” clinical markers, such as pyramidal signs and cognitive decline, that distinguish PARK15 from other recessive forms of parkinsonism like PARK2 and PARK6. The dual role of FBXO7 in mitochondrial quality control and proteasomal assembly suggests a broad disruption of cellular homeostasis. These observations refine genotype–phenotype correlations and may guide variant interpretation in routine diagnostic settings.

The increasing burden of cancer globally necessitates innovative approaches for diagnosis, prognosis, and treatment. This article explores the transformative impact of genomics and artificial intelligence (AI) in precision oncology, addressing how their convergence is reshaping cancer care and its challenges. Methods: This review synthesizes current research on the applications of genomics, including next-generation sequencing, and AI, such as machine learning and deep learning, across the cancer care continuum. It examines their roles in identifying genetic variants, assessing cancer risk, guiding targeted therapies and immunotherapy, predicting treatment response, and enabling early detection through liquid biopsies. Results: Genomics and AI are revolutionizing oncology by enabling personalized treatment strategies, improving early detection, and overcoming drug resistance. AI enhances the interpretation of complex genomic data, facilitates drug repurposing, and accelerates the development of novel therapeutics. However, challenges remain regarding data standardization, interpretability, bias in AI algorithms, and ethical considerations. Conclusions: The integration of genomics and AI holds immense potential to advance precision oncology, offering more effective, equitable, and sustainable cancer care. Addressing current challenges and fostering interdisciplinary training will be crucial to fully harness these technologies and redefine oncology practice.

Background/Objectives: Centralizing genetic sequencing in specialized facilities is pivotal for reducing the costs associated with diagnostic testing. These centers must be able to verify data quality and ensure sample integrity. This study aims at developing a protocol for tracking NGS-analyzed samples to prevent errors and mix-ups, ensuring proper quality control, accuracy, and reliability in genetic testing procedures. To this purpose, a protocol based on the genotyping of a panel of 60 single-nucleotide polymorphisms (SNPs) by OpenArray^TM technology was employed. Methods: The protocol was initially tested on a cohort of 758 samples and subsequently validated on a cohort of 100 samples. Furthermore, its ability to accurately detect identical and different samples was evaluated through a simulation test conducted on an additional 100 samples. Results: In total, 55 probes achieved a call rate ≥90% and were subjected to the sample matching process performed by an R tool specifically developed. The SNP panel achieved a random match probability of 3.29 × 10⁻¹⁵, proving its suitability for efficiently tracking samples and rapidly identifying any errors or mix-up during the analytical processing. Conclusions: The features of OpenArray^TM technology, cost-effectiveness, rapid analysis, and high discriminative power make it a suitable tool for sample tracking. In conclusion, this method represents a valuable example for promoting laboratory centralization and minimizing the risks related to different laboratory procedures and the management of a high number of samples.

Background/Objectives: Artificial intelligence and large language models like ChatGPT and Google’s Gemini are promising tools with remarkable potential to assist healthcare professionals. This study explores ChatGPT and Gemini’s potential utility in assisting clinicians during the first evaluation of patients with suspected neurogenetic disorders. Methods: By analyzing the model’s performance in identifying relevant clinical features, suggesting differential diagnoses, and providing insights into possible genetic testing, this research seeks to determine whether these AI tools could serve as a valuable adjunct in neurogenetic assessments. Ninety questions were posed to ChatGPT (Versions 4o, 4, and 3.5) and Gemini: four questions about clinical diagnosis, seven about genetic inheritance, estimable recurrence risks, and available tests, and four questions about patient management, each for six different neurogenetic rare disorders (Hereditary Spastic Paraplegia type 4 and type 7, Huntington Disease, Fragile X-associated Tremor/Ataxia Syndrome, Becker Muscular Dystrophy, and FacioScapuloHumeral Muscular Dystrophy). Results: According to the results of this study, GPT chatbots demonstrated significantly better performance than Gemini. Nonetheless, all AI chatbots showed notable gaps in diagnostic accuracy and a concerning level of hallucinations. Conclusions: As expected, these tools can empower clinicians in assessing neurogenetic disorders, yet their effective use demands meticulous collaboration and oversight from both neurologists and geneticists.

Recent advancements in Next-Generation Sequencing (NGS) technologies have revolutionized genomic research, presenting unprecedented opportunities for personalized medicine and population genetics. However, issues such as data silos, privacy concerns, and regulatory challenges hinder large-scale data integration and collaboration. Federated Learning (FL) has emerged as a transformative solution, enabling decentralized data analysis while preserving privacy and complying with regulations such as the General Data Protection Regulation (GDPR). This review explores the potential use of FL in genomics, detailing its methodology, including local model training, secure aggregation, and iterative improvement. Key challenges, such as heterogeneous data integration and cybersecurity risks, are examined alongside regulations like GDPR. In conclusion, successful implementations of FL in global and national initiatives demonstrate its scalability and role in supporting collaborative research. Finally, we discuss future directions, including AI integration and the necessity of education and training, to fully harness the potential of FL in advancing precision medicine and global health initiatives.

Rare diseases are heterogeneous diseases characterized by various symptoms and signs. Due to the low prevalence of such conditions (less than 1 in 2000 people), medical expertise is limited, knowledge is poor and patients’ care provided by medical centers is inadequate. An accurate diagnosis is frequently challenging and ongoing research is also insufficient, thus complicating the understanding of the natural progression of the rarest disorders. This review aims at presenting the multimodal approach supported by the integration of multiple analyses and disciplines as a valuable solution to clarify complex genotype–phenotype correlations and promote an in-depth examination of rare disorders. Taking into account the literature from large-scale population studies and ongoing technological advancement, this review described some examples to show how a multi-skilled team can improve the complex diagnosis of rare diseases. In this regard, Facio-Scapulo-Humeral muscular Dystrophy (FSHD) represents a valuable example where a multimodal approach is essential for a more accurate and precise diagnosis, as well as for enhancing the management of patients and their families. Given their heterogeneity and complexity, rare diseases call for a distinctive multidisciplinary approach to enable diagnosis and clinical follow-up.

Autosomal dominant retinitis pigmentosa (AD-RP) is caused by several genes, among which RHO is one of the most investigated. This article will be focused on RHO and its role in explaining AD-RP cases in the Italian population, taking advantage of the experience of the Genomic Medicine Laboratory UILDM at the Santa Lucia Foundation IRCCS. The retrospective evaluation of the distribution of RHO variants in the Italian patients with a clinical suspicion of RP pointed out eight variants. Of them, four variants (c.632A>T, c.1040C>T, c.1030C>T, c.383_392del) were pathogenic and made it possible to confirm the diagnosis of AD-RP in nine affected patients, highlighting a lower frequency (17%) of RHO variants compared to previous studies (30–40%). In addition, this study identified four variants classified as Variants of Uncertain Significance (VUS). In conclusion, the experience of the Genomic Medicine Laboratory provides an overview of the distribution of RHO variants in the Italian population, highlighting a slightly lower frequency of these variants in our cases series compared to previous reports. However, further studies on RHO variants are essential to characterize peculiar RP phenotypes and extend the spectrum of disease associated with this gene.