Therapeutic drug monitoring (TDM) of Antiseizure Medications (ASMs) is an essential tool for persons with epilepsy (PwE). Compared to traditional venipuncture, microsampling requires lower blood volume through less painful and invasive fingerprick offering a sampling methodology potentially performed at-home. This study aimed to validate the extraction method of ASMs from Capitainer®-qDBS microsampling. Five ASMs were considered. According to EMA guidelines, through technical and clinical validation, ASMs' quantification from qDBS device was performed by UHPLC-MS/MS. Extraction parameters were optimized by Design of Experiment. Clinical validation was performed to compare ASMs concentrations in Capitainer®-qDBS with those in plasma in 30 PwE. The method used in the chosen extraction procedure was proven to be accurate and precise. Intra and inter-assay reproducibility analyses showed accuracy and precision ≤15 % across the calibration range. Recovery was >75 %, and matrix effect >75 %, for most of the ASMs analyzed. Stability was tested at 7, 15, and 30 days of storage, showing mutual robustness at 30 days at room temperature. No hematocrit effect was observed over a range of 20-70 %. Linear regression and Bland-Altman analysis indicated a good correlation for the ASMs considered. A UHPLC-MS/MS assay was developed and validated according to EMA guidelines for quantifying ASMs from qDBS devices. This validation has the advantage of allowing the potential to utilize the new microsampling qDBS device, providing a patient-friendly approach to blood sampling eventually even at-home.

Several computational pipelines for biomedical data have been proposed to stratify patients and to predict their prognosis through survival analysis. However, these analyses are usually performed independently, without integrating the information derived from each of them. Clustering of survival data is an underexplored problem, and current approaches are limited for biomedical applications, whose data are usually heterogeneous and multimodal, with poor scalability for high-dimensionality. We introduce VAE-Surv, a multimodal computational framework for patients' stratification and prognosis prediction. VAE-Surv integrates a Variational Autoencoder (VAE), which reduces the high-dimensional space characterizing the molecular data, with a deep survival model, which combines the embedded information with the clinical features. The VAE embedding step prioritizes local coherence within the feature space to detect potential nonlinear relationships among the molecular markers. The latent representation is then exploited to perform K-means clustering. To test the clinical robustness of the algorithm, VAE-Surv was applied to the Genomed4all cohort of Myelodysplastic Syndromes (MDS), comparing the identified subtypes with the World Health Organization (WHO) classification. The survival outcome was compared with the state-of-the-art Cox model and its penalized versions. Finally, to assess the generalizability of the results, the method was also validated on an external MDS cohort. Tested on 2,043 patients in the GenomMed4All cohort, VAE-Surv achieved a median C-index of 0.78, outperforming classical approaches. In addition, the latent space enhanced the clustering performance compared to a traditional approach that applies the clustering directly to the input data. Compared to the WHO 2016 MDS subtypes, the analysis of the identified clusters showed that the proposed framework can capture existing clinical categorizations while also suggesting novel, data-driven patient groups. Even tested in an external MDS cohort of 2,384 patients, VAE-Surv achieved a good prediction performance (median C-index=0.74), preserving the interpretability of the main clinical and genetic features. VAE-Surv enables automatic identification of patients' clusters, while outperforming the traditional CoxPH model in survival prediction tasks at the same time. Applied to MDS use case, the obtained genetic-based clusters exhibit a clear survival stratification, and the application of the clinical information allowed high performance in prognosis prediction.

Hematological malignancies are a diverse group of cancers developing in the peripheral blood, the bone marrow or the lymphatic system. Due to their heterogeneity, the identification of novel and advanced molecular signatures is essential for enhancing their characterization and facilitate its translation to new pharmaceutical solutions and eventually to clinical applications. In this study, we collected publicly available microarray data for more than five thousand subjects, across thirteen hematological malignancies. Using PANDA to estimate gene regulatory networks (GRNs), we performed hierarchical clustering and network analysis to explore transcription factor (TF) interactions and their implications on biological pathways. Our findings reveal distinct clustering patterns among leukemias and lymphomas, with notable differences in gene and TF expression profiles. Gene Set Enrichment Analysis (GSEA) identified 57 significantly enriched KEGG pathways, highlighting both common and unique biological processes across HMs. We also identified potential drug targets within these pathways, emphasizing the role of TFs such as CEBPB and NFE2L1 in disease pathophysiology. Our comprehensive analysis enhances the understanding of the molecular landscape of HMs and suggests new avenues for targeted therapeutic strategies. These findings also motivate the adoption of regulatory networks, combined with modern biotechnological possibilities, for insightful pan-cancer exploratory studies.

We present a comprehensive molecular and histopathological characterization of nine patients with mitochondrial myopathy, predominantly manifesting progressive external ophthalmoplegia (PEO), associated with heteroplasmic variants in mitochondrial tRNA genes (mt-tRNA). Among the ten variants identified, four were novel and previously unreported in MITOMAP. Using laser capture microdissection and deep next-generation sequencing, we quantified heteroplasmy at the single-muscle-fibre level, demonstrating that cytochrome c oxidase (COX)–deficient fibres consistently reached near-homoplasmic mutant loads, whereas COX-positive fibres remained heteroplasmic with lower variant fractions. These findings firmly support the pathogenic role of all variants. Furthermore, digital droplet PCR revealed an increased mitochondrial DNA (mtDNA) content in COX-deficient fibres, indicating compensatory mitochondrial biogenesis. Of particular note, one patient harboured two novel heteroplasmic variants, m.10009G > A and m.15961G > A, for which long-read sequencing identified mitogenomes carrying both variants also in cis, suggesting the occurrence of mtDNA recombination in human tissue. By applying refined American College of Medical Genetics and Genomics (ACMG) criteria specific for mt-tRNA, we reclassified several variants as pathogenic or likely pathogenic, including three previously deemed of uncertain significance. Overall, our integrative approach—combining single-fibre molecular dissection, mtDNA quantification, and long-read sequencing—broadens the mutational spectrum of pathogenic mt-tRNA variants, highlights the diagnostic value of single-fibre analyses in confirming pathogenicity, and provides new insights into mitochondrial genome dynamics and compensatory responses in mitochondrial disease.

Expansion repeat disorders are caused by abnormal expansions of short tandem repeats (STRs). We evaluated targeted long-read nanopore sequencing for comprehensive characterization of STR expansions in 132 participants, including patients with myotonic dystrophy type 1 (DM1, n = 51) and DM2 (n = 3) already characterized by Southern blot, C9orf72-associated amyotrophic lateral sclerosis (ALS n = 11) evaluated by repeat primed polymerase chain reaction (RP-PCR; validation group), suspected spinocerebellar ataxia (SCA, n = 46), cerebellar ataxia with neuropathy and vestibular areflexia syndrome (CANVAS; n = 13), Huntington’s disease (1), Kennedy disease (1), Friedreich ataxia (FRDA, 2), and 4 healthy controls (test group). Native DNA was sequenced using adaptive sampling (AS) on Oxford Nanopore GridION or PromethION-2Solo platforms. Long-PCR sequencing was performed for comparison. We also developed NanoExpansion, a Python-based tool for sizing expanded alleles and detecting sequence interruptions. We correctly characterized all DM1, DM2, ALS, FRDA, Huntington’s disease, and Kennedy disease cases, and identified eight SCA27B, two SCA37, three SCA31, one SCA3, and nine CANVAS cases. Compared with the AS approach, long-PCR underestimated alleles exceeding 800 repeats. Repeat interruptions were detected in 27/51 (52.9%) DM1 cases and 1/10 (10%) ALS, but not in CANVAS, FRDA, Huntington’s disease, Kennedy disease, or SCA cases. This integrated sequencing analysis pipeline provides accurate, comprehensive characterization of repeat expansion disorders and has the potential to improve routine genetic diagnosis.