Improving single nucleotide polymorphisms genotyping accuracy for dihydropyrimidine dehydrogenase testing in pharmacogenetics

Fluoropyrimidines, crucial in cancer treatment, often cause toxicity concerns even at standard doses. Toxic accumulation of fluoropyrimidine metabolites, culminating in adverse effects, can stem from impaired dihydropyrimidine dehydrogenase (DPYD) enzymatic function. Emerging evidence underscores the role of single nucleotide polymorphisms (SNPs) in DPYD gene, capable of inducing DPYD activity deficiency. Consequently, DPYD genotyping’s importance is on the rise in clinical practice before initiating fluoropyrimidine treatment. Although polymerase chain reaction (PCR) followed by Sanger sequencing (SS; PCR-SS) is a prevalent method for DPYD genotyping, it may encounter limitations. In this context, there is reported a case in which a routine PCR-SS approach for genotyping DPYD SNP rs55886062 failed in a proband of African descent. The Clinical Pharmacogenetics Implementation Consortium (CPIC) categorizes the guanine (G) allele of this SNP as non-functional. The enforcement of whole genome sequencing (WGS) approach led to the identification of two adenine (A) insertions near the PCR primers annealing regions in the proband, responsible for a sequence frameshift and a genotyping error for rs55886062. These SNPs (rs145228578, 1-97981199-T-TA and rs141050810, 1-97981622-G-GA) were extremely rare in non-Finnish Europeans (0.05%) but prevalent in African populations (16%). Although limited evidence was available for these SNPs, they were catalogued as benign variants in public databases. Notably, these two SNPs exhibited a high linkage disequilibrium [LD; squared correlation coefficient (R2) = 0.98]. These findings highlighted the importance to consider the prevalence of genetic variants within diverse ethnic populations when designing primers and probes for SNP genotyping in pharmacogenetic testing. This preventive measure is essential to avoid sequence frameshifts or primer misalignments arising from SNP occurrences in the genome, which can compromise PCR-SS and lead to genotyping failures. Furthermore, this case highlights the significance of exploring alternative genotyping approaches, like WGS, when confronted with challenges associated with conventional techniques

Genetic Predisposition to Solid Pediatric Cancers

Progresses over the past years have extensively improved our capacity to use genome-scale analyses-including high-density genotyping and exome and genome sequencing-to identify the genetic basis of pediatric tumors. In particular, exome sequencing has contributed to the evidence that about 10% of children and adolescents with tumors have germline genetic variants associated with cancer predisposition. In this review, we provide an overview of genetic variations predisposing to solid pediatric tumors (medulloblastoma, ependymoma, astrocytoma, neuroblastoma, retinoblastoma, Wilms tumor, osteosarcoma, rhabdomyosarcoma, and Ewing sarcoma) and outline the biological processes affected by the involved mutated genes. A careful description of the genetic basis underlying a large number of syndromes associated with an increased risk of pediatric cancer is also reported. We place particular emphasis on the emerging view that interactions between germline and somatic alterations are a key determinant of cancer development. We propose future research directions, which focus on the biological function of pediatric risk alleles and on the potential links between the germline genome and somatic changes. Finally, the importance of developing new molecular diagnostic tests including all the identified risk germline mutations and of considering the genetic predisposition in screening tests and novel therapies is emphasized

Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry

Image-based identification of circulating tumor cells in microfluidic cytometry condition is one of the most challenging perspectives in the Liquid Biopsy scenario. Here we show a machine learning-powered tomographic phase imaging flow cytometry system capable to provide high-throughput 3D phase-contrast tomograms of each single cell. In fact, we show that discrimination of tumor cells against white blood cells is potentially achievable with the aid of artificial intelligence in a label-free flow-cyto-tomography method. We propose a hierarchical machine learning decision-maker, working on a set of features calculated from the 3D tomograms of the cells' refractive index. We prove that 3D morphological features are adequately distinctive to identify tumor cells versus the white blood cell background in the first stage and, moreover, in recognizing the tumor type at the second decision step. Proof-of-concept experiments are shown, in which two different tumor cell lines, namely neuroblastoma cancer cells and ovarian cancer cells, are used against monocytes. The reported results allow claiming the identification of tumor cells with a success rate higher than 97% and with an accuracy over 97% in discriminating between the two cancer cell types, thus opening in a near future the route to a new Liquid Biopsy tool for detecting and classifying circulating tumor cells in blood by stain-free method

Somatic mutations enriched in cis-regulatory elements affect genes involved in embryonic development and immune system response in neuroblastoma

: Noncoding cis-regulatory variants have gained interest as cancer drivers, yet progress in understanding their significance is hindered by the numerous challenges and limitations of variant prioritization. To overcome these limitations, we focused on active cis-regulatory elements (aCRE) in order to design a customized panel for the deep sequencing of 56 neuroblastoma tumor and normal DNA sample pairs. In order to search for driver mutations, aCREs were defined by reanalysis of H3K27ac ChiP-seq peaks in 25 neuroblastoma cell lines. These regulatory genomic regions were tested for an excess of somatic mutations and assessed for statistical significance using a global approach that accounted for chromatin accessibility and replication timing. Additional validation was provided by whole genome sequence analysis of 151 neuroblastomas. Analysis of Hi-C data determined the presence of candidate target genes interacting with mutated regions. An excess of somatic mutations in aCREs of diverse genes were identified, including IPO7, HAND2, and ARID3A. CRISPR-Cas9 editing was utilized to assess the functional consequences of mutations in the IPO7 aCRE. Patients with noncoding mutations in aCREs showed inferior overall and event-free survival independent of age at diagnosis, stage, risk stratification, and MYCN status. Expression of aCRE-interacting genes correlated strongly with negative prognostic markers and low survival rates. Moreover, a convergence between the biological functions of aCRE target genes and transcription factors with mutated binding motifs was associated with embryonic development and immune system response. Overall, this strategy enabled the identification of somatic mutations in regulatory elements that collectively promote neuroblastoma tumorigenesis

HIF-1 transcription activity: HIF1A driven response in normoxia and in hypoxia

Abstract Background HIF1A (Hypoxia-Inducible-Factor 1A) expression in solid tumors is relevant to establish resistance to therapeutic approaches. The use of compounds direct against hypoxia signaling and HIF1A does not show clinical efficiency because of changeable oxygen concentrations in solid tumor areas. The identification of HIF1A targets expressed in both normoxia and hypoxia and of HIF1A/hypoxia signatures might meliorate the prognostic stratification and therapeutic successes in patients with high-risk solid tumors. Methods In this study, we conducted a combined analysis of RNA expression and DNA methylation of neuroblastoma cells silenced or unsilenced for HIF1A expression, grown in normoxia and hypoxia conditions. Results The analysis of pathways highlights HIF-1 (heterodimeric transcription factor 1) activity in normoxia in metabolic process and HIF-1 activity in hypoxia in neuronal differentiation process. HIF1A driven transcriptional response in hypoxia depends on epigenetic control at DNA methylation status of gene regulatory regions. Furthermore, low oxygen levels generate HIF1A-dependent or HIF1A-independent signatures, able to stratify patients according to risk categories. Conclusions These findings may help to understand the molecular mechanisms by which low oxygen levels reshape gene signatures and provide new direction for hypoxia targeting in solid tumor

La valutazione dei rischi lavorativi: criticità ed indicazioni operative

La procedura di Valutazione dei Rischi è fondamentale per la tutela dei lavoratori (art. 15 D.Lgs. 81/08 s.m.i.) e trova la sua applicazione operativa nell'elaborazione del documento di valutazione dei rischi. La redazione di tale documento, un obbligo del Datore di Lavoro, che può avvalersi a tal fine di vari professionisti ed a cui deve collaborare il Medico Competente, non può essere considerata un mero obbligo legislativo, ma una fotografia reale e continuamente dinamica dei rischi lavorativi e delle misure di prevenzione-protezione atte a ridurli. L'inevitabile complessità dell'argomento, oltre alla rapida evoluzione delle conoscenze scientifiche e delle disposizioni normative, determina svariate difficoltà nell'applicazione corretta della procedura, che richiede competenze specialistiche e multidisciplinari. Nell'ambito della Società Italiana di Igiene (SItI) il Gruppo di Lavoro 'Igiene del Lavoro' ha intrapreso la stesura di un volume che affronta le criticità che possono emergere nel processo di valutazione dei rischi e nella successiva stesura del DVR, anche indicando possibili azioni migliorative che contribuiscano a garantire la qualità e la completezza del processo. Il volume ha un carattere multidisciplinare e specialistico in quanto la sua redazione è stata curata da numerosi esperti di diverse discipline, mantenendo comunque una visione unitaria dell'approccio metodologico, basato sui fondamenti dell'analisi del rischio. Il testo si articola in due parti: nella prima sono descritti gli aspetti generali che caratterizzano il processo, con particolare riferimento all'inquadramento normativo, alla formazione necessaria a ciascuna figura coinvolta nella prevenzione dei rischi in ambiente lavorativo e alle metodologie e alle criticità da considerare nella valutazione di ciascuno dei rischi indicati nella normativa vigente. Nell'ambito di questo ultimo aspetto, particolare attenzione è stata rivolta ai rischi trasversali; sono stati, infatti, affrontati in maniera specifica tematiche quali la comunicazione dei rischi, la valutazione di questi in ottica di genere e l'influenza che stili di vita e abitudini voluttuarie possono esercitare in ambiente di lavoro. In questa prima parte del volume sono inoltre illustrate le caratteristiche fondamentali e le indicazioni all'applicazione degli approcci probabilistici o degli algoritmi nella valutazione dei rischi. Nella seconda parte del volume sono descritte le principali categorie di rischi da valutare in ambiente di lavoro (fisici, chimici, biologici, ergonomici, infortunistici e psico-sociali) considerando le criticità che possono emergere nel processo di valutazione ed indicando possibili soluzioni applicative, anche attraverso indicazioni sintetiche e schematiche quali le check list operative. Riguardo alla valutazione dei rischi di natura biologica e chimica sono riportati due esempi applicativi: la tubercolosi nelle strutture sanitarie ed il comparto rifiuti per i primi, la produzione e l'uso di nanomateriali per i secondi. Il volume può essere, pertanto, considerato un utile strumento di supporto sia per i professionisti direttamente coinvolti nel processo di valutazione dei rischi in ambiente di lavoro, sia per coloro che si avvicinano a tale tematica ed è un manuale operativo da utilizzare sul campo. La multidisciplinarità del testo è espressione della principale caratteristica della vocazione igienista che presenta sempre una visione globale e integrata delle tematiche trattate

Phenotyping neuroblastoma cells through intelligent scrutiny of stain-free biomarkers in holographic flow cytometry

To efficiently tackle certain tumor types, finding new biomarkers for rapid and complete phenotyping of cancer cells is highly demanded. This is especially the case for the most common pediatric solid tumor of the sympathetic nervous system, namely, neuroblastoma (NB). Liquid biopsy is in principle a very promising tool for this purpose, but usually enrichment and isolation of circulating tumor cells in such patients remain difficult due to the unavailability of universal NB cell-specific surface markers. Here, we show that rapid screening and phenotyping of NB cells through stain-free biomarkers supported by artificial intelligence is a viable route for liquid biopsy. We demonstrate the concept through a flow cytometry based on label-free holographic quantitative phase-contrast microscopy empowered by machine learning. In detail, we exploit a hierarchical decision scheme where at first level NB cells are classified from monocytes with 97.9% accuracy. Then we demonstrate that different phenotypes are discriminated within NB class. Indeed, for each cell classified as NB its belonging to one of four NB sub-populations (i.e., CHP212, SKNBE2, SHSY5Y, and SKNSH) is evaluated thus achieving accuracy in the range 73.6%–89.1%. The achieved results solve the realistic problem related to the identification circulating tumor cell, i.e., the possibility to recognize and detect tumor cells morphologically similar to blood cells, which is the core issue in liquid biopsy based on stain-free microscopy. The presented approach operates at lab-on-chip scale and emulates real-world scenarios, thus representing a future route for liquid biopsy by exploiting intelligent biomedical imaging

Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry

Quantitative Phase Imaging (QPI) has gained popularity in bioimaging because it can avoid the need for cell staining, which in some cases is difficult or impossible. However, as a result, QPI does not provide labelling of various specific intracellular structures. Here we show a novel computational segmentation method based on statistical inference that makes it possible for QPI techniques to identify the cell nucleus. We demonstrate the approach with refractive index tomograms of stain-free cells reconstructed through the tomographic phase microscopy in flow cytometry mode. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal fluorescence microscopy (FM) data and microfluidic cytofluorimeter outputs. This is a significant step towards extracting specific three-dimensional intracellular structures directly from the phase-contrast data in a typical flow cytometry configuration