Mutation spectra and mutational signatures in cancerous and non-cancerous tissues can be identified by various established techniques of massively parallel sequencing (or next-generation sequencing) including whole-exome or whole-genome sequencing, and more recently by error-corrected/duplex sequencing. One rather underexplored area has been the genome-scale analysis of mutational signatures as markers of mutagenic exposures, and their impact on cancer driver events applied to formalin-fixed or alcohol-fixed paraffin embedded archived biospecimens. This review showcases successful applications of the next-generation sequencing methodologies in archived fixed tissues, including the delineation of the specific tissue fixation-related DNA damage manifesting as artifactual signatures, distinguishable from the true signatures that arise from biological mutagenic processes. Overall, we discuss and demonstrate how next-generation sequencing techniques applied to archived fixed biospecimens can enhance our understanding of cancer causes including mutagenic effects of extrinsic cancer risk agents, and the implications for prevention efforts aimed at reducing avoidable cancer-causing exposures.
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder (NDD) influenced by genetic, epigenetic, and environmental factors. Recent advancements in genomic analysis have shed light on numerous genes associated with ASD, highlighting the significant role of both common and rare genetic mutations, as well as copy number variations (CNVs), single nucleotide polymorphisms (SNPs) and unique de novo variants. These genetic variations disrupt neurodevelopmental pathways, contributing to the disorder's complexity. Notably, CNVs are present in 10 %-20 % of individuals with autism, with 3 %-7 % detectable through cytogenetic methods. While the role of submicroscopic CNVs in ASD has been recently studied, their association with genomic loci and genes has not been thoroughly explored. In this review, we focus on 47 CNV regions linked to ASD, encompassing 1632 genes, including protein-coding genes and long non-coding RNAs (lncRNAs), of which 659 show significant brain expression. Using a list of ASD-associated genes from SFARI, we detect 17 regions harboring at least one known ASD-related protein-coding gene. Of the remaining 30 regions, we identify 24 regions containing at least one protein-coding gene with brain-enriched expression and a nervous system phenotype in mouse mutants, and one lncRNA with both brain-enriched expression and upregulation in iPSC to neuron differentiation. This review not only expands our understanding of the genetic diversity associated with ASD but also underscores the potential of lncRNAs in contributing to its etiology. Additionally, the discovered CNVs will be a valuable resource for future diagnostic, therapeutic, and research endeavors aimed at prioritizing genetic variations in ASD.
Oral squamous cell carcinoma (OSCC) is the most common oral malignancy, often preceded by oral potentially malignant disorders (OPMDs). Currently, no clinical biomarker exists to predict malignancy, necessitating OPMD follow-up. Habits and environmental factors, such as smoking, and alcohol consumption, influence OSCC onset. Increased micronuclei (MNs) formation has been observed in the development of OSCC. Non-invasive diagnostic tests like exfoliative cytology offer painless and regular monitoring options. This study evaluates the impact of tobacco, alcohol, and pesticide exposure on MNs occurrence in exfoliative cytology-collected oral mucosal cells, assessing their potential as non-invasive biomarker for OSCC development prediction and monitoring in high-risk patients. Despite results from this meta-analysis supporting the existence of a stepwise increase from controls to patients with OPMD to OSCC, the translation of these findings into clinical practice is limited due to intra- and inter-individual heterogeneity, as well as methodological variability in MNs quantification. Various factors contribute to this heterogeneity, including demographic variables, methodological variability of different laboratories, staining techniques, sample collection location, and patient characteristics. All these points were discussed to provide further insights and improve standardization for future studies.

