Next-generation sequencing (NGS) has revolutionized personalized oncology care by providing exceptional insights into the complex genomic landscape. NGS offers comprehensive cancer profiling, which enables clinicians and researchers to better understand the molecular basis of cancer and to tailor treatment strategies accordingly. Targeted therapies based on genomic alterations identified through NGS have shown promise in improving patient outcomes across various cancer types, circumventing resistance mechanisms and enhancing treatment efficacy. Moreover, NGS facilitates the identification of predictive biomarkers and prognostic indicators, aiding in patient stratification and personalized treatment approaches. By uncovering driver mutations and actionable alterations, NGS empowers clinicians to make informed decisions regarding treatment selection and patient management. However, the full potential of NGS in personalized oncology can only be realized through bioinformatics analyses. Bioinformatics plays a crucial role in processing raw sequencing data, identifying clinically relevant variants, and interpreting complex genomic landscapes. This comprehensive review investigates the diverse NGS techniques, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and single-cell RNA sequencing (sc-RNA-Seq), elucidating their roles in understanding the complex genomic/transcriptomic landscape of cancer. Furthermore, the review explores the integration of NGS data with bioinformatics tools to facilitate personalized oncology approaches, from understanding tumor heterogeneity to identifying driver mutations and predicting therapeutic responses. Challenges and future directions in NGS-based cancer research are also discussed, underscoring the transformative impact of these technologies on cancer diagnosis, management, and treatment strategies.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease (COVID-19), induced a global pandemic with a diverse array of clinical manifestations. While the acute phase of the pandemic may be waning, the intricacies of COVID-19′s impact on neurological health remain a crucial area of investigation. Early recognition of the spectrum of COVID-19 symptoms, ranging from mild fever and cough to life-threatening respiratory distress and multi-organ failure, underscored the significance of neurological complications, including anosmia, seizures, stroke, disorientation, encephalopathy, and paralysis. Notably, patients requiring intensive care unit (ICU) admission due to neurological challenges or due to them exhibiting neurological abnormalities in the ICU have shown increased mortality rates. COVID-19 can lead to a range of neurological complications such as anosmia, stroke, paralysis, cranial nerve deficits, encephalopathy, delirium, meningitis, seizures, etc., in affected patients. This review elucidates the burgeoning landscape of neurological sequelae associated with SARS-CoV-2 infection and explores the underlying neurobiological mechanisms driving these diverse manifestations. A meticulous examination of potential neuroinvasion routes by SARS-CoV-2 underscores the intricate interplay between the virus and the nervous system. Moreover, we dissect the diverse neurological manifestations emphasizing the necessity of a multifaceted approach to understanding the disease’s neurological footprint. In addition to elucidating the pathophysiological underpinnings, this review surveys current therapeutic modalities and delineates prospective avenues for neuro-COVID research. By integrating epidemiological, clinical, and diagnostic parameters, we endeavor to foster a comprehensive analysis of the nexus between COVID-19 and neurological health, thereby laying the groundwork for targeted therapeutic interventions and long-term management strategies.

Circulating tumor cells (CTCs) are shed from the primary tumor into the bloodstream and represent dynamic molecular biomarkers for monitoring the progression of cancer. While profiling tumor tissues with over expression of cell surface markers, such as PD-L1 or HER2, is standard in guiding therapy, tissue samples are often inaccessible and inadequate, especially post-surgery or in cases of recurrence. Emerging clinical evidence indicates that CTC counts and biomarker surface expression can predict prognosis and therapeutic resistance more accurately than imaging or tissue-based approaches. Recent advancements in the CTC detection methods, based on physical properties or surface markers (e.g., EpCAM), coupled with next-generation sequencing (NGS) have enabled the isolation of these rare cells and their molecular characterization. Consequently, CTCs provide a real-time alternative, enabling repeated, longitudinal assessment of tumor phenotype and therapeutic response. This review emphasizes the translational potential of surface protein biomarkers on CTCs for profiling, namely PD-L1, HER2, and EGFR, as a clinically actionable approach to stratify patients, guide immunotherapy decisions, and monitor minimal residual disease (MRD), especially when longitudinal tissue biopsies are not feasible.

Background: Comprehensive genomic profiling (CGP) is increasingly used in precision oncology to identify actionable genomic alterations and guide targeted therapies in solid tumors. However, the clinical implementation of CGP assays requires rigorous analytical validation to ensure accurate and reproducible detection of diverse genomic alterations across heterogeneous tumor samples. Despite rapid advancements in next-generation sequencing technologies, there remains a need for validated CGP platforms that demonstrate reliable performance and readiness for routine clinical use. Methods: This study evaluated the analytical and clinical performance of a CGP assay capable of detecting multiple genomic alteration types, including single nucleotide variants (SNVs), insertions/deletions (Indels), copy number variations (CNVs), gene fusions, and tumor mutational burden (TMB). Validation was conducted using patient-derived 117 FFPE tumor samples, external proficiency testing materials, and reference standards. Assay performance was assessed through comparison with orthogonal methods and through evaluation of reproducibility, limit of detection, and TMB concordance. Results: The assay demonstrated excellent analytical performance, achieving 100% sensitivity, specificity, and accuracy for variant detection across evaluated samples. Strong concordance was observed for TMB estimation (R² = 0.9925), with consistent classification of TMB-high cases. The assay showed robust inter- and intra-run reproducibility and reliable detection of low-frequency variants. Limit-of-detection (LOD) analysis confirmed accurate SNV detection at approximately 1% variant allele frequency and reliable RNA fusion detection at low input levels. Conclusions: The validated CGP assay provides accurate, reproducible, and comprehensive detection of clinically relevant genomic alterations in solid tumors. These results support its suitability for routine clinical deployment, enabling reliable genomic profiling to inform precision oncology treatment decisions.

Background: Comprehensive genomic profiling (CGP) via next-generation sequencing (NGS) is pivotal for precision oncology, yet many laboratories face challenges with incomplete genomic coverage, complex bioinformatics workflows, and limited integration of key biomarkers. Methods: We evaluated the analytical performance and clinical utility of a CGP assay using 119 tumor samples representing 18 types of cancer, previously analyzed with an orthogonal NGS panel. Concordance was assessed across 81 genes, covering 176 single-nucleotide variants (SNVs), eight copy number variations (CNVs), four deletions, one duplication, and four gene fusions. Limit of detection (LOD) studies employed AcroMetrix Mutant Hotspot Control and SeraSeq Lung and Brain CNV Mix. Microsatellite instability (MSI) and tumor mutational burden (TMB) were quantified. Inter- and intra-run reproducibility were evaluated to assess precision. Results: The CGP assay demonstrated high analytical performance, with >99% sensitivity, 100% specificity, and complete accuracy for variant detection. LOD studies revealed robust detection of SNVs at ≤5% variant allele frequencies (VAF) and CNVs at three copies. MSI and TMB results were consistent with clinical expectations, showing minimal bias compared to the orthogonal panel. Inter- and intra-run testing confirmed 100% reproducibility, indicating strong assay precision. Post-sequencing variant reporting was streamlined using the iCare platform, enabling direct FASTQ-to-report generation without intermediate bioinformatic steps. Conclusions: These findings support the present assay’s clinical utility in personalized oncology assessment.