Tuberculosis (TB) remains a global health challenge, with timely and accurate diagnosis being critical for effective disease management and control. Recent advancements in the field of TB diagnostics have focused on the identification and utilization of blood-based biomarkers, offering a non-invasive, rapid, and scalable approach to disease detection. This review provides a comprehensive overview of the latest progress in blood-based biomarkers for TB, highlighting their potential to revolutionize diagnostic strategies. Furthermore, we explore emerging technologies such as NGS, PET-CT, Xpert and line probe assays, which have enhanced the sensitivity, specificity, and accessibility of biomarker-based diagnostics. The integration of artificial intelligence (AI) and machine learning (ML) in biomarker analysis is also examined, showcasing its potential to improve diagnostic accuracy and predictive capabilities. This review underscores the need for multidisciplinary collaboration and continued innovation to translate these promising technologies into practical, point-of-care solutions. By addressing these challenges, blood-based biomarkers and emerging technologies hold the potential to significantly improve TB diagnosis, ultimately contributing to global efforts to eradicate this devastating disease.

Comprehensive genomic profiling (CGP) refers to the detailed genomic analysis of cancers for oncology patients. With the rapid development of next-generation sequencing (NGS) technologies, CGP has been widely applied to clinical practice and managing oncology patients. CGP can be performed on the tumor DNA and RNA, as well as non-tumor tissues (e.g., blood, pleural effusion, and ascites). In this article, we review the current evidence supporting the use of CGP in the management of oncology patients, both in real-world practice and the bridging to clinical trials. We also discuss the role of the molecular tumor board on the application of CGP in oncology patients. We provide an overview of the current scheme of CGP reimbursement in Taiwan and the precision oncology branch of the National Biobank Consortium of Taiwan. Finally, we discuss about the potential barriers and challenges of applying CGP in managing oncology patients and the future perspectives of CGP in precision oncology.

Cancer, characterized by the uncontrolled proliferation of cells, is one of the leading causes of death globally, with approximately one in five people developing the disease in their lifetime. While many driver genes were identified decades ago, and most cancers can be classified based on morphology and progression, there is still a significant gap in knowledge about genetic aberrations and nuclear DNA damage. The study of two critical groups of genes-tumor suppressors, which inhibit proliferation and promote apoptosis, and oncogenes, which regulate proliferation and survival-can help to understand the genomic causes behind tumorigenesis, leading to more personalized approaches to diagnosis and treatment. Aberration of tumor suppressors, which undergo two-hit and loss-of-function mutations, and oncogenes, activated forms of proto-oncogenes that experience one-hit and gain-of-function mutations, are responsible for the dysregulation of key signaling pathways that regulate cell division, such as p53, Rb, Ras/Raf/ERK/MAPK, PI3K/AKT, and Wnt/β-catenin. Modern breakthroughs in genomics research, like next-generation sequencing, have provided efficient strategies for mapping unique genomic changes that contribute to tumor heterogeneity. Novel therapeutic approaches have enabled personalized medicine, helping address genetic variability in tumor suppressors and oncogenes. This comprehensive review examines the molecular mechanisms behind tumor-suppressor genes and oncogenes, the key signaling pathways they regulate, epigenetic modifications, tumor heterogeneity, and the drug resistance mechanisms that drive carcinogenesis. Moreover, the review explores the clinical application of sequencing techniques, multiomics, diagnostic procedures, pharmacogenomics, and personalized treatment and prevention options, discussing future directions for emerging technologies.

Comprehensive genomic profiling (CGP) testing is used worldwide for personalized cancer treatment. Unstained slides from formalin-fixed paraffin-embedded (FFPE) blocks are required for tissue-based CGP testing. However, the use of low quality FFPE specimens can result in testing failure or invalid results. Although several previous studies have indicated that the quality of genomic testing is affected by various factors, the factor(s) that has the greatest influence on CGP testing has not been well studied in real-world data. Thus, we conducted a large-scale multi-institutional study in Hiroshima, Japan to investigate the factors associated with quality check status in FoundationOne CDx CGP testing using real-world data from 1,204 participants from an area with a population of approximately 2.7 million. This study revealed low percentage of tumor nuclei in FFPE specimen, long-term storage of FFPE block, and pancreatic cancer as independent risk factors for qualified status. Of the three factors, percentage of tumor nuclei had the largest effect on quality check status, while the magnitudes of the effects of storage time of FFPE or pancreatic cancer were minor. Collectively, our real-world data indicate that tumor purity is the most important factor for successful CGP, and we would suggest greater than 35% as an ideal percentage of tumor nuclei for CGP submission.

Microhaplotypes are small linked genomic regions comprising two or more single-nucleotide polymorphisms (SNPs) that are being applied in forensics and are emerging in wildlife monitoring studies and genomic epidemiology. Typically, targeted in non-coding regions, microhaplotypes in exonic regions can be designed with larger amplicons to capture functional non-synonymous sites and minimise insertion/deletion (indel) polymorphisms. Quality control is an important first step for high-confidence genotyping to counteract such false-positive variants. As genetic markers with higher polymorphism compared to biallelic SNPs, it is critical to ensure sequencing errors across the microhaplotype amplicon are filtered out to avoid introducing false-haplotypes. We developed the MhGeneS pipeline which works in tandem with Seq2Sat to help validate microhaplotype genotyping of the coding region of genes, with broader applicability to any microhaplotype profiling. We genotyped microhaplotype regions of the Zfx (≅ 160 bp) and Zfy (≅ 140 bp) genes, as well as an exon of the prion protein (Prnp) gene (≅ 370 bp) in caribou (Rangifer tarandus) using paired-end Illumina technology. As important quality metrics affecting microhaplotype calling, we identified the sequencing error rate profile related to the overlap or non-overlap of paired-end reads as well as the read depth as significant. In the case of Prnp, we achieved confident microhaplotype calling through MhGeneS by removing small sections of the 5' and 3' amplicons and using a minimum read depth of 20. Read depth and sequence trimming may be locus-specific, and validation of these parameters is recommended before the high-throughput profiling of samples.

The genetic information that dictates the structure and function of all life forms is encoded in the DNA. In 1953, Watson and Crick first presented the double helical structure of a DNA molecule. Their findings unearthed the desire to elucidate the exact composition and sequence of DNA molecules. Discoveries and the subsequent development and optimization of techniques that allowed for deciphering the DNA sequence has opened new doors in research, biotech, and healthcare. The application of high-throughput sequencing technologies in these industries has positively impacted and will continue to contribute to the betterment of humanity and the global economy. Improvements, such as the use of radioactive molecules for DNA sequencing to the use of florescent dyes and the implementation of polymerase chain reaction (PCR) for amplification, led to sequencing a few hundred base pairs in days, to automation, where sequencing of thousands of base pairs in hours became possible. Significant advances have been made, but there is still room for improvement. Here, we look at the history and the technology of the currently available next-generation sequencing platforms and the possible applications of such technologies to biomedical research and beyond.

Targeted next-generation sequencing (NGS) in rare disease focuses on genetic analysis of specific regions in genome that are linked to a rare disease. In addition to library preparation, sequencing, and data analysis, targeted NGS includes an additional step of target enrichment of selected genes and regions. It allows for more sensitive and profound sequencing, as it is a fast and cost-effective approach with less data burden and is therefore often a method of choice for identifying rare variants in known genes, especially in diagnostics of rare diseases. Several in silico tools address the pathogenicity predictions of rare variants of unknown significance (VUS) and can therefore facilitate clinical interpretation.

Comprehensive biomarker testing is a crucial requirement for the optimal treatment of advanced-stage non-small cell lung cancer (NSCLC), with emerging relevance in the adjuvant treatment setting. To advance its goal of ensuring optimal therapy for persons diagnosed with lung cancer, the American Cancer Society National Lung Cancer Roundtable (ACS NLCRT) held The Summit on Optimizing Lung Cancer Biomarkers in Practice in September 2020 to align its partners toward the goal of ensuring comprehensive biomarker testing for all eligible patients with NSCLC. The ACS NLCRT's Strategic Plan for Advancing Comprehensive Biomarker Testing in NSCLC, a product of the summit, comprises actions to promote comprehensive biomarker testing for all eligible patients. The approach is multifaceted, including policy-level advocacy and the development and dissemination of targeted educational materials, clinical decision tools, and guides to patients, physicians, and payers aimed at ameliorating barriers to testing experienced by each of these groups. PLAIN LANGUAGE SUMMARY: The ACS NLCRT works to improve care for patients with lung cancer. The ACS NLCRT supports comprehensive biomarker testing as essential to determine treatment options for all eligible patients with non-small cell lung cancer. Many factors lead to some patients not receiving optimal biomarker testing. The ACS NLCRT held a collaborative summit and developed a strategic plan to achieve and promote comprehensive biomarker testing for all patients. These plans include developing educational materials and physician tools and advocating for national policies in support of biomarker testing.

Individuals with HIV experience an increased risk of lymphoma, making this an important cause of death among people with HIV. Nevertheless, little is known regarding the underlying genetic aberrations, which we therefore set out to characterize.

We conducted next-generation panel sequencing to explore the mutational status of diagnostic lymphoma biopsies from 18 patients diagnosed with lymphoma secondary to HIV infection.

Ion Torrent next-generation sequencing was performed with an AmpliSeq panel on diagnostic lymphoma biopsies from HIV-associated B-cell lymphomas ( n  = 18), comprising diffuse large B-cell lymphoma ( n  = 9), classic Hodgkin lymphoma ( n  = 6), Burkitt lymphoma ( n  = 2), follicular lymphoma ( n  = 1), and marginal zone lymphoma ( n  = 1). The panel comprised 69 lymphoid and/or myeloid-relevant genes, in which either the entire coding sequence or a hotspot region was sequenced.

Among the 18 lymphomas, we detected 213 variants. The number of detected mutations ranged from 4 to 41 per tumor distributed among 42 genes, including both exonic and intronic regions. The most frequently mutated genes included KMT2D (67%), TNFAIP3 (50%), and TP53 (61%). Notably, no gene was found to harbor variants across all the HIV-associated lymphomas, nor did we find subtype-specific variants. While some variants were shared among patients, most were unique to the individual patient and were often not reported as malignant genetic variants in databases.

Our findings demonstrate genetic heterogeneity across histological subtypes of HIV-associated lymphomas and thus help elucidate the genetics and pathophysiological mechanisms underlying the disease.

Multiple myeloma is a complex and challenging type of blood cancer that affects plasma cells in the bone marrow. In recent years, the development of advanced research techniques, such as omics approaches-which involve studying large sets of biological data like genes and proteins-and high-throughput sequencing technologies, has allowed researchers to analyze vast amounts of genetic information rapidly and gain new insights into the disease. Additionally, the advent of artificial intelligence tools has accelerated data analysis, enabling more accurate predictions and improved treatment strategies. This review aims to highlight recent research advances in multiple myeloma made possible by these novel techniques and to provide guidance for researchers seeking effective approaches in this field.

A majority of pancreatic malignancies are unresectable at the time of presentation and require EUS-guided fine-needle aspiration or fine-needle biopsy (EUS-FNA/FNB) for diagnosis. With the advent of precision therapy, there is an increasing need to use EUS-FNA/FNB sample for genetic analysis. Next-generation sequencing (NGS) is a preferred technology to detect genetic mutations with high sensitivity in small specimens. We performed a meta-analysis to evaluate the adequacy of EUS-FNA/FNB for NGS in pancreatic malignancies.

PubMed, Embase, Cochrane Library, and Web of Science were searched from database inception to November 11, 2023. The primary outcome was the proportion of sufficient sample acquired by EUS-FNA/FNB in pancreatic malignancies for NGS. Secondary outcomes were the proportion of sufficient sample for NGS in pancreatic ductal adenocarcinoma (PDAC) and the detection rates of mutations in KRAS, TP53, CDKN2A, and SMAD4 and actionable mutations in PDAC. The pooled proportions were calculated using a random-effects model. Potential sources of heterogeneity were investigated with subgroup analyses and meta-regression.

Twenty studies with 881 samples were included. The pooled adequacy of EUS-FNA/FNB sample for NGS was 89.9% (95% CI, 80.8%-96.7%) in pancreatic malignancies and 92.0% (95% CI, 81.3%-98.8%) in PDAC. Screening sample suitability before NGS testing was associated with lower adequacy in subgroup analysis (79.7% vs. 98.4%, P = 0.001). The pooled prevalences of mutations in KRAS, TP53, CDKN2A, and SMAD4 in PDAC were 87.4% (95% CI, 83.2%-91.2%), 62.6% (95% CI, 53.2%-71.7%), 20.6% (95% CI, 11.9%-30.8%), and 19.4% (95% CI, 11.2%-29.1%), respectively. The pooled prevalence of potentially actionable mutations in PDAC was 14.5% (95% CI, 8.2%-22.0%).

In the majority of cases, EUS-FNA/FNB can acquire adequate sample for NGS and identify tumor-specific mutations in patients with pancreatic malignancies. Strict pre-analysis screening criteria may negatively impact the sample adequacy and the success rate for NGS.

Massively parallel sequencing helps create new knowledge on genes, variants and their association with disease phenotype. This important technological advancement simultaneously makes clinical decision making, using genomic information for cancer patients, more complex. Currently, identifying actionable pathogenic variants with diagnostic, prognostic, or predictive impact requires substantial manual effort. Objective: The purpose is to design a solution for clinical diagnostics of lymphoma, specifically for systematic variant filtering and interpretation. Methods: A scoping review and demonstrations from specialists serve as a basis for a blueprint of a solution for massively parallel sequencing-based genetic diagnostics. Results: The solution uses machine learning methods to facilitate decision making in the diagnostic process. A validation round of interviews with specialists consolidated the blueprint and anchored it across all relevant expert disciplines. The scoping review identified four components of variant filtering solutions: algorithms and Artificial Intelligence (AI) applications, software, bioinformatics pipelines and variant filtering strategies. The blueprint describes the input, the AI model and the interface for dynamic browsing. Conclusion: An AI-augmented system is designed for predicting pathogenic variants. While such a system can be used to classify identified variants, diagnosticians should still evaluate the classification's accuracy, make corrections when necessary, and ultimately decide which variants are truly pathogenic.

Single nucleotide variant (SNV) detection is pivotal in various fields, including disease diagnosis, viral screening, genetically modified organism (GMO) identification, and genotyping. However, detecting SNVs presents significant challenges due to the fragmentation of nucleic acids caused by cellular apoptosis, molecular shearing, and physical degradation processes such as heating. Fragmented nucleic acids often exhibit variable lengths and inconsistent breakpoints, complicating the accurate detection of SNVs. This article delves into the underlying causes of nucleic acid fragmentation and synthesizes the strengths and limitations of next-generation sequencing technology, high-resolution melting curves, molecular probes, and CRISPR-based approaches for SNV detection in fragmented nucleic acids. By providing a detailed comparative analysis, it seeks to offer valuable insights for researchers working to overcome the challenges of SNV detection in fragmented samples, ultimately advancing the accurate and efficient detection of single nucleotide variants across diverse applications.

The etiology of childhood cancer remains largely unknown. Recent evidence suggests that genetic factors play a substantial role in pediatric tumorigenesis. Unlike adult cancers, pediatric cancers typically have a higher prevalence of germline pathogenic variants in cancer predisposition genes. Inherited cancer predisposition syndromes account for approximately 10% of all childhood cancers. Over the years, the diagnosis of cancer predisposition syndromes was based on clinical suspicion prompting referral to a specialized geneticist. However, advances in molecular technologies have led to a shift toward a "genotype-first" approach. Identification of genetic variants related to cancer predisposition enables tailored treatment, improves clinical outcome, optimizes surveillance, and facilitates genetic counseling of the affected child and the family.

Fundamentally precision oncology illustrates the path in which molecular profiling of tumors can illuminate their biological behavior, diversity, and likely outcomes by identifying distinct genetic mutations, protein levels, and other biomarkers that underpin cancer progression. Next-generation sequencing became an indispensable diagnostic tool for diagnosis and treatment guidance in current clinical practice. Nowadays, tissue analysis benefits from further support through methods like comprehensive genomic profiling and liquid biopsies. However, precision medicine in the field of oncology presents specific hurdles, such as the cost-benefit balance and widespread accessibility, particularly in countries with low- and middle-income. A key issue is how to effectively extend next-generation sequencing to all cancer patients, thus empowering treatment decision-making. Concerns also extend to the quality and preservation of tissue samples, as well as the evaluation of health technologies. Moreover, as technology advances, novel next-generation sequencing assessments are being developed, including the study of Fragmentomics. Therefore, our objective was to delineate the primary uses of next-generation sequencing, discussing its' applications, limitations, and prospective paths forward in Oncology.

Gene panel sequencing has become a common diagnostic tool for detecting somatically acquired mutations in myeloid neoplasms. However, many panels have restricted content, provide insufficient sensitivity levels, or lack clinically validated workflows. We here describe the development and validation of the Genomic Medicine Sweden myeloid gene panel (GMS-MGP), a capture-based 191 gene panel including mandatory genes in contemporary guidelines as well as emerging candidates. The GMS-MGP displayed uniform coverage across all targets, including recognized difficult GC-rich areas. The validation of 117 previously described somatic variants showed a 100% concordance with a limit-of-detection of a 0.5% variant allele frequency (VAF), achieved by utilizing error correction and filtering against a panel-of-normals. A national interlaboratory comparison investigating 56 somatic variants demonstrated highly concordant results in both detection rate and reported VAFs. In addition, prospective analysis of 323 patients analyzed with the GMS-MGP as part of standard-of-care identified clinically significant genes as well as recurrent mutations in less well-studied genes. In conclusion, the GMS-MGP workflow supports sensitive detection of all clinically relevant genes, facilitates novel findings, and is, based on the capture-based design, easy to update once new guidelines become available. The GMS-MGP provides an important step toward nationally harmonized precision diagnostics of myeloid malignancies.

The Alpine Merino is a new breed of fine-wool sheep adapted to the cold and arid climate of the plateau in the world. It has been popularized in Northwest China due to its superior adaptability as well as excellent production performance. Those traits related to body weight, wool yield, and wool fiber characteristics, which are economically essential traits in Alpine Merino sheep, are controlled by QTL (Quantitative Trait Loci). Therefore, the identification of QTL and genetic markers for these key economic traits is a critical step in establishing a MAS (Marker-Assisted Selection) breeding program.

In this study, we constructed the high-density genetic linkage map of Alpine Merino sheep by sequencing 110 F1 generation individuals using WGR (Whole Genome Resequencing) technology. 14,942 SNPs (Single Nucleotide Polymorphism) were identified and genotyped. The map spanned 2,697.86 cM, with an average genetic marker interval of 1.44 cM. A total of 1,871 high-quality SNP markers were distributed across 27 linkage groups, with an average of 69 markers per LG (Linkage Group). Among them, the smallest genetic distance is 19.62 cM for LG2, while the largest is 237.19 cM for LG19. The average genetic distance between markers in LGs ranged from 0.24 cM (LG2) to 3.57 cM (LG17). The marker density in the LGs ranged from LG14 (39 markers) to LG1 (150 markers).

The first genetic map of Alpine Merino sheep we constructed included 14,942 SNPs, while 46 QTLs associated with body weight, wool yield and wool fiber traits were identified, laying the foundation for genetic studies and molecular marker-assisted breeding. Notably, there were QTL intervals for overlapping traits on LG4 and LG8, providing potential opportunities for multi-trait co-breeding and further theoretical support for selection and breeding of ultra-fine and meaty Alpine Merino sheep.

The advancements in the detection and characterization of circulating tumor DNA (ctDNA) have revolutionized precision medicine and are likely to transform standard clinical practice. The non-invasive nature of this approach allows for molecular profiling of the entire tumor entity, while also enabling real-time monitoring of the effectiveness of cancer therapies as well as the identification of resistance mechanisms to guide targeted therapy. Although the field of ctDNA studies offers a wide range of applications, including in early disease, in this review we mainly focus on the role of ctDNA in the dynamic molecular characterization of unresectable locally advanced and metastatic BC (mBC). Here, we provide clinical practice guidance for the rapidly evolving field of molecular profiling of mBC, outlining the current landscape of liquid biopsy applications and how to choose the right ctDNA assay. Additionally, we underline the importance of exploring the clinical relevance of novel molecular alterations that potentially represent therapeutic targets in mBC, along with mutations where targeted therapy is already approved. Finally, we present a potential roadmap for integrating ctDNA analysis into clinical practice.

Genomic mosaicism describes the phenomenon where some but not all cells within a tissue harbor unique genetic mutations. Traditionally, research focused on the impact of genomic mosaicism on clinical phenotype-motivated by its involvement in cancers and overgrowth syndromes. More recently, we increasingly shifted towards the plethora of neutral mosaic variants that can act as recorders of cellular lineage and environmental exposures. Here, we summarize the current state of the field of genomic mosaicism research with a special emphasis on our current understanding of this phenomenon in brain development and homeostasis. Although the field of genomic mosaicism has a rich history, technological advances in the last decade have changed our approaches and greatly improved our knowledge. We will provide current definitions and an overview of contemporary detection approaches for genomic mosaicism. Finally, we will discuss the impact and utility of genomic mosaicism.

Next-generation sequencing (NGS) has been increasingly popular in genomics studies over the last decade and is now commonly used in clinical applications for precision diagnostics. Many disease areas typically involve different kinds of sample specimens, sample qualities and quantities. The quality of the DNA can range from intact, high molecular weight molecules to degraded, damaged and very short molecules. The differences in quality and quantity pose challenges for downstream molecular analyses. To overcome the challenge with the need of different molecular methods for different types of samples, we have developed a joint procedure for preparing enriched DNA libraries from high molecular weight DNA and DNA from formalin-fixed, paraffin-embedded tissue, fresh frozen tissue material, as well as cell-free DNA.

Inherited retinal diseases (IRDs) are a clinically and genetically heterogeneous group of diseases which cause visual loss due to Mendelian mutations in over 250 genes, making genetic diagnosis challenging and time-consuming. Here, we developed a new tool, CDIP (Cost-effective Deep-sequencing IRD Panel) in which a simultaneous sequencing of common mutations is performed. CDIP is based on simultaneous amplification of 47 amplicons harboring common mutations followed by next-generation sequencing (NGS). Following five rounds of calibration of NGS-based steps, CDIP was used in 740 IRD samples. The analysis revealed 151 mutations in 131 index cases. In 54 (7%) of these cases, CDIP identified the genetic cause of disease (the remaining were single-heterozygous recessive mutations). These include a patient that was clinically diagnosed with retinoschisis and found to be homozygous for NR2E3-c.932G>A (p.R311Q), and a patient with RP who is hemizygous for an RPGR variant, c.292C>A (p.H98N), which was not included in the analysis but is located in proximity to one of these mutations. CDIP is a cost-effective deep sequencing panel for simultaneous detection of common founder mutations. This protocol can be implemented for additional populations as well as additional inherited diseases, and mainly in populations with strong founder effects.

Melanoma heterogeneity is a hurdle in metastatic disease management. Although the advent of targeted therapy has significantly improved patient outcomes, the occurrence of resistance makes monitoring of the tumor genetic landscape mandatory. Liquid biopsy could represent an important biomarker for the real-time tracing of disease evolution. Thus, we aimed to correlate liquid biopsy dynamics with treatment response and progression by devising a multiplatform approach applied to longitudinal melanoma patient monitoring. We conceived an approach that exploits Next Generation Sequencing (NGS) and droplet digital PCR, as well as the FDA-cleared platform CellSearch, to analyze circulating tumor DNA (ctDNA) trend and circulating melanoma cell (CMC) count, together with their customized genetic and copy number variation analysis. The approach was applied to 17 stage IV melanoma patients treated with BRAF/MEK inhibitors, followed for up to 28 months. BRAF mutations were detected in the plasma of 82% of patients. Single nucleotide variants known or suspected to confer resistance were identified in 70% of patients. Moreover, the amount of ctDNA, both at baseline and during response, correlated with the type and duration of the response itself, and the CMC count was confirmed to be a prognostic biomarker. This work provides proof of principle of the power of this approach and paves the way for a validation study aimed at evaluating early ctDNA-guided treatment decisions in stage IV melanoma. The NGS-based molecular profile complemented the analysis of ctDNA trend and, together with CMC analysis, revealed to be useful in capturing tumor evolution.

In the enduring challenge against disease, advancements in medical technology have empowered clinicians with novel diagnostic platforms. Whilst in some cases, a single test may provide a confident diagnosis, often additional tests are required. However, to strike a balance between diagnostic accuracy and cost-effectiveness, one must rigorously construct the clinical pathways. Here, we developed a framework to build multi-platform precision pathways in an automated, unbiased way, recommending the key steps a clinician would take to reach a diagnosis. We achieve this by developing a confidence score, used to simulate a clinical scenario, where at each stage, either a confident diagnosis is made, or another test is performed. Our framework provides a range of tools to interpret, visualize and compare the pathways, improving communication and enabling their evaluation on accuracy and cost, specific to different contexts. This framework will guide the development of novel diagnostic pathways for different diseases, accelerating the implementation of precision medicine into clinical practice.

Despite the different possible paths of treatment, lung cancer remains one of the leading causes of death in oncological patients. New tools guiding the therapeutic process are under scientific investigation, and one of the promising indicators of the effectiveness of therapy in patients with NSCLC is variant allele frequency (VAF) analysis. VAF is a metric characterized as the measurement of the specific variant allele proportion within a genomic locus, and it can be determined using methods based on NGS or PCR. It can be assessed using not only tissue samples but also ctDNA (circulating tumor DNA) isolated from liquid biopsy. The non-invasive characteristic of liquid biopsy enables a more frequent collection of material and increases the potential of VAF analysis in monitoring therapy. Several studies have been performed on patients with NSCLC to evaluate the possibility of VAF usage. The research carried out so far demonstrates that the evaluation of VAF dynamics may be useful in monitoring tumor progression, remission, and recurrence during or after treatment. Moreover, the use of VAF analysis appears to be beneficial in making treatment decisions. However, several issues require better understanding and standardization before VAF testing can be implemented in clinical practice. In this review, we discuss the difficulties in the application of ctDNA VAF analysis in clinical routine, discussing the diagnostic and methodological challenges in VAF measurement in liquid biopsy. We highlight the possible applications of VAF-based measurements that are under consideration in clinical trials in the monitoring of personalized treatments for patients with NSCLC.

Introduction: PIK3CA gene mutations occur in approximately 40% of hormone receptor-positive/HER2-negative (HR+/HER2-) metastatic breast cancers (MBCs), electing them to targeted therapy. Testing PIK3CA status is complex due to selection of biological specimen and testing method. Materials & methods: This work investigates real-life experience on PIK3CA testing in HR+/HER2- MBC. Clinical, technical and molecular data on PIK3CA testing were collected from two referral laboratories. Additionally, the results of a nationwide PIK3CA survey involving 116 institutions were assessed. Results: Overall, n = 35 MBCs were PIK3CA-mutated, with mutations mostly occurring in exons 9 (n = 19; 51.4%) and 20 (n = 15; 40.5%). The nationwide survey revealed significant variability across laboratories in terms of sampling methodology, technical assessment and clinical report signing healthcare figures for PIK3CA molecular testing in diagnostic routine practice. Conclusion: This study provides insights into the real-world routine of PIK3CA testing in HR+/HER2- MBC and highlights the need for standardization and networking in predictive pathology.

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.

Targeted genomic sequencing (TS) greatly benefits precision oncology by rapidly detecting genetic variations with better accuracy and sensitivity owing to its high sequencing depth. Multiple sequencing platforms and variant calling tools are available for TS, making it excruciating for researchers to choose. Therefore, benchmarking study across different platforms and pipelines available for TS is imperative. In this study, we performed a TS of Reference OncoSpan FFPE (HD832) sample enriched by TSO500 panel using four commercially available sequencers, and analyzed the output 50 datasets using five commonly-used bioinformatics pipelines. We systematically investigated the sequencing quality and variant detection sensitivity, expecting to provide optimal recommendations for future research. Four sequencing platforms returned highly concordant results in terms of base quality (Q20 > 94%), sequencing coverage (>97%) and depth (>2000×). Benchmarking revealed good concordance of variant calling across different platforms and pipelines, among which, FASTASeq 300 platform showed the highest sensitivity (100%) and precision (100%) in high-confidence variants calling when analyzed by SNVer and VarScan 2 algorithms. Furthermore, this sequencer demonstrated the shortest sequencing time (∼21 h) at the sequencing mode PE150. Through the intersection of 50 datasets generated in this study, we recommended a novel set of variant genes outside the truth set published by HD832, expecting to replenish HD832 for future research on tumor variant diagnosis. Besides, we applied these five tools to another panel (TargetSeq One) for Twist cfDNA Pan-cancer Reference Standard, comprehensive consideration of SNP and InDel sensitivity, SNVer and VarScan 2 performed best among them. Furthermore, SNVer and VarScan 2 also performed best for six cancer cell lines samples regarding SNP and InDel sensitivity. Considering the dissimilarity of variant calls across different pipelines for datasets from the same platform, we recommended an integration of multiple tools to improve variant calling sensitivity and accuracy for the cancer genome. Illumina and GeneMind technologies can be used independently or together by public health laboratories performing tumor TS. SNVer and VarScan 2 perform better regarding variant detection sensitivity for three typical tumor samples. Our study provides a standardized target sequencing resource to benchmark new bioinformatics protocols and sequencing platforms.

Cholangiocarcinomas (CCA) are a heterogeneous group of tumors that are classified as intrahepatic, perihilar, or distal according to the anatomic location within the biliary tract. Each CCA subtype is associated with distinct genomic alterations, including single nucleotide variants, copy number variants, and chromosomal rearrangements or gene fusions, each of which can influence disease prognosis and/or treatment outcomes. Molecular profiling using next-generation sequencing (NGS) is a powerful technique for identifying unique gene variants carried by an individual tumor, which can facilitate their accurate diagnosis as well as promote the optimal selection of gene variant-matched targeted treatments. NGS is particularly useful in patients with CCA because between one-third and one-half of these patients have genomic alterations that can be targeted by drugs that are either approved or in clinical development. NGS can also provide information about disease evolution and secondary resistance alterations that can develop during targeted therapy, and thus facilitate assessment of prognosis and choice of alternative targeted treatments. Pathologists play a critical role in assessing the viability of biopsy samples for NGS, and advising treating clinicians whether NGS can be performed and which of the available platforms should be used to optimize testing outcomes. This review aims to provide clinical pathologists and other healthcare professionals with practical step-by-step guidance on the use of NGS for molecular profiling of patients with CCA, with respect to tumor biopsy techniques, pre-analytic sample preparation, selecting the appropriate NGS panel, and understanding and interpreting results of the NGS test.

The focus of this study has been to understand the evolutionary relationships and taxonomy of a widely distributed parapatric species pair of wild silk moths in Europe: Saturnia pavonia and Saturnia pavoniella (Lepidoptera: Saturniidae). To address species delimitation in these parapatric taxa, target enrichment and mtDNA sequencing was employed alongside phylogenetic, admixture, introgression, and species delimitation analyses. The dataset included individuals from both species close to and farther away from the contact zone as well as two hybrids generated in the lab. Nuclear markers strongly supported both S. pavonia and S. pavoniella as two distinct species, with hybrids forming a sister group to S. pavoniella. However, the Maximum Likelihood (ML) tree generated from mtDNA sequencing data presented a different picture, showing both taxa to be phylogenetically intermixed. This inconsistency is likely attributable to mitonuclear discordance, which can arise from biological factors (e.g., introgressive hybridization and/or incomplete lineage sorting). Our analyses indicate that past introgressions have taken place, but that there is no evidence to suggest an ongoing admixture between the two species, demonstrating that the taxa have reached full postzygotic reproductive isolation and hence represent two distinct biological species. Finally, we discuss our results from an evolutionary point of view taking into consideration the past climatic oscillations that have likely shaped the present dynamics between the two species. Overall, our study demonstrates the effectiveness of the target enrichment approach in resolving shallow phylogenetic relationships under complex evolutionary circumstances and that this approach is useful in establishing robust and well-informed taxonomic delimitations involving parapatric taxa.

The impact of polyphenols in ovarian cancer is widely studied observing gene expression, epigenetic alterations, and molecular mechanisms based on new 'omics' technologies. Therefore, the combination of omics technologies with the use of phenolic compounds may represent a promising approach to precision nutrition in cancer. This article provides an updated review involving the current applications of high-throughput technologies in ovarian cancer, the role of dietary polyphenols and their mechanistic effects in ovarian cancer, and the current status and challenges of precision nutrition and their relationship with big data. High-throughput technologies in different omics science can provide relevant information from different facets for identifying biomarkers for diagnosis, prognosis, and selection of specific therapies for personalized treatment. Furthermore, the field of omics sciences can provide a better understanding of the role of polyphenols and their function as signaling molecules in the prevention and treatment of ovarian cancer. Although we observed an increase in the number of investigations, there are several approaches to data acquisition, analysis, and integration that still need to be improved, and the standardization of these practices still needs to be implemented in clinical trials.

We previously reported that dickkopf WNT signaling pathway inhibitor 3 (DKK3) expression is correlated with poorer prognosis in head and neck squamous cell carcinoma (HNSCC). Here we investigated DKK3 expression by using The Cancer Genome Atlas (TCGA) public database and bioinformatic analyses.

We used the RNA sequence data and divided the tumor samples into "DKK3-high" and "DKK3-low" groups according to median DKK3 expression. The correlations between DKK3 expression and the clinical data were investigated. Differentially expressed genes (DEGs) were detected using DESEq2 and analyzed by ShinyGO 0.77. A gene set enrichment analysis (GSEA) was also performed using GSEA software. The DEGs were also analyzed with TargetMine to establish the protein-protein interaction (PPI) network.

DKK3 expression was significantly increased in cancer samples, and a high DKK3 expression was significantly associated with shorter overall survival. We identified 854 DEGs, including 284 up-regulated and 570 down-regulated. Functional enrichment analyses revealed several Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with extracellular matrix remodeling. The PPI network identified COL8A1, AGTR1, FN1, P4HA3, PDGFRB, and CEP126 as the key genes.

These results suggested the cancer-promoting ability of DKK3, the expression of which is a promising prognostic marker and therapeutic target for HNSCC.

A growing number of druggable targets and national initiatives for precision oncology necessitate broad genomic profiling for many cancer patients. Whole exome sequencing (WES) offers unbiased analysis of the entire coding sequence, segmentation-based detection of copy number alterations (CNAs), and accurate determination of complex biomarkers including tumor mutational burden (TMB), homologous recombination repair deficiency (HRD), and microsatellite instability (MSI). To assess the inter-institution variability of clinical WES, we performed a comparative pilot study between German Centers of Personalized Medicine (ZPMs) from five participating institutions. Tumor and matched normal DNA from 30 patients were analyzed using custom sequencing protocols and bioinformatic pipelines. Calling of somatic variants was highly concordant with a positive percentage agreement (PPA) between 91 and 95% and a positive predictive value (PPV) between 82 and 95% compared with a three-institution consensus and full agreement for 16 of 17 druggable targets. Explanations for deviations included low VAF or coverage, differing annotations, and different filter protocols. CNAs showed overall agreement in 76% for the genomic sequence with high wet-lab variability. Complex biomarkers correlated strongly between institutions (HRD: 0.79-1, TMB: 0.97-0.99) and all institutions agreed on microsatellite instability. This study will contribute to the development of quality control frameworks for comprehensive genomic profiling and sheds light onto parameters that require stringent standardization.

Complex regions in the human genome such as repeat motifs, pseudogenes and structural (SVs) and copy number variations (CNVs) present ongoing challenges to accurate genetic analysis, particularly for short-read Next-Generation-Sequencing (NGS) technologies. One such region is the highly polymorphic CYP2D loci, containing CYP2D6, a clinically relevant pharmacogene contributing to the metabolism of >20% of common drugs, and two highly similar pseudogenes, CYP2D7 and CYP2D8. Multiple complex SVs, including CYP2D6/CYP2D7-derived hybrid genes are known to occur in different configurations and frequencies across populations and are difficult to detect and characterize accurately. This can lead to incorrect enzyme activity assignment and impact drug dosing recommendations, often disproportionally affecting underrepresented populations. To improve CYP2D6 genotyping accuracy, we developed a PCR-free CRISPR-Cas9 based enrichment method for targeted long-read sequencing that fully characterizes the entire CYP2D6-CYP2D7-CYP2D8 loci. Clinically relevant sample types, including blood, saliva, and liver tissue were sequenced, generating high coverage sets of continuous single molecule reads spanning the entire targeted region of up to 52 kb, regardless of SV present (n = 9). This allowed for fully phased dissection of the entire loci structure, including breakpoints, to accurately resolve complex CYP2D6 diplotypes with a single assay. Additionally, we identified three novel CYP2D6 suballeles, and fully characterized 17 CYP2D7 and 18 CYP2D8 unique haplotypes. This method for CYP2D6 genotyping has the potential to significantly improve accurate clinical phenotyping to inform drug therapy and can be adapted to overcome testing limitations of other clinically challenging genomic regions.

Hydrocarbon spills in cold climates are a prominent and enduring form of anthropogenic contamination. Bioremediation is one of a suite of remediation tools that has emerged as a cost-effective strategy for transforming these contaminants in soil, ideally into less harmful products. However, little is understood about the molecular mechanisms driving these complex, microbially mediated processes. The emergence of -omic technologies has led to a revolution within the sphere of environmental microbiology allowing for the identification and study of so called 'unculturable' organisms. In the last decade, -omic technologies have emerged as a powerful tool in filling this gap in our knowledge on the interactions between these organisms and their environment in vivo. Here, we utilize the text mining software Vosviewer to process meta-data and visualize key trends relating to cold climate bioremediation projects. The results of text mining of the literature revealed a shift over time from optimizing bioremediation experiments on the macro/community level to, in more recent years focusing on individual organisms of interest, interactions within the microbiome and the investigation of novel metabolic degradation pathways. This shift in research focus was made possible in large part by the rise of omics studies allowing research to focus not only what organisms/metabolic pathways are present but those which are functional. However, all is not harmonious, as the development of downstream analytical methods and associated processing tools have outpaced sample preparation methods, especially when dealing with the unique challenges posed when analyzing soil-based samples.

This review summarizes the published evidence on the clinical impact of using next-generation sequencing (NGS) tests to guide management of patients with cancer in the United States.

We performed a comprehensive literature review to identify recent English language publications that presented progression-free survival (PFS) and overall survival (OS) of patients with advanced cancer receiving NGS testing.

Among 6,475 publications identified, 31 evaluated PFS and OS among subgroups of patients who received NGS-informed cancer management. PFS and OS were significantly longer among patients who were matched to targeted treatment in 11 and 16 publications across tumor types, respectively.

Our review indicates that NGS-informed treatment can have an impact on survival across tumor types.

Immunotherapy has remained at the vanguard of promising cancer therapeutic regimens due to its exceptionally high specificity for tumor cells and potential for significantly improved treatment-associated quality of life compared to other therapeutic approaches such as surgery and chemoradiation. This is especially true in the digestive system, where high rates of mutation give rise to a host of targetable tumor-specific antigens. Many patients, however, do not exhibit measurable improvements under immunotherapy due to intrinsic or acquired resistance, making predictive biomarkers necessary to determine which patients will benefit from this line of treatment. Many of these biomarkers are assessed empirically by pathologists according to nuanced scoring criteria and algorithms. This review serves to inform clinicians and pathologists of extant and promising upcoming biomarkers predictive of immunotherapeutic efficacy among digestive system malignancies and the ancillary testing required for interpretation by pathologists according to tumor site of origin.