Whole genome sequencing analysis for cancer genomics and precision medicine

Genetic variant classification by predicted protein structure: A case study on IRF6

Next-generation genome sequencing has revolutionized genetic testing, identifying numerous rare disease-associated gene variants. However, to impute pathogenicity, computational approaches remain inadequate and functional testing of gene variant is required to provide the highest level of evidence. The emergence of AlphaFold2 has transformed the field of protein structure determination, and here we outline a strategy that leverages predicted protein structure to enhance genetic variant classification. We used the gene IRF6 as a case study due to its clinical relevance, its critical role in cleft lip/palate malformation, and the availability of experimental data on the pathogenicity of IRF6 gene variants through phenotype rescue experiments in irf6-/- zebrafish. We compared results from over 30 pathogenicity prediction tools on 37 IRF6 missense variants. IRF6 lacks an experimentally derived structure, so we used predicted structures to explore associations between mutational clustering and pathogenicity. We found that among these variants, 19 of 37 were unanimously predicted as deleterious by computational tools. Comparing in silico predictions with experimental findings, 12 variants predicted as pathogenic were experimentally determined as benign. Even with the recently published AlphaMissense model, 15/18 (83%) of the predicted pathogenic variants were experimentally determined as benign. In comparison, mapping variants to the protein revealed deleterious mutation clusters around the protein binding domain, whereas N-terminal variants tend to be benign, suggesting the importance of structural information in determining pathogenicity of mutations in this gene. In conclusion, incorporating gene-specific structural features of known pathogenic/benign mutations may provide meaningful insights into pathogenicity predictions in a gene-specific manner and facilitate the interpretation of variant pathogenicity.

Transcriptomic analysis of rat prefrontal cortex following chronic stress induced by social isolation - Relevance to psychiatric and neurodevelopmental illness, and implications for treatment

Social isolation is an established risk factor for psychiatric illness, and became increasingly topical with the spread of SARS-CoV-2. We used RNA sequencing (RNA-Seq) to enable unbiased assessment of transcriptomic changes within the prefrontal cortex (PFC) of isolation-reared rats. To provide insight into the relevance of this manipulation for studying human illness, we compared differentially expressed genes (DEGs) and enriched biological functions against datasets involving post-mortem frontal cortical tissue from patients with psychiatric and neurodevelopmental illnesses. Sixteen male Sprague-Dawley rats were reared in groups of four or individually from weaning on postnatal day (PND) 22-24 until PFC tissue collection for RNA-Seq (PND64-66). We identified a total of 183 DEGs in isolates, of which 128 mirrored those in PFC tissue from patients with stress-related mental illnesses and/or neurodevelopmental conditions featuring social deficits. Seventy-one encode proteins classed as druggable by the gene-drug interaction database. Interestingly there are antagonists or inhibitors for the products of three of these up-regulated DEGs (Hrh3, Snca and Sod1) and agonists or activators for products of six of these down-regulated DEGs (Chrm4, Klf2, Lrrk2, Nr4a1, Nr4a3 and Prkca). Some have already undergone pre-clinical and clinical evaluation, and studies with the remainder may be warranted. Changes to Hrh3, Sod1, Chrm4, Lrrk2, Nr4a1 and Prkca were replicated in an independent cohort of sixteen male Sprague-Dawley rats via quantitative reverse transcription polymerase chain reaction (qRT-PCR). Our findings support the continued use of post-weaning isolation rearing to investigate the neurobiology of stress-related disorders and evaluate therapeutic targets.

Clinical relationships between the intratumoral microbiome and risk factors for head and neck cancer

A bioinformatic analysis is a promising approach to understand the relationship between the vast tumor microbiome and cancer development. In the present study, we studied the relationships between the intratumoral microbiome and classical clinical risk factors using bioinformatics analysis of the Cancer Genome Atlas (TCGA) and the Cancer Microbiome Atlas (TCMA) datasets. We used TCMA database and investigated the abundance of microbes at the genus level in solid normal tissue (n = 22) and the primary tumors of patients with head and neck squamous cell carcinoma (HNSCC) (n = 154) and identified three major tumor microbiomes, Fusobacterium, Prevotella, and Streptococcus. The tissue level of Fusobacterium was higher in primary tumors than in solid normal tissue. However, univariate and multivariate analyses of these 3 microbes showed no significant effects on patient survival. We then extracted 43, 55, or 59 genes that were differentially expressed between the over and under the median groups for Fusobacterium, Prevotella, or Streptococcus using the criteria of >2.5, >1.5, or >2.0 fold and p < 0.05 in the Mann-Whitney U test. The results of a pathway analysis revealed the association of Fusobacterium- and Streptococcus-related genes with the IL-17 signaling pathway and Staphylococcus aureus infection, while Prevotella-associated pathways were not extracted. A protein-protein interaction analysis revealed a dense network in the order of Fusobacterium, Streptococcus, and Prevotella. An investigation of the relationships between the intratumoral microbiome and classical clinical risk factors showed that high levels of Fusobacterium were associated with a good prognosis in the absence of alcohol consumption and smoking, while high levels of Streptococcus were associated with a poor prognosis in the absence of alcohol consumption. In conclusion, intratumoral Fusobacterium and Streptococcus may affect the prognosis of patients with HNSCC, and their effects on HNSCC are modulated by the impact of drinking and smoking.

Fast and accurate DNASeq variant calling workflow composed of LUSH toolkit

BACKGROUND

Whole genome sequencing (WGS) is becoming increasingly prevalent for molecular diagnosis, staging and prognosis because of its declining costs and the ability to detect nearly all genes associated with a patient's disease. The currently widely accepted variant calling pipeline, GATK, is limited in terms of its computational speed and efficiency, which cannot meet the growing analysis needs.

RESULTS

Here, we propose a fast and accurate DNASeq variant calling workflow that is purely composed of tools from LUSH toolkit. The precision and recall measurements indicate that both the LUSH and GATK pipelines exhibit high levels of consistency, with precision and recall rates exceeding 99% on the 30x NA12878 dataset. In terms of processing speed, the LUSH pipeline outperforms the GATK pipeline, completing 30x WGS data analysis in just 1.6 h, which is approximately 17 times faster than GATK. Notably, the LUSH_HC tool completes the processing from BAM to VCF in just 12 min, which is around 76 times faster than GATK.

CONCLUSION

These findings suggest that the LUSH pipeline is a highly promising alternative to the GATK pipeline for WGS data analysis, with the potential to significantly improve bedside analysis of acutely ill patients, large-scale cohort data analysis, and high-throughput variant calling in crop breeding programs. Furthermore, the LUSH pipeline is highly scalable and easily deployable, allowing it to be readily applied to various scenarios such as clinical diagnosis and genomic research.

Targeted next-generation sequencing analysis in Italian patients with keratoconus

OBJECTIVE

To report variants in 26 candidate genes and describe the clinical features of Italian patients with keratoconus (KC).

SUBJECTS/METHODS

Sixty-four patients with a confirmed diagnosis of KC were enrolled in this genetic association study. Patients were classified into two study groups according to whether they had a confirmed diagnosis of progressive or stable KC. A purpose-developed Next Generation Sequencing (NGS) panel was used to identify and analyse the coding exons and flanking exon/intron boundaries of 26 genes known to be associated with KC and corneal dystrophies. Interpretation of the pathogenic significance of variants was performed using in silico predictive algorithms.

RESULT

The targeted NGS research identified a total of 167 allelic variants of 22 genes in the study population; twenty-four patients had stable keratoconus (n. 54 variants) and forty patients had progressive disease (n. 113 variants). We identified genetic variants of certain pathogenic significance in five patients with progressive KC; in addition, eight novel genetic variants were found in eight patients with progressive KC. Mutations of FLG, LOXHD1, ZNF469, and DOCK9 genes were twice more frequently identified in patients with progressive than stable disease. Filaggrin gene variants were found in 49 patients (76% of total), of whom 32 patients (80% of progressive KC group) had progressive disease.

CONCLUSIONS

Targeted NGS research provided new insights into the causative effect of candidate genes in the clinical phenotype of keratoconus. Filaggrin mutations were found to represent a genetic risk factor for development of progressive disease in Italy.

The Prevalence of Cancer Predisposition Syndromes (CPSs) in Children with a Neoplasm: A Cohort Study in a Central and Eastern European Population

IMPORTANCE

The etiology of pediatric cancers is often unclear; however, advancements in genetics have identified significant roles for genetic disorders in their development. Over time, the number of cancer predisposition syndromes (CPSs) and awareness of them have increased, providing the possibility of cancer prevention and early detection.

PURPOSE

In this study, we present data concerning the number and type of oncological cases and their correlation with CPS occurrence in a cohort of Central and Eastern European pediatric patients.

MATERIALS

The data were collected between 2000 and 2019 at the Karol Jonscher Clinical Hospital of Poznan University of Medical Sciences, resulting in a cohort of 2190 cases in total, of which 193 children (8.81%) were confirmed to have a CPS.

RESULTS

CPSs occurred most frequently in infancy (22.90% of all children suffering from any diagnosed cancer during the first year of life; p < 0.0001), accounting for more than one-quarter of all CPS cases in our cohort. CPSs were least likely to be observed in patients aged 14 and 15 years (2.17% and 2.44% of children diagnosed with any of the listed cancers at the exact age, respectively; p < 0.05). Among CPSs, the most common were neurofibromatosis type I (NF1), Li-Fraumeni syndrome (LFS), and Down syndrome (DS).

CONCLUSIONS

To conclude, it is important to emphasize the need for personalized treatment for each patient affected by both CPSs and subsequent cancer in order to reduce the toxicity of therapy and improve quality of life by reducing the risk of side effects.

Nrf2 in human cancers: biological significance and therapeutic potential

The nuclear factor-erythroid 2-related factor 2 (Nrf2) is able to control the redox balance in the cells responding to oxidative damage and other stress signals. The Nrf2 upregulation can elevate the levels of antioxidant enzymes to support against damage and death. In spite of protective function of Nrf2 in the physiological conditions, the stimulation of Nrf2 in the cancer has been in favour of tumorigenesis. Since the dysregulation of molecular pathways and mutations/deletions are common in tumors, Nrf2 can be a promising therapeutic target. The Nrf2 overexpression can prevent cell death in tumor and by increasing the survival rate of cancer cells, ensures the carcinogenesis. Moreover, the induction of Nrf2 can promote the invasion and metastasis of tumor cells. The Nrf2 upregulation stimulates EMT to increase cancer metastasis. Furthermore, regarding the protective function of Nrf2, its stimulation triggers chemoresistance. The natural products can regulate Nrf2 in the cancer therapy and reverse drug resistance. Moreover, nanostructures can specifically target Nrf2 signaling in cancer therapy. The current review discusses the potential function of Nrf2 in the proliferation, metastasis and drug resistance. Then, the capacity of natural products and nanostructures for suppressing Nrf2-mediated cancer progression is discussed.

Current progress in CRISPR-Cas systems for cancer

Cancer has been a primary contributor to morbidity and mortality worldwide. With an increasing trend of incidence and prevalence of cancer, progress has also been made in its treatment, starting from radiation and chemotherapy to immunotherapy and gene therapy. CRISPR-Cas technique, a promising gene editing tool, has been employed in cancer research for novel treatment regimens, identification of therapeutic targets, and unraveling the genetic mechanisms behind oncogenesis. CRISPR-based genome editing helped in identifying the roles of specific genetic factors linked to treatment resistance, metastasis, and cancer development. CRISPR allows the discovery of genes and treatment options through specifically interrupting tumor activators or activating tumor suppressor genes in cancer cells. Advancements in CRISPR technology, especially the use of immune cells like chimeric antigen receptor (CAR) T cells, has the potential to revolutionize personalized cancer treatment by precisely targeting and killing cancer cells. Furthermore, reactivating tumor suppressor genes makes cancer cells more susceptible to chemotherapy or immunotherapy. CRISPR-mediated genome editing can, hence, help to overcome resistance to traditional cancer treatments. The current manuscript covers that how is the CRISPR technology propelling revolutionary development in the field of cancer research, providing advance perspectives on the molecular causes of the disease and creating new lines for the development of more precise and potent cancer therapies.

Implications of noncoding regulatory functions in the development of insulinomas

Insulinomas are rare neuroendocrine tumors arising from pancreatic β cells, characterized by aberrant proliferation and altered insulin secretion, leading to glucose homeostasis failure. With the aim of uncovering the role of noncoding regulatory regions and their aberrations in the development of these tumors, we coupled epigenetic and transcriptome profiling with whole-genome sequencing. As a result, we unraveled somatic mutations associated with changes in regulatory functions. Critically, these regions impact insulin secretion, tumor development, and epigenetic modifying genes, including polycomb complex components. Chromatin remodeling is apparent in insulinoma-selective domains shared across patients, containing a specific set of regulatory sequences dominated by the SOX17 binding motif. Moreover, many of these regions are H3K27me3 repressed in β cells, suggesting that tumoral transition involves derepression of polycomb-targeted domains. Our work provides a compendium of aberrant cis-regulatory elements affecting the function and fate of β cells in their progression to insulinomas and a framework to identify coding and noncoding driver mutations.

Advances in the molecular biology of the solitary fibrous tumor and potential impact on clinical applications

Solitary fibrous tumor (SFT) is a rare fibroblastic mesenchymal neoplasm. The current classification has merged SFT and hemangiopericytoma (HPC) into the same tumor entity, while the risk stratification models have been developed to compensate for clinical prediction. Typically, slow-growing and asymptomatic, SFT can occur in various anatomical sites, most commonly in the pleura. Histologically, SFT consists of spindle to oval cells with minimal patterned growth, surrounded by stromal collagen and unique vascular patterns. Molecularly, SFT is defined by the fusion of NGFI-A-binding protein 2 (NAB2) and signal transducer and activator of transcription 6 (STAT6) genes as NAB2-STAT6. This fusion transforms NAB2 into a transcriptional activator, activating early growth response 1 (EGR1) and contributing to SFT pathogenesis and development. There are several fusion variants of NAB2-STAT6 in tumor tissues, with the most frequent ones being NAB2ex4-STAT6ex2 and NAB2ex6-STAT6ex16/ex17. Diagnostic methods play a crucial role in SFT clinical practice and basic research, including RT-PCR, next-generation sequencing (NGS), FISH, immunohistochemistry (IHC), and Western blot analysis, each with distinct capabilities and limitations. Traditional treatment strategies of SFT encompass surgical resection, radiation therapy, and chemotherapy, while emerging management regimes include antiangiogenic agents, immunotherapy, RNA-targeting technologies, and potential targeted drugs. This review provides an update on SFT's clinical and molecular aspects, diagnostic methods, and potential therapies.

A Comprehensive Review of Bioinformatics Tools for Genomic Biomarker Discovery Driving Precision Oncology

The rapid advancement of high-throughput technologies, particularly next-generation sequencing (NGS), has revolutionized cancer research by enabling the investigation of genetic variations such as SNPs, copy number variations, gene expression, and protein levels. These technologies have elevated the significance of precision oncology, creating a demand for biomarker identification and validation. This review explores the complex interplay of oncology, cancer biology, and bioinformatics tools, highlighting the challenges in statistical learning, experimental validation, data processing, and quality control that underpin this transformative field. This review outlines the methodologies and applications of bioinformatics tools in cancer genomics research, encompassing tools for data structuring, pathway analysis, network analysis, tools for analyzing biomarker signatures, somatic variant interpretation, genomic data analysis, and visualization tools. Open-source tools and repositories like The Cancer Genome Atlas (TCGA), Genomic Data Commons (GDC), cBioPortal, UCSC Genome Browser, Array Express, and Gene Expression Omnibus (GEO) have emerged to streamline cancer omics data analysis. Bioinformatics has significantly impacted cancer research, uncovering novel biomarkers, driver mutations, oncogenic pathways, and therapeutic targets. Integrating multi-omics data, network analysis, and advanced ML will be pivotal in future biomarker discovery and patient prognosis prediction.

Clinical application of whole-genome sequencing of solid tumors for precision oncology

Genomic alterations in tumors play a pivotal role in determining their clinical trajectory and responsiveness to treatment. Targeted panel sequencing (TPS) has served as a key clinical tool over the past decade, but advancements in sequencing costs and bioinformatics have now made whole-genome sequencing (WGS) a feasible single-assay approach for almost all cancer genomes in clinical settings. This paper reports on the findings of a prospective, single-center study exploring the real-world clinical utility of WGS (tumor and matched normal tissues) and has two primary objectives: (1) assessing actionability for therapeutic options and (2) providing clarity for clinical questions. Of the 120 patients with various solid cancers who were enrolled, 95 (79%) successfully received genomic reports within a median of 11 working days from sampling to reporting. Analysis of these 95 WGS reports revealed that 72% (68/95) yielded clinically relevant insights, with 69% (55/79) pertaining to therapeutic actionability and 81% (13/16) pertaining to clinical clarity. These benefits include the selection of informed therapeutics and/or active clinical trials based on the identification of driver mutations, tumor mutational burden (TMB) and mutational signatures, pathogenic germline variants that warrant genetic counseling, and information helpful for inferring cancer origin. Our findings highlight the potential of WGS as a comprehensive tool in precision oncology and suggests that it should be integrated into routine clinical practice to provide a complete image of the genomic landscape to enable tailored cancer management.

PN, Gimenes N, Reis MX, Matsas S, Ferreira CG. Next-generation sequencing impact on cancer care: applications, challenges, and future directions

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.

GWAS advancements to investigate disease associations and biological mechanisms

Genome-wide association studies (GWAS) have been instrumental in elucidating the genetic architecture of various traits and diseases. Despite the success of GWAS, inherent limitations such as identifying rare and ultra-rare variants, the potential for spurious associations, and in pinpointing causative agents can undermine diagnostic capabilities. This review provides an overview of GWAS and highlights recent advances in genetics that employ a range of methodologies, including Whole Genome Sequencing (WGS), Mendelian Randomization (MR), the Pangenome's high-quality T2T-CHM13 panel, and the Human BioMolecular Atlas Program (HuBMAP), as potential enablers of current and future GWAS research. State of the literature demonstrate the capabilities of these techniques in enhancing the statistical power of GWAS. WGS, with its comprehensive approach, captures the entire genome, surpassing the capabilities of the traditional GWAS technique focused on predefined Single Nucleotide Polymorphism (SNP) sites. The Pangenome's T2T-CHM13 panel, with its holistic approach, aids in the analysis of regions with high sequence identity, such as segmental duplications (SDs). Mendelian Randomization has advanced causative inference, improving clinical diagnostics and facilitating definitive conclusions. Furthermore, spatial biology techniques like HuBMAP, enable 3D molecular mapping of tissues at single-cell resolution, offering insights into pathology of complex traits. This study aims to elucidate and advocate for the increased application of these technologies, highlighting their potential to shape the future of GWAS research.

It Is in Our DNA: Bringing Electronic Health Records and Genomic Data Together for Precision Medicine

Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing clinicians with detailed information for each patient and analytic support for decision-making at the point of care, digital health technologies are enabling a new era of precision medicine. Genomic data also provide clinicians with information that can improve the accuracy and timeliness of diagnosis, optimize prescribing, and target risk reduction strategies, all of which are key elements for precision medicine. However, genomic data are predominantly seen as diagnostic information and are not routinely integrated into the clinical workflows of electronic medical records. The use of genomic data holds significant potential for precision medicine; however, as genomic data are fundamentally different from the information collected during routine practice, special considerations are needed to use this information in a digital health setting. This paper outlines the potential of genomic data integration with electronic records, and how these data can enable precision medicine.

Acute lymphoblastic leukaemia

Acute lymphoblastic leukaemia (ALL) is a haematological malignancy characterized by the uncontrolled proliferation of immature lymphoid cells. Over past decades, significant progress has been made in understanding the biology of ALL, resulting in remarkable improvements in its diagnosis, treatment and monitoring. Since the advent of chemotherapy, ALL has been the platform to test for innovative approaches applicable to cancer in general. For example, the advent of omics medicine has led to a deeper understanding of the molecular and genetic features that underpin ALL. Innovations in genomic profiling techniques have identified specific genetic alterations and mutations that drive ALL, inspiring new therapies. Targeted agents, such as tyrosine kinase inhibitors and immunotherapies, have shown promising results in subgroups of patients while minimizing adverse effects. Furthermore, the development of chimeric antigen receptor T cell therapy represents a breakthrough in ALL treatment, resulting in remarkable responses and potential long-term remissions. Advances are not limited to treatment modalities alone. Measurable residual disease monitoring and ex vivo drug response profiling screening have provided earlier detection of disease relapse and identification of exceptional responders, enabling clinicians to adjust treatment strategies for individual patients. Decades of supportive and prophylactic care have improved the management of treatment-related complications, enhancing the quality of life for patients with ALL.

DNA damage response and neoantigens: A favorable target for triple-negative breast cancer immunotherapy and vaccine development

Triple-negative breast cancer (TNBC) poses a significant clinical challenge due to its aggressive nature and limited therapeutic options. The interplay between DNA damage response (DDR) mechanisms and the emergence of neoantigens represents a promising avenue for developing targeted immunotherapeutic strategies and vaccines for TNBC. The DDR is a complex network of cellular mechanisms designed to maintain genomic integrity. In TNBC, where genetic instability is a hallmark, dysregulation of DDR components plays a pivotal role in tumorigenesis and progression. This review explores the intricate relationship between DDR and neoantigens, shedding light on the potential vulnerabilities of TNBC cells. Neoantigens, arising from somatic mutations in cancer cells, represent unique antigens that can be recognized by the immune system. TNBC's propensity for genomic instability leads to an increased mutational burden, consequently yielding a rich repertoire of neoantigens. The convergence of DDR and neoantigens in TNBC offers a distinctive opportunity for immunotherapeutic targeting. Immunotherapy has revolutionized cancer treatment by harnessing the immune system to selectively target cancer cells. The unique immunogenicity conferred by DDR-related neoantigens in TNBC positions them as ideal targets for immunotherapeutic interventions. This review also explores various immunotherapeutic modalities, including immune checkpoint inhibitors (ICIs), adoptive cell therapies, and cancer vaccines, that leverage the DDR and neoantigen interplay to enhance anti-tumor immune responses. Moreover, the potential for developing vaccines targeting DDR-related neoantigens opens new frontiers in preventive and therapeutic strategies for TNBC. The rational design of vaccines tailored to the individual mutational landscape of TNBC holds promise for precision medicine approaches. In conclusion, the convergence of DDR and neoantigens in TNBC presents a compelling rationale for the development of innovative immunotherapies and vaccines. Understanding and targeting these interconnected processes may pave the way for personalized and effective interventions, offering new hope for patients grappling with the challenges posed by TNBCs.

A Novel Germline Mutation of BRCA1 and Integrated Analysis With Somatic Mutation in a Chinese Multi-Cancer Family

The presence of mutations in the BRCA1 gene (MIM: 113705) is widely recognized as a significant genetic predisposition for ovarian cancer. This study investigated the genomic mutations in a Chinese family with a history of ovarian, breast, and rectal adenocarcinoma. A novel germline mutation (Phe1695Val) in BRCA1 was identified through whole-exome sequencing. Subsequently, we performed whole-genome sequencing to identify somatic mutations and analyze mutational signatures in individuals carrying the novel germline mutation. Our findings revealed a correlation between somatic mutational signatures and the BRCA1 germline mutation in the proband with ovarian cancer, while no such association was observed in the tumor tissue from the patient with breast cancer. Furthermore, distinct somatic driver mutations were identified, a truncated mutation in the TP53 gene in the ovarian tumor tissue, and a hotspot mutation in the PIK3CA gene in the breast cancer. According to our findings, the BRCA1 F1695V mutation is linked to ovarian cancer susceptibility in the family and causes specific somatic mutational profiles.

Genetic Analyses of Primary Liver Cancer Cell Lines: Correspondence With Morphological Features of Original Tumors

BACKGROUND/AIM

Advancements in genetic analysis technologies have led to establishment of molecular classifications systems for primary liver cancers. The correlation between pathological morphology and genetic mutations in hepatocellular carcinoma (HCC) is becoming increasingly evident. To construct appropriate experimental models, it is crucial to select cell lines based on their morphology and genetic mutations. In this study, we conducted comprehensive genetic analyses of primary liver cancer cell lines and examined their correlations with morphology.

MATERIALS AND METHODS

Thirteen primary liver cancer cell lines established in our Department were investigated. Eleven cell lines were HCC cell lines, whereas 2 were combined hepatocellular-cholangiocarcinoma (CHC) cell line characteristics. Whole exome sequencing and fusion gene analyses were conducted using a next generation sequencing platform. We also examined correlations between cell mutations and morphological findings and conducted experiments to clarify the association between morphological findings and genetic alterations.

RESULTS

Mutations in TP53, HMCN1, PCLO, HYDIN, APOB, and EYS were found in 11, 5, 4, 4, 3, and 3 cell lines, respectively. CTNNB1 mutation was not identified in any cell line. The original tumor of four cell lines (KYN-1, KYN-2, KYN-3, and HAK-6) showed morphologically macrotrabecular massive patterns and these cell lines harbor TP53 mutations. Two cell lines (KYN-2 and KMCH-2) showed an extremely high tumor mutation burden. These two cell lines possess ultra-mutations associated with DNA repair and/or DNA polymerase.

CONCLUSION

The study identified correlations between morphological findings and genetic mutations in several HCC cell lines. Cell lines with unique genetic mutations were found. This information will be a valuable tool for the selection of suitable experimental models in HCC research.

Emerging biomarkers and molecular targets for precision medicine in cervical cancer

Cervical cancer remains a significant global health burden, necessitating innovative approaches for improved diagnostics and personalized treatment strategies. Precision medicine has emerged as a promising paradigm, leveraging biomarkers and molecular targets to tailor therapy to individual patients. This review explores the landscape of emerging biomarkers and molecular targets in cervical cancer, highlighting their potential implications for precision medicine. By integrating these biomarkers into comprehensive diagnostic algorithms, clinicians can identify high-risk patients at an earlier stage, enabling timely intervention and improved patient outcomes. Furthermore, the identification of specific molecular targets has paved the way for the development of targeted therapies aimed at disrupting key pathways implicated in cervical carcinogenesis. In conclusion, the evolving landscape of biomarkers and molecular targets presents exciting opportunities for advancing precision medicine in cervical cancer. By harnessing these insights, clinicians can optimize treatment selection, enhance patient outcomes, and ultimately transform the management of this devastating disease.

[Molecular pathological analysis through the ages]

BACKGROUND

Molecular pathological examinations of tumor samples encompass a wide range of diagnostic analyses. Especially in recent years, numerous new biomarkers have come to the forefront-the analysis of which is crucial for therapy decisions.

OBJECTIVES

Within the field of molecular pathology, the demands of next generation sequencing (NGS)-based requirements have experienced massive growth in recent years. To meet this demand, methods are constantly being adapted and further developed. The following sections aim to illuminate how this trend arises and which analyses are gaining importance.

METHODS

The article provides an overview of the essential nucleic acid-based analysis techniques in the field of massive parallel sequencing. Terms such as DNA- and RNA-based techniques, as well as the associated analysis methods, are described, particularly with regard to their use in routine molecular pathological diagnostics.

RESULTS

The breadth of genomic sequencing has been steadily growing in recent years, particularly due to the increasing relevance of personalized medicine, along with the rising approvals of targeted therapeutics. This necessitates, among other things, the analysis of new biomarkers. The diagnostics as part of interdisciplinary molecular tumor boards (MTB) are now based on large gene panels (> 1 megabase). Furthermore, through the "Modellvorhaben Genomsequenzierung" § 64e, whole exome or whole genome sequencing has been made available for oncological patients. Given these developments, it is evident that future analyses will require the integration of additional omics fields, such as whole transcriptome analysis, epigenomics, and proteomics.

CONCLUSION

The challenges of personalized medicine along with the necessity of simultaneously assessing numerous new biomarkers require the implementation and execution of new techniques in molecular pathology whose complexity is steadily increasing.

Genetics of liver disease in adults

Chronic liver disease stands as a significant global health problem with an estimated 2 million annual deaths across the globe. Combining the use of next-generation sequencing technologies with evolving knowledge in the interpretation of genetic variation across the human genome is propelling our understanding, diagnosis, and management of both rare and common liver diseases. Here, we review the contribution of risk and protective alleles to common forms of liver disease, the rising number of monogenic diseases affecting the liver, and the role of somatic genetic variants in the onset and progression of oncological and non-oncological liver diseases. The incorporation of genomic information in the diagnosis and management of patients with liver disease is driving the beginning of a new era of genomics-informed clinical hepatology practice, facilitating personalized medicine, and improving patient care.

Screening and verification of hub genes in esophageal squamous cell carcinoma by integrated analysis

Esophageal squamous cell carcinoma (ESCC) is one of the most common malignant tumors. However, the mechanisms underlying ESCC tumorigenesis have not been fully elucidated. Thus, we aimed to determine the key genes involved in ESCC tumorigenesis. The following bioinformatics analyses were performed: identification of differentially expressed genes (DEGs); gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis; integrated analysis of the protein-protein interaction network and Gene Expression Profiling Interactive Analysis database for validation of hub genes. Finally, western blotting and qPCR were used to explore the expression of cell division cycle 6 (CDC6) in ESCC cell lines. Immunohistochemistry analysis of ESCC samples from patients and matched clinical characteristics was used to determine the effects of CDC6. A total of 494 DEGs were identified, and functional enrichment was mainly focused on cell cycle and DNA replication. Biological pathway analysis of the hub genes was closely related to the cell cycle. We found that CDC6 was upregulated in ESCC cell lines and patient tissues and was related to the clinicopathological characteristics of ESCC. In conclusion, this study identified hub genes and crucial biological pathways related to ESCC tumorigenesis and integrated analyses indicated that CDC6 may be a novel diagnostic and therapeutic target for ESCC.

Pharmacogenomics - a minor rather than major force in clinical medicine

INTRODUCTION

Pharmacogenomics (PGx) is touted as essential for the future of precision medicine. But the opportunity cost of PGx from the prescribers' perspective is rarely considered. The aim of this article is to critique PGx-guided prescribing using clinical pharmacology principles so that important cases for PGx testing are not missed by doctors responsible for therapeutic decision making.

AREAS COVERED

Three categories of PGx and their limitations are outlined - exposure PGx, response PGx, and immune-mediated safety PGx. Clinical pharmacology reasons are given for the narrow scope of PGx-guided prescribing apart from a few medical specialties. Clinical problems for doctors that may arise from PGx are then explained, including mismatch between patients' expectations of PGx testing and the benefits or answers it provides.

EXPERT OPINION

Contrary to popular opinion, PGx is unlikely to become the cornerstone of precision medicine. Sound clinical pharmacology reasons explain why PGx-guided prescribing is unnecessary for most drugs. Pharmacogenomics is important for niche areas of prescribing but has limited clinical utility more broadly. The opportunity cost of PGx-guided prescribing is currently too great for most doctors.

Optimized whole-genome sequencing workflow for tumor diagnostics in routine pathology practice

Two decades after the genomics revolution, oncology is rapidly transforming into a genome-driven discipline, yet routine cancer diagnostics is still mainly microscopy based, except for tumor type-specific predictive molecular tests. Pathology laboratories struggle to quickly validate and adopt biomarkers identified by genomics studies of new targeted therapies. Consequently, clinical implementation of newly approved biomarkers suffers substantial delays, leading to unequal patient access to these therapies. Whole-genome sequencing (WGS) can successfully address these challenges by providing a stable molecular diagnostic platform that allows detection of a multitude of genomic alterations in a single cost-efficient assay and facilitating rapid implementation, as well as by the development of new genomic biomarkers. Recently, the Whole-genome sequencing Implementation in standard Diagnostics for Every cancer patient (WIDE) study demonstrated that WGS is a feasible and clinically valid technique in routine clinical practice with a turnaround time of 11 workdays. As a result, WGS was successfully implemented at the Netherlands Cancer Institute as part of routine diagnostics in January 2021. The success of implementing WGS has relied on adhering to a comprehensive protocol including recording patient information, sample collection, shipment and storage logistics, sequencing data interpretation and reporting, integration into clinical decision-making and data usage. This protocol describes the use of fresh-frozen samples that are necessary for WGS but can be challenging to implement in pathology laboratories accustomed to using formalin-fixed paraffin-embedded samples. In addition, the protocol outlines key considerations to guide uptake of WGS in routine clinical care in hospitals worldwide.

Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)

BACKGROUND

Cell-free DNA (cfDNA) contains a large amount of molecular information that can be used for multi-cancer early detection (MCED), including changes in epigenetic status of cfDNA, such as cfDNA fragmentation profile. The fragmentation of cfDNA is non-random and may be related to cfDNA methylation. This study provides clinical evidence for the feasibility of inferring cfDNA methylation levels based on cfDNA fragmentation patterns. We performed whole-genome bisulfite sequencing and whole-genome sequencing (WGS) on both healthy individuals and cancer patients. Using the information of whole-genome methylation levels, we investigated cytosine-phosphate-guanine (CpG) cleavage profile and validated the method of predicting the methylation level of individual CpG sites using WGS data.

RESULTS

We conducted CpG cleavage profile biomarker analysis on data from both healthy individuals and cancer patients. We obtained unique or shared potential biomarkers for each group and built models accordingly. The modeling results proved the feasibility to predict the methylation status of single CpG sites in cfDNA using cleavage profile model from WGS data.

CONCLUSION

By combining cfDNA cleavage profile of CpG sites with machine learning algorithms, we have identified specific CpG cleavage profile as biomarkers to predict the methylation status of individual CpG sites. Therefore, methylation profile, a widely used epigenetic biomarker, can be obtained from a single WGS assay for MCED.

Public health genomics research in Italy: an overview of ongoing projects

Public health genomics (PHG) aims to integrate advances in genomic sciences into healthcare for the benefit of the general population. As in many countries, there are various research initiatives in this field in Italy, but a clear picture of the national research portfolio has never been sketched. Thus, we aimed to provide an overview of current PHG research projects at the national or international level by consultation with Italian institutional and academic experts. We included 68 PHG projects: the majority were international projects in which Italian researchers participated (n = 43), mainly funded by the European Commission, while the remainder were national initiatives (N = 25), mainly funded by central government. Funding varied considerably, from € 50,000 to € 80,803,177. Three main research themes were identified: governance (N = 20); precision medicine (PM; N = 46); and precision public health (N = 2). We found that research activities are preferentially aimed at the clinical application of PM, while other efforts deal with the governance of the complex translation of genomic innovation into clinical and public health practice. To align such activities with national and international priorities, the development of an updated research agenda for PHG is needed.

CDCA8, a mitosis-related gene, as a prospective pan-cancer biomarker: implications for survival prognosis and oncogenic immunology

BACKGROUND

Human cell division cycle-associated protein 8 (CDCA8), a critical regulator of mitosis, has been identified as a prospective prognostic biomarker in several cancer types, including breast, colon, and lung cancers. This study analyzed the diagnostic/prognostic potential and clinical implications of CDCA8 across diverse cancers.

METHODS

Bioinformatics and molecular experiments.

RESULTS

Analyzing TCGA data via TIMER2 and GEPIA2 databases revealed significant up-regulation of CDCA8 in 23 cancer types compared to normal tissues. Prognostically, elevated CDCA8 expression correlated with poorer overall survival in KIRC, LUAD, and SKCM, emphasizing its potential as a prognostic marker. UALCAN analysis demonstrated CDCA8 up-regulation based on clinical variables, such as cancer stage, race, and gender, in these cancers. Epigenetic exploration indicated reduced CDCA8 promoter methylation levels in Kidney Renal Clear Cell Carcinoma (KIRC), Lung Adenocarcinoma (LUAD), and Skin Cutaneous Melanoma (SKCM) tissues compared to normal controls. Promoter methylation and mutational analyses showcased a hypomethylation and low mutation rate for CDCA8 in these cancers. Correlation analysis revealed positive associations between CDCA8 expression and infiltrating immune cells, particularly CD8+ and CD4+ T cells. Protein-protein interaction (PPI) network analysis unveiled key interacting proteins, while gene enrichment analysis highlighted their involvement in crucial cellular processes and pathways. Additionally, exploration of CDCA8-associated drugs through DrugBank presented potential therapeutic options for KIRC, LUAD, and SKCM. In vitro validation using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) confirmed elevated CDCA8 expression in LUAD cell lines (A549 and H1299) compared to control cell lines (Beas-2B and NL-20).

CONCLUSION

This study provides concise insights into CDCA8's multifaceted role in KIRC, LUAD, and SKCM, covering expression patterns, diagnostic and prognostic relevance, epigenetic regulation, mutational landscape, immune infiltration, and therapeutic implications.

Overcoming the limitations of immunotherapy in pancreatic ductal adenocarcinoma: Combining radiotherapy and metabolic targeting therapy

As a novel anticancer therapy, immunotherapy has demonstrated robust efficacy against a few solid tumors but poor efficacy against pancreatic ductal adenocarcinoma (PDAC). This poor outcome is primarily attributable to the intrinsic cancer cell resistance and T-cell exhaustion, which is also the reason for the failure of conventional therapy. The present review summarizes the current PDAC immunotherapy avenues and the underlying resistance mechanisms. Then, the review discusses synergistic combination therapies, such as radiotherapy (RT) and metabolic targeting. Research suggests that RT boosts the antigen of PDAC, which facilitates the anti-tumor immune cell infiltration and exerts function. Metabolic reprogramming contributes to restoring the exhausted T cell function. The current review will help in tailoring combination regimens to enhance the efficacy of immunotherapy. In addition, it will help provide new approaches to address the limitations of the immunosuppressive tumor microenvironment (TME) by examining the relationship among immunotherapy, RT, and metabolism targeting therapy in PDAC.

A comprehensive review on anticancer evaluation techniques

The development of effective anticancer strategies and the improvement of our understanding of cancer need analytical tools. Utilizing a variety of analytical approaches while investigating anti-cancer medicines gives us a thorough understanding of the traits and mechanisms concerned to cancer cells, which enables us to develop potent treatments to combat them. The importance of anticancer research may be attributed to various analytical techniques that contributes to the identification of therapeutic targets and the assessment of medication efficacy, which are crucial things in expanding our understanding of cancer biology. The study looks at methods that are often used in cancer research, including cell viability assays, clonogenic assay, flow cytometry, 2D electrophoresis, microarray, immunofluorescence, western blot caspase activation assay, bioinformatics, etc. The fundamentals, applications, and how each technique analytical advances our understanding of cancer are briefly reviewed.

Assessment of Prostate and Bladder Cancer Genomic Biomarkers Using Artificial Intelligence: a Systematic Review

PURPOSE OF REVIEW

The aim of the systematic review is to assess AI's capabilities in the genetics of prostate cancer (PCa) and bladder cancer (BCa) to evaluate target groups for such analysis as well as to assess its prospects in daily practice.

RECENT FINDINGS

In total, our analysis included 27 articles: 10 articles have reported on PCa and 17 on BCa, respectively. The AI algorithms added clinical value and demonstrated promising results in several fields, including cancer detection, assessment of cancer development risk, risk stratification in terms of survival and relapse, and prediction of response to a specific therapy. Besides clinical applications, genetic analysis aided by the AI shed light on the basic urologic cancer biology. We believe, our results of the AI application to the analysis of PCa, BCa data sets will help to identify new targets for urological cancer therapy. The integration of AI in genomic research for screening and clinical applications will evolve with time to help personalizing chemotherapy, prediction of survival and relapse, aid treatment strategies such as reducing frequency of diagnostic cystoscopies, and clinical decision support, e.g., by predicting immunotherapy response. These factors will ultimately lead to personalized and precision medicine thereby improving patient outcomes.

An FM-Index Based High-Throughput Memory-Efficient FPGA Accelerator for Paired-End Short-Read Mapping

This article presents an Ferragina-Manzini index (FM-index) based paired-end short-read mapping hardware accelerator. Four techniques are proposed to significantly reduce the number of memory accesses and operations to improve the throughput. First, an interleaved data structure is proposed to reduce the processing time by 51.8% by leveraging the data locality. Second, the boundaries of possible mapping location candidates can be retrieved within only one memory access by constructing a lookup table along with the FM-index. This reduces the number of DRAM accesses by 60% with only a 64 MB memory overhead. Third, an additional step is added to skip the time-consuming repetitive location candidates filtering conditionally, avoiding unnecessary operations. Lastly, an early termination method is proposed to terminate the mapping process if any location candidate with a high enough alignment score is detected, greatly decreasing the execution time. Overall, the computation time is reduced by 92.6% with only a 2% memory overhead in DRAM. The proposed methods are realized on a Xilinx Alveo U250 FPGA. The proposed FPGA accelerator processes 1,085,812,766 short-reads from the U.S. Food and Drug Administration (FDA) dataset within 35.4 minutes at 200 MHz. It achieves a 1.7-to-18.6× higher throughput and the highest 99.3% accuracy by exploiting the paired-end short-read mapping, compared to state-of-the-art FPGA-based designs.

Uncovering the Genetic Basis of Congenital Heart Disease: Recent Advancements and Implications for Clinical Management

Congenital heart disease (CHD) is the most prevalent hereditary disorder, affecting approximately 1% of all live births. A reduction in morbidity and mortality has been achieved with advancements in surgical intervention, yet challenges in managing complications, extracardiac abnormalities, and comorbidities still exist. To address these, a more comprehensive understanding of the genetic basis underlying CHD is required to establish how certain variants are associated with the clinical outcomes. This will enable clinicians to provide personalized treatments by predicting the risk and prognosis, which might improve the therapeutic results and the patient's quality of life. We review how advancements in genome sequencing are changing our understanding of the genetic basis of CHD, discuss experimental approaches to determine the significance of novel variants, and identify barriers to use this knowledge in the clinics. Next-generation sequencing technologies are unravelling the role of oligogenic inheritance, epigenetic modification, genetic mosaicism, and noncoding variants in controlling the expression of candidate CHD-associated genes. However, clinical risk prediction based on these factors remains challenging. Therefore, studies involving human-induced pluripotent stem cells and single-cell sequencing help create preclinical frameworks for determining the significance of novel genetic variants. Clinicians should be aware of the benefits and implications of the responsible use of genomics. To facilitate and accelerate the clinical integration of these novel technologies, clinicians should actively engage in the latest scientific and technical developments to provide better, more personalized management plans for patients.

A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer

Studies continue to uncover contributing risk factors for breast cancer (BC) development including genetic variants. Advances in machine learning and big data generated from genetic sequencing can now be used for predicting BC pathogenicity. However, it is unclear which tool developed for pathogenicity prediction is most suited for predicting the impact and pathogenicity of variant effects. A significant challenge is to determine the most suitable data source for each tool since different tools can yield different prediction results with different data inputs. To this end, this work reviews genetic variant databases and tools used specifically for the prediction of BC pathogenicity. We provide a description of existing genetic variants databases and, where appropriate, the diseases for which they have been established. Through example, we illustrate how they can be used for prediction of BC pathogenicity and discuss their associated advantages and disadvantages. We conclude that the tools that are specialized by training on multiple diverse datasets from different databases for the same disease have enhanced accuracy and specificity and are thereby more helpful to the clinicians in predicting and diagnosing BC as early as possible.

Integrative approaches based on genomic techniques in the functional studies on enhancers

With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.

Targeting cellular metabolism in head and neck cancer precision medicine era: A promising strategy to overcome therapy resistance

Head and neck squamous cell carcinoma (HNSCC) is among the most prevalent cancer worldwide, with the most severe impact on quality of life of patients. Despite the development of multimodal therapeutic approaches, the clinical outcomes of HNSCC are still unsatisfactory, mainly caused by relatively low responsiveness to treatment and severe drug resistance. Metabolic reprogramming is currently considered to play a pivotal role in anticancer therapeutic resistance. This review aimed to define the specific metabolic programs and adaptations in HNSCC therapy resistance. An extensive literature review of HNSCC was conducted via the PubMed including metabolic reprogramming, chemo- or immune-therapy resistance. Glucose metabolism, fatty acid metabolism, and amino acid metabolism are closely related to the malignant biological characteristics of cancer, anti-tumor drug resistance, and adverse clinical results. For HNSCC, pyruvate, lactate and almost all lipid categories are related to the occurrence and maintenance of drug resistance, and targeting amino acid metabolism can prevent tumor development and enhance the response of drug-resistant tumors to anticancer therapy. This review will provide a better understanding of the altered metabolism in therapy resistance of HNSCC and promote the development of new therapeutic strategies against HNSCC, thereby contribute to a more efficacious precision medicine.

Advocating for PCR-RFLP as molecular tool within malaria programs in low endemic areas and low resource settings

The road to malaria elimination for low- and middle-income countries is paved with obstacles, including the complexity and high costs of advanced molecular methods for genomic analysis. The usefulness of PCR-RFLP as less complex and affordable molecular surveillance tool in low-endemic malaria regions was assessed in a cross-sectional study conducted in Suriname, currently striving for malaria elimination, but plagued by recent P. vivax outbreaks. Molecular analysis of two highly polymorphic genes Pvmsp-1 F2 and Pvmsp-3α was performed for 49 samples, collected during October 2019 through September 2021 from four different regions with varying malaria transmission risks. RFLP-profiling revealed that outbreak samples from three indigenous villages, almost exclusively, harbored a single clonal type, matching the "Palumeu" lineage previously described in 2019, despite multiple relapses and drug pressure exerted by mass drug administration events, suggesting a limited P. vivax hypnozoite reservoir in Suriname. In contrast, isolates originating from Sophie, a mining area in neighboring French Guiana displayed a highly heterogeneous parasite population consistent with its endemic malaria status, demonstrating the differentiating capacity and thus the usefulness of PCR-RFLP for P. vivax genetic diversity studies. Outbreak reconstruction emphasized the impact of undetected human movement and relapses on reintroduction and resurgence of P. vivax malaria and PCR-RFLP monitoring of circulating parasites guided the roll-out of targeted interventions. PCR-RFLP seems a suitable molecular alternative in low-endemic areas with restricted resources for outbreak analysis, for monitoring the spread or containment of circulating strains and for identification of imported cases or potential foci.

Potential Role of the Intratumoral Microbiota in Prognosis of Head and Neck Cancer

The tumor microbiome, a relatively new research field, affects tumor progression through several mechanisms. The Cancer Microbiome Atlas (TCMA) database was recently published. In the present study, we used TCMA and The Cancer Genome Atlas and examined microbiome profiling in head and neck squamous cell carcinoma (HNSCC), the role of the intratumoral microbiota in the prognosis of HNSCC patients, and differentially expressed genes in tumor cells in relation to specific bacterial infections. We investigated 18 microbes at the genus level that differed between solid normal tissue (n = 22) and primary tumors (n = 154). The tissue microbiome profiles of Actinomyces, Fusobacterium, and Rothia at the genus level differed between the solid normal tissue and primary tumors of HNSCC patients. When the prognosis of groups with rates over and under the median for each microbe at the genus level was examined, rates for Leptotrichia which were over the median correlated with significantly higher overall survival rates. We then extracted 35 differentially expressed genes between the over- and under-the-median-for-Leptotrichia groups based on the criteria of >1.5 fold and p < 0.05 in the Mann-Whitney U-test. A pathway analysis showed that these Leptotrichia-related genes were associated with the pathways of Alzheimer disease, neurodegeneration-multiple diseases, prion disease, MAPK signaling, and PI3K-Akt signaling, while protein-protein interaction analysis revealed that these genes formed a dense network. In conclusion, probiotics and specific antimicrobial therapy targeting Leptotrichia may have an impact on the prognosis of HNSCC.

PAICS as a potential target for cancer therapy linking purine biosynthesis to cancer progression

Tumor cells are required to undergo metabolic reprogramming for rapid development and progression, and one of the metabolic characteristics of cancer cells is the excessive synthesis and utilization of nucleotides. Abnormally increased nucleotides and their metabolites not only directly accelerate tumor cell progression but also indirectly act on stromal cells in the tumor microenvironment (TME) via a paracrine manner to regulate tumor progression. Purine nucleotides are mainly produced via de novo nucleotide synthesis in tumor cells; therefore, intervening in their synthesis has emerged as a promising strategy in anti-tumor therapy. De novo purine synthesis is a 10-step reaction catalyzed by six enzymes to synthesize inosine 5-monophosphate (IMP) and subsequently synthesize AMP and GMP. Phosphoribosylaminoimidazole carboxylase/phosphori-bosylaminoimidazole succinocarboxamide synthetase (PAICS) is a bifunctional enzyme that catalyzes de novo purine synthesis. Aberrantly elevated PAICS expression in various tumors is associated with poor prognosis. Evidence suggests that PAICS and its catalytic product, N-succinylcarboxamide-5-aminoimidazole ribonucleotide (SAICAR), could inhibit tumor cell apoptosis and promote the growth, epithelial-mesenchymal transition (EMT), invasion, and metastasis by regulating signaling pathways such as pyruvate kinase M2 (PKM2), extracellular signal-related kinases 1 and 2 (ERK1/2), focal adhesion kinase (FAK) and so on. This review summarizes the structure, biological functions and the molecular mechanisms of PAICS in cancer development and discusses its potential to be a target for tumor therapy.

Automated and robust extraction of genomic DNA from various leftover blood samples

With the development of genomic technologies, the isolation of genomic DNA (gDNA) from clinical samples is increasingly required for clinical diagnostics and research studies. In this study, we explored the potential of utilizing various leftover blood samples obtained from routine clinical tests as a viable source of gDNA. Using an automated method with optimized pre-treatments, we obtained gDNA from seven types of clinical leftover blood, with average yields of gDNA ranging from 3.11 ± 0.45 to 22.45 ± 4.83 μg. Additionally, we investigated the impact of storage conditions on gDNA recovery, resulting in yields of 8.62-68.08 μg when extracting gDNA from EDTA leftover blood samples stored at 4 °C for up to 13 weeks or -80 °C for up to 78 weeks. Furthermore, we successfully obtained sequenceable gDNA from both Serum Separator Tube and EDTA Tube using a 96-well format extraction, with yields ranging from 0.61 to 71.29 μg and 3.94-215.98 μg, respectively. Our findings demonstrate the feasibility of using automated high-throughput platforms for gDNA extraction from various clinical leftover blood samples with the proper pre-treatments.

Potential agnostic role of BRCA alterations in patients with several solid tumors: One for all, all for one?

Germline BRCA1/2 alterations in the Homologous Recombination (HR) pathway are considered as main susceptibility biomarkers to Hereditary Breast and Ovarian Cancers (HBOC). The modern molecular biology technologies allowed to characterize germline and somatic BRCA1/2 alterations in several malignancies, broadening the landscape of BRCA1/2-alterated tumors. In the last years, BRCA genetic testing, beyond the preventive value, also assumed a predictive and prognostic significance for patient management. The approval of molecules with agnostic indication is leading to a new clinical model, defined "mutational". Among these drugs, the Poly (ADP)-Ribose Polymerase inhibitors (PARPi) for BRCA1/2-deficient tumors were widely studied leading to increasing therapeutic implications. In this Review we provided an overview of the main clinical studies describing the association between BRCA-mutated tumors and PARPi response, focusing on the controversial evidence about the potential agnostic indication based on BRCA1/2 alterations in several solid tumors.

Visual genetic typing of glioma using proximity-anchored in situ spectral coding amplification

Gliomas are histologically and genetically heterogeneous tumors. However, classical histopathological typing often ignores the high heterogeneity of tumors and thus cannot meet the requirements of precise pathological diagnosis. Here, proximity-anchored in situ spectral coding amplification (ProxISCA) is proposed for multiplexed imaging of RNA mutations, enabling visual typing of brain gliomas with different pathological grades at the single-cell and tissue levels. The ligation-based padlock probe can discriminate one-nucleotide variations, and the design of proximity primers enables the anchoring of amplicons on target RNA, thus improving localization accuracy. The DNA module-based spectral coding strategy can dramatically improve the multiplexing capacity for imaging RNA mutations through one-time labelling, with low cost and simple operation. One-target-one-amplicon amplification confers ProxISCA the ability to quantify RNA mutation copy number with single-molecule resolution. Based on this approach, it is found that gliomas with higher malignant grades express more genes with high correlation at the cellular and tissue levels and show greater cellular heterogeneity. ProxISCA provides a tool for glioma research and precise diagnosis, which can reveal the relationship between cellular heterogeneity and glioma occurrence or development and assist in pathological prognosis.

Biomarkers and biosensors for early cancer diagnosis, monitoring and prognosis

Cancers continue to be of major concern due to their serious global socioeconomic impact, apart from the continued increase in the incidence of various cancer types. A major challenge that this disease poses is due to the low "early detection" rates which limit the therapeutic outcomes for the affected individuals. Current research has highlighted the discovering biomarkers that help in early cancer detection and the development of technologies for the detection and quantification of such biomarkers. Biomarkers range from proteins to nucleic acids, and can be specific to a particular cancer type. Detection and quantification of such biomarkers at low levels from biological samples is being made possible by the advent of developing biosensors and by using biomedical engineering technologies such as tumor-on-a-chip models. Here, we present biomarkers that can be helpful for the early detection of breast, colorectal, esophageal, lung, liver, ovarian, and prostate cancer. In addition, we discuss the potential of circulating tumor cell DNA (ctDNA) as an early diagnostic marker. Finally, biosensors available for the detection of cancer biomarkers, which is a recent advancement in this area of research, are discussed.

Lung cancer associated with combustion particles and fine particulate matter (PM2.5) - The roles of polycyclic aromatic hydrocarbons (PAHs) and the aryl hydrocarbon receptor (AhR)

Air pollution is the leading cause of lung cancer after tobacco smoking, contributing to 20% of all lung cancer deaths. Increased risk associated with living near trafficked roads, occupational exposure to diesel exhaust, indoor coal combustion and cigarette smoking, suggest that combustion components in ambient fine particulate matter (PM2.5), such as polycyclic aromatic hydrocarbons (PAHs), may be central drivers of lung cancer. Activation of the aryl hydrocarbon receptor (AhR) induces expression of xenobiotic-metabolizing enzymes (XMEs) and increase PAH metabolism, formation of reactive metabolites, oxidative stress, DNA damage and mutagenesis. Lung cancer tissues from smokers and workers exposed to high combustion PM levels contain mutagenic signatures derived from PAHs. However, recent findings suggest that ambient air PM2.5 exposure primarily induces lung cancer development through tumor promotion of cells harboring naturally acquired oncogenic mutations, thus lacking typical PAH-induced mutations. On this background, we discuss the role of AhR and PAHs in lung cancer development caused by air pollution focusing on the tumor promoting properties including metabolism, immune system, cell proliferation and survival, tumor microenvironment, cell-to-cell communication, tumor growth and metastasis. We suggest that the dichotomy in lung cancer patterns observed between smoking and outdoor air PM2.5 represent the two ends of a dose-response continuum of combustion PM exposure, where tumor promotion in the peripheral lung appears to be the driving factor at the relatively low-dose exposures from ambient air PM2.5, whereas genotoxicity in the central airways becomes increasingly more important at the higher combustion PM levels encountered through smoking and occupational exposure.

Next-generation sequencing in dermatology

Over the past decade, Next-Generation Sequencing (NGS) has advanced our understanding, diagnosis, and management of several areas within dermatology. NGS has emerged as a powerful tool for diagnosing genetic diseases of the skin, improving upon traditional PCR-based techniques limited by significant genetic heterogeneity associated with these disorders. Epidermolysis bullosa and ichthyosis are two of the most extensively studied genetic diseases of the skin, with a well-characterized spectrum of genetic changes occurring in these conditions. NGS has also played a critical role in expanding the mutational landscape of cutaneous squamous cell carcinoma, enhancing our understanding of its molecular pathogenesis. Similarly, genetic testing has greatly benefited melanoma diagnosis and treatment, primarily due to the high prevalence of BRAF hot spot mutations and other well-characterized genetic alterations. Additionally, NGS provides a valuable tool for measuring tumor mutational burden, which can aid in management of melanoma. Lastly, NGS demonstrates promise in improving the sensitivity of diagnosing cutaneous T-cell lymphoma. This article provides a comprehensive summary of NGS applications in the diagnosis and management of genodermatoses, cutaneous squamous cell carcinoma, melanoma, and cutaneous T-cell lymphoma, highlighting the impact of NGS on the field of dermatology.

Global impact of somatic structural variation on the cancer proteome

Both proteome and transcriptome data can help assess the relevance of non-coding somatic mutations in cancer. Here, we combine mass spectrometry-based proteomics data with whole genome sequencing data across 1307 human tumors spanning various tissues to determine the extent somatic structural variant (SV) breakpoint patterns impact protein expression of nearby genes. We find that about 25% of the hundreds of genes with SV-associated cis-regulatory alterations at the mRNA level are similarly associated at the protein level. SVs associated with enhancer hijacking, retrotransposon translocation, altered DNA methylation, or fusion transcripts are implicated in protein over-expression. SVs combined with altered protein levels considerably extend the numbers of patients with tumors somatically altered for critical pathways. We catalog both SV breakpoint patterns involving patient survival and genes with nearby SV breakpoints associated with increased cell dependency in cancer cell lines. Pan-cancer proteogenomics identifies targetable non-coding alterations, by virtue of the associated deregulated genes.

Identification of CHMP7 as a promising immunobiomarker for immunotherapy and chemotherapy and impact on prognosis of colorectal cancer patients

Introduction: ESCRT is a molecular machine involved in various important physiological processes, such as the formation of multivesicular bodies, cellular autophagy, and cellular membrane repair. CHMP7 is a regulatory subunit of ESCRT-III and is necessary for the proper functioning of ESCRT. In this study, public databases were exploited to explore the role of CHMP7 in tumors. Methods: The research on CHMP7 in oncology is rather limited. In this study, the differential expression of CHMP7 in multiple tumor tissues was analyzed with information from public databases and clinically collected colorectal cancer tissue samples. Subsequently, the mutational landscape of CHMP7, methylation levels, and the relationship between its expression levels and genomic instability were resolved. The immune microenvironment is a compelling emerging star in tumor research. The correlation of CHMP7 with various infiltrating immune cell types in TME was analyzed by online datasets and single-cell sequencing. In terms of clinical treatment, the impact of CHMP7 expression levels on chemotherapy and immunotherapy and the evaluation of small molecule drugs related to CHMP7 were assessed. Results: CHMP7 has a predictive value for the prognosis of patients with tumors and is highly involved in tumor immunity. The downregulation of CHMP7 may lead to genomic instability. A strong correlation between CHMP7 and TME immune cell infiltration has been observed, participating in the formation of suppressive TME and promoting tumor progression. The expression level of CHMP7 is significantly lower in the non-responder group of multiple chemotherapeutic agents. CHMP7 can potentially serve as a new biomarker for predicting the efficacy of tumor chemotherapy and immunotherapy. Conclusion: As a gene of interest, CHMP7 is expected to provide novel and promising targets for further treatment of patients with tumor.