Lhermitte-Duclos disease (LDD) is a rare dysplastic gangliocytoma of the cerebellum, typically manifesting as a hamartomatous lesion in the posterior fossa. Currently, LDD has been only linked to PTEN pathogenic variants, with the PI3K/AKT/mTOR pathway acting as the primary signaling cascade responsible for its pathogenesis. We present a case of LDD in which a novel germline heterozygous splice site variant (c.183-2 A > G) in the SUFU gene and a somatic heterozygous missense variant (c.389 G > A) in the PTEN gene, identified from tumor tissue were detected by targeted next-generation sequencing (NGS). SUFU, a tumor suppressor gene, primarily inhibits the hedgehog (Hh) signaling pathway and furthermore influences the AKT/mTOR pathway. Pathogenic variants in SUFU have been linked to medulloblastoma, and their potential role in LDD remains under investigation. Given that both conditions involve granule cell progenitors and are influenced by impaired Hh signaling, they may share a similar developmental path. This is the first research indicating that SUFU may play a role in the etiology of LDD, despite SUFU variants being associated with several central nervous system malignancies. The SUFU variant was shown to disrupt splicing via Sanger sequencing and gel electrophoresis of RNA extracted from blood. Analysis of DNA from tumor tissue using the TWIST Exome 2.0 Panel revealed de novo pathogenic SUFU (c.183-2 A > G) and PTEN (c.389G > A) variants. This paper establishes an initial link between LDD and germline SUFU along with somatic PTEN variants identified from tumor tissue, providing novel insights into the molecular pathogenesis of this rare condition.

Simultaneous identification of natural and chemically modified DNA nucleotides at molecular resolution remains a pivotal challenge in genomic science. Despite significant advances in current sequencing technologies, the ability to identify subtle changes in natural and chemically modified nucleotides is hindered by structural and configurational complexity. Given the critical role of nucleobase modifications in data storage and personalized medicine, we propose a computational approach using a graphene nanopore coupled with machine learning (ML) to simultaneously recognize both natural and chemically modified nucleotides, exploring a wide range of modifications in the nucleobase, sugar, and phosphate moieties while investigating quantum transport mechanisms to uncover distinct molecular signatures and detailed electronic and orbital insights of the nucleotides. Integrating with the best-fitted model, the graphene nanopore achieves a good classification accuracy of up to 96% for each natural, chemically modified, purine, and pyrimidine nucleotide. Our approach offers a rapid and precise solution for real-time DNA sequencing by decoding natural and chemically modified nucleotides on a single platform.

The Hong Kong Genome Project (HKGP) is the first population-wide whole genome sequencing (WGS) programme in Hong Kong and aimed to integrate genomic medicine into the healthcare system. Implementing genetic counselling is essential to help participants understand the genetic basis of diseases and guide informed decision making. We assessed participant experiences during the initial HKGP pilot phase that enrolled patients with undiagnosed diseases and hereditary cancers.

Participants were recruited from three partnering centres at public hospitals during June-September 2023. Participant surveys covered four domains: (1) overall satisfaction, (2) informed consent process, (3) genetic counselling, and (4) attitude towards HKGP. Associations with demographic and socioeconomic characteristics were assessed with multivariable logistic regression. Qualitative feedback was collected in focus group interviews.

Among 422 eligible participants, 341 completed the survey (80.8% response) and five focus group interviews were held (21 participants). We found 89.8% [95% CI: 86.1-92.7] were satisfied with their HKGP experience. Almost all felt that HKGP participation could benefit others (86.8% [95% CI: 82.7-90.0]) and advance genomic research in Hong Kong (88.9% [95% CI: 85.0-91.9]). The survey item with the lowest agreement among respondents was feeling that HKGP participation could improve their/child's medical treatment (73.5% [95% CI: 68.5-78.0]). Those with secondary and tertiary education were less likely to agree genetic counselling was helpful (Odds Ratio [OR]: 0.02 [95% CI: 0.001-0.41]; 0.02 [0.001-0.51]), or the appropriate length of time (OR: 0.12 [95% CI: 0.014-0.81]; 0.11 [0.01-0.91]). Focus group participants cited helping scientific advances and shortening the diagnostic odyssey of future patients as key reasons for participation. Participants hoped for a shorter reporting time of WGS results, additional medical follow-up, and allowing referral of relatives.

Participants were overall highly satisfied with the HKGP and genetic counselling experience. Satisfaction levels were comparable to overseas genomic programmes and locally provided healthcare services. Participants' major concerns on WGS reporting time could be addressed by strengthening the informed consent process to ensure their expectations align with project implementation. Emphasizing the long-term value of genomic research and its potential for personalized treatments may increase participant engagement.

With an increased uptake of genomic profiling in clinical practice and the evolving complexity of diagnostic modalities, vast amounts of complex data need to be properly interpreted and integrated into an individualised care plan. To address these challenges, molecular tumour boards (MTBs) have been widely established. As of today, no international recommendations regulating the composition and workflows of MTBs have been defined.

ESMO's Precision Oncology Working Group (POWG) established an international expert panel in precision oncology and defined core areas of interest. After several consultations and through an expert consensus process, the group reached a consensus level for each recommendation.

The group defined five components in the MTB process that are critical to its function and clinical use: (i) the primary task of MTBs consists in providing genomic-informed clinical recommendations, particularly for cases exhibiting complex genomic alterations; (ii) to achieve this, MTBs should encompass interdisciplinary expertise, with key roles for oncologists with genomic expertise, pathologists with molecular training and clinical geneticists; (iii) MTBs' recommendations should be documented in a structured report that includes genomic-informed treatment strategies, management plans for potential tumour-detected germline alterations and guidance for additional genomic testing; (iv) structured follow-up processes should be implemented for monitoring the clinical effectiveness of MTBs recommendations and (v) finally, the panel proposed quality indicators for operating MTBs, including turnaround times for cases discussion and the proportion of cases for which actionable recommendations and clinical trial enrolments were successfully implemented.

These ESMO's POWG recommendations can serve as a guidance and help to define quality standards for MTBs to allow for harmonisation and to further expedite the integration of precision oncology into clinical practice.

HIV-1 infection continues to be a significant public health concern, notwithstanding the expanded utilization of antiretroviral treatment (ART), due to the emergence of drug resistance. The prevalence of transmitted drug resistance remains uncertain, particularly concerning integrase inhibitors. This study aimed to assess the extent of HIV resistance in both ART-naïve and experienced individuals living with HIV (PLHIV) at the University Hospital in Palermo, Italy.

Genotyping and mutation analysis were performed on ART naïve and experienced PLHIV admitted from June 2021 to October 2023 by the NGS method. Mutations were detected by testing different NGS frequency cut-offs: ≥5 %, ≥10 %, and ≥20 %. Demographic, clinical, virological, and immunological data were retrospectively collected.

Of the PLHIV, 85 (70 %) were ART-naïve, while 36 (30 %) were ART-experienced with virological failure. The main HIV-1 subtype was B (54 %), which was significantly associated with Italy-born (P < 0.001) and experienced PLHIV (P = 0.024). In the remaining cases, A1 (6 %), C (3 %), F1 (7 %), G (2 %), and Circulating Recombinant Forms (28 %) were reported. At least one mutation for a drug class was detected in 39.7 %, 45.4 %, and 53.7 % of cases at HIV-1 NGS thresholds of 20 %, 10 %, and 5 %, respectively. Drug resistance was found in 18.2 %, 25.6 %, and 33.0 %, by NGS cut-off of 20 %, 10 %, and 5 % respectively. The lowering of NGS cut-offs mainly increased the rates of integrase strand transfer inhibitor resistance. For overall resistance, no difference was observed between B and non-B subtypes for any NGS cut-offs.

Promoter prediction is essential for analyzing gene structures, understanding regulatory networks, transcription mechanisms, and precisely controlling gene expression. Recently, computational and deep learning methods for promoter prediction have gained attention. However, there is still room to improve their accuracy. To address this, we propose the HybProm model, which uses DNA2Vec to transform DNA sequences into low-dimensional vectors, followed by a CNN-BiLSTM-Attention architecture to extract features and predict promoters across species, including E. coli, humans, mice, and plants. Experiments show that HybProm consistently achieves high accuracy (90%-99%) and offers good interpretability by identifying key sequence patterns and positions that drive predictions.

One of the most important crop pathogens is Botrytis cinerea. It overcomes plant defenses using laccase, an enzyme which is frequently researched. Yet the differences between strains regarding their laccase activity is poorly understood. The aim of this study was to analyze laccase genes in the context of the regionality, vintage, and laccase activity of the strains. Eight strains were analyzed using whole genome sequencing, and the laccase activity was assessed. The strains were differentiated by SSR-PCR. We looked at all 14 known laccase genome regions as well as the promoter and terminator regions using variant metrics and phylogenetic trees. The laccase genes seem to be correlated with the regionality of the strains rather than the laccase activity, which provides new understanding to the study of pathogen adaption in specific environments. Some of the laccase gene regions showed little to no evolutionary change, while other regions showed a great variety of changes. This research highlights taking different laccase gene regions into context. We provide fundamental information for further research. Further studies, especially on gene expression, could provide insightful information regarding the potential of pathogen infection.

Colorectal cancer (CRC) is a common cancer globally, and chemotherapy often causes severe complications, necessitating effective drugs with minimal side effects. As Areca catechu L. extract (ACE) is a Traditional Chinese Medicine that contains numerous active compounds with anticancer effects, in this study, the Cell Counting Kit-8 (CCK-8) assay was used to determine ACE's effect on CRC cell lines, revealing that it significantly inhibits CoLo320DM and HCT116 cells. In vivo experiments with NU-Foxn1nu mice indicated that ACE inhibits tumor growth, while a flow cytometry assay revealed that higher ACE concentrations increased cell apoptosis and ROS levels. Next-generation sequencing (NGS) showed that ACE increases the fold changes in apoptosis, DNA damage, and autophagy-related genes while inhibiting the fold changes in cell proliferation and Wnt signaling pathway genes. We conducted Western blotting to confirm these findings. Overall, ACE demonstrates potential as a drug candidate by promoting apoptosis and autophagy, and significantly reducing cell viability and tumor growth, thus offering a new approach for effective colorectal cancer treatment with minimal side effects.

Bacterial identification and antimicrobial susceptibility testing is an important step in timely therapeutic decisions for canine superficial bacterial folliculitis (SBF), commonly caused by Staphylococcus pseudintermedius. Next-generation sequencing (NGS) offers the appeal of potentially expedited results with complete detection of bacterial organisms and associated resistance genes compared to culture. Limited studies exist comparing the two methodologies for clinical samples.

To compare and contrast genotypic and phenotypic methods for bacterial identification and antimicrobial susceptibility from cases of canine SBF.

Twenty-four client-owned dogs with lesions consistent with SBF were enrolled.

A sterile culturette swab was used to sample dogs with SBF lesions. The swab was rinsed in 0.9 mL of sterile phosphate-buffered saline and vortexed to create a homogenous solution. Two swabs for NGS laboratories (Labs) and one swab for culture (Culture Lab) were randomly sampled from this solution and submitted for bacterial identification and antimicrobial susceptibility.

No statistical difference regarding turnaround time for NGS Labs compared to Culture Lab was found. NGS Lab 1 identified more organisms than NGS Lab 2 and Culture Lab, which were both statistically significant. There was no statistical difference in detection frequency for Staphylococcus spp. among all laboratories. There was poor agreement for the presence of meticillin resistance and most antimicrobials among all laboratories.

Utilisation of NGS as a replacement for traditional culture when sampling canine SBF lesions is not supported at this time.

Pancreatic ductal adenocarcinoma (PDAC) is projected to be the second leading cause of cancer-related death by 2030. Early identification is rare, with a 5-year overall survival (OS) of less than 10%. Advances in the understanding of PDAC tumor biology are needed to improve these outcomes. Circulating tumor DNA (ctDNA) represents a promising novel biomarker in the identification and management of PDAC. Drawn from peripheral blood and analyzed using a variety of techniques, the detection of ctDNA in PDAC has been associated with shorter OS, minimal residual disease presence, and shorter recurrence-free survival. The use of ctDNA has also been examined as an indicator of therapeutic resistance, susceptibility to targeted therapy, and therapeutic response. While promising, ctDNA analysis is limited by its low rates of detection in some settings and lack of predictive ability in others. Many studies examining the utility of ctDNA for the management of PDAC have been relatively small retrospective cohort studies. The current findings will need to be validated by incorporation of ctDNA analysis into cancer registries and larger prospective studies. Given the current, rapid evolution in the field, it is possible that with time, ctDNA will be more routinely incorporated into the clinical management of PDAC.

Mass spectrometry (MS) has become a critical tool in the characterization of covalently modified nucleic acids. Well-developed bottom-up approaches, where nucleic acids are digested with an endonuclease and the resulting oligonucleotides are separated before MS and MS/MS analysis, provide substantial insight into modified nucleotides in biological and synthetic nucleic. Top-down MS presents an alternative approach where the entire nucleic acid molecule is introduced to the mass spectrometer intact and then fragmented by MS/MS. Current top-down MS workflows have incorporated automated, on-line HPLC workflows to enable rapid desalting of nucleic acid samples for facile mass analysis without complication from adduction. Furthermore, optimization of MS/MS parameters utilizing collision, electron, or photon-based activation methods have enabled effective bond cleavage throughout the phosphodiester backbone while limiting secondary fragmentation, allowing characterization of progressively larger (~100 nt) nucleic acids and localization of covalent modifications. Development of software applications to perform automated identification of fragment ions has accelerated the broader adoption of mass spectrometry for analysis of nucleic acids. This review focuses on progress in tandem mass spectrometry for characterization of nucleic acids with particular emphasis on the software tools that have proven critical for advancing the field.

As the most common pediatric malignancy, B-cell acute lymphoblastic leukemia (B-ALL) has multiple distinct subtypes characterized by recurrent and sporadic somatic and germline genetic alterations. Identifying B-ALL subtypes can facilitate risk stratification and enable tailored therapeutic design. Existing methods for B-ALL subtyping primarily depend on immunophenotyping, cytogenetic tests and genomic profiling, which would be costly, complicated, and laborious. To overcome these challenges, we present RanBALL (an ensemble Random projection-based model for identifying B-ALL subtypes), an accurate and cost-effective model for B-ALL subtype identification. By leveraging random projection (RP) and ensemble learning, RanBALL can preserve patient-to-patient distances after dimension reduction and yield robustly accurate classification performance for B-ALL subtyping. Benchmarking results based on > 1700 B-ALL patients demonstrated that RanBALL achieved remarkable performance (accuracy: 0.93, F1-score: 0.93, and Matthews correlation coefficient: 0.93), significantly outperforming state-of-the-art methods like ALLSorts in terms of all performance metrics. In addition, RanBALL performs better than tSNE in terms of visualizing B-ALL subtype information. We believe RanBALL will facilitate the discovery of B-ALL subtype-specific marker genes and therapeutic targets to have consequential positive impacts on downstream risk stratification and tailored treatment design. To extend its applicability and impacts, a Python-based RanBALL package is available at https://github.com/wan-mlab/RanBALL.

Targeted next-generation sequencing (tNGS) is promising alternative to phenotypic drug susceptibility testing (pDST) for detecting drug-resistant tuberculosis (DRTB). This study explored the potential cost-effectiveness of tNGS for the diagnosis of DR-TB across 3 settings: India, South Africa and Georgia.

To inform WHO guideline development group (GDG) on tNGS we developed a stochastic decision analysis model and assessed cost-effectiveness of tNGS for DST among rifampicin resistance individuals. We also assessed tNGS as initial test for TB drug resistance in bacteriologically confirmed TB. Diagnostic accuracy and cost data were sourced from a systematic review conducted for GDG, covering studies published until September 2022. The primary outcome was incremental cost (2021 US$) per disability-adjusted life year (DALY) averted.

tNGS when compared with in-country DST, tNGS proved cost-effective in South Africa (ICER: $15,619/DALY averted, WTP: $21,165) but not in Georgia (ICER: $18,375/DALY averted, WTP: $15,069). In India, tNGS dominated in-country DST practice, providing greater health impact at lower cost. When comparing tNGS with universal pDST, tNGS was dominated by pDST in all three countries. In Georgia, using tNGS as initial test for TB drug-resistance compared to Xpert MTB/Rif followed by pDST appeared cost-effective. Scenario with 50% reduction in tNGS test kit costs made tNGS cost-effective across all three countries, while a high Bedaquiline resistance prevalence (30%) led to a worsening cost-effectiveness.

tNGS may be cost-effective in India, South Africa and Georgia when comprehensive DST is not routinely performed. Thus, existing DST practice and healthcare infrastructure should be considered before implementation and scale-up of tNGS.

Global Tuberculosis Program, World Health Organization (2022/1249364-0).

Molecular tumor boards (MTB) are interdisciplinary conferences involving various experts discussing patients with advanced tumors, to derive individualized treatment suggestions based on molecular variants. These discussions involve using heterogeneous internal data, such as patient clinical data, but also external resources such as knowledge databases for annotations and search for relevant clinical studies. This imposes a certain level of complexity that requires huge effort to homogenize the data and use it in a speedy manner to reach the needed treatment. For this purpose, most institutions involving an MTB are heading toward automation and digitalization of the process, hence reducing manual work requiring human intervention and subsequently time in deriving personalized treatment suggestions. The tools are also used to better visualize the patient's data, which allows a refined overview for the board members. In this paper, we present the results of our thorough literature research about MTBs, their process, the most common knowledge bases, and tools used to support this decision-making process.

The climate change-associated abnormal weather patterns negatively influences the productivity and performance of farm animals. Heat stress is the major detrimental factor hampering production, causing substantial economic loss to the livestock industry. Therefore, it is important to identify heat-tolerant breeds that can survive and produce optimally in any given environment. To achieve this goal, a clearer understanding of the genetic differences and the underlying molecular mechanisms associated with climate change impacts and heat tolerance are a prerequisite. Adopting next-generation biotechnological and statistical tools like whole transcriptome analysis, whole metagenome sequencing, bisulphite sequencing, genome-wide association studies (GWAS), and selection signatures provides an opportunity to achieve this goal. Through these techniques, it is possible to identify permanent genetic markers for heat tolerance, and by incorporating those markers in marker-assisted breeding selection, it is possible to achieve the target of breeding for heat tolerance in livestock. This review gives an overview of the recent advancements in assessing heat tolerance in livestock using such 'omics' approaches and statistical models. The salient findings from this research highlighted several candidate biomarkers that have the potential to be incorporated into future heat-tolerance studies. Such approaches could revolutionise livestock production in the changing climate scenario and support the food demands of the growing human population.

Epilepsy is a chronic neurological disorder related to various etiologies, and the prevalence of active epilepsy is estimated to be between 4 and 10 per 1000 individuals having a significant role in genetic mutations. Next-Generation Sequencing (NGS) panels are utilized for genetic testing, but a substantial proportion of the results remain uncertain and are not considered directly causative of epilepsy. This study aimed to reevaluate pediatric patients diagnosed with epilepsy who underwent genetic investigation using NGS panels, focusing on inconclusive variant findings or multiple variants of uncertain significance (VUSs).

A subgroup of pediatric patients aged 0-25 years, diagnosed with epilepsy, who underwent genetic investigation with an NGS epilepsy panel at the Child Neurology Unit, The Edmond and Lily Safra Children's Hospital, Sheba Medical Center, between 2018 and 2022 through Invitae, was reevaluated. Patients with inconclusive variant findings or multiple VUSs in their test results were included. Genetic data were analyzed to identify potentially pathogenic variants and frequent genetic combinations.

Two unrelated potentially pathogenic variants were identified in the SCN9A and QARS1 genes. A frequent genetic combination, RANBP2&RYR3, was also observed among other combinations. The RANBP2 gene consistently co-occurred with RYR3 variants in uncertain results, suggesting potential pathogenicity. Analysis of unaffected parents' data revealed certain combinations inherited from different parents, suggesting specific gene combinations as possible risk factors for the disease.

This study highlights the importance of reevaluating genetic data from pediatric epilepsy patients with inconclusive variant findings or multiple VUSs. Identification of potentially pathogenic variants and frequent genetic combinations, such as RANBP2&RYR3, could aid in understanding the genetic basis of epilepsy and identifying potential hotspots.

We have performed a retrospective analysis on a subpopulation of pediatric patients diagnosed with epilepsy. We found that specific genetic variants were repeatable, indicating their potential pathogenicity to the disease.

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.

Within Polynoidae, a diverse aphroditiform family, the subfamily Macellicephalinae comprises anchialine cave-dwelling and deep-sea scaleworms. In this study, Lepidonotopodinae is synonymized with Macellicephalinae, and the tribe Lepidonotopodini is applied to a well-supported clade inhabiting deep-sea chemosynthetic-based ecosystems. Newly sequenced "genome skimming" data for 30 deep-sea polynoids and the comparatively shallow living Eulagisca gigantea is used to bioinformatically assemble their mitogenomes. When analyzed with existing scaleworm mitogenomes, deep-sea scaleworms exhibit increased gene order rearrangement events compared to shallow-water relatives. Additionally, comparative analyses of shallow-water vs. deep-sea polynoid substitution rates in mitochondrial protein-coding genes show an overall relaxed purifying selection and a positive selection of several amino acid sites in deep-sea species, indicating that polynoid mitogenomes have undergone selective pressure to evolve metabolic adaptations suited to deep-sea environments. Furthermore, the inclusion of skimming data for already known Lepidonotopodini species allowed for an increased coverage of DNA data and a representation of the taxa necessary to create a more robust phylogeny using 18 genes, as opposed to the six genes previously used. The phylogenetic results support the erection of Cladopolynoe gen. nov., Mamiwata gen. nov., Photinopolynoe gen. nov., Stratigos gen. nov., and Themis gen. nov., and emended diagnoses for Branchinotogluma, Branchipolynoe, Lepidonotopodium, and Levensteiniella.

A contaminated viral stock results in considerable loss of time, resources and financial means and is generally discovered only by chance after years of research. Thus, it is necessary to implement a technique that can detect contamination without prior knowledge or assumptions, such as next-generation sequencing (NGS). Here, we describe the discovery of a cross-contaminated viral stock from a biological repository of an African Zika virus isolate with Toscana virus after performing NGS on infected cells. In addition, we also describe the consequences that we faced using this contaminated stock. These included the economic and time loss to the lab that needed to repeat all previously performed experiments, the generation of biologically flawed results with a subsequent potential retraction and the severe risk of infection of lab members who manipulated the contaminated stock.

This study aimed to evaluate the cost-effectiveness of next-generation sequencing (NGS) versus sequential single-gene testing (SGT), including the long-term costs and survival outcomes of relevant treatments for advanced EGFR/ALK-negative non-small cell lung cancer (NSCLC).

We developed a decision tree linked to a partitioned survival model to estimate the clinical outcomes and costs over the five-year analysis period. The decision tree consisted of treatment types based on molecular biomarker (ROS1, BRAF, NTRK, MET, RET, and KRAS alterations) test results. The probability of receiving each targeted therapy was estimated based on 1) the testing rate, 2) the proportion of alterations detected, and 3) the proportion of patients receiving treatment consistent with the testing results. We estimated the long-term overall survival and progression-free survival for each treatment using parametric estimation by reconstructing patient-level data from clinical trials. The costs of testing, drugs, administration, physician visits, monitoring, adverse events, post-progression, and end-of-life care were included. The utility values were obtained from a previous study. The incremental cost-effectiveness ratio (ICER) was used to evaluate the cost-effectiveness of NGS within a threshold of $38,701 (50,000,000 KRW) per quality-adjusted life year (QALY).

The incremental life-years (LYs) and QALYs for the NGS group versus the SGT group were 0.028 and 0.023, respectively. The total medical cost for the NGS group was $8,375 higher than that for the SGT group. The difference in drug costs accounted for most of the differences in total medical costs. NGS was not cost-effective compared to sequential SGT, with an ICER of $300,233/LY and $359,405/QALY, respectively.

NGS is not cost-effective for advanced EGFR/ALK-negative NSCLC, but has a survival benefit over sequential SGT. Our findings provide a basis for decision-making regarding the coverage and clinical utilization of NGS in regions where EGFR alterations are prevalent.

Synovial sarcoma is a rare type of soft tissue sarcoma that primarily affects adolescents and young adults, featured by aggressive behavior and a high potential for metastasis. Genetically, synovial sarcoma is defined by the fusion oncogene SS18-SSX arising from the translocation of t(X;18)(p11;q11). SS18-SSX fusion gene is the major driver of the oncogenic event in synovial sarcoma. SS18-SSX fusion protein, while not containing any DNA-binding motifs, binds to the SWI/SNF (BAF) complex, a major epigenetic regulator, leading to the disruption of gene expression which results in tumor initiation and progression. Emerging studies on the molecular mechanisms of SS18-SSX associated signaling pathway hold promise for developments in diagnosis and treatments. Advanced diagnostic methods facilitate early and precise detection of the tumor, enabling disease monitoring and prognostic improvements. Treatment of synovial sarcoma typically comprises local surgery, radiotherapy and chemotherapy, while novel managements such as immunotherapy, targeted therapies and epigenetic modifiers are explored. This review focuses on the recent studies of SS18-SSX fusion gene, epigenetic landscape, signaling pathways, diagnostic techniques, and relevant therapeutic advances, aiming to inhibit the oncogenic processes and improve outcomes for patients with synovial sarcoma.

Traditional treatment strategies for recurrent pregnancy loss (RPL) and recurrent implantation failure (RIF) often result in limited success, placing significant emotional and financial burdens on couples. However, novel approaches such as diagnostic gene profiling, cell therapy, stem cell-derived exosome therapy, and pharmacogenomics offer promising, personalized treatments. Combining traditional treatments with precision and regenerative medicine may enhance the efficacy of these approaches and improve pregnancy outcomes. This review explores how integrating these strategies can potentially transform the lives of couples experiencing repeated pregnancy loss or implantation failure, providing hope for improved treatment success. Precision and regenerative medicine represent a new frontier for managing RPL and RIF, offering promising solutions.

Molecular screening using next-generation sequencing (NGS) in the pathologic evaluation of lung cancer is considered the standard in clinical practice; hence, we evaluated the diagnostic yields of various sampling methods for NGS.

NGS data from patients with lung cancer at the Pusan National University Hospital (Busan, South Korea), admitted October, 2020-April, 2023, was obtained. The sampling methods by which NGS data was obtained were divided into surgical and nonsurgical. Surgical methods included thoracoscopic surgery, surgical biopsy from the metastatic site, and lymph node excisional biopsy, whereas nonsurgical methods included bronchoscopy procedures and medical thoracoscopic biopsy.

In total, we obtained 319 patients' NGS data:150 (47.0%) and 169 (53.0%) was obtained using surgical and nonsurgical methods, respectively. The overall diagnostic yield of NGS analysis was 97.5% for all samples. There were no significant differences in the success rates of deoxyribonucleic acid sequencing between surgical and nonsurgical sampling methods (98.0% vs. 96.4%, p = 0.313). On the other hand, the success rate of ribonucleic acid (RNA) sequencing was significantly lower in the surgical method group (78.0% vs. 92.3%; p < 0.001). Multivariate analysis showed that surgical sampling significantly correlated with RNA sequencing failure (Odd Ratio 4.128, 95% Confidence Interval 1.681-10.133, p = 0.002).

Small samples obtained using nonsurgical procedures are suitable for NGS analysis in clinical practice. However, surgical sampling showed a relatively lower success rate for RNA sequencing than nonsurgical sampling. This information may help in the development of protocols to reduce RNA degradation during the surgical process.

Tuberculous meningitis (TBM) is a severe central nervous system (CNS) infection with a challenging diagnosis due to inadequate detection methods. This study evaluated current clinical detection methods and their applicability.

A cohort of 514 CNS infection patients from 2018 to 2020 was studied. Data on general demographics, Cerebrospinal Fluid (CSF) analysis, epidemiology, and clinical outcomes were collected. TBM patients were identified, and the sensitivities of mmetagenomic next-generation sequencing (NGS), GeneXpert, and microbial culture were compared. Kappa statistic assessed the consistency between methods.

Among the patients involved, TBM (29%) and neurosyphilis (25%) were the two most prevalent CNS infections. CSF analysis indicated that 76% of patients had leukocytosis, suggesting a potential CNS inflammation. In TBM cases, 92.5% had elevated CSF protein and leukocyte counts. Moreover, the percentage of positive mNGS results was 55.6%. GeneXpert and MTB cultures alone had lower sensitivity, but combined use resulted in a 53.4% positive rate.

This study highlights the high sensitivity of mNGS, comparable to GeneXpert and MTB culture. The combined methods are cost-effective and straightforward, and can partially substitute for mNGS, offering valuable alternatives for TBM diagnosis and providing insights into multiple diagnostic strategies in clinical practice.

RNA structure is essential for the function of many non-coding RNAs. Using multiple homologous sequences, which share structure and function, secondary structure can be predicted with much higher accuracy than with a single sequence. It can be difficult, however, to establish a set of homologous sequences when their structure is not yet known. We developed a method to identify sequences in a set of putative homologs that are in fact non-homologs.

Previously, we developed TurboFold to estimate conserved structure using multiple, unaligned RNA homologs. Here, we report that the positive predictive value of TurboFold is significantly reduced by the presence of contamination by non-homologous sequences, although the reduction is less than 1%. We developed a method called DecoyFinder, which applies machine learning trained with features determined by TurboFold, to detect sequences that are not homologous with the other sequences in the set. This method can identify approximately 45% of non-homologous sequences, at a rate of 5% misidentification of true homologous sequences.

DecoyFinder and TurboFold are incorporated in RNAstructure, which is provided for free and open source under the GPL V2 license. It can be downloaded at http://rna.urmc.rochester.edu/RNAstructure.html.

Familial tall stature (FTS) is considered to be a benign variant of growth with a presumed polygenic etiology. However, monogenic disorders with possible associated pathological features could also be hidden under the FTS phenotype.

To elucidate the genetic etiology in families with FTS and to describe their phenotype in detail.

Children with FTS (the life-maximum height in both the child and his/her taller parent > 2 SD for age and sex) referred to the Endocrinology center of Motol University Hospital were enrolled into the study. Their DNA was examined cytogenetically and via a next-generation sequencing panel of 786 genes associated with growth. The genetic results were evaluated by the American College of Molecular Genetics and Genomics guidelines. All of the participants underwent standard endocrinological examination followed by specialized anthropometric evaluation.

In total, 34 children (19 girls) with FTS were enrolled in the study. Their median height and their taller parent's height were 3.1 SD and 2.5 SD, respectively. The genetic cause of FTS was elucidated in 11/34 (32.4%) children (47,XXX and 47,XYY karyotypes, SHOX duplication, and causative variants in NSD1 [in 2], SUZ12 [in 2], FGFR3, CHD8, GPC3, and PPP2R5D genes). Ten children had absent syndromic signs and 24 had dysmorphic features.

Monogenic (and cytogenetic) etiology of FTS can be found among children with FTS. Genetic examination should be considered in all children with FTS regardless of the presence of dysmorphic features.

The primary objective of this study is to develop a prediction model for peritoneal metastasis (PM) in colorectal cancer by integrating the genomic features of primary colorectal cancer, along with clinicopathological features. Concurrently, we aim to identify potential target implicated in the peritoneal dissemination of colorectal cancer through bioinformatics exploration and experimental validation. By analyzing the genomic landscape of primary colorectal cancer and clinicopathological features from 363 metastatic colorectal cancer patients, we identified 22 differently distributed variables, which were used for subsequent LASSO regression to construct a PM prediction model. The integrated model established by LASSO regression, which incorporated two clinicopathological variables and seven genomic variables, precisely discriminated PM cases (AUC 0.899; 95% CI 0.860-0.937) with good calibration (Hosmer-Lemeshow test p = .147). Model validation yielded AUCs of 0.898 (95% CI 0.896-0.899) and 0.704 (95% CI 0.622-0.787) internally and externally, respectively. Additionally, the peritoneal metastasis-related genomic signature (PGS), which was composed of the seven genes in the integrated model, has prognostic stratification capability for colorectal cancer. The divergent genomic landscape drives the driver genes of PM. Bioinformatic analysis concerning these driver genes indicated SERINC1 may be associated with PM. Subsequent experiments indicate that knocking down of SERINC1 functionally suppresses peritoneal dissemination, emphasizing its importance in CRCPM. In summary, the genomic landscape of primary cancer in colorectal cancer defines peritoneal metastatic pattern and reveals the potential target of SERINC1 for PM in colorectal cancer.

Heart failure (HF) remains a major cause of mortality and morbidity worldwide. Understanding the genetic basis of HF allows for the development of disease-modifying therapies, more appropriate risk stratification, and personalised management of patients. The advent of next-generation sequencing has enabled genome-wide association studies; moving beyond rare variants identified in a Mendelian fashion and detecting common DNA variants associated with disease. We summarise the latest GWAS and rare variant data on mixed and refined HF aetiologies, and cardiomyopathies. We describe the recent understanding of the functional impact of titin variants and highlight FHOD3 as a novel cardiomyopathy-associated gene. We describe future directions of research in this field and how genetic data can be leveraged to improve the care of patients with HF.

Accurate knowledge of the genome, virus and bacteria that have invaded our bodies is crucial for diagnosing many human diseases. The field of bioinformatics encompasses the complex computational methods required for this purpose. Metagenomics employs next-generation sequencing (NGS) technology to study and identify microbial communities in environmental samples. This technique allows for the measurement of the relative abundance of different microbes. Various tools are available for detecting bacterial species in sequenced metagenomic samples. In this study, we focus on well-known taxonomic classification tools such as MetaPhlAn4, Centrifuge, Kraken2, and Bracken, and evaluate their performance at the species level using synthetic and real datasets. The results indicate that MetaPhlAn4 exhibited high precision in identifying species in the simulated dataset, while Kraken2 had the best area under the precision-recall curve (AUPR) performance. Centrifuge, Kraken2, and Bracken showed accurate estimation of species abundances, unlike MetaPhlAn4, which had a higher L2 distance. In the real dataset analysis with samples from an inflammatory bowel disease (IBD) research, MetaPhlAn4, and Kraken2 had faster execution times, with differences in performance at family and species levels among the tools. Enterobacteriaceae and Pasteurellaceae were highlighted as the most abundant families by Centrifuge, Kraken2, and MetaPhlAn4, with variations in abundance among ulcerative colitis (UC), Crohn's disease (CD), and control non-IBD (CN) groups. Escherichia coli (E. coli) has the highest abundance among Enterobacteriaceae species in the CD and UC groups in comparison with the CN group. Bracken overestimated E. coli abundance, emphasizing result interpretation caution. The findings of this research can assist in selecting the appropriate short-read classifier, thereby aiding in the diagnosis of target diseases.

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Time to receive effective therapy is a primary determinant of mortality in patients with sepsis. Blood culture is the reference standard for the microbiological diagnosis of bloodstream infections, despite its low sensitivity and prolonged time to receive a pathogen detection. In recent years, rapid tests for pathogen identification, antimicrobial susceptibility, and sepsis identification have emerged, both culture-based and culture-independent methods. This rapid narrative review presents currently commercially available approved diagnostic molecular technologies in bloodstream infections, including their clinical performance and impact on patient outcome, when available. Peer-reviewed publications relevant to the topic were searched through PubMed, and manufacturer websites of commercially available assays identified were also consulted as further sources of information. We have reviewed data about the following technologies for pathogen identification: fluorescence in situ hybridization with peptide nucleic acid probes (Accelerate PhenoTM), microarray-based assay (Verigene®), multiplex polymerase chain reaction (cobas® eplex, BioFire® FilmArray®, Molecular Mouse, Unyvero BCU SystemTM), matrix-assisted laser desorption-ionization time-of-flight mass spectrometry (Rapid MBT Sepsityper®), T2 magnetic resonance (T2Bacteria Panel), and metagenomics-based assays (Karius©, DISQVER®, Day Zero Diagnostics). Technologies for antimicrobial susceptibility testing included the following: Alfed 60 ASTTM, VITEK® REVEALTM, dRASTTM, ASTar®, Fastinov®, QuickMIC®, ResistellTM, and LifeScale. Characteristics, microbiological performance, and issues of each method are described, as well as their clinical performance, when available.

Next-generation DNA sequencing (NGS) technologies have increased the sensitivity for detecting the bacterial presence and have been used in other areas of orthopaedics to better understand the native microbiome of various joints. This study uses NGS to determine whether (1) a unique microbiome exists in human ankle tissues, (2) if components of the ankle microbiome affect patient outcomes, and (3) whether microbes found on the skin are a normal part of the ankle microbiome.

A prospective study recruited 32 patients undergoing total ankle arthroplasty (n = 23) or ankle arthrodesis (n = 9) via direct anterior approach between November 2020 and October 2021. During surgery, five layers of the ankle were swabbed: skin (n = 32), retinaculum (n = 31), tibialis anterior tendon (n = 31), joint capsule (n = 31), and distal tibia (n = 32). These swabs (N = 157) were sent to MicroGen Diagnostics (Lubbock) for NGS. Demographics, medical comorbidities, surgical indication, postoperative complications, readmission, and revision surgery rates were collected from patient records.

The mean age was 60.7 (range, 19 to 85) years, and the mean follow-up duration was 10.2 (range, 4.8 to 20.6) months. Of 157 swabs sent for NGS, 19 (12.1%) indicated that bacteria were present (positive), whereas the remaining 138 (87.9%) had no bacteria present (negative). The most common organisms were Cutibacterium acnes in eight ankles (25.0%) and Staphylococcus epidermidis in two ankles (6.25%). Most bacteria were found in the retinaculum (29.6%). Complications, nonunions, infections, 90-day readmission, and revision surgery rates did not differ by NGS profile.

This study found that C acnes and S epidermidis were the most common bacteria in the ankle microbiome, with C acnes being present in 25% of ankles. Complication rates did not differ between patients with or without positive bacterial DNA remnants. Thus, we concluded that a unique ankle microbiome is present in some patients, which is unique from that of the skin of the ankle.

Level II, Prospective cohort study.

Building on our prior work that RNA alternative splicing modulates the druggability of kinase fusions, this study probes the clinical significance of sole reciprocal fusions. These rare genomic arrangements, despite lacking kinase domains at the DNA level, demonstrated potential RNA-level druggability in sporadic cases from our prior research.

Utilizing the large-scale multicenter approach, we performed RNA sequencing and clinical follow-up to evaluate a broad spectrum of kinase fusions, including ALK, ROS1, RET, BRAF, NTRK, MET, NRG1, and EGFR, in 1943 patients.

Our findings revealed 51 instances (2.57%) of sole reciprocal fusions, predominantly in lung (57%), colorectal (14%), and glioma (10%) cancers. Comparative analysis with an MSKCC cohort confirmed the prevalence in diverse cancer types and identified unique fusion partners and chromosomal locales. Cross-validation through RNA-NGS and FISH authenticated the existence of functional kinase domains in subsets including ALK, ROS1, RET, and BRAF, which correlated with positive clinical responses to targeted kinase inhibitors (KIs). Conversely, fusions involving EGFR, NRG1, and NTRK1/2/3 generated nonfunctional transcripts, suggesting the need for alternative therapeutic interventions.

This inaugural multicenter study introduces a novel algorithm for detecting and treating sole reciprocal fusions in advanced cancers, expanding the patient population potentially amenable to KIs.

DNA sequencing is transforming the field of medical diagnostics and personalized medicine development by providing a pool of genetic information. Recent advancements have propelled solid-state material-based sequencing into the forefront as a promising next-generation sequencing (NGS) technology, offering amplification-free, cost-effective, and high-throughput DNA analysis. Consequently, a comprehensive framework for diverse sequencing methodologies and a cross-sectional understanding with meticulous documentation of the latest advancements is of timely need. This review explores a broad spectrum of progress and accomplishments in the field of DNA sequencing, focusing mainly on electrical detection methods. The review delves deep into both the theoretical and experimental demonstrations of the ionic blockade and transverse tunneling current methods across a broad range of device architectures, nanopore, nanogap, nanochannel, and hybrid/heterostructures. Additionally, various aspects of each architecture are explored along with their strengths and weaknesses, scrutinizing their potential applications for ultrafast DNA sequencing. Finally, an overview of existing challenges and future directions is provided to expedite the emergence of high-precision and ultrafast DNA sequencing with ionic and transverse current approaches.

Synthetic biology is a crucial component of the "cyber-biological revolution" in this new industrial revolution. Owing to breakthroughs in synthetic biology, deoxyribonucleic acid (DNA), the storehouse of hereditary material in biological systems, can now be used as a medium for storage (synthesis) and reading (sequencing) of information. However, integrating synthetic biology with computerization has also caused cyberbiosecurity concerns, encompassing biosecurity and information security issues. Malicious codes intended to attack computer systems can be stored as artificially synthesized DNA fragments, which can be released during DNA sequencing and decoding and attack computer and network systems. As these cyberbiosecurity threats become increasingly realistic, spreading awareness and information about how they can be prevented and controlled is crucial. This review aims to address this need by offering crucial theoretical backing for cyberbiosecurity research and raising awareness of risk mitigation and control measures in information security, biosecurity, and national security.

The emergence and resurgence of pathogens have led to significant global health challenges. Wastewater surveillance has historically been used to track water-borne or fecal-orally transmitted pathogens, providing a sensitive means of monitoring pathogens within a community. This technique offers a comprehensive, real-time, and cost-effective approach to disease surveillance, especially for diseases that are difficult to monitor through individual clinical screenings.

This narrative review examines the current state of knowledge on wastewater surveillance, emphasizing important findings and techniques used to detect potential pathogens from wastewater. It includes a review of literature on the detection methods, the pathogens of concern, and the challenges faced in the surveillance process.

Wastewater surveillance has proven to be a powerful tool for early warning and timely intervention of infectious diseases. It can detect pathogens shed by asymptomatic and pre-symptomatic individuals, providing an accurate population-level view of disease transmission. The review highlights the applications of wastewater surveillance in tracking key pathogens of concern, such as gastrointestinal pathogens, respiratory pathogens, and viruses like SARS-CoV-2.

The review discusses the benefits of wastewater surveillance in public health, particularly its role in enhancing existing systems for infectious disease surveillance. It also addresses the challenges faced, such as the need for improved detection methods and the management of antimicrobial resistance. The potential for wastewater surveillance to inform public health mitigation strategies and outbreak response protocols is emphasized.

Wastewater surveillance is a valuable tool in the fight against infectious diseases. It offers a unique perspective on the spread and evolution of pathogens, aiding in the prevention and control of disease epidemics. This review underscores the importance of continued research and development in this field to overcome current challenges and maximize the potential of wastewater surveillance in public health.

This study aimed to investigate the impact of analyzing somatic alterations using next-generation sequencing (NGS) on treatment management in patients with metastatic solid cancers and their ability to access NGS recommended treatments.

This retrospective study included eligible patients who underwent NGS on somatic tumor tissue. We examined the clinical and pathological characteristics of these patients and the alterations in their treatment following NGS results.

A total of 101 patients who underwent NGS were included in the study. The most common cancers were non-small cell lung cancer (NSCLC), colorectal, and breast cancers, in that order. The median age was 58 (range 21-82) years, with 60 (59.4%) male participants. The median NGS turnaround time was 23 (range 17-29) days. NGS was performed on tissue from the primary lesion in 89(88%) patients. Predictive, prognostic, actionable, or variants of unknown significance were detected in 62(61.4%) patients. The most frequent variants identified were KRAS, EGFR, TP53, PIK3CA, and other rare mutations. Treatment was altered in 17(16.8%) patients based on NGS results. Of the 30 (29.7%) patients for whom NGS-informed treatment was recommended, only seven (6.9%) received the recommended therapy. There was no significant difference in overall survival (OS) between patients whose treatment was changed based on NGS results and those whose treatment remained unchanged (p = 0.897). There was no difference in OS between patients with and without variants (p = 0.384).

NGS analysis of somatic alterations in patients with metastatic cancer may reveal additional variants beyond those identified by baseline tests. However, based on the recommendations of the reimbursement institution in Turkey, only a limited number of patients are able to access treatments recommended by NGS results. Therefore, baseline tests established in Turkey need to be made available in more centers in an appropriate time.

Therapeutic cancer vaccines have been considered in recent decades as important immunotherapeutic strategies capable of leading to tumor regression. In the development of these vaccines, the identification of neoepitopes plays a critical role, and different computational methods have been proposed and employed to direct and accelerate this process. In this context, this review identified and systematically analyzed the most recent studies published in the literature on the computational prediction of epitopes for the development of therapeutic vaccines, outlining critical steps, along with the associated program's strengths and limitations. A scoping review was conducted following the PRISMA extension (PRISMA-ScR). Searches were performed in databases (Scopus, PubMed, Web of Science, Science Direct) using the keywords: neoepitope, epitope, vaccine, prediction, algorithm, cancer, and tumor. Forty-nine articles published from 2012 to 2024 were synthesized and analyzed. Most of the identified studies focus on the prediction of epitopes with an affinity for MHC I molecules in solid tumors, such as lung carcinoma. Predicting epitopes with class II MHC affinity has been relatively underexplored. Besides neoepitope prediction from high-throughput sequencing data, additional steps were identified, such as the prioritization of neoepitopes and validation. Mutect2 is the most used tool for variant calling, while NetMHCpan is favored for neoepitope prediction. Artificial/convolutional neural networks are the preferred methods for neoepitope prediction. For prioritizing immunogenic epitopes, the random forest algorithm is the most used for classification. The performance values related to the computational models for the prediction and prioritization of neoepitopes are high; however, a large part of the studies still use microbiome databases for training. The in vitro/in vivo validations of the predicted neoepitopes were verified in 55% of the analyzed studies. Clinical trials that led to successful tumor remission were identified, highlighting that this immunotherapeutic approach can benefit these patients. Integrating high-throughput sequencing, sophisticated bioinformatics tools, and rigorous validation methods through in vitro/in vivo assays as well as clinical trials, the tumor neoepitope-based vaccine approach holds promise for developing personalized therapeutic vaccines that target specific tumor cancers.

With the advancement of next-generation sequencing, clinicians are now able to detect ultra-rare mutations that are barely encountered by the majority of physicians. Ultra-rare and rare diseases cumulatively acquire a prevalence equivalent to type 2 diabetes with 80% being genetic in origin and more prevalent among high consanguinity communities including Saudi Arabia. The challenge of these diseases is the ability to predict their prevalence and define clear phenotypic features.

This is a non-interventional retrospective multicenter study. We included pediatric patients with a pathogenic variant designated as ultra-rare according to the National Institute for Clinical Excellence's criteria. Demographic, clinical, laboratory, and radiological data of all patients were collected and analyzed using multinomial regression models.

We included 30 patients. Their mean age of diagnosis was 16.77 months (range 3-96 months) and their current age was 8.83 years (range = 2-15 years). Eleven patients were females and 19 were males. The majority were of Arab ethnicity (96.77%). Twelve patients were West-Saudis and 8 patients were South-Saudis. SCN1A mutation was reported among 19 patients. Other mutations included SZT2, ROGDI, PRF1, ATP1A3, and SHANK3. The heterozygous mutation was reported among 67.86%. Twenty-nine patients experienced seizures with GTC being the most frequently reported semiology. The mean response to ASMs was 45.50% (range 0-100%).

The results suggest that ultra-rare diseases must be viewed as a distinct category from rare diseases with potential demographic and clinical hallmarks. Additional objective and descriptive criteria to detect such cases are needed.

With significant advancements of next generation sequencing technologies, large amounts of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have been accumulated, offering an unprecedented opportunity to explore the heterogeneity and complexity of cancer across various molecular levels and scales. One of the promising aspects of multi-omics lies in its capacity to offer a holistic view of the biological networks and pathways underpinning cancer, facilitating a deeper understanding of its development, progression, and response to treatment. However, the exponential growth of data generated by multi-omics studies present significant analytical challenges. Processing, analyzing, integrating, and interpreting these multi-omics datasets to extract meaningful insights is an ambitious task that stands at the forefront of current cancer research. The application of artificial intelligence (AI) has emerged as a powerful solution to these challenges, demonstrating exceptional capabilities in deciphering complex patterns and extracting valuable information from large-scale, intricate omics datasets. This review delves into the synergy of AI and multi-omics, highlighting its revolutionary impact on oncology. We dissect how this confluence is reshaping the landscape of cancer research and clinical practice, particularly in the realms of early detection, diagnosis, prognosis, treatment and pathology. Additionally, we elaborate the latest AI methods for multi-omics integration to provide a comprehensive insight of the complex biological mechanisms and inherent heterogeneity of cancer. Finally, we discuss the current challenges of data harmonization, algorithm interpretability, and ethical considerations. Addressing these challenges necessitates a multidisciplinary collaboration, paving the promising way for more precise, personalized, and effective treatments for cancer patients.

Comprehensive genomic profiling (CGP) provides new opportunities for patients with advanced cancer to receive genome-matched therapies, but the availability rate of these remains low. We reviewed our CGP cases and suggested possible strategies to improve the current status from a clinical perspective.

Druggable genomic alterations and barriers to accessing genome-matched therapies were investigated in 653 patients with 30 various types of cancers who underwent CGP.

While the availability rate of genome-matched therapies as a whole was 9.5%, CGP was useful in some cancer types. Patients with thyroid cancer and lung cancer harbored druggable genomic alterations at high rates, while sarcoma rarely harbored these alterations (100%, 76%, and 15.2%, respectively). In contrast, the availability rate of genome-matched therapies was highest in patients with sarcoma and head and neck cancer (HNC) (60% and 40%, respectively). One hundred thirteen patients (63.5%) had multiple barriers to accessing genome-matched therapy. Of 178 patients, 21 patients (11.8%) could not be considered for genome-matched therapies solely because of the deterioration of their performance status.

This study demonstrated the usefulness of CGP for patients with sarcoma and HNC in addition to lung cancer in clinical practice. Performing CGP at the front line has the potential to improve the availability of genome-matched therapy.

Nucleic acid testing is the cornerstone of modern molecular diagnostics. This review describes the current status and future directions of molecular diagnostics, focusing on four major techniques: polymerase chain reaction (PCR), next-generation sequencing (NGS), isothermal amplification methods such as recombinase polymerase amplification (RPA) and loop-mediated isothermal amplification (LAMP), and clustered regularly interspaced short palindromic repeats (CRISPR)-based detection methods. We explore the advantages and limitations of each technique, describe how each overlaps with or complements other techniques, and examine current clinical offerings. This review provides a broad perspective into the landscape of molecular diagnostics and highlights potential future directions in this rapidly evolving field.