Cost-effective Sequencing Research Articles

The Genomic Landscape of Wilson Disease in a Pan India Disease Cohort and Population-Scale Data.

Wilson's disease (WD) results from pathogenic ATP7B gene variations, causing copper accumulation mainly in the liver, brain, and kidneys. In India, despite studies on ATP7B variants, WD often goes undiagnosed, with the prevalence, carrier rate, and mutation spectrum remaining unknown. A multicenter study examined genetic variations in WD among individuals of Indian origin via whole exome sequencing. The study used the InDelible structural variants calling pipeline and conducted molecular dynamic simulations on variants of uncertain significance (VUS) in ATP7B AlphaFold protein structures. Additionally, a high-throughput gene screening panel for WD was developed. This study examined 128 clinically diagnosed cases of WD, revealing 74 genetically confirmed cases, 22 with ATP7B variants, and 32 without. Twenty-two novel ATP7B gene variants were identified, including a 322 bp deletion classified as a structural variant. Molecular dynamics simulations highlighted the potential deleterious effects of 11 ATP7B VUS. Gene burden analysis suggested associations with ANO8, LGR4, and CDC7. ATP7B gene hotspots for pathogenic variants were identified. Prevalence and carrier rates were determined as one in 18,678 and one in 67, respectively. A multiplex sequencing panel showed promise for accurate WD diagnosis. This study offers crucial insights into WD's genetic variations and prevalence in India, addressing its underdiagnosis. It highlights the novel genetic variants in the ATP7B gene, the involvement of other genes, a scalable, cost-effective multiplex sequencing panel for WD diagnosis and management and promising advancements in WD care.

BakRep - a searchable large-scale web repository for bacterial genomes, characterizations and metadata.

Bacteria are fascinating research objects in many disciplines for countless reasons, and whole-genome sequencing (WGS) has become the paramount methodology to advance our microbiological understanding. Meanwhile, access to cost-effective sequencing platforms has accelerated bacterial WGS to unprecedented levels, introducing new challenges in terms of data accessibility, computational demands, heterogeneity of analysis workflows and, thus, ultimately its scientific usability. To this end, a previous study released a uniformly processed set of 661 405 bacterial genome assemblies obtained from the European Nucleotide Archive as of November 2018. Building on these accomplishments, we conducted further genome-based analyses like taxonomic classification, multilocus sequence typing and annotation of all genomes. Here, we present BakRep, a searchable large-scale web repository of these genomes enriched with consistent genome characterizations and original metadata. The platform provides a flexible search engine combining taxonomic, genomic and metadata information, as well as interactive elements to visualize genomic features. Furthermore, all results can be downloaded for offline analyses via an accompanying command line tool. The web repository is accessible via https://bakrep.computational.bio.

TRANSFORMING HEALTHCARE DELIVERY THROUGH BIG DATA IN HOSPITAL MANAGEMENT SYSTEMS: A REVIEW OF RECENT LITERATURE TRENDS

This systematic review investigates the transformative role of big data technologies in biomedical research, analyzing 40 peer-reviewed articles published between 2010 and 2023. The review specifically explores advancements in next-generation sequencing (NGS), multi-omics approaches, machine learning, and artificial intelligence (AI), all of which have significantly enhanced the understanding of complex biological systems and diseases. NGS has emerged as a key tool in personalized medicine, enabling rapid and cost-effective genome sequencing that has facilitated the identification of genetic mutations and biomarkers associated with various diseases, particularly in oncology. Of the 40 studies reviewed, 12 focused on the integration of multi-omics data—genomics, transcriptomics, proteomics, and metabolomics—to provide a comprehensive view of biological processes. These multi-omics approaches have been instrumental in identifying biomarkers for disease progression and response to treatments, offering new avenues for drug development and precision medicine. Additionally, 15 studies highlighted the growing application of machine learning and AI algorithms in managing and analyzing vast biomedical datasets. These tools are now critical in uncovering hidden patterns within large datasets, predicting disease outcomes, and improving the accuracy of clinical decision-making. However, 10 studies emphasized ongoing challenges related to data storage, privacy concerns, and the lack of standardized data formats, which hinder effective data sharing across institutions. Despite these challenges, the integration of AI, IoT devices, and big data analytics is paving the way for more personalized, real-time healthcare monitoring and treatment solutions. This review concludes that while significant advancements have been made, further efforts are required to address the ethical and technical barriers that limit the full potential of big data technologies in biomedical research.

Navigating directed evolution efficiently: optimizing selection conditions and selection output analysis.

Directed evolution is a powerful tool that can bypass gaps in our understanding of the sequence-function relationship of proteins and still isolate variants with desired activities, properties, and substrate specificities. The rise of directed evolution platforms for polymerase engineering has accelerated the isolation of xenobiotic nucleic acid (XNA) synthetases and reverse transcriptases capable of processing a wide array of unnatural XNAs which have numerous therapeutic and biotechnological applications. Still, the current generation of XNA polymerases functions with significantly lower efficiency than the natural counterparts and retains a significant level of DNA polymerase activity which limits their in vivo applications. Although directed evolution approaches are continuously being developed and implemented to improve XNA polymerase engineering, the field lacks an in-depth analysis of the effect of selection parameters, library construction biases and sampling biases. Focusing on the directed evolution pipeline for DNA and XNA polymerase engineering, this work sets out a method for understanding the impact of selection conditions on selection success and efficiency. We also explore the influence of selection conditions on fidelity at the population and individual mutant level. Additionally, we explore the sequencing coverage requirements in directed evolution experiments, which differ from genome assembly and other -omics approaches. This analysis allowed us to identify the sequencing coverage threshold for the accurate and precise identification of significantly enriched mutants. Overall, this study introduces a robust methodology for optimizing selection protocols, which effectively streamlines selection processes by employing small libraries and cost-effective NGS sequencing. It provides valuable insights into critical considerations, thereby enhancing the overall effectiveness and efficiency of directed evolution strategies applicable to enzymes other than the ones considered here.

Advancements in genetic analysis: Insights from a case study and review of next-generation sequencing techniques for veterinary oncology applications.

Acute myeloid leukemia (AML) poses significant challenges in veterinary medicine, with limited treatment options and poor survival rates. While substantial progress has been made in characterizing human AML, translating these advancements to veterinary practice has been hindered by limited molecular understanding and diagnostic tools. The case study presented illustrates the application of whole genome sequencing in diagnosing AML in a dog, showcasing its potential in veterinary oncology. Our approach facilitated comprehensive genomic analysis, identifying mutations in genes that may be associated with AML pathogenesis in dogs, such as KRAS, IKZF1, and RUNX1. However, without supportive evidence of its clinical utility (eg, association with response to treatment or prognosis), the information is limited to exploration. This article reviews the comparative features of canine AML with human AML and discusses strategies to shrink the knowledge gap between human and veterinary medicine with cost-effective next-generation sequencing (NGS) techniques. By utilizing these approaches, the unique and shared molecular features with human AML can be identified, aiding in molecular classification and therapeutic development for both species. Despite the promise of NGS, challenges exist in implementing it into routine veterinary diagnostics. Cost considerations, turnaround times, and the need for robust bioinformatics pipelines and quality control measures must be addressed. Most importantly, analytical and clinical validation processes are essential to ensure the reliability and clinical utility of NGS-based assays. Overall, integrating NGS technologies into veterinary oncology holds great potential for advancing our understanding of AML and improving disease stratification, in hopes of improving clinical outcomes.

37 Cost-effectiveness of treatment sequences with front-line nivolumab+ipilimumab therapy vs immuno-oncology+tyrosine kinase inhibitor therapies in intermediate/poor-risk metastatic renal cell carcinoma

Abstract Background Immuno-oncology (IO) and vascular endothelial growth factor (VEGF) targeted tyrosine kinase inhibitor (TKI) combinations have transformed the treatment of metastatic renal cell carcinoma (mRCC). However, there is a lack of evidence regarding the most cost-effective sequencing of these systemic therapies. This study aimed to assess the expected health and economic outcomes of various treatment sequences for newly diagnosed patients with intermediate/poor-risk RCC from a US payer perspective. Methods We developed a model using a continuous time micro-simulation framework. First-line treatment options included: nivolumab+ipilimumab, nivolumab+cabozantinib, pembrolizumab+lenvatinib, and pembrolizumab+axitinib. Second-line treatment options included: cabozantinib and everolimus+lenvatinib. In both lines, the source data for the overall survival (OS) and progression-free survival (PFS) distributions were obtained from the registrational randomized controlled trials (RCTs) of each therapy. The analysis included costs associated with drug acquisition in the US wholesale setting, drug administration and monitoring, management of treatment-related adverse events, disease management, and terminal care. Treatment duration for all first-line IO and TKI therapies was capped at 2 years. Quality-adjusted life-years (QALYs) were estimated by multiplying time in each phase of the disease by phase-specific utility values estimated from the CheckMate 214 trial data using US tariffs. The analysis was run 1000 times for cohorts of 100 patients over a 20-year time horizon. All costs and health outcomes were discounted annually at a 3% rate. Results Average per-patient QALYs were estimated for each sequence of interest: nivolumab+ipilimumab followed by cabozantinib (3.59), nivolumab+ipilimumab followed by lenvatinib+everolimus (3.26), pembrolizumab+axitinib followed by cabozantinib (3.14), pembrolizumab+axitinib followed by lenvatinib+everolimus (2.97), pembrolizumab+lenvatinib followed by cabozantinib (3.51), and nivolumab+cabozantinib followed by lenvatinib+everolimus (2.99). Average per-patient costs were estimated for each sequence of interest: nivolumab+ipilimumab followed by cabozantinib ($468,856), nivolumab+ipilimumab followed by lenvatinib+everolimus ($491,259), pembrolizumab+axitinib followed by cabozantinib ($675,714), pembrolizumab+axitinib followed by lenvatinib+everolimus ($700,411), pembrolizumab+lenvatinib followed by cabozantinib ($837,888), nivolumab+cabozantinib followed by lenvatinib+everolimus ($873,377). First-line drug acquisition costs accounted for the majority of costs for all sequences. Conclusions Based upon our model using data from registrational RCTs, first-line nivolumab+ipilimumab followed by cabozantinib represents the dominant treatment strategy for intermediate/poor-risk mRCC patients in the US, offering estimated cost savings of up to 45% when compared to IO+TKI options. Additional studies are needed to elucidate whether these cost savings translate to the real-world setting.

Depletion-assisted multiplexed cell-free RNA sequencing reveals distinct human and microbial signatures in plasma versus extracellular vesicles.

Cell-free long RNAs in human plasma and extracellular vesicles (EVs) have shown promise as biomarkers in liquid biopsy, despite their fragmented nature. To investigate these fragmented cell-free RNAs (cfRNAs), we developed a cost-effective cfRNA sequencing method called DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing). DETECTOR-seq utilised a meticulously tailored set of customised guide RNAs to remove large amounts of unwanted RNAs (i.e., fragmented ribosomal and mitochondrial RNAs) in human plasma. Early barcoding strategy was implemented to reduce costs and minimise plasma requirements. Using DETECTOR-seq, we conducted a comprehensive analysis of cell-free transcriptomes in both whole human plasma and EVs. Our analysis revealed discernible distributions of RNA types in plasma and EVs. Plasma exhibited pronounced enrichment in structured circular RNAs, tRNAs, Y RNAs and viral RNAs, while EVs showed enrichment in messenger RNAs (mRNAs) and signal recognition particle RNAs (srpRNAs). Functional pathway analysis highlighted RNA splicing-related ribonucleoproteins (RNPs) and antimicrobial humoral response genes in plasma, while EVs demonstrated enrichment in transcriptional activity, cell migration and antigen receptor-mediated immune signals. Our study indicates the comparable potential of cfRNAs from whole plasma and EVs in distinguishing cancer patients (i.e., colorectal and lung cancer) from healthy donors. And microbial cfRNAs in plasma showed potential in classifying specific cancer types. Our comprehensive analysis of total and EV cfRNAs in paired plasma samples provides valuable insights for determining the need for EV purification in cfRNA-based studies. We envision the cost effectiveness and efficiency of DETECTOR-seq will empower transcriptome-wide investigations in the fields of cfRNAs and liquid biopsy. DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing) enabled efficient and specific depletion of sequences derived from fragmented ribosomal and mitochondrial RNAs in plasma. Distinct human and microbial cell-free RNA (cfRNA) signatures in whole Plasma versus extracellular vesicles (EVs) were revealed. Both Plasma and EV cfRNAs were capable of distinguishing cancer patients from normal individuals, while microbial RNAs in Plasma cfRNAs enabled better classification of cancer types than EV cfRNAs.

An improved bacterial mRNA enrichment strategy in dual RNA sequencing to unveil the dynamics of plant-bacterial interactions

BackgroundDual RNA sequencing is a powerful tool that enables a comprehensive understanding of the molecular dynamics underlying plant-microbe interactions. RNA sequencing (RNA-seq) poses technical hurdles in the transcriptional analysis of plant-bacterial interactions, especially in bacterial transcriptomics, owing to the presence of abundant ribosomal RNA (rRNA), which potentially limits the coverage of essential transcripts. Therefore, to achieve cost-effective and comprehensive sequencing of the bacterial transcriptome, it is imperative to devise efficient methods for eliminating rRNA and enhancing the proportion of bacterial mRNA. In this study, we modified a strand-specific dual RNA-seq method with the goal of enriching the proportion of bacterial mRNA in the bacteria-infected plant samples. The enriched method involved the sequential separation of plant mRNA by poly A selection and rRNA removal for bacterial mRNA enrichment followed by strand specific RNA-seq library preparation steps. We assessed the efficiency of the enriched method in comparison to the conventional method by employing various plant-bacterial interactions, including both host and non-host resistance interactions with pathogenic bacteria, as well as an interaction with a beneficial rhizosphere associated bacteria using pepper and tomato plants respectively.ResultsIn all cases of plant-bacterial interactions examined, an increase in mapping efficiency was observed with the enriched method although it produced a lower read count. Especially in the compatible interaction with Xanthmonas campestris pv. Vesicatoria race 3 (Xcv3), the enriched method enhanced the mapping ratio of Xcv3-infected pepper samples to its own genome (15.09%; 1.45-fold increase) and the CDS (8.92%; 1.49-fold increase). The enriched method consistently displayed a greater number of differentially expressed genes (DEGs) than the conventional RNA-seq method at all fold change threshold levels investigated, notably during the early stages of Xcv3 infection in peppers. The Gene Ontology (GO) enrichment analysis revealed that the DEGs were predominantly enriched in proteolysis, kinase, serine type endopeptidase and heme binding activities.ConclusionThe enriched method demonstrated in this study will serve as a suitable alternative to the existing RNA-seq method to enrich bacterial mRNA and provide novel insights into the intricate transcriptomic alterations within the plant-bacterial interplay.

Developing Asparagaceae1726: An Asparagaceae-specific probe set targeting 1726 loci for Hyb-Seq and phylogenomics in the family.

Target sequence capture (Hyb-Seq) is a cost-effective sequencing strategy that employs RNA probes to enrich for specific genomic sequences. By targeting conserved low-copy orthologs, Hyb-Seq enables efficient phylogenomic investigations. Here, we present Asparagaceae1726-a Hyb-Seq probe set targeting 1726 low-copy nuclear genes for phylogenomics in the angiosperm family Asparagaceae-which will aid the often-challenging delineation and resolution of evolutionary relationships within Asparagaceae. Here we describe and validate the Asparagaceae1726 probe set (https://github.com/bentzpc/Asparagaceae1726) in six of the seven subfamilies of Asparagaceae. We perform phylogenomic analyses with these 1726 loci and evaluate how inclusion of paralogs and bycatch plastome sequences can enhance phylogenomic inference with target-enriched data sets. We recovered at least 82% of target orthologs from all sampled taxa, and phylogenomic analyses resulted in strong support for all subfamilial relationships. Additionally, topology and branch support were congruent between analyses with and without inclusion of target paralogs, suggesting that paralogs had limited effect on phylogenomic inference. Asparagaceae1726 is effective across the family and enables the generation of robust data sets for phylogenomics of any Asparagaceae taxon. Asparagaceae1726 establishes a standardized set of loci for phylogenomic analysis in Asparagaceae, which we hope will be widely used for extensible and reproducible investigations of diversification in the family.

Cost-Effective Whole Transcriptome Sequencing Landscape and Diagnostic Potential Biomarkers in Active Tuberculosis.

Tuberculosis (TB) is a prevalent and severe infectious disease that poses a significant threat to human health. However, it is frequently disregarded as there are not enough quick and accurate ways to diagnose tuberculosis. Here, we develop a strategy for tuberculosis detection to address the challenges, including an experimental strategy, namely, Double Adapter Directional Capture sequencing (DADCSeq), an easily operated and low-cost whole transcriptome sequencing method, and a computational method to identify hub differentially expressed genes as well as the diagnosis of TB based on whole transcriptome data using DADCSeq on peripheral blood mononuclear cells (PBMCs) from active TB and latent TB or healthy control. Applying our approach to create a robust and stable TB multi-mRNA risk probability model (TBMMRP) that can accurately distinguish active and latent TB patients, including active TB and healthy controls in clinical cohorts, this diagnostic biomarker was successfully validated by several independent cross-platform cohorts with favorable performance in differentiating active TB from latent TB or active TB from healthy controls and further demonstrated superior or similar diagnostic accuracy compared to previous diagnostic markers. Overall, we develop a low-cost and effective strategy for tuberculosis diagnosis; as the clinical cohort increases, we can expand to different disease kinds and learn new features through our disease diagnosis strategy.

Novel liquid biopsy assay to distinguish benign from malignant pelvic masses.

e17563 Background: Despite not ranking among the top ten most common cancers in women in the United States, ovarian cancer stands as the fifth leading cause of cancer-related deaths in this population. According to the American Cancer Society, only 20% of ovarian cancers are found at stages I-II, when 94% of patients are expected to live longer than five years after diagnosis. Instead, the majority of ovarian cancers are detected at stages III-IV, when long term survival is 20% or less. Early identification of ovarian cancer is challenging due to the wide range of symptoms with which a patient may present, most of which lack specificity for ovarian cancer. Even in cases where a physician may want to rule in or out ovarian cancer, the available methods for screening have a low positive predictive value. These methods include bimanual exams, transvaginal ultrasounds, and CA-125 blood tests. In a screening capacity, the sensitivity for a bimanual exam finding ovarian cancer is only about 44%, transvaginal ultrasound is approximately 85%, and CA-125 testing registers at 77%. The need for better tools to detect ovarian cancer is clear. Genece Health has developed an assay utilizing a proprietary algorithm that leverages deep learning to analyze cfDNA fragment size, end motif, and coverage patterns, along with other genomic features, to detect the presence of ctDNA in blood originating from ovarian cancer. The data presented herein demonstrate that the Genece Health assay has the ability to detect ctDNA in a single tube of blood using low pass whole genome sequencing in a cost-effective way to distinguish between malignant and benign pelvic masses. Methods: Blood was prospectively collected from 56 females with pelvic masses in advance of removal and histopathology (approximately 32 cancer and 24 benign masses) in Streck cfDNA BCT Devices. Additionally, healthy female donor blood from 50 individuals procured through the San Diego Blood Bank were collected in Streck cfDNA BCT Devices. cfDNA was extracted from double-spun plasma and low-pass whole genome sequencing (2X – 5X coverage) was performed. Data were analyzed with the Genece Health Algorithm. Results: The Genece Health Algorithm demonstrated the ability to distinguish between pelvic masses that were deemed benign versus malignant via histopathology. There was a significant (P<0.01) difference between the classifier assigned to the malignant and benign and presumed normal groups. Conclusions: The investigational assay described herein shows progress toward the ability to use non-invasive molecular testing to identify ovarian cancer in a blood sample. A blood test to differentiate between a benign and malignant pelvic mass will enhance the ability of physicians to detect ovarian cancer at earlier stage, improving clinical outcomes for women. The cost-effective low-pass whole genome sequencing approach taken by Genece will be further explored in expanded cohorts.

Simultaneous Detection of Common Founder Mutations Using a Cost-Effective Deep Sequencing Panel.

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

Specific Mycobacterium tuberculosis Strain Circulating in Prison Revealed by Cost-Effective Amplicon Sequencing.

This scientific study focuses on tuberculosis (TB) within prison settings, where persons deprived of liberty (PDL) face significantly higher rates of the disease compared to the general population. The research employs the low-cost amplicon sequencing of Mycobacterium tuberculosis strains, aiming first to identify specific lineages and also to detect mutations associated with drug resistance. The method involves multiplex amplification, DNA extraction, and sequencing, providing valuable insights into TB dynamics and resistance-mutation profiles within the prison system at an affordable cost. The study identifies a characteristic lineage (X) circulating among PDLs in the penitentiary system in Uruguay, absent in the general population, and notes its prevalence at prison entry. No high-confidence mutations associated with drug resistance were found. The findings underscore the importance of molecular epidemiology in TB control, emphasizing the potential for intra-prison transmissions and the need for broader studies to understand strain dynamics.

MicroPredict: predicting species-level taxonomic abundance of whole-shotgun metagenomic data using only 16S amplicon sequencing data

BackgroundThe importance of the human microbiome in the analysis of various diseases is emerging. The two main methods used to profile the human microbiome are 16S rRNA gene sequencing (16S sequencing) and whole-genome shotgun sequencing (WGS). Owing to the full coverage of the genome in sequencing, WGS has multiple advantages over 16S sequencing, including higher taxonomic profiling resolution at the species-level and functional profiling analysis. However, 16S sequencing remains widely used because of its relatively low cost. Although WGS is the standard method for obtaining accurate species-level data, we found that 16S sequencing data contained rich information to predict high-resolution species-level abundances with reasonable accuracy.ObjectiveIn this study, we proposed MicroPredict, a method for accurately predicting WGS-comparable species-level abundance data using 16S taxonomic profile data.MethodsWe employed a mixed model using two key strategies: (1) modeling both sample- and species-specific information for predicting WGS abundances, and (2) accounting for the possible correlations among different species.ResultsWe found that MicroPredict outperformed the other machine learning methods.ConclusionWe expect that our approach will help researchers accurately approximate the species-level abundances of microbiome profiles in datasets for which only cost-effective 16S sequencing has been applied.

Detecting Hachimoji DNA: An Eight-Building-Block Genetic System with MoS2 and Janus MoSSe Monolayers.

In the pursuit of personalized medicine, the development of efficient, cost-effective, and reliable DNA sequencing technology is crucial. Nanotechnology, particularly the exploration of two-dimensional materials, has opened different avenues for DNA nucleobase detection, owing to their impressive surface-to-volume ratio. This study employs density functional theory with van der Waals corrections to methodically scrutinize the adsorption behavior and electronic band structure properties of a DNA system composed of eight hachimoji nucleotide letters adsorbed on both MoS2 and MoSSe monolayers. Through a comprehensive conformational search, we pinpoint the most favorable adsorption sites, quantifying their adsorption energies and charge transfer properties. The analysis of electronic band structure unveils the emergence of flat bands in close proximity to the Fermi level post-adsorption, a departure from the pristine MoS2 and MoSSe monolayers. Furthermore, leveraging the nonequilibrium Green's function approach, we compute the current-voltage characteristics, providing valuable insights into the electronic transport properties of the system. All hachimoji bases exhibit physisorption with a horizontal orientation on both monolayers. Notably, base G demonstrates high sensitivity on both substrates. The obtained current-voltage (I-V) characteristics, both without and with base adsorption on MoS2 and the Se side of MoSSe, affirm excellent sensing performance. This research significantly advances our understanding of potential DNA sensing platforms and their electronic characteristics, thereby propelling the endeavor for personalized medicine through enhanced DNA sequencing technologies.

Abstract LB103: Cell-free DNA methylation as a noninvasive prognostic biomarker in hepatocellular carcinoma

Abstract The current noninvasive prognostic evaluation of hepatocellular carcinoma (HCC) using markers such as imaging features and serum biomarkers like alpha-fetoprotein (AFP) is limited due to low sensitivity. Pathologic tumor features can enhance prognosis beyond imaging and AFP, yet pre-surgical percutaneous biopsies are invasive, not routinely pursued, and may not be representative of the entire tumor. There is a critical unmet need to develop noninvasive blood-based HCC biomarkers to guide therapeutic decisions. Epigenetic changes in circulating cell-free DNA (cfDNA) have shown promise in early cancer diagnosis and prognosis. Therefore, we aim to evaluate the potential of cfDNA methylation as a noninvasive predictor for pre-treatment prognostication in HCC patients. Using Illumina HumanMethylation450 array data of 377 HCC tumors and 50 adjacent normal tissues obtained from The Cancer Genome Atlas (TCGA), we systematically identified 158 HCC-related DNA methylation markers associated with overall survival (OS). Functional analyses of these markers indicated their roles in regulating gene expression and revealed biological pathways associated with HCC patient survival. We predicted an OS risk score utilizing the DNA methylation panel, referred to as “methRisk”, and validated its predictive power using the Reduced Representation Bisulfite Sequencing data of HCC tumor tissue samples from 29 surgically resected patients. To assess the noninvasive prognostic value of these markers, we collected plasma cfDNA samples from an independent validation cohort of 52 HCC patients, and profiled the methylation patterns of these samples using our newly developed cost-effective cfDNA methylome sequencing assay, cfMethyl-Seq. We utilized random survival forest models combined with 10-fold cross-validation to predict the methRisk using the circulating markers. The cfDNA-based methRisk effectively stratifies patients into clinically relevant risk groups and predicts patient survival with high accuracy (3-year AUC = 0.81, 95% CI: 0.68-0.94). Integrating the cfDNA-based methRisk with existing risk indices like the Barcelona clinic liver cancer (BCLC) staging greatly improves the noninvasive prognostic assessments (3-year AUC = 0.91, 95% CI: 0.80-0.99). In summary, these findings indicate that cfDNA methylation is a promising noninvasive prognostic biomarker for HCC, providing a proof-of-concept for its potential clinical utility. Incorporating cfDNA methylation analysis into the existing noninvasive prognostic framework for HCC patients holds great potential for enhancing the effectiveness of current staging systems and optimizing patient selection for curative therapies like liver transplantation. Citation Format: Ran Hu, Benjamin Tran, Shuo Li, Mary L. Stackpole, Weihua Zeng, Yonggang Zhou, Andrew Melehy, Xianghong Zhou, Wenyuan Li, Vatche Agopian. Cell-free DNA methylation as a noninvasive prognostic biomarker in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB103.

Abstract 2946: Characterizing the genomic landscapes of breast and lung tumors using cost-effective whole genome sequencing

Abstract Whole Genome Sequencing (WGS) has emerged as a pivotal tool for unraveling the intricate genomic alterations underlying cancer, enabling the detection of a broad spectrum of somatic changes in cancer genomes, including single nucleotide variants, insertions, deletions, copy number variations, and structural rearrangements. Characterizing somatic variants across the entire genome significantly benefits from novel cost-efficient sequencing platforms, such as UG100. In the current study, we aim to demonstrate the utility of UG100 WGS in characterizing the landscape of somatic events in breast and lung cancer. Over 35 tumor-normal cancer sample pairs were profiled using UG100 WGS (mean tumor coverage: >100x; normal: >50x). Variant calling was done using a somatic deep-variant algorithm, optimized to UG100 data. Results were compared to Illumina sequencing and high concordance was observed, with over 97% of SNVs and over 93% of indels detected by Illumina identified also by UG100. UG100 sequencing was deeper and allowed for identification of a larger number of genomic variations, including known driver events in cancer-related genes. The somatic mutational signatures by UG100 were concordant with the Illumina platform, with a cosine similarity higher than 99%. Copy number variation and structural variation analysis was performed on all sample pairs, revealing a wide range of genomic aberrations. The integration of genetic sequencing into routine clinical oncology practices has proven instrumental in identifying actionable mutations, predicting treatment responses, and monitoring minimal residual disease. Moving from targeted sequencing to WGS provides a more comprehensive and unbiased assessment of the entire genome, uncovering rare or unexpected mutations that might be missed by targeted approaches. WGS allows for the simultaneous evaluation of both coding and non-coding regions of the genome, providing a deeper understanding of the regulatory elements influencing gene expression. This broader scope enhances our understanding of cancer biology and contributes to a more accurate assessment of the tumor's functional landscape. Citation Format: Hila Benjamin, Ilya Soifer, Doron Shem-Tov, Maya Levy, Islam Oguz Tuncay, Baek-Lok Oh, Jong-Yeon Shin, Young Seok Ju, Sangmoon Lee, Omer Barad, Doron Lipson. Characterizing the genomic landscapes of breast and lung tumors using cost-effective whole genome sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2946.

Abstract 7560: Microsatellite instability (MSI) detection using flow-based sequencing

Abstract Background: Microsatellite instability (MSI) is an important genomic biomarker associated with various cancers and hereditary conditions. Accurate and efficient classification of MSI status is essential for diagnosis, prognosis, and treatment decisions. Different approaches for testing MSI have been presented in the literature, including NGS-based methods which either rely on comparison with a matched control sample, a set of reference control samples, or the reference genome (no control). In this study, we explore the feasibility of accurately identifying MSI status using the UG100 platform, which utilizes flow-based sequencing chemistry. Since MSI detection is typically based on long homopolymers, we examined here whether the flow-based sequencing data can be effectively used for MSI classification. Methods: We performed whole genome sequencing (WGS) on a cohort of 24 cell lines with known MSI status, as well as on 15 tumor tissue samples, encompassing various cancer types, including colon, lung, bladder, and endometrial cancer. To determine the MSI status, polyA/T homopolymer loci were considered. We measured the MSI status in these loci using two methods: 1) Counting short indel variants detected using somatic Deep Variant, in comparison with matched blood normal, and 2) a novel MSI score based on the difference of the polyA/T homopolymer length, in comparison to the reference genome. We assessed the ability to evaluate the score for the full sequenced coverage (~100X) as well as when limiting to exome boundaries, and when sampling down to 1X coverage. Results and Conclusions: We established the performance of the MSI score on the cell lines with known MSI status. The score was verified on cell line mixtures (simulating lower tumor purity) as well as on clinical samples from various tissue sources, including colon samples annotated as mismatch-repair deficient. We demonstrate the ability to use the cost-effective UG100 sequencing combined with the two methodologies to identify MSI-high samples in these scenarios. Citation Format: Gila Lithwick-Yanai, Hila Benjamin, Jorge Cano-Nistal, Shir Geisler, Ariel Jaimovich, Nika Iremadze, Dror Kessler, Ilya Soifer, Danit Lebanony, Keren Ben Simhon, Shlomit Gilad, Eti Meiri, Doron Lipson, Yosef E. Maruvka. Microsatellite instability (MSI) detection using flow-based sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7560.

ProTInSeq: transposon insertion tracking by ultra-deep DNA sequencing to identify translated large and small ORFs

Identifying open reading frames (ORFs) being translated is not a trivial task. ProTInSeq is a technique designed to characterize proteomes by sequencing transposon insertions engineered to express a selection marker when they occur in-frame within a protein-coding gene. In the bacterium Mycoplasma pneumoniae, ProTInSeq identifies 83% of its annotated proteins, along with 5 proteins and 153 small ORF-encoded proteins (SEPs; ≤100 aa) that were not previously annotated. Moreover, ProTInSeq can be utilized for detecting translational noise, as well as for relative quantification and transmembrane topology estimation of fitness and non-essential proteins. By integrating various identification approaches, the number of initially annotated SEPs in this bacterium increases from 27 to 329, with a quarter of them predicted to possess antimicrobial potential. Herein, we describe a methodology complementary to Ribo-Seq and mass spectroscopy that can identify SEPs while providing other insights in a proteome with a flexible and cost-effective DNA ultra-deep sequencing approach.

Environmental DNA metabarcoding of freshwater fish in Malaysian tropical rivers using short-read nanopore sequencing as a potential biomonitoring tool.

The approach of combining cost-effective nanopore sequencing and emerging environmental DNA (eDNA) metabarcoding could prove to be a promising tool for biodiversity documentation, especially in Malaysia. Given the substantial funding constraints in recent years, especially in relation to the country's biodiversity, many researchers have been limited to conduct restricted research without extended monitoring periods, potentially hindering comprehensive surveys and could compromise the conservation efforts. Therefore, the present study aimed to evaluate the application of eDNA metabarcoding on freshwater fish using short reads generated through nanopore sequencing. This assessment focused on species detection in three selected rivers within the Endau Rompin Landscape in Malaysia. Additionally, the study compared levels of species detection between eDNA metabarcoding and conventional sampling methods, examined the effectiveness of primer choice, and applied both metabarcoding and shotgun sequencing to the eDNA approach. We successfully identified a total of 22 and 71 species with an identification threshold of >97% and >90%, respectively, through the MinION platform. The eDNA metabarcoding approach detected over 13% more freshwater fish species than when the conventional method was used. Notably, the distinction in freshwater fish detection between eDNA primers for 12S rRNA and cytochrome oxidase I was insignificant. The cost for eDNA metabarcoding proved to be more effective compared to conventional sampling with cost reduction at 33.4%. With favourable cost-effectiveness and increased species detection, eDNA metabarcoding could complement existing methods, enhance holistic diversity documentation for targeted habitats and facilitate effective conservation planning.