Genome Research Articles

Draft genome sequences of six multidrug-resistant Escherichia coli isolates from pigeons in Bangladesh.

Household pigeons are a potential source of multidrug-resistant (Escherichia coli (E. coli) and act as a transmission vehicle of this bacterium to humans. Here, we characterized the antimicrobial resistance profile and molecular properties of the whole genome sequences of six E. coli strains from household pigeons.

NS1-mediated DNMT1 degradation regulates human bocavirus 1 replication and RNA processing.

Methylation of the DNA genome plays an important role in viral gene inactivation. However, the role of DNA methylation in human bocavirus (HBoV) remains unclear. In this study, the HBoV1 genomic DNA was found extensively methylated at the CHG and CHH sites. Inhibiting DNA methylation with 5-aza-2'-deoxycytidine (DAC) altered the methylation status and reduced viral DNA production, while enhanced the RNA splicing at D1 and D3 sites and the polyadenylation at the proximal polyadenylation site, (pA)p. Knockdown of DNA methyltransferase 1 (DNMT1) had the same effect on viral DNA synthesis and RNA processing as the DAC treatment, indicating that DNMT1 is the major host methyltransferase involved in viral DNA methylation. In addition, the nonstructural protein NS1 promoted DNMT1 degradation through the ubiquitin-proteasome pathway to regulate viral replication and RNA processing. Collectively, the results suggest that DNA methylation and DNMT1 facilitate HBoV replication and are essential for appropriate NS1 localization in the nucleus. DNMT1 degradation through NS1 promotes the virus RNA processing, leading to viral protein expression.

Abstract B029: Ultra-sensitive molecular residual disease detection through whole genome sequencing with single-read error correction

Abstract While whole genome sequencing (WGS) of cell-free DNA (cfDNA) holds enormous promise for detection of molecularresidual disease (MRD), its performance is limited by WGS error rate. Here we introduce AccuScan, an efficient cfDNA WGS technology that enables genome-wide error correction at single read level, achieving an error rate of 4.2 × 10-7, which is about two orders of magnitude lower than a read-centric de-noising method. Application of AccuScan to MRD demonstrated analytical sensitivity down to 10-6 circulating tumor allele fraction at 99% sample level specificity. AccuScan showed 90% landmark sensitivity (within 6 weeks after surgery) and 100% specificity for predicting relapse in colorectal cancer. It also showed 67% sensitivity and 100% specificity with esophageal cancer using samples collected within one week after surgery. When AccuScan was applied to monitor immunotherapy in melanoma patients, the circulating tumor DNA (ctDNA) levels and dynamic profiles were consistent with clinical outcomes. Overall, AccuScan provides a highly accurate WGS solution for MRD detection, empowering ctDNA detection at parts per million rangewithout requiring high sample input or personalized reagents. Citation Format: Xinxing Li, Tao Liu, Antonella Bacchiocchi, Mengxing Li, Wen Cheng, Tobias Wittkop, Fernando Mendez, Yingyu Wang, Paul Tang, George Yeung, Qianqian Yao, Marcus Bosenberg, Mario Sznol, Qin Yan, Malek Faham, Li Weng, Ruth Halaban, Hai Jin, Zhiqian Hu. Ultra-sensitive molecular residual disease detection through whole genome sequencing with single-read error correction [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B029.

Prospective Screening of Cancer Syndromes in Patients with Mesenchymal Tumors

Background: The etiology of most mesenchymal tumors is unknown, and knowledge about syndromes with an increased risk of tumors in bone or soft tissue is sparse. Methods: We present a prospective germline analysis of 312 patients with tumors suspected of being sarcomas at a tertiary sarcoma center. Germline and tumor whole genome sequencing, tumor transcriptome, and methylome analyses were performed. Results: Germline pathogenic or likely pathogenic variants associated with an increased risk of tumors were detected in 24 patients (8%), of which 11 (4%) harbored a detectable second hit in the tumor. Second hits were confirmed in genes with (NF1, RB1, TP53, EXT2, and SDHC) and without (ATM, CDC73, MLH1, MSH6, POLG, and KCNQ1) known association with mesenchymal tumor predisposition. Sarcomas from two Lynch syndrome patients showed mismatch repair deficiency, predicting a treatment response to immune checkpoint inhibitors (Level 1 biomarker according to the FDA (Federal Drug Administration) and ESMO (European Society for Medical Oncology)). None of the three CHEK2 carriers had a second hit in the tumor, suggesting a weak link to sarcoma. Conclusions: We conclude that second-hit analyses can be used in standard of care to identify syndrome-related tumors. This approach can help distinguish true manifestations of tumor syndromes from unrelated germline findings and enhance the understanding of germline predisposition in soft tissue tumors. Prospective screening using germline whole genome sequencing should be considered when comprehensive somatic sequencing is introduced into clinical practice.

Machine Learning in Genomics: Applications in Whole Genome Sequencing, Whole Exome Sequencing, Single-Cell Genomics, and Spatial Transcriptomics

The application of machine learning (ML) to genomics has transformed the process of analyzing and interpreting large-scale, complex datasets, leading to important breakthroughs in our knowledge of biological systems. This review provides a comprehensive overview of ML applications in key genomic areas: Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES), single-cell genomics, and spatial transcriptomics. In WGS and WES, ML techniques are employed for variant calling, genome-wide association studies, rare variant analysis, and the prediction of pathogenicity. In single-cell genomics, ML facilitates clustering, trajectory inference, and cell type identification, while in spatial transcriptomics, it aids in deciphering spatial patterns of gene expression and tissue heterogeneity. This review further explores the application of ML in related omics fields, including proteomics, transcriptomics, metagenomics, epigenomics, and microbiome research. These applications encompass protein structure prediction, functional annotation, microbial community profiling, and the analysis of epigenetic modifications. We address the challenges caused by high dimensionality, variability in the data, and the requirement for interpretable machine learning models when dealing with genomic data. Emerging technologies like explainable AI and federated learning are highlighted for their potential to address these challenges. Additionally, the review addresses ethical considerations, data privacy issues, and the necessity for standardized protocols in ML applications. This comprehensive examination underscores the transformative impact of ML in genomics and highlights its potential to drive future innovations in personalized medicine and biological research.

Distribution and Implications of Mycobacterium Tuberculosis Genotypes in Animals: A Comprehensive Review

This narrative review discusses the spread of Mycobacterium tuberculosis among animal populations, focusing on M. tuberculosis genotype distribution and frequency across different species. To develop comprehensive tuberculosis control strategies under the One Health approach, it is important to have an understanding of the genotypes of M. tuberculosis in animals. Animal infections are discussed, as well as the pathogenesis and epidemiology of M. tuberculosis. Various genotyping methods, including spoligotyping, MIRU-VNTR, and whole genome sequencing, are discussed in the review, emphasizing their role in understanding strain diversity and transmission dynamics. There are many factors influencing the distribution of M. tuberculosis genotypes in animals, including environmental conditions, host factors, human-animal interactions, and animal trade practices. It is clear from the review that the Euro-American lineage is widely distributed across animal species, with the Beijing genotype becoming increasingly prevalent in regions where it is prevalent in humans. The study highlights the need for more comprehensive genotyping studies as well as the development of better diagnostic tools. This will enhance our understanding of M. tuberculosis in animal populations. A conclusion to the review, emphasizes that standardizing genotyping protocols, utilizing whole genome sequencing, and fostering international collaboration are essential for improving our understanding of M. tuberculosis diversity in animals and developing targeted tuberculosis control strategies. The impact of Mycobacterium tuberculosis genotypes in animals is multi-faceted, affecting both public health and veterinary practices. Addressing these challenges requires a coordinated and comprehensive approach that considers the interconnectedness of human and animal health systems, fosters research, enhances surveillance, and implements effective control strategies.

Abstract A050: Early detection of liver cancer from diverse populations using cfDNA fragmentome and protein biomarkers

Abstract Hepatocellular carcinoma (HCC) is a leading cause of cancer death world-wide. In the US, liver cancer has increased in incidence and mortality due to a growing population from South and Central America, which faces higher risk of disease in part due to aflatoxin exposure. Recently, we showed that the DELFI (DNA evaluation of fragments for early interception) approach using genome-wide cell free DNA (cfDNA) fragmentation profiles and machine learning can be used for detection of liver and other cancers. Having previously demonstrated that a DELFI classifier accurately detects HCC in populations from the US and Hong Kong, we evaluated the classifier in other clinically relevant cohorts worldwide. Here we show that approach generalizes to diverse populations, and that including other genomic and protein features from the same blood draw improves performance. We examined plasma samples from 377 individuals, including 244 individuals with HCC and 133 without cancer, including 85 with cirrhosis. Plasma samples were collected from individuals with or without HCC in a case-control study in Guatemala (n=203) and from a prospective collection in Romania (n=174) and cfDNA was analyzed by whole- genome sequencing at ∼10x coverage. The median DELFI scores using a locked classifier (Foda et al., Cancer Discovery, 2023) were higher in both cohorts for patients with cancer across all stages compared to individuals without cancer, regardless of the presence of cirrhosis. Using the locked model with a threshold that corresponded to 80% specificity in prior work, DELFI detected individuals with HCC with sensitivities of 90% and 69% in the two cohorts at specificities of 92% and 86%, respectively. For early-stage HCC within Milan Criteria for liver transplantation, sensitivities were 85% in the case-control cohort and 64% in the prospective cohort. In these cohorts, the fixed DELFI model outperformed AFP at the clinically used threshold of 20 ng/mL with a sensitivity in the combined cohorts of 78% at 91% specificity, compared to 63% sensitivity at 91% specificity for AFP. A combined approach using either DELFI at a threshold that corresponded to a 90% specificity in our previous study or AFP at the clinical threshold resulted in 82% sensitivity (74% in early stage) at 92% specificity and was superior to estimates of AFP and ultrasound for early-stage disease (63% sensitivity at 84% specificity). Individuals who tested positive with DELFI had a significantly shorter overall survival (p=0.002, log rank test), even amongst individuals at the earliest stage, while AFP alone did not stratify survival for patients with early-stage disease. Single-molecule analyses from low coverage WGS of cfDNA revealed genome-wide mutational profiles that were similar to those of HCC and in individuals from Guatemala that were characteristic of aflatoxin exposure. Overall, this work provides insight into the origins of cfDNA in populations at risk for HCC and validates our genome-wide fragmentome approach for non-invasive cancer detection that may facilitate liver cancer screening. Citation Format: Zachariah H Foda, Daniel Bruhm C Bruhm, Akshaya V Annapragada, Shashikant Koul, Sarah Short, Keerti Boyapati, Adrianna Bartolomucci, Vilmos Adleff, Nicholas A Vulpescu, Hope Orjuela, Andrei Sorop, Razvan Iacob, Liana Gheorghe, Simona Dima, Katherine A McGlynn, Manuel Ramírez-Zea, Jillian Phallen, John Groopman, Robert B Sharpf, Victor E Velculescu. Early detection of liver cancer from diverse populations using cfDNA fragmentome and protein biomarkers [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A050.

Abstract B071: Identifying the clinical utility of CSF as a source of liquid biopsy in pediatric brain tumors

Abstract Pediatric tumors of the central nervous system (CNS) are a leading cause of cancer-related childhood mortality. Along with imaging, it is essential to molecularly characterize tumor tissue for the diagnosis, treatment, and prognosis of these cancers. Biopsies of deeply-seeded lesions may not be possible or may carry significant risks. On the other hand, liquid biopsy, using cerebral spinal fluid (CSF), provides a minimally invasive alternative to identify and monitor tumor DNA at crucial time points, from diagnosis to follow-up. This allows for molecular diagnosis, identification of therapeutic targets, monitoring response to therapy, and early detection of recurrence or progression. The aim of our study was to assess the practicality and usefulness of liquid biopsies in a diverse group of CNS tumor patients. We used a combination of methods, including a targeted hybridization capture panel and low-pass whole genome sequencing (LP-WGS) based on the sample characteristics. We analyzed a total of 243 CSF samples from patients with CNS tumors including: low-grade glioma (LGG; N=57), high-grade glioma (HGG; N=53), medulloblastoma (MB; N=40), germinoma (CNS-GCT; N=29), and no prior biopsy (N=42). We found somatic mutations in 88% of CSF samples from HGG and 67% from LGG with known tumor DNA variants and sufficient sequencing quality. It's important to note that in LGG patients, samples collected from lumbar puncture were positive only in disseminated cases, while a higher positivity rate was found in cases where CSF was collected from ventricular spaces. Moreover, we observed positive copy number alterations (CNA) in CSF samples collected from HGG, embryonal tumors, and CNS-GCT patients at the time of diagnosis and follow-up with a sensitivity of 64%. Within our sample cohort that had an unknown or query tumor due to no prior biopsy, we were able to detect diagnostic, targetable mutations and/or a positive CNA profile in 30% of cases. This supports the utility of liquid biopsy as a diagnostic tool in in CNS lesions. This tool may also assist in monitoring for relapse in high-grade tumors. For example, in a case study, we were able to detect recurrent MB by indication of a positive CNA profile in keeping with the primary tumor CNV, six years post-surgical resection. In CNS- GCT patients, we found that LP-WGS was highly sensitive at diagnosis as we have observed similar CNV in both CSF and tumor DNA. In summary, the utility of our liquid biopsy platform in the context of CNS cancers is an impactful tool that can aid clinicians in diagnosis, treatment and monitoring disease in a minimally invasive manner. Citation Format: Liana Nobre, Mansuba Rana, Yoshiko Nakano, Ian Burns, Robert Siddaway, Richard Yuditskiy, Cyril Li, Andrew Bondoc, Anthony Liu, Uri Taboori, Cynthia Hawkins. Identifying the clinical utility of CSF as a source of liquid biopsy in pediatric brain tumors [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B071.

Abstract IA021: Plasma-only ultrasensitive liquid biopsy to monitor tumor burden and clonal evolution

Abstract Individual bulk tumor biopsies are invasive and may fail to capture clonal heterogeneity within primary tumors and metastatic sites of disease. Further, analyses of subclonal variants are often hindered by background error rates related to tissue preservation methods (e.g. FFPE) and sequencing artifact. Use of plasma-only ultrasensitive liquid biopsy approaches may allow for noninvasive inference of clonality and dynamically track changes in tumor burden. We previously developed MRD-EDGE, a tumor-informed machine learning classifier for ultrasensitive liquid biopsy monitoring of single nucleotide variants (SNVs) and copy number variants (CNVs) using standard plasma whole genome sequencing. For SNVs, MRD- EDGE uses a novel machine learning framework to classify individual cell free DNA (cfDNA) fragments as circulating tumor DNA or cfDNA sequencing artifact. We demonstrated the potential of plasma-only MRD-EDGE to identify SNVs in advanced melanoma and monitor tumor burden throughout treatment with immune checkpoint inhibiton. In tumor-informed settings, MRD-EDGE was used to identify minimal residual disease at tumor fractions (TFs) of 1:100,000 and below. Accurate SNV classification is a function of classifier performance and background error rate. To reduce error rates, we combined MRD-EDGE SNV with paired plus- minus sequencing (ppmSeq) from Ultima Genomics. ppmSeq is a PCR-free approach that leverages the hydrogen bonds of complementary DNA to carry matched native strands directly into sequencing, enabling scalable duplex sequencing. Use of MRD-EDGE SNV together with ppmSeq enables ultrasensitive tracking of early-stage non-small cell lung cancer throughout neoadjuvant immunotherapy. Plasma-only MRD-EDGE is therefore poised to enable dynamic monitoring of tumor burden and clonal composition outside the scope of tumor-informed approaches, including the comparison of SNV composition in cancer recurrence with preoperative disease. Citation Format: Adam Widman, Alexandre P. Cheng, John Zinno, Srinivas Rajagopalan, Ashish Saxena, Nasser Altorki, Dan A. Landau. Plasma-only ultrasensitive liquid biopsy to monitor tumor burden and clonal evolution [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr IA021.

Whole genome sequencing of CRISPR/Cas9-engineered NF-κB reporter mice for validation and variant discovery.

Targeted knockout, mutations, or knock-in of genomic DNA fragments in model organisms have been used widely for functional and cell-tracking studies. The desired genetic perturbation is often accomplished by recombination-based or CRISPR/Cas9-based genome engineering. For validating the intended genetic modification, a local region surrounding the targeted locus is typically examined based on enzymatic cleavage and consequent length patterns, e.g. in a Southern analysis. Despite its wide use, this approach is open to incomplete and ambiguous readouts. With decreasing costs of high-throughput sequencing, it is becoming feasible to consider a large-scale validation of a new strain after a targeted genetic perturbation. Here we describe a dataset of whole-genome sequences and the variant analysis results from four novel reporter mouse strains. This served to validate the strains and identified all the off-target effects on the genome, thereby increasing the genetic diversity of genomic sequences over those represented in the public databases for inbred mice.

Abstract B044: Fragmentomic features of cell-free DNA predict late-stage melanoma treatment benefit and survival

Abstract The levels of circulating tumour DNA (ctDNA) in cancer patients are associated with response and survival outcomes. Analysis of ctDNA generally requires knowledge of tumour mutations or the application of expensive next generation targeted sequencing. Here we utilize a cutting-edge approach, combining cell-free DNA molecules (cfDNA) fragmentomics and machine learning, to infer the presence of ctDNA to predict response and survival to immune checkpoint inhibitors in stage III unresectable and IV melanoma patients. Genome libraries were constructed from cfDNA derived from 48 (discovery cohort) and 96 (validation cohort) stage III unresectable and IV melanoma patients prior to the commencement of immune-checkpoint inhibitors. The discovery cohort included any-line therapy, patients had a median age of 66, 69% were male, 33% were BRAF V600 mutant, and 88% had an ECOG of 0-1. The validation cohort included first-line therapy, patients had a median age of 69, 59% were male, 22% were BRAF V600 mutant, and 89% had an ECOG of 0-1. Genome libraries were subjected to shallow whole genome sequencing (8x) and the following metrics were calculated: tumour content prediction, aneuploidy levels, various proportions of fragments ranging from 20 bp to 320 bp, the proportion of mitochondrial DNA, and the amplitude of 10 bp size oscillations between 75-150 bp, and the Shannon diversity of trinucleotide motifs at each end of the read. Non-penalized logistic modelling was used to predict clinical benefit, and the resulting logistic model was transformed into a “white-box” model. The white-box model was applied to the cohorts and used to test progression-free and overall survival. The removal of reads with inserts <90 and >150 bp greatly improved chromosome aneuploidy detection from 19% to 31%. Logistic modelling of the fragmentomic features of cfDNA yielded an AUROC of 0.83 for prediction of clinical benefit in the discovery cohort. A cutoff was set for the fragmentomic model score and used to stratify groups for survival analysis. Patients with a low score showed significantly worse progression- free (mPFS 931 vs 286 days, HR 3.4, 95% CI 1.55-7.46, p<0.002) and overall (mOS NR vs 363 days, HR 8.1, 95% CI 2.7-24.3, p<0.001) survival. These results were replicated in the validation cohort with an AUROC of 0.78 for prediction of clinical benefit and significant associations with progression-free (mPFS 1986 vs 926 days, HR 2.33 95% CI 1.27-3.92, p=0.005) and overall (mOS NR vs 1238 days, HR 4.51 95% CI 1.7-11.96, p=0.002) survival. Fragmentomic features of plasma cfDNA enable the prediction of clinical benefit, progression-free, and overall survival in stage IV melanoma patients treated with immune-checkpoint inhibitors. Citation Format: Aaron B Beasley, Leslie Calapre, Ashleigh Stewart, Anna L Reid, Russell Diefenbach, Wei Yen Chan, Rebecca Auzins, Luisa Pinnel, Sanjeev Adhikari, Muhammad A Khattak, Peter Lau, Tarek M Meniawy, Jenny H Lee, Lydia Warburton, Michael Millward, Alexander M Menzies, Richard A Scolyer, Georgina V Long, Helen Rizos, Elin S Gray. Fragmentomic features of cell-free DNA predict late-stage melanoma treatment benefit and survival [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B044.

Abstract A051: Identifying patterns of cell-free DNA associated with elevated risk of diffuse large B-cell lymphoma in adult dogs

Abstract Background: Nonrandom patterns of cell-free DNA (cfDNA) are closely associated with malignancy. We hypothesize that they are also found in the systemic microenvironment that gives rise to cancer, and that such signatures can inform a risk stratification test. To achieve this, we have initiated the Lymphoma Risk Assessment (LyRA) study, using machine learning on a training set of dogs with known health status and a test set of otherwise healthy adult pet dogs to identify cfDNA signatures that are associated with elevated risk of developing diffuse large B- cell lymphoma (DLBCL). We will validate the predictive accuracy of these signatures, including the persistence of the risk environment and the interval between risk detection and development of clinical disease. Since cfDNA is the primary input of the LyRA test, we first performed a pilot study to assess how sample characteristics affect cfDNA quality and subsequent low-pass whole genome sequencing (WGS) data. Methods: Blood samples were collected from pet dogs with the owner’s informed consent. Paired samples were collected into potassium (K3)EDTA tubes and additive-free red top tubes (n = 5 dogs) or K3EDTA tubes and Cell-free DNA BCT® Streck Tubes (n = 6 dogs). Additional serum samples (n=3) were generously provided by the Morris Animal Foundation Golden Retriever Lifetime Study. cfDNA was isolated using the QIAamp® Circulating Nucleic Acid kit. Total cfDNA concentration was measured by PicoGreen™ and fragment distribution by Agilent High Sensitivity D500 ScreenTape® Assay, with comparisons made by starting material (plasma vs. serum) and collection tubes. Results: Serum had both higher total concentrations of cfDNA (PicoGreen™) and calibrated concentrations of long fragment cfDNA (515-658 bp Agilent traces) compared to plasma. In addition, total cfDNA concentrations increased with malignancy, following the pattern: no cancer < cancer < hematological cancer. Median fragment lengths of Agilent peaks also increased with malignancy for short (125-181 bp) and medium (340-425 bp) fragments, in both serum and plasma, but decreased for long fragments. In these pilot data the trends seen were consistent with our hypothesis and with prior studies, although due to sample size, no statistical testing was performed. Conclusions: The pilot study demonstrated that the effects of sample parameters on cfDNA quality are consistent with existing literature, validating our methods and supporting the rationale for the LyRA study. These features may prove valuable when integrated with genomic data to identify high-risk microenvironments. Recent research has shown that a majority of cfDNA in tumor patients originates from non-malignant cells and current methods often overlook potentially important patterns in this so-called 'discard cfDNA.' By focusing on these overlooked patterns, with an emphasis on risk stratification, the LyRA test could shift clinical management to a prevention-focused approach, serving as a model of testing and interception for DLBCL and other cancers in dogs, and potentially in humans. Citation Format: Lauren E Burt, Amy E Treeful, Mitzi Lewellen, Kimberly Demos-Davies, Andrea Chehadeh, Sara Pracht, Ashley J Schulte, Caitlin Feiock, Julia Medland, Davis Seelig, Kelly M Makielski, Ali Khammanivong, Antonella Borgatti, Michael S Henson, Michael Linden, Daniel Vallera, Gary R Cutter, Aaron L Sarver, Jaime F Modiano. Identifying patterns of cell-free DNA associated with elevated risk of diffuse large B-cell lymphoma in adult dogs [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A051.

A single upstream mutation of whiB7 underlies amikacin and clarithromycin resistance in Mycobacterium abscessus.

We aimed to investigate the molecular mechanisms underlying the survival of Mycobacterium abscessus when faced with antibiotic combination therapy. By conducting evolution experiments and whole genome sequencing (WGS), we sought to identify genetic variants associated with stress response mechanisms, with a particular focus on drug survival and resistance. We conducted evolution experiments on M. abscessus, exposing the bacteria to a combination therapy of amikacin and rifabutin. Genetic mutations associated with increased antibiotic survival and altered susceptibility were subsequently identified by WGS. We focused on mutations that contribute to stress response mechanisms and tolerance. Of particular interest was a novel frameshift mutation in MAB_3509c, a gene of unknown function within the upstream open reading frame of whiB7. A MAB_3509c knockout mutant was constructed, and expression of downstream drug resistance genes was assessed by RT-qPCR. Mutation of MAB_3509c results in increased RNA levels of whiB7 and downstream stress response genes such as eis2, which is responsible for aminoglycoside resistance. Our findings demonstrate the importance of whiB7 in the adaptive stress response in M. abscessus. Moreover, our results highlight the complexity of M. abscessus adapting to drug stress and underscore the need for further research.

Genomic sequencing and neutralizing serological profiles during acute dengue infection: A 2017 cohort study in Nepal.

Dengue virus (DENV) is a mosquito-borne flavivirus that poses a threat to nearly 50% of the global population. DENV has been endemic in Nepal since 2006; however, little is known about how DENV is evolving or the prevalence of anti-DENV immunity within the Nepalese population. To begin to address these gaps, we performed a serologic and genetic study of 49 patients from across Nepal who presented at central hospitals during the 2017 dengue season with suspected DENV infection. Of the 49 subjects assessed, 21 (43%) were positive for DENV NS1 antigen; of these; 5 were also anti-DENV IgM+ IgG+; 7 were DENV IgM+ IgG-, 2 were IgM- IgG+, and 7 were IgM-IgG- by specific ELISAs. Seven of the 21 NS1 positive sera were RNA positive by RT-PCR (six DENV2, one DENV3), suggesting that DENV2 was the dominant serotype in our cohort. Whole-genome sequencing of two DENV2 isolates showed similarity with strains circulating in Singapore in 2016, and the envelope genes were also similar to strains circulating in India in 2017. DENV-neutralizing antibodies (nAbs) were present in 31 of 47 sera tested (66%); among these, 20, 24, 26, and 12 sera contained nAbs against DENV1, 2, 3, and 4 serotypes, respectively. Additionally, 27 (58%) samples had nAbs against multiple serotypes (2 or more). Serology analysis suggested that 12 (26%) and 19 (40%) of the 47 subjects were experiencing primary and secondary DENV infections, respectively. Collectively, our results provide evidence for current and/or past exposure to multiple DENV serotypes in our cohort. These data suggest that expanded local surveillance of circulating DENV genotypes and population immunity will be important to effectively manage and mitigate future dengue outbreaks in Nepal.

Abstract B028: Analytical validation of tumor-informed whole genome sequencing analyses for detection of molecular residual disease in solid tumors

Abstract Introduction: Across all patients diagnosed with solid tumors, approximately two-thirds initially present with locoregional disease and are potentially eligible for curative-intent intervention. However, there is a significant subset of patients for which residual tumor cells remain afterwards, potentially leading to disease recurrence. It has been reported that these residual tumor cells, representative of molecular residual disease (MRD), can be detected through analyses of circulating tumor DNA (ctDNA), often earlier than can be achieved through radiographic imaging or clinical presentation. Methods: Labcorp Plasma Detect was utilized to analyze patient-matched tumor, germline (white blood cells), and cell-free or contrived DNA samples through whole genome sequencing at approximately 80x, 40x, and 30x depth, respectively. Raw sequencing data were demultiplexed and trimmed for read quality, then aligned to the hg19 human reference genome. Tumor-specific single nucleotide variants were identified from the tumor and germline datasets and used to determine the presence of cancer within cell-free or contrived DNA samples through a random forest machine learning model. ctDNA status was determined based on the level of signal compared to a reference cohort of noncancerous donor plasma samples (n=80). Results: Analytical specificity was determined through analysis of five noncancerous donor plasma samples with five replicates each, evaluated against 60 somatic mutation profiles obtained from patients with stage III colon cancer, demonstrating a specificity of 99.4% (835/840). Analytical sensitivity was assessed using three contrived cell line models from breast and melanoma tumors, analyzed across three tumor content levels with five replicates at each level and resulted in a limit of detection of 0.005% tumor content using a 100% hit-rate model. Precision, repeatability, and reproducibility were demonstrated within and across runs, operators, and instruments, resulting in a 100% detection rate at 0.05% tumor content with a coefficient of variation of 8.5%. Finally, analytical accuracy compared to an independent tumor-informed ctDNA MRD approach was determined through analysis of 39 clinical cases obtained from patients with bladder, breast, colorectal, and lung cancer, yielding a positive percent agreement of 91% (10/11) and negative percent agreement of 100% (24/24). The prognostic value of ctDNA MRD was demonstrated in stage III colon cancer and clinical validation results for HPV-negative head and neck squamous cell carcinoma will be presented separately. Conclusions: There is significant potential for tumor-informed, non- bespoke MRD approaches for ctDNA detection across a broad range of solid tumor types and curative-intent clinical settings. These data demonstrate the analytical performance of Labcorp Plasma Detect in a CAP/CLIA laboratory for pan-solid tumor research use together with investigational use in early stage colon cancer to support the interventional MEDOCC-CrEATE clinical trial. Citation Format: Kaitlin Victor, Andrew Georgiadis, Ellen Verner, Antoine Simmons, David Riley, James R White, Christopher Greco, Liam Cox, Jesse Fox, Jennifer B Jackson, Eric A Severson, Brian J Caveney, Marcia Eisenberg, Taylor J Jensen, Shakti Ramkissoon, Samuel V Angiuoli, Amy Greer, Kenneth Valkenburg, Mark Sausen. Analytical validation of tumor-informed whole genome sequencing analyses for detection of molecular residual disease in solid tumors [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B028.

Abstract B058: Novel machine learning lung cancer classifier shows significant correlation with cancer-specific fragmentomics features

Abstract Lung cancer is the second most prevalent cancer, and the leading cause of cancer-related deaths, in the US. Since the survival rate of patients diagnosed with lung cancer at earlier stages is dramatically higher than those diagnosed at later stages, technologies that shift lung cancer diagnosis to an earlier stage would be expected to improve survival rates. Fragmentomics, the study of short circulating cell-free DNA (cfDNA) fragments in the blood, is an emerging field in cancer diagnostics that has the potential to catalyze such a shift. Studies have shown that fragmentomics features, such as fragment sizes and end motifs, can be used to discriminate normal cfDNA from circulating tumor DNA (ctDNA) generated through tumor necrosis and apoptosis. Using this knowledge, machine learning tools, trained to identify ctDNA-specific features, are being leveraged in novel liquid biopsy assays to improve early-stage lung cancer detection. To create a machine learning classifier that detects discrete signals of ctDNA in individuals with lung cancer, Genece Health utilized low pass (∼3x coverage) whole genome sequencing (Illumina) of plasma extracted cfDNA from >400 lung cancer samples and >1,000 negative controls. This data was used to generate our novel FEMS (Fragment End Motifs and Sizes) dataframe, containing >30,000 features, by combining fragment lengths with their corresponding 5’ 4bp end motifs. The FEMS dataframe was then used to train a neural network machine learning classifier. To evaluate the performance of our machine learning classifier, we investigated whether the classifier scores correlated with well-known hallmarks of ctDNA. First, consistent with published ctDNA characteristics, we observed significant (p<0.01) enrichment of fragment sizes ≤167bp in lung cancer samples. Concordantly, our classifier scores are significantly correlated with this fragment size enrichment. Also, both fragment size enrichment and our classifier scores are correlated with lung cancer stage, with higher enrichment and higher scores observed in later stages. Next, we show that our classifier scores are correlated with specific end motif enrichments or depletions in lung cancer samples. For example, in lung cancer samples, CCCA end motifs were significantly enriched (p<0.01) in fragment sizes 120-140bp (max at 128bp), while significantly depleted in fragment sizes 190-210bp (min at 204bp). Once again, our classifier scores were significantly correlated with both the enrichment of this end motif in short fragments and its depletion in long fragments. Last, we assessed the classifier’s performance in an independent cohort (46 lung cancer and 132 non-cancer samples) and show 89.2% sensitivity with 90.4% specificity (auROC=0.957). The correlation observed between our classifier scores and published ctDNA characteristics, combined with the strong performance in an independent cohort, suggests that Genece’s novel machine learning classifier is effectively leveraging ctDNA-specific fragmentomics features for the identification of lung cancer in patient samples. Citation Format: Carlos Guzman, Michael L Salmans, Mengchi Wang, Byung In Lee, Andrew R Carson. Novel machine learning lung cancer classifier shows significant correlation with cancer-specific fragmentomics features [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B058.

Abstract B035: Detection and monitoring of translocation renal cell carcinoma via epigenomic profiling of cell-free DNA

Abstract We evaluated the ability of plasma chromatin immunoprecipitation and sequencing (ChIP-seq) and methylated DNA immunoprecipitation sequencing (MeDIP-seq) to detect translocation renal cell carcinoma (tRCC), to distinguish tRCC from clear cell renal cell carcinoma (ccRCC), and to monitor the disease evolution. tRCC is an aggressive subtype of kidney cancer usually driven by a fusion involving the TFE3 gene. Due to histologic overlap with other subtypes of RCC, tRCC is frequently misclassified. Methods for accurate diagnosis and detection of this molecularly distinct entity are therefore a pressing need. Most cell-free DNA (cfDNA) technologies are genomic-based and not optimized for tRCC as molecular fusions are not easily detected in circulating tumor DNA (ctDNA), and 50% of tRCC cases do not have other alteration. Epigenomic profiling of ctDNA via ChIP-seq has recently emerged as a powerful tool to detect and molecularly subtype cancers and may offer a more sensitive and specific detection of fusion- driven tumors. We first identified differentially methylated regions (DMRs) and regulatory elements (REs) specific to tRCC vs. ccRCC via MeDIP-seq and H3K4me3/H3K27Ac ChIP-Seq, of 4 tRCC and 5 ccRCC cell lines. We also identified TFE3 transcription factor binding sites (TFBS) via TFE3 ChIP-seq in 3 tRCC cell lines. We collected 30 plasma samples from metastatic patients with tRCC, 12 with ccRCC and 9 healthy individuals. Ultra low pass whole genome sequencing (ulpWGS) was performed to infer ctDNA fraction (TF), and cfMeDIP-seq, H3K4me3/H3K27ac cfChIP-seq for epigenomic profiling. Signal at tRCC-specific regions derived from cell lines profiling was aggregated for each mark and normalized to common active REs/methylated regions and used to discriminate tRCC vs. ccRCC vs. healthy plasma. Classification performance was evaluated using the area under the receiver operating characteristic (AUROC) curve. Overall 10/30 tRCC and 5/12 ccRCC samples had >3% TF by ulpWGS. H3K4me3 cfChIP-seq signal was significantly higher in all tRCC samples compared to healthy plasma (AUROC=1) at tRCC-specific REs and TFE3 TFBS. Furthermore, H3K27ac and H3K4me3 cfChIP-seq signal in plasma were also significantly higher at tRCC-specific REs and TFE3 TFBS in tRCC samples compared to ccRCC (AUROC=0.86-0.89). A cfDNA-based classifier combining the four scores distinguished tRCC from ccRCC with an AUROC=0.94. MeDIP-seq could not discriminate between tRCC and ccRCC. Moreover, three patients had multiple time points, and the integrated cfChIP H3K4me3/H3K27Ac score was able to accurately monitor the evolution of the disease. In conclusion, although a majority of tRCC plasma samples profiled had TF <3%, all could be distinguished from healthy samples on the basis of cfChIP. The combined score of H3K27ac and H3K4me3 cfChIP-Seq was also discriminatory for tRCC vs. ccRCC. Epigenomic profiling of cfDNA appears as a powerful tool for both detection of tRCC and discrimination from ccRCC/healthy plasma, with potential implications for diagnosis, disease monitoring and guiding therapy. Citation Format: Simon Garinet, Karl Semaan, John Caniff, Noa Phillips, Jacob Berchuck, Jiao Li, Kevin Lyons, Ananthan Sadagopan, Brad Fortunato, Mary Lee, Jack Horst, Rachel Trowbridge, Ji-Heui Seo, Matthew Freedman, Toni Choueiri, Sylvan Baca, Srinivas Viswanathan. Detection and monitoring of translocation renal cell carcinoma via epigenomic profiling of cell-free DNA [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B035.

Serotypes, Antimicrobial Susceptibility, and Potential Mechanisms of Resistance Gene Transfer in Erysipelothrix rhusiopathiae Strains from Waterfowl in Poland

Erysipelas is a significant problem in the waterfowl farming in Poland, and information on the characteristics of the Erysipelothrix rhusiopathiae strains causing this disease is limited. In this study, we determined the serotypes, antimicrobial susceptibility, and potential mechanisms of resistance gene transfer in E. rhusiopathiae isolates (n = 60) from domestic geese and ducks. We also developed a multiplex PCR for the detection of resistance genes. The antimicrobial susceptibility of the isolates was assessed using the broth microdilution method. Resistance genes, integrative conjugative element (ICE)-specific genes, phage-specific genes, and serotype determinants were detected by PCR. Multilocus sequence typing (MLST) was performed for selected resistant strains. The comparative analyses included 260 E. rhusiopathiae strains whose whole genome sequences (WGSs) are publicly available. E. rhusiopathiae isolates represented 7 serotypes, among which serotypes 5 (38.3%) and 1b (28.3%) were the most common. All strains were susceptible to β-lactams, and the vast majority of them were resistant to tetracycline (85%) and enrofloxacin (80%). The percentages of isolates resistant to other antimicrobials used ranged from 3.3% to 16.7%. Ten isolates (16.7%) were found to be multidrug resistant (MDR). The genotypic resistance profiles of the E. rhusiopathiae strains corresponded to their phenotypic resistance, and the amplification patterns obtained using the 10-plex PCR developed in this study were fully consistent with the results of single PCRs. The most prevalent resistance gene was tetM. In enrofloxacin-resistant strains, nonsynonymous mutations in the gyrA and parC genes were identified. The presence of ICE-specific genes was confirmed in resistant strains, and in MDR isolates of serotype 8 that represented sequence type (ST) 113, prophage DNA (Javan630-like) linked to the lsaE gene was additionally detected. The results indicate that β-lactam antibiotics should be the first choice for the treatment of waterfowl erysipelas in Poland. ICEs, including a transposon from the Tn916/Tn1545 family, and bacteriophages are most likely responsible for the transfer of resistance genes in E. rhusiopathiae.

Abstract A043: Tumor-informed whole genome sequencing of ctDNA from lymph and plasma detects molecular residual disease in HPV-negative head and neck cancer patients

Abstract Introduction: Recurrence after tumor resection is a major cause of failure in HPV-negative head and neck squamous cell carcinoma (HNSCC), with up to 50% of patients recurring within 2 years. There is unmet need for an accurate diagnostic test that can predict risk of recurrence prior to adjuvant therapy selection. We previously demonstrated that ctDNA in lymphatic exudate collected via surgical drains (“lymph”) accurately identifies molecular residual disease (MRD) and outperforms plasma in HNSCC patients using a targeted sequencing (TS) approach. While TS assays are robust and sensitive, they have key limitations, namely limited ctDNA features, higher cost, longer turnaround times, and lower throughput. Here we evaluate whole genome sequencing (WGS) for detection of MRD in both lymph and plasma sampled 24 hours after surgery. Methods: Lymph, plasma, and whole blood were collected 24 hours postoperatively from 30 HPV-negative HNSCC patients (stages I – IV) along with resected tumor. To detect ctDNA, we utilized WGS analyses of patient matched tumor, germline (blood), and cfDNA from lymph or plasma samples at approximately 150x, 30x, and 30x depth, respectively. Tumor- specific single nucleotide variants were identified from tumor and germline datasets, from which a candidate variant set was used to determine the presence of ctDNA within lymph or plasma DNA through a random forest machine learning model. ctDNA status was determined based on the level of signal compared to a reference population of noncancerous donor plasma samples (n=80). 5 tumors and 1 lymph sample failed QC; in total, 24 patients remained, yielding 10 patients with disease recurrence and 14 with no evidence of disease with >1 year of follow-up. The Kaplan-Meier (KM) estimator with log-rank test and Cox proportional-hazards model were used for survival analyses. Results: KM survival analyses indicated that WGS of lymph ctDNA accurately predicts recurrence (sensitivity (SN) = 50%, specificity (SP) = 93%; p = 0.003, Hazard ratio (HR) = 6.0), consistent with previously published results using a TS MRD approach in this cohort (SN = 80%, SP = 64%, p = 0.02, HR = 5.3). WGS of lymph ctDNA outperformed plasma at this timepoint (SN = 30%, SP = 93%, p = 0.03, HR = 4.2). WGS of lymph ctDNA also showed enhanced performance in locoregional relapse (n=21, SN = 57%, SP = 93%; p = 0.006, HR = 6.7). Finally, the ctDNA fraction of proximal lymph was higher than paired plasma in all locoregional recurrences (n=7), while peripheral plasma had a higher ctDNA fraction in 2 out of 3 distant recurrences. Conclusions: Tumor-informed WGS analysis of lymph ctDNA represents a robust MRD approach in HPV-negative HNSCC. Lymph outperforms time-matched plasma for prediction of recurrence, particularly in patients with locoregional relapse. Accurate MRD identification in the immediate post-surgical timeframe suggests that lymph analysis has potential to augment pathology and longitudinal plasma testing to provide more personalized adjuvant treatment decision-making in patients with HPV-negative HNSCC. Citation Format: Zhuosheng Gu, Seka Lazare, Noah Earland, Marra S Francis, Adam Harmon, Megan Long, Amy Greer, Ellen Verner, Andrew Georgiadis, Mark Sausen, Shakti Ramkissoon, Jose P Zevallos, Aadel A Chaudhuri, Taylor J Jensen, Wendy Winckler. Tumor-informed whole genome sequencing of ctDNA from lymph and plasma detects molecular residual disease in HPV-negative head and neck cancer patients [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A043.

Antibiotic efficacy and resistance patterns of urinary tract infection-causing bacteria in dogs and resistome of multidrug-resistant Klebsiella pneumoniae via whole genome sequencing in South Korea

Bacterial urinary tract infections (UTIs) are prevalent in dogs and necessitate antibiotic intervention. However, the emergence of multidrug-resistant (MDR) bacteria poses significant challenges to antibiotic therapy. Although fosfomycin has been demonstrated to achieve and maintain high concentrations in urine, suggesting its potential for treating UTIs in dogs, its efficacy and the resistance profiles of urinary pathogens from canine UTIs remain elusive. Therefore, this study was conducted to investigate the antibiotic susceptibility of bacterial pathogens isolated from companion dogs with UTIs, with a particular focus on their susceptibility and resistance to fosfomycin. A total of 70 isolates from urine samples were analyzed, of which Escherichia coli (n = 18), Proteus mirabilis (n = 9), Klebsiella pneumoniae (n = 5), and Staphylococcus pseudintermedius (n = 5) were predominant. Resistance to erythromycin was most prevalent (94.59%), followed by clindamycin (91.89%) and ampicillin (78.37%), whereas the lowest resistance rate was observed for amikacin (5.40%). Resistance to fosfomycin was observed in 15 out of the 37 predominant isolates (40.54%), including all K. pneumoniae isolates (100%). All isolates, except 4 E. coli strains, were categorized as MDR (33 out of 37; 89.18%). The resistance rates for amoxicillin/clavulanic acid and trimethoprim-sulfamethoxazole, which are common first-line antibiotics for canine UTIs, were 48.64 and 56.75%, respectively. Whole-genome sequencing of K. pneumoniae isolates, which exhibited high resistance to fosfomycin, revealed multiple antibiotic resistance genes, with chromosomal fosA present in all isolates. Among the 27 dogs with recurrent infection included in this study, 2 were administered fosfomycin, resulting in clinical remission, as evidenced by negative urine culture tests. Overall, this study is the first to demonstrate the importance of assessing fosfomycin resistance profile for optimal treatment of canine UTIs, particularly in cases involving MDR strains.