Structural Alterations Driving Castration-Resistant Prostate Cancer Revealed by Linked-Read Genome Sequencing.

Nearly all prostate cancer deaths are from metastatic castration-resistant prostate cancer (mCRPC), but there have been few whole-genome sequencing (WGS) studies of this disease state. We performed linked-read WGS on 23 mCRPC biopsy specimens and analyzed cell-free DNA sequencing data from 86 patients with mCRPC. In addition to frequent rearrangements affecting known prostate cancer genes, we observed complex rearrangements of the AR locus in most cases. Unexpectedly, these rearrangements include highly recurrent tandem duplications involving an upstream enhancer of AR in 70%-87% of cases compared with <2% of primary prostate cancers. A subset of cases displayed AR or MYC enhancer duplication in the context of a genome-wide tandem duplicator phenotype associated with CDK12 inactivation. Our findings highlight the complex genomic structure of mCRPC, nominate alterations that may inform prostate cancer treatment, and suggest that additional recurrent events in the non-coding mCRPC genome remain to be discovered.

Circulating tumor DNA in advanced solid tumors: Clinical relevance and future directions.

The application of genomic profiling assays using plasma circulating tumor DNA (ctDNA) is rapidly evolving in the management of patients with advanced solid tumors. Diverse plasma ctDNA technologies in both commercial and academic laboratories are in routine or emerging use. The increasing integration of such testing to inform treatment decision making by oncology clinicians has complexities and challenges but holds significant potential to substantially improve patient outcomes. In this review, the authors discuss the current role of plasma ctDNA assays in oncology care and provide an overview of ongoing research that may inform real-world clinical applications in the near future.

Aspiring toward equitable benefits from genomic advances to individuals of ancestrally diverse backgrounds.

Advancements in genomic technologies have shown remarkable promise for improving health trajectories. The Human Genome Project has catalyzed the integration of genomic tools into clinical practice, such as disease risk assessment, prenatal testing and reproductive genomics, cancer diagnostics and prognostication, and therapeutic decision making. Despite the promise of genomic technologies, their full potential remains untapped without including individuals of diverse ancestries and integrating social determinants of health (SDOHs). The NHGRI launched the 2020 Strategic Vision with ten bold predictions by 2030, including "individuals from ancestrally diverse backgrounds will benefit equitably from advances in human genomics." Meeting this goal requires a holistic approach that brings together genomic advancements with careful consideration to healthcare access as well as SDOHs to ensure that translation of genetics research is inclusive, affordable, and accessible and ultimately narrows rather than widens health disparities. With this prediction in mind, this review delves into the two paramount applications of genetic testing-reproductive genomics and precision oncology. When discussing these applications of genomic advancements, we evaluate current accessibility limitations, highlight challenges in achieving representativeness, and propose paths forward to realize the ultimate goal of their equitable applications.

Bridging biological cfDNA features and machine learning approaches.

Liquid biopsies (LBs), particularly using circulating tumor DNA (ctDNA), are expected to revolutionize precision oncology and blood-based cancer screening. Recent technological improvements, in combination with the ever-growing understanding of cell-free DNA (cfDNA) biology, are enabling the detection of tumor-specific changes with extremely high resolution and new analysis concepts beyond genetic alterations, including methylomics, fragmentomics, and nucleosomics. The interrogation of a large number of markers and the high complexity of data render traditional correlation methods insufficient. In this regard, machine learning (ML) algorithms are increasingly being used to decipher disease- and tissue-specific signals from cfDNA. Here, we review recent insights into biological ctDNA features and how these are incorporated into sophisticated ML applications.

Current and new frontiers in hereditary cancer surveillance: Opportunities for liquid biopsy.

At least 5% of cancer diagnoses are attributed to a causal pathogenic or likely pathogenic germline genetic variant (hereditary cancer syndrome-HCS). These individuals are burdened with lifelong surveillance monitoring organs for a wide spectrum of cancers. This is associated with substantial uncertainty and anxiety in the time between screening tests and while the individuals are awaiting results. Cell-free DNA (cfDNA) sequencing has recently shown potential as a non-invasive strategy for monitoring cancer. There is an opportunity for high-yield cancer early detection in HCS. To assess clinical validity of cfDNA in individuals with HCS, representatives from eight genetics centers from across Canada founded the CHARM (cfDNA in Hereditary and High-Risk Malignancies) Consortium in 2017. In this perspective, we discuss operationalization of this consortium and early data emerging from the most common and well-characterized HCSs: hereditary breast and ovarian cancer, Lynch syndrome, Li-Fraumeni syndrome, and Neurofibromatosis type 1. We identify opportunities for the incorporation of cfDNA sequencing into surveillance protocols; these opportunities are backed by examples of earlier cancer detection efficacy in HCSs from the CHARM Consortium. We seek to establish a paradigm shift in early cancer surveillance in individuals with HCSs, away from highly centralized, regimented medical screening visits and toward more accessible, frequent, and proactive care for these high-risk individuals.

Language model enables end-to-end accurate detection of cancer from cell-free DNA.

We present a language model Affordable Cancer Interception and Diagnostics (ACID) that can achieve high classification performance in the diagnosis of cancer exclusively from using raw cfDNA sequencing reads. We formulate ACID as an autoregressive language model. ACID is pretrained with language sentences that are obtained from concatenation of raw sequencing reads and diagnostic labels. We benchmark ACID against three methods. On testing set subjected to whole-genome sequencing, ACID significantly outperforms the best benchmarked method in diagnosis of cancer [Area Under the Receiver Operating Curve (AUROC), 0.924 versus 0.853; P < 0.001] and detection of hepatocellular carcinoma (AUROC, 0.981 versus 0.917; P < 0.001). ACID can achieve high accuracy with just 10 000 reads per sample. Meanwhile, ACID achieves the best performance on testing sets that were subjected to bisulfite sequencing compared with benchmarked methods. In summary, we present an affordable, simple yet efficient end-to-end paradigm for cancer detection using raw cfDNA sequencing reads.

Large language model produces high accurate diagnosis of cancer from end-motif profiles of cell-free DNA.

Instruction-tuned large language models (LLMs) demonstrate exceptional ability to align with human intentions. We present an LLM-based model-instruction-tuned LLM for assessment of cancer (iLLMAC)-that can detect cancer using cell-free deoxyribonucleic acid (cfDNA) end-motif profiles. Developed on plasma cfDNA sequencing data from 1135 cancer patients and 1106 controls across three datasets, iLLMAC achieved area under the receiver operating curve (AUROC) of 0.866 [95% confidence interval (CI), 0.773-0.959] for cancer diagnosis and 0.924 (95% CI, 0.841-1.0) for hepatocellular carcinoma (HCC) detection using 16 end-motifs. Performance increased with more motifs, reaching 0.886 (95% CI, 0.794-0.977) and 0.956 (95% CI, 0.89-1.0) for cancer diagnosis and HCC detection, respectively, with 64 end-motifs. On an external-testing set, iLLMAC achieved AUROC of 0.912 (95% CI, 0.849-0.976) for cancer diagnosis and 0.938 (95% CI, 0.885-0.992) for HCC detection with 64 end-motifs, significantly outperforming benchmarked methods. Furthermore, iLLMAC achieved high classification performance on datasets with bisulfite and 5-hydroxymethylcytosine sequencing. Our study highlights the effectiveness of LLM-based instruction-tuning for cfDNA-based cancer detection.

Oxidized mitochondrial DNA: a protective signal gone awry.

Despite the emergence of mitochondria as key regulators of innate immunity, the mechanisms underlying the generation and release of immunostimulatory alarmins by stressed mitochondria remains nebulous. We propose that the major mitochondrial alarmin in myeloid cells is oxidized mitochondrial DNA (Ox-mtDNA). Fragmented Ox-mtDNA enters the cytosol where it activates the NLRP3 inflammasome and generates IL-1ß, IL-18, and cGAS-STING to induce type I interferons and interferon-stimulated genes. Inflammasome activation further enables the circulatory release of Ox-mtDNA by opening gasdermin D pores. We summarize new data showing that, in addition to being an autoimmune disease biomarker, Ox-mtDNA converts beneficial transient inflammation into long-lasting immunopathology. We discuss how Ox-mtDNA induces short- and long-term immune activation, and highlight its homeostatic and immunopathogenic functions.

Epigenetic liquid biopsies: a novel putative biomarker in immunology and inflammation.

Immune and inflammatory processes occurring within tissues are often undetectable by blood cell counts, standard circulating biomarkers, or imaging, representing an unmet biomedical need. Here, we outline recent advances indicating that liquid biopsies can broadly inform human immune system dynamics. Nucleosome-size fragments of cell-free DNA (cfDNA) released from dying cells into blood contain rich epigenetic information such as methylation, fragmentation, and histone mark patterns. This information allows to infer the cfDNA cell of origin, as well as pre-cell death gene expression patterns. We propose that the analysis of epigenetic features of immune cell-derived cfDNA can shed light on immune cell turnover dynamics in healthy people, and inform the study and diagnosis of cancer, local inflammation, infectious or autoimmune diseases, as well as responses to vaccination.

Bioinspired nanogels as cell-free DNA trapping and scavenging organelles for rheumatoid arthritis treatment.

Excessive cell-free DNA (cfDNA) in the serum and synovium is considered a causative factor of rheumatoid arthritis (RA). Thus, cfDNA scavenging by using cationic polymers has been an effective therapeutic avenue, while these stratagems still suffer from systemic toxicity and unstable capture of cfDNA. Here, inspired by the biological charge-trapping effects and active degradation function of enzyme-containing organelles in vivo, we proposed a cationic peptide dendrimer nanogel with deoxyribonuclease I (DNase I) conjugation for the treatment of RA. Benefitting from their naturally derived peptide components, the resultant nanogels were highly biocompatible. More attractively, by tailoring them with a larger size and higher surface charge density, these cationic nanogels could achieve the fastest targeting capability, highest accumulation amounts, longer persistence time, and superior DNA scavenging capacity in inflamed joints. Based on these features, we have demonstrated that the organelle mimicking cationic nanogels could significantly down-regulate toll-like receptor (TLR)-9 signaling pathways and attenuate RA symptoms in collagen-induced arthritis mice. These results make the bioinspired DNase I conjugated cationic nanogels an ideal candidate for treating RA and other immune dysregulation diseases.

Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring.

Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high-resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort, for sensitive and accurate tissue deconvolution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical utilities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring.

Lesion Shedding Model: unraveling site-specific contributions to ctDNA.

Sampling circulating tumor DNA (ctDNA) using liquid biopsies offers clinically important benefits for monitoring cancer progression. A single ctDNA sample represents a mixture of shed tumor DNA from all known and unknown lesions within a patient. Although shedding levels have been suggested to hold the key to identifying targetable lesions and uncovering treatment resistance mechanisms, the amount of DNA shed by any one specific lesion is still not well characterized. We designed the Lesion Shedding Model (LSM) to order lesions from the strongest to the poorest shedding for a given patient. By characterizing the lesion-specific ctDNA shedding levels, we can better understand the mechanisms of shedding and more accurately interpret ctDNA assays to improve their clinical impact. We verified the accuracy of the LSM under controlled conditions using a simulation approach as well as testing the model on three cancer patients. The LSM obtained an accurate partial order of the lesions according to their assigned shedding levels in simulations and its accuracy in identifying the top shedding lesion was not significantly impacted by number of lesions. Applying LSM to three cancer patients, we found that indeed there were lesions that consistently shed more than others into the patients' blood. In two of the patients, the top shedding lesion was one of the only clinically progressing lesions at the time of biopsy suggesting a connection between high ctDNA shedding and clinical progression. The LSM provides a much needed framework with which to understand ctDNA shedding and to accelerate discovery of ctDNA biomarkers. The LSM source code has been available in the IBM BioMedSciAI Github (https://github.com/BiomedSciAI/Geno4SD).

Identify gestational diabetes mellitus by deep learning model from cell-free DNA at the early gestation stage.

Gestational diabetes mellitus (GDM) is a common complication of pregnancy, which has significant adverse effects on both the mother and fetus. The incidence of GDM is increasing globally, and early diagnosis is critical for timely treatment and reducing the risk of poor pregnancy outcomes. GDM is usually diagnosed and detected after 24 weeks of gestation, while complications due to GDM can occur much earlier. Copy number variations (CNVs) can be a possible biomarker for GDM diagnosis and screening in the early gestation stage. In this study, we proposed a machine-learning method to screen GDM in the early stage of gestation using cell-free DNA (cfDNA) sequencing data from maternal plasma. Five thousand and eighty-five patients from north regions of Mainland China, including 1942 GDM, were recruited. A non-overlapping sliding window method was applied for CNV coverage screening on low-coverage (~0.2×) sequencing data. The CNV coverage was fed to a convolutional neural network with attention architecture for the binary classification. The model achieved a classification accuracy of 88.14%, precision of 84.07%, recall of 93.04%, F1-score of 88.33% and AUC of 96.49%. The model identified 2190 genes associated with GDM, including DEFA1, DEFA3 and DEFB1. The enriched gene ontology (GO) terms and KEGG pathways showed that many identified genes are associated with diabetes-related pathways. Our study demonstrates the feasibility of using cfDNA sequencing data and machine-learning methods for early diagnosis of GDM, which may aid in early intervention and prevention of adverse pregnancy outcomes.

MIMESIS: minimal DNA-methylation signatures to quantify and classify tumor signals in tissue and cell-free DNA samples.

DNA-methylation alterations are common in cancer and display unique characteristics that make them ideal markers for tumor quantification and classification. Here we present MIMESIS, a computational framework exploiting minimal DNA-methylation signatures composed by a few dozen informative DNA-methylation sites to quantify and classify tumor signals in tissue and cell-free DNA samples. Extensive analyses of multiple independent and heterogenous datasets including >7200 samples demonstrate the capability of MIMESIS to provide precise estimations of tumor content and to enable accurate classification of tumor type and molecular subtype. To assess our framework for clinical applications, we designed a MIMESIS-informed assay incorporating the minimal signatures for breast cancer. Using both artificial samples and clinical serial cell-free DNA samples from patients with metastatic breast cancer, we show that our approach provides accurate estimations of tumor content, sensitive detection of tumor signal and the ability to capture clinically relevant molecular subtype in patients' circulation. This study provides evidence that our extremely parsimonious approach can be used to develop cost-effective and highly scalable DNA-methylation assays that could support and facilitate the implementation of precision oncology in clinical practice.

Liquid Biopsy Based on Cell-Free DNA and RNA.

This review delves into the rapidly evolving landscape of liquid biopsy technologies based on cell-free DNA (cfDNA) and cell-free RNA (cfRNA) and their increasingly prominent role in precision medicine. With the advent of high-throughput DNA sequencing, the use of cfDNA and cfRNA has revolutionized noninvasive clinical testing. Here, we explore the physical characteristics of cfDNA and cfRNA, present an overview of the essential engineering tools used by the field, and highlight clinical applications, including noninvasive prenatal testing, cancer testing, organ transplantation surveillance, and infectious disease testing. Finally, we discuss emerging technologies and the broadening scope of liquid biopsies to new areas of diagnostic medicine.

Exploring mitochondrial DNA copy number in circulating cell-free DNA and extracellular vesicles across cardiovascular health status: A prospective case-control pilot study.

Cardiovascular disease (CVD) is a leading global cause of mortality, difficult to predict in advance. Evidence indicates that the copy number of mitochondrial DNA (mtDNAcn) in blood is altered in individuals with CVD. MtDNA released into circulation may act as a mediator of inflammation, a recognized factor in the development of CVD, in the long distance. This pilot study aims to test if levels of mtDNAcn in buffy coat DNA (BC-mtDNA), in circulating cellfree DNA (cf-mtDNA), or in DNA extracted from plasma extracellular vesicles (EV-mtDNA) are altered in CVD patients and if they can predict heart attack in advance. A group of 144 people with different CVD statuses (50 that had CVD, 94 healthy) was selected from the LifeLines Biobank according to the incidence of new cardiovascular event monitored in 6 years (50 among controls had heart attack after the basal assessment). MtDNAcn was quantified in total cf-DNA and EV-DNA from plasma as well as in buffy coat. EVs have been characterized by their size, polydispersity index, count rate, and zeta potential, by Dynamic Light Scattering. BC-mtDNAcn and cf-mtDNAcn were not different between CVD patients and healthy subjects. EVs carried higher mtDNAcn in subject with a previous history of CVD than controls, also adjusting the analysis for the EVs derived count rate. Despite mtDNAcn was not able to predict CVD in advance, the detection of increased EV-mtDNAcn in CVD patients in this pilot study suggests the need for further investigations to determine its pathophysiological role in inflammation.

Transcription and Remodeling Produce Asymmetrically Unwrapped Nucleosomal Intermediates.

Nucleosomes are disrupted during transcription and other active processes, but the structural intermediates during nucleosome disruption in vivo are unknown. To identify intermediates, we mapped subnucleosomal protections in Drosophila cells using Micrococcal Nuclease followed by sequencing. At the first nucleosome position downstream of the transcription start site, we identified unwrapped intermediates, including hexasomes that lack either proximal or distal contacts. Inhibiting topoisomerases or depleting histone chaperones increased unwrapping, whereas inhibiting release of paused RNAPII or reducing RNAPII elongation decreased unwrapping. Our results indicate that positive torsion generated by elongating RNAPII causes transient loss of histone-DNA contacts. Using this mapping approach, we found that nucleosomes flanking human CTCF insulation sites are similarly disrupted. We also identified diagnostic subnucleosomal particle remnants in cell-free human DNA data as a relic of transcribed genes from apoptosing cells. Thus identification of subnucleosomal fragments from nuclease protection data represents a general strategy for structural epigenomics.

The current status and trends of DNA extraction.

DNA extraction, playing an irreplaceable role in molecular biology as it is an essential step prior to various downstream biological analyses. Thus, the accuracy and reliability of downstream research outcomes depend largely on upstream DNA extraction methodology. However, with the advancement of downstream DNA detection techniques, the development of corresponding DNA extraction methods is lagging behind. The most innovative DNA extraction techniques are silica- or magnetic-based. Recent studies have demonstrated that plant fiber-based adsorbents (PF-BAs) have stronger DNA capturing ability than classic materials. Moreover, magnetic ionic liquid (MIL)-based DNA extraction has gathered attention lately, and extrachromosomal circular DNA (eccDNA), cell-free DNA (cfDNA), and microbial community DNA are current research hotspots. These require specific extraction methods, along with constant improvements in the way they are used. This review discusses the significance as well as the direction of innovation of DNA extraction methods to try to provide valuable references including current status and trends for DNA extraction.

Cell-Free DNA Maps Tissue Injury and Correlates with Disease Severity in Lung Transplant Candidates.

Rationale: Plasma cell-free DNA levels correlate with disease severity in many conditions. Pretransplant cell-free DNA may risk stratify lung transplant candidates for post-transplant complications. Objectives: To evaluate if pretransplant cell-free DNA levels and tissue sources identify patients at high risk of primary graft dysfunction and other pre- and post-transplant outcomes. Methods: This multicenter, prospective cohort study recruited 186 lung transplant candidates. Pretransplant plasma samples were collected to measure cell-free DNA. Bisulfite sequencing was performed to identify the tissue sources of cell-free DNA. Multivariable regression models determined the association between cell-free DNA levels and the primary outcome of primary graft dysfunction and other transplant outcomes, including Lung Allocation Score, chronic lung allograft dysfunction, and death. Measurements and Main Results: Transplant candidates had twofold greater cell-free DNA levels than healthy control patients (median [interquartile range], 23.7 ng/ml [15.1-35.6] vs. 12.9 ng/ml [9.9-18.4]; P < 0.0001), primarily originating from inflammatory innate immune cells. Cell-free DNA levels and tissue sources differed by native lung disease category and correlated with the Lung Allocation Score (P < 0.001). High pretransplant cell-free DNA increased the risk of primary graft dysfunction (odds ratio, 1.60; 95% confidence interval [CI], 1.09-2.46; P = 0.0220), and death (hazard ratio, 1.43; 95% CI, 1.07-1.92; P = 0.0171) but not chronic lung allograft dysfunction (hazard ratio, 1.37; 95% CI, 0.97-1.94; P = 0.0767). Conclusions: Lung transplant candidates demonstrate a heightened degree of tissue injury with elevated cell-free DNA, primarily originating from innate immune cells. Pretransplant plasma cell-free DNA levels predict post-transplant complications.

Cell-free DNA profiling informs all major complications of hematopoietic cell transplantation.

Allogeneic hematopoietic cell transplantation (HCT) provides effective treatment for hematologic malignancies and immune disorders. Monitoring of posttransplant complications is critical, yet current diagnostic options are limited. Here, we show that cell-free DNA (cfDNA) in blood is a versatile analyte for monitoring of the most important complications that occur after HCT: graft-versus-host disease (GVHD), a frequent immune complication of HCT, infection, relapse of underlying disease, and graft failure. We demonstrate that these therapeutic complications are informed from a single assay, low-coverage bisulfite sequencing of cfDNA, followed by disease-specific bioinformatic analyses. To inform GVHD, we profile cfDNA methylation marks to trace the cfDNA tissues-of-origin and to quantify tissue-specific injury. To inform infection, we implement metagenomic cfDNA profiling. To inform cancer relapse, we implement analyses of tumor-specific genomic aberrations. Finally, to detect graft failure, we quantify the proportion of donor- and recipient-specific cfDNA. We applied this assay to 170 plasma samples collected from 27 HCT recipients at predetermined timepoints before and after allogeneic HCT. We found that the abundance of solid-organ-derived cfDNA in the blood at 1 mo after HCT is predictive of acute GVHD (area under the curve, 0.88). Metagenomic profiling of cfDNA revealed the frequent occurrence of viral reactivation in this patient population. The fraction of donor-specific cfDNA was indicative of relapse and remission, and the fraction of tumor-specific cfDNA was informative of cancer relapse. This proof-of-principle study shows that cfDNA has the potential to improve the care of allogeneic HCT recipients by enabling earlier detection and better prediction of the complex array of complications that occur after HCT.