Biology subjects

Ni, M.

Publications and source records attributed to Ni, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Genome-wide evolutionary shifts shape the emergence of dominant circulating clones in Mycobacterium abscessus

Mycobacterium abscessus exhibits extensive genomic diversity, yet independently emerged dominant circulating clones (DCCs) have achieved widespread distribution. However, the evolutionary changes associated with their emergence and subsequent diversification remain poorly understood. Here, we analyzed 11,314 genomes from 30 countries using a conservative framework integrating population-wide validation and read-supported sequence reconstruction, defining a stable core genome of 3,001 genes. Across seven DCCs, accessory-gene gains consistently exceeded losses on the ancestral branches leading to clone formation, with recurrent functions involving metal homeostasis, metabolism, environmental sensing and stress responses. Recombination also contributed substantial variation in specific lineages; in DCC3, ancestral recombinant regions encompassed core genes involved in iron acquisition, respiratory metabolism and protein homeostasis. Following DCC establishment, the relative contribution of homologous recombination to mutation consistently declined during within-clone diversification, indicating a broad shift toward mutation-dominated core-genome evolution. Together, these findings suggest a recurring pattern in DCC evolution, with accessory-genome gain and lineage-specific recombination occurring during clone formation, followed by increasingly mutation-dominated diversification after establishment.

genomics↗

bioRxiv2026

An operon encoding two secreted nucleases mediates virulence in Methicillin-resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen that colonizes a significant proportion of humans, contains numerous virulence factors promoting infection, and continues to threaten human lives and burden healthcare systems globally. Many MRSA virulence factors are known to be either secreted or anchored on the outer leaflet of the cell surface. Although many virulence factors have been studied intensively in MRSA, there remains a significant proportion of secreted and surface proteins that are unstudied for their potential as virulence factors. We began with identifying proteins secreted from MRSA in axenic culture using an unbiased mass-spectrometry based approach. 2 secreted proteins thus identified mapped to an operon of 6 genes, SAUSA300_1739 to SAUSA300_1744. Mutation of each of the individual genes in the operon resulted in attenuation in a mouse model of subcutaneous infection. We demonstrate that two genes in the operon, SAUSA300_1739, and SAUSA300_1740, encode nucleases with DNase activity. Genetic analysis of the SAUSA300_1739 to SAUSA300_1744 operon across several Staphylococcus aureus strains indicate that the operon is highly conserved, highlighting its importance for virulence.

microbiology↗

bioRxiv2026

Genos-m: a foundation model for human-associated microbial genomes

Human-associated microbial genomes encode extensive strain-level diversity and niche-specific gene repertoires that are critical to host health. However, these complex sequence features remain difficult to capture using general-purpose DNA foundation models, highlighting the need for dedicated representation learning tailored to the human microbiome. Here, we introduce Genos-m, an open-source foundation model for human-associated microbial genome representation. Genos-m was pretrained on approximately 1.2 trillion nucleotide tokens from a curated microbial genome corpus, including human-associated prokaryotic isolates, high-quality metagenome-assembled genomes (MAGs) and bacteriophages, supplemented with GTDB species-level representative genomes to broaden prokaryotic taxonomic breadth. The model uses a sparsely activated Mixture-of-Experts (MoE) Transformer architecture, with 4.7 billion total parameters, approximately 330 million activated parameters per forward pass and a maximum context length of one million base pairs. We evaluated frozen Genos-m representations across short-sequence and gene-level tasks, biosynthetic gene cluster (BGC)-based regional sequence tasks, whole-genome strain phenotype prediction, and zero-shot transfer on prokaryote-related RNAfitness assays. Across these benchmarks, Genos-m consistently ranked among the leading comparison models, with the best performance in five of eight gene-fitness regression tasks and in BGC type classification. Using sparse autoencoders, we identified sparse features in Genos-m hidden activations that aligned with annotated ORFs, intergenic regions, and tRNA and rRNA loci. In downstream applications, Genos-m-derived genome-informed species representations in-corporated into a human microbiome self-supervised learning model improved colorectal cancer (CRC)-control classification over conventional species-abundance random forest models. Genos-m also generated stable sample-level embeddings from as few as 10,000 metagenomic reads, retaining gut microbial community structure that distinguished geographic origin and aligned with enterotypes defined from full-depth taxonomic profiles. Together, these results support Genos-m as a reusable representation model for microbial genomes and metagenomes, with conclusions bounded by the reported datasets, task definitions and evaluation protocols. Genos-m model weights, inference code, and usage documentation are publicly available on GitHub (https://github.com/BGI-HangzhouAI/Genos-m) and Hugging-Face (https://huggingface.co/BGI-HangzhouAI/Genos-m).

bioinformatics↗

bioRxiv2026

PanVariants: Best Practice for Pangenome-based Variant Calling Pipeline and Framework

BackgroundAlthough pangenome references offer richer population diversity compared to linear references, current mainstream pangenome-based variant callers are limited to detecting only known variants stored in the graph. To address this limitation, we developed PanVariants, a novel pipeline designed to improve the detection of both known and novel variants accurately. We systematically evaluated its performance against the traditional linear alignment solution (BWA+GATK/Manta) and the existing pangenome-aware solution (DRAGEN/PanGenie) in three contexts: small variants (SNVs/indels) and structural variants (SVs) accuracy in Genome in a Bottle samples, clinical detection on positive samples, and application in cohort-based joint calling. ResultsBy integrating k-mer-based and mapping-based methods, PanVariants significantly reduced variant errors (FPs + FNs), achieving a 73% reduction compared to BWA+GATK and a 45% reduction compared to DRAGEN for SNVs. Retraining the DeepVariant model with high-quality DNBSEQ data further decreased errors by 15%. For SVs detection, PanVariants attained an F1-score of 89.39%, markedly outperforming DRAGEN (68.18%) and BWA+Manta (58.33%), approaching long-read sequencing performance (95.22%). In validation using clinical positive samples, PanVariants successfully detected all expected pathogenic variants while PanGenie failed. In the cohort joint-calling analysis, PanVariants detected more variants, made fewer Mendelian inheritance errors, and gave better per-sample accuracy than GATK. ConclusionsPanVariants establishes a robust framework and best-practice pipeline for pangenome-based variant detection, achieving both sensitive novel variant discovery and high accuracy for SNVs, indels and SVs. Our systematic evaluation of optional processing steps and input variables offers practical guidance for users. Validated across diagnostic and population-based applications, our findings strongly support the transition from linear to pangenome references in future genomics.

bioinformatics↗

bioRxiv2026

UFMylation of Pyruvate Dehydrogenase Regulates Mitochondrial Metabolism

The ubiquitin-fold modifier 1 (UFM1) post-translational modification (PTM), or UFMylation, regulates protein homeostasis and is essential for human development. Yet the roles of the de-UFMylase, UFM1-specific peptidase 2 (UFSP2), which removes UFM1 from UFMylated proteins, remain poorly characterized. Here, we demonstrate that UFMylation and UFSP2 regulate mitochondrial metabolism. Quantitative proteomics in UFSP2-deficient cells revealed the accumulation of many proteins previously unknown to be impacted by UFMylation. These included components of the mitochondrial ribosome, electron transport chain (ETC), and pyruvate dehydrogenase (PDH) complex. Functional analyses demonstrated that excessive UFMylation in UFSP2-deficient cells increases mitochondrial respiration, glucose oxidation in the tricarboxylic acid (TCA) cycle, and PDH enzymatic activity. We identified dihydrolipoamide S-acetyltransferase (DLAT), the E2 component of PDH, as a direct UFMylation substrate, with lysine 118 (K118) as the primary conjugation site. Mutating K118 to arginine (K118R) abolished DLAT UFMylation and reduced pyruvate oxidation, identifying this modification as an activator of PDH. These findings reveal a UFMylation-based regulatory mechanism that controls mitochondrial function by inducing utilization of pyruvate as a TCA cycle fuel.

biochemistry↗

bioRxiv2026

Critical assessment of intratumor and low-biomass microbiome using long-read sequencing

The detection of low-biomass microbial DNA in human tissues is often confounded by contamination, as demonstrated in the debates over the existence of microbiomes in the placenta, brain, blood, and tumors. Here we show that genomic DNA fragment length serves as a discriminator: while genuine microbiome genomes have long genomic DNA fragments, contaminant DNA is typically short. Using germ-free mouse tissues with bacterial spike-ins and human cell lines, we developed a metric that normalizes microbial read length to host read length. Across multiple human tumor and normal tissues, we found genuine microbiome signals are largely limited to tissues with natural microbial exposure (e.g., gastrointestinal tract, cervix, vagina, skin), while other tissues (e.g. kidney, brain, blood, and placenta) showed no evidence of resident microbiome. These findings support DNA fragment length as a metric for quality controlling low-biomass microbiome profiling, clarifying the debates and strengthen future studies of resident microbiome in tissues.

genomics↗

bioRxiv2025

A single DNA methylation site regulates cell fate during Clostridioides difficile sporulation.

DNA methylation is a widespread phenomenon in bacteria that can regulate gene expression, although the mechanisms underlying this epigenetic regulation are often poorly understood. In Clostridioides difficile, the orphan DNA methyltransferase CamA promotes sporulation, a process critical for the persistence and transmission of this nosocomial pathogen. However, the specific CamA target genes that drive this increased sporulation phenotype were unknown. Here, we show that methylation of a single CamA motif in the promoter region of spoIIE, which encodes a factor critical for activating the early-acting sporulation sigma factor {sigma}F, is sufficient to promote spoIIE transcription, {sigma}F activation, and spore formation. Surprisingly, the CamA-dependent increase in spoIIE expression also increases the frequency of premature {sigma}F activation prior to asymmetric division, resulting in miscompartmentalization of {sigma}F activity. While this premature activation event triggers cell lysis in the well-studied spore-former Bacillus subtilis, we show that C. difficile cells retain developmental plasticity: predivisional cells that have prematurely activated {sigma}F can abort sporulation and resume vegetative growth, whereas cells that activate {sigma}F in the forespore after asymmetric division remain committed to sporulation. Thus, DNA methylation controls a critical cell fate decision in C. difficile without compromising its capacity to adapt to fluctuating environmental conditions. Finally, we show that CamA confers a significant fitness advantage during murine infection through mechanisms largely independent of its ability to promote sporulation. Since CamA is specific to C. difficile and epigenetically regulates multiple pathways critical for pathogen persistence, these analyses imply that CamA could be a promising antimicrobial target. SignificanceWhile DNA methylation of the bacterial genome is ubiquitous and can regulate diverse processes, identifying the methylation sites that drive a specific phenotype remains challenging. The Clostridioides difficile-specific orphan DNA methyltransferase CamA promotes sporulation and persistence in mice, but the mechanisms underlying these phenotypes were unknown. We identified a single methylation site in the promoter of a key sporulation gene that enhances sporulation, a process critical for this clinically relevant pathogen to transmit disease. Our analyses uncovered an unexpected developmental plasticity to C. difficiles sporulation program while also revealing that CamA regulates processes beyond sporulation that are important for maintaining long-term infection. Thus, targeting CamA may offer therapeutic benefit.

microbiology↗

bioRxiv2025

Carbohydrate adaptation drives liver-brain axis maturation

Mammalian postnatal life requires adaptation to a carbohydrate-rich diet, yet how metabolic programs are coordinated within and across organs is unclear. Using time-resolved transcriptomic and metabolomic analyses from the neonatal period through adulthood, we show that mouse liver rapidly acquires oxidative and detoxification capacity after weaning. This transition enables the brain to establish energy-sufficient, low-toxicity metabolic environment for neuronal development. This maturation process is marked by progressive activation of the hepatic electron transport chain (ETC), with the mitochondrial RNA endoribonuclease LACTB2 acting as a key regulator. LACTB2 prevents the accumulation of mitochondrial RNAs and sustains expression of mtDNA-encoded ETC subunits, thereby preserving mitochondrial competence for oxidative metabolism. LACTB2 is postnatally induced in hepatocytes, and its loss causes defective glucose utilization, systemic metabolic toxicity, and impaired brain metabolism and myelination, leading to prepubertal lethality, particularly in males. Restoring ETC function through liver-targeted expression of yeast NADH dehydrogenase NDI1, inhibiting the integrated stress response or ammonia scavenging improved survival. Our findings identify LACTB2-dependent hepatic mitochondrial maturation as a central mechanism that aligns carbohydrate adaptation with the liver-brain metabolic coordination to support early-life development.

developmental biology↗

bioRxiv2025

TREM2 macrophages are associated with enhanced response to PD-1 blockade in human hepatocellular carcinoma

Macrophages are known to dampen tumor immunity. However, identifying druggable targets that modulate these cells to improve existing immunotherapies has been limited by a dearth of studies identifying macrophages that associate with pathological response to immune checkpoint blockade. To fulfill this unmet clinical need, we leveraged transcriptional and spatial profiling of specimens collected from a Phase II clinical trial studying neoadjuvant PD-1 blockade in patients with hepatocellular carcinoma (HCC). We determined that the intratumoral abundance of TREM2-expressing macrophages and serological levels of soluble TREM2 are elevated in patients who responded to PD-1 blockade, compared to non-responders. We validated these findings in a second HCC cohort and in the IMbrave150 trial. These highlight the robust potential for TREM2 macrophages to predict therapeutic responses of HCC to immunotherapy. Therefore, our study provides a novel basis for the use of TREM2 macrophages to strategize treatment for patients with HCC to maximize therapeutic benefit.

immunology↗

bioRxiv2025

Loss of the actin remodeling protein Flightless-1 impairs CD8 and regulatory T cell function

T cell immunity depends on the precise coordination of signaling networks with actin cytoskeleton remodeling, yet the molecular regulators of these processes remain incompletely defined. Flightless-1 (FLII) is a gelsolin-family actin regulator with unique leucine-rich repeats that can couple cytoskeletal dynamics to diverse signaling pathways. Here, using conditional knockout mice, we identify essential roles for FLII in both CD8+ and regulatory T cells. Loss of FLII in CD8+ T cells caused a profound loss of naive cells from the spleen, impaired CCR7-dependent migration, and defective accumulation in the lung parenchyma during antigen-specific responses to respiratory vesicular stomatitis virus infection, despite largely preserved activation, effector differentiation, and cytotoxic function. FLII-deficient Foxp3+ regulatory T cells maintained normal numbers but exhibited diminished CD25 expression, defective IL-2 signaling, and failed to restrain spontaneous, tissue-specific autoimmunity. These findings identify FLII as a critical and previously unrecognized orchestrator of T cell trafficking and immune regulation, which may link chemokine receptor signaling to actin remodeling and is essential for proper T cell migration and function.

immunology↗

bioRxiv2025

Viral Infection Detector: Ensemble Learning for Predicting Viral Infection in Single-cell Transcriptomics of Virus-Induced Cancers

Oncoviruses cause around 15-20% of all human cancers. As 3 end-based single-cell RNA-sequencing (scRNA-seq) methods become more popular, the relationship between viral infection and tumor progression at the transcriptomic level has been increasingly studied at single-cell resolution. However, the identification of infected cells is challenging as viral reads from infected cells may not be captured, leading to high false negative rates. To recover these infected cells that have been missed, we developed VID, a stacked ensemble machine-learning model that predicts virus-infected cells in scRNA-seq datasets. Using VID, we uncovered biologically meaningful differences between infected and uninfected cells across different oncovirus-mediated cancers and different cell types. Our VID tool provides a user-friendly way to study changes induced by viral infections in scRNA-seq datasets.

bioinformatics↗

bioRxiv2025

Epigenetic phase variation in the gut microbiome enhances bacterial adaptation

The human gut microbiome within the gastrointestinal tract continuously adapts to variations in diet, medications, and host physiology. A strategy for bacterial genetic adaptation is epigenetic phase variation (ePV) mediated by bacterial DNA methylation, which can regulate gene expression, enhance clonal heterogeneity, and enable a single bacterial strain to exhibit variable phenotypic states. Genome-wide and site-specific ePVs have been characterized in human pathogens antigenic variation and virulence factor production. However, the role of ePV in facilitating adaptation within the human microbiome remains poorly understood. Here, we comprehensively cataloged genome-wide and site-specific ePV in human infant and adult gut microbiomes. First, using long-read metagenomic sequencing, we detected genome-wide ePV mediated by complex structural variations of DNA methyltransferases, highlighting those associated with antibiotics or fecal microbiota transplantation. Second, we analyzed a collection of public short-read metagenomic sequencing datasets, uncovering a great prevalence of genome-wide ePV in the human gut microbiome. Third, we quantitatively detected site-specific ePVs using single-molecule methylation analysis to identify dynamic variation associated with antibiotic treatment or probiotic engraftment. Finally, we performed an in-depth assessment of an Akkermansia muciniphila isolate from an infant, highlighting that ePVs can regulate gene expression and enhance the bacterial adaptive capacity by employing a bet-hedging strategy to increase tolerance to differing antibiotics. Our findings indicate that epigenetic modifications are a common strategy used by gut bacteria to adapt to the fluctuating environment.

microbiology↗

bioRxiv2024

Critical assessment of nanopore sequencing for the detection of multiple forms of DNA modifications

While nanopore sequencing is increasingly used for mapping DNA modifications, it is important to recognize associated false-positive calls, as they can mislead biological interpretations. To assist biologists and methods developers, we describe a framework, modFDR, for rigorous evaluation that emphasizes the use of the false discovery rate with rationally designed negative controls capturing both general background and confounding modifications. Our critical assessment across multiple DNA modifications shows that while nanopore sequencing performs reliably for high-abundance modifications--including 5-methylcytosine (5mC) at CpG sites in mammalian cells and 5-hydroxymethylcytosine (5hmC) in mammalian brain cells--it produces a substantial fraction of false-positive detections for low-abundance modifications, such as 5mC at CpH sites, 5hmC, and N6-methyldeoxyadenine (6mA) in most mammalian cell types. Although newer models improve certain aspects, systematic false positives remain, and we further observe elevated false negatives for 5mCpG when benchmarked against orthogonal enzymatic methods. This study highlights the urgent need to incorporate modFDR into future methods development, evaluation, and biological studies, and advocates prioritizing nanopore sequencing for mapping abundant rather than rare modifications in biomedical applications.

genomics↗

bioRxiv2024

Revolutionizing large-scale DNA synthesis with microchip-based massive in parallel synthesis system

DNA synthesis serves as the fundamental enabling technology of engineering biology, aiming to provide DNA molecules of designed composition, length, and complexity at scale and low cost. Current high-throughput DNA synthesis technologies rely on intricate chip manufacturing and microfluidic systems to provide large-scale synthetic oligonucleotides, at the expense of low concentration and limited compatibility in the processing of longer DNA constructs assembly. Here, we report a microchip-based massive in parallel synthesis (mMPS), pioneering an "identification-sorting-synthesis-recycling" iteration mechanism to microchips for high throughput DNA synthesis. In comparison to microarray-based methods, we demonstrate that our method can increase the DNA product concentration by 4 magnitudes (to picomole-scale per sequence) and greatly simplifies the downstream processes for large-scale gene synthesis construction. By the construction of 1.97 million-diversity variant libraries that cover 1,254 human protein domains, we demonstrated the uniformity of the constructed variant libraries using mMPS-derived oligos is greatly improved, with amino acid distribution highly consistent as designed. In addition, by synthesizing 285 1kb-to-3kb genes with varying degrees of sequence complexity from previously reported strains A501 and 3DAC, potential ancestor of early archaea and bacteria, our result shows that the overall gene assembly success rate using mMPS-derived oligos is increased by 10-fold in comparison to other methods. Our mMPS technology holds the potential to close the gap between the quality and cost of writing DNA in increasing demand across many sectors of research and industrial activities.

synthetic biology↗

bioRxiv2024

Long-read metagenomics empowers precise tracking of bacterial strains and their genomic changes after fecal microbiota transplantation

Fecal microbiota transplantation (FMT) has revolutionized the treatment of recurrent Clostridioides difficile infection (rCDI) and is being evaluated across other diseases. Accurate tracking of bacterial strains that stably engraft in recipients is critical for understanding the determinants of strain engraftment, evaluating their correlation with clinical outcomes, and guiding the development of therapeutic bacterial consortia. While short-read sequencing has advanced FMT research, it faces challenges in strain-level de novo metagenomic assembly. In this study, we described a novel framework, LongTrack, which uses long-read metagenomic assemblies and rigorous informatics tailored for FMT strain tracking. We highlighted LongTracks advantage over short-read approaches especially when multiple strains co-exist in the same sample. We showed LongTrack uncovered hundreds of engrafted strains across six FMT cases of rCDI and inflammatory bowel disease patients. Furthermore, long reads also allowed us to assess the genomic and epigenomic stability of engrafted strains during the 5-year follow-ups, revealing structural variations that may be associated with strain adaptation in a new host environment. Combined, our study advocates the use of long-read metagenomics and LongTrack to enhance strain tracking in future FMT studies, paving the way for the development of more effective defined biotherapeutic as an alternative to FMT.

microbiology↗

bioRxiv2024

Somatic mtDNA Mutation Burden Shapes Metabolic Plasticity in Leukemogenesis

The role of somatic mitochondrial DNA (mtDNA) mutations in leukemogenesis remains poorly characterized. To determine the impact of somatic mtDNA mutations on the process, we assessed the leukemogenic potential of hematopoietic progenitor cells (HPCs) from mtDNA mutator mice (Polg D257A) with or without NMyc overexpression. We observed a higher incidence of spontaneous leukemogenesis in recipients transplanted with heterozygous Polg HPCs and a lower incidence of NMyc-driven leukemia in those with homozygous Polg HPCs compared to controls. Although mtDNA mutations in heterozygous and homozygous HPCs caused similar baseline impairments in mitochondrial function, only heterozygous HPCs responded to and supported altered metabolic demands associated with NMyc overexpression. Homozygous HPCs showed altered glucose utilization with pyruvate dehydrogenase inhibition due to increased phosphorylation, exacerbated by NMyc overexpression. The impaired growth of NMyc-expressing homozygous HPCs was partially rescued by inhibiting pyruvate dehydrogenase kinase, highlighting a relationship between mtDNA mutation burden and metabolic plasticity in leukemogenesis. TEASERSomatic mtDNA mutations as drivers of metabolic change in the development of leukemia.

cancer biology↗

bioRxiv2024

Prediction of protein biophysical traits from limited data: a case study on nanobody thermostability through NanoMelt

1In-silico prediction of protein biophysical traits is often hindered by the limited availability of experimental data and their heterogeneity. Training on limited data can lead to overfitting and poor generalisability to sequences distant from those in the training set. Additionally, inadequate use of scarce and disparate data can introduce biases during evaluation, leading to unreliable model performances being reported. Here, we present a comprehensive study exploring various approaches for protein fitness prediction from limited data, leveraging pre-trained embeddings, repeated stratified nested cross-validation, and ensemble learning to ensure an unbiased assessment of the performances. We applied our framework to introduce NanoMelt, a predictor of nanobody thermostability trained with a dataset of 640 measurements of apparent melting temperature, obtained by integrating data from the literature with 129 new measurements from this study. We find that an ensemble model stacking multiple regression using diverse sequence embeddings achieves state-of-the-art accuracy in predicting nanobody thermostability. We further demonstrate NanoMelts potential to streamline nanobody development by guiding the selection of highly stable nanobodies. We make the curated dataset of nanobody thermostability freely available and NanoMelt accessible as a downloadable software and webserver. 2 Significance StatementRapidly predicting protein biophysical traits with accuracy is a key goal in protein engineering, yet efforts to develop reliable predictors are often hindered by limited and disparate experimental measurements. We introduce a framework to predict biophysical traits using few training data, leveraging diverse machine learning approaches via a semi-supervised framework combined with ensemble learning. We applied this framework to develop NanoMelt, a tool to predict nanobody thermostability trained on a new dataset of apparent melting temperatures. Nanobodies are increasingly important in research and therapeutics due to their ease of production and small size, which allows deeper tissue penetration and seamless combination into multi-specific compounds. NanoMelt outperforms available methods for protein thermostability prediction and can streamline nanobody development by guiding the design and selection of highly stable nanobodies during discovery and optimization campaigns.

bioengineering↗

bioRxiv2024

Monitoring of Cell-free Human Papillomavirus DNA in Metastatic or Recurrent Cervical Cancer: Clinical Significance and Treatment Implications

PurposeMonitoring of circulating human papillomavirus (HPV) cell-free DNA (cfDNA) is a minimally invasive approach for surveillance in HPV-associated cancers, particularly cervical cancer. The aim of this study was to monitor circulating HPV cfDNA levels in patients with recurrent or metastatic cervical cancer during treatment and follow-up, assessing the utility of HPV cfDNA as a tumor marker for disease surveillance and in guiding clinical treatment decisions. Experimental DesignIn this prospective pilot observational study, levels of HPV cfDNA in serum samples from 28 patients with recurrent or metastatic HPV-positive cervical cancer were measured via digital droplet polymerase chain reaction. Results for HPV cfDNA levels were matched to clinical outcomes and to serum levels of squamous cell carcinoma antigen (SCC-Ag) to assess the clinical potential of HPV cfDNA as a tumor marker. ResultsHPV cfDNA was detected in all 28 patients (100% detection rate). Notably, median baseline HPV cfDNA levels varied according to the metastatic pattern observed in individual patients (P=0.019). Patients with a combined multi-metastatic pattern (lymph node + hematogenous {+/-} diffuse serosal metastasis) exhibited a higher median baseline HPV cfDNA level compared to those with a single-metastasis pattern (local recurrence, lymph node metastasis, or hematogenous metastasis) (P = 0.003). All participants exhibited changes in HPV cfDNA levels over a median monitoring period of 2 months (range 0.3 to 16.9 months) prior to evaluations for treatment response or disease progression. Among 26 patients initially diagnosed with squamous cell cervical cancer, the positivity rate was 100% for HPV cfDNA and 69.2% for SCC-Ag (P = 0.004, 95% confidence interval, 0-0.391). Among 20 patients longitudinally monitored for squamous cell cervical cancer, the concordance with changes in disease status was 90% for HPV cfDNA and 50% for SCC-Ag (P = 0.014, 95% confidence interval, 0.022-0.621). ConclusionsHPV cfDNA is a promising tumor marker for HPV-positive cervical cancer. In the context of precision medicine, HPV cfDNA is poised to play an increasingly pivotal role in monitoring treatment efficacy, providing valuable insights into disease progression, and guiding clinical decisions.

cancer biology↗