Cell-free DNA 5-hydroxymethylcytosine is an emerging marker of acute myeloid leukemia

Clinical applications of circulating tumor DNA in hematological malignancies: From past to the future

Liquid biopsy, particularly circulating tumor DNA (ctDNA), has drawn a lot of attention as a non- or minimal-invasive detection approach for clinical applications in patients with cancer. Many hematological malignancies are well suited for serial and repeated ctDNA surveillance due to relatively high ctDNA concentrations and high loads of tumor-specific genetic and epigenetic abnormalities. Progress of detecting technology in recent years has improved sensitivity and specificity significantly, thus broadening and strengthening the potential utilities of ctDNA including early diagnosis, prognosis estimation, treatment response evaluation, minimal residual disease monitoring, targeted therapy selection, and immunotherapy surveillance. This manuscript reviews the detection methodologies, clinical application and future challenges of ctDNA in hematological malignancies, especially for lymphomas, myeloma and leukemias.

Deep5hmC: predicting genome-wide 5-hydroxymethylcytosine landscape via a multimodal deep learning model

MOTIVATION

5-Hydroxymethylcytosine (5hmC), a crucial epigenetic mark with a significant role in regulating tissue-specific gene expression, is essential for understanding the dynamic functions of the human genome. Despite its importance, predicting 5hmC modification across the genome remains a challenging task, especially when considering the complex interplay between DNA sequences and various epigenetic factors such as histone modifications and chromatin accessibility.

RESULTS

Using tissue-specific 5hmC sequencing data, we introduce Deep5hmC, a multimodal deep learning framework that integrates both the DNA sequence and epigenetic features such as histone modification and chromatin accessibility to predict genome-wide 5hmC modification. The multimodal design of Deep5hmC demonstrates remarkable improvement in predicting both qualitative and quantitative 5hmC modification compared to unimodal versions of Deep5hmC and state-of-the-art machine learning methods. This improvement is demonstrated through benchmarking on a comprehensive set of 5hmC sequencing data collected at four developmental stages during forebrain organoid development and across 17 human tissues. Compared to DeepSEA and random forest, Deep5hmC achieves close to 4% and 17% improvement of Area Under the Receiver Operating Characteristic (AUROC) across four forebrain developmental stages, and 6% and 27% across 17 human tissues for predicting binary 5hmC modification sites; and 8% and 22% improvement of Spearman correlation coefficient across four forebrain developmental stages, and 17% and 30% across 17 human tissues for predicting continuous 5hmC modification. Notably, Deep5hmC showcases its practical utility by accurately predicting gene expression and identifying differentially hydroxymethylated regions (DhMRs) in a case-control study of Alzheimer's disease (AD). Deep5hmC significantly improves our understanding of tissue-specific gene regulation and facilitates the development of new biomarkers for complex diseases.

AVAILABILITY AND IMPLEMENTATION

Deep5hmC is available via https://github.com/lichen-lab/Deep5hmC.

A 5-Hydroxymethylcytosine-Based Noninvasive Model for Early Detection of Colorectal Carcinomas and Advanced Adenomas: The METHOD-2 Study

PURPOSE

Detection of colorectal carcinomas at a time when there are more treatment options is associated with better outcomes. This prospective case-control study assessed the 5-hydroxymethylcytosine (5hmC) biomarkers in circulating cell-free DNA (cfDNA) for early detection of colorectal carcinoma and advanced adenomas (AA).

EXPERIMENTAL DESIGN

Plasma cfDNA samples from 2,576 study participants from the multicenter METHOD-2 study (NCT03676075) were collected, comprising patients with newly diagnosed colorectal carcinoma (n = 1,074), AA (n = 356), other solid tumors (n = 80), and non-colorectal carcinoma/AA controls (n = 1,066), followed by genome-wide 5hmC profiling using the 5hmC-Seal technique and the next-generation sequencing. A weighted diagnostic model for colorectal carcinoma (stage I-III) and AA was developed using the elastic net regularization in a discovery set and validated in independent samples.

RESULTS

Distribution of 5hmC in cfDNA reflected gene regulatory relevance and tissue of origin. Besides being confirmed in internal validation, a 96-gene model achieved an area under the curve (AUC) of 90.7% for distinguishing stage I-III colorectal carcinoma from controls in 321 samples from multiple centers for external validation, regardless of primary location or mutation status. This model also showed cancer-type specificity as well as high capacity for distinguishing AA from controls with an AUC of 78.6%. Functionally, differential 5hmC features associated with colorectal carcinoma and AA demonstrated relevance to colorectal carcinoma biology, including pathways such as calcium and MAPK signaling.

CONCLUSIONS

Genome-wide mapping of 5hmC in cfDNA shows promise as a highly sensitive and specific noninvasive blood test to be integrated into screening programs for improving early detection of colorectal carcinoma and high-risk AA.

5-Hydroxymethylcytosine in Cell-Free DNA Predicts Immunotherapy Response in Lung Cancer

Immune checkpoint inhibitors (ICIs) drastically improve therapeutic outcomes for lung cancer, but accurately predicting individual patient responses to ICIs remains a challenge. We performed the genome-wide profiling of 5-hydroxymethylcytosine (5hmC) in 85 plasma cell-free DNA (cfDNA) samples from lung cancer patients and developed a 5hmC signature that was significantly associated with progression-free survival (PFS). We built a 5hmC predictive model to quantify the 5hmC level and validated the model in the validation, test, and control sets. Low weighted predictive scores (wp-scores) were significantly associated with a longer PFS compared to high wp-scores in the validation [median 7.6 versus 1.8 months; p = 0.0012; hazard ratio (HR) 0.12; 95% confidence interval (CI), 0.03-0.54] and test (median 14.9 versus 3.3 months; p = 0.00074; HR 0.10; 95% CI, 0.02-0.50) sets. Objective response rates in patients with a low or high wp-score were 75.0% (95% CI, 42.8-94.5%) versus 0.0% (95% CI, 0.0-60.2%) in the validation set (p = 0.019) and 80.0% (95% CI, 44.4-97.5%) versus 0.0% (95% CI, 0.0-36.9%) in the test set (p = 0.0011). The wp-scores were also significantly associated with PFS in patients receiving single-agent ICI treatment (p < 0.05). In addition, the 5hmC predictive signature demonstrated superior predictive capability to tumor programmed death-ligand 1 and specificity to ICI treatment response prediction. Moreover, we identified novel 5hmC-associated genes and signaling pathways integral to ICI treatment response in lung cancer. This study provides proof-of-concept evidence that the cfDNA 5hmC signature is a robust biomarker for predicting ICI treatment response in lung cancer.

Deep5hmC: Predicting genome-wide 5-Hydroxymethylcytosine landscape via a multimodal deep learning model

5-hydroxymethylcytosine (5hmC), a critical epigenetic mark with a significant role in regulating tissue-specific gene expression, is essential for understanding the dynamic functions of the human genome. Using tissue-specific 5hmC sequencing data, we introduce Deep5hmC, a multimodal deep learning framework that integrates both the DNA sequence and the histone modification information to predict genome-wide 5hmC modification. The multimodal design of Deep5hmC demonstrates remarkable improvement in predicting both qualitative and quantitative 5hmC modification compared to unimodal versions of Deep5hmC and state-of-the-art machine learning methods. This improvement is demonstrated through benchmarking on a comprehensive set of 5hmC sequencing data collected at four time points during forebrain organoid development and across 17 human tissues. Notably, Deep5hmC showcases its practical utility by accurately predicting gene expression and identifying differentially hydroxymethylated regions in a case-control study of Alzheimer's disease.

Specific inhibition of TET1 in the spinal dorsal horn alleviates inflammatory pain in mice by regulating synaptic plasticity

DNA demethylation mediated by ten-eleven translocation 1 (TET1) is a critical epigenetic mechanism in which gene expression is regulated via catalysis of 5-methylcytosine to 5-hydroxymethylcytosine. Previously, we demonstrated that TET1 is associated with the genesis of chronic inflammatory pain. However, how TET1 participates in enhanced nociceptive responses in chronic pain remains poorly understood. Here, we report that conditional knockout of Tet1 in dorsal horn neurons via intrathecal injection of rAAV-hSyn-Cre in Tet1fl/fl mice not only reversed the inflammation-induced upregulation of synapse-associated proteins (post-synaptic density protein 95 (PSD95) and synaptophysin (SYP)) in the dorsal horn but also ameliorated abnormalities in dendritic spine morphology and alleviated pain hypersensitivities. Pharmacological blockade of TET1 by intrathecal injection of a TET1-specific inhibitor-Bobcat 339-produced similar results, as did knockdown of Tet1 by intrathecal injection of siRNA. Thus, our data strongly suggest that increased TET1 expression during inflammatory pain upregulates the expression of multiple synapse-associated proteins and dysregulates synaptic morphology in dorsal horn neurons, suggesting that Tet1 may be a potential target for analgesic strategies.

Cell-Free DNA 5-Hydroxymethylcytosine Signatures for Lung Cancer Prognosis

Accurate prognostic markers are essential for guiding effective lung cancer treatment strategies. The level of 5-hydroxymethylcytosine (5hmC) in tissue is independently associated with overall survival (OS) in lung cancer patients. We explored the prognostic value of cell-free DNA (cfDNA) 5hmC through genome-wide analysis of 5hmC in plasma samples from 97 lung cancer patients. In both training and validation sets, we discovered a cfDNA 5hmC signature significantly associated with OS in lung cancer patients. We built a 5hmC prognostic model and calculated the weighted predictive scores (wp-score) for each sample. Low wp-scores were significantly associated with longer OS compared to high wp-scores in the training [median 22.9 versus 8.2 months; p = 1.30 × 10-10; hazard ratio (HR) 0.04; 95% confidence interval (CI), 0.00-0.16] and validation (median 18.8 versus 5.2 months; p = 0.00059; HR 0.22; 95% CI: 0.09-0.57) sets. The 5hmC signature independently predicted prognosis and outperformed age, sex, smoking, and TNM stage for predicting lung cancer outcomes. Our findings reveal critical genes and signaling pathways with aberrant 5hmC levels, enhancing our understanding of lung cancer pathophysiology. The study underscores the potential of cfDNA 5hmC as a superior prognostic tool for guiding more personalized therapeutic strategies for lung cancer patients.

Advances in next-generation sequencing and emerging technologies for hematologic malignancies

Innovations in molecular diagnostics have often evolved through the study of hematologic malignancies. Examples include the pioneering characterization of the Philadelphia chromosome by cytogenetics in the 1970s, the implementation of polymerase chain reaction for high-sensitivity detection and monitoring of mutations and, most recently, targeted next- generation sequencing to drive the prognostic and therapeutic assessment of leukemia. Hematologists and hematopath- ologists have continued to advance in the past decade with new innovations improving the type, amount, and quality of data generated for each molecule of nucleic acid. In this review article, we touch on these new developments and discuss their implications for diagnostics in hematopoietic malignancies. We review advances in sequencing platforms and library preparation chemistry that can lead to faster turnaround times, novel sequencing techniques, the development of mobile laboratories with implications for worldwide benefits, the current status of sample types, improvements to quality and reference materials, bioinformatic pipelines, and the integration of machine learning and artificial intelligence into mol- ecular diagnostic tools for hematologic malignancies.

Cell-free DNA 5-hydroxymethylcytosine is highly sensitive for MRD assessment in acute myeloid leukemia

Measurable residual disease (MRD) is an important biomarker in acute myeloid leukemia (AML). However, MRD cannot be detected in many patients using current methods. We developed a highly sensitive 5-hydroxymethylcytosine (5hmC) signature in cell-free DNA by analyzing 115 AML patients and 86 controls. The 5hmC method detected MRD in 20 of 29 patients with negative MRD by multiparameter flow cytometry and 11 of 14 patients with negative MRD by molecular methods. MRD detection by the 5hmC method was significantly associated with relapse-free survival. This novel method can be used in most AML patients and may significantly impact AML patient management.

Classification of Acute Myeloid Leukemia by Cell-Free DNA 5-Hydroxymethylcytosine

Epigenetic abnormality is a hallmark of acute myeloid leukemia (AML), and aberrant 5-hydroxymethylcytosine (5hmC) levels are commonly observed in AML patients. As epigenetic subgroups of AML correlate with different clinical outcomes, we investigated whether plasma cell-free DNA (cfDNA) 5hmC could categorize AML patients into subtypes. We profiled the genome-wide landscape of 5hmC in plasma cfDNA from 54 AML patients. Using an unbiased clustering approach, we found that 5hmC levels in genomic regions with a histone mark H3K4me3 classified AML samples into three distinct clusters that were significantly associated with leukemia burden and survival. Cluster 3 showed the highest leukemia burden, the shortest overall survival of patients, and the lowest 5hmC levels in the TET2 promoter. 5hmC levels in the TET2 promoter could represent TET2 activity resulting from mutations in DNA demethylation genes and other factors. The novel genes and key signaling pathways associated with aberrant 5hmC patterns could add to our understanding of DNA hydroxymethylation and highlight the potential therapeutic targets in AML. Our results identify a novel 5hmC-based AML classification system and further underscore cfDNA 5hmC as a highly sensitive marker for AML.

Liquid biopsy in hematological malignancies: current and future applications

The assessment of the cancer mutational profile is crucial for patient management, stratification, and therapeutic decisions. At present, in hematological malignancies with a solid mass, such as lymphomas, tumor genomic profiling is generally performed on the tissue biopsy, but the tumor may harbor genetic lesions that are unique to other anatomical compartments. The analysis of circulating tumor DNA (ctDNA) on the liquid biopsy is an emerging approach that allows genotyping and monitoring of the disease during therapy and follow-up. This review presents the different methods for ctDNA analysis and describes the application of liquid biopsy in different hematological malignancies. In diffuse large B-cell lymphoma (DLBCL) and Hodgkin lymphoma (HL), ctDNA analysis on the liquid biopsy recapitulates the mutational profile of the tissue biopsy and can identify mutations otherwise absent on the tissue biopsy. In addition, changes in the ctDNA amount after one or two courses of chemotherapy significantly predict patient outcomes. ctDNA analysis has also been tested in myeloid neoplasms with promising results. In addition to mutational analysis, liquid biopsy also carries potential future applications of ctDNA, including the analysis of ctDNA fragmentation and epigenetic patterns. On these grounds, several clinical trials aiming at incorporating ctDNA analysis for treatment tailoring are currently ongoing in hematological malignancies.

Expression pattern, tumor immune landscape, and prognostic value of N7‑methylguanosine regulators in bladder urothelial carcinoma

N7-Methylguanosine (m7G) modification is important in post-transcriptional regulation. dysregulation of m7G RNA modification has been reported to be markedly associated with cancer. However, its importance in bladder urothelial carcinoma (BLCA) remains poorly characterized. The present study systematically analyzed mRNA gene expression data and clinical information from The Cancer Genome Atlas and further constructed robust risk signatures for the four regulators of m7G RNA modification (nudix hydrolase 11, gem nuclear organelle-associated protein 5, eukaryotic translation initiation factor 3 subunit D and cytoplasmic FMR1 interacting protein 1). The differential expression and cell function of m7G-related genes in bladder cancer cells were verified by reverse transcription-quantitative PCR, Cell Counting Kit-8 and colony formation assays. The four-gene-based model could accurately predict the prognosis of BLCA. Nomogram-based clinical decisions had a higher net benefit compared with that of individual predictors. Through immune infiltration analysis, it was found that immune cell infiltration affected the prognosis of patients with BLCA. Finally, the present study identified potential therapeutics that differ between high and low-risk groups based on four genes. In summary, the current findings revealed an essential role for m7G RNA modification regulators in BLCA, and developed risk signatures as promising prognostic markers in patients with BLCA.

Third-Generation Sequencing of Epigenetic DNA

The discovery of epigenetic bases has revolutionised the understanding of disease and development. Among the most studied epigenetic marks are cytosines covalently modified at the 5 position. In order to gain insight into their biological significance, the ability to determine their spatiotemporal distribution within the genome is essential. Techniques for sequencing on "next-generation" platforms often involve harsh chemical treatments leading to sample degradation. Third-generation sequencing promises to further revolutionise the field by providing long reads, enabling coverage of highly repetitive regions of the genome or structural variants considered unmappable by next generation sequencing technology. While the ability of third-generation platforms to directly detect epigenetic modifications is continuously improving, at present chemical or enzymatic derivatisation presents the most convenient means of enhancing reliability. This Review presents techniques available for the detection of cytosine modifications on third-generation platforms.

Epigenetic reprogramming in cancer: From diagnosis to treatment

Disruption of the epigenetic program of gene expression is a hallmark of cancer that initiates and propagates tumorigenesis. Altered DNA methylation, histone modifications and ncRNAs expression are a feature of cancer cells. The dynamic epigenetic changes during oncogenic transformation are related to tumor heterogeneity, unlimited self-renewal and multi-lineage differentiation. This stem cell-like state or the aberrant reprogramming of cancer stem cells is the major challenge in treatment and drug resistance. Given the reversible nature of epigenetic modifications, the ability to restore the cancer epigenome through the inhibition of the epigenetic modifiers is a promising therapy for cancer treatment, either as a monotherapy or in combination with other anticancer therapies, including immunotherapies. Herein, we highlighted the main epigenetic alterations, their potential as a biomarker for early diagnosis and the epigenetic therapies approved for cancer treatment.

Liquid biopsies and minimal residual disease in myeloid malignancies

Minimal residual disease (MRD) assessment through blood component sampling by liquid biopsies (LBs) is increasingly being investigated in myeloid malignancies. Blood components then undergo molecular analysis by flow cytometry or sequencing techniques and can be used as a powerful tool for prognostic and predictive purposes in myeloid malignancies. There is evidence and more is evolving about the quantification and identification of cell-based and gene-based biomarkers in myeloid malignancies to monitor treatment response. MRD based acute myeloid leukemia protocol and clinical trials are currently incorporating LB testing and preliminary results are encouraging for potential widespread use in clinic in the near future. MRD monitoring using LBs are not standard in myelodysplastic syndrome (MDS) but this is an area of active investigation. In the future, LBs can replace more invasive techniques such as bone marrow biopsies. However, the routine clinical application of these markers continues to be an issue due to lack of standardization and limited number of studies investigating their specificities. Integrating artificial intelligence (AI) could help simplify the complex interpretation of molecular testing and reduce errors related to operator dependency. Though the field is rapidly evolving, the applicability of MRD testing using LB is mostly limited to research setting at this time due to the need for validation, regulatory approval, payer coverage, and cost issues. This review focuses on the types of biomarkers, most recent research exploring MRD and LB in myeloid malignancies, ongoing clinical trials, and the future of LB in the setting of AI.