Cell-free DNA 5-hydroxymethylcytosine for immunotherapy response prediction in lung cancer
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PROVIDER: PRJNA993686 | ENA |
REPOSITORIES: ENA
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Project description:Immune checkpoint inhibitors (ICIs) drastically improve therapeutic outcomes for lung cancer, but accurate prediction of individual patient responses to ICIs remains a challenge. We performed a genome-wide analysis of 5-hydroxymethylcytosine (5hmC) in plasma cell-free DNA (cfDNA) samples from 83 lung cancer patients. Using machine learning approaches, we developed a 5hmC signature to predict ICI treatment response and calculated a weighted-predictive score (wp-score) based on the 5hmC levels of signature genes in each sample. A low wp-score was significantly correlated with longer progression-free survival across three independent patient sample sets, and demonstrated superior predictive capability to tumor programmed death-ligand 1. Moreover, we identified novel 5hmC-associated genes and signaling pathways integral to ICI treatment response in lung cancer. Our study suggests that cfDNA 5hmC analysis is a minimally invasive, innovative strategy for guiding treatment selection in lung cancer patients.
2024-02-28 | GSE237087 | GEO
Project description:We used a highly sensitive nano-5hmC-Seal method and profiled the genome-wide distribution of 5-hydroxymethylcytosine (5hmC) in plasma cell-free DNA (cfDNA) from 384 patients with bladder, breast, colorectal, kidney, lung, or prostate cancer and 221 controls. We used machine learning and developed plasma cfDNA 5hmC signatures that are highly sensitive for cancer detection and cancer origin determination. We also identified genes and signaling pathways with aberrant DNA hydroxymethylation in six cancers.
2022-09-23 | GSE202988 | GEO
Project description:Analysis of gene expression data to evaluate candidate targets for immunotherapy. Analysis of gene expression data to evaluate candidate targets for immunotherapy, We analyse 7 lung cancer samples and 3 reference samples (2x kidney, 1x lung).
2011-07-01 | E-GEOD-27556 | biostudies-arrayexpress
Project description:Prediction of prognosis, immunogenicity and efficacy of immunotherapy based on glutamine metabolism in lung adenocarcinoma
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Project description:Background: One of the main fields of lung cancer research is identifying patients who are at high risk of post-resection recurrence. Individual recurrence risk evaluation by accurate but simple and reproducible method is needed for the clinical practice. Results: The log-rank test and further selection by our criteria of assayability generated 87 genes from microarray data with significant level 5%. Of these, by PTQ-PCR, the expression of most significant 18 genes was obtained. Using these gene expression information and clinical parameters, by stepwise variable selection method, the recurrence prediction model, which composed of 6 genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, IFI44) and pStage and cell differentiation, were developed. Validation into the two independent cohorts showed good results of the proposed model (p=0.0314, 0.0305, respectively). The predicted median recurrence-free survival times for each patient were reflected real ones well. Conclusions: Our method of individualized recurrence risk prediction is accurate, technically simple and reproducible to be used in clinical practice. Therefore, it would be useful in customizing the lung cancer management strategies. Keywords: Recurrence Free Survival Analysis
2007-11-20 | GSE8894 | GEO
Project description:High-resolution detection of genome-wide 5-hydroxymethylcytosine (5hmC) sites of small-scale samples represents a continuous challenge. Here, we present CATCH-seq, a bisulfite-free, base-resolution method for the genome-wide detection of 5hmC. CATCH-seq is based on selective 5hmC oxidation, labeling and subsequent C-to-T transition during PCR. Applications of CATCH-seq to nano-scale DNA samples reveal previously underappreciated non-CG 5hmCs in the hESC genome and base-resolution hydroxymethylome in human cell-free DNA.
2018-10-09 | GSE112048 | GEO
Project description:Glutamine (Gln) metabolism has been reported to play an essential role in cancer. However, a comprehensive analysis of its role in lung adenocarcinoma is still unavailable. This study established a novel system of quantification of Gln metabolism to predict the prognosis and immunotherapy efficacy in lung cancer. Further, the Gln metabolism in tumor microenvironment (TME) was characterized and the Gln metabolism-related genes were identified for targeted therapy. We comprehensively evaluated the patterns of Gln metabolism in 513 patients diagnosed with lung adenocarcinoma (LUAD) based on 73 Gln metabolism-related genes. Based on differentially expressed genes (DEGs), a risk model was constructed using Cox regression and Lasso regression analysis. The prognostic efficacy of the model was validated using an individual LUAD cohort form Shandong Provincial Hospital, an integrated LUAD cohort from GEO and pan-cancer cohorts from TCGA databases. Five independent immunotherapy cohorts were used to validate the model performance in predicting immunotherapy efficacy. Next, a series of single-cell sequencing analyses were used to characterize Gln metabolism in TME. Finally, single-cell sequencing analysis, transcriptome sequencing, and a series of in vitro experiments were used to explore the role of EPHB2 in LUAD. Patients with LUAD were eventually divided into low- and high-risk groups. Patients in low-risk group were characterized by low levels of Gln metabolism, survival advantage, “hot” immune phenotype and benefit from immunotherapy. Compared with other cells, tumor cells in TME exhibited the most active Gln metabolism. Among immune cells, tumor-infiltrating T cells exhibited the most active levels of Gln metabolism, especially CD8 T cell exhaustion and Treg suppression. EPHB2, a key gene in the model, was shown to promote LUAD cell proliferation, invasion and migration, and regulated the Gln metabolic pathway. Finally, we found that EPHB2 was highly expressed in macrophages, especially M2 macrophages. It may be involved in the M2 polarization of macrophages and mediate the negative regulation of M2 macrophages in NK cells. This study revealed that the Gln metabolism-based model played a significant role in predicting prognosis and immunotherapy efficacy in lung cancer. We further characterized the Gln metabolism of TME and investigated the Gln metabolism-related gene EPHB2 to provide a theoretical framework for anti-tumor strategy targeting Gln metabolism.
2022-07-31 | GSE209973 | GEO
Project description: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.
2023-12-31 | GSE237057 | GEO
Project description:Barcode-free prediction of cell lineages from scRNA-seq datasets
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Project description:Bisulfite-free, nano-scale analysis of 5-hydroxymethylcytosine at single base resolution
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