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dna methylation signature  (Epimmune Inc)

 
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    Epimmune Inc dna methylation signature
    Dna Methylation Signature, supplied by Epimmune Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/dna+methylation+microarray/microarray+dna+methylation+signature/pmc12095235-303-13-18
    Average 90 stars, based on 1 article reviews
    dna methylation signature - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    Microarray:

    Article Title: PD-L1 versus tumor mutation burden: Which is the better immunotherapy biomarker in advanced non-small cell lung cancer?
    Article Snippet: This article is protected by copyright.. All rights reserved.. Aims: PD-L1 and tumor mutation burden (TMB) are the most widely used immunotherapy biomarkers to identify populations who would attain clinical benefit, with the higher values predicting the better therapeutic efficacy.

    Article Title: Opportunities and challenges of using circulating tumor DNA to predict lung cancer immunotherapy efficacy
    Article Snippet: .. An epigenomic profile based on a microarray DNA methylation signature (EPIMMUNE) was created in a sample of 34 NSCLC patients receiving pembrolizumab or nivolumab. ..

    Article Title: Tumor Methylation Burden (TMeB) in Non-Small Cell Lung Cancer: A New Way of Thinking About Epigenetics.
    Article Snippet: .. The epigenomic profile was based on a microarray DNA methylation signature (EPIMMUNE) in a discovery set of tumor samples from 142 patients treated with nivolumab or pembrolizumab (34 in the discovery cohort, 47 in the EPIMMUNE validation cohort, and 61 in the derived methylation marker cohort). .. An intriguing result came from the EPIMMUNE signature (forkhead box P1, FOXP1 status), since this was significantly associated with progression-free survival (PFS) and overall survival (OS), suggesting that EPIMMUNE may be a good predictor of anti-PD-1-ICI response [24].

    DNA Methylation Assay:

    Article Title: PD-L1 versus tumor mutation burden: Which is the better immunotherapy biomarker in advanced non-small cell lung cancer?
    Article Snippet: This article is protected by copyright.. All rights reserved.. Aims: PD-L1 and tumor mutation burden (TMB) are the most widely used immunotherapy biomarkers to identify populations who would attain clinical benefit, with the higher values predicting the better therapeutic efficacy.

    Article Title: Recent advances in biomarkers for predicting the efficacy of immunotherapy in non-small cell lung cancer
    Article Snippet: .. In another study involving stage IV NSCLC patients treated with anti-PD-1 agents, a DNA methylation signature known as EPIMMUNE was found to correlate with both PFS and OS ( ). ..

    Article Title: Tumor mutation burden in the prognosis and response of lung cancer patients to immune-checkpoint inhibition therapies.
    Article Snippet: .. Importantly, EPIMMUNE is a DNA methylation signature that has been associated with ICI efficacy and overall survival in NSCLC patients [112]. ..

    Article Title: Opportunities and challenges of using circulating tumor DNA to predict lung cancer immunotherapy efficacy
    Article Snippet: .. An epigenomic profile based on a microarray DNA methylation signature (EPIMMUNE) was created in a sample of 34 NSCLC patients receiving pembrolizumab or nivolumab. ..

    Article Title: Tumor mutation burden in the prognosis and response of lung cancer patients to immune-checkpoint inhibition therapies
    Article Snippet: .. Importantly, EPIMMUNE is a DNA methylation signature that has been associated with ICI efficacy and overall survival in NSCLC patients [ ]. ..

    Article Title: Tumour-infiltrating lymphocytes: from prognosis to treatment selection
    Article Snippet: .. Duruisseaux et al. found a correlation between epigenetic features based on DNA methylation signature (EPIMMUNE) and clinical benefit with PD-1 blockade in NSCLC. ..

    Article Title: Tumor Methylation Burden (TMeB) in Non-Small Cell Lung Cancer: A New Way of Thinking About Epigenetics.
    Article Snippet: .. The epigenomic profile was based on a microarray DNA methylation signature (EPIMMUNE) in a discovery set of tumor samples from 142 patients treated with nivolumab or pembrolizumab (34 in the discovery cohort, 47 in the EPIMMUNE validation cohort, and 61 in the derived methylation marker cohort). .. An intriguing result came from the EPIMMUNE signature (forkhead box P1, FOXP1 status), since this was significantly associated with progression-free survival (PFS) and overall survival (OS), suggesting that EPIMMUNE may be a good predictor of anti-PD-1-ICI response [24].

    Biomarker Discovery:

    Article Title: Tumor Methylation Burden (TMeB) in Non-Small Cell Lung Cancer: A New Way of Thinking About Epigenetics.
    Article Snippet: .. The epigenomic profile was based on a microarray DNA methylation signature (EPIMMUNE) in a discovery set of tumor samples from 142 patients treated with nivolumab or pembrolizumab (34 in the discovery cohort, 47 in the EPIMMUNE validation cohort, and 61 in the derived methylation marker cohort). .. An intriguing result came from the EPIMMUNE signature (forkhead box P1, FOXP1 status), since this was significantly associated with progression-free survival (PFS) and overall survival (OS), suggesting that EPIMMUNE may be a good predictor of anti-PD-1-ICI response [24].

    Derivative Assay:

    Article Title: Tumor Methylation Burden (TMeB) in Non-Small Cell Lung Cancer: A New Way of Thinking About Epigenetics.
    Article Snippet: .. The epigenomic profile was based on a microarray DNA methylation signature (EPIMMUNE) in a discovery set of tumor samples from 142 patients treated with nivolumab or pembrolizumab (34 in the discovery cohort, 47 in the EPIMMUNE validation cohort, and 61 in the derived methylation marker cohort). .. An intriguing result came from the EPIMMUNE signature (forkhead box P1, FOXP1 status), since this was significantly associated with progression-free survival (PFS) and overall survival (OS), suggesting that EPIMMUNE may be a good predictor of anti-PD-1-ICI response [24].

    Methylation:

    Article Title: Tumor Methylation Burden (TMeB) in Non-Small Cell Lung Cancer: A New Way of Thinking About Epigenetics.
    Article Snippet: .. The epigenomic profile was based on a microarray DNA methylation signature (EPIMMUNE) in a discovery set of tumor samples from 142 patients treated with nivolumab or pembrolizumab (34 in the discovery cohort, 47 in the EPIMMUNE validation cohort, and 61 in the derived methylation marker cohort). .. An intriguing result came from the EPIMMUNE signature (forkhead box P1, FOXP1 status), since this was significantly associated with progression-free survival (PFS) and overall survival (OS), suggesting that EPIMMUNE may be a good predictor of anti-PD-1-ICI response [24].

    Marker:

    Article Title: Tumor Methylation Burden (TMeB) in Non-Small Cell Lung Cancer: A New Way of Thinking About Epigenetics.
    Article Snippet: .. The epigenomic profile was based on a microarray DNA methylation signature (EPIMMUNE) in a discovery set of tumor samples from 142 patients treated with nivolumab or pembrolizumab (34 in the discovery cohort, 47 in the EPIMMUNE validation cohort, and 61 in the derived methylation marker cohort). .. An intriguing result came from the EPIMMUNE signature (forkhead box P1, FOXP1 status), since this was significantly associated with progression-free survival (PFS) and overall survival (OS), suggesting that EPIMMUNE may be a good predictor of anti-PD-1-ICI response [24].



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    Comparison of candidate gene studies, microarrays, and methylome sequencing approaches for <t>DNA</t> <t>methylation</t> biomarker studies. Genome CpG coverage varies significantly across methods. Candidate gene approaches can typically assess tens to thousands of CpG sites, microarrays (such as Illumina 450 k and 850 k platforms) cover hundreds of thousands of CpG sites, and methylome-wide approaches capture nearly all 29.4 million CpG sites within the human genome (hg38, autosomes, X and Y). Base- and strand-level resolution highlights the ability to measure methylation at single-CpG resolution on individual DNA strands, and is only practically feasible with methylome sequencing. Microarrays provide site-level resolution based on reference genome sequence, but do not typically distinguish between strands, while candidate gene approaches are limited to specific loci typically without strand information. The potential for participant re-identification increases with the scale and resolution of the data. Sequencing-based methods pose a higher risk due to the comprehensive and individual-specific nature of the genomic data, requiring robust data governance and privacy protections. Microarrays and candidate gene studies present lower re-identification risks, as they capture less data and provide limited genomic context. Relative cost per sample reflects the resources needed for data production and analysis. Candidate gene approaches are the most cost-effective, while microarrays offer a balance of affordability, with methylome sequencing being the most costly due to sequencing and computational demands. Raw data sizes illustrate the storage demands of each method. Candidate gene studies generate minimal data (<10 MB per sample), while microarrays produce 16–20 MB per sample. In contrast, methylomes at 30x coverage produce approximately 110 GB of raw data per sample (compressed FASTQ format) and about 62 GB of mapped data (CRAM format), accounting for ∼10% data loss through PCR duplicates and read filtering, however this can be highly variable. The computational resources required increase with data complexity. Candidate gene and <t>microarray</t> studies can typically be processed on desktop computers or small servers, while methylome analyses often require servers or high-performance computing (HPC) environments. The shift to advanced computing infrastructure is driven by the large datasets and computationally intensive analyses associated with sequencing-based studies.
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    Comparison of candidate gene studies, microarrays, and methylome sequencing approaches for DNA methylation biomarker studies. Genome CpG coverage varies significantly across methods. Candidate gene approaches can typically assess tens to thousands of CpG sites, microarrays (such as Illumina 450 k and 850 k platforms) cover hundreds of thousands of CpG sites, and methylome-wide approaches capture nearly all 29.4 million CpG sites within the human genome (hg38, autosomes, X and Y). Base- and strand-level resolution highlights the ability to measure methylation at single-CpG resolution on individual DNA strands, and is only practically feasible with methylome sequencing. Microarrays provide site-level resolution based on reference genome sequence, but do not typically distinguish between strands, while candidate gene approaches are limited to specific loci typically without strand information. The potential for participant re-identification increases with the scale and resolution of the data. Sequencing-based methods pose a higher risk due to the comprehensive and individual-specific nature of the genomic data, requiring robust data governance and privacy protections. Microarrays and candidate gene studies present lower re-identification risks, as they capture less data and provide limited genomic context. Relative cost per sample reflects the resources needed for data production and analysis. Candidate gene approaches are the most cost-effective, while microarrays offer a balance of affordability, with methylome sequencing being the most costly due to sequencing and computational demands. Raw data sizes illustrate the storage demands of each method. Candidate gene studies generate minimal data (<10 MB per sample), while microarrays produce 16–20 MB per sample. In contrast, methylomes at 30x coverage produce approximately 110 GB of raw data per sample (compressed FASTQ format) and about 62 GB of mapped data (CRAM format), accounting for ∼10% data loss through PCR duplicates and read filtering, however this can be highly variable. The computational resources required increase with data complexity. Candidate gene and microarray studies can typically be processed on desktop computers or small servers, while methylome analyses often require servers or high-performance computing (HPC) environments. The shift to advanced computing infrastructure is driven by the large datasets and computationally intensive analyses associated with sequencing-based studies.

    Journal: Frontiers in Molecular Biosciences

    Article Title: Type-2 diabetes epigenetic biomarkers: present status and future directions for global and Indigenous health

    doi: 10.3389/fmolb.2025.1502640

    Figure Lengend Snippet: Comparison of candidate gene studies, microarrays, and methylome sequencing approaches for DNA methylation biomarker studies. Genome CpG coverage varies significantly across methods. Candidate gene approaches can typically assess tens to thousands of CpG sites, microarrays (such as Illumina 450 k and 850 k platforms) cover hundreds of thousands of CpG sites, and methylome-wide approaches capture nearly all 29.4 million CpG sites within the human genome (hg38, autosomes, X and Y). Base- and strand-level resolution highlights the ability to measure methylation at single-CpG resolution on individual DNA strands, and is only practically feasible with methylome sequencing. Microarrays provide site-level resolution based on reference genome sequence, but do not typically distinguish between strands, while candidate gene approaches are limited to specific loci typically without strand information. The potential for participant re-identification increases with the scale and resolution of the data. Sequencing-based methods pose a higher risk due to the comprehensive and individual-specific nature of the genomic data, requiring robust data governance and privacy protections. Microarrays and candidate gene studies present lower re-identification risks, as they capture less data and provide limited genomic context. Relative cost per sample reflects the resources needed for data production and analysis. Candidate gene approaches are the most cost-effective, while microarrays offer a balance of affordability, with methylome sequencing being the most costly due to sequencing and computational demands. Raw data sizes illustrate the storage demands of each method. Candidate gene studies generate minimal data (<10 MB per sample), while microarrays produce 16–20 MB per sample. In contrast, methylomes at 30x coverage produce approximately 110 GB of raw data per sample (compressed FASTQ format) and about 62 GB of mapped data (CRAM format), accounting for ∼10% data loss through PCR duplicates and read filtering, however this can be highly variable. The computational resources required increase with data complexity. Candidate gene and microarray studies can typically be processed on desktop computers or small servers, while methylome analyses often require servers or high-performance computing (HPC) environments. The shift to advanced computing infrastructure is driven by the large datasets and computationally intensive analyses associated with sequencing-based studies.

    Article Snippet: The latest Illumina EPIC DNA methylation microarray (900 K) includes the addition of probes to study open chromatin as well as additional enhancer locations ( ).

    Techniques: Comparison, Sequencing, DNA Methylation Assay, Biomarker Discovery, Methylation, Microarray