Abstract 1942: MIRAGE: A ctDNA methylation-driven computational algorithm designed for sensitive detection of minimal residual disease.

Abstract Background: Detection of minimal residual disease (MRD) following surgical resection or a treatment regimen remains a significant clinical challenge, primarily due to the typically low concentrations of circulating tumor DNA (ctDNA) in the bloodstream post-treatment. In situations where circulating tumor cells (CTCs) are undetectable or ctDNA levels fall below the thresholds of conventional detection methods, DNA methylation analysis has emerged as a promising alternative. Utilizing computational algorithms further improve the detection of tumor signal in cancers with as low as 0.001% circulating tumor fraction. Methods: MIRAGE (Minimal Residual Assessment using Genome-wide Epigenomics) algorithm was validated using 156 samples, among which 16.7% (n=26) were control reference samples. The algorithm assessed DNA methylation patterns across genome-wide differentially methylated regions (DMRs). The algorithm quantifies methylation by evaluating the number of methylated and unmethylated cytosines at each CpG site and integrating these values into a composite methylation score, reflecting overall hyper- or hypomethylation. The resulting methylation score is further normalized against a predefined cut-off established from a reference cohort of non-cancerous healthy individuals to enable standardized interpretation. Results: MIRAGE achieved a specificity of 96.8% from 127 clinical samples (64 tumor, 63 non-cancerous healthy controls) tested. Among the clinical tumor samples, 64% (n=41/64) were detected as ctDNA-positive which showed an overall sensitivity of 64.1%. Conclusion: The study demonstrates the specificity of MIRAGE algorithm for ctDNA classification-based minimal residual detection with high specificity. Further validation on larger cohort is still warranted to show more clinical insights. Citation Format: Gowhar Shafi, Aarthi Ramesh, Sandhya Iyer, Aparna Sornapudi, Alain D'Souza, Sumit Halder, Bhagwat Jadhav, Sangeeta Prajapati, Mohan Uttarwar. MIRAGE: A ctDNA methylation-driven computational algorithm designed for sensitive detection of minimal residual disease [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1942.

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Publication Details

Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-1942
Primary Topic
Cancer Genomics and Diagnostics
Type
article
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article

Abstract 1942: MIRAGE: A ctDNA methylation-driven computational algorithm designed for sensitive detection of minimal residual disease.

Sumit Halder, Sangeeta Prajapati, Aarthi Ramesh, Gowhar Shafi et al.
Cancer Research
Cancer Genomics and Diagnostics
article

Abstract 1942: MIRAGE: A ctDNA methylation-driven computational algorithm designed for sensitive detection of minimal residual disease.

Sumit Halder, Sangeeta Prajapati, Aarthi Ramesh, Gowhar Shafi, Bhagwat Jadhav, Sandhya Iyer, Mohan Uttarwar, Aparna Sornapudi, Alain D'Souza
article en

Abstract

Abstract Background: Detection of minimal residual disease (MRD) following surgical resection or a treatment regimen remains a significant clinical challenge, primarily due to the typically low concentrations of circulating tumor DNA (ctDNA) in the bloodstream post-treatment. In situations where circulating tumor cells (CTCs) are undetectable or ctDNA levels fall below the thresholds of conventional detection methods, DNA methylation analysis has emerged as a promising alternative. Utilizing computational algorithms further improve the detection of tumor signal in cancers with as low as 0.001% circulating tumor fraction. Methods: MIRAGE (Minimal Residual Assessment using Genome-wide Epigenomics) algorithm was validated using 156 samples, among which 16.7% (n=26) were control reference samples. The algorithm assessed DNA methylation patterns across genome-wide differentially methylated regions (DMRs). The algorithm quantifies methylation by evaluating the number of methylated and unmethylated cytosines at each CpG site and integrating these values into a composite methylation score, reflecting overall hyper- or hypomethylation. The resulting methylation score is further normalized against a predefined cut-off established from a reference cohort of non-cancerous healthy individuals to enable standardized interpretation. Results: MIRAGE achieved a specificity of 96.8% from 127 clinical samples (64 tumor, 63 non-cancerous healthy controls) tested. Among the clinical tumor samples, 64% (n=41/64) were detected as ctDNA-positive which showed an overall sensitivity of 64.1%. Conclusion: The study demonstrates the specificity of MIRAGE algorithm for ctDNA classification-based minimal residual detection with high specificity. Further validation on larger cohort is still warranted to show more clinical insights. Citation Format: Gowhar Shafi, Aarthi Ramesh, Sandhya Iyer, Aparna Sornapudi, Alain D'Souza, Sumit Halder, Bhagwat Jadhav, Sangeeta Prajapati, Mohan Uttarwar. MIRAGE: A ctDNA methylation-driven computational algorithm designed for sensitive detection of minimal residual disease [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1942.

Cancer ResearchVol. 86(7_Supplement)
Openalex Percentile: Top 9%
Cancer Genomics and Diagnostics
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