Abstract 6425: Evaluation of a plasma cell-free DNA methylation-based multi-cancer detection test

Abstract Introduction: Blood-based multi-cancer detection (MCD) tests hold promise for early cancer detection. MCDs should demonstrate high specificity, clinically meaningful sensitivity, and provide information to guide clinical diagnostic evaluation. Methods: We developed an MCD test that leverages a next-generation sequencing epigenomics hybrid capture assay (Shield™) to measure DNA methylation in regions differentially methylated across cancers. We then implemented a two-step classification algorithm. First, a regression model (multi-cancer classifier) was trained to distinguish cancer from non-cancer samples at 98% target specificity. Next, a cancer signal of origin (CSO) model was trained to predict 10 solid tumors (Table 1) in samples predicted to be cancer. The test was evaluated in a blinded case-control cohort of plasma samples (3mL; Streck) from adults with treatment naïve cancer, or who were self-reported cancer free with 1 year of follow-up (NCT05334069). Results: Final evaluable dataset was 778 participants (excluding 18 (2.3%) that failed QC). Median age was 62 years (range: 40-78). 55% were female. Self-reported race was 79% White. Specificity was 98.5% (397/403). Overall sensitivity for the 10 cancers was 59.7% (224/375); 34.6% Stage I/II (64/185), 84.2% Stage III/IV (160/190) and ranged from 96% for esophageal-stomach to 21% for prostate. Primary or secondary CSO prediction was 89% accurate. Sensitivity in cancers without average risk standard-of-care screening was 73% and was 74% in the 6 most aggressive cancers (shortest survival rates; Table 1). Conclusions: In this cohort, this MCD has 59.7% sensitivity, 98.5% specificity, and 89% primary or secondary CSO accuracy. Ongoing development is aimed at expanding CSO calling range. Integrating an MCD with an FDA approved blood-based CRC screening test may increase the MCD clinical value in the intended use population. A version of this MCD is being studied in a prospective, interventional study evaluating MCD testing feasibility. Citation Format: Rachel Gittelman, Alan Selewa, Yupeng He, Jordan Burke, Victoria M. Raymond, Sven Duenwald, Craig Eagle, AmirAli Talasaz, Darya Chudova. Evaluation of a plasma cell-free DNA methylation-based multi-cancer detection test [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6425.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2025-04-21
DOI
https://doi.org/10.1158/1538-7445.am2025-6425
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 6425: Evaluation of a plasma cell-free DNA methylation-based multi-cancer detection test

Jordan E. Burke, Rachel Gittelman, Darya Chudova, Sven Duenwald et al.
Cancer Research
Cancer Genomics and Diagnostics
article

Abstract 6425: Evaluation of a plasma cell-free DNA methylation-based multi-cancer detection test

Jordan E. Burke, Rachel Gittelman, Darya Chudova, Sven Duenwald, AmirAli Talasaz, Victoria M. Raymond, Yupeng He, Craig Eagle, Alan Selewa
article en

Abstract

Abstract Introduction: Blood-based multi-cancer detection (MCD) tests hold promise for early cancer detection. MCDs should demonstrate high specificity, clinically meaningful sensitivity, and provide information to guide clinical diagnostic evaluation. Methods: We developed an MCD test that leverages a next-generation sequencing epigenomics hybrid capture assay (Shield™) to measure DNA methylation in regions differentially methylated across cancers. We then implemented a two-step classification algorithm. First, a regression model (multi-cancer classifier) was trained to distinguish cancer from non-cancer samples at 98% target specificity. Next, a cancer signal of origin (CSO) model was trained to predict 10 solid tumors (Table 1) in samples predicted to be cancer. The test was evaluated in a blinded case-control cohort of plasma samples (3mL; Streck) from adults with treatment naïve cancer, or who were self-reported cancer free with 1 year of follow-up (NCT05334069). Results: Final evaluable dataset was 778 participants (excluding 18 (2.3%) that failed QC). Median age was 62 years (range: 40-78). 55% were female. Self-reported race was 79% White. Specificity was 98.5% (397/403). Overall sensitivity for the 10 cancers was 59.7% (224/375); 34.6% Stage I/II (64/185), 84.2% Stage III/IV (160/190) and ranged from 96% for esophageal-stomach to 21% for prostate. Primary or secondary CSO prediction was 89% accurate. Sensitivity in cancers without average risk standard-of-care screening was 73% and was 74% in the 6 most aggressive cancers (shortest survival rates; Table 1). Conclusions: In this cohort, this MCD has 59.7% sensitivity, 98.5% specificity, and 89% primary or secondary CSO accuracy. Ongoing development is aimed at expanding CSO calling range. Integrating an MCD with an FDA approved blood-based CRC screening test may increase the MCD clinical value in the intended use population. A version of this MCD is being studied in a prospective, interventional study evaluating MCD testing feasibility. Citation Format: Rachel Gittelman, Alan Selewa, Yupeng He, Jordan Burke, Victoria M. Raymond, Sven Duenwald, Craig Eagle, AmirAli Talasaz, Darya Chudova. Evaluation of a plasma cell-free DNA methylation-based multi-cancer detection test [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6425.

Cancer ResearchVol. 85(8_Supplement_1)
Guardant (United States) (US)
Openalex Percentile: Top 89%
Cancer Genomics and Diagnostics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.