Abstract 1113: Clinical validation of a targeted multi-cancer early detection (MCED) test using cfDNA mutation and integrated genomic signatures analysis

Abstract Early cancer detection significantly improves survival, particularly for high-burden cancers across Asia that are often diagnosed late. Plasma-derived cell-free DNA (cfDNA) is a promising non-invasive biomarker for identifying cancer earlier than conventional methods. This study evaluated the performance of a targeted multi-cancer early detection (MCED) test that analyzes tumour-associated mutations in cfDNA to detect 20 cancer types commonly seen in Asian populations. In this retrospective, multicentre validation study, participants aged ≥20 years with either confirmed cancer or cancer-free status were recruited. Plasma cfDNA was sequenced using a targeted panel constructed from large genomic datasets. Machine learning models were trained and validated in a blinded manner to assess diagnostic performance. Among 2342 cancer cases and 1646 controls, the MCED test achieved a sensitivity of 83.1% (95% CI: 81.5-84.6%) and specificity of 95.4% (95% CI: 94.4-96.4%), with a false-positive rate of 4.6%. Performance surpassed that of traditional biomarkers such as CEA. Notably, high sensitivity was observed in cancers of major concern in Asia—liver, gastric, pancreatic, bile duct, and nasopharyngeal cancers—with early-stage detection ranging from 75.0-91.3% and late-stage detection from 95.5-100%. These findings support the potential of cfDNA-based MCED testing as a non-invasive, complementary screening approach for populations in Asia. Its strong performance across multiple high-mortality cancers underscores its value for early detection, risk stratification, and integration into regional cancer control strategies. Citation Format: Chee-Onn Leong, Boon Shing Tan, Hiu Ching Toh, Ee Mun Loo, Anand Mohan, Teryna Thu, Zhi Win Ng, Xin Yi Chew, Chin King Looi, Felicia Fei Lei Chung, Chun Wai Mai. Clinical validation of a targeted multi-cancer early detection (MCED) test using cfDNA mutation and integrated genomic signatures analysis [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 1113.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-1113
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 1113: Clinical validation of a targeted multi-cancer early detection (MCED) test using cfDNA mutation and integrated genomic signatures analysis

Boon Shing Tan, Anand Mohan, Hiu Ching Toh, Xin Yi Chew et al.
Cancer Research
Cancer Genomics and Diagnostics
article

Abstract 1113: Clinical validation of a targeted multi-cancer early detection (MCED) test using cfDNA mutation and integrated genomic signatures analysis

Boon Shing Tan, Anand Mohan, Hiu Ching Toh, Xin Yi Chew, Chun Wai Mai, Chee-Onn Leong, Felicia Fei Lei Chung, Chin King Looi, Zhi Win Ng, Teryna Thu, Ee Mun Loo
article en

Abstract

Abstract Early cancer detection significantly improves survival, particularly for high-burden cancers across Asia that are often diagnosed late. Plasma-derived cell-free DNA (cfDNA) is a promising non-invasive biomarker for identifying cancer earlier than conventional methods. This study evaluated the performance of a targeted multi-cancer early detection (MCED) test that analyzes tumour-associated mutations in cfDNA to detect 20 cancer types commonly seen in Asian populations. In this retrospective, multicentre validation study, participants aged ≥20 years with either confirmed cancer or cancer-free status were recruited. Plasma cfDNA was sequenced using a targeted panel constructed from large genomic datasets. Machine learning models were trained and validated in a blinded manner to assess diagnostic performance. Among 2342 cancer cases and 1646 controls, the MCED test achieved a sensitivity of 83.1% (95% CI: 81.5-84.6%) and specificity of 95.4% (95% CI: 94.4-96.4%), with a false-positive rate of 4.6%. Performance surpassed that of traditional biomarkers such as CEA. Notably, high sensitivity was observed in cancers of major concern in Asia—liver, gastric, pancreatic, bile duct, and nasopharyngeal cancers—with early-stage detection ranging from 75.0-91.3% and late-stage detection from 95.5-100%. These findings support the potential of cfDNA-based MCED testing as a non-invasive, complementary screening approach for populations in Asia. Its strong performance across multiple high-mortality cancers underscores its value for early detection, risk stratification, and integration into regional cancer control strategies. Citation Format: Chee-Onn Leong, Boon Shing Tan, Hiu Ching Toh, Ee Mun Loo, Anand Mohan, Teryna Thu, Zhi Win Ng, Xin Yi Chew, Chin King Looi, Felicia Fei Lei Chung, Chun Wai Mai. Clinical validation of a targeted multi-cancer early detection (MCED) test using cfDNA mutation and integrated genomic signatures analysis [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 1113.

Cancer ResearchVol. 86(7_Supplement)
University Kebangsaan Malaysia Medical Centre (MY), University of Kuala Lumpur (MY), Asia e University (MY), IMU University (MY)
Good health and well-being
Openalex Percentile: Top 30%
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.