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Updated: May 7, 2025

Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions
Published on: September 25, 2018
Combination of Hotspot Mutations With Methylation and Fragmentomic Profiles to Enhance Multi-Cancer Early Detection
Thi Hue Hanh Nguyen1, Giang Hoang Vu1, Tu Thi Nguyen1
1Medical Genetics Institute, Ho Chi Minh, Vietnam.
Background:
Multi-cancer early detection (MCED) through a single blood test significantly advances cancer diagnosis. However, most MCED tests rely on a single type of biomarkers, leading to limited sensitivity, particularly for early-stage cancers. We previously developed SPOT-MAS, a multimodal ctDNA-based assay analyzing methylation and fragmentomic profiles to detect five common cancers. Despite its potential, SPOT-MAS exhibited moderate sensitivities for early-stage cancers. This study investigated whether integrating hotspot mutations into SPOT-MAS could enhance its detection rates.
Method:
A targeted amplicon sequencing approach was developed to profile 700 hotspot mutations in cell-free DNA and integrated into the SPOT-MAS assay, creating a single-blood draw workflow. This workflow, namely SPOT-MAS Plus was retrospectively validated in a cohort of 255 non-metastatic cancer patients (breast, colorectal, gastric, liver, and lung) and 304 healthy individuals.
Results:
Hotspot mutations were detected in 131 of 255 (51.4%) cancer patients, with the highest rates in liver cancer (96.5%), followed by colorectal (59.3%) and lung cancer (53.7%). Lower detection rates were found for cancers with low tumor mutational burden, such as breast (31.3%) and gastric (41.9%) cancers. In contrast, SPOT-MAS demonstrated higher sensitivities for these cancers (51.6% for breast and 62.9% for gastric). The combination of hotspot mutations with SPOT-MAS predictions improved early-stage cancer detection, achieving an overall sensitivity of 78.5% at a specificity of 97.7%. Enhanced sensitivities were observed for colorectal (81.36%) and lung cancer (82.9%).
Conclusion:
The integration of genetic and epigenetic alterations into a multimodal assay significantly enhances the early detection of various cancers. Further validation in larger cohorts is necessary to support broader clinical applications.
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