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Methyl-binding DNA capture Sequencing for Patient Tissues
Published on: October 31, 2016
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Tissue of origin prediction for cancer of unknown primary using a targeted methylation sequencing panel
Miaomiao Sun1, Bo Xu2, Chao Chen1
1Department of Pathology, Henan Key Laboratory of Tumor Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Clinical Epigenetics
|February 9, 2024
Summary
A novel methylation classifier accurately predicts cancer tissue of origin for unknown primary cancers. This DNA methylation-based approach aids in risk assessment and guides targeted cancer therapy.
Area of Science:
- Oncology
- Genomics
- Epigenetics
Background:
- Cancer of unknown primary (CUP) presents a diagnostic challenge due to its rarity, poor prognosis, and unidentifiable tissue of origin.
- Distinct DNA methylation patterns across different tissues and cancer types offer a potential avenue for identifying the origin of CUPs.
- Accurate tissue of origin identification is crucial for risk stratification and guiding site-directed cancer therapies.
Purpose of the Study:
- To develop and validate a machine learning-based classifier utilizing DNA methylation profiles for predicting the tissue of origin in Cancer of Unknown Primary (CUP).
- To establish a targeted methylation sequencing panel for accurate tissue of origin prediction in CUP patients.
- To explore the potential of specific CpG methylation levels as biomarkers for cancer diagnosis and screening.
Main Methods:
- Genome-wide DNA methylation profile datasets from The Cancer Genome Atlas (TCGA) were analyzed using machine learning.
- A 200-CpG methylation feature classifier for CUP tissue of origin prediction (MFCUP) was developed.
- MFCUP was validated using public methylation array data and targeted bisulfite sequencing on Formalin-fixed paraffin-embedded (FFPE) samples.
Main Results:
- The MFCUP classifier achieved high accuracy (97.2%) in a large validation cohort (n=5923) across 25 cancer types.
- Validation on Infinium 450K and EPIC array datasets yielded accuracies of 93.4% and 84.8%, respectively.
- A targeted bisulfite sequencing panel, based on MFCUP, correctly identified tissue of origin in 88.5% of FFPE samples from 78 patients.
Conclusions:
- A methylation-based cancer classifier and a targeted methylation sequencing panel demonstrate high accuracy in predicting tissue of origin across diverse cancer types.
- These tools hold significant potential for improving the diagnosis and management of Cancer of Unknown Primary (CUP).
- Specific CpG methylation patterns may serve as valuable biomarkers for early cancer detection and screening.
