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Inferring gene expression from cell-free DNA fragmentation profiles.
Mohammad Shahrokh Esfahani1,2,3, Emily G Hamilton4, Mahya Mehrmohamadi1,2
1Divisions of Oncology and of Hematology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA.
Nature Biotechnology
|April 1, 2022
Summary
New epigenetic analysis of cell-free DNA (cfDNA) predicts gene expression for noninvasive cancer detection. This method, EPIC-seq, shows potential for diagnosing cancer subtypes and monitoring treatment response.
Area of Science:
- Genomics
- Epigenetics
- Molecular Biology
Background:
- Circulating tumor DNA (ctDNA) profiling offers noninvasive cancer detection potential.
- Current fragmentomic methods for cfDNA analysis have limitations in sensitivity and resolution.
- Inferring gene expression from cfDNA is an emerging area in cancer research.
Purpose of the Study:
- To introduce promoter fragmentation entropy as a novel epigenomic cfDNA feature.
- To develop and validate a method, EPIC-seq, for inferring gene expression from cfDNA.
- To assess the utility of EPIC-seq for cancer subtyping and treatment response prediction.
Main Methods:
- Developed EPIC-seq, a targeted sequencing method focusing on gene promoters.
- Analyzed cfDNA from 329 blood samples (201 cancer patients, 87 healthy adults).
- Applied EPIC-seq to serial samples from patients receiving PD-(L)1 inhibitors.
Main Results:
- Promoter fragmentation entropy accurately predicts RNA expression at individual genes.
- EPIC-seq successfully classified lung carcinoma and diffuse large B cell lymphoma subtypes.
- Inferred gene expression profiles correlated with clinical response to immune-checkpoint inhibitors.
Conclusions:
- EPIC-seq provides a high-resolution, noninvasive method for epigenomic profiling of cfDNA.
- This approach holds promise for early cancer detection, diagnosis, prognosis, and therapeutic monitoring.
- EPIC-seq could enable high-throughput tissue-of-origin characterization for personalized medicine.
