Journal
NATURE BIOTECHNOLOGY
Volume 40, Issue 4, Pages 585-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41587-022-01222-4
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Funding
- National Cancer Institute [R25CA180993, T32CA009302, F99CA212457, R01CA188298, R01CA254179, R01CA257655, R01CA244526, R01CA233975, R01CA229766]
- US National Institutes of Health Director's New Innovator Award Program [1-DP2-CA186569]
- Virginia and D.K. Ludwig Fund for Cancer Research
- Bakewell Foundation
- CRK Faculty Scholar Fund
- Troper-Wojcicki Family Gift
- Shanahan Family Lymphoma Fund
- Skeff Family Lymphoma Fund
- Arzang Family Lymphoma Fund
- Cane-Nowak Family Foundation
- Marc Benioff Fund
- Jewish Communal Fund for Lymphoma Research
- Sara Schottenstein Memorial Fund
- Moghadam Family Endowed Professorship
- NIH [P30 CA008748, 5R25CA180993]
- American Cancer Society [134031-PF-19-164-01-TBG]
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EPIC-seq is a method that predicts gene expression levels at individual genes from cfDNA. We demonstrate the potential of EPIC-seq in cancer subtype classification and prediction of clinical response.
EPIC-seq predicts expression of individual genes from cell-free DNA. Profiling of circulating tumor DNA (ctDNA) in the bloodstream shows promise for noninvasive cancer detection. Chromatin fragmentation features have previously been explored to infer gene expression profiles from cell-free DNA (cfDNA), but current fragmentomic methods require high concentrations of tumor-derived DNA and provide limited resolution. Here we describe promoter fragmentation entropy as an epigenomic cfDNA feature that predicts RNA expression levels at individual genes. We developed 'epigenetic expression inference from cell-free DNA-sequencing' (EPIC-seq), a method that uses targeted sequencing of promoters of genes of interest. Profiling 329 blood samples from 201 patients with cancer and 87 healthy adults, we demonstrate classification of subtypes of lung carcinoma and diffuse large B cell lymphoma. Applying EPIC-seq to serial blood samples from patients treated with PD-(L)1 immune-checkpoint inhibitors, we show that gene expression profiles inferred by EPIC-seq are correlated with clinical response. Our results indicate that EPIC-seq could enable noninvasive, high-throughput tissue-of-origin characterization with diagnostic, prognostic and therapeutic potential.
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