EPIC-Seq employs a Dirichlet-Multinomial statistical model to evaluate cell-free DNA fragment size diversity at the TSSs. This model generates a new fragmentomic feature called, Promoter Fragmentation Entropy (PFE). PFE is generalizable and can be calculated at any window of interest (e.g., at the ATAC peaks instead of TSSs).

In addition to PFE, EPIC-seq extracts the normalized coverage at the Nucleosome Depleted Regions (NDR) to infer gene expression across all genes (in the deep whole-genome) samples and for a subset of samples for the targeted strategy introduced by EPIC-Seq.

The predicted expression values can be used for different applications, including cancer vs normal classification or cancer subtype classifications.

Profiling of circulating tumor DNA (ctDNA) in the bloodstream shows promise for non-invasive cancer detection and classification. While chromatin fragmentation features in cell-free DNA (cfDNA) have previously been explored, current fragmentomic methods require high concentrations of tumor-derived DNA and are limited by insufficient resolution to infer individual gene expression. Here, we describe promoter fragmentation entropy (PFE) at transcription start sites (TSS) as a novel epigenomic cfDNA feature strongly correlated with RNA expression levels. PFE is complementary to other fragmentomic features in predicting gene-specific transcription levels and has advantages over them. We leverage these insights within EPIC-Seq, a method for high-resolution cancer detection and tissue-of-origin classification from cfDNA that extracts features of chromatin fragmentation using targeted sequencing from promoters of genes of interest. Profiling 249 blood samples from 158 cancer patients and 68 healthy adults, we demonstrate the ability of EPIC-Seq to infer gene expression at the level of individual TSS. We describe the utility of this approach for noninvasive classification of subtypes of lung carcinomas and of diffuse large B-cell lymphomas, and for noninvasive cancer detection purposes. Finally, by applying EPIC-Seq to serial blood samples from patients treated with PD-(L)1 immune checkpoint inhibitors, we show that inferred gene expression profiles inferred by EPIC-Seq after a single infusion can help identify patients likely to achieve durable clinical benefit. Our results suggest that EPIC-Seq could augment current personalized profiling efforts, enabling noninvasive, high-throughput tissue-of-origin characterization with diagnostic, prognostic, and therapeutic potential.