It’s time to put down that well-thumbed copy of Homer’s Odyssey, as there is a new epic in town. Now, the “epigenetic expression inference from cell-free (cf)DNA-sequencing” (EPIC-seq) technique heroically enables gene expression analysis using a process known as “fragmentomics.”
Circulating cfDNA comes as nucleosome-associated fragments that reflect the chromatin configuration in the cell of origin; therefore, fragmentation features could reflect gene expression profiles in cancer. In a best-selling leap forward in this genre, well-read researchers led by Ash A. Alizadeh (Stanford Medicine) describe how non-invasive high-resolution profiling of cfDNA fragmentation at critical genome regions using EPIC-seq infers gene expression and supports tumor detection, subtype classification, therapeutic response assessment, and analysis of prognostic genes.
Let’s hear more on this EPIC epigenetic technique direct from the quills of Esfahani and colleagues:
- EPIC-seq uses “promoter fragmentation entropy” as an epigenomic cfDNA feature predicting RNA expression levels at individual genes
- Higher gene expression levels and the associated lower level of nucleosome occupation at gene promoters lead to more random cleavage patterns (higher entropy) and vice-versa
- EPIC-seq combines hybrid capture-based targeted deep sequencing of transcription start site-flanking regions in cfDNA with machine learning to predict cancer subtype-specific gene expression
- EPIC-seq analysis of blood from non-small cell lung carcinoma (NSCLC) patients uncovers higher NKX2-1 (TTF1) expression, a gene used in the histopathological diagnosis of lung adenocarcinoma
- EPIC-seq also distinguishes lung cancer cases from healthy patients, evaluates tumor burden, and non-invasively classifies NSCLC at the histological level
- A similar analysis in diffuse large B cell lymphoma (DLBCL) patients reveals higher MS4A1 (CD20) expression, a gene used for lymphoma immunophenotyping/classification
- EPIC-seq also non-invasively detects tissue-derived DLBCL signals, reflects disease burden before and after therapy, classifies DLBCL cell-of-origin, and robustly stratifies patients
- Proof-of-concept studies suggest that EPIC-seq-based profiling of blood from NSCLC patients treated with immune-checkpoint inhibitors reliably detects tissue-specific signals to predict the durability of clinical benefit
- EPIC-seq non-invasively measures the expression and prognostic value of individual genes and resolves individual transcription start site regions, which aids risk stratification and therapeutic management
- LMO2 expression significantly associates with progression-free survival in DLBCL patients, where a distal transcription start site associates with patient outcome
While this epic new epigenetic technique has far-reaching potential for cancer management, the authors have read between the lines and have foreseen the potential of EPIC-seq to detect tissue injury, ischemia, and pharmacodynamic effects on therapeutically targeted pathways and toxicity in challenging to non-invasively monitor human tissues.
To immerse yourself in the details of this finely written epigenetic epic of our times, see Nature Biotechnology, March 2022.