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Inferring gene expression from cell-free DNA fragmentation profiles.

ABSTRACT: 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.

SUBMITTER: Esfahani MS

PROVIDER: S-EPMC9337986 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Inferring gene expression from cell-free DNA fragmentation profiles.

Esfahani Mohammad Shahrokh MS Hamilton Emily G EG Mehrmohamadi Mahya M Nabet Barzin Y BY Alig Stefan K SK King Daniel A DA Steen Chloé B CB Macaulay Charles W CW Schultz Andre A Nesselbush Monica C MC Soo Joanne J Schroers-Martin Joseph G JG Chen Binbin B Binkley Michael S MS Stehr Henning H Chabon Jacob J JJ Sworder Brian J BJ Hui Angela B-Y AB Frank Matthew J MJ Moding Everett J EJ Liu Chih Long CL Newman Aaron M AM Isbell James M JM Rudin Charles M CM Li Bob T BT Kurtz David M DM Diehn Maximilian M Alizadeh Ash A AA

Nature biotechnology 20220331 4

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 'e ...[more]

PMID: 35361996

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Project description:MotivationSingle cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired.ResultsWe developed SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS) for the inference of GRNs from single cell transcriptional profiles. We focused on time-stamped cross-sectional expression data, commonly generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers directed regulatory relationships among genes by employing regularized linear regression (ridge regression), using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and single cell transcriptional profiles of THP-1 monocytic human leukemia cells. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Moreover, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality. Finally, an application of SINCERITIES to single cell expression data of T2EC chicken erythrocytes pointed to BATF as a candidate novel regulator of erythroid development.Availability and implementationMATLAB and R version of SINCERITIES are freely available from the following websites: http://www.cabsel.ethz.ch/tools/sincerities.html and https://github.com/CABSEL/SINCERITIES. The single cell THP-1 and T2EC transcriptional profiles are available from the original publications (Kouno et al., 2013; Richard et al., 2016). The in silico single cell data are available on SINCERITIES websites.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:Unique bits of genetic, biological and pathological information occur in differently sized cell-free DNA (cfDNA) populations. This is a significant discovery, but much of the phenomenon remains to be explored. We investigated cfDNA fragmentation patterns in cultured human bone cancer (143B) cells using increasingly sensitive electrophoresis assays, including four automated microfluidic capillary electrophoresis assays from Agilent, i.e., DNA 1000, High Sensitivity DNA, dsDNA 915 and dsDNA 930, and an optimized manual agarose gel electrophoresis protocol. This comparison showed that (i) as the sensitivity and resolution of the sizing methods increase incrementally, additional nucleosomal multiples are revealed (hepta-nucleosomes were detectable with manual agarose gel electrophoresis), while the estimated size range of high molecular weight (HMW) cfDNA fragments narrow correspondingly; (ii) the cfDNA laddering pattern extends well beyond the 1–3 nucleosomal multiples detected by commonly used methods; and (iii) the modal size of HMW cfDNA populations is exaggerated due to the limited resolving power of electrophoresis, and instead consists of several poly-nucleosomal subpopulations that continue the series of DNA laddering. Furthermore, the most sensitive automated assay used in this study (Agilent dsDNA 930) revealed an exponential decay in the relative contribution of increasingly longer cfDNA populations. This power-law distribution suggests the involvement of a stochastic inter-nucleosomal DNA cleavage process, wherein shorter populations accumulate rapidly as they are fed by the degradation of all larger populations. This may explain why similar size profiles have historically been reported for cfDNA populations originating from different processes, such as apoptosis, necrosis, accidental cell lysis and purported active release. These results not only demonstrate the diversity of size profiles generated by different methods, but also highlight the importance of caution when drawing conclusions on the mechanisms that generate different cfDNA size populations, especially when only a single method is used for sizing.

Dataset Information

Inferring gene expression from cell-free DNA fragmentation profiles.

Publications

Inferring gene expression from cell-free DNA fragmentation profiles.

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