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Inferring gene expression from cell-free DNA fragmentation profiles

Nature Biotechnology volume 40, pages 585–597 (2022)Cite this article

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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.

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Fig. 1: Correlation of gene expression and cfDNA molecular features.
Fig. 2: Fragment size entropy in relation to gene structure informs expression inferences from whole-exome cfDNA profiling.
Fig. 3: EPIC-seq design and workflow.
Fig. 4: Application of EPIC-seq for lung cancer detection and histological classification.
Fig. 5: Application of EPIC-seq for DLBCL detection.
Fig. 6: Application of EPIC-seq for DLBCL COO classification.

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Data availability

For each sample profiled in this study, we also provide anonymized fragmentomic data for fragments meeting minimal MAPQ and read FLAGs, which are available at https://epicseq.stanford.edu. These data are summarized across TSS regions by fragment size distributions (as in Fig. 1b). Moreover, the anonymized sequencing reads of samples profiled whole-genome (n = 3 deep whole-genome; n = 3 whole-genome and n = 24 shallow whole-genome) and whole-exome (n = 39 deep WES) are deposited at SRA PRJNA795275.

Code availability

The custom EPIC-seq software code for fragmentomic featurization and gene expression inference from cfDNA BAM files can be accessed at https://epicseq.stanford.edu/.

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Acknowledgements

We thank the patients and families who participated in this study, and we are grateful for all the insightful discussions with the members of the Alizadeh and Diehn laboratories. Funding sources: this work was supported by the National Cancer Institute (M.S.E., grant no. R25CA180993; M.S.E., grant no. T32CA009302; M.M., grant no. F99CA212457, M.D. and A.A.A., grant nos. R01CA188298, R01CA254179, R01CA257655, R01CA244526, R01CA233975, and R01CA229766), the US National Institutes of Health Director’s New Innovator Award Program (M.D.; grant no. 1-DP2-CA186569), the Virginia and D.K. Ludwig Fund for Cancer Research (M.D. and A.A.A.), and philanthropic support from the Bakewell Foundation (to M.D. and A.A.A.), the CRK Faculty Scholar Fund (M.D.), the Troper-Wojcicki Family Gift, The Shanahan Family Lymphoma Fund, Arzang Family Lymphoma Fund, The Skeff Family Lymphoma Fund, The Cane-Nowak Family Foundation, The Marc Benioff Fund, Jewish Communal Fund for Lymphoma Research, The Sara Schottenstein Memorial Fund, and The Moghadam Family Endowed Professorship (to A.A.A.). A.A.A. is a scholar of the Leukemia and Lymphoma Society. J.M.I., C.M.R. and B.T.L. are supported by the MSKCC Support grant no. P30 CA008748 from the NIH. B.Y.N. is a Stanford Cancer Systems Biology Scholar and supported by the NIH (grant no. 5R25CA180993) and by the Postdoctoral Research Fellowship (grant no. 134031-PF-19-164-01-TBG) from the American Cancer Society. Present address for M.M. is the Department of Biotechnology, College of Science, University of Tehran, Iran, and for D.A.K. is the Northwell Health Cancer Institute and Feinstein Institute of Research, Lake Success, NY, USA.

Author information

Authors and Affiliations

  1. Divisions of Oncology and of Hematology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA

    Mohammad Shahrokh Esfahani, Mahya Mehrmohamadi, Stefan K. Alig, Daniel A. King, Chloé B. Steen, Charles W. Macaulay, Joanne Soo, Joseph G. Schroers-Martin, Binbin Chen, Brian J. Sworder, Chih Long Liu, David M. Kurtz & Ash A. Alizadeh

  2. Department of Radiation Oncology, Stanford School of Medicine, Stanford, CA, USA

    Mohammad Shahrokh Esfahani, Mahya Mehrmohamadi, Barzin Y. Nabet, Michael S. Binkley, Jacob J. Chabon, Angela B-Y Hui, Everett J. Moding & Maximilian Diehn

  3. Stanford Cancer Institute, Stanford School of Medicine, Stanford, CA, USA

    Mohammad Shahrokh Esfahani, Barzin Y. Nabet, Andre Schultz, Joseph G. Schroers-Martin, Henning Stehr, David M. Kurtz, Maximilian Diehn & Ash A. Alizadeh

  4. Program in Cancer Biology, Stanford School of Medicine, Stanford, CA, USA

    Emily G. Hamilton & Monica C. Nesselbush

  5. Institute for Stem Cell Biology and Regenerative Medicine, Stanford School of Medicine, Stanford, CA, USA

    Chloé B. Steen, Aaron M. Newman, Maximilian Diehn & Ash A. Alizadeh

  6. Department of Biomedical Informatics, Stanford School of Medicine, Stanford, CA, USA

    Chloé B. Steen & Aaron M. Newman

  7. Division of Blood and Marrow Transplantation and Cellular Therapy, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA

    Matthew J. Frank

  8. Thoracic Surgery Service, Memorial Sloan Kettering Cancer Center and Weill Cornell Medicine, New York, NY, USA

    James M. Isbell

  9. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Charles M. Rudin & Bob T. Li

Authors
  1. Mohammad Shahrokh Esfahani
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  2. Emily G. Hamilton
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  19. Matthew J. Frank
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  20. Everett J. Moding
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  21. Chih Long Liu
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  22. Aaron M. Newman
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  27. Maximilian Diehn
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  28. Ash A. Alizadeh
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Contributions

M.S.E., M.M., M.D. and A.A.A. developed the concept of EPIC-seq. M.S.E., M.D. and A.A.A. developed the concept of PFE, designed the experiments, analyzed the data and wrote the manuscript. M.S.E. and M.M. designed the EPIC-seq selector with inputs from H.S., A.M.N., M.D. and A.A.A. M.S.E. performed all the statistical and machine learning analyses and the mechanistic modeling with assistance from M.M., B.C. and C.B.S. M.S.E., M.M., E.G.H. and A.S. performed the bioinformatics analyses with assistance from D.A.K., C.L.L., M.C.N. and J.J.C. E.G.H., C.W.M., J.S., B.Y.N., A.B.-Y.H., J.G.S-M. and E.J.M. performed molecular biology experiments related to profiling clinical specimens with assistance from J.J.C. S.K.A., B.J.S., B.Y.N., M.J.F., J.M.I., C.M.R., B.T.L., D.M.K., M.D. and A.A.A. provided patient specimens and clinical data. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Maximilian Diehn or Ash A. Alizadeh.

Ethics declarations

Competing interests

A.A.A. reports research funding from Celgene, Pfizer, ownership interests in FortySeven, CiberMed, ForeSight and paid consultancy from Roche, Genentech, Janssen, Pharmacyclics, Gilead, Celgene and Chugai. M.D. reports research funding from Varian Medical Systems and Illumina, ownership interest in CiberMed, ForeSight and paid consultancy from Roche, AstraZeneca, Illumina, RefleXion and BioNTech. J.J.C. reports paid consultancy from Lexent Bio Inc. and ownership interests in ForeSight. A.M.N. has patent filings related to expression deconvolution and cancer biomarkers and has served as a consultant for Roche, Merck and CiberMed. D.M.K. reports paid consultancy from Roche. B.T.L. has served as an uncompensated advisor and consultant to Amgen, Genentech, Boehringer Ingelheim, Lilly, AstraZeneca and Daiichi Sankyo. B.T.L. reports receiving research grants to his institution from Amgen, Genentech, AstraZeneca, Daiichi Sankyo, Lilly, Illumina, GRAIL, Guardant Health, Hengrui Therapeutics, MORE Health and Bolt Biotherapeutics. B.T.L. has received academic travel support from MORE Health and Jiangsu Hengrui Medicine. B.T.L. reports to be inventor on two institutional patents at MSKCC (US62/685,057, US62/514,661) and has intellectual property rights as a book author at Karger Publishers and Shanghai Jiao Tong University Press. J.M.I. reports serving as an unpaid consultant to Amgen and Roche-Genentech, institutional research support from Guardant Health and GRAIL, and ownership interest in LumaCyte. A.A.A., M.D., M.S.E., D.M.K., J.J.C., and B.Y.N. report patent filings related to cancer biomarkers. M.S.E., M.M., A.A.A. and M.D. have patent filing related to this paper. B.Y.N. is currently an employee and holds stock from Roche/Genentech. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Fragment length density at the transcription start sites varies with gene expression.

Fragment length density at the transcription start sites varies with gene expression. (a) A heatmap of fragment length densities across 1,748 groups of genes (similar to Fig. 1a). Three regions R1 (100–150 bps), R2 (151–210 bps), and R3 (211–300 bps) show enrichment in either high or low expression gene groups. (b) The percent of fragments within each region defined in panel (a) in the deep whole-genome sample across deciles of the reference PBMC gene expression vector, that is, 10 groups of genes when sorted by their expression values in PBMC. Highly expressed genes include fewer monosome fragments, indicating a wider distribution and thereby a higher PFE. (c) Fraction of fragments within the three regions, R1-R3, for exons vs introns vs TSS sites for the top (and bottom) 2000 genes as ranked by expression. The fraction of monosomal fragments within TSS regions is substantially lower than within intronic and exonic regions (63.5% at TSS vs ~71% at non-TSS). Pearson’s Chi-Squared goodness-of-fit tests resulted in the following test statistics (TSS vs Exon: G = 62,133 [P < 2.2E-16]; TSS vs Intron: G = 84,110 [P < 2.2E-16]). (d) Fraction of fragments falling within each region (R1, R2, and R3) for mutant cfDNA fragments and their wildtype counterparts. Each dot represents one tuple (variant-patient) and the connecting lines indicate the paired mutant-wildtype status. These results show that the mutant cfDNA fragments are enriched for R1 and R3 while wildtype fragments are enriched in R2. (e) A contour plot capturing the relationship between expression level (depicted by heat) as a function of two cfDNA fragmentomic features used in the gene inference model: PFE and NDR. (f) ROC analysis of a ‘NSCLC Score’ for noninvasively distinguishing patients with NSCLC from healthy controls (AUC = 0.76). The genes comprising this score were first defined from external RNA-Seq profiling data of primary NSCLC tumor tissues and blood samples, allowing subsequent calculation of their corresponding PFE in cfDNA samples profiled by WGS for independent NSCLC cases and healthy controls. (g) A schematic for the analyses performed for Fig. 2d–h. (h) Sample-level ‘SCLC Score’ from deep whole exome analysis of cfDNA and associated diagnostic performance. As in the exercise for NSCLC depicted in panel f, the genes comprising this SCLC score were first defined from external RNA-Seq profiling data of primary SCLC tumor tissues and blood samples. The corresponding PFEs (as the difference between the overall PFE level of top and bottom gene signatures) were subsequently calculated in cfDNA samples we profiled by deep WES for independent SCLC cases and healthy controls. Using these scores, an AUC of 0.9 was achieved in distinguishing cases from controls. (i) The Venn diagram of SCLC high genes identified in cfDNA (whole exome profiling) and tumor biopsy (RNA-Seq transcriptome profiling), with significance of overlap assessed by hypergeometric test.

Extended Data Fig. 2 Ensemble model accurately predicts gene expression in validation samples.

Ensemble model accurately predicts gene expression in validation samples. (a) The scatterplot of the predicted vs a population-averaged gene expression across 1,748 groups of genes. The underlying data are from a merged cfDNA ‘meta-sample’ (pooled from merger of 27 healthy subjects profiled by relatively shallow WGS), achieving a correlation of 0.9 in initial validation. (b) The meta sample from panel (a) was used to assess model performance, when considering TSS-level expression values without gene grouping (n = 1), as well as scenarios with 2, 3, 5 and 10 genes per group. The Pearson correlation between observed expression in PBMC versus predicted expression from our model (combining PFE and NDR) is shown in green bars. This correlation substantially improves as number of genes per group increases. The Pearson correlation values between observed gene expression and those predicted by NDR or PFE expression are shown in blue and green bars, respectively. (c) Scatterplot depicts predicted versus observed gene expression measurements across 1,748 groups of genes (dots), when comparing expression measurements by RNA-Seq on matched PBMC (x-axis) against plasma cfDNA inferences (y-axis), for a validation sample from a healthy adult that we also profiled by deep WGS (~200x). This achieved a Pearson correlation of 0.86. (d) Similar to panel c, but for a second healthy adult control subject also profiled for validation, by deep WGS of cfDNA and matched RNA-Seq of PBMC (Pearson r = 0.91). (e-f) The same analysis as in panels (a-b) for a meta whole-genome sample generated from healthy subjects from Zviran et al. (g) The whole genome samples (depth ~20-40x) from Zviran et al. were used with every ten genes grouped and the concordance between model-predicted expression and PBMC expression are evaluated using Pearson correlation (that is, each dot is one subject). The non-cancer samples show a significantly higher correlation with normal PBMC than lung cancer cases (Wilcoxon P = 0.018). (h) The ichorCNA tumor fraction estimates of the lung cancer cases in panel f are used to compare with the correlations in panel f. As shown in a scatterplot, as tumor fraction increases, the correlation decreases (r = −0.69, P = 0.00052).

Extended Data Fig. 3 Case-level information of samples profiled by EPIC-Seq.

Cohorts and cell-free DNA samples profiled by EPIC-seq in this study, including Cancer Cases and Control Subjects. (a) Schema depicts the full set of specimens profiled by EPIC-Seq (n = 373), including those meeting Quality Control (QC) criteria (n = 352, 95%). A subset of samples were used for the initial gene expression model tuning (n = 2) and TSS filtering (n = 21). The remaining 329 samples were profiled by EPIC-Seq to address disease-specific questions, including utility for cancer detection, classification of histology and cell-of-origin, and response monitoring. These included 252 samples (76.6%) from 226 subjects that comprised our Discovery/Training cohort (large light purple rectangle), as well as subsequent profiling of a Validation Cohort of 77 samples (23.4%) from 69 subjects, after models were ‘locked down’ (large light green rectangle). A subset of 22 NSCLC patients where a pair of serial blood samples were monitored for ICI response (to allow comparisons of both EPIC-Seq and CAPP-Seq and assess biological plausibility), but this exercise was not subject to any model training. No samples were shared between Training and Validation exercises, with all models locked down before independent validations. Four healthy subjects (4.5%) provided more than one cfDNA specimen with one used for Training and the second for Validation. (b) Distribution of demographic, clinical, anatomic, and pathological variables for subjects profiled by EPIC-Seq. Tabulated are the relevant indices for cancer cases (235 blood samples 201 patients), including NSCLC patients (light blue; 109 blood samples from 87 patients), DLBCL patients (light orange; 126 blood samples from 104 patients), and non-cancer control subjects (gray; 94 blood samples from 87 adults).

Extended Data Fig. 4 Correlation between EPIC-lung score and clinical factors.

Concordance between EPIC-Seq measurements and established NSCLC risk factors including metabolic tumor burden, ctDNA level, and ctDNA response. (a) Concordance between EPIC-lung score and metabolic tumor volume (MTV), as measured by Spearman correlation (ρ = 0.67; P = 0.04). (b) Concordance between EPIC-lung score and the ctDNA mean allele fractions as measured by CAPP-Seq, evaluated using Spearman correlation (ρ = 0.5; P = 3E-5). (c) Relationships between genetic versus epigenetic molecular responses to Immune Checkpoint Inhibitor (ICI) therapy in advanced NSCLC. Scatterplot compares molecular responses measured noninvasively by CAPP-Seq (x-axis; fold change, Log10) and EPIC-Seq (lung dynamics score; y-axis) using serial plasma profiling before and after ICI therapy. The two orthogonal measures show moderate but significant correlation (r = 0.53, P = 0.012).

Extended Data Fig. 5 Correlation between EPIC-lymphoma score and clinical factors, results of the validation set and prognostic value of the LMO2 distal promoter.

Concordance between EPIC-Seq measurements and established DLBCL risk factors impacting outcome, including metabolic tumor volume, ctDNA level, and Cell-of-Origin. (a) The boxplots illustrate the two groups of patients stratified by their metabolic tumor volumes (>220 vs <220 mL; Wilcoxon P = 0.015). (b) Similar to panel a, but for the DLBCL Validation Cohort. (c) Concordance between EPIC-DLBCL scores and ctDNA mean allele fractions (from CAPP-Seq), evaluated using Spearman correlation (ρ = 0.66; P < 2E-16). (d) The EPIC-DLBCL model is applied to the cfDNA profiles of 13 samples from two DLBCL patients (DLBCL002 [ABC] and DLBCL007 [GCB]). The concordance between the resulting scores and the ctDNA mean allele fractions is evaluated by Spearman correlation (ρ = 0.79; P = 0.004). (e) Relationship between DLBCL cell-of-origin EPIC-Seq GCB scores and mutation-based GCB scores as measured by CAPP-Seq in the validation set (Spearman ρ = 0.64, P = 0.01). Each dot represents one sample (related to Fig. 6a). (f) Relationship between EPIC-Seq GCB scores from cfDNA and matched tumor tissue classification by routine Hans immunohistochemical algorithm in the validation set (Wilcoxon P = 0.001; related to Fig. 6b). (g) Relationship between EPIC-Seq GCB scores from cfDNA and tumor classification by RNA-seq of paired tumor tissue (Jonckheere’s trend test, P = 0.015). Box-and-whisker plots depict the EPIC-Seq GCB score in individual samples profiled by EPIC-Seq (dots), with boxes spanning the inter-quartile range; the median is horizontally marked with a line in each box, and whiskers span the 1.5 IQRs. (h) The Kaplan-Meier curves of EFS of the patients when labeled by the Hans algorithm. The non-GCB group contains both Non-GCB and Unknown. (i) The violin plot shows the distributions of Cox Proportional Hazard Model Z-scores when genes are grouped according to their effects on outcome (measured as EFS) in three prior tumor studies.

Extended Data Fig. 6 Pre-analytical factors and TSS GC-content correction effect on PFE.

Effect of preanalytical factors on fragment size entropy and effect of GC-content correction on expression model performance. (a) The concordance between PFE values for three healthy controls profiled by EPIC-Seq using paired Streck BCT and K2EDTA tubes. A Pearson correlation of 0.94 was observed between tube types. (b) Effect of time on the bench (that is, in days) on the PFEs in a cohort of plasma cfDNA samples. (c) Effect of additional PCR cycles on PFE. Here we profiled 4 healthy control cfDNA samples by the CAPP-Seq lung cancer selector when 3 additional PCR cycles were included to study their effect. A Pearson correlation of 0.95 was observed between standard conditions versus those incorporating additional PCR cycles. (d) Effect of correction for GC-content of TSS regions on gene expression model accuracy. Four scenarios were studied when correcting features using the GC values for NDR and PFE: PFE alone corrected, NDR alone corrected, both corrected, and neither corrected. The correction was performed using a LOESS function with a span of 0.5. Two healthy control cfDNA samples were profiled by deep whole genome sequencing. For these two subjects, we also profiled the matched PBMC by RNA-Sequencing. We then compared the predicted values from cfDNA against observed values from RNA-Seq for each of the different GC-correction scenarios and tested concordance. The concordance was evaluated using three metrics: Pearson correlation, Spearman correlation, and root-mean-square error (RMSE). When considering both cfDNA samples, none of the four GC-correction approaches seemed to consistently improve correlations or reduce associated error profiles. (e) Whole exome profiling of small-cell lung cancer samples in Fig. 2 are used to investigate association between PFEs and copy number aberrations. We first determined genes with PFE significantly higher in SCLC cfDNA samples (n = 11) compared with healthy control cfDNA samples (n = 28) (‘High’ PFE). Similarly, we determined genes with significantly lower PFEs in SCLC cfDNA samples (‘Low’ PFE). Then, the copy number states (CNS) corresponding to all genes were identified by overlapping copy number profiles from CNVkit with the genomic coordinates of the first exons. The CNS values were then dichotomized into (i) amplification vs no-amplification and (ii) deletion vs no-deletion. Next, we summarized these by contingency tables for (i) vs PFE levels (top table) and (ii) vs PFE levels (bottom table). Finally, the association between the two was examined via Fisher’s exact test, which showed insignificant associations in both tests (P = 0.97 and P = 0.17; for amplifications and deletions, respectively).

Extended Data Fig. 7

Mechanistic model and gene detection sensitivity with various parameters. (a) The cartoon shows four scenarios considered in our simulations: (i) protected, meaning that nucleosomes are well-positioned and are all present, (ii) one nucleosome-free position is present, (iii) two nucleosome-free positions are present and (iv) three nucleosome-free positions are present. (b) The density plots show the results of generating fragment lengths via the model described in panel a. Three panels correspond to scenarios (ii-iv) vs (i) in a. (c) A varying mixture parameters is considered and its effect on the entropy for three different coverages: 500x, 2500x and 5000x. (d) A summary of panel c for active gene detection sensitivity while achieving a specificity of 85%. The error bars are from the sensitivities calculated using the ‘ci.se’ function in R pROC package. The colors correspond to three different coverages in panel c.

Supplementary information

Supplementary Information

Supplementary Information.

Reporting Summary

Supplementary Tables 1–4

Supplementary Table 1: List of samples analyzed or profiled in this study. Supplementary Table 2: Gene groups and their corresponding features in the deep whole-genome sample. Supplementary Table 3: Targeted TSSs in the EPIC-seq panel. Supplementary Table 4: Clinical characteristics of the EPIC-seq cohort and scores from different classifiers.

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Esfahani, M.S., Hamilton, E.G., Mehrmohamadi, M. et al. Inferring gene expression from cell-free DNA fragmentation profiles. Nat Biotechnol 40, 585–597 (2022). https://doi.org/10.1038/s41587-022-01222-4

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