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Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA

Nature Genetics volume 49, pages 635–642 (2017)Cite this article

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Abstract

Adjacent CpG sites in mammalian genomes can be co-methylated owing to the processivity of methyltransferases or demethylases, yet discordant methylation patterns have also been observed, which are related to stochastic or uncoordinated molecular processes. We focused on a systematic search and investigation of regions in the full human genome that show highly coordinated methylation. We defined 147,888 blocks of tightly coupled CpG sites, called methylation haplotype blocks, after analysis of 61 whole-genome bisulfite sequencing data sets and validation with 101 reduced-representation bisulfite sequencing data sets and 637 methylation array data sets. Using a metric called methylation haplotype load, we performed tissue-specific methylation analysis at the block level. Subsets of informative blocks were further identified for deconvolution of heterogeneous samples. Finally, using methylation haplotypes we demonstrated quantitative estimation of tumor load and tissue-of-origin mapping in the circulating cell-free DNA of 59 patients with lung or colorectal cancer.

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Figure 1: Identification and characterization of human methylation haplotype blocks (MHBs).
Figure 2: Comparison of methylation haplotype load with four other metrics used in the literature.
Figure 3: Tissue clustering based on methylation haplotype load.
Figure 4: Quantitative estimation of the proportion of DNA derived from cancer cells in cell-free DNA, using the MHL of informative MHBs.
Figure 5: MHL-based prediction of cancer tissue of origin from plasma DNA.

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Acknowledgements

We thank S. Kaushal for managing and handling patient samples in the UCSD Moores Cancer Center Biorepository Tissue Technology Shared Resource, and S.M. Lippman, R. Liu and B. Ren for insightful discussions. This study was supported by US National Institutes of Health grants R01GM097253 (Kun Zhang), R01CA217642 (Kang Zhang), R01EY025090 (Kang Zhang) and P30CA23100 (S.M.L.), and a VA Merit Award (Kang Zhang).

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  1. Shicheng Guo and Dinh Diep: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Bioengineering, University of California at San Diego, La Jolla, California, USA

    Shicheng Guo, Dinh Diep, Nongluk Plongthongkum, Ho-Lim Fung & Kun Zhang

  2. Institute for Genomic Medicine, University of California at San Diego, La Jolla, California, USA

    Kang Zhang & Kun Zhang

  3. Shiley Eye Institute, University of California at San Diego, La Jolla, California, USA

    Kang Zhang

  4. Veterans Administration Healthcare System, San Diego, California, USA

    Kang Zhang

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  1. Shicheng Guo
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Contributions

Kun Zhang conceived the initial concept and oversaw the study; S.G., D.D. and Kun Zhang performed the bioinformatics analyses; N.P., D.D. and H.-L.F. performed the experiments; Kang Zhang contributed plasma samples from healthy individuals; and Kun Zhang, S.G. and D.D. wrote the manuscript with input from all co-authors.

Corresponding author

Correspondence to Kun Zhang.

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Competing interests

S.G., D.D. and Kun Zhang are listed as inventors in patent applications related to the methods disclosed in this manuscript, and Kun Zhang is a co-founder and scientific advisor of Singlera Genomics, Inc.

Integrated supplementary information

Supplementary Figure 1 Characteristics of MHBs in the human genome.

(a) Distribution of MHB sizes. (b) Distribution of MHBs CpG densities (CpGs/bp). (c) Co-localization of known genomic features broken down by CpG density. We split all MHBs into quartiles where each quartile is as follows: (0, 0.046], (0.046, 0.097], (0.097, 0.155], (0.155, 6]. Note that closed brackets are inclusive. The 1st quartile (MHBs with the lowest CpG densities) are mostly in CGI shelf or shore, and are enriched for LAD, LOCK and enhancers.

Supplementary Figure 2 Loss of CpG linkage disequilibrium replicated in two additional samples tumor tissues from patients with kidney cancer.

Two kidney cancer WGBS data were downloaded from NCBI GEO GSE63183), and processed with the same computational procedures.

Supplementary Figure 3 Validation of MHBs with TCGA Methylation HM450K beadchip and ENCODE RRBS data.

(a) Squared Pearson correlation coefficient r2 versus methylation LD r2. (b) The Pearson correlation coefficient for CpGs in RRBS and HM450K data were significantly higher in regions overlapping with MHBs compared with the CpGs without overlapping with MHBs. IN denotes RRBS or HM450K CpGs within MHBs. OUT denotes RRBS or HM450K regions beyond MHBs.

Supplementary Figure 4 Profiles of H3K27ac, H3K4me3 and H3K4me1 over methylation haplotype blocks for 12 human adult tissue types.

X-axis denote the distances from the centers of MHBs (+/- 1000 bp) and y-axis denotes the average reads density in RPKM (input normalized reads per kilobase per million). Epigenomics Roadmap histones data were downloaded from NCBI GEO (https://www.ncbi.nlm.nih.gov/geo/roadmap/epigenomics/).

Supplementary Figure 5 PCA of human tissues and cells based on methylation haplotype loads in MHBs regions.

Tissues and cells from WGBS datasets were downloaded from 5 other studies and 10 adult tissues WGBS were from generated in this study.

Supplementary Figure 6 Distinct patterns of functional enrichment for TFBS associated with layer-specific MHBs.

(a) Venn diagrams of transcription factors (TF) with binding sites associated with layer specific hypo- or hyper- MHL regions. (b) Functional enrichment analysis of associated TFBS using GREAT (http://bejerano.stanford.edu/great/public/html/).

Supplementary Figure 7 Distribution of incidence of cancer-associated HMH in plasma samples from patients with colorectal cancer or lung cancer.

Y-axis denotes the frequency of caHMH and x-axis denotes the incidence (sample number) of the caHMH in CRC plasma samples (a) or LC plasma samples (b). The majority of caHMH are patient specific while a few have high incidence among the cancer plasma samples.

Supplementary Figure 8 Deconvolution of cancer and normal plasma samples using non-negative decomposition with quadratic programming.

(a) Deconvolution accuracy as a function of tumor fraction using simulated data. (b) Cancer DNA proportions estimated by deconvolution of plasma samples using CCT or LCT as the tumor reference.

Supplementary Figure 9 Estimated tumor fraction in plasma correlated with the normalized yield of DNA extraction from plasma.

Supplementary Figure 10 Distribution of tissue-specific MHBs counts in human plasma samples.

Color bar represents the number of tissue specific MHBs (for each respective tissue) over the MHL threshold in each plasma sample.

Supplementary Figure 11 Distributions of counts of highly methylated tsMHBs in human plasma samples.

(a) Distributions of counts of ts-MHBs infor normal plasma samples forto 11 reference tissues. (b) Distributions of count of ts-MHBs infor lung cancer plasma samples forto lung tissue or to pan-cancer tissue (CT). (c) Distributions of counts of ts-MHBs infor colorectal cancer plasma samples forto colon tissue or to pan-cancer tissue (CT).

Supplementary Figure 12 Joint prediction of cancer status and tissue of origin on plasma DNA.

Distribution of enrichment Z-score in each set of reference-specific MHBs for colon cancer plasma samples (a) and lung cancer plasma samples (b). Integrating signatures from cancer and tissue-of-origin (Colon+CT; Lung+CT) improveds the prediction accuracy (c,d), on both types of plasma samples, compared withover focusing on cancer signatures alone (CT). The ROC curves were created by adjusting the Z-score cutoff for calculating specificities and sensitivities. AUC denotes area under the curve.

Supplementary Figure 13 Flowchart of data processing and samples used in this study.

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Guo, S., Diep, D., Plongthongkum, N. et al. Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA. Nat Genet 49, 635–642 (2017). https://doi.org/10.1038/ng.3805

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