The circulation receives DNA from different tissues and organs within the body. The analysis of plasma DNA from specific tissues or organs is useful for revealing and monitoring the pathological processes in different tissues. In scenarios where the genetic composition of a target tissue or organ is different from the host constitutional genome, plasma DNA carrying the tissue- or organ-specific variants can be used to identify DNA molecules released by the tissue or organ. For example, in pregnant women, plasma DNA carrying fetal-specific alleles can be used for prenatal analysis of the fetal genetic constitution (Kitzman et al., 2012; Lo et al., 2010). In organ transplant recipients, the concentrations of donor-specific DNA has been used to reflect the tissue damage associated with acute rejection (De Vlaminck et al., 2014; De Vlaminck et al., 2015; Knight et al., 2019; Lo et al., 1998; Schütz et al., 2017). Notably, immediately after organ transplantation, the plasma concentration of donor-derived DNA surges (De Vlaminck et al., 2015). Because of this initial surge, the analysis for donor-derived DNA has limited value in identifying graft rejection and infection during the first 60 days of transplantation (De Vlaminck et al., 2015). The exact mechanism of this initial surge is unclear. It is possible that the hematopoietic cells within the transplanted organ are more likely to release a significant amount of DNA into the circulation during the initial days after the transplantation. However, existing methods for detecting DNA derived from a transplanted organ in plasma rely on identifying genetic differences between the organ donor and the recipient (De Vlaminck et al., 2014; De Vlaminck et al., 2015; Knight et al., 2019; Lo et al., 1998; Schütz et al., 2017). These methods cannot be used to further distinguish the exact cell types the donor DNA is derived from.

In situations where the genetic compositions of the different organs are the same, tissue composition analysis based on detecting organ-specific alleles would not be applicable. To overcome this, recent efforts have been made to measure the composition of DNA using epigenetic approaches. These approaches include methylation deconvolution (Moss et al., 2018; Sun et al., 2015), mapping nucleosomal patterns (Snyder et al., 2016; Sun et al., 2019), analysis of end DNA motifs, end positions and jaggedness (Chan et al., 2016; Jiang et al., 2018; Jiang et al., 2020b; Jiang et al., 2020a), and the profiling of RNA transcripts (Koh et al., 2014; Tsui et al., 2014). In these methods, the features of interest, for example, methylation patterns, of the plasma DNA were profiled and compared with those of the candidate tissues. Then the relative contribution of the different tissues to the circulating DNA was determined mathematically. One potential application of plasma DNA tissue composition analysis is to reveal the likely location of a concealed cancer. Recently, it has been shown that the analysis for circulating cell-free tumor DNA (ctDNA) is useful for the screening of early asymptomatic cancers (Chan et al., 2017; Lennon et al., 2020; CCGA Consortium et al., 2020). As cancer-associated genetic and epigenetic changes are present in virtually all types of cancers (Chan et al., 2013a; Chan et al., 2013b; Leary et al., 2012; Wong et al., 1999), the detection of these cancer-associated aberrations in plasma can potentially serve as universal tumor markers for the screening of cancers in general. However, how subjects with positive results of a universal cancer test can be further worked up is an important but relatively under-explored topic. In a study by Lennon et al., subjects tested positive with ctDNA test that detected a wide variety of cancers were investigated with whole body positron emission tomography-computed tomography (PET-CT) (Lennon et al., 2020). If the potential tissue origin of the cancer can be obtained from ctDNA analysis, more focused investigations, for example, high-resolution imaging of an affected organ, can be performed. These organ-specific investigations could provide better sensitivity and specificity and could be achieved with a lower dose of radiation to the patients. In previous proof-of-principle studies, the tissue origin of cancers was successfully revealed by plasma DNA deconvolution (CCGA Consortium et al., 2020; Moss et al., 2018; Sun et al., 2015). However, existing approaches only allow tissue composition analysis of the whole pool circulating DNA rather than specifically to the tumor-derived DNA. The accuracy of these approaches would be affected by the fractional concentration of tumor-derived DNA in the sample.

In this study, we developed a method called genetic-epigenetic tissue mapping (GETMap) to determine the tissue composition of plasma DNA carrying genetic variants which are different from the host constitutional genome. This method is based on the comparison of the methylation profiles of the plasma DNA carrying genetic variants and the relevant tissues or organs that plasma DNA is potentially derived from. First, we validated this approach using a pregnancy model through the analysis of the tissue origin of the plasma DNA carrying fetal-specific alleles. Then, we applied this method to measure the tissue compositions of plasma DNA carrying cancer-associated mutations (i.e., present in tumor cells or plasma but absent from buffy coats) in hepatocellular cancer (HCC) patients and those molecules carrying donor-specific alleles in lung transplant recipients. The former analysis can provide information regarding the tissue origin of the cancer and the latter analysis provided insights on the reason for the surge of donor-derived DNA in the plasma of organ transplant recipients during the early post-transplantation period.

In this study, we developed GETMap analysis to determine the tissue origin of plasma DNA molecules carrying genetic variants. In this method, we first identified a subset of plasma DNA molecules carrying specific alleles. Then, by comparing the methylation status of these molecules and the methylation profiles of the candidate tissue organs, we could determine the tissue composition of the DNA molecules. In the first part of the study, we used the pregnancy model to validate the GETMap analysis. The plasma DNA molecules carrying the fetal-specific alleles were deduced to be 100% derived from the placenta. For the molecules carrying the alleles shared by the fetus and the mother, the percentage contribution from the placenta showed a positive linear relationship with the fractional concentration of fetal DNA based on SNP analysis. These results are consistent with the previous studies which showed that the fetal DNA in maternal plasma is indeed derived from the placenta. For the plasma DNA molecules carrying maternal-specific alleles, no contribution from the placenta was observed. A large proportion was derived from the hematopoietic cells, neutrophils, and lymphocytes, with a median total contribution of 80%. These figures are comparable to those reported previously in healthy subjects (Gai et al., 2018; Sun et al., 2015). These results demonstrate the feasibility of determining the tissue contributions to the different genetic components of plasma DNA using GETMap analysis.

We then showed that, in patients who had received lung transplantation, a substantial proportion of donor-derived DNA was derived from the hematopoietic cells during the early post-transplant period. Previous studies have shown that a high level of DNA carrying donor genotypes would be present in the plasma of organ transplant recipients during the early post-transplant period even in the absence of any evidence of organ rejection (De Vlaminck et al., 2015). Hence, quantitative analysis for donor DNA in plasma cannot be used for reflecting transplant organ damage or rejection within 60 days of transplantation. The reason for this elevation in donor DNA was unclear. Using GETMap analysis, we determined the tissue composition of plasma DNA molecules carrying donor-specific alleles for samples collected at different time intervals after transplantation. Importantly, at 72 hr after transplantation, the median contribution from the lung was only 17% and a substantial contribution of 78% was from hematopoietic cells. This is likely due to the presence of residual blood cells in the transplanted organ and they could release DNA with donor genotypes into the circulation. The contribution of the lung gradually increases with time together with a parallel decline in the contribution of the hematopoietic cells. The median contribution of hematopoietic cells dropped to 21% after 50 weeks. The persistent contribution from the hematopoietic cells may be due to imprecision of measurement as the concentrations of donor DNA after 50 weeks were very low in patients without evidence of rejection. Alternatively, there may be persistence of donor hematopoietic cells in the body of the transplant recipient. In this regard, it has been shown that some immune cells resident in the donor tissue can be long-lived and self-renewing (Gasteiger et al., 2015). The lung fraction appeared to be higher for samples collected during graft rejection compared with those collected during remission. However, the difference did not reach statistical significance. Future studies with larger sample size would be useful to further explore this point.

We then investigated if GETMap analysis could be used to identify the tissue origin of plasma DNA derived from the tumor. Circulating DNA analysis has increasingly been used in the management of cancer patients, in particular for guiding the use of target therapy and monitoring disease progression (Mok et al., 2017; Wan et al., 2020; Yung et al., 2009). Recently, it has been demonstrated that the analysis for cancer-derived DNA in plasma is useful for the screening of cancers in asymptomatic individuals (Chan et al., 2017; Lennon et al., 2020). As genetic and methylation aberrations are present in almost all cancers, the detection of cancer-associated alterations in plasma DNA can potentially serve as a universal tumor marker for a wide variety of cancers. The capability of a tumor marker for picking up multiple types of cancers can greatly enhance the cost-effectiveness of a cancer screening program. However, the lack of tissue or organ specificity of these tests also poses practical challenges on the workup of subjects with positive test results. In the screening study by Lennon et al., subjects tested positive were further investigated with PET-CT to confirm and localize a possible tumor (Lennon et al., 2020). However, if the tissue origin and location can be obtained from the ctDNA analysis, more targeted investigation on the potentially affected organ may be performed. For example, a colonoscopy can be performed for individuals who are suspected of having colorectal cancers. This targeted investigation approach not only provides a more accurate assessment for cancers, it also reduces the radiation exposure of the tested positive subjects. Here, we used the GETMap analysis to determine the tissue origin of plasma DNA carrying cancer-associated mutations. First, we compared the sequencing results of the tumor tissues and the blood cells to identify the mutations in the tumor tissues of two HCC patients. In contrast to the pregnancy and transplantation models which used microarray for genotyping, we used whole-genome sequencing to identify the cancer-associated mutations as these mutations would not be covered by the whole-genome arrays. In our simulation analysis, the numbers of informative SNPs and mutations identified are shown to provide a median accuracy of 98.3%. After bisulfite sequencing of the plasma DNA, DNA molecules carrying the cancer-associated mutations were identified and their methylation profiles were used to deduce the contribution from different tissues. The liver was deduced to be the key contributor to these cancer-derived plasma DNA molecules with a contribution of 90% and 87% for the two male HCC patients. The remaining portion, that is, 10% and 13%, were attributed to placental contribution. The attribution of a small proportion of ctDNA to originate from the placenta may be due to the fact that global hypomethylation and hypermethylation of tumor suppressor genes are common features in both the placenta and tumor tissues (Chan et al., 2013a; Feinberg and Vogelstein, 1983; Lun et al., 2013). Although this analysis suggests that GETMap analysis may be useful for revealing the tissue origin of ctDNA, the requirement of tumor tissues for mutation identification limits its practical application in cancer screening. To overcome this, we further attempted to identify cancer mutations directly from plasma DNA sequencing. In this regard, non-bisulfite sequencing for the plasma DNA and the blood cells of the cancer patients were performed. The single nucleotide variants present in the plasma DNA but not in the blood cells were regarded as cancer-associated mutations. GETMap analysis was performed on the plasma DNA molecules carrying these mutations using the bisulfite sequencing data. Despite a smaller number of cancer-associated mutations could be identified by directly sequencing plasma DNA compared with sequencing the tumor tissues, the liver was again correctly identified as the key contributor to these cancer-derived DNA molecules. These results suggest that the GETMap analysis could be useful in revealing the tissue origin and location of a concealed cancer in patients who are screened positive with a tumor marker that detects various types of cancers.

We further challenged GETMap analysis with a complex scenario where a woman developed lymphoma during pregnancy. Her plasma consisted of DNA derived from the lymphoma tissues, the fetus, and the normal cells. As fetal tissue was not available, fetal genotypes were deduced by sequencing plasma DNA, maternal blood cells/buccal cells, and tumor tissues. Sequence variants present in plasma that overlap with the dbSNP database but absent in the tumor tissues were regarded as fetal-specific alleles. Variants detected in plasma and tumor tissues, but not overlapping with the dbSNP database were regarded as tumor-specific. Plasma DNA molecules carrying these fetal-specific alleles were deduced to be predominantly (95%) derived from the placenta, whereas those carrying the tumor-specific alleles were solely from lymphocytes.

There has been increasing interest in the tissue composition circulating cell-free DNA. Methods based on analysis of DNA methylation (Gai et al., 2018; Lehmann-Werman et al., 2016; Sun et al., 2015), nucleosome footprint (Snyder et al., 2016; Sun et al., 2019), sequence motifs, end coordinates, and jaggedness (Chan et al., 2016; Jiang et al., 2018; Jiang et al., 2020a; Jiang et al., 2020b) have been developed. However, existing methods only allow the deconvolution of all the DNA as a single entity. In contrast, GETMap analysis can determine the tissue origin of subsets of plasma DNA that carry different genetic variations. The specific analysis of a particular component can enhance the signal-to-noise ratio and eliminate the variation caused by the difference in the concentrations of the target DNA, for example, DNA derived from the tumor. Furthermore, clonal hematopoiesis has been identified as one important source of false-positive results for liquid biopsy-based cancer screening tests. In this regard, GETMap would be useful for identifying the hematopoietic origin of the abnormal signal in such cases. Although the number of cases is relatively small in this proof-of-principle study, we have illustrated the potential applications in cancer detection, prenatal testing, and organ transplant monitoring. As the current format of this method is based on whole-genome bisulfite sequencing, identification of cytosine to thymine alteration is less efficient because bisulfite treatment would convert unmethylated cytosine to thymine. A targeted sequencing approach enriching for regions with mutation hotspots and differential methylation across different tissues can be developed to enhance the cost-effectiveness of this approach.

Sequencing data have been deposited in EGA under the accession code EGAS00001004788.

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Author details

1. Wanxia Gai

   1. Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
   2. Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
   3. State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong, China

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

2. Ze Zhou

   1. Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
   2. Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China

   Contribution

   Data curation, Software, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Sean Agbor-Enoh

   1. Genomic Research Alliance for Transplantation (GRAfT), Bethesda, United States
   2. Division of Pulmonary and Critical Care Medicine, The Johns Hopkins School of Medicine, Baltimore, United States
   3. Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, United States

   Contribution

   Data curation, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

4. Xiaodan Fan

   Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China

   Contribution

   Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2744-9030

5. Sheng Lian

   Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China

   Contribution

   Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

6. Peiyong Jiang

   1. Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
   2. Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
   3. State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong, China

   Contribution

   Data curation, Investigation, Methodology, Writing - review and editing

   Competing interests

   Holds equities in Grail. Serves as a director of KingMed Future. Received patent royalties from Grail, Illumina, Sequenom, DRA, Take2 and Xcelom. Filed a patent application (US15/214,998).

7. Suk Hang Cheng

   1. Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
   2. Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China

   Contribution

   Formal analysis, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

8. John Wong

   Department of Surgery, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

9. Stephen L Chan

   Department of Clinical Oncology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China

   Contribution

   Investigation

   Competing interests

   No competing interests declared

10. Moon Kyoo Jang

    1. Genomic Research Alliance for Transplantation (GRAfT), Bethesda, United States
    2. Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, United States

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

11. Yanqin Yang

    1. Genomic Research Alliance for Transplantation (GRAfT), Bethesda, United States
    2. Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, United States

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

12. Raymond HS Liang

    Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Hong Kong, China

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

13. Wai Kong Chan

    Department of Pathology, Hong Kong Sanatorium & Hospital, Hong Kong, China

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

14. Edmond SK Ma

    Department of Pathology, Hong Kong Sanatorium & Hospital, Hong Kong, China

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

15. Tak Y Leung

    Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

16. Rossa WK Chiu

    1. Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
    2. Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration

    Competing interests

    Holds equities in DRA, Take2 and Grail. Is a consultant to Grail and Illumina. Receives research funding from Grail. Receives royalties from Grail, Illumina, Sequenom, DRA, Take2 and Xcelom. Filed a patent application (US15/214,998).

17. Hannah Valantine

    1. Genomic Research Alliance for Transplantation (GRAfT), Bethesda, United States
    2. Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, United States

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

18. KC Allen Chan

    1. Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
    2. Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
    3. State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong, China

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Investigation, Methodology, Writing - original draft, Project administration

    Competing interests

    Holds equities in DRA, Take2 and Grail. Is a consultant to and receives research funding from Grail. Receives royalties from Grail, Illumina, Sequenom, DRA, Take2 and Xcelom. Filed a patent application (US15/214,998).

    "This ORCID iD identifies the author of this article:" 0000-0003-1780-1691

19. YM Dennis Lo

    1. Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
    2. Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
    3. State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong, China

    Contribution

    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    loym@cuhk.edu.hk

    Competing interests

    Reviewing editor, eLife. Holds equities in DRA, Take2 and Grail. Serves as a scientific cofounder and consultant of Grail. Receives research funding from Grail. Receives royalties from Grail, Illumina, Sequenom, DRA, Take2 and Xcelom. Filed a patent application (US15/214,998).

    "This ORCID iD identifies the author of this article:" 0000-0001-8746-0293

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