TABLE 2.
Feasibility of tissue-of-origin analysis in oncology using cell-free DNA methylation markers.
| Disease | Methylation data type | Marker type | Deconvolution method | Publication |
| HCC, NIPT, Transplant | WGBS | Tissue-specific | QP | Sun et al., 2015 |
| PDAC, CRC, Diabetes, Transplant, MS, TBI, IBD | BSAS | Tissue-specific | Read-specific binary classification | Lehmann-Werman et al., 2016, 2018 |
| Transplant | WGBS | Tissue-specific | QP | Cheng et al., 2017 |
| CRC, LCP | RRBS, WGBS | Both | Multi-class prediction, RF, feature extraction “haplotype blocks” | Guo et al., 2017 |
| MI, sepsis | BSAS | Tissue-specific | Read-specific binary classification | Zemmour et al., 2018 |
| CRC, BRCA, PDAC, CUP, Transplant, Sepsis | 450K array | Tissue-specific | NNLS regression | Moss et al., 2018 |
| Transplant, infection | WGBS | Tissue-specific | QP | Cheng A. P. et al., 2019 |
| Neurotrauma + neurodegenerative disease | tNGBS (multiplex 35 amplicons) | Tissue-specific | Read-specific binary classification (k-mer analysis) | Chatterton et al., 2019 |
| HCT, GVHD, transplant | WGBS | Tissue-specific | QP | Cheng et al., 2020 |
| HCC, cirrhosis, cholelithiasis, acute pancreatitis | MCTA-seq | Tissue-specific | PSO | Liu Y. et al., 2019 |
| BRCA | BSAS | Tissue-specific | Read-specific binary classification | Moss et al., 2020 |
| mCRPC | Cpature-seq/WGBS | Both | PCA | Wu et al., 2020 |
| 12 cancer types | Cpature-seq/WGBS | Both | Ensemble logistic regression | Liu et al., 2020 |
| ALS, pregnancy | WGBS | Tissue-specific | Bayesian EM algorithm (CelFiE) likelihood-based | Caggiano et al., 2020 |
| Transplant, AKI | cfNOME-seq | Tissue-specific | LSM (QP) | Erger et al., 2020 |
| COVID-19 | WGBS | Tissue-specific | NNLS regression | Cheng et al., 2021 |
| HCC, CRC, LCP | WGBS | Cancer-specific | Read-specific, likelihood-based | Kang et al., 2017; Li et al., 2018 |
| LCP, HCC. PDAC, GBM, CRC, BRCA | hMe-Seal (5hmc) | Cancer-specific | RF, Mclust | Song et al., 2017 |
| PDAC, AML, BRCA, CRC, RCC, PLC | MeDIP-seq | Cancer-specific | Limma, binomial GLM | Shen et al., 2018 |
| Pediatric MB | WGBS/CMS-IP-seq | Cancer-specific | Multivariate Cox regression linear model | Li et al., 2020 |
| Glioma, intracranial tumors | MeDIP-seq | Cancer-specific | Binomial RF | Nassiri et al., 2020 |
HCC, Hepatocellular Cancer; NIPT, Non-Invasive Prenatal Testing; PDAC, Pancreatic Cancer; CRC, Colorectal Cancer; MS, Multiple Sclerosis; TBI, Traumatic Brain Injury; IBD, Inflammatory Bowel Disease; LCP, Lung Cancer Primary; MI, Myocardial Infarction; BRCA, Breast Cancer; CUP, Cancer Unknown Primary; GBM, Glioblastoma Multiforme; AML, Acute Myeloid Leukemia; RCC, Renal Cell Carcinoma; HBC, Hepatobiliary Cancer; NSCLC, Non-Small Cell Lung Cancer; HCT, Hematopoietic Cell Transplant; GVHD, Graft-vs.-Host Disease; AKI, Acute Kidney Injury; ALS, Amyotrophic Lateral Sclerosis; MB, Medulloblastoma; WGBS, Whole Genome Bisulfite Sequencing; BSAS, Bisulfite Amplicon Sequencing; RRBS, Reduced Representation Bisulfite Sequencing; ddPCR, Droplet Digital PCR; tNGBS, targeted Next Generation Bisulfite Sequencing; MeDIP-seq, Methylated DNA immunoprecipitation Sequencing; CMS-IP-seq, Cytosine 5-methyenesulphonate-immunoprecipitation sequencing; MCTA-seq, Methylated CpG Tandems Amplification Sequencing; cfNOME-seq, cell-free Nucleosome Occupancy and Methylation Sequencing; RF, random forest; GLM, generalized linear model; NNLS, Non-Negative Least Squares; LSM, Linear Least Squares Minimization; QP, Quadratic Programming; PSO, Particle Swarm Optimization; EM, Expectation-Maximization. Some of the materials are based on Barefoot et al. (2021).