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Definitions

• the invention relates to the field of cell free DNA and, more specifically, to methods and uses of capturing cell-free methylated DNA.
• DNA methytetion is a covalent modification of DMA and a stable gene regulatory mechanism that plays an important role In the chromatin architecture.
• DNA methylation primarily occurs at cytokine residues In CpG dlnudeotldes. Unlike other dinucleotides, CpGs are not evenly distributed across the genome but are instead concentrated in short CpG-rich DNA regions called CpG islands.
• DNA methylation can lead to gene repression by two main mechanisms: 1) recruiting methyl-binding domain proteins, which can in turn recruit h ' etone deacetylases (HDACs) and 2) blocking the access to binding sites of transcription factors (TFs), such as c-MYC 1 .
• HDACs h ' etone deacetylases
• DNA methylation patterns have been used to stratify cancer patients fnto clinically relevant subgroups with prognostic value In glioblastoma', ependymomas 4 , colorectal 1 , breast 1 ' 7 , among many other cancer types.
• DNA methylation is a good biomarker that can be used to represent tumor characteristics and phenotypic states and therefore, has high potential for personalized medicine.
• Many sample types are suitable for DNA methylation mapping and for blomarker discovery including fresh and FFPE tumor tissue, blood cells, urine, saliva, stool, among others'.
• cfDNA circulating celt-free DNA
• the use of circulating celt-free DNA (cfDNA) as a blomarker is gaining momentum, especiaRy in situations where genomic distinctions exist, such as In cancer (somatic mutations) 8 , transplants (donor versus recipient DNA) 10 and pregnancy (fetus versus mother DNA) 11,12 .
• DNA methylation mapping of cfDNA could have a significant Impact, as it could allow for the identification of the fissue-of-orlgin and stratify cancer patients in a minimally invasive fashion. Moreover, it could enable the use of cfDNA as a biomaricer In situations where genomic distinctions do not exist, such as monitoring immune response, neurodegenerative diseases or myocardial Infarction, where the epigenetic aberration can be detected in the cfDNA.
• genome-wide DNA methylation mapping of cfDNA could overcome a critical sensitivity problem in detecting circulating tumor DNA (dDNA) in patients with early-stage cancer with no radiographic evidence of disease.
• Existing ctQNA detection methods are based on sequencing mutations and have limited sensitivity In part due to the limited number of recurrent mutations available to distinguish between tumor and normal circulating cfDNA 1a,u .
• genome-wide DNA methylation mapping leverages large numbers of epigenetic alterations that may be used to distinguish circulating tumor DNA (ctDNA) from normal circulating cell-free DNA (cfDNA). For example, some tumor types, such as ependymomas, can have extensive DNA methylation aberrations without any significant recurrent somatic mutations 4 .
• pan-cancer data from The Cancer Genome Adas shows large numbers of DMRs between tumor and normal tissues across virtually all tumor types 19 . Therefore, these findings highlighted that an assay that successfully recovered cancer- specific DNA methylation alterations from ctDNA could serve as a very sensitive tool to detect, classify, and monitor malignant disease with low sequencing-associated costs.
• a method of capturing cell-free methylated DNA from a sample having less than 100 ng of cell-free DNA comprising the steps of: subjecting the sample to library preparation to permit subsequent sequencing of the cell-free methylated DNA; adding a first amount of filler DNA to the sample, wherein at least a portion of the filler DNA is methylated; denaturing the sample; and capturing cell-free methylated DNA using a binder selective for methylated polynucleotides.
• Figure 1 shows the methytome analysis of cfDNA Is a highly sensitive approach to enrich and detect ctDNA in low amounts of input DNA.
• cfMeDiP-seq was performed in pure HCT116 DNA (100% CRC), pure MM1.S DNA (100% MM) and 10%, 1%, 0.1%, 0.01%, and 0.001% CRC DNA diluted into MM DNA. All DNA was fragmented to mimic plasma cfDNA. We observed an almost perfect linear correlation (1 ⁇ 0.99, p ⁇ 0.0001) between the observed versus expected (D) numbers of DMRs and (E) the DNA methylation signal (In RPKM) within those DMRs. F) In the same dilution series, known somatic mutations are only detectable at 1/100 allele fraction by ultra-deep (>10.000X) targeted sequencing, above the background sequencer and polymerase error rate.
• Figure 2 shows the schematic representation of the cfMeDIP-seq protocol.
• Figure 3 shows sequencing saturation analysis and qualty controls.
• A) The figure shows the results of the saturation analysis from the Bioconductor package MEDIPS analyzing cfMeDIP-seq data from each replicate for each input concentration from the HCT116 DNA fragmented to mimic plasma cfDNA.
• the horizontal dotted line indicates a fold-enrichment ratio threshold of 25. Error bars represent ⁇ 1 s.e.m.
• Figure 4 shows quality controls from cfMeDIP-seq from serial dilution.
• Figure S shows that the cfMeOIP-seq method can Identify thousands of differentially methylated regions on circulating cfDNA obtained from pancreatic adenocarcinoma patients.
• D Permutation analysis to estimate the frequency of expected versus the observed overlap between the DMRs identified In the plasma (cases versus controls) and the cancer-specific DMCs identified in the primary tumor tissue (primary tumor versus normal tissue).
• the box-plots represent the null distribution for the overlap.
• the diamonds represent the experimentally observed number of overlap between primary tumor tissue and DMA methylation from circulating cfDNA. Red diamonds mean the observed number of overlaps Is significantly more than expected by chance.
• Green diamonds mean that the observed number of overlaps is significantly less than expected by chance and blue diamonds are non-significant
• Figure 6 shows quality controls for cfMeDIP-seq from circulating cfDNA from pancreatic adenocarcinoma patients (cases) and healthy donors (controls).
• A-B Specificity of reaction for each case (A) and each control (B) sample was calculated using methylated and unmethyiated spiked-ln A. thaliana DNA. Fold enrichment ratio was not calculated due to the very limited amount of DNA available.
• C-D CpG Enrichment Scores of fhe sequenced samples show a strong enrichment of CpGs within the genomic regions from the immunoprecfpteted samples.
• Figure 7 shows A) PCA on the 48 plasma cfDNA methylation from healthy donors and early stage pancreatic adenocarcinoma patients using the top million most variable genome-wide windows. For each window, variability was calculated using the MAD (Mean Absolute Deviation) metric, which is a robust measurement that returns the median of the absolute deviations from the data's median value; in this case, the data Is the RPKM values across all the 48 samples for a given window. PC1 versus PC2 (left) and PC1 versus PC3 (right) are shown. B) Percentage of variance for each principal component
• DMCs Densetralfy Methylated CpGs
• Red dots indicate the windows that reached significance after correction for multiple tests and having absolute methylation difference (absolute delta beta) > 0.25.
• X-axis shows the tog10 q values for the primary pancreatic adenocarcinoma tumor versus normal tissue from the RRBS data. If the region Is hypermethylated in the tumor, the significance Is showed on a positive scale. Hypomethylated regions are shown on a negative scale.
• Y-axis shows the log 10 q values for the plasma cfDNA methylation from pancreatic adenocarcinoma patients versus healthy donors from the cfMeDIP-eeq data. Blue dots are significant in both. Red line shows the trend line.
• X-axis shows the DNA methylation difference for the primary pancreatic adenocarcinoma tumor versus normal tissue from the RRBS data.
• Y-axis shows the DNA methylation difference for the plasma cfDNA methylation from pancreatic adenocarcinoma patients versus healthy donors from the cfMeDIP-seq data. Blue line shows the trend line.
• F Volcano plot for LCM pancreatic adenocarcinoma tissue versus normal PBMCs using RRBS. Total numbers of DMCs (Differentially Methylated CpGs) Identified are listed. Red dots Indicate the windows that reached significance after correction for multiple tests and having absolute methylation difference (absolute delta beta) > 0.25.
• G Scatter-plot showing the significance of the DNA methylation difference for each overlapping window. X-axis shows the log10 q values for the primary pancreatic adenocarcinoma tumor versus normal PBMCs from the RRBS data. If the region is hypermethylated in the tumor, the significance is showed on a positive scale. Hypomethylated regions are shown on a negative scale.
• Y-axis shows the log 10 q values for the plasms cfDNA metfiylatton from pancreatic adenocarcinoma patients versus healthy donors from the cfMeDIP-seq data. Blue dots are significant in both. Red line shows the trend line. H) Scatter-plot showing the DNA methylatlon difference far each overlapping window. X-axis shows the DMA methylatlon difference for the primary pancreatic adenocarcinoma tumor versus normal PBMCs from the RRBS data.
• Y-axis shows the DNA methylatlon difference for the plasma cfDNA methylatlon from pancreatic adenocarcinoma patients versus healthy donors from the cfMeDIP-seq data.
• Figure 8 shows circulating cfDNA methylatlon profile can be used to identify transcription factors (TFs) footprints and infer active transcriptional networks In the tissue-of-origln.
• TFs transcription factors
• A) Expression profile of all TPs (n 33) whose motifs were enriched (using the software HOMER 20 ) in the regions hypomethylated in the cfDNA from healthy donors (hypomethytated footprints In contra le) across multiple human tissues.
• the expression data was obtained from the Genotype-Tissue Expression (GTEx) project 21 .
• TFs preferentially expressed In the hematopoietic system were identified (PU.1, Fill , STAT5B, KLF1).
• pancreas-specific or pancreatic cancer-associated TFs were identified. Moreover, hallmark TFs that drive molecular subtypes of pancreatic cancer were also identified.
• the filler DNA used varied in the composition of % artificially methylated to % unmethylated lambda DNA present to increase final amount prior to immunoprecipitation to 100ng.
• the % recovery of spitocUn unmethylated DNA desired is ⁇ 1.0%, with lower recovery resulting In higher % specificity of reaction.
• Figure 10 shows % Recovery of aplked-in methylated A. thaiiana DNA after cfMeDIP- seq using 10ng, 6ng and 1ng of starting cancer cell-free DNA amounts (n*3), combined with 90ng, 85ng and 99ng of filler DNA respectively or no filler DNA, prior to lmmunopreclpitation.
• the filler DNA used varied In the composition of % artificially methylated to % unmethylated lambda DNA present to increase final amount prior to lmmunopreclpitation to 100ng. Minimum % recovery of sp!ked-in methylated DNA desired is 20%.
• the filler DNA consisted of ampllcons similar In size to an adapter-llgated cfDNA library and was composed of unmethylated and m vitro methylated DNA at different methylation levels ( Figure 9 and Figure 10).
• the addition of this filler DNA also serves a practical use, as different patients will yield different amounts of cfDNA, allowing for the normalization of input DNA amount to 100 ng. This ensures that the downstream protocol remains exactly the same for all samples regardless of the amount of available cfDNA.
• a method of capturing ceH-free methylated DNA from a sample having less than 100 ng of ceil-free DNA comprising the steps of: a. subjecting the sample to library preparation to permit subsequent ' sequencing of the cell-free methylated DNA; b. adding a first amount of filler DNA to the sample, wherein at least a portion of the filler DNA is methylated; c denaturing the sample; and d. capturing cell-tree methylated DNA using a binder selective for methylated polynucleotides.
• this method further comprises the step of amplifying and subsequently sequencing the captured cell-free methylated DNA.
• NGS next-generation sequencing
• Illumine (Solexa) sequencing Roche 454 sequencing
• Ion torrent Proton / PGM sequencing SOLiD sequencing.
• SOLiD sequencing SOLiD sequencing.
• NGS allow for the sequencing of DNA and RNA much more quickly and cheaply than the previously used Sanger sequencing.
• said sequencing is optimized for short read sequencing.
• Cell-free methylated DNA is DNA that is circulating freely in the blood stream, and are methylated at various known regions of the DNA. Samples, for example, ptasma samples can be taken to analyze cell-free methylated DNA.
• 'library preparation includes list end-repair, A-talBng, adapter ligation, or any other preparation performed on the cefl free DNA to permit subsequent sequencing of DNA.
• filler DNA can be noncoding DNA or it can consist of ampllcons.
• DNA samples may be denatured, for example, using sufficient heat In some embodiments, samples have less than 50 ng of cell-free DNA.
• the first amount of filler DNA comprises about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% methylated filler DNA. In preferred embodiments, the first amount of filler DNA comprises about 50% methylated filler DNA.
• the first amount of filler DNA is from 20 ng to 100 ng. In preferred embodiments, 30 ng to 100 ng of filler DNA. In more preferred embodiments 50 ng to 100 ng of fiPer DNA.
• the cell-free DNA from the sample and the first amount of filler DNA are combined together, there comprises at least 50 ng of total DNA, and preferably at least 100 ng of total DNA.
• the filler DNA is 50 bp to BOO bp long. In preferred embodiments, 100 bp to 600 bp long; and In more preferred embodiments 200 bp to 600 bp long.
• the filler DNA is double stranded.
• the filler DNA can be Junk DNA.
• the filler DNA may also be endogenous or exogenous DNA.
• the filler DNA is non-human DNA, and In preferred embodiments, ⁇ DNA.
• ⁇ DNA refers to Enterobacteria phage ⁇ DNA.
• the filler DNA has no alignment to human DNA.
• the binder is a protein comprising a Methyl-CpO-blndlng domain.
• MBD2 protein is a protein comprising a Methyl-CpO-blndlng domain.
• MBD2 protein is a protein comprising a Methyl-CpO-blndlng domain.
• MBD2 protein is MBD2 protein.
• MBD Metal-CpG- binding domain
• MBD Metal-CpG- binding domain
• Human proteins MECP2, MBD1, MBD2, MBD3, and MBD4 comprise a family of nuclear proteins related by the presence in each of a methyl-CpG-binding domain (MBD). Each of these proteins, with the exception of MBD3, is capable of binding specifically to methylated DNA.
• the binder is an antibody and capturing cell-free methylated DNA comprises immunopredpltating the cell-free methylated DNA using the antibody.
• Immurtoprecipitation refers a technique of precipitating an antigen (such as polypeptides and nucleotides) out of solution using an antibody that specifically binds to that particular antigen. This process can be used to Isolate and concentrate a particular protein or DMA from a sample and requires that the antibody be coupled to a solid substrate at some point in the procedure.
• the solid substrate includes for examples beads, such as magnetic beads. Other types of beads and solid substrates are known in the art.
• One exemplary antibody is 5-MeC antibody.
• the method described herein further comprises the step of adding a second amount of control DMA to the sample after step (b).
• Another exemplary antibody is or 5-hydroxymethyl cytosfne antibody.
• the method described herein further comprises the step of adding a second amount of control DNA to the sample after step (b) for confirming the capture of cell-free methylated DNA.
• control* may comprise both positive and negative control, or at least a positive control.
• the use further comprising the use of described herein for Identifying tissue of origin of the cancer cells within the cell-free DNA within the sample.
• Pancreatic adenocarcinoma (PDAC) patient samples were obtained from the University Health Network BioBank; healthy controls were recruited through the Family Medicine Centre at Mount Sinai Hospital (MSH) in Toronto, Canada. All samples collected with patient consent were obtained with Institutional approval from the Research Ethics Board, from University Hearth Network and Mount Sinai Hospital in Toronto, Canada.
• LCM Laser capture microdissection
• Mlcrodlaaected tumor cells were collected by gravity into the caps of sterile, RNAse- free microcentrifuge tubes. Approximately 150,000-200,000 tumor cells were collected for DNA sample and stored at -80 'C until further processing. LCM typically took 1-2 days per case to collect sufficient amounts of purified tumor cells. Q lag en Cell Lysis Buffer was used to extract genomic DNA. Matched normal, histologically reviewed reference tissue was collected for each patient from frozen duodenal or gastric mucosa by scraping unstained frozen sections on glass slides Into the appropriate DNA extraction buffer. Specimen Processing - cfDNA
• EDTA and ACD plasma samples were obtained from the BloBank and from the Family Medicine Centre at Mount Sinai Hospital (MSH) in Toronto, Canada. All samples were either stored at -BO'C or in vapour phase Gquid nitrogen until use.
• Cell-free DNA was extracted from 0.5-3.5 ml of plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen). The extracted DNA was quantified through Qublt prior to use.
• Human colorectal tumor tissue obtained with patient consent from the University Health Network Btobank as approved by the Research Ethics Board at University Health Network, was digested to single cells using coilagenase A. Single cells were subcutaneousiy injected into 4-6 week old NOD/SCID male mouse. Mice were euthanized by C02 Inhalation prior to blood collection by cardiac puncture and stored in EDTA tubes. From the collected blood samples, the plasma was isolated and stored at -8QC. Cell-free DNA was extracted from 0.3-0.7 ml of plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen). All animal work was carried out in compliance with the ethical regulations approved by the Animal Care Committee at University Hearth Network.
• Enterobacteria phage ⁇ DNA was amplified using the primers Indicated in Table 1 , generating 6 different PCR am pi icons products.
• the PCR reaction was carried out using KAPA HIFI Hotstart ReadyMix with the following condition: activation of enzyme at 95'C for 3 mln, 30 cycles of 98 ' C for 20 sec, 60'C for 15 sec, 72'C for 30 sec and a final extension at 72'C for 1 min.
• the PCR amplicons were purified with QlAQuick PCR purification kit (Qiagen) and ran on a gel to verify . size and amplification.
• Amplicons for 1CpG, SCpG, 10CpG, 15CpG and 20CpGL were methylated using CpG Methy!transferase (M.Sss ⁇ ) (ThermoFischer Scientific) and purified with the QlAQuick PCR purification kit Methytatton of the PCR amplicons was tested using restriction enzyme HpyCHffl (New England Biotabs Canada) and ran on a gel to ensure its methylation.
• M.Sss ⁇ CpG Methy!transferase
• Methytatton of the PCR amplicons was tested using restriction enzyme HpyCHffl (New England Biotabs Canada) and ran on a gel to ensure its methylation.
• the DNA concentration of the unmethylated (20CpGS) and methylated (1CpG, 5CpG, 10CpG, 15CpG, 20CpGL) amplicons was measured using picogreen prior to pooGng with 50% of methylated and 50% unmethylated ⁇ PCR product.
• FIG. 1 A schematic representation of the cfMeDIP-seq protocol Is shown in Figure 2.
• the DNA samples were subjected to library preparation using the Kapa Hyper Prep Kit (Kapa Bfosystems). The manufacturer protocol was followed with some modifications. Briefly, the DNA of interest was added to 0.2 mL PCR tube and subjected to end-repefr and A-Tafflng. Adapter ligation was followed using NEBNext adapter (from the NEBNext Multiplex Oitgos for lllumina kit, New England Bioiabs) at a final concentration of 0.181 ⁇ , Incubated at 20'C for 20 mine and purified with AMPure XP beads. The eluted library was digested using the USER enzyme (New England Bioiabs Canada) followed by purification with Qiagen MinElute PCR Purification Kit prior to MeDIP.
• NEBNext adapter from the NEBNext Multiplex Oitgos for lllumina kit, New England Bioiabs
• the prepared libraries were combined with the pooled methylated/unmethylated ⁇ PCR product to a final DNA amount of 100 ng and subjected to MeDIP using the protocol from TaJwo et al. 2012 17 with some modifications.
• MeDIP the Diagenode MagMeDIP kit (Cat* C02010021) was used following the manufacturer's protocol with soma modifications. After the addition of 0.3 ng of the control methylated and 0.3ng of the control unmethylated A.
• Washed magnetic beads were also added prior to Incubation at 4'C for 17 hours.
• the samples were purified using the Diagenode iPure Kit and edited In SO ⁇ of Buffer C.
• the success of the reaction (QC1) was valdated through qPCR to detect the presence of the spiked-in A. thaliana DMA, ensuring a % recovery of unmethylated splked-ln DNA ⁇ 1% and the % specificity of the reaction >99% (as calculated by 1- [recovery of spKed-in unmethylated control DNA aver recovery of splked-ln methylated control DNA] ⁇ , prior to proceeding to the next step.
• the optimal number of cycles to ampDfy each library was determined through the use of qPCR, after which the samples were amplified using the KAPA HJF1 Hotstart Mastermix and the NEBNext multiplex oligos added to a final concentration of 0.3 ⁇ .
• the PCR settings used to amplify the libraries were as follows: activation at 95 * C for 3 mln, followed by predetermined cycles of 98'C for 20 sec, 65'C for 15 sec and 72'C for 30 sec and a final extension of 72'C for 1 min.
• the amplified libraries were purified using MmElute PCR purification column and then gel size selected with 3% Nusieve QTO agarose gel to remove any adapter dimers.
• the filler DNA used to Increase the final amounts prior to immunoprecipitatJon to 100ng. should preferably have some artificially methylated DMA In Its composition (from 100%-15%) in order to have the minimal recovery unmethylated DNA (Figure 9), while stHl getting a good yield in terms of recovery of methylated DNA ( Figure 10).
• the filler DNA helps to occupy the excess antibody present in the reaction, minimizing the amount of uns pacific binding to unmethylated DNA found in the sample.
• DMA Circulating Nucleic Add kit to isolate celWree DMA from -20 mL of plasma (4-Sx 10mL EDTA blood tubes) from patients with matched tumor tissue molecular profiling data generated prior to enrolment in early phase clinical trials at the Princess Margaret Cancer Centre.
• DMA was extracted from cell lines (dilution of CRC and MM cell lines) using the Pure Gene Gerrtra kit, fragmented to -180 bp using a Covaris sonlcator. and larger size fragments excluded using Ampure beads to mimic the fragment size of cell-free DMA.
• DMA sequencing libraries were constructed from 83 ng of fragmented DMA using the KAPA Hyper Prep Kit (Kapa Biosystems, WOmtngton.
• DMRa Differentially Methylated Regions
• PC Pancreatic Cancer
• Healthy Donors were calculated using the MEDIPS R package 21 .
• BAM alignment to human genome hg19 files were used to create MEDIPS R objects.
• DMRe were calculated by comparing the RPKMs from the two sets of samples using t-tests. The raw p-valuea from the t-tests were adjusted using the Benjaminl-Hochberg procedure. DMRs were then defined as an the windows with adjusted p-values less than 0.1; 38,085 total DMRs were found: 6,651 Hyper in Pancreatic Cancer patients and 31,544 Hypo.
• DMCs were obtained using the criteria of Benjarnini-Hochberg adjusted p-value ⁇ 0.01 and Delta Beta > 0.25, and 134,021 DMCs were found to be Hyper in Pancreatic Cancer compared to PBMCs. Analogously, using the same q-value cutoff and Delta Beta ⁇ - 0.25, we obtained 179.662 Hypo DMCs.
• Permutation analysis was carried out to compare the frequency of expected versus the observed overlap between the DMRs identified in the plasma (with circulating cfDMA subjected to our cfMeOlP-eeq protocol) and the cancer-specific DMCs Identified in the primary tumor tissue (with RRBS).
• RNA-Seq data was obtained from the GTEx database in the form of median RPKMe by tissue for ail human genes (obtained from file GTEx.j ⁇ natysia_vep_RNA-eeq_RNA- SeQCv1.1.8jjeneji ⁇ ian_roton.gct.gz under httpsy/gtexportaLorg/home/datBsets).
• TFs of Interest were matched to their gene names, and heatmaps ( Figure 8A, 8C) were constructed with the median RPKMs of each TF scaled across all tissues. The distance function "manhattan” and clustering function "average” were used for both row-wise and column-wise clustering. Violin Plots with GTEx Expression Profiles of TFs associated wfth motifs hvpomethvlated In 24 PC and 24 HaaHhv cfDMA samples
• the filler DNA consisted of amplicons similar in size to an adapter-Hgated cfDNA library and was composed of unmethyiated and in wfro methylated DNA at different CpG densities.
• the addition of this filler DNA also serves a practical use, as different patients will yield different amounts of cfDNA. alowing for the normalization of input DNA amount to 100 ng. This ensures that the downstream protocol remains exactly the same for all samples regardless of the amount of available cfDNA.
• Cancer DNA is frequently hypermethylated at CpO-rich regions 1 . Since cfMeDIP-seq specifically targets methylated CpG-rksh sequences, we hypothesized that ctDNA would be preferentially enriched during the immunoprecipitation procedure. To test this, we generated patient-derived xenografts (PDXs) from two colorectal cancer patients and collected the mouse plasma. Tumor-derived human cfDNA was present at less than 1% frequency within the total cfDNA pool in the input samples and at 2- fold greater abundance following immunoprstipitatfon (Figure 1G). These results suggest that through biased sequencing of ctONA. the cfMeDIP procedure could further Increase ctDMA detection sensitivity. Methvlorne analysis of Plasma cfDNA distinguishes eariv stage pancreatic adenocarcinoma patients from healthy donors
• TF transcription factor
• pancreatic adenocarcinoma cases were also frequently overexpressed In pancreatic adenocarcinoma patients from TCGA ( Figure 8E). Furthermore, we were able to Identify several hypometnylated footprints in the pancreatic adenocarcinoma cases that correspond to TFs previously identified as drivers of each molecular subtypes of pancreatic cancer 24 . These included c-MYC and HIF1a (Squamous subtype drivers), NR5A2, MAFA, RBPJL, and NEUROD1 (AD EX drivers) and finally FOXA2 and HNF4A (pancreatic progenitor subtype).
• cfMeDIP-eeq relies on epigenetic, rather than genomic information, It could potentially be used to non-invasively monitor tissue damage h a broad set of non-malignant diseases. For instance, it could be used to monitor immune responee to an infection or after cancer immunotherapy; It could be used to monitor heart DNA in the circulation after myocardial infarction or brain DNA during early stages of neurodegenerative diseases.
• pancreatic cancer patients can be stratified into four subgroups driven by several mechanisms 24 : squamous, pancreatic progenitor, Immunogenic and aberrantly differentiated endocrine exocrine (AD EX).
• Table 1 PCR primers used to generate Enterobactaria phage ⁇ PCR product from Tahvo et el., 2012
• Table SB Number of roads and mapping efficiency of sequenced cfMeDIP-seq libraries
• Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell 22, 425-437, doi:10.1016/].ccr.20l2.08.024 (2012).

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