Tumors or circulating tumor cells release circulating tumor DNA (ctDNA) into the blood when undergoing apoptosis or necrosis. Approximately 0.1% to 10% of cell-free DNA originates from cancer cells. Tumors or circulating tumor cells release circulating tumor DNA (ctDNA) into the blood when undergoing apoptosis or necrosis. ctDNA markers are typically single-nucleotide variants (SNVs), insertion-deletion mutations (indels) and copy number alterations (CNAs), however the measurement of these molecules are hampered by factors such as molecule loss during library construction, PCR artifacts, and sequencing errors.

By limiting the target of the measurement to DNA mutations from cancerous cells, and being able to only reliably look at a tiny fraction (0.1% is one mutant in a thousand wild-type copies), the task of early detection using only DNA markers is formidable. With approximately 140 genes that 'drive' tumorigenesis there are several targeted gene sequencing methods that various groups are using to look at specific driver genes mutated in cancer.

Yet by by looking downstream of the original causative mutation in DNA, there are common behaviors associated with cancer in general. In two seminal papers, The Hallmarks of Cancer (Hanahan and Weinberg, Cell 2000) and The Hallmarks of Cancer: The Next Generation (Hanahan and Weinberg, Cell 2011) stated in 2000 "We suggest that research over the past decades has revealed a small number of molecular, biochemical, and cellular traits—acquired capabilities—shared by most and perhaps all types of human cancer" and in 2011, "The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease."

At its most basic level, regardless of how a normal cell becomes a cancer cell, these cancer cells have traits they share in common, and these traits will be characterized by genes being expressed. While gene expression has been studied exhaustively for its connection to cancer (and both RNA microarrays and RNA-Seq methods are still in active use today in cancer research worldwide), relatively less attention has been paid to methylation, which play a fundamental role in which genes are expressed to begin with.

One main reason DNA methylation has not been studied as extensively as RNA expression or DNA mutations is due to its method of detection. The primary method of detection is bisulfite conversion of unlabeled Cytosine residues to Uracil, while 5'-methyl Cytosines remain protected and remain as Cytosines. While sequencing the converted Uracils (which were unlabled Cytosines before conversion), these Uracils are read as Thymidines. Thus if a region of DNA is completely unlabeled with a methyl group, all the Cytosines will be read as Thymidines, and the canonical four DNA codes of G's, T's, C's and A's becomes only three (G's, T's and A's).

One consequence of this reduced complexity genome when analyzing sequencing data (from a four-base code to a three-base one) is the complexity of bioinformatic analysis. However, a worse consequence to utilize bisulfite treatment is loss of material due to bisufite-induced DNA damage.

Historically methylation has been studied via microarrays or real-time PCR, where a particular methylated CpG site is interrogated independently of any adjacent site. Through the use of retaining methylation status across a given strand of DNA being sequenced (the CpG methylation occurs in CpG-rich regions called 'CpG islands' and 'CpG shores'), this adjacency information yields a much more powerful methylation signature than single CpG methylation status measured as separate entities.

You can think of it as a large jar of beads with one of 100 colors, compared to the same jar of beads where the 100 colors are arranged in certain patterns on short pieces of string. It is much easier to discern a pattern looking at the short stretches, compared to looking at individual beads, if any pattern can be discerned at all.

Singlera researchers have used their extensive expertise in single-cell methylation analysis (see our Publications here) to apply these techniques to examine methylation haplotypes in cell-free DNA. Singlera has developed proprietary experimental and bioinformatics approaches to maximize the amount of cell-free DNA retained during sequencing library construction (mGuard and mTitan), and retain adjacency information for CpG sites across the genome in “methylation haplotype blocks” (MONOD).

The combination of these approaches both reduces the overall sample input requirement and amount of sequencing necessary, while retaining superior sensitivity and specificity to standard methylation analysis methods.