Quantification method of ctDNA using cell-free DNA methylation profile for noninvasive screening and monitoring of colon cancer.

BACKGROUND: Colon cancer ranks as the second most lethal form of cancer globally. In recent years, there has been active investigation into using the methylation profile of circulating tumor DNA (ctDNA), derived from blood, as a promising indicator for diagnosing and monitoring colon cancer. RESULTS: We propose a liquid biopsy-based epigenetic method developed by utilizing 49 patients and 260 healthy controls methylation profile data to screen and monitor colon cancer. Our method initially identified 901 colon cancer-specific hypermethylated (CaSH) regions in the tissues of the 49 cancer patients. We then used these CaSH regions to accurately quantify the amount of circulating tumor DNA (ctDNA) in the blood samples of these same patients, utilizing cell-free DNA methylation profiles. Notably, the methylation profiles of ctDNA in the blood exhibited high sensitivity (82%) and specificity (93%) in distinguishing patients with colon cancer from the control group, with an area under the curve of 0.903. Furthermore, we confirm that our method for ctDNA quantification is effective for monitoring cancer patients and can serve as a valuable tool for postoperative prognosis. CONCLUSIONS: This study demonstrated a successful application of the quantification of ctDNA among cfDNA using the original cancer tissue-derived CaSH region for screening and monitoring colon cancer.

FAT10 Induces cancer cell migration by stabilizing phosphorylated ABI3/NESH.

The WAVE regulatory complex (WRC) is involved in various cellular processes by regulating actin polymerization. The dysregulation of WRC components is associated with cancer development. ABI family member 3 (ABI3)/new molecule including SH3 (NESH) is one of the WRC components and it has been reported that ABI3 phosphorylation can affect WRC function. Although several residues of ABI3 have been reported to be possible phosphorylation sites, it is still unclear which residues are important for the function of ABI3. Furthermore, it is unclear how the phosphorylated form of ABI3 is regulated. Here, we demonstrate that ABI3 is stabilized by its interaction with human leukocyte antigen-F adjacent transcript 10 (FAT10). Using phospho-dead or phospho-mimetic mutants of ABI3, we showed that serine 213 and 216 are important phosphorylation sites of ABI3. In particular, FAT10 has a higher affinity for the phosphorylated form of ABI3 than the non-phosphorylated form, and it stabilizes the phosphorylated form more than the non-phosphorylated form through this differential affinity. The interaction between FAT10 and the phosphorylated form of ABI3 promoted cancer cell migration. Therefore, our results suggest that FAT10 stabilizes the phosphorylated form of ABI3, which may lead to WRC activation, thereby promoting cancer cell migration.

A method for early diagnosis of lung cancer from tumor originated DNA fragments using plasma cfDNA methylome and fragmentome profiles.

Early detection is critical for minimizing mortality from cancer. Plasma cell-free DNA (cfDNA) contains the signatures of tumor DNA, allowing us to quantify the signature and diagnose early-stage tumors. Here, we report a novel tumor fragment quantification method, TOF (Tumor Originated Fragment) for the diagnosis of lung cancer by quantifying and analyzing both the plasma cfDNA methylation patterns and fragmentomic signatures. TOF utilizes the amount of ctDNA predicted from the methylation density information of each cfDNA read mapped on 6243 lung-tumor-specific CpG markers. The 6243 tumor-specific markers were derived from lung tumor tissues by comparing them with corresponding normal tissues and healthy blood from public methylation data. TOF also utilizes two cfDNA fragmentomic signatures: 1) the short fragment ratio, and 2) the 5' end-motif profile. We used 298 plasma samples to analyze cfDNA signatures using enzymatic methyl-sequencing data from 201 lung cancer patients and 97 healthy controls. The TOF score showed 0.98 of the area under the curve in correctly classifying lung cancer from normal samples. The TOF score resolution was high enough to clearly differentiate even the early-stage non-small cell lung cancer patients from the healthy controls. The same was true for small cell lung cancer patients.