DNA methylation-based patterns for early diagnostic prediction and prognostic evaluation in colorectal cancer patients with high tumor mutation burden

Epigenetic mechanisms underlying endometrial cancer outcomes: race-specific patterns of DNA methylation associated with molecular subtypes and survival

Endometrial cancer (EC) is the fourth most common cancer in women in the USA. Stark racial disparities are present in EC outcomes in which Black women have significantly higher EC-related mortality than White women. The social and biologic factors that contribute to these disparities are complex and may include racial differences in epigenetic landscapes. To investigate race-specific epigenetic differences in EC tumor characteristics and outcomes, we utilized the most recent data within the Cancer Genome Atlas (TCGA). Genome-wide CpG methylation data for more than 850 000 CpG sites were analyzed across 245 tumor samples, including 52 from Black women and 181 from White women. Race-adjusted and race-stratified associations among CpG methylation in ECs and molecular subtypes and disease-free survival were examined. Race-specific analysis identified subtype-associated CpGs within 9572 genes in tumors from White women and only 10 genes in tumors that were from Black women. Race-specific analyses also identified survival-associated CpGs with 1119 unique genes identified in tumors from White women and none identified in tumors from Black women. Genes identified with differential methylation among subtypes included those involved in oxidative stress (HIF3A), and DNA repair (MLH1). Data from a replication cohort highlighted genes overlapping with those identified within the TCGA, such as G Protein Subunit Beta 1 (GNB1), involved in G-protein signaling, and Interleukin 37 (IL37), involved in cytokine signaling. Identification of these racial differences in EC tumor epigenetic landscapes and associated changes in gene expression may provide insight into strategies to improve outcomes and reduce disparities.

Evaluation of a plasma cell-free DNA methylation test for colorectal cancer diagnosis: a multicenter clinical study

BACKGROUND

A blood-based diagnostic test is a promising strategy for colorectal cancer (CRC). The MethyDT test (IColohunter), which detects methylation levels of NTMT1 and MAP3K14-AS1, exhibited potential in discriminating CRC, but its clinical performance needs to be validated in large-scale populations.

METHODS

A multicenter, double-blinded, cross-sectional study that enrolled 1194 participants was performed. Plasma samples were collected to detect methylation levels of NTMT1 and MAP3K14-AS1 using quantitative methylation-specific PCR with the MethyDT test, and the accuracy was further evaluated by Sanger sequencing.

RESULTS

The sensitivities of the MethyDT test for detecting CRC, early stages of CRC (I and II), advanced adenoma (AA), and high-grade intraepithelial neoplasia (HGIN) were 91.2% (95% confidence interval [CI], 88.4-94.0), 87.4% (95% CI, 82.5-92.2), 43.5% (95% CI, 35.7-51.4), and 72.7% (95% CI, 57.5-87.9), respectively. The specificities for participants with non-AA, interfering diseases (ID), and no evidence of disease (NED) were 85.0% (95% CI, 78.8-91.3), 93.7% (95% CI, 91.4-95.9) and 97.3% (95% CI, 90.5-99.7), respectively, and its overall specificity for all-controls was 92.4% (95% CI, 90.3-94.4). The consistency of the MethyDT test with pathology for CRC was high with a kappa value of 0.830 (95% CI, 0.795-0.865). Additionally, the MethyDT test was comparable to Sanger sequencing for detecting methylation with kappa values > 0.97.

CONCLUSIONS

The MethyDT test demonstrates excellent sensitivity and specificity for CRC and high consistency with Sanger sequencing for methylation, suggesting it may serve as a potential noninvasive diagnostic tool for the detection of CRC.

TRIAL REGISTRATION

This clinical trial has been registered in ClinicalTrials.gov (NCT05508503).

Alteration in DNA methylation patterns: Epigenetic signatures in gastrointestinal cancers

Abnormalities in epigenetic modifications can cause malignant transformations in cells, leading to cancers of the gastrointestinal (GI) tract, which accounts for 20% of all cancers worldwide. Among the epigenetic alterations, DNA hypomethylation is associated with genomic instability. In addition, CpG methylation and promoter hypermethylation have been recognized as biomarkers for different malignancies. In GI cancers, epigenetic alterations affect genes responsible for cell cycle control, DNA repair, apoptosis, and tumorigenic-specific signaling pathways. Understanding the pattern of alterations in DNA methylation in GI cancers could help scientists discover new molecular-based pharmaceutical treatments. This study highlights alterations in DNA methylation in GI cancers. Understanding epigenetic differences among GI cancers may improve targeted therapies and lead to the discovery of new diagnostic biomarkers.

[Molecular pathology of colorectal cancer]

In recent years, the treatment of colorectal carcinoma has experienced increasing individualization. In addition to RAS and BRAF mutational status that is firmly established in routine diagnostics, new therapeutic options evolved based on MSI and HER2 status as well as primary tumour localization. Offering the best targeted options in therapy requires new evidence-based decision-making algorithms regarding timing and scope of molecular pathological diagnostics in order for patients to receive an optimized therapy according to current treatment guidelines. New targeted therapies, some of which are about to be approved and for which pathology has to provide new molecular pathological biomarkers, will also play an increasingly important role in the future.

Data-driven decision-making for precision diagnosis of digestive diseases

Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.