Machine learning unveils an immune-related DNA methylation profile in germline DNA from breast cancer patients.

BACKGROUND: There is an unmet need for precise biomarkers for early non-invasive breast cancer detection. Here, we aimed to identify blood-based DNA methylation biomarkers that are associated with breast cancer. METHODS: DNA methylation profiling was performed for 524 Asian Chinese individuals, comprising 256 breast cancer patients and 268 age-matched healthy controls, using the Infinium MethylationEPIC array. Feature selection was applied to 649,688 CpG sites in the training set. Predictive models were built by training three machine learning models, with performance evaluated on an independent test set. Enrichment analysis to identify transcription factors binding to regions associated with the selected CpG sites and pathway analysis for genes located nearby were conducted. RESULTS: A methylation profile comprising 51 CpGs was identified that effectively distinguishes breast cancer patients from healthy controls achieving an AUC of 0.823 on an independent test set. Notably, it outperformed all four previously reported breast cancer-associated methylation profiles. Enrichment analysis revealed enrichment of genomic loci associated with the binding of immune modulating AP-1 transcription factors, while pathway analysis of nearby genes showed an overrepresentation of immune-related pathways. CONCLUSION: This study has identified a breast cancer-associated methylation profile that is immune-related to potential for early cancer detection.

Degradation of methylation signals in cryopreserved DNA.

BACKGROUND: Blood-based DNA methylation has shown great promise as a biomarker in a wide variety of diseases. Studies of DNA methylation in blood often utilize samples which have been cryopreserved for years or even decades. Therefore, changes in DNA methylation associated with long-term cryopreservation can introduce biases or otherwise mislead methylation analyses of cryopreserved DNA. However, previous studies have presented conflicting results with studies reporting hypomethylation, no effect, or even hypermethylation of DNA following long-term cryopreservation. These studies may have been limited by insufficient sample sizes, or by their profiling of methylation only on an aggregate global scale, or profiling of only a few CpGs. RESULTS: We analyzed two large prospective cohorts: a discovery (n = 126) and a validation (n = 136) cohort, where DNA was cryopreserved for up to four years. In both cohorts there was no detectable change in mean global methylation across increasing storage durations as DNA. However, when analysis was performed on the level of individual CpG methylation both cohorts exhibited a greater number of hypomethylated than hypermethylated CpGs at q-value < 0.05 (4049 hypomethylated but only 50 hypermethylated CpGs in discovery, and 63 hypomethylated but only 6 hypermethylated CpGs in validation). The results were the same even after controlling for age, storage duration as buffy coat prior to DNA extraction, and estimated cell type composition. Furthermore, we find that in both cohorts, CpGs have a greater likelihood to be hypomethylated the closer they are to a CpG island; except for CpGs at the CpG islands themselves which are less likely to be hypomethylated. CONCLUSION: Cryopreservation of DNA after a few years results in a detectable bias toward hypomethylation at the level of individual CpG methylation, though when analyzed in aggregate there is no detectable change in mean global methylation. Studies profiling methylation in cryopreserved DNA should be mindful of this hypomethylation bias, and more attention should be directed at developing more stable methods of DNA cryopreservation for biomedical research or clinical use.