Besides, the turnaround time for results may take several weeks, delaying important therapeutic decisions. 13ĭNA methylation holds great promise as biomarkers in cancer, however, generating DNA methylation data of cancer patients has yet to become common clinical practice, since the performance of DNA methylation biomarkers varies across diseases and remains to be evaluated and improved, which is an active area of research.
SOURCE YPOS 2014 DRIVER
12 Furthermore, proteomic data can also be leveraged in the MethylMix analytical framework to further narrows down the candidate methylation driver genes in cancer. 11 A similar hypo-methylated subtype defined by NSD1 inactivation was also identified across squamous cell carcinoma. These DNA methylation subtypes better segregate with etiological subgroups defined by HPV status and smoking in head and neck cancer. For example, in head and neck cancer, we discovered five distinct DNA methylation subtypes identified by DM values differing from previously reported gene expression subtypes. MethylMix has been used to identify methylation driver genes and reveal cancer subtypes across heterogeneous samples. The main output of MethylMix are the “Differential Methylation” values or DM-values, defined as the difference between an abnormal methylation state (e.g., hyper-methylated or hypo-methylated) and the normal methylation state. 7– 10 MethylMix integrates DNA methylation and gene expression data from normal and disease samples and identifies differentially methylated genes that are also predicative of gene expression levels.
For example, we have developed MethylMix, a beta mixture model-based method that identifies DNA methylation driver genes in cancer. Several approaches have been developed to profile DNA methylation pattern in cancers and identify differentially methylated genes from DNA methylation profiling assays. For example, the Cancer Genome Atlas (TCGA) project generated a rich source of epigenomic data for cancers of various organs, 6 including the profiling of DNA methylation using microarray technology in over 10,000 samples. 1– 5 High-throughput DNA methylation assays are being used more frequently in cancer research, generating vast amounts of genome-wide DNA methylation measurements.
SOURCE YPOS 2014 DRIVERS
Aberrant DNA methylation is one of the most common and well-studied molecular alterations in cancer and DNA methylation changes have emerged as important biomarkers and epigenetic drivers of cancer. DNA hyper-methylation and hypo-methylation are important mechanisms that deregulate gene expression in a wide range of cancers. Our results provide new insights into the link between histopathological and molecular data.ĭNA methylation is an important epigenetic mechanism regulating various biological processes. The well-predicted genes are enriched in key pathways in carcinogenesis including hypoxia in glioma and angiogenesis in renal cell carcinoma. Furthermore, grouping the genes into methylation clusters greatly improves the performance of the models. We demonstrate that classical machine learning algorithms can associate the DNA methylation profiles of cancer samples with morphometric features extracted from whole slide images. In this work, we investigate the interaction between cancer histopathology images and DNA methylation profiles to provide a better understanding of tumor pathobiology at the epigenetic level. Histopathology images are commonly obtained in cancer treatment, given that tissue sampling remains the clinical gold-standard for diagnosis. High-throughput DNA methylation assays have been used broadly in cancer research. DNA methylation is an important epigenetic mechanism regulating gene expression and its role in carcinogenesis has been extensively studied.
0 kommentar(er)
