Current Research Projects
 
Using Aberrant Methylation Patterns for Tumor Classification and Prediction of Malignant Progression
Principal Investigator: Joseph F. Costello PhD
 
Brain tumors of similar malignancy grades and histology often have a vastly different clinical and biological course. Classifying tumors based on large scale analyses of DNA or RNA is a promising approach for distinguishing similar appearing tumors. Aberrant methylation of CpG islands in DNA will be particularly useful in classifying tumors, as it plays an important role in inappropriate silencing of cancer genes in sporadic human tumors, including low-grade brain tumors in which genetic alterations are far less frequent. Here we propose that the aberrant methylation is intimately associated with and contributes to the malignant behavior of brain tumor cells. A prediction based on this hypothesis is that gliomas of different malignancy grades may exhibit aberrant methylation patterns that are specific to a grade. The hypothesis also suggests that aberrant methylation might allow prediction of future behavior of tumor cells, prior to recurrence. We will test our hypothesis and predictions by determining the methylation status of 1184 CpG islands in low-grade and high-grade gliomas, using Restriction Landmark Genome Scanning (RLGS). RLGS is a method that separates radiolabeled NotI restriction in two dimensions, in a reproducible and quantitative fashion. As a prerequisite to tumor classification and prediction, we will first identify each of the 1184 genes corresponding to the anonymous CpG islands displayed on the RLGS profile by using our arrayed CpG island library. To classify the tumors, and to allow prediction of malignant progression based on methylation patterns, we will apply statistical methods and clustering techniques that are well suited for analysis of large data sets. Predicting progression at the time of the primary tumor diagnosis is critical to the appropriate application of aggressive therapy and to avoiding unwarranted toxicity. Successful prediction strategies will present a therapeutic window for preventing malignant dedifferentiation that is potentially months to years in duration.
 
Convergent Mechanisms Contributing to Cancer
Principal Investigator: Joseph F. Costello PhD
 
Tumorigenesis is fueled in part by an accumulation of genetic and epigenetic (e.g., aberrant methylation of CpG islands) alterations that inactivate tumor suppressor genes. However, it has not been possible to understand the interaction of these mechanisms on a genome-wide scale, since whole-genome methylation profiling has not been amenable to alignment with chromosomal deletion maps. The genesis of low-grade brain tumors (WHO grade II astrocytomas) is accompanied by widespread aberrant CpG island methylation and a relatively small number of deletions, whereas in tumors that have progressed to malignant high-grade astrocytoma (WHO Grades III, IV), large deletions are commonplace. We hypothesize that methylation and deletion converge on particular genes during gliomagenesis, and that this convergence in low-grade tumors negatively affects patients' survival. To determine the independent and potentially convergent effects of these mechanisms on tumorigenesis and patients' survival, we will; 1) generate whole-chromosome maps of potential methylation sites (CpGs within CpG islands); 2) identify chromosomal regions that are deleted in low-grade and high-grade tumors and align these with the maps of potentially methylated sites; 3) identify the loci where deletion and aberrant CpG island methylation converge, particularly those present in a proportion of both low-grade and high-grade tumors and; 4) determine if the length of survival of patients with low-grade astrocytoma can be predicted from the patterns of aberrant methylation and deletion. By understanding where and when methylation and deletion interact, we will gain a more complete understanding of tumorigenesis in general, and we hope to devise an objective guide for improving the therapy and therapeutic decisions for patients with low-grade astrocytoma.