Margaret R. Karagas

DNA Methylation, Isocitrate Dehydrogenase Mutation, and Survival in Glioma

BACKGROUND Although much is known about molecular and chromosomal characteristics that distinguish glioma histological subtypes, DNA methylation patterns of gliomas and their association with other tumor features such as mutation of isocitrate dehydrogenase (IDH) genes have only recently begun to be investigated. METHODS DNA methylation of glioblastomas, astrocytomas, oligodendrogliomas, oligoastrocytomas, ependymomas, and pilocytic astrocytomas (n = 131) from the Brain Tumor Research Center at the University of California San Francisco, as well as nontumor brain tissues (n = 7), was assessed with the Illumina GoldenGate methylation array. Methylation data were subjected to recursively partitioned mixture modeling (RPMM) to derive methylation classes. Differential DNA methylation between tumor and nontumor was also assessed. The association between methylation class and IDH mutation (IDH1 and IDH2) was tested using univariate and multivariable analysis for tumors (n = 95) with available substrate for sequencing. Survival of glioma patients carrying mutant IDH (n = 57) was compared with patients carrying wild-type IDH (n = 38) using a multivariable Cox proportional hazards model and Kaplan-Meier analysis. All statistical tests were two-sided. RESULTS We observed a statistically significant association between RPMM methylation class and glioma histological subtype (P < 2.2 × 10(-16)). Compared with nontumor brain tissues, across glioma tumor histological subtypes, the differential methylation ratios of CpG loci were statistically significantly different (permutation P < .0001). Methylation class was strongly associated with IDH mutation in gliomas (P = 3.0 × 10(-16)). Compared with glioma patients whose tumors harbored wild-type IDH, patients whose tumors harbored mutant IDH showed statistically significantly improved survival (hazard ratio of death = 0.27, 95% confidence interval = 0.10 to 0.72). CONCLUSION The homogeneity of methylation classes for gliomas with IDH mutation, despite their histological diversity, suggests that IDH mutation is associated with a distinct DNA methylation phenotype and an altered metabolic profile in glioma.

Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions.

BackgroundEpigenetics is the study of heritable changes in gene function that cannot be explained by changes in DNA sequence. One of the most commonly studied epigenetic alterations is cytosine methylation, which is a well recognized mechanism of epigenetic gene silencing and often occurs at tumor suppressor gene loci in human cancer. Arrays are now being used to study DNA methylation at a large number of loci; for example, the Illumina GoldenGate platform assesses DNA methylation at 1505 loci associated with over 800 cancer-related genes. Model-based cluster analysis is often used to identify DNA methylation subgroups in data, but it is unclear how to cluster DNA methylation data from arrays in a scalable and reliable manner.ResultsWe propose a novel model-based recursive-partitioning algorithm to navigate clusters in a beta mixture model. We present simulations that show that the method is more reliable than competing nonparametric clustering approaches, and is at least as reliable as conventional mixture model methods. We also show that our proposed method is more computationally efficient than conventional mixture model approaches. We demonstrate our method on the normal tissue samples and show that the clusters are associated with tissue type as well as age.ConclusionOur proposed recursively-partitioned mixture model is an effective and computationally efficient method for clustering DNA methylation data.

Safety and Immunogenicity of a Five-Dose Series of Inactivated Mycobacterium vaccae Vaccination for the Prevention of HIV-Associated Tuberculosis

Five doses of inactivated Mycobacterium vaccae vaccine were administered intradermally to 22 human immunodeficiency virus (HIV)-infected patients (11 bacille Calmette-Guérin [BCG]-positive and 11 BCG-negative) in Zambia whose CD4 lymphocyte counts were >/=200 cells/mm(3). HIV viral load and lymphocyte proliferation responses were compared for vaccine recipients and 22 HIV-infected control patients (11 BCG-positive and 11 BCG-negative). Immunization was safe and well tolerated in all patients, and induration at the vaccine site decreased from dose 1 to dose 5. A transient decrease in HIV viral load was observed in BCG-positive vaccine recipients after dose 3 but not after subsequent doses. Median lymphocyte stimulation indices to M. vaccae were 6.0 in vaccine recipients and 2.3 in control patients (P<.001). Stimulation indices were >/=3.0 in 19 vaccine recipients (86%) and 7 control patients (32%; P=.001). A 5-dose series of vaccination with inactivated M. vaccae is safe in HIV-infected patients and induces lymphocyte proliferation responses to the vaccine antigen. M. vaccae vaccine is a candidate for the prevention of tuberculosis in HIV infection.

Semi-supervised recursively partitioned mixture models for identifying cancer subtypes

MOTIVATION Patients with identical cancer diagnoses often progress differently. The disparity we see in disease progression and treatment response can be attributed to the idea that two histologically similar cancers may be completely different diseases on the molecular level. Methods for identifying cancer subtypes associated with patient survival have the capacity to be powerful instruments for understanding the biochemical processes that underlie disease progression as well as providing an initial step toward more personalized therapy for cancer patients. We propose a method called semi-supervised recursively partitioned mixture models (SS-RPMM) that utilizes array-based genetic and patient-level clinical data for finding cancer subtypes that are associated with patient survival. RESULTS In the proposed SS-RPMM, cancer subtypes are identified using a selected subset of genes that are associated with survival time. Since survival information is used in the gene selection step, this method is semi-supervised. Unlike other semi-supervised clustering classification methods, SS-RPMM does not require specification of the number of cancer subtypes, which is often unknown. In a simulation study, our proposed method compared favorably with other competing semi-supervised methods, including: semi-supervised clustering and supervised principal components analysis. Furthermore, an analysis of mesothelioma cancer data using SS-RPMM, revealed at least two distinct methylation profiles that are informative for survival. AVAILABILITY The analyses implemented in this article were carried out using R (http://www.r.project.org/). CONTACT devin_koestler@brown.edu; e_andres_houseman@brown.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Copy number variation has little impact on bead-array-based measures of DNA methylation

MOTIVATION Integration of various genome-scale measures of molecular alterations is of great interest to researchers aiming to better define disease processes or identify novel targets with clinical utility. Particularly important in cancer are measures of gene copy number DNA methylation. However, copy number variation may bias the measurement of DNA methylation. To investigate possible bias, we analyzed integrated data obtained from 19 head and neck squamous cell carcinoma (HNSCC) tumors and 23 mesothelioma tumors. RESULTS Statistical analysis of observational data produced results consistent with those anticipated from theoretical mathematical properties. Average beta value reported by Illumina GoldenGate (a bead-array platform) was significantly smaller than a similar measure constructed from the ratio of average dye intensities. Among CpGs that had only small variations in measured methylation across tumors (filtering out clearly biological methylation signatures), there were no systematic copy number effects on methylation for three and more than four copies; however, one copy led to small systematic negative effects, and no copies led to substantial significant negative effects. CONCLUSIONS Since mathematical considerations suggest little bias in methylation assayed using bead-arrays, the consistency of observational data with anticipated properties suggests little bias. However, further analysis of systematic copy number effects across CpGs suggest that though there may be little bias when there are copy number gains, small biases may result when one allele is lost, and substantial biases when both alleles are lost. These results suggest that further integration of these measures can be useful for characterizing the biological relationships between these somatic events.

Effect of voriconazole on risk of nonmelanoma skin cancer after hematopoietic cell transplantation

Background: Voriconazole has previously been associated with increased risk for cutaneous squamous cell carcinoma (SCC) in solid organ transplant recipients. Less is known about the risk in patients after hematopoietic cell transplantation (HCT). Objective: We evaluated the effect of voriconazole on the risk for nonmelanoma skin cancer (NMSC), including SCC and basal cell carcionoma, among those who have undergone allogeneic and autologous HCT. Methods: In all, 1220 individuals who had undergone allogeneic HCT and 1418 who had undergone autologous HCT were included in a retrospective cohort study. Multivariate analysis included voriconazole exposure and other known risk factors for NMSC. Results: In multivariate analysis, voriconazole use increased the risk for NMSC (hazard ratio, 1.82; 95% confidence interval, 1.13‐2.91) among those who had undergone allogeneic HCT, particularly for SCC (hazard ratio, 2.25; 95% confidence interval, 1.30‐3.89). Voriconazole use did not appear to confer increased risk for NMSC among those who had undergone autologous HCT. Limitations: This is a retrospective study. Conclusion: Voriconazole use represents an independent factor that may contribute to increased risk specifically for SCC in the allogeneic HCT population.