Abstract 3348: UniD: unified and integrated diagnostic pipeline for malignant gliomas based on DNA methylation data

Molecular profiling of long-term IDH-wildtype glioblastoma survivors

BACKGROUND

Glioblastoma (GBM) represents an aggressive cancer type with a median survival of only 14 months. With fewer than 5% of patients surviving 5 years, comprehensive profiling of these rare patients could elucidate prognostic biomarkers that may confer better patient outcomes. We utilized multiple molecular approaches to characterize the largest patient cohort of isocitrate dehydrogenase (IDH)-wildtype GBM long-term survivors (LTS) to date.

METHODS

Retrospective analysis was performed on 49 archived formalin-fixed paraffin embedded tumor specimens from patients diagnosed with GBM at the Mayo Clinic between December 1995 and September 2013. These patient samples were subdivided into 2 groups based on survival (12 LTS, 37 short-term survivors [STS]) and subsequently examined by mutation sequencing, copy number analysis, methylation profiling, and gene expression.

RESULTS

Of the 49 patients analyzed in this study, LTS were younger at diagnosis (P = 0.016), more likely to be female (P = 0.048), and MGMT promoter methylated (UniD, P = 0.01). IDH-wildtype STS and LTS demonstrated classic GBM mutations and copy number changes. Pathway analysis of differentially expressed genes showed LTS enrichment for sphingomyelin metabolism, which has been linked to decreased GBM growth, invasion, and angiogenesis. STS were enriched for DNA repair and cell cycle control networks.

CONCLUSIONS

While our findings largely report remarkable similarity between these LTS and more typical STS, unique attributes were observed in regard to altered gene expression and pathway enrichment. These attributes may be valuable prognostic markers and are worth further examination. Importantly, this study also underscores the limitations of existing biomarkers and classification methods in predicting patient prognosis.

Spatial Distance Correlates With Genetic Distance in Diffuse Glioma

Background: Treatment effectiveness and overall prognosis for glioma patients depend heavily on the genetic and epigenetic factors in each individual tumor. However, intra-tumoral genetic heterogeneity is known to exist and needs to be managed. Currently, evidence for genetic changes varying spatially within the tumor is qualitative, and quantitative data is lacking. We hypothesized that a greater genetic diversity or “genetic distance” would be observed for distinct tumor samples taken with larger physical distances between them.

Methods: Stereotactic biopsies were obtained from untreated primary glioma patients as part of a clinical trial between 2011 and 2016, with at least one biopsy pair collected in each case. The physical (Euclidean) distance between biopsy sites was determined using coordinates from imaging studies. The tissue samples underwent whole exome DNA sequencing and epigenetic methylation profiling and genomic distances were defined in three separate ways derived from differences in number of genes, copy number variations (CNV), and methylation profiles.

Results: Of the 31 patients recruited to the trial, 23 were included in DNA methylation analysis, for a total of 71 tissue samples (14 female, 9 male patients, age range 21–80). Samples from an 8 patient subset of the 23 evaluated patients were further included in whole exome and copy number variation analysis. Physical and genomic distances were found to be independently and positively correlated for each of the three genomic distance measures. The correlation coefficients were 0.63, 0.65, and 0.35, respectively for (a) gene level mutations, (b) copy number variation, and (c) methylation status. We also derived quantitative linear relationships between physical and genomic distances.

Conclusion: Primary brain tumors are genetically heterogeneous, and the physical distance within a given glioma correlates to genomic distance using multiple orthogonal genomic assessments. These data should be helpful in the clinical diagnostic and therapeutic management of glioma, for example by: managing sampling error, and estimating genetic heterogeneity using simple imaging inputs.