BioProject

Allele-specific copy number analysis of tumors (ASCAT) assesses copy number variations (CNV) while accounting for aberrant cell fraction (ACF) and tumor ploidy. Here, we evaluate if ASCAT-assessed CNV are associated with survival outcomes in patients with WHO grade IV gliomas. Methods: We identified 56 patients with WHO grade IV gliomas. Tumor data analyzed by Affymetrix OncoScan FFPE Assay yielded the log ratio (R) and B-allele frequency (BAF). Input into ASCAT quantified CNV using the segmentation function to measure copy number inflection points throughout the genome. Actuarial overall survival (OS) and progression-free survival (PFS) assessed with Kaplan-Meier method and subgroups compared by the log-rank test. Results were validated on The Cancer Genome Atlas (TCGA) glioblastoma dataset. Results: Our cohort’s median age was 60.4 years (range: 26.1-86.3). Tumors were hyper-methylated at MGMT in 25 (44.6%) with IDH1 mutations in 6 (10.7%). Median follow-up time was 36.4 months, and median PFS and OS were 8 and 14.7 months, respectively. PFS was longer for higher log R and BAF segment counts with hazard ratios (HR) of 0.32 and 0.49 respectively (p<0.001 and p=0.022). Patients with higher log R segment counts had significantly longer 12-month OS (81% vs 46.3%, p=0.0088). In the TCGA validation cohort, higher BAF segment counts had longer 12-month OS with a trend for log R (62.3% vs. 51.9%, p=0.0129 and 63.6% vs. 55.2%, p=0.0696 respectively). Conclusions: Our analysis demonstrated a longer PFS and OS for patients with increased genomic CNV. This may provide a novel method for assessing glioblastoma outcomes. Overall design: After obtaining IRB approval, 56 patients treated at Emory University between 1/2009 and 8/2015 were evaluated and their charts were reviewed. Inclusion criteria included a pathologic diagnosis of a primary glioblastoma, no prior brain radiotherapy and an OncoScan analysis for the ASCAT inputs. Treatment At Emory University, all glioblastoma patients were discussed at a multi-disciplinary tumor board, including surgical oncology, neurosurgery, pathology, radiology, and medical and radiation oncology services. Decisions for therapeutic management were done in a joint manner. Baseline characteristics recorded included age, gender, tumor location, performance status, MGMT methylation, 1p19q co-deletion and IDH1/2 mutation status. Radiation characteristics were also recorded, including radiation total dose, dose per fraction, and receipt of concurrent and adjuvant temozolomide. SNP array For the Emory Institutional dataset, Affymetrix OncoScan FFPE single nucleotide polymorphism (SNP) array was used for raw genomic data acquisition. Two data tracks are produced from SNP arrays: total signal intensity and allelic contrast. Log ratio (R) represents the total signal intensity, which reflects the total copy number on a logarithmic scale. The B allele frequency (BAF) represents the allelic contrast and demonstrates the relative presence at each SNP locus evaluated of the two alternative nucleotides. For the TCGA dataset, Affymetrix Genome-Wide Human SNP Array 6.0 was utilized for the raw genomic data. ASCAT Analysis Following acquisition of the raw genomic data from biopsy or resected tumor samples, we opened each microarray case in Affymetrix Chromosome Analysis Suite v3.0 and used the "Export Probe Level Data" function to create a text file with the microarray probe set name, log 2 ratio (log R) signal, and B-allele frequency (BAF) BAF signal suitable for processing with the ASCAT v2.4.4 library in R v3.4.1. These data were input into the ASCAT algorithm. As a function of the SNP data, ASCAT models the allele-specific copy number and arrives at the solution that is closest to integer copies at all assessed loci. To do this accurately, the ASCAT algorithm accounts for important factors including polyploidy and aberrant cell fraction when calculating the CNV profile.8 Aneuploidy, a deviation from the normal chromosomal number, can confound copy number analysis. Non-aberrant cell admixture or aberrant cell fraction reflects the non-tumor component of the sample and can differ significantly between samples again confounding copy number studies. As an intermediate output of the ASCAT algorithm, we quantified the number of segments with changes in the log R and BAF. A segment is defined as a region of the genome with a fixed copy number. For a representative log R data sample, there were an average of 4,866,610 base pairs and 332 probes per segment with a range of 109,439 – 8,276,5227 base pairs and 7 – 5,997 probes. The total number of segments reflects the number of CNV inflection points or changes in copy number in the genome. Statistical analysis PFS was defined as months from diagnosis to progression, death or last follow-up, where those alive without progression were censored at last follow-up. Progression was defined radiographically based on the T1 post-contrast MRI. OS was defined as months from diagnosis to death or last follow-up, where those who survived were censored at last follow-up. Genomic characteristics considered for analysis were ASCAT ploidy, ASCAT aberrant cell fraction, ASCAT log ratio segment count, and ASCAT BAF segment count. These variables were analyzed as continuous variables, and as categorical variables dichotomized at their median values. A cut point analysis was also performed to identify statistically significant cut points for each genomic variable and each endpoint using an outcome-oriented approach.15 All reported survival data used the optimal cut point. The log-rank statistic was maximized, and the significance of each cut points was assessed. Statistically significant cut points were considered for further analysis. Descriptive statistics were reported for each variable. PFS and OS curves were estimated using the Kaplan-Meier method, and compared using log-rank tests. Median follow-up is estimated using the reverse Kaplan-Meier approach, where the censoring indicator is switched such that deaths are now censored. Univariate Cox proportional hazards models were also fit, modeling PFS and OS as a function of age, gender, KPS, surgery type, the four genomic characteristics, IDH1 mutation (positive vs. negative), and MGMT (methylated vs. unmethylated). Multivariable Cox proportional hazards models were fit for OS and PFS as function of the statistically significant ASCAT variables, IDH1, MGMT, KPS and 1p19q codeletion. Multivariable Cox models also were fit for OS as a function of the statistically significant ASCAT variables, IDH1, MGMT, KPS, and surgery type. Model assumptions were checked and verified. Statistical analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC), and statistical significance was assessed at the 0.05 level.