Establishment of prognostic models for astrocytic and oligodendroglial brain tumors with standardized quantification of marker gene expression and clinical variables

Abstract

Background: Prognosis models established using multiple molecular markers in cancer along with clinical variables should enable prediction of natural disease progression and residual risk faced by patients. In this study, multivariate Cox proportional hazards analyses were done based on overall survival (OS) of 100 glioblastoma multiformes (GBMs, 92 events), 49 anaplastic astrocytomas (AAs, 33 events), 45 gliomas with oligodendroglial features, including anaplastic oligodendroglioma (AO, 13 events) and oligodendraglioma (O, 9 events). The modeling included two clinical variables (patient age and recurrence at the time of sample collection) and the expression variables of 13 genes selected based on their proven biological and/or prognosis functions in gliomas (ABCG2, BMI1, MELK, MSI1, PROM1, CDK4, EGFR, MMP2, VEGFA, PAX6, PTEN, RPS9, and IGFBP2). Gene expression data was a log-transformed ratio of marker and reference (ACTB) mRNA levels quantified using absolute real-time qRT-PCR.

Results: Age is positively associated with overall grade (4 for GBM, 3 for AA, 2_1 for AO_O), but lacks significant prognostic value in each grade. Recurrence is an unfavorable prognostic factor for AA, but lacks significant prognostic values for GBM and AO_O. Univariate models revealed opposing prognostic effects of ABCG2, MELK, BMI1, PROM1, IGFBP2, PAX6, RPS9, and MSI1 expressions for astrocytic (GBM and AA) and oligodendroglial tumors (AO_O). Multivariate models revealed independent prognostic values for the expressions of MSI1 (unfavorable) in GBM, CDK4 (unfavorable) and MMP2 (favorable) in AA, while IGFBP2 and MELK (unfavorable) in AO_O. With all 13 genes and 2 clinical variables, the model R(2) was 14.2% (P = 0.358) for GBM, 45.2% (P = 0.029) for AA, and 62.2% (P = 0.008) for AO_O.

Conclusion: The study signifies the challenge in establishing a significant prognosis model for GBM. Our success in establishing prognosis models for AA and AO_O was largely based on identification of a set of genes with independent prognostic values and application of standardized gene expression quantification to allow formation of a large cohort in analysis.

Keywords: gene expression markers; glioma; model; prognosis.

Figure 1

Hazard ratio vs. gene expression logRatios curves for GBM, AA, and AO_O based on the univariate Cox PH model. The hazard ratio for a particular marker corresponding to each of the three grades was computed using a Cox PH model with 3 terms (grade, marker, and grade-marker interaction), for detail statistical analyses see Method. The hazard ratios are shown using zero as the comparator value. A decreasing curve indicates a favorable prognostic effect from the gene expression. In contrast, an increasing curve indicates an unfavorable prognostic effect, while a flat curve signifies no prognostic effect from the gene expression.

Figure 2

Opposing effect of MSI1 in prognosis for GBM and oligodendroglia tumors. Upper panel shows Kaplan-Meier survival curves for GBM, AA, and AO_O based on absolute ratio of MSI1 to ACTB dichotomized at the overall median of 0.0012 for all 194 gliomas. Bottom panel shows log scaled MSI1 univariate models for GBM, AA and AO_O.