Gene expression profiling for survival prediction in pediatric rhabdomyosarcomas: a report from the children's oncology group

Abstract

Purpose: We investigated whether tumors from diagnostic biopsies of primary rhabdomyosarcoma (RMS) contain relevant prognostic information in the form of gene expression signatures that can be used to model and predict outcome of patients.

Patients and methods: A 22,000-probe set microarray was used to evaluate 120 RMS specimens and correlate gene expression patterns to survival. Multivariate gene expression models or metagenes were developed using cross-validated Cox regression proportional hazards modeling and were evaluated using Kaplan-Meier analysis.

Results: A 34-metagene, based on expression patterns of 34 genes, was highly predictive of outcome. It was not highly correlated with individual clinical risk factors such as patient age, stage, tumor size, or histology. However, it was correlated with a risk classification used by the Children's Oncology Group and the biologic subsets of alveolar histology tumors.

Conclusion: These data support further evaluation of RMS metagenes to discriminate patients with good prognosis from those with poor prognosis, with the potential to direct risk-adapted therapy.

Fig 1.

Metagene predictor scores determine outcome in rhabdomyosarcoma patients. (A) Histogram showing the binned distribution of the 34-metagene predictor scores for 120 patients (vertical purple lines highlight the tertile cut points). Kaplan-Meier survival analysis of all 120 rhabdomyosarcoma patients (B) using tertiles as groups and (C) for 113 RMS patients with known Children's Oncology Group (COG) risk groups (Table 2). Numbers below the curves indicate the number of patients at risk, and P values are from log-rank test. Int, intermediate.

Fig 2.

Evaluation of metagene predictor scores within Children's Oncology Group (COG) risk groups. Kaplan-Meier survival analysis of COG low-risk (A), intermediate-risk (B), and high-risk (C) patients using 34-metagene tertiles as groups. Numbers below the curves indicate the number of patients at risk, and P values are from log-rank test.

Fig A1.

Development, evaluation, and validation of metagene continuous predictors of rhabdomyosarcoma (RMS) patient outcome. Metagene models were developed using a model-building cohort of 120 patients from North American Intergroup Rhabdomyosarcoma Study (IRS) and Children's Oncology Group (COG) clinical trials under reiterative Cox regression modeling with 50% sample cross-validation.

Fig A2.

Evaluation of the best metagene model. Metagenes were generated by building in a step-wise procedure from the ranked list of the best single gene predictors as determined by reiterative cross-validation. Cox regression χ² values plotted for each metagene generated (blue curve, triangles) were compared with those generated from a permuted data set (gold curve) for the rhabdomyosarcoma metagenes built in a step-wise procedure from the ranked gene list (Supplementary Table 1). Arrow indicates the peak χ² statistics for a 34-probe set metagene or 34-metagene. Generation of metagenes on a permuted data set indicates that the Cox regression model χ² values are not likely attributed to chance alone (gold curve, circles).

Fig A3.

Interquartile range of 34-metagene scores for postsurgical risk groups (A) and histopathologic subtypes (B). The box plots show the 25th and 75th percentiles, the error bars show the minimum and maximum scores, and the middle bars show the median score on a logarithmic scale. RMS, rhabdomyosarcoma.