A three-gene expression signature model for risk stratification of patients with neuroblastoma

A three-gene expression signature model for risk stratification of patients with neuroblastoma

1. Idoia Garcia1,
2. Gemma Mayol2,
3. Jose Rios3,
4. Gema Domenech4,
5. Nai-Kong V Cheung5,
6. Andre Oberthuer6,
7. Matthias Fischer7,
8. John M Maris8,
9. Garrett M Brodeur9,
10. Barbara Hero6,
11. Eva Rodriguez10,
12. Mariona Sunol11,
13. Patricia Galvan2,
14. Carmen de Torres10,
15. Jaume Mora12, and
16. Cinzia Lavarino2,*

+ Author Affiliations

1. ¹Oncology, Hospital Sant joan de Déu
2. ²Oncology, Hospital Sant Joan de Déu
3. ³Biostatistics and Epidemiology, Universitat Autonoma
4. ⁴Laboratory of Biostatistics & Epidemiology, Hospital Clinic
5. ⁵Dept of Pediatrics, Memorial Sloan-Kettering
6. ⁶Department of Pediatric Oncology and Hematology, University of Cologne, Children's Hospital
7. ⁷Pediatric Oncology, University Children's Hospital of Cologne
8. ⁸Pediatrics/Oncology, Children's Hospital of Philadelphia
9. ⁹Division of Oncology, The Children's Hospital of Philadelphia
10. ¹⁰Developmental Tumor Biology Laboratory, Hospital Sant Joan de Déu
11. ¹¹Pathology, Hospital Sant Joan de Déu
12. ¹²Oncology, Hospital Sant Joan de Deu de Barcelona

1. ↵* Corresponding Author:
   Cinzia Lavarino, Oncology, Hospital Sant Joan de Déu, Edificio Docete 4th Floor, calle Santa Rosa 39-57, Barcelona, Barcelona, 08950, Spain clavarino@fsjd.org

Abstract

Background: Neuroblastoma is an embryonal tumor with contrasting clinical courses. Despite elaborate stratification strategies, precise clinical risk assessment still remains a challenge. The purpose of this study was to develop a PCR-based predictor model to improve clinical risk assessment of neuroblastoma patients. Methods: The model was developed using real-time PCR gene expression data from 96 samples, and tested on separate expression data sets obtained from real-time PCR and microarray studies comprising 362 patients. Results: Based on our prior study of differentially expressed genes in favorable and unfavorable neuroblastoma subgroups, we identified three genes, CHD5, PAFAH1B1 and NME1, strongly associated with patient outcome. The expression pattern of these genes was used to develop a PCR-based single score predictor model. The model discriminated patients into two groups with significantly different clinical outcome (Set 1 5-year overall survival [OS]:0.93±0.03 vs 0.53±0.06, 5-year event free survival [EFS]:0.85±0.04 vs 0.042±0.06, both P<0.001; Set 2 OS:0.97±0.02 vs 0.61±0.1, P=0.005, EFS:0.91±0.8 vs 0.56±0.1, P<0.001 and Set 3 OS:0.99±0.01 vs 0.56±0.06, EFS:0.96±0.02 vs 0.43±0.05, both P<0.001). Multivariate analysis showed that the model was an independent marker for survival (P<0.001, for all). In comparison with accepted risk stratification systems, the model robustly classified patients in the total cohort, and in different clinically relevant risk subgroups. Conclusion: We propose for the first time in neuroblastoma, a technically simple PCR-based predictor model that could help refine current risk stratification systems.

• Received September 27, 2011.
• Revision received February 7, 2012.
• Accepted February 7, 2012.

• Copyright © 2012, American Association for Cancer Research.