André Oberthuer

Customized Oligonucleotide Microarray Gene Expression–Based Classification of Neuroblastoma Patients Outperforms Current Clinical Risk Stratification

PURPOSE To develop a gene expression-based classifier for neuroblastoma patients that reliably predicts courses of the disease. PATIENTS AND METHODS Two hundred fifty-one neuroblastoma specimens were analyzed using a customized oligonucleotide microarray comprising 10,163 probes for transcripts with differential expression in clinical subgroups of the disease. Subsequently, the prediction analysis for microarrays (PAM) was applied to a first set of patients with maximally divergent clinical courses (n = 77). The classification accuracy was estimated by a complete 10-times-repeated 10-fold cross validation, and a 144-gene predictor was constructed from this set. This classifiers predictive power was evaluated in an independent second set (n = 174) by comparing results of the gene expression-based classification with those of risk stratification systems of current trials from Germany, Japan, and the United States. RESULTS The first set of patients was accurately predicted by PAM (cross-validated accuracy, 99%). Within the second set, the PAM classifier significantly separated cohorts with distinct courses (3-year event-free survival [EFS] 0.86 +/- 0.03 [favorable; n = 115] v 0.52 +/- 0.07 [unfavorable; n = 59] and 3-year overall survival 0.99 +/- 0.01 v 0.84 +/- 0.05; both P < .0001) and separated risk groups of current neuroblastoma trials into subgroups with divergent outcome (NB2004: low-risk 3-year EFS 0.86 +/- 0.04 v 0.25 +/- 0.15, P < .0001; intermediate-risk 1.00 v 0.57 +/- 0.19, P = .018; high-risk 0.81 +/- 0.10 v 0.56 +/- 0.08, P = .06). In a multivariate Cox regression model, the PAM predictor classified patients of the second set more accurately than risk stratification of current trials from Germany, Japan, and the United States (P < .001; hazard ratio, 4.756 [95% CI, 2.544 to 8.893]). CONCLUSION Integration of gene expression-based class prediction of neuroblastoma patients may improve risk estimation of current neuroblastoma trials.

Distinct transcriptional MYCN/c-MYC activities are associated with spontaneous regression or malignant progression in neuroblastomas

BackgroundAmplified MYCN oncogene resulting in deregulated MYCN transcriptional activity is observed in 20% of neuroblastomas and identifies a highly aggressive subtype. In MYCN single-copy neuroblastomas, elevated MYCN mRNA and protein levels are paradoxically associated with a more favorable clinical phenotype, including disseminated tumors that subsequently regress spontaneously (stage 4s-non-amplified). In this study, we asked whether distinct transcriptional MYCN or c-MYC activities are associated with specific neuroblastoma phenotypes.ResultsWe defined a core set of direct MYCN/c-MYC target genes by applying gene expression profiling and chromatin immunoprecipitation (ChIP, ChIP-chip) in neuroblastoma cells that allow conditional regulation of MYCN and c-MYC. Their transcript levels were analyzed in 251 primary neuroblastomas. Compared to localized-non-amplified neuroblastomas, MYCN/c-MYC target gene expression gradually increases from stage 4s-non-amplified through stage 4-non-amplified to MYCN amplified tumors. This was associated with MYCN activation in stage 4s-non-amplified and predominantly c-MYC activation in stage 4-non-amplified tumors. A defined set of MYCN/c-MYC target genes was induced in stage 4-non-amplified but not in stage 4s-non-amplified neuroblastomas. In line with this, high expression of a subset of MYCN/c-MYC target genes identifies a patient subtype with poor overall survival independent of the established risk markers amplified MYCN, disease stage, and age at diagnosis.ConclusionsHigh MYCN/c-MYC target gene expression is a hallmark of malignant neuroblastoma progression, which is predominantly driven by c-MYC in stage 4-non-amplified tumors. In contrast, moderate MYCN function gain in stage 4s-non-amplified tumors induces only a restricted set of target genes that is still compatible with spontaneous regression.

The Tumor-Associated Antigen PRAME Is Universally Expressed in High-Stage Neuroblastoma and Associated with Poor Outcome

Purpose: The tumor-associated antigen PRAME, a potential candidate for immunotherapeutic targeting, is frequently expressed in a variety of cancers. However, no information about its presence in neuroblastoma is available to date. We therefore evaluated and quantified PRAME expression in a considerable number of neuroblastoma tumors and assessed its impact on the outcome of patients. Experimental Design: Qualitative analysis of PRAME expression was assessed by reverse transcription (RT)-PCR screening of 94 patients with primary neuroblastoma. The same cohort was used for semiquantitative determination of transcript levels by Northern blotting, comparing the signal intensities of patients with those of testis total RNA. For more precise quantification of PRAME expression, real-time RT-PCR was performed in 88 patients of the above cohort and 7 additional patients, thus leaving a total of 101 patients that were analyzed with either method. Furthermore, association with tumor stage, age of patients at diagnosis, and MYCN amplification was determined as well as the prognostic impact of PRAME expression. Results: RT-PCR screening detected PRAME expression in 93% of primary neuroblastoma and 100% of patients with advanced disease. Furthermore, RT-PCR and Northern blot analysis showed a highly significant association of PRAME expression with both higher tumor stage (P < 0.01) and the age of patients at diagnosis (P < 0.01). Finally, precise quantification of PRAME expression by quantitative real-time reverse transcription-PCR displayed significant impact on the outcome of patients. Conclusions: PRAME expression in neuroblastoma is extraordinarily common and was universally seen in patients with advanced-stage disease in our study. Furthermore, significant impact of PRAME expression on the outcome of patients was shown. Thus, PRAME may present a particularly attractive target for immunotherapeutic strategies in neuroblastoma.

Clinical Significance of Tumor-Associated Inflammatory Cells in Metastatic Neuroblastoma

PURPOSE Children diagnosed at age ≥ 18 months with metastatic MYCN-nonamplified neuroblastoma (NBL-NA) are at high risk for disease relapse, whereas those diagnosed at age < 18 months are nearly always cured. In this study, we investigated the hypothesis that expression of genes related to tumor-associated inflammatory cells correlates with the observed differences in survival by age at diagnosis and contributes to a prognostic signature. METHODS Tumor-associated macrophages (TAMs) in localized and metastatic neuroblastomas (n = 71) were assessed by immunohistochemistry. Expression of 44 genes representing tumor and inflammatory cells was quantified in 133 metastatic NBL-NAs to assess age-dependent expression and to develop a logistic regression model to provide low- and high-risk scores for predicting progression-free survival (PFS). Tumors from high-risk patients enrolled onto two additional studies (n = 91) served as independent validation cohorts. RESULTS Metastatic neuroblastomas had higher infiltration of TAMs than locoregional tumors, and metastatic tumors diagnosed in patients at age ≥ 18 months had higher expression of inflammation-related genes than those in patients diagnosed at age < 18 months. Expression of genes representing TAMs (CD33/CD16/IL6R/IL10/FCGR3) contributed to 25% of the accuracy of a novel 14-gene tumor classification score. PFS at 5 years for children diagnosed at age ≥ 18 months with NBL-NA with a low- versus high-risk score was 47% versus 12%, 57% versus 8%, and 50% versus 20% in three independent clinical trials, respectively. CONCLUSION These data suggest that interactions between tumor and inflammatory cells may contribute to the clinical metastatic neuroblastoma phenotype, improve prognostication, and reveal novel therapeutic targets.

Prognostic Impact of Gene Expression–Based Classification for Neuroblastoma

PURPOSE To evaluate the impact of a predefined gene expression-based classifier for clinical risk estimation and cytotoxic treatment decision making in neuroblastoma patients. PATIENTS AND METHODS Gene expression profiles of 440 internationally collected neuroblastoma specimens were investigated by microarray analysis, 125 of which were examined prospectively. Patients were classified as either favorable or unfavorable by a 144-gene prediction analysis for microarrays (PAM) classifier established previously on a separate set of 77 patients. PAM classification results were compared with those of current prognostic markers and risk estimation strategies. RESULTS The PAM classifier reliably distinguished patients with contrasting clinical courses (favorable [n = 249] and unfavorable [n = 191]; 5-year event free survival [EFS] 0.84 +/- 0.03 v 0.38 +/- 0.04; 5-year overall survival [OS] 0.98 +/- 0.01 v 0.56 +/- 0.05, respectively; both P < .001). Moreover, patients with divergent outcome were robustly discriminated in both German and international cohorts and in prospectively analyzed samples (P <or= .001 for both EFS and OS for each). In subgroups with clinical low-, intermediate-, and high-risk of death from disease, the PAM predictor significantly separated patients with divergent outcome (low-risk 5-year OS: 1.0 v 0.75 +/- 0.10, P < .001; intermediate-risk: 1.0 v 0.82 +/- 0.08, P = .042; and high-risk: 0.81 +/- 0.08 v 0.43 +/- 0.05, P = .001). In multivariate Cox regression models based on both EFS and OS, PAM was a significant independent prognostic marker (EFS: hazard ratio [HR], 3.375; 95% CI, 2.075 to 5.492; P < .001; OS: HR, 11.119, 95% CI, 2.487 to 49.701; P < .001). The highest potential clinical impact of the classifier was observed in patients currently considered as non-high-risk (n = 289; 5-year EFS: 0.87 +/- 0.02 v 0.44 +/- 0.07; 5-year OS: 1.0 v 0.80 +/- 0.06; both P < .001). CONCLUSION Gene expression-based classification using the 144-gene PAM predictor can contribute to improved treatment stratification of neuroblastoma patients.

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

BackgroundGene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.ResultsWe generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models.ConclusionsWe demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Oligonucleotide array-based comparative genomic hybridization (aCGH) of 90 neuroblastomas reveals aberration patterns closely associated with relapse pattern and outcome.

The study of genomic alterations in neuroblastoma is of particular importance since several cytogenetic markers proved to be closely associated with the clinical phenotype. To disclose patterns of gains and losses, we performed high‐resolution oligonucleotide array‐based comparative genomic hybridization (aCGH). A total cohort of 90 patients was classified into 6 subsets according to tumor stage and outcome: Stages 1‐3+ (with event), Stage 1‐3− (no event), Stage 4+/−, and Stage 4S+/−. The aberration patterns in Stages 1‐3− and 4S− tumors differed from all other groups as they were predominantly characterized by losses (3, 4, 14, X) and gains (7, 17) of whole chromosomes. However, 59/65 (91%) tumors of Stages 1‐3+ or Stage 4 revealed numerous structural copy number alterations (sCNA). While deletions in chromosomes 1, 3, and 11 discriminated outcome in Stage 4, there were no specific sCNA that distinguished tumor stage within the subgroup of unfavorable tumors. sCNA in 1p, 3p, 11q, 17q, or MYCN amplification (MNA) was seen among 22/24 patients who died, 10/12 with metastatic relapses, and 5/9 with local recurrences. Detailed breakpoint analyses on chromosomes 1, 3, 11, and 17 disclosed preferred breaking areas, although breakpoints were not identical. Amplifications were found in 18 patients and involved 2p24 (MYCN) and other segments of chromosome 2, as well as regions on chromosome arms 6q, 12q, and 17q. One single feature in 21q21.1 (BU678720, without known function yet) attracted particular attention since five patients showed a homozygous loss of this sequence.

k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction.

In the clinical application of genomic data analysis and modeling, a number of factors contribute to the performance of disease classification and clinical outcome prediction. This study focuses on the k-nearest neighbor (KNN) modeling strategy and its clinical use. Although KNN is simple and clinically appealing, large performance variations were found among experienced data analysis teams in the MicroArray Quality Control Phase II (MAQC-II) project. For clinical end points and controls from breast cancer, neuroblastoma and multiple myeloma, we systematically generated 463 320 KNN models by varying feature ranking method, number of features, distance metric, number of neighbors, vote weighting and decision threshold. We identified factors that contribute to the MAQC-II project performance variation, and validated a KNN data analysis protocol using a newly generated clinical data set with 478 neuroblastoma patients. We interpreted the biological and practical significance of the derived KNN models, and compared their performance with existing clinical factors.

Accurate Outcome Prediction in Neuroblastoma across Independent Data Sets Using a Multigene Signature

Purpose: Reliable prognostic stratification remains a challenge for cancer patients, especially for diseases with variable clinical course such as neuroblastoma. Although numerous studies have shown that outcome might be predicted using gene expression signatures, independent cross-platform validation is often lacking. Experimental Design: Using eight independent studies comprising 933 neuroblastoma patients, a prognostic gene expression classifier was developed, trained, tested, and validated. The classifier was established based on reanalysis of four published studies with updated clinical information, reannotation of the probe sequences, common risk definition for training cases, and a single method for gene selection (prediction analysis of microarray) and classification (correlation analysis). Results: Based on 250 training samples from four published microarray data sets, a correlation signature was built using 42 robust prognostic genes. The resulting classifier was validated on 351 patients from four independent and unpublished data sets and on 129 remaining test samples from the published studies. Patients with divergent outcome in the total cohort, as well as in the different risk groups, were accurately classified (log-rank P < 0.001 for overall and progression-free survival in the four independent data sets). Moreover, the 42-gene classifier was shown to be an independent predictor for survival (odds ratio, >5). Conclusion: The strength of this 42-gene classifier is its small number of genes and its cross-platform validity in which it outperforms other published prognostic signatures. The robustness and accuracy of the classifier enables prospective assessment of neuroblastoma patient outcome. Most importantly, this gene selection procedure might be an example for development and validation of robust gene expression signatures in other cancer entities. Clin Cancer Res; 16(5); 1532–41

Hox-C9 activates the intrinsic pathway of apoptosis and is associated with spontaneous regression in neuroblastoma

Neuroblastoma is an embryonal malignancy of the sympathetic nervous system. Spontaneous regression and differentiation of neuroblastoma is observed in a subset of patients, and has been suggested to represent delayed activation of physiologic molecular programs of fetal neuroblasts. Homeobox genes constitute an important family of transcription factors, which play a fundamental role in morphogenesis and cell differentiation during embryogenesis. In this study, we demonstrate that expression of the majority of the human HOX class I homeobox genes is significantly associated with clinical covariates in neuroblastoma using microarray expression data of 649 primary tumors. Moreover, a HOX gene expression-based classifier predicted neuroblastoma patient outcome independently of age, stage and MYCN amplification status. Among all HOX genes, HOXC9 expression was most prominently associated with favorable prognostic markers. Most notably, elevated HOXC9 expression was significantly associated with spontaneous regression in infant neuroblastoma. Re-expression of HOXC9 in three neuroblastoma cell lines led to a significant reduction in cell viability, and abrogated tumor growth almost completely in neuroblastoma xenografts. Neuroblastoma growth arrest was related to the induction of programmed cell death, as indicated by an increase in the sub-G1 fraction and translocation of phosphatidylserine to the outer membrane. Programmed cell death was associated with the release of cytochrome c from the mitochondria into the cytosol and activation of the intrinsic cascade of caspases, indicating that HOXC9 re-expression triggers the intrinsic apoptotic pathway. Collectively, our results show a strong prognostic impact of HOX gene expression in neuroblastoma, and may point towards a role of Hox-C9 in neuroblastoma spontaneous regression.