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  • 1
    Abstract: The majority of patients with neuroblastoma have tumors that initially respond to chemotherapy, but a large proportion will experience therapy-resistant relapses. The molecular basis of this aggressive phenotype is unknown. Whole-genome sequencing of 23 paired diagnostic and relapse neuroblastomas showed clonal evolution from the diagnostic tumor, with a median of 29 somatic mutations unique to the relapse sample. Eighteen of the 23 relapse tumors (78%) showed mutations predicted to activate the RAS-MAPK pathway. Seven of these events were detected only in the relapse tumor, whereas the others showed clonal enrichment. In neuroblastoma cell lines, we also detected a high frequency of activating mutations in the RAS-MAPK pathway (11/18; 61%), and these lesions predicted sensitivity to MEK inhibition in vitro and in vivo. Our findings provide a rationale for genetic characterization of relapse neuroblastomas and show that RAS-MAPK pathway mutations may function as a biomarker for new therapeutic approaches to refractory disease.
    Type of Publication: Journal article published
    PubMed ID: 26121087
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  • 2
    Abstract: An urgent need remains for new paediatric oncology drugs to cure children who die from cancer and to reduce drug-related sequelae in survivors. In 2007, the European Paediatric Regulation came into law requiring industry to create paediatric drug (all types of medicinal products) development programmes alongside those for adults. Unfortunately, paediatric drug development is still largely centred on adult conditions and not a mechanism of action (MoA)-based model, even though this would be more logical for childhood tumours as these have much fewer non-synonymous coding mutations than adult malignancies. Recent large-scale sequencing by International Genome Consortium and Paediatric Cancer Genome Project has further shown that the genetic and epigenetic repertoire of driver mutations in specific childhood malignancies differs from more common adult-type malignancies. To bring about much needed change, a Paediatric Platform, ACCELERATE, was proposed in 2013 by the Cancer Drug Development Forum, Innovative Therapies for Children with Cancer, the European Network for Cancer Research in Children and Adolescents and the European Society for Paediatric Oncology. The Platform, comprising multiple stakeholders in paediatric oncology, has three working groups, one with responsibility for promoting and developing high-quality MoA-informed paediatric drug development programmes, including specific measures for adolescents. Key is the establishment of a freely accessible aggregated database of paediatric biological tumour drug targets to be aligned with an aggregated pipeline of drugs. This will enable prioritisation and conduct of early phase clinical paediatric trials to evaluate these drugs against promising therapeutic targets and to generate clinical paediatric efficacy and safety data in an accelerated time frame. Through this work, the Platform seeks to ensure that potentially effective drugs, where the MoA is known and thought to be relevant to paediatric malignancies, are evaluated in early phase clinical trials, and that this approach to generate pre-clinical and clinical data is systematically pursued by academia, sponsors, industry, and regulatory bodies to bring new paediatric oncology drugs to front-line therapy more rapidly.
    Type of Publication: Journal article published
    PubMed ID: 27258969
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  • 3
    Keywords: STAGE ; PROGRESSION ; AMPLIFICATION ; chemotherapy ; DELETIONS ; SPONTANEOUS REGRESSION ; PREDICTION ; pathology ; N-MYC ; EXPRESSION-BASED CLASSIFICATION
    Abstract: Purpose: To optimize neuroblastoma treatment stratification, we aimed at developing a novel risk estimation system by integrating gene expression-based classification and established prognostic markers. Material and Methods: Gene expression profiles were generated from 709 neuroblastoma specimens using customized 4x44K microarrays. Classification models were built using 75 tumors with contrasting courses of disease. Validation was performed in an independent test set (n=634) by Kaplan-Meier estimates and Cox regression analyses. Results: The best-performing classifier predicted patient outcome with an accuracy of 0.95 (sensitivity 0.93, specificity 0.97) in the validation cohort. The highest potential clinical value of this predictor was observed for current low-risk patients (LR: 5-year EFS 0.84+/-0.02 vs 0.29+/-0.10; 5-year OS 0.99+/-0.01vs 0.76+/-0.11; both p〈0.001) and intermediate-risk patients (IR: 5-year EFS 0.88+/-0.06 vs 0.41+/-0.10; 5-year OS 1.0 vs 0.70+/-0.09; both p〈0.001). In multivariate Cox regression models for LR/IR patients the classifier outperformed risk assessment of the current German trial NB2004 (EFS: HR 5.07, 95%-CI 3.20-8.02, OS: HR 25.54, 95%-CI 8.40-77.66; both p〈0.001). Based on these findings, we propose to integrate the classifier into a revised risk stratification system for LR/IR patients. According to this system, we identified novel subgroups with poor outcome (5-year EFS 0.19+/-0.08; 5-year OS 0.59+/-0.1), for whom we propose intensified treatment, and with beneficial outcome (5-year EFS 0.87+/-0.05; 5-year OS 1.0), who may benefit from treatment de-escalation. Conclusion: Combination of gene expression-based classification and established prognostic markers improves risk estimation of LR/IR neuroblastoma patients. We propose to implement our revised treatment stratification system in a prospective clinical trial.
    Type of Publication: Journal article published
    PubMed ID: 25231397
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  • 4
    Abstract: New drugs are crucially needed for children with cancer. The European Paediatric Regulation facilitates paediatric class waivers for drugs developed for diseases only occurring in adults. In this Review, we retrospectively searched oncology drugs that were class waivered between June, 2012, and June, 2015. 147 oncology class waivers were confirmed for 89 drugs. Mechanisms of action were then assessed as potential paediatric therapeutic targets by both a literature search and an expert review. 48 (54%) of the 89 class-waivered drugs had a mechanisms of action warranting paediatric development. Two (2%) class-waivered drugs were considered not relevant and 16 (18%) required further data. In light of these results, we propose five initiatives: an aggregated database of paediatric biological tumour drug targets; molecular profiling of all paediatric tumours at diagnosis and relapse; a joint academic-pharmaceutical industry preclinical platform to help analyse the activity of new drugs (Innovative Therapy for Children with Cancer Paediatric Preclinical Proof-of-Concept Platform); paediatric strategy forums; and the suppression of article 11b of the European Paediatric Regulation, which allows product-specific waivers on the grounds that the associated condition does not occur in children. These initiatives and a mechanism of action-based approach to drug development will accelerate the delivery of new therapeutic drugs for front-line therapy for those children who have unmet medical needs.
    Type of Publication: Journal article published
    PubMed ID: 28677575
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  • 5
    Keywords: CANCER ; EXPRESSION ; SURVIVAL ; CLASSIFICATION ; DIAGNOSIS ; INFORMATION ; SYSTEM ; COHORT ; DISEASE ; RISK ; GENE ; GENE-EXPRESSION ; GENES ; microarray ; validation ; PATIENT ; prognosis ; SEQUENCE ; SEQUENCES ; IDENTIFICATION ; AMPLIFICATION ; gene expression ; MICROARRAY DATA ; microarrays ; DESIGN ; NUMBER ; CANCER-PATIENTS ; PREDICTION ; SELECTION ; CANCER PATIENTS ; neuroblastoma ; ONCOLOGY ; GENE-EXPRESSION PROFILES ; development ; prospective ; RISK STRATIFICATION ; outcome ; SIGNATURES ; EXPRESSION SIGNATURES ; STRATIFICATION
    Abstract: 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. (C)2010 AACR
    Type of Publication: Journal article published
    PubMed ID: 20179214
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  • 6
    Keywords: EXPRESSION ; SURVIVAL ; Germany ; MODEL ; MODELS ; CLASSIFICATION ; DIAGNOSIS ; COHORT ; DEATH ; DISEASE ; MORTALITY ; RISK ; GENE ; GENE-EXPRESSION ; microarray ; PATIENT ; MARKER ; IMPACT ; STAGE ; AMPLIFICATION ; gene expression ; microarrays ; AGE ; MARKERS ; HIGH-RISK ; STRATEGIES ; SPONTANEOUS REGRESSION ; PREDICTION ; INFANTS ; pathology ; CHILDREN ; MICROARRAY ANALYSIS ; neuroblastoma ; ONCOLOGY ; REGRESSION ; overall survival ; INDEPENDENT PROGNOSTIC MARKER ; methods ; PROGNOSTIC MARKER ; PROFILES ; EXPRESSION PROFILES ; RISK STRATIFICATION ; SUBGROUPS ; MYC ; PROFILE ; outcome ; STRATEGY ; CONTRIBUTE ; COHORTS ; COX REGRESSION ; clinical oncology ; STRATIFICATION ; prognostic
    Abstract: 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 = .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; intermediaterisk: 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
    Type of Publication: Journal article published
    PubMed ID: 20567016
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  • 7
  • 8
    Publication Date: 2018-02-16
    Description: Purpose: Neuroblastoma displays important clinical and genetic heterogeneity, with emergence of new mutations at tumor progression. Experimental Design: To study clonal evolution during treatment and follow-up, an innovative method based on circulating cell-free DNA (cfDNA) analysis by whole-exome sequencing (WES) paired with target sequencing was realized in sequential liquid biopsy samples of 19 neuroblastoma patients. Results: WES of the primary tumor and cfDNA at diagnosis showed overlap of single-nucleotide variants (SNV) and copy number alterations, with 41% and 93% of all detected alterations common to the primary neuroblastoma and cfDNA. CfDNA WES at a second time point indicated a mean of 22 new SNVs for patients with progressive disease. Relapse-specific alterations included genes of the MAPK pathway and targeted the protein kinase A signaling pathway. Deep coverage target sequencing of intermediate time points during treatment and follow-up identified distinct subclones. For 17 seemingly relapse-specific SNVs detected by cfDNA WES at relapse but not tumor or cfDNA WES at diagnosis, deep coverage target sequencing detected these alterations in minor subclones, with relapse-emerging SNVs targeting genes of neuritogenesis and cell cycle. Furthermore a persisting, resistant clone with concomitant disappearance of other clones was identified by a mutation in the ubiquitin protein ligase HERC2 . Conclusions: Modelization of mutated allele fractions in cfDNA indicated distinct patterns of clonal evolution, with either a minor, treatment-resistant clone expanding to a major clone at relapse, or minor clones collaborating toward tumor progression. Identification of treatment-resistant clones will enable development of more efficient treatment strategies. Clin Cancer Res; 24(4); 939–49. ©2017 AACR .
    Print ISSN: 1078-0432
    Electronic ISSN: 1557-3265
    Topics: Medicine
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