Dataset Information

Neuroblastoma Risk Assessment and Treatment Stratification with Hybrid Capture-Based Panel Sequencing.

ABSTRACT: For many years, the risk-based therapy stratification of children with neuroblastoma has relied on clinical and molecular covariates. In recent years, genome analysis has revealed further alterations defining risk, tumor biology, and therapeutic targets. The implementation of a robust and scalable method for analyzing traditional and new molecular markers in routine diagnostics is an urgent clinical need. Here, we investigated targeted panel sequencing as a diagnostic approach to analyze all relevant genomic neuroblastoma risk markers in one assay. Our "neuroblastoma hybrid capture sequencing panel" (NB-HCSP) assay employs a technology for the high-coverage sequencing (>1000×) of 55 selected genes and neuroblastoma-relevant genomic regions, which allows for the detection of single nucleotide changes, structural rearrangements, and copy number alterations. We validated our assay by analyzing 15 neuroblastoma cell lines and a cohort of 20 neuroblastomas, for which reference routine diagnostic data and genome sequencing data were available. We observed a high concordance for risk markers identified by the NB-HSCP assay, clinical routine diagnostics, and genome sequencing. Subsequently, we demonstrated clinical applicability of the NB-HCSP assay by analyzing routine clinical samples. We conclude that the NB-HCSP assay may be implemented into routine diagnostics as a single assay that covers all essential covariates for initial neuroblastoma classification, extended risk stratification, and targeted therapy selection.

SUBMITTER: Szymansky A

PROVIDER: S-EPMC8398598 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Neuroblastoma Risk Assessment and Treatment Stratification with Hybrid Capture-Based Panel Sequencing.

Szymansky Annabell A Kruetzfeldt Louisa-Marie LM Heukamp Lukas C LC Hertwig Falk F Theissen Jessica J Deubzer Hedwig E HE Willing Eva-Maria EM Menon Roopika R Fuchs Steffen S Thole Theresa T Schulte Stefanie S Schmelz Karin K Künkele Annette A Lang Peter P Fuchs Jörg J Eggert Angelika A Eckert Cornelia C Fischer Matthias M Henssen Anton G AG Rodriguez-Fos Elias E Schulte Johannes H JH

Journal of personalized medicine 20210722 8

For many years, the risk-based therapy stratification of children with neuroblastoma has relied on clinical and molecular covariates. In recent years, genome analysis has revealed further alterations defining risk, tumor biology, and therapeutic targets. The implementation of a robust and scalable method for analyzing traditional and new molecular markers in routine diagnostics is an urgent clinical need. Here, we investigated targeted panel sequencing as a diagnostic approach to analyze all rel ...[more]

PMID: 34442335

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Project description:A suitable diagnostic classification of myeloid neoplasms and acute leukemias requires testing for a large number of molecular biomarkers. Next-generation sequencing is a technology able to integrate identification of the vast majority of them in a single test. This manuscript includes the design, analytical validation and clinical feasibility evaluation of a molecular diagnostic kit for onco-hematological diseases. It is based on sequencing of the coding regions of 76 genes (seeking single-nucleotide variants, small insertions or deletions and CNVs), as well as the search for fusions in 27 target genes. The kit has also been designed to detect large CNVs throughout the genome by including specific probes and employing a custom bioinformatics approach. The analytical and clinical feasibility validation of the Haematology OncoKitDx panel has been carried out from the sequencing of 170 patient samples from 6 hospitals (in addition to the use of commercial reference samples). The analytical validation showed sensitivity and specificity close to 100% for all the parameters evaluated, with a detection limit of 2% for SNVs and SVs, and 20% for CNVs. Clinically relevant mutations were detected in 94% of all patients. An analysis of the correlation between the genetic risk classification of AML (according to ELN 2017) established by the hospitals and that obtained by the Haematology OncoKitDx panel showed an almost perfect correlation (K = 0.94). Among the AML samples with a molecular diagnosis, established by the centers according to the WHO, the Haematology OncoKitDx analysis showed the same result in 97% of them. The panel was able to adequately differentiate between MPN subtypes and also detected alterations that modified the diagnosis (FIP1L1-PDGFRA). Likewise, the cytogenetic risk derived from the CNV plot generated by the NGS panel correlated substantially with the results of the conventional karyotype (K = 0.71) among MDS samples. In addition, the panel detected the main biomarkers of prognostic value among patients with ALL. This validated solution enables a reliable analysis of a large number of molecular biomarkers from a DNA sample in a single assay.

Project description:PurposeThe substratification of high-risk neuroblastoma is challenging, and new predictive imaging biomarkers are warranted for better patient selection. The aim of the study was to evaluate the prognostic role of PET-based intratumor heterogeneity and its potential ability to improve risk stratification in neuroblastoma.MethodsPretreatment 18F-FDG PET/CT scans from 112 consecutive children with newly diagnosed neuroblastoma were retrospectively analyzed. The primary tumor was segmented in the PET images. SUVs, volumetric parameters including metabolic tumor volume (MTV) and total lesion glycolysis (TLG), and texture features were extracted. After the exclusion of imaging features with poor and moderate reproducibility, the relationships between the imaging indices and clinicopathological factors, as well as event-free survival (EFS), were assessed.ResultsThe median follow-up duration was 33 months. Multivariate analysis showed that PET-based intratumor heterogeneity outperformed clinicopathological features, including age, stage, and MYCN, and remained the most robust independent predictor for EFS [training set, hazard ratio (HR): 6.4, 95% CI: 3.1-13.2, p < 0.001; test set, HR: 5.0, 95% CI: 1.8-13.6, p = 0.002]. Within the clinical high-risk group, patients with a high metabolic heterogeneity showed significantly poorer outcomes (HR: 3.3, 95% CI: 1.6-6.8, p = 0.002 in the training set; HR: 4.4, 95% CI: 1.5-12.9, p = 0.008 in the test set) compared to those with relatively homogeneous tumors. Furthermore, intratumor heterogeneity outran the volumetric indices (MTVs and TLGs) and yielded the best performance of distinguishing high-risk patients with different outcomes with a 3-year EFS of 6% vs. 47% (p = 0.001) in the training set and 9% vs. 51% (p = 0.004) in the test set.ConclusionPET-based intratumor heterogeneity was a strong independent prognostic factor in neuroblastoma. In the clinical high-risk group, intratumor heterogeneity further stratified patients with distinct outcomes.

Dataset Information

Neuroblastoma Risk Assessment and Treatment Stratification with Hybrid Capture-Based Panel Sequencing.

Publications

Neuroblastoma Risk Assessment and Treatment Stratification with Hybrid Capture-Based Panel Sequencing.

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