Shahab Asgharzadeh

PURPOSE Treatment planning for children with neuroblastoma requires accurate assessment of prognosis. The most recent Children's Oncology Group (COG) risk classification system used tumor stage as defined by the International Neuroblastoma Staging System. Here, we validate a revised classifier using the International Neuroblastoma Risk Group Staging System (INRGSS) and incorporate segmental chromosome aberrations (SCA) as an additional genomic biomarker. METHODS Newly diagnosed patients enrolled on the COG neuroblastoma biology study ANBL00B1 between 2007 and 2017 with known age, International Neuroblastoma Staging System, and INRGSS stage were identified (N = 4,832). Tumor MYCN status, ploidy, SCA status (1p and 11q), and International Neuroblastoma Pathology Classification histology were determined centrally. Survival analyses were performed for combinations of prognostic factors used in COG risk classification according to the prior version 1, and to validate a revised algorithm (version 2). RESULTS Most patients with locoregional tumors had excellent outcomes except for those with image-defined risk factors (INRGSS L2) with MYCN amplification (5-year event-free survival and overall survival: 76.3% ± 5.8% and 79.9% ± 5.5%, respectively) or patients age ≥ 18 months with L2 MYCN nonamplified tumors with unfavorable International Neuroblastoma Pathology Classification histology (72.7% ± 5.4% and 82.4% ± 4.6%), which includes the majority of L2 patients with SCA. For patients with stage M (metastatic) and MS (metastatic, special) disease, genomic biomarkers affected risk group assignment for those < 12 months ( MYCN) or 12-18 months ( MYCN, histology, ploidy, and SCA) of age. In a retrospective analysis of patient outcome, the 5-year event-free survival and overall survival using COG version 1 were low-risk: 89.4% ± 1.1% and 97.9% ± 0.5%; intermediate-risk: 86.1% ± 1.3% and 94.9% ± 0.8%; high-risk: 50.8% ± 1.4% and 61.9% ± 1.3%; and using COG version 2 were low-risk: 90.7% ± 1.1% and 97.9% ± 0.5%; intermediate-risk: 85.1% ± 1.4% and 95.8% ± 0.8%; high-risk: 51.2% ± 1.4% and 62.5% ± 1.3%, respectively. CONCLUSION A revised 2021 COG neuroblastoma risk classifier (version 2) that uses the INRGSS and incorporates SCAs has been adopted to prospectively define COG clinical trial eligibility and treatment assignment.

BACKGROUND: Gene 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. RESULTS: We 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. CONCLUSIONS: We 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.

BACKGROUND: How exosomic microRNAs (miRNAs) contribute to the development of drug resistance in the context of the tumor microenvironment has not been previously described in neuroblastoma (NBL). METHODS: Coculture experiments were performed to assess exosomic transfer of miR-21 from NBL cells to human monocytes and miR-155 from human monocytes to NBL cells. Luciferase reporter assays were performed to assess miR-155 targeting of TERF1 in NBL cells. Tumor growth was measured in NBL xenografts treated with Cisplatin and peritumoral exosomic miR-155 (n = 6 mice per group) CD163, miR-155, and TERF1 levels were assessed in 20 NBL primary tissues by Human Exon Arrays and quantitative real-time polymerase chain reaction. Student's t test was used to evaluate the differences between treatment groups. All statistical tests were two-sided. RESULTS: miR-21 mean fold change (f.c.) was 12.08±0.30 (P < .001) in human monocytes treated with NBL derived exosomes for 48 hours, and miR-155 mean f.c. was 4.51±0.25 (P < .001) in NBL cells cocultured with human monocytes for 48 hours. TERF1 mean luciferase activity in miR-155 transfected NBL cells normalized to scrambled was 0.36 ± 0.05 (P <.001). Mean tumor volumes in Dotap-miR-155 compared with Dotap-scrambled were 322.80±120mm(3) and 76.00±39.3mm(3), P = .002 at day 24, respectively. Patients with high CD163 infiltrating NBLs had statistically significantly higher intratumoral levels of miR-155 (P = .04) and lower levels of TERF1 mRNA (P = .02). CONCLUSIONS: These data indicate a unique role of exosomic miR-21 and miR-155 in the cross-talk between NBL cells and human monocytes in the resistance to chemotherapy, through a novel exosomic miR-21/TLR8-NF-кB/exosomic miR-155/TERF1 signaling pathway.