Abstract 1323
Background
Credible prognostic stratification remains a challenge for neuroblastoma (NBL) with variable clinical manifestations. RNA expression signatures might predict the outcomes; notwithstanding, independent cross-platform validation is still rare.
Methods
RNA expression data were obtained from NBL patients and then analyzed. In TARGET-NBL data, an RNA-based prognostic signature was developed and validated. Survival prediction was assessed using a time-dependent receiver operating characteristic (ROC) curve. Functional enrichment analysis of the RNAs was conducted using bioinformatics methods.
Results
A total of 1,119 differentially expressed RNAs and 149 prognosis-related RNAs were identified sequentially. Then, in the training cohort, 12 RNAs were identified as significantly associated with overall survival (OS) and were combined to develop a model that stratified NBL patients into low- and high-risk groups. Twelve RNA signature high-risk patients had poorer OS in the training cohort (n = 105, Hazard Ratios (HR)= 0.10 (0.05-0.20), P < 0.001) and in the validation cohort (n = 44, HR = 0.25 (0.09-0.69), P = 0.008). ROC curve analysis also showed that both the training and validation cohorts performed well in predicting OS (12-month AUC values of 0.852 and 0.438, 36-month AUC values of 0.824 and 0.737, and 60-month AUC values of 0.802 and 0.702, respectively). Moreover, these 12 RNAs may be involved in certain events that are known to be associated with NBL through functional enrichment analysis.
Conclusions
This study identified and validated a novel 12-RNA prognostic signature to reliably distinguish NBL patients at low and high risk of death. Further larger, multicenter prospective studies are desired to validate this model.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
National Natural Science Foundation of China (Grant No. 81660512).
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
4614 - Predictors of Response to Checkpoint Inhibitors in Naïve and Ipilimumab-Refractory Melanoma
Presenter: Domenico Mallardo
Session: Poster Display session 3
Resources:
Abstract
2901 - IFN-γ/IL-10 ratio as predictive biomarker for response to anti-PD-1 therapy in metastatic melanoma patients
Presenter: Emilio Giunta
Session: Poster Display session 3
Resources:
Abstract
2306 - Multiplex Chromogenic Immunohistochemistry (IHC) for Spatial Analysis of Checkpoint-Positive Tumor Infiltrating Lymphocytes (TILs)
Presenter: Scott Ely
Session: Poster Display session 3
Resources:
Abstract
1678 - The role of PD-L1 expression as a predictive biomarker in advanced renal cell carcinoma: a meta-analysis of randomized clinical trials.
Presenter: Alberto Carretero-Gonzalez
Session: Poster Display session 3
Resources:
Abstract
5138 - Radiomic Features as a Non-invasive Biomarker to Predict Response to Immunotherapy in Recurrent or Metastatic Urothelial Carcinoma
Presenter: Kye Jin Park
Session: Poster Display session 3
Resources:
Abstract
5800 - Integrative combination of high-plex digital profiling techniques and cluster analysis to reveal complex immune biology in the tumor microenvironment of mesothelioma
Presenter: Carmen Ballesteros-Merino
Session: Poster Display session 3
Resources:
Abstract
5736 - Predictive factors of response to immunotherapy in 198 patients with metastatic non-microcytic lung cancer (mNSCLC): real world data from 2 university hospitals in Spain
Presenter: Juan Felipe Cordoba Ortega
Session: Poster Display session 3
Resources:
Abstract
5645 - Evaluating Lung CT Density Changes Among Patients with Extensive Stage Small Cell Lung Cancer (ES-SCLC) Treated with Thoracic Radiotherapy (TRT) alone or TRT Followed by Combined Ipilimumab (IPI) and Nivolumab (NIVO).
Presenter: Kujtim Latifi
Session: Poster Display session 3
Resources:
Abstract
1540 - Immuno-oncology therapy biomarkers differences between polyoma-virus positive and negative Merkel cell carcinomas
Presenter: Zoran Gatalica
Session: Poster Display session 3
Resources:
Abstract
4538 - Can we improve patient selection for phase 1 clinical trials (Ph1) based on Immuno-Oncology score prognostic index (VIO)?
Presenter: Ignacio Matos Garcia
Session: Poster Display session 3
Resources:
Abstract