Development and validation of a 21-gene prognostic signature in neuroblastoma

Abstract

Survival outcomes for patients with neuroblastoma vary markedly and reliable prognostic markers and risk stratification tools are lacking. We sought to identify and validate a transcriptomic signature capable of predicting risk of mortality in patients with neuroblastoma. The TARGET NBL dataset (n = 243) was used to develop the model and two independent cohorts, E-MTAB-179 (n = 478) and GSE85047 (n = 240) were used as validation sets. EFS was the primary outcome and OS was the secondary outcome of interest for all analysis. We identified a 21-gene signature capable of stratifying neuroblastoma patients into high and low risk groups in the E-MTAB-179 (HR 5.87 [3.83–9.01], p < 0.0001, 5 year AUC 0.827) and GSE85047 (HR 3.74 [2.36–5.92], p < 0.0001, 5 year AUC 0.815) validation cohorts. Moreover, the signature remained independent of known clinicopathological variables, and remained prognostic within clinically important subgroups. Further, the signature was effectively incorporated into a risk model with clinicopathological variables to improve prognostic performance across validation cohorts (Pooled Validation HR 6.93 [4.89–9.83], p < 0.0001, 5 year AUC 0.839). Similar prognostic utility was also demonstrated with OS. The identified signature is a robust independent predictor of EFS and OS outcomes in neuroblastoma patients and can be combined with clinically utilized clinicopathological variables to improve prognostic performance.

Development and validation of a novel stemness-related prognostic model for neuroblastoma using integrated machine learning and bioinformatics analyses

“…Various prognostic signatures for NB have been reported in previous studies ( 12 , 46 , 47 ). However, to our knowledge, no previous study in NB has reported measuring stemness for prognostication.…”

Radiofrequency Ablation of Small Thyroid Cancer–A Solution Looking for a Problem?

“…2 Studies increasingly report the connection of molecular profiling results with thyroid tumor biology and outcomes, 3,4 offering important prognostic information from a fine-needle biopsy alone and, thus, perfectly suited to improve patient selection for nonoperative strategies. The use of molecular profiling for prognosis is common in other cancers 5,6 and its application routinely guides clinical care of breast cancer 7 -why should thyroid cancer care lag behind?…”