Replicating and identifying large cell neuroblastoma using high-dose intra-tumoral chemotherapy and automated digital analysis

Abstract

Purpose: Large cell neuroblastomas (LCN) are frequently seen in recurrent, high-risk neuroblastoma but are rare in primary tumors. LCN, characterized by large nuclei with prominent nucleoli, predict a poor prognosis. We hypothesize that LCN can be created with high-dose intra-tumoral chemotherapy and identified by a digital analysis system.

Methods: Orthotopic mouse xenografts were created using human neuroblastoma and treated with high-dose chemotherapy delivered locally via sustained-release silk platforms, inducing tumor remission. After recurrence, LCN populations were identified on H&E sections manually. Clusters of typical LCN and non-LCN cells were divided equally into training and test sets for digital analysis. Marker-controlled watershed segmentation was used to identify nuclei and characterize their features. Logistic regression was developed to distinguish LCN from non-LCN.

Results: Image analysis identified 15,000 nuclei and characterized 70 nuclear features. A 19-feature model provided AUC >0.90 and 100% accuracy when >30% nuclei/cluster were predicted as LCN. Overall accuracy was 87%.

Conclusions: We recreated LCN using high-dose chemotherapy and developed an automated method for defining LCN histologically. Features in the model provide insight into LCN nuclear phenotypic changes that may be related to increased activity. This model could be adapted to identify LCN in human tumors and correlated with clinical outcomes.

Keywords: Intra-tumoral chemotherapy; KELLY; Large cell neuroblastoma; Neuroblastoma; Orthotopic xenograft; SKNAS.