Revealing an unexpected homogeneity in neuroblastoma

Tumors are usually very heterogeneous and consist of various cancer cell subtypes. Single-cell sequencing can be applied to understand this heterogeneity and often leads to identifying cancer cell subtypes. In this study, there is a surprising outcome: a homogeneous phenotype.  

In this scientific collaboration between many institutes in the Netherlands and the UK, researchers were able to identify a pan-neuroblastoma cell type. Their research was published in Science Advances in February.  

Neuroblastoma is a diverse type of childhood cancer, which arises from aberrant differentiation of the neural crest during early development. It’s most frequently located at the adrenal glands but can develop in other parts of the body. The exact developmental processes that neuroblastoma cells represent have not been defined.  

Here, neuroblastoma cancer cells and normal reference cells were directly compared to identify developmental processes and cell types. 

What did they find?  

Their principal finding is that neuroblastoma cancer cells have a sympathoblast-like phenotype. A sympathoblast is a pluripotent cell that differentiates in a sympathetic nerve cell or a chromaffin cell during development.  The sympathoblast state was only found among malignant cells in both low-risk and high-risk tumors. This indicates that this state predominates among all types of neuroblastoma.  

The identification of the predominant sympathoblast phenotype among all risk groups may have implications for novel targeted drug studies.  

Single-cell sequencing  

Single-cell sequencing allowed these researchers to compare neuroblastoma cells with fetal reference cells at a high resolution. Two platforms were used in this study: 10x Genomics* and SORT-seq.

We are proud of our contribution to this study by generating the raw SORT-seq from 16 tumors obtained at the Princess Máxima Center. The data was further analyzed by the Máxima Single Cell Genomics Facility.  

This resulted in a dataset of 13,281 neuroblastoma cells. This dataset contributed to the identification of cancer cells in the tumor tissue. 

Furthermore, the SORT-seq dataset, together with the 10x data set, allowed for a differential gene expression analysis between fetal medulla single cells and tumor cells. This analysis revealed the sympathoblast-like phenotype of neuroblastoma cells, the primary outcome of this study.  

We are looking forward to future scientific breakthroughs in neuroblastoma fueled by single-cell sequencing.   

*All 10x Genomics data in this study was not generated by Single Cell Discoveries 

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