Neuroblastoma, a subtype of neural-crest-derived malignancy, is the most common extracranial solid tumour in early childhood and frequently presents as a widely metastatic disease in most children. Although neuroblastoma prognosis has improved over time, its intratumoural heterogeneity remains a significant barrier to successful therapy in high-risk neuroblastoma patients, attributed to minor malignant cell clones that persist post-chemotherapy, leading to high relapse rates of 50 to 60%. There is no study which has used post-chemotherapy transcriptomes to identify treatment-resistant clones and its component RNA targets at the single-cell level. We aim to use single cell analysis with combined transcriptomic/genomic analyses, to better direct subsequent treatment. As part of the SCRIPT (Single Cell RNA-seq in Paediatric Tumours) clinical trial which we opened at Sydney Children’s Hospital, we have applied high-throughput droplet-sequencing and shown that we can identify enriched neuroblastoma drug resistant subpopulations having strong prognostic significance. We generated transcriptomes from 59,902 cells of 14 longitudinally sampled tumours from 5 high-risk neuroblastoma patients taken at 3 different time points; (1) diagnosis; (2) post-chemotherapy; and (3) relapse. Of these cells, 15 broad cell types were classified, and the malignant cell population consisting of 12,363 cells was identified. In this malignant cell population, we observed an enrichment of various differentially expressed genes in three oncogenic cell states in malignant cells associated with neuronal processes, VEGF/MAPK signalling, gap junction formation and/or mitosis. The top gene candidates were selected through bioinformatic analyses based on criteria such as correlation between gene expression and; (1) patient prognosis, (2) disease stage; (3) MYCN status; and (4) patient age. We are currently in the process of validating selected genes such as NPY, HSP90AA1, DNAJB1,
CHGA, CENPF and NDUFA3 through RT-qPCR to determine gene signature levels. Our findings provide insight into resistance trajectories and transcriptional programs underlying drug resistance to chemotherapeutic agents, which can be ultimately used to personalise patient treatment protocols in the future.