Approximately 80% of pediatric tumors occur in low- and middle-income countries (LMIC), where diagnostic tools essential for treatment decisions are often unavailable or incomplete. Development of cost-effective molecular diagnostics will help bridge the cancer diagnostic gap and ultimately improve pediatric cancer outcomes in LMIC settings. We investigated the feasibility of using nanopore whole transcriptome sequencing on formalin-fixed paraffin embedded (FFPE)-derived RNA and a composite machine learning model for pediatric solid tumor diagnosis. Transcriptome cDNA sequencing was performed on a heterogenous set of 221 FFPE and 32 fresh frozen pediatric solid tumor and lymphoma specimens on Oxford Nanopore Technologies' sequencing platforms. A composite machine learning model was then used to classify transcriptional profiles into clinically actionable tumor types and subtypes. In total, 95.6% and 89.7% of pediatric solid tumors and lymphoma specimens were correctly classified, respectively. 71.5% of pediatric solid tumors had prediction probabilities > 0.8 and were classified with 100% accuracy. Similarly, for lymphomas, 72.4% of samples that had prediction probabilities > 0.6 were classified with 97.6% accuracy. Additionally, FOXO1 fusion status was predicted accurately for 97.4% of rhabdomyosarcomas and MYCN amplification was predicted with 88% accuracy in neuroblastoma. Whole transcriptome sequencing from FFPE-derived pediatric solid tumor and lymphoma samples has the potential to provide clinical classification of both tissue lineage and core genomic classification. Further expansion, refinement, and validation of this approach is necessary to explore whether this technology could be part of the solution of addressing the diagnostic limitations in LMIC.
© 2024. The Author(s).