Transcriptome profiling of pediatric extracranial solid tumors and lymphomas enables rapid low-cost diagnostic classification.

ABSTRACT: 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 us

SUBMITTER: Opoku KB 

PROVIDER: S-EPMC11339337 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature