Research Mentor(s): Marcin Cieslik, Assistant Professor
Research Mentor School/College/Department: Computational Medicine and Bioinformatics / Pathology, Michigan Medicine
Presentation Date: Thursday, April 22, 2021
Session: Session 2 (11am – 11:50am)
Breakout Room: Room 7
Acute lymphoblastic leukemia (ALL) is the most common cancer among pediatric patients and is characterized by fewer mutations than adult cancers, suggesting that aberrant gene expression is a critical factor in treatment and prognosis. Gene expression data can identify targeted therapy pathways by identifying more precise cancer profiles based on genetic expression. To assess effectiveness of RNA expression analysis at identifying target genes, mRNA copy counts from tumor samples of 32 pediatric ALL patients were gathered. Sample grouping was explored using principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection. The samples were then split according to expression levels of known neuroblastoma driver ALK and then analyzed for differentially expressed genes using the DESeq2 and limma workflows. As part of the preliminary findings, differential expression analysis revealed differences within the group in expression of known neuroblastoma drivers MYCN, ALK, PHOX2B, and TERT. This also revealed limitations of RNAseq analysis due to noise at low expression levels and outliers that skewed mean expression data. Analysis of RNA expression data shows promising results by identifying groups of similar cancer profiles and identifying differentially expressed genes as therapeutic targets.