Abstract 3033: Molecular classification of pediatric high-risk leukemias using expression profiles of multimodally expressed genes

Abstract Introduction: Leukemia is the most common cancer in children, accounting for approximately one third of all malignancies that occur in the pediatric age group. Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) account for most leukemia diagnosed in this age group. While known markers for poor prognosis include higher age, higher white blood cell count at diagnosis and certain translocations, innovative approaches in tumor RNA sequencing (RNA-Seq) data analysis can discover novel prognostic factors that could be exploited for future therapeutic development in fusion-negative ALL and AML. Methods: To reveal gene expression signatures among fusion-negative leukemias, we used a novel unsupervised analysis model called Hydra. Hydra uses a Dirichlet process mixture model to detect multimodally expressed genes to use in characterizing clusters within cancer cohorts. This approach can detect subtle yet robust differences in gene expression without the reliance on reference normal RNA-Seq datasets. The Hydra model reveals clusters of the cancer cohort, and differences among these clusters can be investigated by finding enriched pathways via Gene Set Enrichment Analysis (GSEA). The cluster-specific enriched pathways can be used in conjunction with survival data to determine how certain pathways are associated with outcome. This analysis used publicly available data from the National Cancer Institute (NCI) Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database that was uniformly processed by the Treehouse Childhood Cancer Initiative. Results: First, 202 fusion-negative AML and fusion-negative B-cell precursor ALL samples were run through Hydra and five clusters were identified. These clusters had different enriched pathways, such as high mitochondrial activity, high cell proliferation, and high cell signaling. Though these are characteristics of all cancer cells, each cluster demonstrated that one pathway was most distinctive of those samples. Most clusters were differentiated by disease, however, one cluster with enriched heme metabolism and immunoglobulin pathways contained almost equal amounts of AML and ALL samples, suggesting that specific cohorts of AML and ALL patients had increased inflammatory response. Another cluster contained 72 AML samples and 4 ALL samples. The four ALL samples in this cluster showed lowered expression of CD19, a B-cell lineage immune marker, and elevated expression of CD14, a myeloid lineage immune marker. These ALL patients exhibited genomic characteristics of AML, which may suggest a more specialized treatment regimen. Discussion: Despite extensive characterization of pediatric high-risk leukemias using genomic approaches, there is ample opportunity to study RNA-Seq-derived gene expression profiles to help accurately diagnose and treat pediatric patients. Citation Format: Sneha S. Jariwala, Alfred Geoffrey Lyle, Jacob Pfeil, Lauren Sanders, Holly C. Beale, Ellen T. Kephart, Katrina Learned, Allison Cheney, Olena M. Vaske. Molecular classification of pediatric high-risk leukemias using expression profiles of multimodally expressed genes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3033.