Molecular Classification of Pediatric High-Risk Leukemias Using Expression Profiles of Multimodally Expressed Genes
Physical and Biological Sciences
Treehouse Childhood Cancer Initiative at the UCSC Genomics Institute
Introduction: Leukemia is the most common cancer in children, accounting for approximately one third of all malignancies that occur in the pediatric age group. Leukemia is characterized by the abnormal expression of leukocytes and cells of the immune system and bone marrow. 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 an unsupervised analysis model called Hydra. Hydra essentially detects multimodally expressed genes, which can provide insight as to what genes have mutated to contribute to the oncogenesis of leukemia. Hydra establishes different groups, called clusters, of samples that have the same disease but express different expression levels of certain genes. These clusters were analyzed in order to find patterns and molecular pathways that are associated with different subtypes of high-risk leukemias. This information can be used in conjunction with survival data to determine how certain pathways are associated with clinical outcome.
Results: First, 202 AML and B-cell precursor ALL samples were run through Hydra and five clusters were identified. These clusters had different enriched molecular 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.
Conclusion: The results of this project indicate that certain patients with ALL can be treated clinically similar to patients with AML based on their gene expression profiles. This type of analysis would be useful to clinicians in determining the most efficient way to treat children with these high-risk leukemias.
