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MicroRNA Expression-Based Model Indicates Event-Free Survival in Pediatric Acute Myeloid Leukemia
Emilia L Lim1, Diane L Trinh1, Rhonda E Ries1
1Emilia L. Lim, Diane L. Trinh, Yussanne Ma, James Topham, Erin Pleasance, Andrew J. Mungall, Richard Moore, Yongjun Zhao, and Marco A. Marra, Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency; Marco A. Marra, University of British Columbia, Vancouver, British Columbia; Lillian Sung, The Hospital for Sick Children, Toronto, Ontario, Canada; Rhonda E. Ries, Maya Hughes, and Soheil Meshinchi, Fred Hutchinson Cancer Research Center; Rhonda E. Ries, Maya Hughes, and Soheil Meshinchi, University of Washington, Seattle, WA; Jim Wang, Robert B. Gerbing, E. Anders Kolb, Alan Gamis, and Todd A. Alonzo, Children's Oncology Group, Monrovia; Todd A. Alonzo, University of Southern California, Los Angeles, CA; Richard Aplenc, The Children's Hospital of Philadelphia, Philadelphia, PA; Malcolm Smith and Daniela S. Gerhard, Office of Cancer Genomics, National Cancer Institute, Bethesda, MD; and Robert J. Arceci, Phoenix Children's Hospital, Phoenix. AZ.
None:
Purpose Children with acute myeloid leukemia (AML) whose disease is refractory to standard induction chemotherapy therapy or who experience relapse after initial response have dismal outcomes. We sought to comprehensively profile pediatric AML microRNA (miRNA) samples to identify dysregulated genes and assess the utility of miRNAs for improved outcome prediction. Patients and Methods To identify miRNA biomarkers that are associated with treatment failure, we performed a comprehensive sequence-based characterization of the pediatric AML miRNA landscape. miRNA sequencing was performed on 1,362 samples-1,303 primary, 22 refractory, and 37 relapse samples. One hundred sixty-four matched samples-127 primary and 37 relapse samples-were analyzed by using RNA sequencing. Results By using penalized lasso Cox proportional hazards regression, we identified 36 miRNAs the expression levels at diagnosis of which were highly associated with event-free survival. Combined expression of the 36 miRNAs was used to create a novel miRNA-based risk classification scheme (AMLmiR36). This new miRNA-based risk classifier identifies those patients who are at high risk (hazard ratio, 2.830; P ≤ .001) or low risk (hazard ratio, 0.323; P ≤ .001) of experiencing treatment failure, independent of conventional karyotype or mutation status. The performance of AMLmiR36 was independently assessed by using 878 patients from two different clinical trials (AAML0531 and AAML1031). Our analysis also revealed that miR-106a-363 was abundantly expressed in relapse and refractory samples, and several candidate targets of miR-106a-5p were involved in oxidative phosphorylation, a process that is suppressed in treatment-resistant leukemic cells. Conclusion To assess the utility of miRNAs for outcome prediction in patients with pediatric AML, we designed and validated a miRNA-based risk classification scheme. We also hypothesized that the abundant expression of miR-106a could increase treatment resistance via modulation of genes that are involved in oxidative phosphorylation.
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