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Molecular and Cellular Pathobiology

Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse

Jason E. Farrar, Heather L. Schuback, Rhonda E. Ries, Daniel Wai, Oliver A. Hampton, Lisa R. Trevino, Todd A. Alonzo, Jaime M. Guidry Auvil, Tanja M. Davidsen, Patee Gesuwan, Leandro Hermida, Donna M. Muzny, Ninad Dewal, Navin Rustagi, Lora R. Lewis, Alan S. Gamis, David A. Wheeler, Malcolm A. Smith, Daniela S. Gerhard and Soheil Meshinchi
Jason E. Farrar
1Arkansas Children's Hospital Research Institute and the University of Arkansas for Medical Sciences, Little Rock, Arkansas.
2Children's Oncology Group, Monrovia, California.
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Heather L. Schuback
3Fred Hutchinson Cancer Research Center and the University of Washington School of Medicine, Seattle, Washington.
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Rhonda E. Ries
3Fred Hutchinson Cancer Research Center and the University of Washington School of Medicine, Seattle, Washington.
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Daniel Wai
4Ron Matricaria Institute of Molecular Medicine, Phoenix Children's Hospital and the University of Arizona College of Medicine, Tucson, Arizona.
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Oliver A. Hampton
5Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas.
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Lisa R. Trevino
5Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas.
6Doctors Hospital at Renaissance, Edinburg, Texas.
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Todd A. Alonzo
2Children's Oncology Group, Monrovia, California.
7University of Southern California, Los Angeles, California.
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Jaime M. Guidry Auvil
8Office of Cancer Genomics, National Cancer Institute, Bethesda, Maryland.
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Tanja M. Davidsen
9Center for Bioinformatics and Information Technology, National Cancer Institute, Rockville, Maryland.
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Patee Gesuwan
9Center for Bioinformatics and Information Technology, National Cancer Institute, Rockville, Maryland.
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Leandro Hermida
9Center for Bioinformatics and Information Technology, National Cancer Institute, Rockville, Maryland.
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Donna M. Muzny
5Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas.
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Ninad Dewal
5Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas.
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Navin Rustagi
5Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas.
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Lora R. Lewis
5Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas.
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Alan S. Gamis
10Children's Mercy Hospitals and Clinics, Kansas City, Missouri.
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David A. Wheeler
5Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas.
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Malcolm A. Smith
11Cancer Therapy Evaluation Program, National Cancer Institute, Rockville, Maryland.
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Daniela S. Gerhard
8Office of Cancer Genomics, National Cancer Institute, Bethesda, Maryland.
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Soheil Meshinchi
2Children's Oncology Group, Monrovia, California.
3Fred Hutchinson Cancer Research Center and the University of Washington School of Medicine, Seattle, Washington.
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  • For correspondence: smeshinc@fredhutch.org
DOI: 10.1158/0008-5472.CAN-15-1015 Published April 2016
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    Figure 1.

    Mutation distribution and types of variants. A, number of verified, nonsynonymous, somatic mutations per patient in all samples. B, types of verified, somatic, nonsynonymous mutations found in diagnostic and relapse samples. C, nucleotide changes observed among the missense mutations.

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    Figure 2.

    Number of diagnostic, verified, somatic nonsynonymous mutations by group. A and B, infant AML, age 0 to <2 years versus 2 to 17 years (A) and risk-group, core-binding factor abnormality (CBF) versus MLL rearrangement and other karyotypic abnormalities versus FLT3/ITD positive (B). Numbers above each cluster signify the median for that risk group. A P value was determined by Wilcoxon rank-sum test for A and Kruskal–Wallis test for B.

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    Figure 3.

    Number of patients with recurrent mutations.

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    Figure 4.

    Pathway analysis of identified mutations. A, the cytogenetic category is indicated below each patient identifier: normal karyotype (CN-AML), core-binding factor AML (CBF) indicating presence of either t(8;21) or inv16, MLL rearrangement, or other karyotype abnormality. Tissue type is indicated by degree of shading per legend above (diagnostic only, dark shading; relapse only, medium shading; both tissues, no shading). Both refer to mutations identified at diagnosis that persist to relapse. B, a collapsed version of A, demonstrating the prevalence of mutations in each pathway.

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    Figure 5.

    Number of mutations and VAF in tissues. A, Venn diagram depicting mutations present at diagnosis only, relapse only, or in both tissues. B, distribution of allele fraction in all diagnostic variants, diagnostic variants that persist at relapse and diagnostic variants only. A P value was determined by the Kruskal–Wallis test. C, proportion of variants present at relapse with diagnostic allele fraction greater than 0.40, between 0.20 and 0.40, and less than 0.20. A P value was determined by ANOVA.

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    Figure 6.

    Copy number segments determined by WXS. A, CNA segments were predicted by LOHcate, filtered to include only regions >200 markers, and visualized by Partek. Segments include somatic amplifications shown in red, loss of heterozygosity (LOH) shown in green, and copy neutral loss of heterozygosity (cnLOH) shown in purple. Paired patient samples (diagnostic and relapse) are on the y-axis while chromosomes are indicated on the x-axis. B, table displaying number of amplifications, deletions, or cnLOH found at diagnosis and relapse. C, Venn diagram depicting copy number segments present in diagnostic only, relapse only, or both tissues.

Additional Files

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  • Supplementary Data

    • Supplemental Table 1 - Patient Characteristics
    • Supplemental Table 2 - List of all verified mutations
    • Supplemental Table 3 - List of all segment variations
    • Supplemental Figure 1 - Demographic Information of Patient Cohort. A) Age category of patients. B) Cytogenetic category.
    • Supplemental Figure 2 - Gene expression (array based) of recurrently mutated genes. Graph shows normalized expression of each patient and normal bone marrow (BM) controls for 9 of the 10 recurrently mutated genes; DHX30 was not represented on the array.
    • Supplemental Figure 3 - Clonal evolution of mutations from diagnosis to relapse. These graphs depict the Variant Allele Frequency (VAF) of each patient's mutations at diagnosis and relapse. Note: Mutations absent at diagnosis or relapse are depicted with VAF of zero. Gene names are noted to the left of each variant and are available in Supplemental Table 2.
    • Supplemental Figure 4 - Clonal cluster analysis. Mutations were clustered using DBSCAN based on the VAF and graphed according to timepoint. Panels A-T illustrate possible scenarios of how individual mutations may originate, evolve, and resolve based on VAF.
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Cancer Research: 76 (8)
April 2016
Volume 76, Issue 8
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Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
Jason E. Farrar, Heather L. Schuback, Rhonda E. Ries, Daniel Wai, Oliver A. Hampton, Lisa R. Trevino, Todd A. Alonzo, Jaime M. Guidry Auvil, Tanja M. Davidsen, Patee Gesuwan, Leandro Hermida, Donna M. Muzny, Ninad Dewal, Navin Rustagi, Lora R. Lewis, Alan S. Gamis, David A. Wheeler, Malcolm A. Smith, Daniela S. Gerhard and Soheil Meshinchi
Cancer Res April 15 2016 (76) (8) 2197-2205; DOI: 10.1158/0008-5472.CAN-15-1015

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Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse
Jason E. Farrar, Heather L. Schuback, Rhonda E. Ries, Daniel Wai, Oliver A. Hampton, Lisa R. Trevino, Todd A. Alonzo, Jaime M. Guidry Auvil, Tanja M. Davidsen, Patee Gesuwan, Leandro Hermida, Donna M. Muzny, Ninad Dewal, Navin Rustagi, Lora R. Lewis, Alan S. Gamis, David A. Wheeler, Malcolm A. Smith, Daniela S. Gerhard and Soheil Meshinchi
Cancer Res April 15 2016 (76) (8) 2197-2205; DOI: 10.1158/0008-5472.CAN-15-1015
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