Gene Expression Profiling Predicts Outcome in De Novo Acute Myeloid Leukemia (AML) with Normal Karyotype: Results of Children’s Oncology Group (COG) Study POG #9421.
Authors: Lacayo Norman J. , O’Brien Maureen , Jain Shweta , Meshinchi Soheil , Yu Ron , Juric Dejan , Chang Myron N. , Tibshirani Robert , Ravindranath Yaddanapudi , Weinstein Howard J. , Sikic Branimir I. , Van Houten Dahl Gary 
Published: October 2019

Abstract
We previously reported a 36% event-free survival (EFS) estimate for patients with normal karyotype (NK) on the COG study POG #9421 (n=144). In addition, we hypothesized that gene expression profiling would identify signatures linked to clinical outcome and useful for retrospective risk determination. Bone marrows in a subset of patients with NK (n=58) were analyzed using a 43,760-element spotted arrays containing 41,751 unique genes and expressed sequence tags; arrays were obtained from the Stanford University Microarray Core Facility. Prediction analysis for microarrays (PAM) was used to find genes that identified samples associated-with and unassociated-with events (relapse or death); after analyzing 28,711 genes with PAM we chose a 727-gene cluster that differentiated patients with NK on the basis of clinical outcome (cumulative classification error rate 19%). The analysis was biased for a larger number of genes in order to obtain a more biologically informative gene pathways analysis. Significance analysis of microarrays (SAM) on the PAM output identified 633 genes (false-discovery rate of 0%) that differed significantly between the event-associated and event-unassociated samples. Spearman based hierarchical clustering on these genes yielded 2 clusters with statistically significant different event-free survivals: 65% (n=24) for the event-unassociated curve and 23% (n=34) for the event-associated curve with P=0.01.
The patients in these clusters did not differ at diagnosis for WBC (70K vs. 100K/microL with P=0.19) and age (10.1 vs. 9.9 yrs with P=0.83) by unpaired t-test; or for sex (P=0.11) and FLT3-ITD status (P=0.76) by Fisher’s exact test. The gene list (GenBank #) and fold-change in gene expression from SAM output were analyzed using Ingenuity Pathway Analysis software (Ingenuity™ Systems, Mountain View, CA). Canonical pathways identified 33 networks associated with event-unassociated outcome using 302 eligible genes that included: underexpressed CDC73, RAD50, SPARC, PTPN12, MXD1, TNF, ABCA1, STAT4, CCNA1, TNF, BCL2A1, JUN, BCL6 and AREG; and overexpressed RUNX3, FKBP9, FKBP8, MAP2K2, CHES1, HOXA11, HRK, CDK6, MGA, MAPK3, ABL1, HDAC7A, SMARCC2, SYK, MXD4, CDC42. Several of these genes have been previously reported to be associated with improved outcome in AML. However, two of these genes (MXD4 and MXD1) previously not identified as related to outcome in AML, but identified in our analysis in two highly interacting networks related to the MYC gene, result in a difference in EFS of 51% vs. 24% (P=0.04), suggesting that a smaller number of genes may be predictive of outcome.
Conclusion: Risk assignment for patients with NK may be feasible by analyzing a limited number of genes. We will validate these findings by correlating gene expression results with quantitative real-time PCR. Prospective validation of this strategy in clinical trials may be warranted.