Abstract 2699: Proteomic landscape of de novo pediatric acute myeloid leukemia
Authors: Fieke W. Hoff, Yihua Qiu, Wendy Hu, Amina A. Qutub, Alan S. Gamis, Richard Aplenc, E Anders Kolb, Todd A. Alonzo, Eveline SJM de Bont, Terzah M. Horton, Steven M. Kornblau
Journal: Cancer Research (2018)

Abstract Background: Despite substantial increases in therapy intensity, the overall survival of pediatric acute myeloid leukemia (AML) is still guarded, with survival rates of approximately 60%. This indicates the need for new therapeutic strategies, as well as improved risk stratification. Chemotherapies target proteins rather than genetic events, yet little is known about the proteomic landscape in pediatric AML. This study provides a global assessment of pediatric AML protein expression and correlates protein expression with outcome. Methods: A reverse phase protein array (RPPA) probed with 298 validated antibodies was performed to determine protein expression in ‘‘bulk'' (CD3-/19-) AML cells from 505 diagnostic pediatric AML patients who participated in the Children's Oncology Group AAML1031 phase 3 clinical trial. Proteomic profiling was applied in the context of 31 protein functional groups (PFG) (e.g., cell cycle, apoptosis) to analyze their expression in relation to related proteins. Progeny clustering was performed to identify patients with correlated protein expression patterns within each PFG (protein cluster). Block clustering searched for protein clusters that recurrently co-occurred (protein constellation), and for subgroups of patients that expressed similar combinations of protein constellations (patient signatures). Signatures were correlated with patient and disease characteristics. Results: For each PFG, protein clusters (n=120) could be discerned that showed different protein expression states. From this we constructed 11 protein constellations and 10 patient signatures. Signatures were correlated with event-free survival (EFS) when we combined signatures into favorable (Sig. 4, 8), intermediate (Sig. 6, 7, 9) and unfavorable (Sig. 1-3, 5, 10) groups (p=0.01). Other significant clinical correlations between signatures included CEPBA (40% in Sig. 6, vs. 9% overall, p&amp;lt;0.001), MRD status (high in Sig. 2 vs. low in Sig. 6+7, p=0.006) and several laboratory features. Proteins that were significantly altered compared to normal CD34+ cells were identified for each signature. From this list, 20 proteins were recognized as universally downregulated (CDKN1A, PPP2R2A) and only PIK3CA was universally upregulated. Many druggable proteins showed association with specific protein signatures: high KIT (Sig. 1, 2, 6), high BCL2 (Sig. 1, 2, 6, 9) and high NPM1 (Sig. 1, 2, 6, 9). Conclusion: We studied the proteomic landscape in 505 pediatric AML patients, and identified 10 protein signatures based on 11 protein constellations. We identified signatures that did well with ADE therapy vs. signatures that did not. Recognition of deregulated proteins could help to select drugs that could potentially improve individualized therapies for the latter signatures. Citation Format: Fieke W. Hoff, Yihua Qiu, Wendy Hu, Amina A. Qutub, Alan S. Gamis, Richard Aplenc, E Anders Kolb, Todd A. Alonzo, Eveline SJM de Bont, Terzah M. Horton, Steven M. Kornblau. Proteomic landscape of de novo pediatric acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2699.