Proteomic Landscape of De Novo Pediatric Acute Myeloid Leukemia

Background: Many genetic drivers that are implicated in disease pathology and risk stratification have been identified for pediatric acute myeloid leukemia (pedi-AML). However, only a minority have been exploited for therapeutic interventions and most of the identified genetic events currently lack...

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Veröffentlicht in:Blood 2018-11, Vol.132 (Supplement 1), p.294-294
Hauptverfasser: Hoff, Fieke W, Qiu, Yihua, Hu, Wendy, Qutub, Amina A, Gamis, Alan S, Aplenc, Richard, Kolb, E. Anders, Alonzo, Todd A, de Bont, Eveline S., Horton, Terzah M., Kornblau, Steven M.
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Sprache:eng
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Zusammenfassung:Background: Many genetic drivers that are implicated in disease pathology and risk stratification have been identified for pediatric acute myeloid leukemia (pedi-AML). However, only a minority have been exploited for therapeutic interventions and most of the identified genetic events currently lack targeted therapy to address the mutations. The combined consequences of genetic and epigenetic events culminate in a net effect manifested at the protein level and most of the chemotherapies target proteins. Yet little is known about the proteomic landscape of pedi-AML. Methods: Reverse Phase Protein Array (RPPA) was performed with 291 strictly validated antibodies to determine the protein expression levels of bulk leukemic cells from 505 pedi-AML patient samples that were collected prior to therapy. All patients participated on the COG AAML1031 Phase 3 clinical trial, that compared standard therapy (ADE) to ADE plus bortezomib (ADE+B). Proteins were allocated into 31 protein functional groups (PFG) (e.g. cell cycle, apoptosis) to analyze proteins 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 (protein signatures). Protein signatures were correlated with known cytogenetics and mutational state. Results: For each PFG, cluster analysis identified an optimal number of protein clusters, resulting in a total of 120. From this we constructed 11 protein constellations (PRCON) and 10 protein signatures (SIG) (Fig. 1A). A training set (n=334) and test set (n=171) showed high reproducibility (Pearson's X2; p < 0.001). SIG were prognostic for event-free survival (EFS) (p = 0.029), with a favorable EFS for SIG 1, 2, 3, 5, 7 & 9, and an unfavorable EFS for SIG 4, 6, 8, 10. Notably, patients that formed SIG 3 had a significant better EFS after receiving ADE+B than patients that received ADE (p = 0.039) (Fig. 1B). This SIG was highly enriched for CEBPA mutated cases; 43% vs. 9% overall (p < 0.001). SIG were associated with cytogenetic aberrations (p < 0.001) and mutational state, as well as with the traditional risk groups (p < 0.001). For example, t(8;21) was overrepresented in SIG 9 (39% vs. 16% overall) and MLL-rearrangement in SIG 6 (61% vs. 19% overall). Multivariat
ISSN:0006-4971
1528-0020
DOI:10.1182/blood-2018-99-110796