Recurrent Patterns of Protein Expression Signatures in Pediatric Acute Lymphoblastic Leukemia: Recognition and...

Pediatric acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy, and the second leading cause of pediatric cancer-related death in developed countries. While the cure rate for newly diagnosed ALL is excellent, the genetic heterogeneity and chemoresistance of leukemia cells at relapse makes individualized curative treatment plans difficult. We hypothesize that genetic events would coalesce into a finite number of protein signatures that could guide the design of individualized therapy. Custom reverse-phase protein arrays were produced from pediatric ALL (n = 73) and normal CD34⁺ (n = 10) samples with 194 validated antibodies. Proteins were allocated into 31 protein functional groups (PFG) to analyze them in the context of other proteins, based on known associations from the literature. The optimal number of protein clusters was determined for each PFG. Protein networks showed distinct transition states, revealing "normal-like" and "leukemia-specific" protein patterns. Block clustering identified strong correlation between various protein clusters that formed 10 protein constellations. Patients that expressed similar recurrent combinations of constellations comprised 7 distinct signatures, correlating with risk stratification, cytogenetics, and laboratory features. Most constellations and signatures were specific for T-cell ALL or pre-B-cell ALL; however, some constellations showed significant overlap. Several signatures were associated with Hispanic ethnicity, suggesting that ethnic pathophysiologic differences likely exist. In addition, some constellations were enriched for "normal-like" protein clusters, whereas others had exclusively "leukemia-specific" patterns.Implications: Recognition of proteins that have universally altered expression, together with proteins that are specific for a given signature, suggests targets for directed combinatorial inhibition or replacement to enable personalized therapy. Mol Cancer Res; 16(8); 1263-74. ©2018 AACRSee related article by Hoff et al., p. 1275.