Subtractive Interaction Proteomics Reveal a Network of Signaling Pathways Activated By an Oncogenic Transcription Factor in High Risk AML

Acute myeloid leukemias (AML) are characterized by recurrent genomic alterations, often in transcriptional regulators, which form the basis on which current prognostication and therapeutic intervention is overlaid. Three subtypes of AML carrying specific translocations, namely t(15;17), t(11;17) and t(6;9), are notable for being associated with a smaller number of co-existing driver mutations than e.g. AML with normal karyotype. This strongly suggests that the function of their aberrant gene products, PML/RAR and DEK/CAN, respectively, may subsume the functions of other driver mutations. Thus we hypothesized that these functions, while as yet elusive, not necessarily require sequential acquisition of secondary genomic alterations. We elected to study AML with the t(6;9), defined as a distinct entity by the WHO classification, because of its particular biological and high risk clinical features and unmet clinical needs. Most t(6;9)-AML patients are young, with a median age of 23-40 years, complete remission rates do not exceed 50% and median survival after diagnosis is only about 1 year. We used a novel "subtractive interaction proteomics" (SIP) approach to understand the mechanisms by which the t(6;9)-DEK/CAN nuclear oncogene induces this highly resistant leukemic phenotype.

Based on Tandem Affinity Precipitation (TAP) for the enrichment of proteins complexes associated with SILAC-technology followed by LC-MS/MS we developed SIP as a comparison between the interactome of an oncogene and those of its functionally inactive mutants in order to obtain eventually only relevant interaction partners (exclusive binders) in the same genetic background. This is achieved by the subtraction of binders that are common to four functionally inactive mutants classifying them as not relevant. Bioinformatic network analysis of the 9 exclusive binders of DEK/CAN revealed by SIP (RAB1A, RAB6A, S100A7, PCBD1, Clusterin, RPS14 and 19, IDH3A, SerpinB3) using BioGrid, IntAct and String together with Ingenuity^© Pathway Analysis (IPA), indicated a functional relationship with ABL1-, AKT/mTOR-, MYC- and SRC family kinases-dependent signaling. Interestingly, we found all these signaling pathways strongly activated in an autonomous manner in four DEK/CAN-positive leukemia models, DEK/CAN expressing U937 cells, t(6;9)-positive FKH-1 cells, primary syngeneic murine DEK/CAN-driven leukemias, and t(6;9)-positive patient samples. Bioinformatic analysis of the phopshoproteomic profile of FKH1 cells upon molecular targeting of single pathways (imatinib for ABL1, PP2 for SFKs, dasatinib for ABL1/SFK and Torin1 or NVP-BEZ-235 for mTOR/AKT) revealed that these signaling pathways were organized in clusters creating a network with nodes that are credible candidates for combinatorial therapeutic interventions. On the other hand inhibition of individual outputs had the potential to activate interconnected pathways in a detrimental manner with consequential clinical impact e.g. the activation of STAT5 by the inhibition of mTOR/AKT in these cells. Treatment of mice injected with primary syngeneic DEK/CAN-induced leukemic cells with dasatinib (10mg/kg) and NVP-BEZ-235 (45mg/kg) alone and in combination for 14 days led to a strong reduction of leukemia burden in all cohorts (each cohort n=7). In fact, as compared to untreated controls (146.6 +/- 36mg), mice treated with NVP-BEZ 235 alone and in combination (61.7 +/-4.7mg and 65.3+/- 4.6mg, respectively) showed a statistically significant reduction of spleen size whereas those treated with dasatinib alone (77.5 8 +/- 5.4mg) did not reach statistical significance.

Taken together the here presented results reveal specific interdependencies between a nuclear oncogene and kinase driven cancer signaling pathways providing a foundation for the design of therapeutic strategies to better address the complexity of cancer signaling. In addition, it provides evidence for the need of a more in depth analysis of indirect effects of molecular targeting strategies in a preclinical setting not only in AML but in all cancer types.