Traer, E. | bioRxiv Science

bioRxiv2021

Utilizing proteomics and phosphoproteomics to predict ex vivo drug sensitivity across genetically diverse AML patients

Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemia. This genetic heterogeneity is difficult to treat using conventional therapies that are generally based on the detection of a single driving mutation. Thus, the use of molecular signatures, consisting of multiple functionally related transcripts or proteins, in making treatment decisions may overcome this hurdle and provide a more effective way to inform drug treatment protocols. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify signatures that could predict patient-specific drug responses. The Clinical Proteomic Tumor Analysis Consortium is in the process of extending this cohort to collect proteomic and phosphoproteomic measurements from a subset of these patient samples to evaluate the hypothesis that proteomic signatures can robustly predict drug response in AML patients. We sought to examine this hypothesis on a sub-cohort of 38 patient samples from Beat AML with proteomic and drug response data and evaluate our ability to identify proteomic signatures that predict drug response with high accuracy. For this initial analysis we built predictive models of patient drug responses across 26 drugs of interest using the proteomics and phosphproteomics data. We found that proteomics-derived signatures provide an accurate and robust signature of drug response in the AML ex vivo samples, as well as related cell lines, with better performance than those signatures derived from mutations or mRNA expression. Furthermore, we found that in specific drug-resistant cell lines, the proteins in our prognostic signatures represented dysregulated signaling pathways compared to parental cell lines, confirming the role of the proteins in the signatures in drug resistance. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to predict drug sensitivity in AML.

bioinformatics↗

bioRxiv2021

A noncanonical FLT3 gatekeeper mutation disrupts gilteritinib binding and confers resistance

The recent FDA approval of the FLT3 inhibitor, gilteritinib, for AML represents a major breakthrough for treatment of FLT3 mutated AML. However, patients only respond to gilteritinib for 6-7 months due to the emergence of drug resistance. Clinical resistance to gilteritinib is often associated with expansion of NRAS mutations, and less commonly via gatekeeper mutations in FLT3, with F691L being the most common. We developed an in vitro model that charts the temporal evolution of resistance to gilteritinib from early microenvironmental-mediated resistance to late intrinsic resistance mutations. Our model system accurately recapitulates the expansion of NRAS mutations and the F691L gatekeeper mutations found in AML patients. As part of this study, we also identified a novel FLT3N701K mutation that also appeared to promote resistance to gilteritinib. Using the Ba/F3 system, we demonstrate that N701K mutations effectively act like a gatekeeper mutation and block gilteritinib from binding to FLT3, thereby promoting resistance. Structural modeling of FLT3 reveals how N701K, and other reported gilteritinib resistance mutations, obstruct the gilteritinib binding pocket on FLT3. Interestingly, FLT3N701K does not block quizartinib binding, suggesting that FLT3N701K mutations are more specific for type 1 FLT3 inhibitors (gilteritinib, midostaurin, and crenolanib). Thus, our data suggests that for the FLT3N701K mutation, switching classes of FLT3 inhibitors may restore clinical response. As the use of gilteritinib expands in the clinic, this information will become critical to define clinical strategies to manage gilteritinib resistance.

cancer biology↗