Ras oncogene-independent activation of RALB signaling is a targetable mechanism of escape from NRAS(V12) oncogene addiction in acute myeloid leukemia.

Somatic mutations that lead to constitutive activation of NRAS and KRAS proto-oncogenes are among the most common in human cancer and frequently occur in acute myeloid leukemia (AML). An inducible NRAS(V12)-driven AML mouse model has established a critical role for continued NRAS(V12) expression in leukemia maintenance. In this model genetic suppression of NRAS(V12) expression results in rapid leukemia remission, but some mice undergo spontaneous relapse with NRAS(V12)-independent (NRI) AMLs providing an opportunity to identify mechanisms that bypass the requirement for Ras oncogene activity and drive leukemia relapse. We found that relapsed NRI AMLs are devoid of NRAS(V12) expression and signaling through the major oncogenic Ras effector pathways, phosphatidylinositol-3-kinase and mitogen-activated protein kinase, but express higher levels of an alternate Ras effector, Ralb, and exhibit NRI phosphorylation of the RALB effector TBK1, implicating RALB signaling in AML relapse. Functional studies confirmed that inhibiting CDK5-mediated RALB activation with a clinically relevant experimental drug, dinaciclib, led to potent RALB-dependent antileukemic effects in human AML cell lines, induced apoptosis in patient-derived AML samples in vitro and led to a 2-log reduction in the leukemic burden in patient-derived xenograft mice. Furthermore, dinaciclib potently suppressed the clonogenic potential of relapsed NRI AMLs in vitro and prevented the development of relapsed AML in vivo. Our findings demonstrate that Ras oncogene-independent activation of RALB signaling is a therapeutically targetable mechanism of escape from NRAS oncogene addiction in AML.

Searching for non-B DNA-forming motifs using nBMST (non-B DNA motif search tool).

This unit describes basic protocols on using the non-B DNA Motif Search Tool (nBMST) to search for sequence motifs predicted to form alternative DNA conformations that differ from the canonical right-handed Watson-Crick double-helix, collectively known as non-B DNA, and on using the associated PolyBrowse, a GBrowse-based genomic browser. The nBMST is a Web-based resource that allows users to submit one or more DNA sequences to search for inverted repeats (cruciform DNA), mirror repeats (triplex DNA), direct/tandem repeats (slipped/hairpin structures), G4 motifs (tetraplex, G-quadruplex DNA), alternating purine-pyrimidine tracts (left-handed Z-DNA), and A-phased repeats (static bending). The nBMST is versatile, simple to use, does not require bioinformatics skills, and can be applied to any type of DNA sequences, including viral and bacterial genomes, up to an aggregate of 20 megabasepairs (Mbp).

Optimization of minimum set of protein-DNA interactions: a quasi exact solution with minimum over-fitting.

MOTIVATION: A major limitation in modeling protein interactions is the difficulty of assessing the over-fitting of the training set. Recently, an experimentally based approach that integrates crystallographic information of C2H2 zinc finger-DNA complexes with binding data from 11 mutants, 7 from EGR finger I, was used to define an improved interaction code (no optimization). Here, we present a novel mixed integer programming (MIP)-based method that transforms this type of data into an optimized code, demonstrating both the advantages of the mathematical formulation to minimize over- and under-fitting and the robustness of the underlying physical parameters mapped by the code. RESULTS: Based on the structural models of feasible interaction networks for 35 mutants of EGR-DNA complexes, the MIP method minimizes the cumulative binding energy over all complexes for a general set of fundamental protein-DNA interactions. To guard against over-fitting, we use the scalability of the method to probe against the elimination of related interactions. From an initial set of 12 parameters (six hydrogen bonds, five desolvation penalties and a water factor), we proceed to eliminate five of them with only a marginal reduction of the correlation coefficient to 0.9983. Further reduction of parameters negatively impacts the performance of the code (under-fitting). Besides accurately predicting the change in binding affinity of validation sets, the code identifies possible context-dependent effects in the definition of the interaction networks. Yet, the approach of constraining predictions to within a pre-selected set of interactions limits the impact of these potential errors to related low-affinity complexes. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.