Inactivating mutations in GNA13 and RHOA in Burkitt's lymphoma and diffuse large B-cell lymphoma: a tumor suppressor function for the Gα13/RhoA axis in B cells.

G proteins and their cognate G protein-coupled receptors (GPCRs) function as critical signal transduction molecules that regulate cell survival, proliferation, motility and differentiation. The aberrant expression and/or function of these molecules have been linked to the growth, progression and metastasis of various cancers. As such, the analysis of mutations in the genes encoding GPCRs, G proteins and their downstream targets provides important clues regarding how these signaling cascades contribute to malignancy. Recent genome-wide sequencing efforts have unveiled the presence of frequent mutations in GNA13, the gene encoding the G protein Gα13, in Burkitt's lymphoma and diffuse large B-cell lymphoma (DLBCL). We found that mutations in the downstream target of Gα13, RhoA, are also present in Burkitt's lymphoma and DLBCL. By multiple complementary approaches, we now show that that these cancer-specific GNA13 and RHOA mutations are inhibitory in nature, and that the expression of wild-type Gα13 in B-cell lymphoma cells with mutant GNA13 has limited impact in vitro but results in a remarkable growth inhibition in vivo. Thus, although Gα13 and RhoA activity has previously been linked to cellular transformation and metastatic potential of epithelial cancers, our findings support a tumor suppressive role for Gα13 and RhoA in Burkitt's lymphoma and DLBCL.

Spatial chemical distance based on atomic property fields.

Similarity of compound chemical structures often leads to close pharmacological profiles, including binding to the same protein targets. The opposite, however, is not always true, as distinct chemical scaffolds can exhibit similar pharmacology as well. Therefore, relying on chemical similarity to known binders in search for novel chemicals targeting the same protein artificially narrows down the results and makes lead hopping impossible. In this study we attempt to design a compound similarity/distance measure that better captures structural aspects of their pharmacology and molecular interactions. The measure is based on our recently published method for compound spatial alignment with atomic property fields as a generalized 3D pharmacophoric potential. We optimized contributions of different atomic properties for better discrimination of compound pairs with the same pharmacology from those with different pharmacology using Partial Least Squares regression. Our proposed similarity measure was then tested for its ability to discriminate pharmacologically similar pairs from decoys on a large diverse dataset of 115 protein-ligand complexes. Compared to 2D Tanimoto and Shape Tanimoto approaches, our new approach led to improvement in the area under the receiver operating characteristic curve values in 66 and 58% of domains respectively. The improvement was particularly high for the previously problematic cases (weak performance of the 2D Tanimoto and Shape Tanimoto measures) with original AUC values below 0.8. In fact for these cases we obtained improvement in 86% of domains compare to 2D Tanimoto measure and 85% compare to Shape Tanimoto measure. The proposed spatial chemical distance measure can be used in virtual ligand screening.