Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation.

Kinases play important roles in diverse cellular processes, including signaling, differentiation, proliferation, and metabolism. They are frequently mutated in cancer and are the targets of a large number of specific inhibitors. Surveys of cancer genome atlases reveal that kinase domains, which consist of 300 amino acids, can harbor numerous (150 to 200) single-point mutations across different patients in the same disease. This preponderance of mutations-some activating, some silent-in a known target protein make clinical decisions for enrolling patients in drug trials challenging since the relevance of the target and its drug sensitivity often depend on the mutational status in a given patient. We show through computational studies using molecular dynamics (MD) as well as enhanced sampling simulations that the experimentally determined activation status of a mutated kinase can be predicted effectively by identifying a hydrogen bonding fingerprint in the activation loop and the αC-helix regions, despite the fact that mutations in cancer patients occur throughout the kinase domain. In our study, we find that the predictive power of MD is superior to a purely data-driven machine learning model involving biochemical features that we implemented, even though MD utilized far fewer features (in fact, just one) in an unsupervised setting. Moreover, the MD results provide key insights into convergent mechanisms of activation, primarily involving differential stabilization of a hydrogen bond network that engages residues of the activation loop and αC-helix in the active-like conformation (in >70% of the mutations studied, regardless of the location of the mutation).

The novel lncRNA BlackMamba controls the neoplastic phenotype of ALK- anaplastic large cell lymphoma by regulating the DNA helicase HELLS.

The molecular mechanisms leading to the transformation of anaplastic lymphoma kinase negative (ALK-) anaplastic large cell lymphoma (ALCL) have been only in part elucidated. To identify new culprits which promote and drive ALCL, we performed a total transcriptome sequencing and discovered 1208 previously unknown intergenic long noncoding RNAs (lncRNAs), including 18 lncRNAs preferentially expressed in ALCL. We selected an unknown lncRNA, BlackMamba, with an ALK- ALCL preferential expression, for molecular and functional studies. BlackMamba is a chromatin-associated lncRNA regulated by STAT3 via a canonical transcriptional signaling pathway. Knockdown experiments demonstrated that BlackMamba contributes to the pathogenesis of ALCL regulating cell growth and cell morphology. Mechanistically, BlackMamba interacts with the DNA helicase HELLS controlling its recruitment to the promoter regions of cell-architecture-related genes, fostering their expression. Collectively, these findings provide evidence of a previously unknown tumorigenic role of STAT3 via a lncRNA-DNA helicase axis and reveal an undiscovered role for lncRNA in the maintenance of the neoplastic phenotype of ALK-ALCL.