Intermittent treatment of BRAFV600E melanoma cells delays resistance by adaptive resensitization to drug rechallenge.

Patients with melanoma receiving drugs targeting BRAFV600E and mitogen-activated protein (MAP) kinase kinases 1 and 2 (MEK1/2) invariably develop resistance and face continued progression. Based on preclinical studies, intermittent treatment involving alternating periods of drug withdrawal and rechallenge has been proposed as a method to delay the onset of resistance. The beneficial effect of intermittent treatment has been attributed to drug addiction, where drug withdrawal reduces the viability of resistant cells due to MAP kinase pathway hyperactivation. However, the mechanistic basis of the intermittent effect is incompletely understood. We show that intermittent treatment with the BRAFV600E inhibitor, LGX818/encorafenib, suppresses growth compared with continuous treatment in human melanoma cells engineered to express BRAFV600E, p61-BRAFV600E, or MEK2C125 oncogenes. Analysis of the BRAFV600E-overexpressing cells shows that, while drug addiction clearly occurs, it fails to account for the advantageous effect of intermittent treatment. Instead, growth suppression is best explained by resensitization during periods of drug removal, followed by cell death after drug readdition. Continuous treatment leads to transcriptional responses prominently associated with chemoresistance in melanoma. By contrast, cells treated intermittently reveal a subset of transcripts that reverse expression between successive cycles of drug removal and rechallenge and include mediators of cell invasiveness and the epithelial-to-mesenchymal transition. These transcripts change during periods of drug removal by adaptive switching, rather than selection pressure. Resensitization occurs against a background of sustained expression of melanoma resistance genes, producing a transcriptome distinct from that of the initial drug-naive cell state. We conclude that phenotypic plasticity leading to drug resensitization can underlie the beneficial effect of intermittent treatment.

Denoising large-scale biological data using network filters.

BACKGROUND: Large-scale biological data sets are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation. RESULTS: We describe a general method for automatically reducing noise in large-scale biological data sets. This method uses an interaction network to identify groups of correlated or anti-correlated measurements that can be combined or "filtered" to better recover an underlying biological signal. Similar to the process of denoising an image, a single network filter may be applied to an entire system, or the system may be first decomposed into distinct modules and a different filter applied to each. Applied to synthetic data with known network structure and signal, network filters accurately reduce noise across a wide range of noise levels and structures. Applied to a machine learning task of predicting changes in human protein expression in healthy and cancerous tissues, network filtering prior to training increases accuracy up to 43% compared to using unfiltered data. CONCLUSIONS: Network filters are a general way to denoise biological data and can account for both correlation and anti-correlation between different measurements. Furthermore, we find that partitioning a network prior to filtering can significantly reduce errors in networks with heterogenous data and correlation patterns, and this approach outperforms existing diffusion based methods. Our results on proteomics data indicate the broad potential utility of network filters to applications in systems biology.

Specificity of Phosphorylation Responses to Mitogen Activated Protein (MAP) Kinase Pathway Inhibitors in Melanoma Cells.

The BRAF-MKK1/2-ERK1/2 pathway is constitutively activated in response to oncogenic mutations of BRAF in many cancer types, including melanoma. Although small molecules that inhibit oncogenic BRAF and MAP kinase kinase (MKK)1/2 have been successful in clinical settings, resistance invariably develops. High affinity inhibitors of ERK1/2 have been shown in preclinical studies to bypass the resistance of melanoma and colon cancer cells to BRAF and MKK1/2 inhibitors, and are thus promising additions to current treatment protocols. But still unknown is how molecular responses to ERK1/2 inhibitors compare with inhibitors currently in clinical use. Here, we employ quantitative phosphoproteomics to evaluate changes in phosphorylation in response to the ERK inhibitors, SCH772984 and GDC0994, and compare these to the clinically used MKK1/2 inhibitor, trametinib. Combined with previous studies measuring phosphoproteomic responses to the MKK1/2 inhibitor, selumetinib, and the BRAF inhibitor, vemurafenib, the outcomes reveal key insights into pathway organization, phosphorylation specificity and off-target effects of these inhibitors. The results demonstrate linearity in signaling from BRAF to MKK1/2 and from MKK1/2 to ERK1/2. They identify likely targets of direct phosphorylation by ERK1/2, as well as inhibitor off-targets, including an off-target regulation of the p38α mitogen activated protein kinase (MAPK) pathway by the MKK1/2 inhibitor, trametinib, at concentrations used in the literature but higher than in vivo drug concentrations. In addition, several known phosphorylation targets of ERK1/2 are insensitive to MKK or ERK inhibitors, revealing variability in canonical pathway responses between different cell systems. By comparing multiple inhibitors targeted to multiple tiers of protein kinases in the MAPK pathway, we gain insight into regulation and new targets of the oncogenic BRAF driver pathway in cancer cells, and a useful approach for evaluating the specificity of drugs and drug candidates.