The clinical impact of using complex molecular profiling strategies in routine oncology practice

Molecular profiling and functional assessment of signalling pathways of advanced solid tumours are becoming increasingly available. However, their clinical utility in guiding patients’ treatment remains unknown. Here, we assessed whether molecular profiling helps physicians in therapeutic decision making by analysing the molecular profiles of 1057 advanced cancer patient samples after failing at least one standard of care treatment using a combination of next-generation sequencing (NGS), immunohistochemistry (IHC) and other specific tests. The resulting information was interpreted and personalized treatments for each patient were suggested. Our data showed that NGS alone provided the oncologist with useful information in 10–50% of cases (depending on cancer type), whereas the addition of IHC/other tests increased extensively the usefulness of the information provided. Using internet surveys, we investigated how therapy recommendations influenced treatment choice of the oncologist. For patients who were still alive after the provision of the molecular information (76.8%), 60.4% of their oncologists followed report recommendations. Most treatment decisions (93.4%) were made based on the combination of NGS and IHC/other tests, and an approved drug- rather than clinical trial enrolment- was the main treatment choice. Most common reasons given by physicians to explain the non-adherence to recommendations were drug availability and cost, which remain barriers to personalised precision medicine. Finally, we observed that 27% of patients treated with the suggested therapies had an overall survival > 12 months. Our study demonstrates that the combination of NGS and IHC/other tests provides the most useful information in aiding treatment decisions by oncologists in routine clinical practice

The effects of perceived competence and sociability on electoral outcomes

Previous research demonstrated that inferences of competence from the face are good predictors of electoral outcomes [Todorov, A., Mandisoza, A. N., Gore, A., & Hall, C. C. (2005). Inferences of competence from faces predict election outcomes. Science, 308, 1623–1626]. In the current work we examined the role of another key dimension in social perception, namely perceived sociability. Results showed that people considered both competence and sociability, as inferred from the face, as related to higher chances of winning the elections. A different pattern emerged in relation to the actual electoral outcomes. Indeed, perceived competence was related to higher chances of winning, whereas perceived sociability was negatively related to electoral success. It is thus shown that these two fundamental dimensions in social perception exert opposite effects on voting behaviors

Metadynamics simulations rationalize the conformational effects induced by N-methylation of RGD cyclohexapeptides

Cyclopeptides are a promising class of compounds with favourable pharmacokinetic characteristics that can be used as therapeutics in modulation of protein-protein interactions. Nevertheless their application has been limited due to the difficulties to predict in silico their three-dimensional structure and inhibitory activity. Because of these challenges, their optimization for specific biological targets has been mainly based on empirical approaches. Computational tools could be fundamental in accelerating the drug design process, reducing the efforts dedicated to expensive and time-consuming compound synthesis. In this scenario a detailed conformational search of ligands followed by docking calculations is highly recommendable to achieve reliable computational predictability. We have developed a multi-stage computational protocol[1] able to i. predict the affinity of a set of cyclopeptides for different integrins, ii. rationalize the interplay between conformational equilibria and receptor affinity. The protocol relies on the combination of enhanced sampling molecular dynamics technique (Bias-Exchange Metadynamics), docking calculations and re-scoring via Molecular Mechanics/Generalized-Born Surface Area. The reliability of our method was tested investigating the impact of single and multiple N-methylation on the equilibrium conformations of five RGD (Arg-Gly-Asp) cyclohexapeptides that were generated to increase their selectivity towards \u3b1IIb\u3b23 integrin.[2] We obtained excellent results: the conformational sampling was in good agreement with available NMR data and we were able to discriminate between binders and non-binders. Additionally we offered a structural rationale for why N-methylation increases peptides affinities towards a specific integrin. Herein we have shown that Metadynamics can represent a promising in silico screening strategy, opening new perspectives in the application of cyclopeptides as therapeutic inhibitors. We expect that this combination of techniques will be successfully exploited in future to predict the conformational effects of methylation and other chemical modifications in cyclopeptides

MetaD simulations rationalize the conformational effects induced by N-methylation of RGD cyclohexapeptides

Cyclic peptides are a promising class of compounds that can be used as therapeutics in modulation of protein-protein interactions thanks to their favourable pharmacokinetic characteristics. Nevertheless their application has been relatively limited due to the difficulties to accurately predict in silico their three-dimensional structure and their inhibitory activity. Because of these challenges, their optimization for specific biological targets has been mainly based on empirical approaches, requiring massive time-consuming synthesis campaigns of different variants to identify sets of molecules with appropriate conformational and target-binding properties. Computational tools could be fundamental in accelerating the drug design process, thus reducing the efforts dedicated to expensive and time consuming compound synthesis In this scenario a detailed conformational search of ligands followed by docking calculations is highly recommendable to achieve reliable computational predictability. We have developed a multi-stage computational protocol[1] able i. to reliably predict the affinity of a set of cyclic-peptides towards different integrins, ii. to rationalize the interplay between conformational equilibria and receptor affinity. This protocol relies on the combination of enhanced sampling molecular dynamics technique (Bias Exchange Metadynamics, BE-META), docking calculations and re-scoring via Molecular Mechanics/Generalized Born Surface Area methods. We have explored the applicability and reliability of our method investigating the impact of single and multiple N-methylation on the equilibrium conformations of five head-to-tail cyclic RGD (Arg-Gly-Asp) hexapeptides that were generated to increase their selectivity towards \u3b1IIb\u3b23 integrin.[2] We obtained excellent results: we validated the conformational sampling obtaining a good agreement with available NMR data and demonstrated our prediction ability discriminating between binders and non-binders. Herein we have shown that BE-META can represent a promising in silico spatial screening strategy to predict the conformational effects of N-methylation in cyclic-peptides, opening new perspectives in their application as therapeutic inhibitors of protein-protein interactions. Moreover our results are relevant in the field of integrin-targeting RGD peptidomimetics, as they offer a structural rationale for why N-methylation increases peptides affinities towards a specific integrin. We expect that this combination of techniques will be successfully exploited in future to predict the conformational effects of methylation also in other classes of cyclopeptides. Herein, the method could be easily extended to predict the conformational effect of other chemical modifications, of flanking residues or of d-amino acids. Such an approach may be well exploited before entering time-consuming chemical synthesis and binding experiments

A combination of Metadynamics and docking calculations rationalizes the effects induced by N-Methylation on RGD-cyclopeptides integrin affinity

Cyclopeptides are a promising class of compounds with favourable pharmacokinetic characteristics that can be used as therapeutics in modulation of protein-protein interactions. Nevertheless their application has been limited due to the difficulties to predict in silico their three-dimensional structure and inhibitory activity. Because of these challenges, their optimization for specific biological targets has been mainly based on empirical approaches. Computational tools could be fundamental in accelerating the drug design process, reducing the efforts dedicated to expensive and time-consuming compound synthesis. In this scenario a detailed conformational search of ligands followed by docking calculations is highly recommendable to achieve reliable computational predictability. We have optimized a multi-stage computational approach[1] able to i. predict the affinity of a set of cyclopeptides for different integrins, ii. rationalize the interplay between conformational equilibria and receptor affinity. The protocol relies on the combination of enhanced sampling molecular dynamics technique (Bias-Exchange Metadynamics), docking calculations and re-scoring via Molecular Mechanics/Generalized-Born Surface Area. The reliability of our method was tested investigating the impact of single and multiple N-methylation on the equilibrium conformations of five RGD (Arg-Gly-Asp) cyclohexapeptides that were generated to increase their selectivity towards \u3b1IIb\u3b23 integrin.[2] We obtained excellent results: the conformational sampling was in good agreement with available NMR data and we were able to discriminate between binders and non-binders. Additionally we offered a structural rationale for why N-methylation increases peptides affinities towards a specific integrin. Herein we have shown that Metadynamics can represent a promising in silico screening strategy, opening new perspectives in the application of cyclopeptides as therapeutic inhibitors. We expect that this combination of techniques will be successfully exploited in future to predict the conformational effects of methylation and other chemical modifications in cyclopeptides

Metadynamics simulations rationalize the conformational effects induced by N-methylation of RGD cyclic hexapeptides

We combined metadynamics, docking and molecular mechanics/generalised born surface area (MM/GBSA) re-scoring methods to investigate the impact of single and multiple N-methylation on a set of RGD cyclopeptides displaying different affinity for integrin \u3b1IIb\u3b23. We rationalised the conformational effects induced by N-methylation and its interplay with receptor affinity, obtaining good agreement with experimental data. This approach can be exploited before entering time-consuming and expensive synthesis and binding experiments