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Sire is a Python/C++ library that is used both to prototype new algorithms and as an interoperability engine for exchanging information between molecular simulation programs. It provides a collection of file parsers and information converters that together make it easier to combine and leverage the functionality of many other programs and libraries. This empowers researchers to use sire to write a single script that can, for example, load a molecule from a PDBx/mmCIF file via Gemmi, perform SMARTS searches via RDKit, parameterize molecules using BioSimSpace, run GPU-accelerated molecular dynamics via OpenMM, and then display the resulting dynamics trajectory in a NGLView Jupyter notebook 3D molecular viewer. This functionality is built on by BioSimSpace, which uses sire's molecular information engine to interconvert with programs such as GROMACS, NAMD, Amber, and AmberTools for automated molecular parameterization and the running of molecular dynamics, metadynamics, and alchemical free energy workflows. Sire comes complete with a powerful molecular information search engine, plus trajectory loading and editing, analysis, and energy evaluation engines. This, when combined with an in-built computer algebra system, gives substantial flexibility to researchers to load, search for, edit, and combine molecular information from multiple sources and use that to drive novel algorithms by combining functionality from other programs. Sire is open source (GPL3) and is available via conda and at a free Jupyter notebook server at https://try.openbiosim.org. Sire is supported by the not-for-profit OpenBioSim community interest company. [ABSTRACT FROM AUTHOR]

The cowpea chlorotic mottle virus (CCMV) is a plant virus explored as a nanotechnological platform. The robust self-assembly mechanism of its capsid protein allows for drug encapsulation and targeted delivery. Additionally, the capsid nanoparticle can be used as a programmable platform to display different molecular moieties. In view of future applications, efficient production and purification of plant viruses are key steps. In established protocols, the need for ultracentrifugation is a significant limitation due to cost, difficult scalability, and safety issues. In addition, the purity of the final virus isolate often remains unclear. Here, an advanced protocol for the purification of the CCMV from infected plant tissue was developed, focusing on efficiency, economy, and final purity. The protocol involves precipitation with PEG 8000, followed by affinity extraction using a novel peptide aptamer. The efficiency of the protocol was validated using size exclusion chromatography, MALDI-TOF mass spectrometry, reversed-phase HPLC, and sandwich immunoassay. Furthermore, it was demonstrated that the final eluate of the affinity column is of exceptional purity (98.4%) determined by HPLC and detection at 220 nm. The scale-up of our proposed method seems to be straightforward, which opens the way to the large-scale production of such nanomaterials. This highly improved protocol may facilitate the use and implementation of plant viruses as nanotechnological platforms for in vitro and in vivo applications.

The intricate three-dimensional geometries of protein tertiary structures underlie protein function and emerge through a folding process from one-dimensional chains of amino acids. The exact spatial sequence and configuration of amino acids, the biochemical environment and the temporal sequence of distinct interactions yield a complex folding process that cannot yet be easily tracked for all proteins. To gain qualitative insights into the fundamental mechanisms behind the folding dynamics and generic features of the folded structure, we propose a simple model of structure formation that takes into account only fundamental geometric constraints and otherwise assumes randomly paired connections. We find that despite its simplicity, the model results in a network ensemble consistent with key overall features of the ensemble of Protein Residue Networks we obtained from more than 1000 biological protein geometries as available through the Protein Data Base. Specifically, the distribution of the number of interaction neighbors a unit (amino acid) has, the scaling of the structure's spatial extent with chain length, the eigenvalue spectrum and the scaling of the smallest relaxation time with chain length are all consistent between model and real proteins. These results indicate that geometric constraints alone may already account for a number of generic features of protein tertiary structures.

Hit-to-lead virtual screening frequently relies on a cascade of computational methods that starts with rapid calculations applied to a large number of compounds and ends with more expensive computations restricted to a subset of compounds that passed initial filters. This work focuses on set up protocols for alchemical free energy (AFE) scoring in the context of a Docking-MM/PBSA-AFE cascade. A dataset of 15 congeneric inhibitors of the ACK1 protein was used to evaluate the performance of AFE set up protocols that varied in the steps taken to prepare input files (using previously docked and best scored poses, manual selection of poses, manual placement of binding site water molecules). The main finding is that use of knowledge derived from X-ray structures to model binding modes, together with the manual placement of a bridging water molecule, improves the R2 from 0.45 ± 0.06 to 0.76 ± 0.02 and decreases the mean unsigned error from 2.11 ± 0.08 to 1.24 ± 0.04 kcal mol-1. By contrast a brute force automated protocol that increased the sampling time ten-fold lead to little improvements in accuracy. Besides, it is shown that for the present dataset hysteresis can be used to flag poses that need further attention even without prior knowledge of experimental binding affinities.

The cowpea chlorotic mottle virus (CCMV) is a plant virus explored as a nanotechnological platform. The robust self-assembly mechanism of its capsid protein allows for drug encapsulation and targeted delivery. Additionally, the capsid nanoparticle can be used as a programmable platform to display different molecular moieties. In view of future applications, efficient production and purification of plant viruses are key steps. In established protocols, the need for ultracentrifugation is a significant limitation due to cost, difficult scalability, and safety issues. In addition, the purity of the final virus isolate often remains unclear. Here, an advanced protocol for the purification of the CCMV from infected plant tissue was developed, focusing on efficiency, economy, and final purity. The protocol involves precipitation with PEG 8000, followed by affinity extraction using a novel peptide aptamer. The efficiency of the protocol was validated using size exclusion chromatography, MALDI-TOF mass spectrometry, reversed-phase HPLC, and sandwich immunoassay. Furthermore, it was demonstrated that the final eluate of the affinity column is of exceptional purity (98.4%) determined by HPLC and detection at 220 nm. The scale-up of our proposed method seems to be straightforward, which opens the way to the large-scale production of such nanomaterials. This highly improved protocol may facilitate the use and implementation of plant viruses as nanotechnological platforms for in vitro and in vivo applications. [ABSTRACT FROM AUTHOR]

Computing the equilibrium properties of complex systems, such as free energy differences, is often hampered by rare events in the dynamics. Enhanced sampling methods may be used in order to speed up sampling by, for example, using high temperatures, as in parallel tempering, or simulating with a biasing potential such as in the case of umbrella sampling. The equilibrium properties of the thermodynamic state of interest (e.g., lowest temperature or unbiased potential) can be computed using reweighting estimators such as the weighted histogram analysis method or the multistate Bennett acceptance ratio (MBAR). weighted histogram analysis method and MBAR produce unbiased estimates, the simulation samples from the global equilibria at their respective thermodynamic states—a requirement that can be prohibitively expensive for some simulations such as a large parallel tempering ensemble of an explicitly solvated biomolecule. Here, we introduce the transition-based reweighting analysis method (TRAM)—a class of estimators that exploit ideas from Markov modeling and only require the simulation data to be in local equilibrium within subsets of the configuration space. We formulate the expanded TRAM (xTRAM) estimator that is shown to be asymptotically unbiased and a generalization of MBAR. Using four exemplary systems of varying complexity, we demonstrate the improved convergence (ranging from a twofold improvement to several orders of magnitude) of xTRAM in comparison to a direct counting estimator and MBAR, with respect to the invested simulation effort. Lastly, we introduce a random-swapping simulation protocol that can be used with xTRAM, gaining orders-of-magnitude advantages over simulation protocols that require the constraint of sampling from a global equilibrium.

We propose a discrete transition-based reweighting analysis method (dTRAM) for analyzing configuration-space-discretized simulation trajectories produced at different thermodynamic states (temperatures, Hamiltonians, etc.) dTRAM provides maximum-likelihood estimates of stationary quantities (probabilities, free energies, expectation values) at any thermodynamic state. In contrast to the weighted histogram analysis method (WHAM), dTRAM does not require data to be sampled from global equilibrium, and can thus produce superior estimates for enhanced sampling data such as parallel/simulated tempering, replica exchange, umbrella sampling, or metadynamics. In addition, dTRAM provides optimal estimates of Markov state models (MSMs) from the discretized state-space trajectories at all thermodynamic states. Under suitable conditions, these MSMs can be used to calculate kinetic quantities (e.g., rates, timescales). In the limit of a single thermodynamic state, dTRAM estimates a maximum likelihood reversible MSM, while in the limit of uncorrelated sampling data, dTRAM is identical to WHAM. dTRAM is thus a generalization to both estimators. [ABSTRACT FROM AUTHOR]

The intricate three-dimensional geometries of protein tertiary structures underlie protein function and emerge through a folding process from one-dimensional chains of amino acids. The exact spatial sequence and configuration of amino acids, the biochemical environment and the temporal sequence of distinct interactions yield a complex folding process that cannot yet be easily tracked for all proteins. To gain qualitative insights into the fundamental mechanisms behind the folding dynamics and generic features of the folded structure, we propose a simple model of structure formation that takes into account only fundamental geometric constraints and otherwise assumes randomly paired connections. We find that despite its simplicity, the model results in a network ensemble consistent with key overall features of the ensemble of Protein Residue Networks we obtained from more than 1000 biological protein geometries as available through the Protein Data Base. Specifically, the distribution of the number of interaction neighbors a unit (amino acid) has, the scaling of the structure's spatial extent with chain length, the eigenvalue spectrum and the scaling of the smallest relaxation time with chain length are all consistent between model and real proteins. These results indicate that geometric constraints alone may already account for a number of generic features of protein tertiary structures. [ABSTRACT FROM AUTHOR]

Hit-to-lead virtual screening frequently relies on a cascade of computational methods that starts with rapid calculations applied to a large number of compounds and ends with more expensive computations restricted to a subset of compounds that passed initial filters. This work focuses on set up protocols for alchemical free energy (AFE) scoring in the context of a Docking–MM/PBSA–AFE cascade. A dataset of 15 congeneric inhibitors of the ACK1 protein was used to evaluate the performance of AFE set up protocols that varied in the steps taken to prepare input files (using previously docked and best scored poses, manual selection of poses, manual placement of binding site water molecules). The main finding is that use of knowledge derived from X-ray structures to model binding modes, together with the manual placement of a bridging water molecule, improves the R2 from 0.45 ± 0.06 to 0.76 ± 0.02 and decreases the mean unsigned error from 2.11 ± 0.08 to 1.24 ± 0.04 kcal mol-1. By contrast a brute force automated protocol that increased the sampling time ten-fold lead to little improvements in accuracy. Besides, it is shown that for the present dataset hysteresis can be used to flag poses that need further attention even without prior knowledge of experimental binding affinities. [ABSTRACT FROM AUTHOR]

BACKGROUND An earlier analysis in this phase 3 trial showed that durvalumab significantly prolonged progression-free survival, as compared with placebo, among patients with stage III, unresectable non-small-cell lung cancer (NSCLC) who did not have disease progression after concurrent chemoradiotherapy. Here we report the results for the second primary end point of overall survival. METHODS We randomly assigned patients, in a 2:1 ratio, to receive durvalumab intravenously, at a dose of 10 mg per kilogram of body weight, or matching placebo every 2 weeks for up to 12 months. Randomization occurred 1 to 42 days after the patients had received chemoradiotherapy and was stratified according to age, sex, and smoking history. The primary end points were progression-free survival (as assessed by blinded independent central review) and overall survival. Secondary end points included the time to death or distant metastasis, the time to second progression, and safety. RESULTS Of the 713 patients who underwent randomization, 709 received the assigned intervention (473 patients received durvalumab and 236 received placebo). As of March 22, 2018, the median follow-up was 25.2 months. The 24-month overall survival rate was 66.3% (95% confidence interval [CI], 61.7 to 70.4) in the durvalumab group, as compared with 55.6% (95% CI, 48.9 to 61.8) in the placebo group (two-sided P = 0.005). Durvalumab significantly prolonged overall survival, as compared with placebo (stratified hazard ratio for death, 0.68; 99.73% CI, 0.47 to 0.997; P = 0.0025). Updated analyses regarding progression-free survival were similar to those previously reported, with a median duration of 17.2 months in the durvalumab group and 5.6 months in the placebo group (stratified hazard ratio for disease progression or death, 0.51; 95% CI, 0.41 to 0.63). The median time to death or distant metastasis was 28.3 months in the durvalumab group and 16.2 months in the placebo group (stratified hazard ratio, 0.53; 95% CI, 0.41 to 0.68). A total of 30.5% of the patients in the durvalumab group and 26.1% of those in the placebo group had grade 3 or 4 adverse events of any cause; 15.4% and 9.8% of the patients, respectively, discontinued the trial regimen because of adverse events. CONCLUSIONS Durvalumab therapy resulted in significantly longer overall survival than placebo. No new safety signals were identified. (Funded by AstraZeneca; PACIFIC ClinicalTrials.gov number, NCT02125461). [ABSTRACT FROM AUTHOR]

The Drug Design Data Resource (D3R) consortium organises blinded challenges to address the latest advances in computational methods for ligand pose prediction, affinity ranking, and free energy calculations. Within the context of the second D3R Grand Challenge several blinded binding free energies predictions were made for two congeneric series of Farsenoid X Receptor (FXR) inhibitors with a semi-automated alchemical free energy calculation workflow featuring FESetup and SOMD software tools. Reasonable performance was observed in retrospective analyses of literature datasets. Nevertheless, blinded predictions on the full D3R datasets were poor due to difficulties encountered with the ranking of compounds that vary in their net-charge. Performance increased for predictions that were restricted to subsets of compounds carrying the same net-charge. Disclosure of X-ray crystallography derived binding modes maintained or improved the correlation with experiment in a subsequent rounds of predictions. The best performing protocols on D3R set1 and set2 were comparable or superior to predictions made on the basis of analysis of literature structure activity relationships (SAR)s only, and comparable or slightly inferior, to the best submissions from other groups. [ABSTRACT FROM AUTHOR]

Background: Most patients with locally advanced, unresectable, non-small-cell lung cancer (NSCLC) have disease progression despite definitive chemoradiotherapy (chemotherapy plus concurrent radiation therapy). This phase 3 study compared the anti-programmed death ligand 1 antibody durvalumab as consolidation therapy with placebo in patients with stage III NSCLC who did not have disease progression after two or more cycles of platinum-based chemoradiotherapy.Methods: We randomly assigned patients, in a 2:1 ratio, to receive durvalumab (at a dose of 10 mg per kilogram of body weight intravenously) or placebo every 2 weeks for up to 12 months. The study drug was administered 1 to 42 days after the patients had received chemoradiotherapy. The coprimary end points were progression-free survival (as assessed by means of blinded independent central review) and overall survival (unplanned for the interim analysis). Secondary end points included 12-month and 18-month progression-free survival rates, the objective response rate, the duration of response, the time to death or distant metastasis, and safety.Results: Of 713 patients who underwent randomization, 709 received consolidation therapy (473 received durvalumab and 236 received placebo). The median progression-free survival from randomization was 16.8 months (95% confidence interval [CI], 13.0 to 18.1) with durvalumab versus 5.6 months (95% CI, 4.6 to 7.8) with placebo (stratified hazard ratio for disease progression or death, 0.52; 95% CI, 0.42 to 0.65; P<0.001); the 12-month progression-free survival rate was 55.9% versus 35.3%, and the 18-month progression-free survival rate was 44.2% versus 27.0%. The response rate was higher with durvalumab than with placebo (28.4% vs. 16.0%; P<0.001), and the median duration of response was longer (72.8% vs. 46.8% of the patients had an ongoing response at 18 months). The median time to death or distant metastasis was longer with durvalumab than with placebo (23.2 months vs. 14.6 months; P<0.001). Grade 3 or 4 adverse events occurred in 29.9% of the patients who received durvalumab and 26.1% of those who received placebo; the most common adverse event of grade 3 or 4 was pneumonia (4.4% and 3.8%, respectively). A total of 15.4% of patients in the durvalumab group and 9.8% of those in the placebo group discontinued the study drug because of adverse events.Conclusions: Progression-free survival was significantly longer with durvalumab than with placebo. The secondary end points also favored durvalumab, and safety was similar between the groups. (Funded by AstraZeneca; PACIFIC ClinicalTrials.gov number, NCT02125461 .). [ABSTRACT FROM AUTHOR]

We explore the dynamical large deviations of a lattice heteropolymer model of a protein by means of path sampling of trajectories. We uncover the existence of nonequilibrium dynamical phase transitions in ensembles of trajectories between active and inactive dynamical phases, whose nature depends on the properties of the interaction potential. We consider three potentials: two heterogeneous interaction potentials and a homogeneous Gö potential. When preserving the full heterogeneity of interactions due to a given amino acid sequence, either in a fully interacting model or in a native contacts interacting model (heterogeneous Gö model), the observed dynamic transitions occur between equilibrium highly native states and highly native but kinetically trapped states. A native activity is defined that allows us to distinguish these dynamic phases. In contrast, for the homogeneous Gö model, where all native interaction energies are uniform and the amino acid sequence plays no role, the dynamical transition is a direct consequence of the static bistability between the unfolded and the native state. In the two heterogeneous interaction models the native-active and native-inactive states, despite their thermodynamic similarity, have widely varying dynamical properties, and the transition between them occurs even in lattice proteins whose sequences are designed to make them optimal folders. [ABSTRACT FROM AUTHOR]

Background: Long-term data with immune checkpoint inhibitors in non-small-cell lung cancer (NSCLC) are limited. Two phase III trials demonstrated improved overall survival (OS) and a favorable safety profile with the anti-programmed death-1 antibody nivolumab versus docetaxel in patients with previously treated advanced squamous (CheckMate 017) and nonsquamous (CheckMate 057) NSCLC. We report results from ≥3 years' follow-up, including subgroup analyses of patients with liver metastases, who historically have poorer prognosis among patients with NSCLC. Patients and methods: Patients were randomized 1 : 1 to nivolumab (3 mg/kg every 2 weeks) or docetaxel (75 mg/m2 every 3 weeks) until progression or discontinuation. The primary end point of each study was OS. Patients with baseline liver metastases were pooled across studies by treatment for subgroup analyses. Results: After 40.3 months' minimum follow-up in CheckMate 017 and 057, nivolumab continued to show an OS benefit versus docetaxel: estimated 3-year OS rates were 17% [95% confidence interval (CI), 14% to 21%] versus 8% (95% CI, 6% to 11%) in the pooled population with squamous or nonsquamous NSCLC. Nivolumab was generally well tolerated, with no new safety concerns identified. Of 854 randomized patients across both studies, 193 had baseline liver metastases. Nivolumab resulted in improved OS compared with docetaxel in patients with liver metastases (hazard ratio, 0.68; 95% CI, 0.50-0.91), consistent with findings from the overall pooled study population (hazard ratio, 0.70; 95% CI, 0.61-0.81). Rates of treatment-related hepatic adverse events (primarily grade 1-2 liver enzyme elevations) were slightly higher in nivolumab-treated patients with liver metastases (10%) than in the overall pooled population (6%).Conclusions: After 3 years' minimum follow-up, nivolumab continued to demonstrate an OS benefit versus docetaxel in patients with advanced NSCLC. Similarly, nivolumab demonstrated an OS benefit versus docetaxel in patients with liver metastases, and remained well tolerated. [ABSTRACT FROM AUTHOR]

Background In the phase 3 PACIFIC study of patients with unresectable, Stage III NSCLC without progression after chemoradiotherapy (CRT), durvalumab demonstrated significant improvements versus placebo in the primary endpoints of progression-free survival (HR, 0.52; 95% CI, 0.42–0.65; P < 0.0001) and overall survival (OS; HR, 0.68; 95% CI, 0.53–0.87; P = 0.00251). Safety was similar and durvalumab had no detrimental effect on patient-reported outcomes. Here, we report 3-year OS rates for all patients randomized in the PACIFIC study. Methods Patients with WHO PS 0/1 (any tumor PD-L1 status) who received ≥2 cycles of platinum-based CRT were randomized (2:1), 1–42 days following CRT, to receive durvalumab 10 mg/kg intravenously every 2 weeks or placebo, up to 12 months, and stratified by age, sex, and smoking history. OS was analyzed using a stratified log-rank test in the ITT population. Medians and OS rates at 12, 24 and 36 months were estimated by Kaplan-Meier method. Results In total, 713 patients were randomized of whom 709 received treatment (durvalumab, n = 473; placebo, n = 236). The last patient had completed the protocol-defined 12 months of study treatment in May 2017. As of January 31, 2019 (data cutoff), 48.2% of patients had died (44.1% and 56.5% in the durvalumab and placebo groups, respectively). The median duration of follow-up was 33.3 months (range, 0.2–51.3). Updated OS remained consistent with that previously reported (stratified HR 0.69, 95% CI, 0.55–0.86), with the median not reached (NR; 95% CI, 38.4 months–NR) with durvalumab versus 29.1 months (95% CI, 22.1–35.1) with placebo. The 12-, 24- and 36-month OS rates with durvalumab and placebo were 83.1% versus 74.6%, 66.3% versus 55.3%, and 57.0% versus 43.5%, respectively. After discontinuation, 43.3% and 57.8% in the durvalumab and placebo groups, respectively, received subsequent anticancer therapy (9.7% and 26.6% subsequently received immunotherapy). OS subgroup results will be presented. Conclusions Updated OS data from PACIFIC, including 3-year survival rates, underscore the long-term clinical benefit with durvalumab following CRT and further establish the PACIFIC regimen as the standard of care in this population. Clinical trial identification NCT02125461. Editorial acknowledgement Medical writing support during the preparation of this abstract, which was in accordance with Good Publication Practice (GPP3) guidelines, was provided by Hashem Dbouk, of Cirrus Communications (Macclesfield, UK), an Ashfield company, and was funded by AstraZeneca. Legal entity responsible for the study AstraZeneca. Funding AstraZeneca. Disclosure Y-L. Wu: Honoraria (self), Advisory / Consultancy: AstraZeneca; Honoraria (self): Eli Lilly; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): Roche; Honoraria (self): Pierre Fabre; Honoraria (self): Pfizer; Honoraria (self): Sanofi; Advisory / Consultancy: Merck; Advisory / Consultancy, Research grant / Funding (institution): Boehringer Ingelheim. J.E. Gray: Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): AstraZeneca; Honoraria (self), Advisory / Consultancy: Janssen; Honoraria (self), Advisory / Consultancy: Celgene; Honoraria (self), Advisory / Consultancy: Takeda; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): Genentech; Honoraria (self), Advisory / Consultancy: Eli Lilly; Honoraria (self), Advisory / Consultancy: Triptych Health Partners; Research grant / Funding (institution): Array; Research grant / Funding (institution): Merck; Research grant / Funding (institution): Epic Sciences; Research grant / Funding (institution): BMS; Research grant / Funding (institution): Boehringer Ingelheim; Research grant / Funding (institution): Trovagene; Research grant / Funding (institution): Loxo; Research grant / Funding (institution): Blueprint; Research grant / Funding (institution): Novartis. A. Villegas: Honoraria (self): AstraZeneca; Honoraria (self): Seattle Genetics. D.B. Daniel: Research grant / Funding (institution): AstraZeneca; Research grant / Funding (institution): E R Squibb & Sons; Research grant / Funding (institution): Boehringer Ingelheim; Research grant / Funding (institution): Genentech; Research grant / Funding (institution): Eli Lilly; Research grant / Funding (institution): Novartis; Research grant / Funding (institution): Pfizer; Research grant / Funding (institution): Celgene; Research grant / Funding (institution): Roche; Research grant / Funding (institution): E1 Therapeutics. D. Vicente: Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: BMS; Advisory / Consultancy: Merck; Advisory / Consultancy: Boehringer Ingelheim; Advisory / Consultancy: Roche. S. Murakami: Research grant / Funding (institution): Takeda; Honoraria (self): AstraZeneca; Honoraria (self): Chugai; Honoraria (self): Ono; Honoraria (self): Merck Sharp and Dohme. R. Hui: Honoraria (self), Advisory / Consultancy: AstraZeneca; Honoraria (self), Advisory / Consultancy: Merck Sharp and Dohme; Honoraria (self), Advisory / Consultancy: Novartis; Advisory / Consultancy: Roche; Honoraria (self), Advisory / Consultancy: Bristol-Myers Squibb. T. Kurata: Honoraria (self), Research grant / Funding (institution): AstraZeneca; Honoraria (self): Ono; Honoraria (self), Research grant / Funding (institution): Bristol Myers Squibb; Honoraria (self), Research grant / Funding (institution): Chugai; Honoraria (self): Lilly; Honoraria (self): Boehringer Ingelheim; Honoraria (self), Research grant / Funding (institution): MSD. A. Chiappori: Speaker Bureau / Expert testimony: Genentech; Speaker Bureau / Expert testimony: Merck; Speaker Bureau / Expert testimony: Takeda; Speaker Bureau / Expert testimony, Research grant / Funding (institution): Novartis; Speaker Bureau / Expert testimony: Boehringer Ingelheim; Speaker Bureau / Expert testimony: Celgene; Research grant / Funding (institution): Bristol Myers Squibb. B.C. Cho: Advisory / Consultancy, Research grant / Funding (institution): AstraZeneca; Advisory / Consultancy, Research grant / Funding (institution): Novartis; Research grant / Funding (institution): Bayer; Research grant / Funding (institution): MOGAM Institute; Research grant / Funding (institution): Dong-A ST; Research grant / Funding (institution): Champions Oncology; Advisory / Consultancy, Research grant / Funding (institution): Janssen; Advisory / Consultancy, Research grant / Funding (institution): Yuhan; Advisory / Consultancy, Research grant / Funding (institution): Ono; Research grant / Funding (institution): Dizal Pharma; Advisory / Consultancy, Research grant / Funding (institution): MSD; Advisory / Consultancy: Boehringer Ingelheim; Advisory / Consultancy: Roche; Advisory / Consultancy: BMS; Advisory / Consultancy: Pfizer; Advisory / Consultancy: Eli Lilly; Advisory / Consultancy: Takeda; Shareholder / Stockholder / Stock options: TheraCanVac Inc. D. Planchard: Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: Boehringer Ingelheim; Advisory / Consultancy: BMS; Advisory / Consultancy: Celgene; Advisory / Consultancy: Novartis; Advisory / Consultancy: Pfizer; Advisory / Consultancy: MSD; Advisory / Consultancy: Roche; Advisory / Consultancy: prIME Oncology; Advisory / Consultancy: PeerCME. L. Paz-Ares: Leadership role: Genomica; Honoraria (self), Travel / Accommodation / Expenses, Spouse / Financial dependant: Roche; Honoraria (self), Travel / Accommodation / Expenses: AstraZeneca; Honoraria (self), Travel / Accommodation / Expenses: MSD; Honoraria (self), Travel / Accommodation / Expenses: BMS; Honoraria (self), Travel / Accommodation / Expenses: Lilly; Honoraria (self), Travel / Accommodation / Expenses, Spouse / Financial dependant: Pfizer; Honoraria (self): Merck Serono; Honoraria (self): Pharma Mar; Honoraria (self), Spouse / Financial dependant: Novartis; Honoraria (self): Celgene; Honoraria (self): Sysmex; Honoraria (self), Spouse / Financial dependant: Amgen; Honoraria (self): Incyte; Spouse / Financial dependant: Ipsen; Spouse / Financial dependant, Leadership Role: European Medicines Agency; Spouse / Financial dependant: SERVIER; Spouse / Financial dependant: Sanofi; Spouse / Financial dependant: Merck. C. Faivre-Finn: Advisory / Consultancy, Speaker Bureau / Expert testimony, To institution: AstraZeneca. J.F. Vansteenkiste: Honoraria (self), Research grant / Funding (institution): MSD; Honoraria (self): AstraZeneca; Honoraria (self): Boehringer Ingelheim; Honoraria (self): Eli-Lilly; Honoraria (self): Apotex; Honoraria (self): Roche. M. Taboada: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca. P.A. Dennis: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca. M. Özgüroğlu: Advisory / Consultancy: Janssen. S.J. Antonia: Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: AstraZeneca; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: BMS; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: Merck; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: Boehringer Ingelheim; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: Novartis; Honoraria (self), Advisory / Consultancy: Memgen; Advisory / Consultancy, Travel / Accommodation / Expenses: FLX Bio; Honoraria (self), Shareholder / Stockholder / Stock options: CBMG. All other authors have declared no conflicts of interest. [ABSTRACT FROM AUTHOR]