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Background Targeting fibroblast growth factor receptor 1 (FGFR1) is a potential treatment for squamous cell lung cancer (SQCLC). So far, treatment decision in clinical studies is based on gene amplification. However, only a minority of patients have shown durable response. Furthermore, former studies have revealed contrasting results regarding the impact of FGFR1 amplification and expression on patient's prognosis. Aims Here, we analyzed prevalence and correlation of FGFR1 gene amplification and protein expression in human lung cancer and their impact on overall survival. Materials & Methods FGFR1 gene amplification and protein expression were analyzed by fluorescence in situ hybridization and immunohistochemistry (IHC) in 208 SQCLC and 45 small cell lung cancers (SCLC). Furthermore, FGFR1 protein expression was analyzed in 121 pulmonary adenocarcinomas (ACs). Amplification and expression were correlated to each other, clinicopathological characteristics, and overall survival. Results FGFR1 was amplified in 23% of SQCLC and 8% of SCLC. Amplification was correlated to males (P = .027) but not to overall survival. Specificity of immunostaining was verified by cellular CRISPR/Cas9 FGFR1 knockout. FGFR1 was strongly expressed in 9% of SQCLC, 35% of AC, and 4% of SCLC. Expression was correlated to females (P = .0187) and to the absence of lymph node metastasis in SQCLC (P = .018) with no significant correlation to overall survival. Interestingly, no significant correlation between amplification and expression was detected. Discussion FGFR1 gene amplification does not seem to correlate to protein expression. Conclusion We believe that patient selection for FGFR1 inhibitors in clinical studies should be reconsidered. Neither FGFR1 amplification nor expression influences patient's prognosis. Fibroblast growh factor receptor 1 (FGFR1) is considered a potential molecular target in squamous cell lung cancer. However, prevalence of gene amplification as well as correlation to protein overexpression have to be established. Our work has evaluated prevalence and correlation of FGFR1 gene amplification and protein expression in 421 lung cancer patient samples.

(1) Background: The main objectives of our study are (i) to determine the prevalence of NTRK (neurotrophic tyrosine kinase) fusions in a routine diagnostic setting in NSCLC (non-small cell lung cancer) and (ii) to investigate the feasibility of screening approaches including immunohistochemistry (IHC) as a first-line test accompanied by fluorescence in situ hybridization (FISH) and RNA-(ribonucleic acid-)based next-generation sequencing (RNA-NGS). (2) Methods: A total of 1068 unselected consecutive patients with NSCLC were screened in two scenarios, either with initial IHC followed by RNA-NGS (n = 973) or direct FISH testing (n = 95). (3) Results: One hundred and thirty-three patients (14.8%) were IHC positive; consecutive RNA-NGS testing revealed two patients (0.2%) with NTRK fusions (NTRK1-EPS15 (epidermal growth factor receptor pathway substrate 15) and NTRK1-SQSTM1 (sequestosome 1)). Positive RNA-NGS was confirmed by FISH, and NTRK-positive patients benefited from targeted treatment. All patients with direct FISH testing were negative. RNA-NGS- or FISH-positive results were mutually exclusive with alterations in EGFR (epidermal growth factor receptor), ALK (anaplastic lymphoma kinase), ROS1 (ROS proto-oncogene 1), BRAF (proto-oncogene B-Raf), RET (rearranged during transfection) or KRAS (kirsten rat sarcoma viral oncogene). Excluding patients with one of these alterations raised the prevalence of NTRK-fusion positivity among panTrk-(tropomyosin receptor kinase-) IHC positive samples to 30.5%. (4) Conclusions: NTRK fusion-positive lung cancers are exceedingly rare and account for less than 1% of patients in unselected all-comer populations. Both RNA-NGS and FISH are suitable to determine clinically relevant NTRK fusions in a real-world setting. We suggest including panTrk-IHC in a diagnostic workflow followed by RNA-NGS. Excluding patients with concurrent molecular alterations to EGFR/ALK/ROS1/BRAF/RET or KRAS might narrow the target population.

The identification of potential molecular alterations is standard in the diagnostic pathway of non-small-cell lung cancer (NSCLC). The aim of this study is to determine the prevalence of NTRK fusions in NSCLC in a routine diagnostic setting using immunohistochemistry, fluorescence in situ hybridization, and RNA-based next-generation sequencing. A total of 1068 unselected consecutive patients with NSCLC were screened in two scenarios, either with initial IHC followed by RNA-NGS (n = 973) or direct FISH testing (n = 95). In total, 0.2% of all patients were NTRK positive. Both RNA-NGS and FISH are suitable to determine clinically relevant NTRK fusions in a real-world setting. RNA-NGS or FISH NTRK positive results were mutually exclusive with alterations in EGFR/ALK/ROS1/BRAF/RET or KRAS. (1) Background: The main objectives of our study are (i) to determine the prevalence of NTRK (neurotrophic tyrosine kinase) fusions in a routine diagnostic setting in NSCLC (non-small cell lung cancer) and (ii) to investigate the feasibility of screening approaches including immunohistochemistry (IHC) as a first-line test accompanied by fluorescence in situ hybridization (FISH) and RNA-(ribonucleic acid-)based next-generation sequencing (RNA-NGS). (2) Methods: A total of 1068 unselected consecutive patients with NSCLC were screened in two scenarios, either with initial IHC followed by RNA-NGS (n = 973) or direct FISH testing (n = 95). (3) Results: One hundred and thirty-three patients (14.8%) were IHC positive; consecutive RNA-NGS testing revealed two patients (0.2%) with NTRK fusions (NTRK1-EPS15 (epidermal growth factor receptor pathway substrate 15) and NTRK1-SQSTM1 (sequestosome 1)). Positive RNA-NGS was confirmed by FISH, and NTRK-positive patients benefited from targeted treatment. All patients with direct FISH testing were negative. RNA-NGS- or FISH-positive results were mutually exclusive with alterations in EGFR (epidermal growth factor receptor), ALK (anaplastic lymphoma kinase), ROS1 (ROS proto-oncogene 1), BRAF (proto-oncogene B-Raf), RET (rearranged during transfection) or KRAS (kirsten rat sarcoma viral oncogene). Excluding patients with one of these alterations raised the prevalence of NTRK-fusion positivity among panTrk-(tropomyosin receptor kinase-) IHC positive samples to 30.5%. (4) Conclusions: NTRK fusion-positive lung cancers are exceedingly rare and account for less than 1% of patients in unselected all-comer populations. Both RNA-NGS and FISH are suitable to determine clinically relevant NTRK fusions in a real-world setting. We suggest including panTrk-IHC in a diagnostic workflow followed by RNA-NGS. Excluding patients with concurrent molecular alterations to EGFR/ALK/ROS1/BRAF/RET or KRAS might narrow the target population.

Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC = 0.85±0.15, p = 0.0009) compared to the features extracted from the manual segmentations (ICC = 0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p = 3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p = 0.007, higher: p = 5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts.

In the present work, the knowledge on target proteins of standard antibiotics was extended to antimicrobial mushroom compounds. Docking studies were performed for 34 compounds in order to evaluate their affinity to bacterial proteins that are known targets for some antibiotics with different mechanism of action: inhibitors of cell wall synthesis, inhibitors of protein synthesis, inhibitors of nucleic acids synthesis and antimetabolites. After validation of the molecular docking approach, virtual screening of all the compounds was performed against penicillin binding protein 1a (PBP1a), alanine racemase (Alr), d-alanyl-d-alanine synthetase (Ddl), isoleucyl-tRNA sinthetase (IARS), DNA gyrase subunit B, topoisomerase IV (TopoIV), dihydropteroate synthetase (DHPS) and dihydrofolate reductase (DHFR) using AutoDock4. Overall, it seems that for the selected mushroom compounds (namely, enokipodins, ganomycins and austrocortiluteins) the main mechanism of the action is the inhibition of cell wall synthesis, being Alr and Ddl probable protein targets.

FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) R D , dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) R B , maintaining a constant intensity resolution B. Clinical feasibility was assessed on 35 lung cancer patients, imaged before and in the second week of radiotherapy. Forty-four textural features were determined for different D and B for both imaging time points. Feature values depended on the intensity resolution and out of both assessed methods, R B was shown to allow for a meaningful inter- and intra-patient comparison of feature values. Overall, patients ranked differently according to feature values–which was used as a surrogate for textural feature interpretation–between both discretization methods. Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

International trade enables us to exploit regional differences in climate change impacts and is increasingly regarded as a potential adaptation mechanism. Here, we focus on hunger reduction through international trade under alternative trade scenarios for a wide range of climate futures. Under the current level of trade integration, climate change would lead to up to 55 million people who are undernourished in 2050. Without adaptation through trade, the impacts of global climate change would increase to 73 million people who are undernourished (+33%). Reduction in tariffs as well as institutional and infrastructural barriers would decrease the negative impact to 20 million (−64%) people. We assess the adaptation effect of trade and climate-induced specialization patterns. The adaptation effect is strongest for hunger-affected import-dependent regions. However, in hunger-affected export-oriented regions, partial trade integration can lead to increased exports at the expense of domestic food availability. Although trade integration is a key component of adaptation, it needs sensitive implementation to benefit all regions.The impacts of climate change on agriculture differ regionally and will increase hunger globally. Reducing tariffs and other barriers to international trade would mitigate this, but trade integration requires a careful approach to avoid reducing domestic food security in food-exporting regions.

The standard primitives of quantum computing include deterministic unitary entangling gates, which are not natural operations in many systems including photonics. Here, we present fusion-based quantum computation, a model for fault tolerant quantum computing constructed from physical primitives readily accessible in photonic systems. These are entangling measurements, called fusions, which are performed on the qubits of small constant sized entangled resource states. Probabilistic photonic gates as well as errors are directly dealt with by the quantum error correction protocol. We show that this computational model can achieve a higher threshold than schemes reported in literature. We present a ballistic scheme which can tolerate a 10.4% probability of suffering photon loss in each fusion, which corresponds to a 2.7% probability of loss of each individual photon. The architecture is also highly modular and has reduced classical processing requirements compared to previous photonic quantum computing architectures. Fusion gates are common operations in photonic quantum information platforms, where they are employed to create entanglement. Here, the authors propose a quantum computation scheme where the same measurements used to generate entanglement can also be used to achieve fault-tolerance leading to an increased tolerance to errors.

Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost. An individual tumour is often heterogeneous and its various features can be visualised noninvasively using medical imaging. Here, the authors analyse large computed tomography data sets using radiomic algorithms to identify heterogeneity, and find that some of these tumour features have prognostic value across cancer types.

This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment with the hyperspectral imaging system to obtain the spatial and spectral response of a batch of marine litter samples; (ii) a real dataset hyperspectral image acquisition was performed using manned and unmanned aerial platforms, of artificial targets composed of the material analyzed in the laboratory; (iii) comparison of the results (spatial and spectral response) obtained in laboratory conditions with the remote observation data acquired during the dataset flights; (iv) implementation of two different supervised machine learning methods, namely Random Forest (RF) and Support Vector Machines (SVM), for marine litter artificial target detection based on previous training. Obtained results show a marine litter automated detection capability with a 70–80% precision rate of detection in all three targets, compared to ground-truth pixels, as well as recall rates over 50%.