Resolution Enhancement of Metabolomic J-Res NMR Spectra Using Deep Learning.

J-Resolved (J-Res) nuclear magnetic resonance (NMR) spectroscopy is pivotal in NMR-based metabolomics, but practitioners face a choice between time-consuming high-resolution (HR) experiments or shorter low-resolution (LR) experiments which exhibit significant peak overlap. Deep learning neural networks have been successfully used in many fields to enhance quality of natural images, especially with regard to resolution, and therefore offer the prospect of improving two-dimensional (2D) NMR data. Here, we introduce the J-RESRGAN, an adapted and modified generative adversarial network (GAN) for image super-resolution (SR), which we trained specifically for metabolomic J-Res spectra to enhance peak resolution. A novel symmetric loss function was introduced, exploiting the inherent vertical symmetry of J-Res NMR spectra. Model training used simulated high-resolution J-Res spectra of complex mixtures, with corresponding low-resolution spectra generated via blurring and down-sampling. Evaluation of peak pair resolvability on J-RESRGAN demonstrated remarkable improvement in resolution across a variety of samples. In simulated plasma data, 100% of peak pairs exhibited enhanced resolution in super-resolution spectra compared to their low-resolution counterparts. Similarly, enhanced resolution was observed in 80.8-100% of peak pairs in experimental plasma, 85.0-96.7% in urine, 94.4-98.9% in full fat milk, and 82.6-91.7% in orange juice. J-RESRGAN is not sample type, spectrometer or field strength dependent and improvements on previously acquired data can be seen in seconds on a standard desktop computer. We believe this demonstrates the promise of deep learning methods to enhance NMR metabolomic data, and in particular, the power of J-RESRGAN to elucidate overlapping peaks, advancing precision in a wide variety of NMR-based metabolomics studies. The model, J-RESRGAN, is openly accessible for download on GitHub at https://github.com/yanyan5420/J-RESRGAN.

Circulating Metabolome and White Matter Hyperintensities in Women and Men.

BACKGROUND: White matter hyperintensities (WMH), identified on T2-weighted magnetic resonance images of the human brain as areas of enhanced brightness, are a major risk factor of stroke, dementia, and death. There are no large-scale studies testing associations between WMH and circulating metabolites. METHODS: We studied up to 9290 individuals (50.7% female, average age 61 years) from 15 populations of 8 community-based cohorts. WMH volume was quantified from T2-weighted or fluid-attenuated inversion recovery images or as hypointensities on T1-weighted images. Circulating metabolomic measures were assessed with mass spectrometry and nuclear magnetic resonance spectroscopy. Associations between WMH and metabolomic measures were tested by fitting linear regression models in the pooled sample and in sex-stratified and statin treatment-stratified subsamples. Our basic models were adjusted for age, sex, age×sex, and technical covariates, and our fully adjusted models were also adjusted for statin treatment, hypertension, type 2 diabetes, smoking, body mass index, and estimated glomerular filtration rate. Population-specific results were meta-analyzed using the fixed-effect inverse variance-weighted method. Associations with false discovery rate (FDR)-adjusted P values (PFDR)<0.05 were considered significant. RESULTS: In the meta-analysis of results from the basic models, we identified 30 metabolomic measures associated with WMH (PFDR<0.05), 7 of which remained significant in the fully adjusted models. The most significant association was with higher level of hydroxyphenylpyruvate in men (PFDR.full.adj=1.40×10-7) and in both the pooled sample (PFDR.full.adj=1.66×10-4) and statin-untreated (PFDR.full.adj=1.65×10-6) subsample. In men, hydroxyphenylpyruvate explained 3% to 14% of variance in WMH. In men and the pooled sample, WMH were also associated with lower levels of lysophosphatidylcholines and hydroxysphingomyelins and a larger diameter of low-density lipoprotein particles, likely arising from higher triglyceride to total lipids and lower cholesteryl ester to total lipids ratios within these particles. In women, the only significant association was with higher level of glucuronate (PFDR=0.047). CONCLUSIONS: Circulating metabolomic measures, including multiple lipid measures (eg, lysophosphatidylcholines, hydroxysphingomyelins, low-density lipoprotein size and composition) and nonlipid metabolites (eg, hydroxyphenylpyruvate, glucuronate), associate with WMH in a general population of middle-aged and older adults. Some metabolomic measures show marked sex specificities and explain a sizable proportion of WMH variance.

Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer's disease mouse model and risk loci carriers.

Alzheimer's disease (AD) is a highly prevalent neurodegenerative disorder. Despite increasing evidence of the importance of metabolic dysregulation in AD, the underlying metabolic changes that may impact amyloid plaque formation are not understood, particularly for late-onset AD. This study analyzed genome-wide association studies (GWAS), transcriptomics, and proteomics data obtained from several data repositories to obtain differentially expressed (DE) multi-omics elements in mouse models of AD. We characterized the metabolic modulation in these data sets using gene ontology, transcription factor, pathway, and cell-type enrichment analyses. A predicted lipid signature was extracted from genome-scale metabolic networks (GSMN) and subsequently validated in a lipidomic data set derived from cortical tissue of ABCA-7 null mice, a mouse model of one of the genes associated with late-onset AD. Moreover, a metabolome-wide association study (MWAS) was performed to further characterize the association between dysregulated lipid metabolism in human blood serum and genes associated with AD risk. We found 203 DE transcripts, 164 DE proteins, and 58 DE GWAS-derived mouse orthologs associated with significantly enriched metabolic biological processes. Lipid and bioenergetic metabolic pathways were significantly over-represented across the AD multi-omics data sets. Microglia and astrocytes were significantly enriched in the lipid-predominant AD-metabolic transcriptome. We also extracted a predicted lipid signature that was validated and robustly modeled class separation in the ABCA7 mice cortical lipidome, with 11 of these lipid species exhibiting statistically significant modulations. MWAS revealed 298 AD single nucleotide polymorphisms-metabolite associations, of which 70% corresponded to lipid classes. These results support the importance of lipid metabolism dysregulation in AD and highlight the suitability of mapping AD multi-omics data into GSMNs to identify metabolic alterations.

Genomewide Association Study of Severe Covid-19 with Respiratory Failure.

BACKGROUND: There is considerable variation in disease behavior among patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (Covid-19). Genomewide association analysis may allow for the identification of potential genetic factors involved in the development of Covid-19. METHODS: We conducted a genomewide association study involving 1980 patients with Covid-19 and severe disease (defined as respiratory failure) at seven hospitals in the Italian and Spanish epicenters of the SARS-CoV-2 pandemic in Europe. After quality control and the exclusion of population outliers, 835 patients and 1255 control participants from Italy and 775 patients and 950 control participants from Spain were included in the final analysis. In total, we analyzed 8,582,968 single-nucleotide polymorphisms and conducted a meta-analysis of the two case-control panels. RESULTS: We detected cross-replicating associations with rs11385942 at locus 3p21.31 and with rs657152 at locus 9q34.2, which were significant at the genomewide level (P<5×10-8) in the meta-analysis of the two case-control panels (odds ratio, 1.77; 95% confidence interval [CI], 1.48 to 2.11; P = 1.15×10-10; and odds ratio, 1.32; 95% CI, 1.20 to 1.47; P = 4.95×10-8, respectively). At locus 3p21.31, the association signal spanned the genes SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6 and XCR1. The association signal at locus 9q34.2 coincided with the ABO blood group locus; in this cohort, a blood-group-specific analysis showed a higher risk in blood group A than in other blood groups (odds ratio, 1.45; 95% CI, 1.20 to 1.75; P = 1.48×10-4) and a protective effect in blood group O as compared with other blood groups (odds ratio, 0.65; 95% CI, 0.53 to 0.79; P = 1.06×10-5). CONCLUSIONS: We identified a 3p21.31 gene cluster as a genetic susceptibility locus in patients with Covid-19 with respiratory failure and confirmed a potential involvement of the ABO blood-group system. (Funded by Stein Erik Hagen and others.).

Optimization of Diffusion-Ordered NMR Spectroscopy Experiments for High-Throughput Automation in Human Metabolic Phenotyping.

The diffusion-ordered nuclear magnetic resonance spectroscopy (DOSY) experiment allows the calculation of diffusion coefficient values of metabolites in complex mixtures. However, this experiment has not yet been broadly used for metabolic profiling due to lack of a standardized protocol. Here we propose a pipeline for the DOSY experimental setup and data processing in metabolic phenotyping studies. Due to the complexity of biological samples, three experiments (a standard DOSY, a relaxation-edited DOSY, and a diffusion-edited DOSY) have been optimized to provide DOSY metabolic profiles with peak-picked diffusion coefficients for over 90% of signals visible in the one-dimensional 1H general biofluid profile in as little as 3 min 36 s. The developed parameter sets and tools are straightforward to implement and can facilitate the use of DOSY for metabolic profiling of human blood plasma and urine samples.

Normal tissue content impact on the GBM molecular classification.

Molecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue. These samples can be misclassified preventing biological and clinical insights into the different tumor subtypes from coming to light. In this work, we present a model that tackles both the molecular classification of samples and discrimination of those with a high content of normal cells. We performed a transcriptomic in silico analysis on glioblastoma (GBM) samples (n = 810) and tested different criteria to optimize the number of genes needed for molecular classification. We used gene expression of normal brain samples (n = 555) to design an additional gene signature to detect samples with a high normal tissue content. Microdissection samples of different structures within GBM (n = 122) have been used to validate the final model. Finally, the model was tested in a cohort of 43 patients and confirmed by histology. Based on the expression of 20 genes, our model is able to discriminate samples with a high content of normal tissue and to classify the remaining ones. We have shown that taking into consideration normal cells can prevent errors in the classification and the subsequent misinterpretation of the results. Moreover, considering only samples with a low content of normal cells, we found an association between the complexity of the samples and survival for the three molecular subtypes.

Blood donor notification rate in a blood bank in Veracruz, Mexico.

Blood banks are primarily responsible for providing safe blood, but they also indirectly act to prevent the spread of infectious diseases by notifying blood donors of positive screening results. The notification process differs between countries and notifications rates are generally low. This study sought to analyze the notification rate of healthy and infection-positive donors who donated blood at CETS-Veracruz. A total of 41790 donors were analyzed, 1585 (3.79%) were positive for one or more of the screened infection markers. Only 4163 (9.96% of the total) were notified about their serology results. Of the positive donors, 157 were contacted by phone call; of them, 91 (57%) returned to the blood bank for their results. The average notification rate for positive donors was only 17.48%. The highest notification rate was for anti-HBc (26.63%), while the lowest was for HBsAg (4.17%). Age significantly influenced the return of donors: Those aged 18-24 and 25-39 years were 4.71 and 1.64 times less likely, respectively, to return for their results compared to the rate for all ages. The advice received in the pre-donation stage about the risks of transfusion-transmitted infections and the relevance of returning for results did not appear to impact donors, since the rate of notification was lower than those reported internationally. These data indicate that CETS-Veracruz should improve donor data registration and communication mechanisms to increase the notification rate, and that donor notification studies should be carried out in other Mexican blood banks to analyze the return rate at the national level.

Conradi-Hünermann-Happle syndrome: Clinical and trichoscopic findings.

Conradi-Hünermann-Happle syndrome (CHHS) is a rare genodermatosis resulting from mutations in the EBP (emopamil binding protein) gene. Dermatologic manifestations may include cicatricial alopecia, ichthyosis, follicular atrophoderma, pigmentary abnormalities, and nail dystrophy. In addition to genetic testing and clinical findings, trichoscopic findings may aid in the diagnosis. In this case report, we discuss the trichoscopic findings in a 3-year-old girl with CHHS and how these findings help us understand the pathophysiology of this disease.

The role of gastrointestinal endoscopy in Kaposi sarcoma.

We are writing to make endoscopists aware of the paramount of a prompt diagnosis of gastrointestinal Kaposi sarcoma (GI-KS). Patients with GI involvement have a two to five times higher risk of death and will benefit from chemotherapy to improve their survival. However, current evidence found that one out of three patients might have a false negative result even with HHV-8 since other entities such as gastrointestinal stromal tumors, angiosarcoma, and lymphoma shared macroscopic and histopathological characteristics. These cause a delay in treatment and significantly worsen the prognosis. We observed a trend for a positive diagnosis from ulcers and nodules. To our knowledge, this is the largest cohort of patients with GI-KS in the world. Our study suggests that in cases where a complete immunochemistry panel for KS is not available, HHV-8 remains as a bare minimum. However, other gastrointestinal lesions shared histopathological characteristics. Therefore, we suggest taking biopsies from nodular and ulcer-type lesions to increase the probability to establish a histopathological diagnosis.

Structure Elucidation and Mitigation of Endogenous Interferences in LC-MS-Based Metabolic Profiling of Urine.

Liquid chromatography-mass spectrometry (LC-MS) is the main workhorse of metabolomics owing to its high degree of analytical sensitivity and specificity when measuring diverse chemistry in complex biological samples. LC-MS-based metabolic profiling of human urine, a biofluid of primary interest for clinical and biobank studies, is not widely considered to be compromised by the presence of endogenous interferences and is often accomplished using a simple "dilute-and-shoot" approach. Yet, it is our experience that broad obscuring signals are routinely observed in LC-MS metabolic profiles and represent interferences that lack consideration in the relevant metabolomics literature. In this work, we chromatographically isolated the interfering metabolites from human urine and unambiguously identified them via de novo structure elucidation as two separate proline-containing dipeptides: N,N,N-trimethyl-l-alanine-l-proline betaine (l,l-TMAP) and N,N-dimethyl-l-proline-l-proline betaine (l,l-DMPP), the latter reported here for the first time. Offline LC-MS/MS, magnetic resonance mass spectrometry (MRMS), and nuclear magnetic resonance (NMR) spectroscopy were essential components of this workflow for the full chemical and spectroscopic characterization of these metabolites and for establishing the coexistence of cis and trans isomers of both dipeptides in solution. Analysis of these definitive structures highlighted intramolecular ionic interactions as responsible for slow interconversion between these isomeric forms resulting in their unusually broad elution profiles. Proposed mitigation strategies, aimed at increasing the quality of LC-MS-based urine metabolomics data, include modification of column temperature and mobile-phase pH to reduce the chromatographic footprint of these dipeptides, thereby reducing their interfering effect on the underlying metabolic profiles. Alternatively, sample dilution and internal standardization methods may be employed to reduce or account for the observed effects of ionization suppression on the metabolic profile.

Predicting microbiomes through a deep latent space.

MOTIVATION: Microbial communities influence their environment by modifying the availability of compounds, such as nutrients or chemical elicitors. Knowing the microbial composition of a site is therefore relevant to improve productivity or health. However, sequencing facilities are not always available, or may be prohibitively expensive in some cases. Thus, it would be desirable to computationally predict the microbial composition from more accessible, easily-measured features. RESULTS: Integrating deep learning techniques with microbiome data, we propose an artificial neural network architecture based on heterogeneous autoencoders to condense the long vector of microbial abundance values into a deep latent space representation. Then, we design a model to predict the deep latent space and, consequently, to predict the complete microbial composition using environmental features as input. The performance of our system is examined using the rhizosphere microbiome of Maize. We reconstruct the microbial composition (717 taxa) from the deep latent space (10 values) with high fidelity (>0.9 Pearson correlation). We then successfully predict microbial composition from environmental variables, such as plant age, temperature or precipitation (0.73 Pearson correlation, 0.42 Bray-Curtis). We extend this to predict microbiome composition under hypothetical scenarios, such as future climate change conditions. Finally, via transfer learning, we predict microbial composition in a distinct scenario with only 100 sequences, and distinct environmental features. We propose that our deep latent space may assist microbiome-engineering strategies when technical or financial resources are limited, through predicting current or future microbiome compositions. AVAILABILITY AND IMPLEMENTATION: Software, results and data are available at https://github.com/jorgemf/DeepLatentMicrobiome. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DNA-Modified Plasmonic Sensor for the Direct Detection of Virus Biomarkers from the Blood.

The rapid spread of viral infections demands early detection strategies to minimize proliferation of the disease. Here, we demonstrate a plasmonic biosensor to detect Dengue virus, which was chosen as a model, via its nonstructural protein NS1 biomarker. The sensor is functionalized with a synthetic single-stranded DNA oligonucleotide and provides high affinity toward NS1 protein present in the virus genome. We demonstrate the detection of NS1 protein at a concentration of 0.1-10 µg/mL in bovine blood using an on-chip microfluidic plasma separator integrated with the plasmonic sensor which covers the clinical threshold of 0.6 µg/mL of high risk of developing Dengue hemorrhagic fever. The conceptual and practical demonstration shows the translation feasibility of these microfluidic optical biosensors for early detection of a wide range of viral infections, providing a rapid clinical diagnosis of infectious diseases directly from minimally processed biological samples at point of care locations.

A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D 1H NMR.

Small Molecule Enhancement SpectroscopY (SMolESY) was employed to develop a unique and fully automated computational solution for the assignment and integration of 1H nuclear magnetic resonance (NMR) signals from metabolites in challenging matrices containing macromolecules (herein blood products). Sensitive and reliable quantitation is provided by instant signal deconvolution and straightforward integration bolstered by spectral resolution enhancement and macromolecular signal suppression. The approach is highly efficient, requiring only standard one-dimensional 1H NMR spectra and avoiding the need for sample preprocessing, complex deconvolution, and spectral baseline fitting. The performance of the algorithm, developed using >4000 NMR serum and plasma spectra, was evaluated using an additional >8800 spectra, yielding an assignment accuracy greater than 99.5% for all 22 metabolites targeted. Further validation of its quantitation capabilities illustrated a reliable performance among challenging phenotypes. The simplicity and complete automation of the approach support the application of NMR-based metabolite panel measurements in clinical and population screening applications.

Planar cell polarity: the prickle gene acts independently on both the Ds/Ft and the Stan/Fz systems.

Epithelial cells are polarised within the plane of the epithelium, forming oriented structures that have a coordinated and consistent polarity (planar cell polarity, PCP). In Drosophila, at least two separate molecular systems generate and interpret intercellular polarity signals: Dachsous/Fat, and the 'core' or Starry night/Frizzled system. Here, we study the prickle gene and its protein products Prickle and Spiny leg. Much research on PCP has focused on the asymmetric localisation of core proteins in the cell and as a result prickle was placed in the heart of the Starry night/Frizzled system. We investigate whether this view is correct and how the prickle gene relates to the two systems. We find that prickle can affect, separately, both systems; however, neither Prickle nor Spiny leg are essential components of the Dachsous/Fat or the Starry night/Frizzled system, nor do they act as a functional link between the two systems.

Chitin- and Keratin-Rich Soil Amendments Suppress Rhizoctonia solani Disease via Changes to the Soil Microbial Community.

Enhancing soil suppressiveness against plant pathogens or pests is a promising alternative strategy to chemical pesticides. Organic amendments have been shown to reduce crop diseases and pests, with chitin products the most efficient against fungal pathogens. To study which characteristics of organic products are correlated with disease suppression, an experiment was designed in which 10 types of organic amendments with different physicochemical properties were tested against the soilborne pathogen Rhizoctonia solani in sugar beet seedlings. Organic amendments rich in keratin or chitin reduced Rhizoctonia solani disease symptoms in sugar beet plants. The bacterial and fungal microbial communities in amended soils were distinct from the microbial communities in nonamended soil, as well as those in soils that received other nonsuppressive treatments. The Rhizoctonia-suppressive amended soils were rich in saprophytic bacteria and fungi that are known for their keratinolytic and chitinolytic properties (i.e., Oxalobacteraceae and Mortierellaceae). The microbial community in keratin- and chitin-amended soils was associated with higher zinc, copper, and selenium, respectively.IMPORTANCE Our results highlight the importance of soil microorganisms in plant disease suppression and the possibility to steer soil microbial community composition by applying organic amendments to the soil.

A Tri-Band Cooled Receiver for Geodetic VLBI.

This paper shows a simultaneous tri-band (S: 2.2-2.7 GHz, X: 7.5-9 GHz and Ka: 28-33 GHz) low-noise cryogenic receiver for geodetic Very Long Baseline Interferometry (geo-VLBI) which has been developed at Yebes Observatory laboratories in Spain. A special feature is that the whole receiver front-end is fully coolable down to cryogenic temperatures to minimize receiver noise. It was installed in the first radio telescope of the Red Atlántica de Estaciones Geodinámicas y Espaciales (RAEGE) project, which is located in Yebes Observatory, in the frame of the VLBI Global Observing System (VGOS). After this, the receiver was borrowed by the Norwegian Mapping Autorithy (NMA) for the commissioning of two VGOS radiotelescopes in Svalbard (Norway). A second identical receiver was built for the Ishioka VGOS station of the Geospatial Information Authority (GSI) of Japan, and a third one for the second RAEGE VGOS station, located in Santa María (Açores Archipelago, Portugal). The average receiver noise temperatures are 21, 23, and 25 Kelvin and the measured antenna efficiencies are 70%, 75%, and 60% in S-band, X-band, and Ka-band, respectively.

[An update on parathyroid carcinoma]. / Puesta al día: carcinoma paratiroideo.

Parathyroid carcinoma is a rare malignant disease that presents as a sporadic or familial primary hyperparathyroidism (PHP). The latter is associated with some genetic syndromes. It occurs with equal frequency in both sexes, unlike PHP caused by parathyroid adenoma that is more common in women. It should be suspected in cases of severe hypercalcemia, with high parathyroid hormone levels and a palpable cervical mass. Given the difficulty in distinguishing between parathyroid carcinoma and adenoma prior to the surgery, the diagnosis is often made after parathyroidectomy. The only curative treatment is complete surgical resection with oncologic block resection of the primary tumor to ensure free margins. Adjuvant therapies with chemotherapy or radiation therapy do not modify overall or disease-free survival. Recurrences are common and re-operation of resectable recurrent disease is recommended. The palliative treatment of symptomatic hypercalcemia is crucial in persistent or recurrent disease after surgery since morbidity and mortality are more associated with hypercalcemia than with tumor burden.

The nPYc-Toolbox, a Python module for the pre-processing, quality-control and analysis of metabolic profiling datasets.

SUMMARY: As large-scale metabolic phenotyping studies become increasingly common, the need for systemic methods for pre-processing and quality control (QC) of analytical data prior to statistical analysis has become increasingly important, both within a study, and to allow meaningful inter-study comparisons. The nPYc-Toolbox provides software for the import, pre-processing, QC and visualization of metabolic phenotyping datasets, either interactively, or in automated pipelines. AVAILABILITY AND IMPLEMENTATION: The nPYc-Toolbox is implemented in Python, and is freely available from the Python package index https://pypi.org/project/nPYc/, source is available at https://github.com/phenomecentre/nPYc-Toolbox. Full documentation can be found at http://npyc-toolbox.readthedocs.io/ and exemplar datasets and tutorials at https://github.com/phenomecentre/nPYc-toolbox-tutorials.

pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES 1H NMR spectra.

MOTIVATION: Data processing is a key bottleneck for 1H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1 D 1H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA ("pJRES Binning Algorithm"), which aims to extend the applicability of SRV to pJRES spectra. RESULTS: The performance of JBA is comprehensively evaluated using 617 plasma 1H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building. AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented using the MWASTools R/Bioconductor package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Impact of performance status on non-small-cell lung cancer patients with a PD-L1 tumour proportion score ≥50% treated with front-line pembrolizumab.

Objectives: We retrospectively analysed patients with advanced non-small-cell lung cancer (NSCLC) harbouring high PD-L1 expression (>50%) and treated with front-line pembrolizumab, comparing outcomes of patients with an Eastern Cooperative Oncology Group (ECOG) performance status (PS) 2 to those with PS 0-1.Methods: Data were collected by 16 participating centres. All patients with NSCLC and high PD-L1, treated with first-line pembrolizumab were included. We collected medical data from patient files, pathology and laboratory reports. Patient characteristics, comorbidities, PS, and tumour characteristics were reported. Overall survival (OS), progression-free survival (PFS) and response rate (RR) were calculated.Results: 302 patients were included, 246 with PS 0-1, 56 with PS 2. RR was 72% among patients with PS 0-1 compared to 45% with PS2 (odds ratio (OR) 0.31 (95% CI: 0.17-0.57), p < .001). Median PFS was 2.6 months (95% CI: 1.9-5.1) among patients with PS2 and 11.3 months (95% CI: 8.5-14.4) among those with PS 0-1. Median OS was 7.8 months (95% CI: 2.5-10.7) in the PS2 group, not reached in the PS 0-1 group. PS 2 remained predictive of poor outcomes in multivariate analysis.Conclusion: PS 2 is a strong independent predictor of poor response and survival in NSCLC patients with high PD-L1, treated with front-line pembrolizumab. Prospective randomised trials comparing immunotherapy to chemotherapy in this population would be welcome.