Breast cancer (BC) is the most common malignant tumor worldwide. Apoptosis and hypoxia are involved in the progression of BC, but reliable biomarkers for these have not been developed. We hope to explore a gene signature that combined apoptosis and hypoxia‐related genes (AHGs) to predict BC prognosis and immune infiltration.We collected the mRNA expression profiles and clinical data information of BC patients from The Cancer Genome Atlas database. The gene signature based on AHGs was constructed using the univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analysis. The associations between risk scores, immune infiltration, and immune checkpoint gene expression were studied using single‐sample gene set enrichment analysis. Besides, gene signature and independent clinicopathological characteristics were combined to establish a nomogram. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on the potential functions of AHGs.We identified a 16‐AHG signature  that could independently predict BC prognosis. The median score of the risk model divided the patients into two subgroups. By contrast, patients in the high‐risk group had poorer prognosis, less abundance of immune cell infiltration, and expression of immune checkpoint genes. The gene signature and nomogram had good predictive effects on the overall survival of BC patients. GO and KEGG analyses revealed that the differential expression of AHGs may be closely related to tumor immunity.We established and verified a 16‐AHG BC signature which may help predict prognosis, assess potential immunotherapy benefits, and provide inspiration for future research on the functions and mechanisms of AHGs in BC. We identified a 16‐AHG signature  that could independently predict BC prognosis. The median score of the risk model divided the patients into two subgroups. By contrast, patients in the high‐risk group had poorer prognosis, less abundance of immune cell infiltration, and expression of immune checkpoint genes. The gene signature and nomogram had good predictive effects on the overall survival of BC patients. GO and KEGG analyses revealed that the differential expression of AHGs may be may be closely related to tumor immunity. Moreover, assessing immune infiltration by risk score was helpful to select the appropriate population for immunotherapy. In addition, we constructed a nomogram by integrating the risk model with several clinicopathological features to quantitatively predict the survival of BC patients.22.1https://portal.gdc.cancer.gov/). These gene expression data were then formatted into fragments per kilobase of transcript per million mapped reads (FPKM) and normalized by log2(FPKM+1) in the gene expression comparative analysis. In addition, we collected detailed clinical data of these BC cases, including age, survival time and status, TNM stage, pathological stage, and expression status of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Moreover, another external verification cohort of 1052 BC cases from the International Cancer Genome Consortium (ICGC) database (https://dcc.icgc.org/projects/BRCA‐US) was obtained to verify our findings. Figure We collected RNA sequencing profiles of 1109 BC samples and 113 healthy controls from the TCGA database (2.2https://www.gsea‐msigdb.org/gsea/msigdb/index.jsp) was originally developed for GSEA and other similar approaches and has been one of the biggest and most influential libraries of gene sets. The most recent version of MSigDB has nine collections (H and C1‐C8), including hallmark gene sets (H), positional gene sets (C1), curated gene sets (C2), regulatory target gene sets (C3), computational gene sets (C4), ontology gene sets (C5), oncogenic signature gene sets (C6), immunologic signature gene sets (C7), and cell type signature gene sets (C8).Gene set enrichment analysis (GSEA) is a tool for analyzing the differential expression of annotated genes or gene sets and interpreting the results in the biological processes involved.The list of 29 apoptosis‐ and 49 hypoxia‐related gene sets selected from the MSigDB for GSEA included 2556 and 4610 genes, respectively. The “limma” R package was used to identify differentially expressed genes in these sets after normalization.2.30(t) × eƩ (coefi * Expri). In this formula, Expri represents gene expression, while h0(t) and coefi represent constant and coefficient obtained in multivariate Cox regression analysis, respectively. Each patient's risk score was determined in both the TCGC and ICGC cohorts, and the high‐ and low‐risk groups were separated based on the median risk score. The survival difference between these two subgroups was assessed using the Kaplan–Meier (KM) survival analysis and the log‐rank test. Likewise, receiver operating characteristic (ROC) analysis was conducted to further evaluate the prognostic signature's accuracy. These studies used the R packages “Survminer” and “survivalROC”.To begin, we conducted univariate Cox regression analysis on differentially expressed genes (DEGs) and screened for genes that were meaningfully linked to overall survival (OS) in BC. Then, to reduce the risk of overfitting, we constructed a penalty function and used the least absolute shrinkage and selection operator (LASSO) regression to obtain a more accurate signature. Lastly, multivariate Cox regression analysis was performed to examine the genes acquired in the previous stage, and the final genes were utilized to create an independent gene prediction signature. The following equation was used to determine the gene signature's risk score: Risk score = h  = h2.4We assessed the tumor purity and immune, stromal, and estimate scores of high‐ and low‐risk groups using the R package “estimate” and unsupervised consensus cluster analysis, and then estimated the distribution of stromal and immune cells in tumor tissues using the “estimate” R package.2.5A nomogram was created to quantitatively estimate the OS in BC patients by incorporating the combined apoptosis and hypoxia gene signature with clinicopathological features that can independently predict prognosis. Cox regression analysis assigned a certain score to each variable in the nomogram to predict the 3‐, and 5‐year survival rates. Scores were negatively correlated with prognosis. Moreover, Harrell's concordance index (C‐index), KM survival analysis, the area under the ROC curve (AUC), and calibration curves were employed to assess the nomogram's prediction performance. The higher the C‐index, the stronger the prediction power of the nomogram. The nomogram‐predicted survival rates and observed survival rates were plotted on the x‐ and y‐axes of the calibration curves, with the 45‐degree line representing the best prediction. The nomogram was evaluated by bootstrap method with 1000 heavy samples.2.62 fold change (FC)| > 1 and false discovery rate (FDR) < 0.05. The “limma” and “clusterProfiler” R package was used to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to explore the underlying effect of the AHGs on the development of BC.p < 0.05.The DEGs between high‐ and low‐risk groups were identified by the cutoff values of |log2.7U test were applied to assess the correlations between risk scores and clinicopathological variables as well as immune cells or pathways. The two‐tailed tests were given a statistical significance of p < 0.05.All statistical analysis and charts were obtained using R (version 4.0.2) and Excel . To evaluate OS differences comparing high‐ and low‐risk groups, KM curves, and log‐rank tests were utilized. The hazard ratio (HR) and 95 percent confidence interval (CI) of prognostic variables were calculated in both univariate and multivariate Cox regression models to select independent prognostic factors. The chi‐square test and Mann–Whitney 33.1Due to the missing values of OS and survival status, and the situation that one patient may has multiple samples, only 1090 BC patients with transcriptome profiles and detailed clinicopathological parameters were selected for subsequent analysis from the TCGA database  in BC patients by univariate Cox regression analysis Table . Next, Lp < 0.001, Figure Among them, BTBD6, ERRFI1, IRF2, JAK1, MAP2K6, NFKBIA, NFKBIZ, RCL1, and SGCE were protective genes, which were underexpressed in tumor tissues Figure A–I. Howe3.4n = 495) or low‐risk (n = 494) patients, respectively. Survival analysis showed that the survival rates of the two risk groups were markedly different , stage , and risk score  were all found to be associated to BC OS in univariate regression analysis , stage , and risk score  were independent prognostic variables for BC patients . The AUC of the nomogram's prediction accuracy was 0.879, 0.831, and 0.796 for 1‐, 3‐, and 5‐year OS, correspondingly  resulting from univariate and multivariate regression studies for quantitative prediction of the 1‐, 3‐, and 5‐year survival rates of BC patients Figure . Patienty Figure . The valy Figure .3.9To explore the relevant biological functions and pathways of different risk groups based on the gene model, GO and KEGG pathway enrichment analyses of DEGs in high‐ and low‐risk groups were performed. The results showed that 217 DEGs were identified between the high‐ group and low‐risk groups, of which 193 genes were upregulated in the low‐risk group and 24 genes were upregulated in the high‐risk group Table . GO enri4As a complex heterogeneous tumor, BC is underpinned by several molecular mechanisms which have not been clarified. Thus, there are limitations to its early diagnosis and treatment. Clinicopathological features including pathological stage and ER, PR, and HER2 receptor status are now crucial in the diagnosis and prognostication of BC but are insufficient for effective clinical management. Nowadays, with the support of high‐throughput sequencing technology and bioinformatics, several molecular markers have been developed. These have been applied in clinical trials and practices of molecular diagnosis to individualize treatment and predict BC survival.This research examined the predictive power of AHGs for OS in BC patients. We applied univariate Cox regression, LASSO, and multivariate Cox regression analyses to obtain a 16‐gene risk model significantly linked to prognosis . According to the survival study, the high‐risk group's OS was considerably shorter. The validation of the ROC curves and ICGC cohort confirmed that the risk model had an excellent forecasting effect on BC prognosis. Clinically, BC is often divided into different subtypes depending on the expression of ER, PR, and HER2 receptors; they are important indicators for the selection of treatment methods, evaluation of malignancy, and prediction of prognosis.As for the impact of the risk gene model on immune infiltration, the results showed high tumor purity and low immune scores in the high‐risk group. This was opposite to that of the low‐risk group, demonstrating that the immunological state of the two risk groups varies markedly. Fractions of M0 and M2 macrophages in the high‐risk group were significantly upregulated, a finding related to poor prognosis. Macrophages stimulate tumor growth by promoting angiogenesis and chemotherapy resistance in tumor cells, as well as by inducing immune dysfunction through interacting with other immune cells in the TME, resulting in tumor cell immune escape.The immune checkpoint pathway has immunosuppressive functions and is involved in immune evasion and progression of tumor cells.The 16‐AHG signature contained AGPAT1, BTBD6, EIF4EBP1, ERRFI1, FAM114A1, GRIP1, IRF2, JAK1, MAP2K6, MCTS1, NFKBIA, NFKBIZ, NUP43, PGK1, RCL1, and SGCE, and the relationship between some genes and BC has been explored in previous studies. Eukaryotic translation initiation factor 4E‐binding protein 1 (EIF4EBP1), as an inhibitor of EIF4E, synergistically induces the expression of c‐MYC and Cyclin D1 with eukaryotic elongation factor‐2 kinase (eEF2K) inhibitor to suppress the growth of TNBC cells.Our current research has several limitations. First, the 16‐AHG signature based on the TCGA database was only verified in the ICGC cohort, and it requires large‐scale multicenter prospective queries for further verification. Second, further basic tests are necessary to ensure the bioinformatics results and explore the internal mechanisms of the gene signature. This also provides ideas for us to continue to study this project.In conclusion, we identified an effective and accurate signature of AHGs in BC. This risk model can not only be utilized to predict prognosis in BC patients and improve clinical management, but also to evaluate the immune microenvironment and identify appropriate patients for immunotherapy.The authors declare that they have no conflict of interest.SL and HK put forward the main principle and guided the research design. XR, HC, JW, and RZ retrieved and analyzed original data. NW, DL, XX, and HZ interpreted the results. XR wrote the first edition of the paper and then DL, XM, and CD modified it. The findings were discussed among all authors, and the manuscript was revised accordingly. The submission of final paper was reviewed and approved by all contributors.Figure S1Click here for additional data file.Figure S2Click here for additional data file.Figure S3Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.


To the Editor 000 protein‐coding and non‐coding genes, and over 90 000 classified gene‐disease associations, to ease the gene‐disease visualization for researchers, medical practitioners and pharmacists.Atrial fibrillation (AF) is defined as the high‐frequency excitation of the atrium, resulting in both dyssynchronous atrial contraction and the irregularity of ventricular excitation.SCN1B, NPPA‐AS1, KCNQ1, KCNE1, VKORC1, ATF7, KCNH2, SELP, PDE4D, ACE, PRKAR1B, NUP155, CYP4F2, ABCC9, KCNJ2‐AS1, CFAP20, KCNJ2, MYBPC3, KCNE3, PF4, PPBP, MYL4)  showed differentially regulated expression  as novel genes for familial AF in the absence of mutations, whereas mutations in MYL4 have been strongly associated with AF disease in humans.First, the transcriptomic data analysis involved the development of an RNA‐seq processing pipeline that contained four operating parts: (I) data pre‐processing, (II) data quality checking, (III) data storage and management and (IV) data visualization ; AF familial 18; familial AF, AF, AF familial 3, AF, respectively , has been found to have a positive relationship with the infiltration of immune cells  and plays a role in the development of AF disease.PF4, PPBP and MYL4 genes as highly expressed with AF disease in both female and male groups  were associated with both diseases (HF and AF). These findings are valuable for future research studies as they signify the potential to further investigate these genes for mutations and disease‐specific variants. This will provide a new path focusing on a more personalized approach to therapy and treatment. In the future, we seek to evaluate the causal basis for AF by moving beyond the one gene‐one disease model through the integration of the expressed genome, characterization of mutations derived from genomic signatures and mapping them on phenotypic traits in the electronic medical records. We aim to contribute to the paradigm shift in the application and interpretation of genetic and genomically informed medicine for AF, moving from a deterministic conceptualization to a probabilistic interpretation of genetic risk. This will support diagnostic and preventive care delivery strategies beyond traditional symptom‐driven, disease‐causal medical practice. We aim to construct machine learning models to identify a baseline transcriptional signature highly predictive of response across these indications.In summary, we performed the systematic transcriptomic characterization of AF‐associated genes. Our findings report three highly expressed genes and their associated diseases as AF phenotypes; The authors declare no conflict of interests regarding financial or non‐financial aspects.Supplementary Material 1: High‐resolution figures.Click here for additional data file.Supplementary Material 2: Population details.Click here for additional data file.Supplementary Material 3: Detailed genes list.Click here for additional data file.Supplementary Material 4: AF analysis – gene expression data.Click here for additional data file.Supplementary Material 5: Information about AF phenotypes.Click here for additional data file.


Due to the lack of effective treatments, osteoarthritis (OA) remains a challenge for clinicians. Quercetin, a bioflavonoid, has shown potent anti‐inflammatory effects. However, its effect on preventing OA progression and the underlying mechanisms are still unclear. In this study, Sprague–Dawley male rats were divided into five groups: control group, OA group (monosodium iodoacetate intra‐articular injection), and three quercetin‐treated groups. Quercetin‐treated groups were treated with intragastric quercetin once a day for 28 days. Gross observation and histopathological analysis showed cartilage degradation and matrix loss in the OA group. High‐dose quercetin‐group joints showed failure in OA progression. High‐dose quercetin inhibited the OA‐induced expression of MMP‐3, MMP‐13, ADAMTS4, and ADAMTS5 and promoted the OA‐reduced expression of aggrecan and collagen II. Levels of most inflammatory cytokines and growth factors tested in synovial fluid and serum were upregulated in the OA group and these increases were reversed by high‐dose quercetin. Similarly, subchondral trabecular bone was degraded in the OA group and this effect was reversed in the high‐dose quercetin group. Our findings indicate that quercetin has a protective effect against OA development and progression possibly via maintaining the inflammatory cascade homeostasis. Therefore, quercetin could be a potential therapeutic agent to prevent OA progression in risk groups. Several factors are known to contribute to joint damage in OA, such as proinflammatory cytokines , tumour necrosis factor‐alpha (TNF‐α) and IL‐6), matrix metalloproteinases (MMPs), and aggrecan ), which mediate extracellular matrix (ECM) degradation, as well as the inducible nitric oxide synthase‐nitric oxide (NO) system.Several dietary polyphenols such as quercetin, resveratrol, curcumin, epigallocatechin‐3‐gallate, rosmarinic acid, genistein, ginger, berries, silver fir, pine bark, and others have shown pain‐relieving and anti‐inflammatory effects and in OA models.The present study aimed to investigate the effect of quercetin treatment on OA progression and synovial and serum levels of inflammatory cytokines and growth factors in a rat OA model. Monosodium iodoacetate (MIA)‐injection in the rat joint was used as an OA inducer. Quercetin protected the MIA‐induced OA progression, cartilage and subchondral bone destruction, and synovial and systemic inflammation.22.1Quercetin and MIA were purchased from Sigma‐Aldrich . Quercetin was dissolved in normal saline. MIA was dissolved in water to make the stock solution and the working concentration was prepared by diluting the stock solution in PBS. A Bio‐plex rat cytokine 22‐plex assay kit (12005641) for TNF‐α, IL‐1α, IL‐1β, IL‐6, IL‐7, IL‐12p70, IL‐18, IL‐4, IL‐5, IL‐10, IL‐2, IL‐13, IL‐17, IFN‐γ, GRO/KC, MCP‐1, MIP‐1α, MIP‐3α, G‐CSF, M‐CSF, GM‐CSF, and VEGF was purchased from Bio‐Rad . Antibodies against MMP‐3 (ab3523), MMP13 (ab39012), ADAMTS4 (ab180953) ADAMTS5 (ab180953), aggrecan (ab3523), collagen II (ab39012) were purchased from Abcam.2.2A total of 30 healthy Sprague–Dawley male rats 6 weeks old weighing 230–250 g were obtained from the Guangdong Medical Science Experiment Center. Visual observation of rats before the experiment showed no sign of illness or joint diseases. Rats were consuming food and water properly. Rats were housed under a controlled temperature (25 ± 2°C), a constant light cycle (12 h light/dark), and suitable humidity (45%–65%). All animals were allowed free access to food and water. The experimental procedure was started 7 days after adaptive feeding. All animal procedures were approved by the Committee of Animal Experiments of Guangzhou University of Chinese Medicine.2.3n = 6/group): control group, MIA (OA) group, and three quercetin‐treated groups . The healthy control group and OA group were used to confirm the OA development after MIA injection. Three doses of quercetin were used to find out the optimal concentration of quercetin to prevent OA development. Pieces of the literature showed the beneficial effect of 10–100 mg/kg oral or peritoneal administration of quercetin on OA treatment in different animal models.Rats were randomly divided into five groups  and 500 μl of PBS was injected into the rat knee articular joint cavity. The PBS was aspirated out after 5 times injections and withdrawal procedures. Then, the synovial fluid was centrifuged, and the supernatants were stored at −80 °C. Blood was collected from the abdominal aorta. After standing for 30 min at room temperature, the samples were centrifuged at 4500 RPM for 10 min and the serum was stored at −80°C.2.5Micro‐CT was used to assess tibial subchondral bone surface and secondary osteoporosis. The knee joints were collected, and the attached soft tissue was removed thoroughly and fixed in 70% ethanol. Micro‐CT was performed with a micro‐CT  with an image pixel size of 10 μm, a source voltage of 60 kV, and a source current of 100 μA. Fifty slices of proximal tibia starting at 2 mm from the end of the growth plate were analysed. Bone mineral density (BMD), bone volume/total volume (BV/TV), bone surface density (BS/TV), trabecular number (Tb.N), trabecular separation (Tb.Sp), trabecular thickness (Tb.Th), trabecular pattern factor (Tb.Pf) were calculated separately as described previously.2.6Following scanning, rat joints were washed and then transferred to 10% EDTA solution for 30 days for decalcification, and the solution was replaced every 3 days. Next, the decalcified tissues were dehydrated and embedded in paraffin, and 5‐μm thick sections were stained with haematoxylin and eosin (H&E), Masson, and safranin O/fast green. The histological evaluation was conducted using the OA Research Society International (OARSI) scoring system in a blinded manner.2.7Briefly, after deparaffinization and rehydration through graded alcohol solutions, sections were used for antigen retrieval in 10 mM sodium citrate (pH 6.0) for 15 min and cooled to room temperature (RT). All the sections were washed with TBST, endogenous peroxidase was quenched with 3% hydrogen peroxide for 15 min, and the sections were washed with TBST. The sections were blocked with 5% donkey serum for an additional 2 h at RT before incubation with primary antibodies  overnight at 4°C followed by peroxidase‐labelled appropriate secondary antibodies for 30 min at RT. All sections were developed with diaminobenzidine (DAB) chromogen, followed by haematoxylin counterstaining, dehydration, and mounting. The semi‐quantification of the positive area was analysed using ImageJ software as described in the literature.2.8OA is characterized as an inflammatory disease, as various inflammatory cytokines are involved in the development and progression of OA.2.9p‐value <0.05All data are presented as the mean ± standard deviation (SD). Data were analysed using one‐way analysis of variance (anova). Relative indices were analysed using SPSS version 22.0 software (SPSS). Data normal distribution was analysed by the Shapiro–Wilk test. LSD (homogeneous variances) and Dunnett's C  were used to perform post hoc tests for multiple comparisons. The data were graphically presented using GraphPad Prism 7 (GraphPad Software Inc.). Differences were considered statistically significant at a 33.1The chemical structure of quercetin and the experimental design are listed in Figure The results from HE, Masson, and safranin O/fast green staining for each group are shown in Figure Furthermore, to evaluate the degree of cartilage degradation, we used the Mankin score system, which scores structural damage, cellular abnormalities, and matrix staining.3.2To investigate the anti‐inflammatory effects of quercetin on MIA‐treated rats, we evaluated cytokines in the serum and synovial fluid of joints when the rats were sacrificed. Elevated levels of inflammatory markers i.e., TNF‐α, IL‐1α, IL‐1β, IL‐6, IL‐7, IL‐12p70, IL‐18, IL‐4, IL‐5, IL‐10, IFN‐γ, GRO/KC, MCP‐1, MIP‐1α, MIP‐3α, G‐CSF, M‐CSF, GM‐CSF, and VEGF were found in the synovial fluid of joints of OA rats compared with those of control rats. However, these increases were reversed in the high‐dose quercetin group , including MMP‐3, MMP‐13, ADAMTS4, and ADAMTS5, which are important cartilage catabolic factors, were analysed by immunohistochemistry. We observed obvious upregulation of the protein levels of MMP‐3, MMP‐13, ADAMTS4, and ADAMTS5 in the OA model group compared to the control group. In contrast, high‐quercetin treatment suppressed MMP‐3, MMP‐13, ADAMTS4, and ADAMTS5 protein expression Figure .3.4Decreases in aggrecan and collagen II result in the compression or calcification of the ECM, followed by the acceleration of arthrosis.3.5The OA group showed significant subchondral bone injury accompanied by massive osteophyte formation compared with the control group Figure . High‐do4OA is the most prevalent arthritic disease that affects the joints. OA is an age‐related degenerative disease with a complicated pathology involving inflammation and ECM degradation. The majority of available therapies focus on relieving symptoms, but there are challenges to slowing the progression of the disease. Therefore, it is important to find new therapeutic agents to mitigate the development and progression of OA in vulnerable patients.An MIA intra‐articular injection is a widely used method to mimic the structural and functional changes that occur in human OA.It is clear that inflammation is not only present in the majority of OA patients but also actively involved in the progression of the disease.Current therapeutic agents alter the OA pathogenesis by targeting the regulation of inflammatory processes or the expression of growth factors or aggrecans.Aggrecan is a high‐molecular‐weight proteoglycan that has a pivotal role in cartilage structure and the joints' functions. Aggrecan is one of the main components of the cartilage ECM, which also protects collagen from degradation. The loss of ECM aggrecans in the early stages of OA is one of the critical events in the development of the disease.Quercetin, a single extract natural product, has anti‐inflammatory properties and has shown potential to treat various inflammatory diseases.5Quercetin showed anti‐degenerative and anti‐inflammatory effects in OA‐progressing joints. Quercetin prevented cartilage damage and subchondral bone degeneration in OA progressing joints. MMPs and ADAMTSs levels were reduced and Collagen II and aggrecan levels in joint cartilage were maintained in quercetin‐treated OA progressing rats. Moreover, quercetin prevents the elevation of the majority of OA pathogenesis‐related cytokines, chemokines, chemokine receptors, and growth factors in synovium and serum of OA‐progressing rats. Our results indicate that quercetin as a potent therapeutic agent to prevent the development and progression of OA in risk groups.Haifeng Lan: Conceptualization ; funding acquisition ; project administration (lead). Haiyan Wang: Data curation ; investigation ; writing – original draft . Yongyong Yan: Data curation ; investigation ; writing – original draft . Janak L. Pathak: Methodology ; writing – review and editing (lead). Wei Hong: Formal analysis ; funding acquisition . Jing Zeng: Resources . Dongyang Qian: Funding acquisition . Binwei Hao: Formal analysis . Haiqing Li: Formal analysis . Jinlan Gu: Investigation . Richard T. Jaspers: Writing – review and editing (supporting). Gang Wu: Writing – review and editing (supporting). Ming Shao: Methodology ; supervision . Gongyong Peng: Methodology ; supervision .This work was supported by Ph.D. Initiation Project of the Third Affiliated Hospital of Guangzhou Medical University (No. 2018B14), Guangdong Medical Research Foundation (No. A2022253), Characteristic Innovation Projects of Universities in Guangdong Province (No. 2019KTSCX139), Open Project of the State Key Laboratory of Respiratory Disease (No. SKLRD‐Z‐202103), Science and technology innovation project of Guangzhou Medical University (No. 202102080045), Natural Science Foundation of Guangdong (No. 2021A1515012424) and Youth Innovative Talents Projects of Universities in Guangdong Province (No. 2019KQNCX121).The authors declare no conflict of interest.


Escherichia coli strains were first characterized for macrolide resistance genes, and susceptibility to different antibiotics, including AZT. A subset of 24 strains was then selected for synergy testing by the checkerboard assay. PMBN was able to re-sensitize the bacteria to AZT, even in strains with high minimum inhibitory concentrations (MIC: 32 to ≥128 μg/ml) for AZT, and in strains resistant to the last resort drugs such as colistin and meropenem. The fractional inhibitory concentration index was lower than 0.5, demonstrating that PMBN and AZT combinations had a synergistic effect. The combinations worked efficiently in strains carrying mphA gene encoding macrolide phosphotransferase which can cause macrolide inactivation. However, the combinations were inactive in strains having an additional ermB gene encoding macrolide methylase which causes ribosomal drug target alteration. Killing kinetics study showed a significant reduction of bacterial growth after 6 h of treatment with complete killing achieved after 24 h. Transmission electron microscopy showed morphological alterations in the bacteria treated with PMBN alone or in combination with AZT, with evidence of damage to the outer membrane. These results suggested that PMBN acted by increasing the permeability of bacterial outer membrane to AZT, which was also evident using a fluorometric assay. Using multiple antimicrobial agents could therefore be a promising strategy in the eradication of MDR bacteria. PMBN is a good candidate for use with other antibiotics to potentiate their activity, but further studies are required in vivo. This will significantly contribute to resolving antimicrobial resistance crisis.Antimicrobial resistance is a global public health threat. Antibiotic development pipeline has few new drugs; therefore, using antibiotic adjuvants has been envisioned as a successful method to preserve existing medications to fight multidrug-resistant (MDR) pathogens. In this study, we investigated the synergistic effect of a polymyxin derivative known as polymyxin B nonapeptide (PMBN) with azithromycin (AZT). A total of 54  Enterobacteriaceae in critically ill patients  is a global public health crisis . The Worpatients . The compatients .Enterobacterales (including Escherichia coli) are among the most common GNB that are resistant to many antibiotics , accounting for more than 80% of the UTI episodes  purchased from Microbiologics, United States, namely, ATCC BAA-2469, CDC AR-0346, and ATCC 25922. In addition, 51 clinical strains isolated from patients attending to Tawam Hospital, Al-Ain, UAE were tested  guidelines using  control . Disk di control .Based on antibiotic susceptibility profiles, the strains were classified as multidrug resistant (MDR) if they were resistant to at least one antibiotic in three or more antimicrobial categories , and extmphA and mphB), methylases , esterase (ereA), and efflux pumps  was done by PCR, as described previously , and extended spectrum beta-lactamase (ESBL) genes  using primers and conditions previously described  + (MICBA + B/MICB).FICI =  for FICI calculation for most of the strains except those with determined MIC. This was done by other investigators if the tested compounds were not active when used alone . The com5 CFU/ml. Drugs were applied as described for the checkerboard assay. Time-kill curves were obtained by removing aliquots from wells with the AZT/PMBN combinations and those with a single agent applied to the bacterial strains grown in a 96-well plate. The plates were incubated at 37°C, and aliquots were collected at different time points . At each time point, an inoculum of 5 μl was taken from each well and 10-fold serially diluted in 0.85% NaCl. Immediately after the dilution, 5 μl drop was pipetted on TSA plate and incubated at 37°C overnight for the enumeration of colony forming units (CFU) in each combination  were added to black 96 well microplates containing 10 μM NPN and colistin, PMBN or AZT , or a combination of PMBN and AZM (at FICI). The plates were incubated for 1 h at 37° C protected from light. Fluorescence intensity was measured after 1 h using infinite M200 PRO fluorescence microplate reader  at 350 nm excitation and 420 nm emission wavelengths. NPN uptake (%) was calculated for each strain as described before (The outer membrane permeation activity of the PMPN and combinations was assessed by the 1-N-phenylnaphthylamine (NPN) assay , as described previously with slight modifications . Brieflyd before , as follFobs − F0) / (F100 − F0) × 100%.NPN uptake (%) =  was reported to be achieved at high concentrations of colistin (Where colistin .600 of 0.5 (approximately 4 × 108 CFU/ml). The bacteria were treated with either AZT (8 μg/ml); PMBN (16 μg/ml) or a combination of PMBN and AZT . Untreated culture was used as a growth control (negative control) and culture treated with colistin (2 μg/ml) was used as a positive control. Both treated and untreated cultures were incubated for 1 h with shaking at 37°C. The cells were harvested by centrifugation at 4°C to avoid cell damage, washed and resuspended in 0.1 M phosphate-buffered saline . Cells were fixed overnight with a fixative containing 4% formaldehyde, 1.25% glutaraldehyde, 4% sucrose, 0.01 M CaCl2, 0.075% ruthenium red in PBS. The fixed cells were washed three times in PBS buffer, post fixed in 1% osmium tetroxide in PBS containing 0.075% ruthenium red for 1 h, and washed with water afterwards. Cells were dehydrated with ascending ethanol grades  each for 15 min, and finally treated with propylene oxide. The cells were infiltrated for 1 h each in propylene oxide: Agar 100 epoxy resin in ratio of 1:1, 1:2, and 1:3 and polymerized at 65° C for 24 h. Blocks were trimmed and then semithin and ultrathin sections were cut with EM UC7 Ultracuts ultramicrotome . Semithin sections (1.5 μm thickness) were collected and stained with 1% aqueous toluidine blue on glass slides with a hot plate at 70°C. Ultrathin sections (95 nm) with gold color were collected on 200 mesh copper grids, air dried and then were contrasted with uranyl acetate followed by lead citrate. Then, grids were examined with transmission electron microscope  and images were taken at 80 Kv at different magnifications.Transmission electron microscopy (TEM) was used to look for changes in cell morphology of a selected bacterial strain (EC26) after treatment with the test agents. The procedure followed as described before with some modifications . A singlp < 0.05.All statistics were performed using IBM SPSS . Graphs were generated using GraphPad Prism® Version 9.0 . Kruskal–Walli’s rank sum test and Mann–Whitney U-tests were performed to compare the groups as appropriate. Statistical significance was determined at E. coli strains tested in this study were highly resistant to multiple antibiotics (MDR), including strains that were also resistant to the last resort drugs including carbapenems (six strains with blaNDM gene and two strains with blaOXA-48 like gene), and colistin . Results of antibiotic susceptibility testing and resistance genes detected in the strains are shown in the Except the quality control strain (ATCC 25922), all the n = 34) of the strains harbored the mphA gene (MIC: 32 to ≥128 μg/ml), from them 14.8% (n = 8) harbored the ermB gene in addition to the mphA gene and exhibited very high MICs for AZT (>128 μg/ml). On the other hand, all the 20 strains (37.04%) with low MICs for AZT (2–8 μg/ml) lacked these genes.For macrolide resistance genes, 62.96% . When the mean MICs of AZT in the combinations were compared, they were not significantly different when different concentrations of PMBN were applied (p > 0.05), although the highest fold-reduction in MIC (9.1–16 folds) was found when the highest concentration of PMBN (32 μg/ml) was used. However, when strains with different MICs for AZT (4- ≥ 128 μg/ml) were compared, a significant difference in the MICs in the combinations was found (p < 0.05), as strains with lower MICs for AZT (4–8 μg/ml) demonstrated better response to treatment with AZT and PMBN  compared to strains having high AZT MICs (32 to ≥128 μg/ml) exhibiting mean MIC of 3–16 μg/ml (mean folds reduction in MIC: 5.7–16) when treated with both AZT and PMBN  which was resistant to colistin, demonstrated the least response to PMBN, compared to the other strains. Nevertheless, synergistic combinations of PMBN and AZT had bactericidal effects as shown in the time-kill study . As for E. coli as shown in Transmission electron microscopy was used to examine the ultrastructural changes in a selected strain EC26) treated with the synergistic combination compared to a single agent. As shown in C26 treatE. coli, even those resistant to the last resort drugs including carbapenems and colistin. The spread of resistance to the latter drugs increased the need for alternatives to these medications to fight highly resistant bacteria  or by the last-line antibiotic colistin . Howevermcr-1-mediated LPS modification associated with colistin resistance can protect the cytoplasmic but not the outer membrane from damage caused by colistin. Despite OM damage of resistant bacteria, it was unable to kill or lyse them, leading to bacterial survival after treatment with colistin , a validated marker for outer membrane permeability of GNB verified that PMBN can increase outer membrane permeability in E. coli . NPN is E. coli . The sammphA with or without ermB. In this study, synergetic combinations were not effective in the presence of ermB gene. This gene encodes a methylase that confers macrolide-lincosamide-streptogramin B resistance. Methylation of the ribosomal target of the antibiotics by ermB enzyme causes resistance due to drug target alteration, resulting in the inability of AZT to interact with ribosome  which is an enzyme that causes inactivation of macrolides  of the bacteria tested were resistant to AZT due to the presence of ribosome . Thus, ecrolides . It has  than majority of mphA harboring strains (MIC: 32 to >128 μg/ml), which could affect the likelihood of success of synergy with PMBN. Unfortunately, the sole effect of ermB gene could not be explored as none of the strains tested in this study possess this gene alone, which is a common limitation encountered in previous studies .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.https://www.frontiersin.org/articles/10.3389/fmicb.2022.998671/full#supplementary-materialThe Supplementary material for this article can be found online at: Click here for additional data file.Click here for additional data file.


Sebaceous carcinoma is a rare cutaneous malignant tumor, usually occurring on the eyelids, head, neck, and trunk. There have been few reports about sebaceous carcinoma with primary lung cancer, for which optimal therapy has not yet been established. A 70‐year‐old man presented with a mass in the left iliac bone and tumor of the lower left lung. The morphological characteristics of the iliac bone biopsy pathology and immunostaining results showed sebaceous gland differentiation. After systemic examination, we diagnosed a primary lung sebaceous carcinoma with intrapulmonary and bone metastases. PD‐L1 was positive in 1%–24% of tumor cells, and microsatellites were stable. We detected protein kinase B (AKT1) mutations using the Oncomine Dx target test. Palliative radiotherapy (RT) of a total of 45 Gy was provided in 15 fractions to the left iliac region, which resulted in a 25% reduction in the tumor size. Subsequently, four courses of first‐line pembrolizumab led to a 30% reduction in the total tumor count. RT and pembrolizumab may be treatment options for certain rare primary sebaceous carcinomas of the lungs. A synergistic effect from RT and subsequent administration of immune checkpoint inhibitors may have contributed to tumor reduction. Primary sebaceous carcinoma of the lung is very rare, and no effective treatment has been yet established. We successfully treated lung sebaceous carcinoma with sequential treatment with radiotherapy and anti‐PD‐1 antibody therapy. The tumor had AKT1 mutation and was positive for PD‐L1 expression, suggesting AKT‐PD‐L1 pathway may be implicated in the response to therapy. Sebaceous carcinoma is a rare malignant tumor of the sebaceous gland.Herein, we present a case of primary lung sebaceous carcinoma with considerable bone metastasis, positive for PD‐L1 and protein kinase B (AKT1), that was successfully treated with radiotherapy (RT) and pembrolizumab.A 70‐year‐old‐man presenting with severe pain extending from his left buttock to the thigh was referred to our institute, after computed tomography (CT) revealed masses in the left lung and left iliac bone. The patient's general condition was poor, with an Eastern Cooperative Oncology Group Performance Status (PS) of 4. The patient was unable to lie supine due to the severity of the pain.Blood tests revealed an elevated carcinoembryonic antigen (CEA) level of 4.2 ng/ml and cytokeratin 19 fragment (CYFRA) level of 111.1 ng/ml. Elevated lactate dehydrogenase and C‐reactive protein levels, as well as anemia were also observed. Contrast‐enhanced CT revealed a mass in the lower lobe of the left lung. Peripherally, a similar mass and an enlarged ipsilateral hilar lymph node were observed Figure . A largeIliac bone biopsy revealed tumor cells with foamy cytoplasm and unevenly distributed nuclei. Immunostaining results were negative for thyroid transcription factor‐1 (TTF‐1), Napsin A, and p40. Additionally, the apocrine differentiation marker GCDFP‐15 was negative, and adipophilin, a marker of sebaceous differentiation, was positive Figure .Positron emission tomography‐CT and magnetic resonance imaging revealed no other neoplastic lesions Figure , and thiInitially, the patient received a palliative RT of total of 45 Gy to the left iliac bone, after which the bone tumor mass was reduced by 25% after RT. During RT, the primary lung tumor increased in size by 19.6% and total tumor count increased by 2.5%. After RT, pembrolizumab was administered as the first‐line therapy. After four courses of pembrolizumab, the primary lung tumor, bone tumor, and total tumor counts were reduced by 38, 46, and 30%, respectively  or near‐CR efficacy.Here, sequential administration of immune checkpoint inhibitors (ICIs) after palliative RT was successful. The result may have been a synergistic effect of RT and ICI. Formenti et al. suggested that ionizing radiation to a tumor‐encompassing target produces effects beyond cell death, sending specific signals to the host immune system.Here, AKT1 mutations were detected using the ODxTT. No other cases of AKT1 mutation positivity in sebaceous carcinomas have been reported. The AKT/extracellular signal‐regulated kinase signaling pathway has been associated with PD‐L1 expressionIn conclusion, we successfully treated an extremely rare primary sebaceous carcinoma of the lung with sequential RT and anti‐PD‐1 antibody therapy. The tumor had an AKT1 mutation and expressed PD‐L1, suggesting that the AKT‐PD‐L1 pathway may influence the response to therapy. A treatment strategy combining ICI with RT may induce synergistic effects and be a treatment option for sebaceous carcinoma.YJ drafted the original manuscript. SH and YT reviewed the manuscript draft and revised it critically on intellectual content. MY, YM, MA, TJ, SS, HI and TS reviewed the manuscript draft. All authors approved the final version of the manuscript to be published.All authors declare no potential conflict of interest.


In the phase 2 double‐blind Study 211, a starting dose of lenvatinib 18 mg/day was compared with the approved starting dose of 24 mg/day in patients with radioiodine‐refractory differentiated thyroid cancer (RR‐DTC). Predefined criteria for noninferiority for efficacy in the 18 mg arm were not met; safety was similar in both arms. Impact of lenvatinib treatment on health‐related quality‐of‐life (HRQoL) was a secondary endpoint of Study 211.Patients with RR‐DTC were randomly assigned to a blinded starting dose of lenvatinib 18 mg/day or 24 mg/day. HRQoL was assessed at baseline, every 8 weeks until Week 24, then every 16 weeks, and at the off‐treatment visit, using the EQ‐5D‐3L and FACT‐G instruments. Completion and compliance rates, mean change from baseline, and times to first and definitive deterioration were evaluated.n = 77) and 24 mg/day arms (n = 75). For the 18 mg versus 24 mg arms, least squares mean differences were −0.42  for EQ‐5D‐VAS and 0.47  for FACT‐G total. Time to first deterioration did not significantly favor either arm; EQ‐5D‐VAS HR [18 mg/24 mg] 0.93 (95% CI 0.61–1.40), EQ‐5D‐HUI HR [18 mg/24 mg] 0.68 (95% CI 0.44–1.05), FACT‐G total HR [18 mg/24 mg] 0.73 (95% CI 0.48–1.12). Time to definitive deterioration did not significantly favor either arm, though EQ‐5D‐VAS showed a trend in favor of the 24 mg arm ; EQ‐5D‐HUI HR [18 mg/24 mg] was 0.96 (95% CI 0.57–1.63), FACT‐G total HR [18 mg/24 mg] was 0.72 (95% CI 0.43–1.21).Baseline EQ‐5D and FACT‐G scores, and overall changes from baseline, were comparable between patients in the lenvatinib 18 mg/day , support the use of the approved lenvatinib starting dose of 24 mg/day in patients with radioiodine‐refractory differentiated thyroid cancer. Cancer diagnoses impair patients' HRQoL both during and after treatmentIn the SELECT study, lenvatinib 24 mg/day demonstrated superior efficacy compared with placebo in patients with RR‐DTC.Study 211 was designed to determine if a lower starting dose of lenvatinib (18 mg/day) would provide comparable efficacy and an improved safety profile versus the approved starting dose of 24 mg/day in patients with RR‐DTC.Few data exist on HRQoL in patients with RR‐DTC receiving kinase inhibitor therapies. Because real‐world analyses typically include few patients and prospective controlled clinical trial data are lacking, the effect of kinase inhibitor treatment, and lenvatinib specifically, on HRQoL in patients with RR‐DTC is an area that requires additional research.22.1Eligibility details of this phase 2 multicenter, randomized, double‐blind trial have been previously published.Written informed consent was provided by all patients before undergoing any study‐specific procedures. The study protocol was approved by relevant institutional review boards and was conducted in accordance with the principles of the World Medical Association Declaration of Helsinki.Patients were randomly assigned (1:1) to receive a starting dose of lenvatinib 18 mg/day or 24 mg/day. Randomization of masked lenvatinib dose packs was performed centrally by an interactive voice‐ and webresponse system. Double‐blind lenvatinib dosing was used to minimize patient and investigator bias. Patients were stratified by age (≤ 65 years vs. > 65 years) and ECOG PS (0 vs. 1 or 2).A secondary objective of Study 211 was to evaluate the effect of lenvatinib treatment on HRQoL as measured by the EuroQol 5 Dimensions 3 Levels (EQ‐5D‐3L) and Functional Assessment of Cancer Therapy‐General (FACT‐G). HRQoL was assessed at baseline, every 8 weeks until Week 24, then every 16 weeks, and at the off‐treatment visit.2.2The EQ‐5D‐3L is divided into the EQ‐5D descriptive system, consisting of 5 dimensions , and the EQ‐5D visual analog scale (VAS).2.3p values and CIs are nominal and should be considered descriptive in nature. All analyses were performed using SAS, version 9.4 . See the Data Statistical tests and CIs were 2‐sided and had an associated alpha level of 0.05. No statistical hypotheses were prespecified for HRQoL outcomes. No adjustments for multiple testing or estimation were used for HRQoL analyses, so all 2.3.1Completion and compliance rates were summarized for each HRQoL instrument and outcome. An instrument was considered complete if at least one valid scale score could be computed from the available response data. The rate was computed as a percentage of patients in the full analysis set. The compliance rate was the percentage of patients who had completed the instrument among all patients who were expected to complete the instrument at a certain time point .2.3.2p value, was also estimated.To assess the effect of lenvatinib starting dose on EQ‐5D and FACT‐G outcomes, mixed models with random coefficients were fitted using the change from baseline for each HRQoL score as the response variable. The least squares (LS) mean change from baseline for each treatment arm was estimated at each timepoint (not including the off‐treatment assessment), along with an overall LS mean. The difference in LS mean for the lenvatinib 18 mg arm versus the 24 mg arm, along with associated 95% CI and 2.3.3Time to deterioration (TTD) was evaluated using the Kaplan–Meier method to estimate the distribution of TTD and median TTD for each treatment arm. Time to first deterioration and time to definitive deterioration  or nonresponders . Time to first deterioration and time to definitive deterioration were assessed for responders versus nonresponders using similar methods as those described in the TTD section.33.1n = 77) and 24 mg starting dose arm (n = 75). Patient demographics and baseline characteristics have been previously reported and were generally well‐balanced between the treatment arms. The median ages were 66.0 years (range 21–89) and 65.0 years (range 36–92) in the lenvatinib 18 mg and 24 mg arms, respectively. In the lenvatinib 18 mg arm, 48.1% of patients were male and 54.7% of patients were male in the lenvatinib 24 mg arm. Baseline thyroid‐stimulating hormone use and ECOG PS scores were similar between the two treatment arms. Most patients had papillary RR‐DTC .Patients were randomly assigned to the lenvatinib 18 mg starting dose arm , 45.5% and 57.3% of patients in the lenvatinib 18 and 24 mg starting‐dose arms, respectively, were still undergoing treatment. Disease progression was the primary reason for discontinuation in 26.0% and 17.3% of patients in the lenvatinib 18 mg and 24 mg starting‐dose arms, respectively; and AEs were the primary reason for discontinuation in 15.6% of patients in the lenvatinib 18 mg starting‐dose arm, and 13.3% of patients in the 24 mg starting dose arm. All TEAEs that resulted in discontinuation of lenvatinib were each reported by no more than one patient in either treatment arm.3.2At baseline and throughout the assessment period, HRQoL instrument completion and compliance rates were similar for both groups Table . While H3.3The EQ‐5D and FACT‐G scores at baseline were comparable between the treatment arms Table . Baselin3.4No significant differences were observed in time to first deterioration between the treatment arms Figure . Among tIn addition, no significant differences were observed between the treatment arms in time to definitive deterioration Figure . Definit3.5There was a numerical, but not statistically significant, trend toward a longer median time to first deterioration among radiologic responders versus nonresponders  for the EQ‐5D VAS, EQ‐5D HUI, and FACT‐G total score. A similar trend was observed for time to definitive deterioration, though EQ‐5D HUI nominally significantly favored responders Figure .4In this analysis, there were no significant differences in HRQoL between the lenvatinib 18 mg/day and 24 mg/day starting dose arms. Specifically, there were no statistically significant differences in LS mean change between treatment arms for both EQ‐5D and FACT‐G HRQoL scores, time to first deterioration, or time to definitive deterioration.The blinded nature of this study allowed us to address lenvatinib starting dose selection while minimizing patient and investigator bias. In real‐world practice, some clinicians initiate lenvatinib treatment at doses lower than the approved 24 mg/day starting dose in an attempt to minimize treatment‐emergent AEs (TEAEs) and optimize HRQoL; however, this may prevent patients from receiving the full efficacy benefits of lenvatinib. The Study 211 HRQoL results combined with the primary analysis suggest that use of a lower lenvatinib starting dose may compromise efficacy without improving HRQoL or safety in patients with RR‐DTC.n = 39) using the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire and the pain VAS.The HRQoL data from Study 211 are particularly valuable because of the dearth of HRQoL data in patients with thyroid cancer treated with targeted therapies. An analysis that assessed studies that included HRQoL outcomes in patients with thyroid cancer reviewed 94 articles published between 2000 and 2019 and found few studies with data on HRQoL in patients with thyroid cancer treated with kinase inhibitors.In Study 211, respective compliance rates in the lenvatinib 18 mg/day and 24 mg/day starting dose arms were high at baseline  and remained high through Week 56 , which support the robustness of the data collected. In this HRQoL analysis, no significant differences were seen in either time to first deterioration or time to definitive deterioration; however, some potential patterns can be observed in the TTD data. TTD data are consistent with the hypothesis that time to first deterioration may reflect patients' experience of TEAEs, and time to definitive deterioration may align with disease progression. Time to dose reduction in Study 211 was shorter in the lenvatinib 24 mg starting dose arm compared with the 18 mg starting dose arm,Although some TEAEs are known to negatively impact HRQoL outcomes, it has been suggested that antitumor efficacy might have a beneficial effect on QoL . In this analysis, HRQoL was assessed in patients who had radiologic tumor responses versus those who did not. As expected, results showed that there was a trend toward longer TTD among patients who had objective radiologic responses. For time to first deterioration, the EQ‐5D VAS, EQ‐5D HUI, and the FACT‐G total score all numerically favored patients with radiologic responses. For time to definitive deterioration, the EQ‐5D HUI nominally significantly favored patients with radiologic responses, and the EQ‐5D VAS and the FACT‐G total score both numerically favored radiologic responders.P values considered nominal. Recall periods for the instruments were relatively short (“today” for the EQ‐5D‐3L and 7 days for the FACT‐G) and, thus, only represent HRQoL status at selected intervals. Study 211 was also limited by the relatively small sample size and duration of follow‐up. Furthermore, patient discontinuation limited the availability of HRQoL data over time. Finally, we noted that some patients in clinical practice experience negative symptoms while undergoing treatment with tyrosine kinase inhibitors and, therefore, this study's HRQoL data may represent an incomplete picture of patients' experience, particularly among those patients who do not meet clinical trial criteria. Despite these limitations, to the best of our knowledge, this is the largest, double‐blind, randomized, controlled study to date with prospectively collected data regarding treatment with a kinase inhibitor in patients with RR‐DTC that includes HRQoL data.The analysis of HRQoL outcomes was a secondary objective of Study 211, with no statistical hypotheses prespecified for these outcomes and, therefore, the results must be viewed as descriptive in nature, and all These results demonstrate that, contrary to common opinions among clinicians, the starting dose of lenvatinib 24 mg/day did not lead to worse HRQoL than the lower dose of lenvatinib 18 mg/day. In the primary analysis, the ORR as of Week 24 was 40.3% in the lenvatinib 18 mg/day starting dose arm versus 57.3% in the 24 mg/day starting dose arm, and the rates of TEAEs with Common Terminology Criteria for Adverse Events grade ≥3 were similar between the arms. Together with the efficacy and safety results,Matthew H. Taylor: Conceptualization ; formal analysis ; investigation ; methodology ; supervision ; writing – original draft ; writing – review and editing . Sophie Leboulleux: Investigation ; writing – original draft ; writing – review and editing . Yury Panaseykin: Investigation ; writing – original draft ; writing – review and editing . Bhavana Konda: Investigation ; writing – original draft ; writing – review and editing . Christelle De La Fouchardiere: Investigation ; writing – original draft ; writing – review and editing . Brett G.M. Hughes: Formal analysis ; investigation ; writing – original draft ; writing – review and editing . Andrew G. Gianoukakis: Investigation ; writing – original draft ; writing – review and editing . Young Joo Park: Investigation ; writing – original draft ; writing – review and editing . Ilia Romanov: Investigation ; writing – original draft ; writing – review and editing . Monika K. Krzyzanowska: Investigation ; writing – original draft ; writing – review and editing . Diana Garibinsky: Formal analysis ; validation ; writing – original draft ; writing – review and editing . Bintu Sherif: Formal analysis ; validation ; writing – original draft ; writing – review and editing . Jie Janice Pan: Formal analysis ; validation ; writing – original draft ; writing – review and editing . Terri A. Binder: Conceptualization ; formal analysis ; methodology ; supervision ; writing – original draft ; writing – review and editing . Nicholas Sauter: Formal analysis ; validation ; writing – original draft ; writing – review and editing . Ran Xie: Formal analysis ; validation ; writing – original draft ; writing – review and editing . Marcia S Brose: Conceptualization ; formal analysis ; investigation ; methodology ; supervision ; writing – original draft ; writing – review and editing .This study was funded by Eisai Inc., and Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc. Eisai Inc. participated in the design and conduct of the study, data management, and data analysis. Eisai Inc. participated in the interpretation of data in conjunction with the investigators, and in the preparation, review, and approval of the manuscript. The authors had full access to the data and control of the final approval and decision to submit the manuscript for publication.Matthew H. Taylor: Consulting/advisory board member (honoraria paid to MHT): Bristol Myers Squibb, Eisai Inc., Novartis, Merck, Pfizer, Bayer, Sanofi/Genzyme, Regeneron, Bayer, Array Biopharma, LOXO Oncology, Blueprint Medicines, Immuneonc, Exelixis, Cascade Prodrug. Speakers' bureau (honoraria paid to MHT): Bristol Myers Squibb, Eisai Inc., Blueprint Medicines, Merck. Research Funding : Bristol Myers Squibb, Merck Sharp & Dohme Corp., Pharmacyclics, AstraZeneca, Eisai, Incyte, EMD Serono, Novartis, Seattle Genetics, AbbVie, Genentech, Eli Lilly, Roche, Acerta Pharma, Genzyme Corporation, Pfizer.Sophie Leboulleux: Advisory Board: Bayer, Eisai, Lilly; Invited Speaker: Eisai.Yury Panaseykin: Nothing to disclose.Bhavana Konda: Research funding : Eisai, Merck, Bristol Myers Squibb, Xencor, Eli Lilly & Co.Christelle de La Fouchardiere: Honoraria: Eisai, Roche, Servier, Amgen, Bayer, Pierre Fabre Oncologie, Bristol Myers Squibb; Non‐financial support: Roche, Servier, Amgen, Bayer, Pierre Fabre Oncologie, Bristol Myers Squibb.Brett G.M. Hughes: Advisory Board: MSD, BMS, Roche, Pfizer, AZ, Eisai, Takeda, Sanofi; Grant : Amgen.Andrew G. Gianoukakis: Advisory Board: Eisai, Exelixis.Young Joo Park: Consultant (Steering committee): Novartis; Research funding: Lilly.Ilia Romanov: Honoraria: Eisai, Bristol Myers Squibb, Merck Serono.Monika K. Krzyzanowska: Advisory Board: Eisai, Ipsen, Novartis; Research funding: Ipsen, Eisai.Diana Garbinsky is a full‐time employee of RTI Health Solutions.Bintu Sherif is a full‐time employee of RTI Health Solutions.Jie Janice Pan is an employee of Eisai Inc.Terri A. Binder is a former employee of, and current consultant to, Eisai Inc., and an Amgen Inc. shareholder.Nicholas Sauter is an employee of Eisai Inc.Ran Xie is an employee of Eisai Inc.Marcia S. Brose: Consulting/advisory board member (honoraria paid to MSB): Eisai Inc, Novartis, Bayer, Sanofi/Genzyme, LOXO Oncology, Blueprint Medicines, Exelixis; Research Funding : Eisai, Novartis, Genentech, Eli Lilly, Roche.Data S1Click here for additional data file.


Pathologic findings were correlated to PSA values at time of sLND, categorized in early (<0.5 ng/ml), low (0.5–0.99 ng/ml), moderate (1–1.5 ng/ml) and high (>1.5 ng/ml). Clinical recurrence (CR)‐free survival after sLND was calculated using multivariable analyses and plotted over pre‐operative PSA value.The study included 164 patients diagnosed with ≤2 pelvic lymph‐node recurrence(s) of PCa documented on p < 0.0001). There were 51 CRs after sLND. After adjusting for confounders, we found a significant, non‐linear relationship between PSA level at sLND and the 12‐month CR‐free survival (p < 0.0001), with the highest probability of freedom from CR for patients who received sLND at PSA level ≥1 ng/ml.Median [interquartile range (IQR)] PSA at sLND was 1.1  ng/ml, and 131 (80%) patients had one positive spot at PET scan. All patients received pelvic sLND, whereas 91 (55%) men received also retroperitoneal dissection. Median (IQR) number of node removed was 15 . The rate of positive pathology increased as a function of pre‐operative PSA value, with highest rates for patients with pre‐operative PSA > 1.5 ng/ml . After sLND, PSA ≤ 0.3 ng/ml was detected in 67 (41%) men. On multivariable analyses, pre‐operative PSA was associated with PSA response (68Ga‐PSMA PET in biochemical recurrent PCa.In case of PET‐detected nodal recurrences amenable to sLND, salvage surgery was associated with the highest short‐term oncologic outcomes when performed in men with PSA ≥ 1 ng/ml. Awaiting confirmatory data from prospective trials, these findings may help physicians to optimize the timing for  However, positive nuclide uptake of 68Ga‐PSMA can be expected in approximately 50% of patients at these low PSA values,In men with recurrent prostate cancer after primary treatment, current guidelines recommend the performance of 68Ga‐PSMA PET in case of PSA rise after primary treatment, a delicate balance between higher detection rate and risk of missing the therapeutic window. For this reason, we assessed the relationship between PSA level at sLND, 68Ga‐PSMA PET scan feature and oncologic outcomes with the aim of identifying the patients who have benefited the most from sLND and thus optimize the timing for 68Ga‐PSMA PET in biochemical recurrent prostate cancer.Salvage lymph node dissection (sLND) is among possible MDT options for men with node‐only recurrence(s) after radical prostatectomy. Despite good short‐term biochemical control rates,268Ga‐PSMA PET who underwent sLND at 11 tertiary referral centres between 2012 and 2019. For all patients, the interval between 68Ga‐PSMA PET and sLND was no longer than 3 months. To exclude other sites of recurrence, pre‐operative imaging included abdominal CT scan, whole‐body MRI or bone scan using technetium Tc99m methylene diphosphonate according to the standard of care at each treating institution. We excluded patients with PSA level at sLND higher than 4 ng/ml (n = 55) and those with missing covariates , resulting in 164 patients eligible for the analyses.We analysed data of 283 patients with up to two pelvic‐only nodal recurrence(s) after radical prostatectomy detected by 2.1All patients underwent a template lymph node dissection with a minimum of resection of lymph nodes surrounding the external, internal and obturator artery, as well as the common iliac artery as described earlier.2.2cluster option in Stata statistical software. Compared with the main cohort that included 164 patients, seven men were not included in the survival analyses due to missing data. Finally, to visualize our findings, the probability of freedom from CR 1 year after sLND was calculated for an average patient, that is, by setting variables at the mean.Our statistical analyses included several steps. First, we assessed descriptive characteristics of our cohort. Second, we correlated pathologic findings in different resected fields during sLND with pre‐operative PSA values. Third, the probability of freedom from CR after sLND [and corresponding 95% confidence interval (CI)] were calculated using Kaplan–Meier analyses. Fourth, multivariable regression models were utilized to assess the association between PSA at sLND and oncologic outcomes after sLND, namely, PSA response and CR‐free survival after sLND. The adjustment for case mix included the following variables that were selected a priori: When PSA response was the outcome of interest, the model included ISUP group at RP (≥4 vs. ≤5), ADT administration at PET scan (yes vs. no) and number of positive spots at PET scan (1 vs. 2), whereas the analyses for CR‐free survival also included variables known after surgery such as PSA response after sLND (yes vs. no), administration of ADT within 6 months from sLND (yes vs. no), administration of RT within 6 months from sLND (yes vs. no). In this latter model, we utilized landmark analyses with follow‐up starting 6 months after sLND. PSA at sLND was included as a non‐linear term using restricted cubic splines with knots at quartiles. Because data from different institutions are correlated, we incorporated institution clustering in our analysis using the 368Ga‐PSMA PET, and approximately four in five patients had one positive spot on imaging. Median [interquartile range (IQR)] PSA and age at sLND were 1.1  ng/ml and 67  years, respectively. Median (IQR) number of resected nodes at sLND was 15 . In the whole study population, final pathology was positive for 131 (80%) patients.Patient characteristics are described in Table Table p < 0.0001). As shown in Figure A total of 67 41%) patients had PSA response after surgery. As shown in Table % patientp < 0.0001). Figure A total of 51 patients experienced CR after sLND. Median (IQR) follow‐up for survivors was 12  months. The 12‐month CR‐free survival was 84% . On multivariable analyses, we found a significant, non‐linear association between PSA value at sLND and the risk of CR after surgery . Maurer et al. first described the technique in 201568Ga‐PSMA PET performed at very early PSA values may have higher chances of false negative findings, and thus, it may miss additional nodal metastases, and/or it may diagnose oligometastatic prostate cancer although the disease is already systemic. In both these scenarios, the potential benefit associated with MDT is inevitably limited. This is compatible with our findings as they suggest that, in candidates to sLND, there might be a certain early temporal window in which surgery performed at low PSA levels might translate in higher rate of PSA response after surgery, whereas the oncologic benefit of sLND might not be maximized. If replicated, our results might have relevant implications for clinical practice as they suggest that, in men with biochemical recurrence after radical prostatectomy, re‐staging PET scan might be performed at PSA of approximately 1 ng/ml.Our data entails a second relevant point for clinical practice. Unlike patients receiving salvage radiotherapy after radical prostatectomy, who have demonstrably more favourable outcomes when treated at very early PSA levels,68Ga‐PSMA PET scan performance and oncologic outcomes after sLND.Our study has several limitations. First, no control group including men managed with either observation or systemic treatment was available. Moreover, the use of additional treatments after sLND was left at the discretion of physicians. As such, it is hard to extrapolate the relative contribution of sLND and subsequent treatments to oncologic outcomes given possible patient selection biases. However, the role of a multi‐modal approach was confirmed at multivariable analyses accounting for all possible significant confounders. Moreover, we cannot exclude residual confounding from known and unknown variables. For instance, the multi‐institutional data collection did not account for central specimen review or for surgical experience of each treating surgeon. Because there is evidence that these factors might influence results and outcomes after surgery,5In men who received sLND for node‐only recurrence after radical prostatectomy, a delicate balance exists between pre‐operative PSA level, imaging performance and oncologic outcomes after surgery. Despite higher rates of PSA response after sLND, patients receiving sLND at lower PSA levels had worse CR‐free survival than those treated at PSA level ≥1 ng/ml. Awaiting results from prospective studies, these findings might help physicians to optimize the timing for re‐staging imaging in biochemical recurrent prostate cancer.Study concept and design: Pfister, Bravi, Heidenreich. Acquisition of data: Mazzone, Stabile, Cucchiara, Devos, Gandaglia, Fossati. Drafting of the manuscript: Bravi, Pfister. Critical revision of the manuscript for important intellectual content: Bravi, Briganti, Montorsi, Karakiewicz, Mottrie, Karnes, Kretschmer, Albers, Joniau, Van Poppel, Shariat, Tilki, Graefen, Gill, Suardi, Osmonov, Juenemann, Buchner, Stief, Hiester, Grubmüller.Data S1. Supporting InformationClick here for additional data file.


The protocol includes ntEdit for correcting base errors and small indels, and for marking potentially problematic regions, then Sealer for filling both assembly gaps and problematic regions flagged by ntEdit. ntEdit+Sealer produces highly accurate, error‐corrected genome assemblies, and is available as a Makefile pipeline from https://github.com/bcgsc/ntedit_sealer_protocol. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.High‐quality genome assemblies are crucial to many biological studies, and utilizing long sequencing reads can help achieve higher assembly contiguity. While long reads can resolve complex and repetitive regions of a genome, their relatively high associated error rates are still a major limitation. Long reads generally produce draft genome assemblies with lower base quality, which must be corrected with a genome polishing step. Hybrid genome polishing solutions can greatly improve the quality of long‐read genome assemblies by utilizing more accurate short reads to validate bases and correct errors. Currently available hybrid polishing methods rely on read alignments, and are therefore memory‐intensive and do not scale well to large genomes. Here we describe ntEdit+Sealer, an alignment‐free, Basic Protocol: Automated long‐read genome finishing with short readsSupport Protocol: Selecting optimal values for k‐mer lengths (k) and Bloom filter size (b) We provide instructions for installing the required software, both from package managers and manually, in the Strategic Planning section. Additionally, we provide a Here we describe a scalable and alignment‐free protocol for correcting base errors and resolving problematic regions in long‐read genome assemblies using short reads. This protocol relies on Bloom filters, a probabilistic data structure that stores and tests for elements in a set in constant time Bloom, . The proThe output of ntEdit+Sealer is a polished long‐read genome assembly with sequence gaps closed. The ntEdit+Sealer protocol provides an accessible solution for correcting errors and producing high quality genome assemblies from long‐ and short‐read sequencing data. This will allow users to take full advantage of the benefits of both technologies. We expect this genome assembly finishing protocol to be invaluable for hybrid sequencing efforts in pursuit of producing contiguous and accurate genome assemblies.ntEdit+Sealer is a command‐line‐based protocol that requires a 64‐bit Linux or Mac operating system and a sufficient amount of RAM and disk space for generating and storing Bloom filters. Requirements will depend on the genome size of the specific organism under study. The peak memory usage is approximately equal to the size of the Bloom filter generated by ABySS‐Bloom. Guidelines for determining this Bloom filter size are described in the https://github.com/bcgsc/nthits)ntHits v0.0.1+ (https://github.com/bcgsc/ntEdit)ntEdit v1.3.5+ (https://github.com/bcgsc/abyss)ABySS v2.3.2+; contains Sealer and ABySS‐Bloom  pipeline available on Github at https://docs.conda.io/en/latest/miniconda.html. Once Miniconda is installed, create a new environment for the protocol with the following commands:conda create ‐n ntedit_sealer python=3.7conda activate ntedit_sealerInstallation via the Conda package manager (Miniconda) is recommended. Conda allows easy installation of tools and their dependencies into standalone environments. Miniconda is a minimal, lightweight installation for Conda and can be obtained freely from conda install ‐c bioconda "nthits>=0.0.1" "ntedit>=1.3.5" "abyss>=2.3.2"ntHits, ntEdit and ABySS can then be installed from the bioconda channel:ntedit_sealer environment. This environment must be activated prior to running the protocol with the command conda activate ntedit_sealer.This command will install all three tools into the https://brew.sh. Once Homebrew is installed and configured, the required tools can be installed with the following commands:brew tap brewsci/biobrew install nthits ntedit abyssThe three packages are also available on Homebrew. The Homebrew package manager can be obtained freely from https://www.gnu.org/software/autoconf)Autoconf (https://www.gnu.org/software/automake)Automake (https://gcc.gnu.org)A C++ compiler that supports OpenMP, such as GCC 4.2 or greater Boost (https://www.open‐mpi.org)OpenMPI (https://github.com/sparsehash/sparsehash)Sparsehash sra‐tools v2.9.1+ (https://github.com/bcgsc/nthits)ntHits v0.0.1+ (https://github.com/bcgsc/ntEdit)ntEdit v1.3.5+ (https://github.com/bcgsc/abyss)ABySS v2.3.2+ ntEdit+Sealer protocol v1.0.0+ (https://github.com/ablab/quast)QUAST v5.0.0+  can be provided in compressed or uncompressed FASTQ format. Paired‐end reads do not need to be interleaved. The long‐read draft genome assembly can be provided as either a multi‐ or single‐line FASTA file.E. coli strain NDM5 draft genome assembly is included in the ntEdit+Sealer Protocol Github Repository under the “demo” subdirectory . The corresponding Illumina short reads will be used as demonstration for polishing and can be obtained from the NCBI Sequencing Read Archive (Accession: SRX12150405). Additionally, we will use the E. coli strain K‐12 substr. MG1655 reference genome assembly (Accession: GCF_000005845.2) to assess the assemblies with QUAST.The example 1PATH environment variable.Install ntHits, ntEdit, ABySS, and the ntEdit+Sealer repository as outlined in the Strategic Planning section and add all binaries to your 2Install protocol‐specific dependencies fasterq‐dump (part of sra‐tools) and QUAST via Conda or manually. Ensure sra‐tools v2.9.1 or newer is installed in order to obtain fasterq‐dump, a more performant, multi‐threaded version of fastq‐dump. If the correct version cannot be installed, fastq‐dump can be used as a replacement (see step 3 below for details). If installing the tools manually, the executables must be added to your PATH environment variable.conda activate <env name>) prior to installing the tools.If Option A of the Strategic Planning section was used to install ntHits, ntEdit, and ABySS, dependencies may be installed into the same environment. Otherwise, a new Conda environment should be created. The Conda environment must be activated . If, for some reason, the correct version of sra‐tools and fasterq‐dump cannot be installed, fastq‐dump may be used instead with the following command: fastq‐dump SRR15859208 ‐‐split‐3 ‐‐skip‐technical.The Create a new directory for running the ntEdit+Sealer protocol. Enter the new directory, soft‐link the draft genome assembly from the ntEdit+Sealer repository and download the reference genome assembly and short reads.4ecoli_shasta.fa with the two reads files SRR15859208_1.fastq and SRR15859208_2.fastq. Specify k‐mer lengths of 80, 65 and 50 with the k parameter, and a Bloom filter size of 200 MB with the b parameter. The protocol should take approximately 5 min to complete and requires under 550 MB of RAM, so can easily be run on a modern laptop or desktop computer.ntedit‐sealer finish seqs=ecoli_shasta.fa \reads="SRR15859208_1.fastq SRR15859208_2.fastq" \k="80 65 50" b=200MEnsure that quotation marks are used to enclose lists of parameter values , and that individual items in lists are space‐separated. k‐mer lengths must be passed in decreasing order. The command will run (1) ntHits and ABySS‐bloom to populate Bloom filters from the short reads, (2) a Bash script to call ntEdit iteratively with decreasing k, (3) a Python script to consolidate (soft‐mask) sequences shorter than the lowest k that are flanked by soft‐masked regions, and finally (4) Sealer.Run the ntEdit+Sealer Makefile with the “finish” command to polish and fill the draft assembly 5ntEdit and Sealer polishing steps complete! Polished assembly can be found in: ecoli_shasta.ntedit_edited.prepd.sealer_scaffold.fa”.Ensure that the ntEdit+Sealer run completes successfully. Successful completion will result in the Makefile reporting “6quast ‐‐fast ‐r GCF_000005845.2_ASM584v2_genomic.fna.gz \‐o ecoli_quast ecoli_shasta.fa \ecoli_shasta.ntedit_edited.prepd.sealer_scaffold.faecoli_quast directory, specified by the ‐o parameter. A summary report will be printed in tab‐separated format to a file named report.tsv, where each column describes one of the input assemblies. The “# mismatches per 100 kbp” metric in this summary describes the base accuracy of the draft and finished assemblies compared to the reference. The “# indels per 100 kbp” metric describes the average proportion of insertions or deletions of either assembly compared to the reference. The expected values for these metrics are shown in Table quast.py.All QUAST output files will be printed to the Run QUAST to analyze the draft and ntEdit+Sealer‐finished genome assemblies.k‐sweep approach, iterating from long to short k‐mer lengths. This approach is beneficial because different k‐mer lengths can provide resolution at different scales. Larger k‐mers can disambiguate repeats as they span longer regions, while shorter k‐mers are useful when the local read coverage is low and for assemblies with lower base quality. The same sequence of k values is used for both tools. k=40 is the practical lower limit for Sealer, as shorter k values cause its runtime to increase sharply. We find that k=80 generally performs well for a variety of datasets and suggest decreasing in intervals of 10‐15. Generally speaking, there is no strict upper limit for k (apart from the read length), so a wide range of k‐mer lengths can be used to achieve the best polishing results. Time and memory restrictions will be the limiting factors in these cases.Both ntEdit and Sealer employ a k‐mer distribution for the input reads, but ABySS‐bloom requires the desired Bloom filter size to be specified. This parameter is controlled by the b parameter when invoking the protocol Makefile.ntHits will automatically select the optimal Bloom filter size for ntEdit by calculating the The optimal size of a Bloom filter depends on several factors, namely the desired false positive rate (FPR), number of hash functions used for insertion, and number of elements that will be inserted. For large Bloom filters, the FPR can be approximated Equation :(1)f=f is the FPR, m is the size of the filter in bits, h is the number of hash functions, and n is the number of elements m=Homo sapiens (3 Gbp genome): 100 GBCaenorhabditis elegans (100 Mbp genome): 2.5 GBEscherichia coli (5 Mbp genome): 200 MBThe following Bloom filter sizes (RAM) generally perform well for common model organisms:b value for other genome sizes can be interpolated from these guidelines, or can be estimated using ntCard ntCard v1.2.2+ ; once=$(grep “˄1\b” freq_k$k.hist | awk '{print $2}'); echo k=$k: n=$((${distinct} ‐ ${once})); doneThis command will calculate and print the number of k‐mers appearing two or more times for each k. See Table Estimate the number of 5k‐mer distributions using the largest n calculated in step 4 (k=65). This will maximize the number of unique k‐mers considered, and therefore produce the lowest FPR for the range of k, a method analogous to that employed by Kmergenie  of 0.005 is m=This equates to approximately 168 MB. We rounded this estimate up to 200 MB for the example in the .ntedit_edited.prepd.sealer_scaffold.fa. The Makefile will exit with a non‐zero code if an error occurs during any of the intermediate steps. Otherwise, the pipeline will log a success message to inform the user of its completion.The final output of ntEdit+Sealer is a base‐corrected assembly with assembly gaps closed. The file will have the suffix _changes.tsv. Each iterative run will also produce an intermediate edited FASTA file. Sealer will print information about how many gaps are closed during each iterative k to a single file with the suffix .sealer_log.txt.ntEdit keeps track of the base corrections made to the draft assembly, and these changes are printed for each iterative run to a tab‐separated file with the suffix de novo assembly for an organism without a reference genome. QUAST is a genome assembly quality‐assessment tool that produces a wide range of metrics and visualizations and is particularly beneficial when a reference genome is available. Of note, the number of mismatches per 100 kbp and number of indels per 100 kbp describe the accuracy of your genome assembly in relation to the reference. These metrics should decrease after using ntEdit+Sealer to polish and finish your draft genome, assuming the assembled genome is highly similar to the reference genome.Genome assembly assessment tools such as BUSCO  characters as assembly gaps by default; the ‐‐lower flag dictates this behavior. The ntEdit+Sealer Makefile has these behaviors configured automatically.When working with Bloom filters, the Bloom filter size and The Makefile should not finish running  if an error occurs during any of the intermediate steps. Carefully inspect the logs, output files, and error codes if an error occurs. See Table https://github.com/bcgsc/ntedit_sealer_protocol/issues. Please address any problems related to specific tools on their respective Github Issues pages.If other problems arise related to the ntEdit+Sealer protocol Makefile and scripts, create a Github Issue on the ntEdit+Sealer Github page at Janet X. Li: software, writing original draft; Lauren Coombe: software, validation, writing review and editing; Johnathan Wong: software, writing review and editing; Inanç Birol: supervision, writing review and editing; René L. Warren: conceptualization, software, supervision, writing review and editing.The authors declare that they have no competing interests.


P = 0.014); in non‐CPAP treatment group, a significant increase in AHI was observed in patients with moderate OSA (P = 0.028). After adjustment for confounding factors, multivariate linear regression showed that △AHI was negatively associated with CPAP treatment . Multinominal logistic regression showed that the AHI of patients not treated with CPAP tended to be unchanged or worsened with the AHI improvement group as a reference . In conclusion, active OSA screening and long‐term CPAP intervention may improve the severity of severe OSA patients following short‐term CPAP withdrawal in the general population.The effect of long‐term continuous positive airway pressure (CPAP) treatment on apnea–hypopnea index (AHI) after CPAP withdrawal remains unclear, especially in obstructive sleep apnea (OSA) patients screened from the population. To examine that, 1241 civil servants who participated in the annual physical examination were screened for OSA between September and December 2017. Screened OSA firstly underwent 1‐week CPAP adherence assessment. Then, patients with good CPAP adherence would be freely provided CPAP to continued treatment. All OSA patients were followed for 2 years. At study end, all OSA patients underwent home sleep testing (HST) again within 1 week of CPAP withdrawal. The effect of 2‐year CPAP treatment on OSA severity was investigated by using linear regression and multinominal logistic regression. In total, 103 OSA patients were screened, including 41 cases (39.8%) in CPAP treatment group and 62 cases (60.2%) in non‐CPAP treatment group. At 2‐year follow‐up, compared with baseline, in CPAP treatment group, following CPAP withdrawal, a significant decrease in AHI was observed in patients with severe OSA ( In general population, active OSA screening and CPAP intervention, a new model for the management of OSA, are feasible. Population‐based OSA screening and long‐term CPAP intervention may improve the severity of severe OSA patients following short‐term CPAP withdrawal. OSA is a risk factor of multisystem disease, such as hypertension, diabetes mellitus, coronary heart disease, and stroke.Continuous positive airway pressure (CPAP) is currently the first‐line therapeutic strategy for OSA.Significant effectiveness with CPAP therapy was observed in improving excessive daytime sleepiness (EDS), sleep‐related quality of life, and blood pressure.In addition, many OSA patients do not tolerate CPAP therapy. In clinical practice, what puzzles clinicians and patients is that whether once CPAP is prescribed, the patients need to use it forever, that is, a key factor impacting the acceptance in CPAP therapy for OSA patients. Current studies have focused only on improving adherence to CPAP therapy, but few studies answer this common clinical question.Therefore, based on active OSA screening and intervention in the general populations, the aim of the study was to examine the impact of long‐term CPAP therapy on AHI after CPAP withdrawal.22.1The subjects were a group of civil servants populations who participated in the annual health examination from September to December 2017 in Guangzhou, Guangdong Province, China. The cohort study consisted of demographic, anthropometric, medical history, sleep‐related questionnaires, OSA diagnosis, CPAP treatment, regular follow‐up, and other related information.2.2First, Berlin questionnaire2 of ≥3%. The AHI was defined as the number of apneas and hypopneas per hour of sleep. Patients were diagnosed as OSA based on an AHI of ≥5 events/h.An obstructive apnea was defined as a decrease in respiratory airflow by ≥90% of pre‐event baseline for ≥10 s with continued respiratory effort; an obstructive hypopnea is defined as a decrease in respiratory airflow by ≥30% of pre‐event baseline for ≥10 s followed by a decrease in SaOBased on AHI, OSA was classified as mild (5 ≤ AHI < 15), moderate (15 ≤ AHI < 30), or severe (AHI ≥ 30). Improvement or worsening of initial AHI was defined as an increase or decrease of ≥25% of AHI.2.3Screened OSA patients firstly underwent 1‐week CPAP adherence assessment based on their willingness. OSA patients with good CPAP adherence2.4All OSA patients were followed up regularly for 2 years at 1, 3, 6, 18, 12, 18, and 24 months. At study end, all OSA patients underwent HST test again within 1 week of CPAP withdrawal. The primary endpoint was AHI, and the secondary endpoints included new‐onset diseases, body mass index (BMI), and oxygen desaturation index (ODI).2.52, and Tsat90 with that of end of follow‐up. Categorical variables were analyzed by using a chi‐square test. Multivariate linear regression was performed to determine the effect of CPAP treatment on △AHI (△AHI = AHI2 years later − AHIbaseline) after adjustment for other variables. Multinominal logistic regression was conducted in order to further identify the effect of CPAP therapy on OSA severity according to three levels of AHI change . All analyses were performed with SPSS 25.0 software . A P value of <0.05 was considered to be statistically significant.As the Shapiro–Wilk test showed some parameters were not normally distributed, the nonparametric Wilcoxon signed rank test was used to compare the mean baseline AHI, BMI, ODI, mean SaO3In total, 1241 civil servants in service underwent on‐site investigation, and 205 were excluded because of incomplete basic information. The Berlin questionnaire identified 228 patients at high risk for OSA among 1036 civil servants, of which 156 accepted HST monitoring, and four were excluded due to insufficient monitoring time. Finally, 103 were diagnosed with OSA, among them, 57 accepted 1‐week CPAP adherence assessment. Forty patients had good 1‐week CPAP compliance, of whom 39 continued CPAP treatment; 17 with poor adherence, and two continued CPAP treatment. Therefore, 41 were identified as CPAP treatment group, and 62 were identified as non‐CPAP treatment group. The flowchart of the study is shown in Figure p = 0.04), and a significant improvement in mean SaO2, Tsat90, and ODI was also reported. In non‐CPAP group, we reported an insignificant increase in AHI (p = 0.120). During the 2 years of follow‐up, we reported one new‐onset diabetes mellitus for CPAP group, three new‐onset hypertension, and one diabetes mellitus for non‐CPAP group.The average age of the OSA patients was 44.8 ± 8.3 years, and 96.1% were male. Characteristics of the total group and for CPAP and non‐CPAP group at baseline and follow‐up are reported in Table Of the 103 patients with OSA, 41 were diagnosed as mild (39.8%), 34 moderate (33.0%), and 28 severe (27.2%). Table Compared with baseline, in CPAP group, following CPAP withdrawal, a significant decrease in AHI was observed for patients with severe OSA, while no significant change in AHI for patients with mild and moderate OSA; in non‐CPAP group, the AHI increased significantly for patients with moderate OSA, while no significant change for patients with mild and severe OSA.2, and time SaO2 < 90%. Variables with a P value of <0.05, plus age, were retained for inclusion in the multivariable linear regression models (lowest SaO2 and time SaO2 < 90% were excluded due to significantly correlated with CPAP therapy). The results showed that △AHI was significantly negatively correlated with CPAP treatment after adjusting for age, △BMI, ESS . Results of the linear regression are shown in Table Univariate linear regression showed that the △AHI was significantly correlated with △BMI, CPAP treatment, ESS, lowest SaOA cut‐off value of 25% was used to define improvement or worsening of initial AHI. In total, 24 (23.3%) OSA patients improved their initial AHI, 61 (59.2%) remained unchanged, and 18 (17.5%) worsened.2 at baseline. Based on a threshold P value < 0.05, we conducted multivariate multinomial logistic regression including these variables and age.Univariate multinomial logistic regression showed that the change in AHI was significantly associated with initial OSA severity, CPAP treatment, and mean SaOP = 0.017; 6.536 , P = 0.032). Tables Adjusting for these potential confounding factors, the results showed that the AHI of OSA patients who were not treated with CPAP tended to be unchanged or worsened with the AHI improvement group as a reference  has considered that insufficient evidence was existing on the benefits and harms of screening for OSA in asymptomatic adults.Previous longitudinal studies have demonstrated that weight change plays a key role in triggering the progression of OSA severity. A study from the Wisconsin Sleep Cohort showed a 10% of weight gain led to a 32% of increase in AHI, while a 10% of weight loss led to a 26% of decrease in AHIRossi and colleaguesOur study shows that long‐term CPAP therapy can delay the progression of OSA severity, especially for severe OSA with a significant reduction in AHI after CPAP withdraws. Previous study has reported that in a subset of OSA patients treated with CPAP, OSA is temporarily “cured” for up to 2 weeks.CPAP therapy significantly decreased AHI in patients with severe OSA; however, as previous studies, a decrease in AHI was also found in untreated severe OSA patients,This study was a population‐based cohort study. The potential strength is that portable home sleep monitoring was performed on all OSA patients at the end of follow‐up, thus reducing the chance of selection bias. However, our study also has some limitations. First, due to the variability of AHI, which is affected by sleep position and the percentage of sleep time in rapid eye movement (REM), the severity of OSA may be misclassified, especially for patients with mild and moderate OSA.In conclusion, the present study provides a new model for the management of OSA, namely, active OSA screening and intervention in general population. Our results have obvious clinical implications in enhancing the beliefs of treating disease and improving adherence to CPAP therapy for OSA patients, especially in the mode of active screening for OSA and intervention.The authors have no conflicts of interest to disclose.Qiong Ou designed the study; Minxia Pan performed the study and data collection; Longlong Wang steered literature search, statistical analysis, and drafted the manuscript; Qiong Ou reviewed and approved the submission of the manuscript.This study was approved by the Ethics Committee of Guangdong Provincial People's Hospital (2017244H). All subjects signed an informed consent form and agreed to participate in the study.