Prognostic model based on B cell marker genes for NSCLC patients under neoadjuvant immunotherapy by integrated analysis of single-cell and bulk RNA-sequencing data.

BACKGROUND: Neoadjuvant immunotherapy has evolved as an effective option to treat non-small cell lung cancer (NSCLC). B cells play essential roles in the immune system as well as cancer progression. However, the repertoire of B cells and its association with clinical outcomes remains unclear in NSCLC patients receiving neoadjuvant immunotherapy. METHODS: Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data for LUAD samples were accessed from the TCGA and GEO databases. LUAD-related B cell marker genes were confirmed based on comprehensive analysis of scRNA-seq data. We then constructed the B cell marker gene signature (BCMGS) and validated it. In addition, we evaluated the association of BCGMS with tumor immune microenvironment (TIME) characteristics. Furthermore, we validated the efficacy of BCGMS in a cohort of NSCLC patients receiving neoadjuvant immunotherapy. RESULTS: A BCMGS was constructed based on the TCGA cohort and further validated in three independent GSE cohorts. In addition, the BCMGS was proven to be significantly associated with TIME characteristics. Moreover, a relatively higher risk score indicated poor clinical outcomes and a worse immune response among NSCLC patients receiving neoadjuvant immunotherapy. CONCLUSIONS: We constructed an 18-gene prognostic signature derived from B cell marker genes based on scRNA-seq data, which had the potential to predict the prognosis and immune response of NSCLC patients receiving neoadjuvant immunotherapy.

Genomic characteristics and immune landscape of super multiple primary lung cancer.

BACKGROUND: In recent years, a growing number of patients with multiple primary lung cancer (MPLC) are being diagnosed, and a subset of these patients is found to have a large number of lesions at the time of diagnosis, which are referred to as 'super MPLC'. METHODS: Here, we perform whole exome sequencing (WES) and immunohistochemistry (IHC) analysis of PD-L1 and CD8 on 212 tumor samples from 42 patients with super MPLC. FINDINGS: We report the genomic alteration landscape of super MPLC. EGFR, RBM10 and TP53 mutation and TERT amplification are important molecular events in the evolution of super MPLC. We propose the conception of early intrapulmonary metastasis, which exhibits different clinical features from conventional metastasis. The IHC analyses of PD-L1 and CD8 reveal a less inflamed microenvironment of super MPLC than that of traditional non-small cell lung cancer (NSCLC). We identify the potentially susceptible germline mutations for super MPLC. INTERPRETATION: Our study depicts the genomic characteristics and immune landscape, providing insights into the pathogenesis and possible therapeutic guidance of super MPLC. FUNDING: A full list of funding bodies that supported this study can be found in the Acknowledgements section.

Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis.

BACKGROUND: Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results. METHODS: A total of 525 patients from two medical centers were retrospectively enrolled. The radiomics features were extracted according to two regions of interest (ROI) segmentation criteria. Multiple logistic regression models were trained to predict the pathology: (1) The clinical model relied on clinical-radiological semantic features; (2) The radiomics models (radiomics+ and radiomics-) utilized radiomics features from different ROIs (including or excluding cavities); (3) the composite models (composite+ and composite-) incorporated both above. RESULTS: In the testing set, the radiomics+/- models and the composite+/- models still possessed efficient prediction performance (AUC ≥ 0.94), while the AUC of the clinical model was 0.881. In the validation set, the AUC of the clinical model was only 0.717, while that of the radiomics+/- models and the composite+/- models ranged from 0.801 to 0.825. The prediction performance of all the radiomics+/- and composite+/- models were significantly superior to that of the clinical model (p < 0.05). Whether the ROI segmentation included or excluded the cavity had no significant effect on these models (radiomics+ vs. radiomics-, composite+ model vs. composite-) (p > 0.05). CONCLUSIONS: The present study established a machine learning-based radiomics strategy for differentiating LUAD from TB lesions. The ROI segmentation including or excluding the cavity region may exert no significant effect on the predictive ability.

CSFV restricts necroptosis to sustain infection by inducing autophagy/mitophagy-targeted degradation of RIPK3.

IMPORTANCE: CSFV infection in pigs causes persistent high fever, hemorrhagic necrotizing multi-organ inflammation, and high mortality, which seriously threatens the global swine industry. Cell death is an essential immune response of the host against pathogen invasion, and lymphopenia is the most typical clinical feature in the acute phase of CSFV infection, which affects the initial host antiviral immunity. As an "old" virus, CSFV has evolved mechanisms to evade host immune response after a long genetic evolution. Here, we show that necroptosis is a limiting host factor for CSFV infection and that CSFV-induced autophagy can subvert this host defense mechanism to promote its sustained replication. Our findings reveal a complex link between necroptosis and autophagy in the process of cell death, provide evidence supporting the important role for CSFV in counteracting host cell necrosis, and enrich our knowledge of pathogens that may subvert and evade this host defense.

Plasma metabonomics of classical swine fever virus-infected pigs.

Classical swine fever (CSF) is an infectious disease caused by Classical swine fever virus (CSFV), which is characterized by depression, high fever, extensive skin bleeding, leukopenia, anorexia, alternating constipation, and diarrhea. Hemorrhagic infarction of the spleen is the main characteristic pathological change following CSFV infection. Large-scale outbreaks of CSF are rare in China and are mainly distributed regionally. The clinical symptoms of CSF are not obvious, and show variation from typical to atypical symptoms, which makes diagnosis based on clinical symptoms and pathology challenging. In recent years, the incidence of CSF-immunized pig farms in China has increased and new CSFV gene subtypes have appeared, posing new challenges to the prevention and control of CSF in China. Changes in metabolites caused by viral infection reflect the pathogenic process. Metabonomics can reveal the trace metabolites of organisms; however, plasma metabonomics of CSFV-infected pigs have rarely been investigated. Therefore, we used an established pig CSFV infection model to study changes in plasma metabolites. The results showed significant differences in forty-five plasma metabolites at different time periods after CSFV infection in pigs, with an increase in twenty-five metabolites and a decrease in twenty metabolites. These changed metabolites were mainly attributed to the tricarboxylic acid cycle, amino acid cycle, sugar metabolism, and fat metabolism. Thirteen metabolic pathways changed significantly in CSFV-infected pigs, including tricarboxylic acid cycle, inositol phosphate metabolism, glycine, serine and threonine metabolism,lysine degradation, alanine, aspartate and glutamic acid metabolism, pantothenate and CoA biosynthesis, ß-alanine metabolism, lysine degradation, arginine and proline metabolism, glycerolipid metabolism, phenylalanine metabolism, arachidonic acid metabolism, linoleic acid metabolism. Among these, changes in fatty acid biosynthesis and metabolism occurred at all time periods post-infection. These results indicate that CSFV infection in pigs could seriously alter metabolic pathways.

Single-cell RNA sequencing analysis reveals transcriptional heterogeneity of multiple primary lung cancer.

INTRODUCTION: With the advancements in early diagnosis, more and more patients with multiple primary lung cancer (MPLC) have been identified. However, the progression of MPLC involves complex changes in cell composition and metabolic function, which remains largely controversial. OBJECTIVE: Our study aims to comprehensively reveal the cellular characteristics and inter-cellular connections of MPLC. METHODS: We performed scRNA-seq from 23 samples of six MPLC patients, combined with bulk whole-exome sequencing. We performed trajectory analysis to investigate the transition of different cell types during the development of MPLC. RESULTS: A total of 1 67 397 cells were sequenced derived from tumour and adjacent tissues of MPLC patients, and tumour, normal, immune and stromal cells were identified. Two states of epithelial cells were identified, which were associated with immune response and cell death, respectively. Furthermore, both CD8+ naïve and memory T cells participated in the differentiation of CD8+ T cells. The terminal states of CD8+ T cells were exhausted T cells and cytotoxic T cells, which positively regulated cell death and were implicated in the regulation of cytokine production, respectively. Two main subpopulations of B cells with distinct functions were identified, which participate in the regulation of the immune response and antigen presentation, respectively. In addition, we found a specific type of endothelial cells that were abundant in tumour samples, with an increasing trend from normal to tumour samples. CONCLUSIONS: Our study showed the comprehensive landscape of different cells of MPLC, which might reveal the key cellular mechanisms and, therefore, may provide new insights into the early diagnosis and treatment of MPLC.

Multimodal analysis of cell-free DNA whole-methylome sequencing for cancer detection and localization.

Multimodal epigenetic characterization of cell-free DNA (cfDNA) could improve the performance of blood-based early cancer detection. However, integrative profiling of cfDNA methylome and fragmentome has been technologically challenging. Here, we adapt an enzyme-mediated methylation sequencing method for comprehensive analysis of genome-wide cfDNA methylation, fragmentation, and copy number alteration (CNA) characteristics for enhanced cancer detection. We apply this method to plasma samples of 497 healthy controls and 780 patients of seven cancer types and develop an ensemble classifier by incorporating methylation, fragmentation, and CNA features. In the test cohort, our approach achieves an area under the curve value of 0.966 for overall cancer detection. Detection sensitivity for early-stage patients achieves 73% at 99% specificity. Finally, we demonstrate the feasibility to accurately localize the origin of cancer signals with combined methylation and fragmentation profiling of tissue-specific accessible chromatin regions. Overall, this proof-of-concept study provides a technical platform to utilize multimodal cfDNA features for improved cancer detection.

Peripheral blood inflammatory biomarkers dynamics reflect treatment response and predict prognosis in non-small cell lung cancer patients with neoadjuvant immunotherapy.

Neoadjuvant immunotherapy has significantly changed the therapeutic approach for treating patients with surgically resectable non-small cell lung cancer (NSCLC). Here, peripheral blood inflammation-based biomarkers as well as previously less focused eosinophil fraction, modified Glasgow prognostic score (mGPS), and prognostic nutritional index (PNI) were systematically included to comprehensively analyze their potential in predicting neoadjuvant immunotherapy efficacy and prognosis. We enrolled 189 patients (94 in training and 95 in validation cohorts) with stage I-III B surgically resectable NSCLC treated with neoadjuvant immunotherapy from the National Cancer Center of China. Baseline and post-treatment eosinophils fraction, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), monocyte-to-lymphocyte ratio (MLR), PNI, mGPS, and their changes were calculated and analyzed for correlation with neoadjuvant immunotherapy efficacy and prognosis. In patients in the major pathological response (MPR) group, the post-treatment eosinophil fraction was significantly high, and NLR, PLR, SII, and MLR were significantly lower compared to the non-MPR group in both the training and validation cohorts. The receiver operating characteristic curve showed that post-treatment, eosinophil fraction and SII and their changing were two of the most important factors. Univariate and multivariate logistic regression analyses showed that post-treatment eosinophil fraction, SII, mGPS, and ΔSII could independently predict MPR in patients treated with neoadjuvant immunotherapy. Survival analysis showed a significant correlation between high post-treatment NLR, PLR, SII, mGPS, and their changes in ΔNLR and ΔSII elevation with poor overall survival and event-free survival of patients. Our results suggest that inflammatory biomarkers could predict the patient's response to neoadjuvant immunotherapy and prognosis.

Plasma extracellular vesicle long RNAs predict response to neoadjuvant immunotherapy and survival in patients with non-small cell lung cancer.

Neoadjuvant immunotherapy has brought new hope for patients with non-small cell lung cancer (NSCLC). However, limited by the lack of clinically feasible markers, it is still difficult to select NSCLC patients who respond well and to predict patients' clinical outcomes before the treatment. Before the treatment, we isolated plasma extracellular vesicles (EVs) from three cohorts (discovery, training and validation) of 78 NSCLC patients treated with neoadjuvant immunotherapy. To identify differentially-expressed EV long RNAs (exLRs), we employed RNA-seq in the discovery cohort. And we subsequently used qRT-PCR to establish and validate the predictive signature in the other two cohorts. We have identified 8 candidate exLRs from 27 top-ranked exLRs differentially expressed between responders and non-responders, and tested their expression with qRT-PCR in the training cohort. We finally identified H3C2 (P = 0.029), MALAT1 (P = 0.043) and RPS3 (P = 0.0086) significantly expressed in responders for establishing the predictive signature. Integrated with PD-L1 expression, our signature performed well in predicting immunotherapeutic responses in the training (AUC=0.892) and validation cohorts (AUC=0.747). Furthermore, our signature was proven to be a predictor for favorable prognosis of patients treated with neoadjuvant immunotherapy, which demonstrates the feasibility of our signature in clinical practices (P = 0.048). Our results demonstrate that the exLR-based signature could accurately predict responses to neoadjuvant immunotherapy and prognosis in NSCLC patients.

Utility of 18F-FDG uptake in predicting major pathological response to neoadjuvant immunotherapy in patients with resectable non­small cell lung cancer.

PURPOSE: The aim of present study was to investigate the efficiency of 18F-FDG uptake in predicting major pathological response (MPR) in resectable non-small cell lung cancer (NSCLC) patients with neoadjuvant immunotherapy. METHODS: A total of 104 patients with stage I-IIIB NSCLC were retrospectively derived from National Cancer Center of China, of which 36 cases received immune checkpoint inhibitors (ICIs) monotherapy (I-M) and 68 cases with ICI combination therapy (I-C). 18F-FDG PET-CT scans were performed at baseline and after neoadjuvant therapy (NAT). Receiver-operating characteristic (ROC) curve analyses were conducted and area under ROC curve (AUC) was calculated for biomarkers including maximum standardized uptake value (SUVmax), inflammatory biomarkers, tumor mutation burden (TMB), PD-L1 tumor proportion score (TPS) and iRECIST. RESULTS: Fifty-four resected NSCLC tumors achieved MPR (51.9%, 54/104). In both neoadjuvant I-M and I-C cohorts, post-NAT SUVmax and the percentage changes of SUVmax (ΔSUVmax%) were significantly lower in the patients with MPR versus non-MPR (p < 0.01), and were also negatively correlated with the degree of pathological regression (p < 0.01). The AUC of ΔSUVmax% for predicting MPR was respectively 1.00 (95% CI: 1.00-1.00) in neoadjuvant I-M cohort and 0.94 (95% CI: 0.86-1.00) in I-C cohort. Baseline SUVmax had a statistical prediction value for MPR only in I-M cohort, with an AUC up to 0.76 at the threshold of 17.0. ΔSUVmax% showed an obvious advantage in MPR prediction over inflammatory biomarkers, TMB, PD-L1 TPS and iRECIST. CONCLUSION: 18F-FDG uptake can predict MPR in NSCLC patients with neoadjuvant immunotherapy.

Single-cell RNA-sequencing data reveals the genetic source of extracellular vesicles in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is invasive cancer and the complex mechanisms underlying carcinogenesis remain unclear. Extracellular vesicles (EVs), secreted by most cell types, serve as a critical factor in tumorigenesis via intercellular communications. Our study aims to investigate the cellular origin of EVs in ESCC, and unveil the unknown molecular and cellular mechanisms underlying cell-cell communications. Six ESCC patients were enrolled and single-cell RNA sequencing (scRNA-seq) analyses were conducted to screen different cell subpopulations. The genetic origin of EVs was tracked using the supernatant from different cellular extracts. Nanoparticle tracking analysis (NTA), western blot analysis, and transmission electron microscopy (TEM) were performed for validation. Using scRNA-seq analysis, eleven cell subpopulations were identified in ESCC. Differences in gene expression in EVs between malignant and non-malignant esophageal tissues were found. Our findings demonstrated that epithelial cells releasing EVs were the most prevalent in malignant tissues, while endothelial cells and fibroblasts releasing EVs were predominant in non-malignant tissues. Furthermore, the high levels of gene expression in EVs released from these cells were correlated significantly with a worse prognosis. Our findings revealed the genetic origin of EVs in malignant and non-malignant esophageal tissues and provided a comprehensive overview of the associated cell-cell interactions in ESCC.

Comparing a PD-L1 inhibitor plus chemotherapy to chemotherapy alone in neoadjuvant therapy for locally advanced ESCC: a randomized Phase II clinical trial : A randomized clinical trial of neoadjuvant therapy for ESCC.

BACKGROUND: A Phase II study was undertaken to evaluate the safety and efficacy of the neoadjuvant socazolimab, a novel PD-L1 inhibitor, in combination with nab-paclitaxel and cisplatin for locally advanced esophageal squamous cell carcinoma (ESCC). METHODS: Sixty-four patients were randomly divided between the Socazolimab + nab-paclitaxel + cisplatin (TP) arm (n = 32) and the control arm (n = 32), receiving either socazolimab (5 mg/kg intravenously (IV), day 1) or a placebo with nab-paclitaxel (125 mg/m2 IV, day 1/8) and cisplatin (75 mg/m2 IV, day 1) repeated every 21 days for four cycles before surgery. The primary endpoint was major pathological response (MPR), and the secondary endpoints were pathological complete response (pCR), R0 resection rate, event-free survival (EFS), overall survival (OS), and safety. RESULTS: A total of 29 (90.6%) patients in each arm underwent surgery, and 29 (100%) and 28 (98.6%) patients underwent R0 resection in the Socazolimab + TP and Placebo + TP arms, respectively. The MPR rates were 69.0 and 62.1% (95% Confidence Interval (CI): 49.1-84.0% vs. 42.4-78.7%, P = 0.509), and the pCR rates were 41.4 and 27.6% (95% CI: 24.1-60.9% vs. 13.5-47.5%, P = 0.311) in the Socazolimab + TP and Placebo + TP arms, respectively. Significantly higher incidence rates of ypT0 (37.9% vs. 3.5%; P = 0.001) and T downstaging were observed in the Socazolimab + TP arm than in the Placebo + TP arm. The EFS and OS outcomes were not mature. CONCLUSIONS: The neoadjuvant socazolimab combined with chemotherapy demonstrated promising MPR and pCR rates and significant T downstaging in locally advanced ESCC without increasing surgical complication rates. TRIAL REGISTRATION: Registration name (on clinicaltrials.gov): A Study of Anti-PD-L1 Antibody in Neoadjuvant Chemotherapy of Esophageal Squamous Cell Carcinoma. REGISTRATION NUMBER: NCT04460066.

Association between radiotherapy and risk of second primary malignancies in patients with resectable lung cancer: a population-based study.

BACKGROUND: The most common form of treatment for non-metastatic lung cancer is surgery-based combination therapy, which may also include adjuvant radiotherapy or chemotherapy. Second primary malignancies (SPMs) are uncommon but significant radiation side effects in patients with resectable lung cancer, and SPMs have not been adequately investigated. Our study aims to assess the correlations of radiotherapy with the development of SPMs in patients with resectable lung cancer. METHODS: We screened for any primary malignancy that occurred more than five years after the diagnosis of resectable lung cancer. Based on the large cohort of the Surveillance, Epidemiology and End Results database, radiotherapy-correlated risks were estimated using the Poisson regression analysis and the cumulative incidence of SPMs was calculated using Fine-Gray competing risk regression analysis. RESULTS: Among the 62,435 patients with non-metastatic lung cancer undergoing surgery, a total of 11,341 (18.16%) patients have received radiotherapy. Our findings indicated that radiotherapy was substantially related to a high risk of main second solid malignancies (RR = 1.21; 95%CI, 1.08 to 1.35) and a negligible risk of main second hematologic malignancies (RR = 1.08; 95%CI, 0.84 to 1.37). With the greatest number of patients, the risk of acquiring a second primary gastrointestinal cancer was the highest overall (RR = 1.77; 95 percent CI, 1.44 to 2.15). The cumulative incidence and standardized incidence ratios of SPMs revealed similar findings. Furthermore, the young and the elderly may be more vulnerable, and the highest risk of acquiring most SPMs was seen more than ten years after lung cancer diagnosis. Additionally, more attention should be paid to the second primary gastrointestinal cancer in young individuals with resectable lung cancer. CONCLUSION: After receiving radiotherapy, an increased risk of developing second primary solid and gastrointestinal cancers was observed for patients with resectable lung cancer. The prevention of SPMs associated with radiotherapy requires further attention.

Global burden and temporal trends in incidence and mortality of oesophageal cancer.

INTRODUCTION: Oesophageal cancer is a prevalent and deadly cancer around the world. OBJECTIVES: We aimed to present a comprehensive analysis of the global geographic patterns and temporal trends in the mortality and incidence of oesophageal cancer. METHODS: The mortality and incidence data of oesophageal cancer in 2020 were obtained from the GLOBOCAN database. Based on World Health Organization (WHO) mortality database and the Cancer Incidence in Five Continents (CI5), we also retrieved the mortality and incidence age-standardized rates (ASRs) of oesophageal cancer. The average annual percentage changes (AAPCs) of mortality and incidence were calculated using the joinpoint regression analysis. RESULTS: Globally, 0.54 million deaths and 0.6 million new cases were identified in 2020. In the majority of countries of South America and Asia, the mortality and incidence trends have substantially decreased, but trends in European countries have varied. The prevalence in European nations varied, but the incidence in most other continents decreased dramatically. In terms of mortality, the global average rate was 5.6 per 100000, ranging from 16.7 (Malawi) to 0.28 (Belize). European countries varied in mortality, such as Norway (AAPC, male: 0.68; female: 0.89) and Ireland (AAPC, male: -0.96; female: -1.52). Most non-European countries saw large decreases in mortality, such as Singapore (AAPC, male: -4.78; female: -6.89). The elderly had more noticeable trends in mortality and incidence in most countries. CONCLUSIONS: We have identified different trends in mortality and incidence among European countries, whereas declining trends were identified in most non-European countries. However, increasing trends were identified in specific subgroups of some countries, such as men in Thailand. For populations with rising mortality and incidence trends, more preventative efforts are required.

Comprehensive analysis of the immunological implication and prognostic value of CXCR4 in non-small cell lung cancer.

CXCR4 (C-X-C chemokine receptor type 4) is the most commonly expressed of all chemokine receptors in malignant tumors. However, studies on CXCR4 in non-small cell lung cancer (NSCLC) tumor immune microenvironment, including those determining its immune efficacy and prognostic potential, are still scarce. Therefore, in this study, we determined the ability of CXCR4 to predict immunotherapy response and prognosis in NSCLC using immunohistochemical staining and RT-PCR, respectively, in two independent cohorts from the National Cancer Center of China. We analyzed transcriptome sequencing data and clinical information from multiple public databases to assess immune cell infiltration in NSCLC and constructed immune risk prognostic signatures based on CXCR4-related immunomodulators. We found that immune cell infiltration is significant differences in NSCLC tissues and is moderately correlated with CXCR4 expression. High CXCR4 expression was significantly associated with poor prognosis in NSCLC patients and a higher response rate to immunotherapy. The ROC curve showed that CXCR4 expression exhibited excellent performance in predicting the efficacy of immunotherapy in NSCLC. We identified 30 CXCR4-related immunomodulators in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and constructed immune prognostic signatures based on CXCR4-related immunomodulators and CXCR4-related mutant genes. The signature-based prognostic risk score showed good performance in predicting patient prognosis in both LUAD and LUSC; high risk scores were significantly associated with poor prognosis (P < 0.0001) and was established as an independent prognostic factor by multivariate Cox regression. We postulate that CXCR4 is a potential predictive marker of immunotherapy efficacy in NSCLC and should be used in clinical settings. Moreover, the constructed signatures may be valuable in predicting patient prognosis in NSCLC.

Immunogenicity and Immunoprotection of PCV2 Virus-like Particles Incorporating Dominant T and B Cell Antigenic Epitopes Paired with CD154 Molecules in Piglets and Mice.

Porcine circovirus type 2 (PCV2) is capable of causing porcine circovirus-associated disease (PCVAD) and is one of the major threats to the global pig industry. The nucleocapsid protein Cap encoded by the PCV2 ORF2 gene is an ideal antigen for the development of PCV2 subunit vaccines, and its N-terminal nuclear localization sequence (NLS) structural domain is essential for the formation of self-assembling VLPs. In the present study, we systematically expressed and characterized full-length PCV2 Cap proteins fused to dominant T and B cell antigenic epitopes and porcine-derived CD154 molecules using baculovirus and found that the Cap proteins fusing epitopes were still capable of forming virus-like particles (VLPs). Both piglet and mice experiments showed that the Cap proteins fusing epitopes or paired with the molecular adjuvant CD154 were able to induce higher levels of humoral and cellular responses, particularly the secretion of PCV2-specific IFN-γ and IL-4. In addition, vaccination significantly reduced clinical signs and the viral load of PCV2 in the blood and tissues of challenged piglets. The results of the study provide new ideas for the development of a more efficient, safe and broad-spectrum next-generation PCV2 subunit vaccine.

RNA modification writer expression profiles predict clinical outcomes and guide neoadjuvant immunotherapy in non-small cell lung cancer.

BACKGROUND: RNA modifications, including adenosine-to-inosine RNA editing, alternative polyadenylation, m1A and m6A, play a significant role in tumorigenesis and tumor immunity. However, the functions of RNA modification enzymes (writers) in immunotherapy and tumor microenvironment (TME) remain unknown. METHODS: Nonnegative matrix factorization clustering was applied to identify RNA modification clusters in lung adenocarcinoma, one of the most prevalent subtypes of non-small cell lung cancer (NSCLC). CIBERSORT and ESTIMATE algorithms were performed to depict TME characteristics. Additionally, a scoring system called Writer-Score was established to quantify RNA modification patterns and subsequently predict clinical outcomes. We subsequently used RNA sequencing, targeted DNA sequencing and multiplex immunofluorescence to further evaluate the efficacy of Writer-Score in NSCLC patients receiving neoadjuvant immunotherapy. FINDINGS: We identified three distinct RNA modification clusters and two DEGclusters, which were shown to be strongly associated with a variety of TME features and biological processes. Additionally, the Writer-Score served as an important factor in post-transcriptional events and immunotherapy. The Writer-Score was capable of properly predicting the prognosis of NSCLC patients receiving neoadjuvant PD-1 inhibitor therapy. INTERPRETATION: Our work systematically analyzed four types of RNA modifications and constructed a scoring system to guide neoadjuvant immunotherapy in NSCLC, which highlighted the writers' roles in post-transcriptional events, TME and neoadjuvant immunotherapy. FUNDING: A full list of funding bodies that supported this study can be found in the Acknowledgements section.

A new N descriptor for non-small cell lung cancer: the classification based on anatomic location, number and ratio of metastatic lymph nodes.

Background: The current N classification, which is determined by the anatomical location of positive lymph nodes, does not effectively stratify N1 and N2 non-small cell lung cancer (NSCLC) patients into prognostically significant subgroups. Methods: We acquired the clinical data of 3,234 N1 and N2 NSCLC patients from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015). We eliminated patients undergoing chemotherapy or radiation because chemotherapy and radiotherapy might lower lymph node stage, and the SEER database does not distinguish between therapy administered before and after surgery. We developed the N-new classification based on the former N stage, the number and ratio of lymph nodes. Patients were finally classified into four categories (N1a, N1b, N2a, N2b). Then, the N-new classification was validated in subgroups based on a variety of clinical characteristics, such as tumor size. The multivariable Cox regression analysis, the decision curve analysis (DCA) and the time-dependent receiver operating characteristic (ROC) analysis were conducted to compare the performance of the N-new classification and the current N classification. Results: The cancer-specific survival (CSS) and overall survival were significantly different among each pair of N-new classification. And the same results were shown in the majority of the subgroups determined by various clinical characteristics. Compared with the current N classification (C-index, 0.639), the N-new classification (C-index, 0.652) performed better in classifying N1 and N2 NSCLC patients into subgroups with distinctive clinical outcomes. The 5-year CSS rates were 49.7%, 41.4%, 30.4% and 20.4% for N1a, N1b, N2a and N2b, respectively. Conclusions: When compared to the current N classification, the N-new classification could be a more reliable and accurate prognostic determinant, which is worth considering in the revision of the 9th edition of the tumor, node, metastasis (TNM) staging system.

Integrating microarray-based spatial transcriptomics and single-cell RNA-sequencing reveals tissue architecture in esophageal squamous cell carcinoma.

BACKGROUND: The tumor microenvironment (TME) serves as an important factor in tumorigenesis and metastasis. Although distinct cell subsets can be identified via single-cell RNA sequencing (scRNA-seq), the spatial composition of cells within the TME is difficult to characterise. METHODS: Tissue samples were collected from three patients with esophageal squamous cell carcinoma (ESCC), and scRNA-seq was performed to identify distinct cell subsets. In addition, a microarray-based spatial transcriptomics (ST) method was used to characterise the spatial landscape of expression data via an array of spots. Using multimodal intersection analysis (MIA) to integrate scRNA-seq and ST, the exact cellular components of the tumor and stromal regions were annotated. FINDINGS: The subpopulations of seven stromal cells were identified within the TME of ESCC, and the architecture of scRNA-seq-determined subsets was mapped in cancer and stromal regions. The distribution of various stromal cells and their subpopulations was heterogeneous. Compared with immune cells, non-immune stromal cells were significantly enriched in the TME. Most subsets of epithelial cells were enriched in the cancer regions, whereas inflammatory cancer-associated fibroblasts were correlated with the stromal regions. Furthermore, TME features were different between metastatic and non-metastatic samples and between the primary and metastatic sites of the metastatic sample. INTERPRETATION: This study revealed the spatial landscape of various cell subsets within the TME and the potential cross-talk among diverse cells, which provides novel insights into cancer intervention. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Three-Year Follow-Up of Neoadjuvant Programmed Cell Death Protein-1 Inhibitor (Sintilimab) in NSCLC.

INTRODUCTION: Programmed cell death protein-1 (PD-1) inhibitors have been proved to be feasible and to have efficacy in multiple cancers, including NSCLC. But few studies have evaluated the effectiveness of PD-1 inhibitor as neoadjuvant therapy with a long-term follow-up. Here, in this phase 1b study with a 3-year follow-up, we reported the clinical outcomes of patients who received the PD-1 inhibitor as neoadjuvant therapy. METHODS: Two doses of sintilimab (intravenously, 200 mg) were used for patients with stages IA to IIIB NSCLC (registration number: ChiCTR-OIC-17013726). Then, surgery was performed within 29 to 43 days after the first dose. All patients underwent positron emission tomography-computed tomography at enrolment and before surgery to evaluate tumor metabolism after administration of PD-1 inhibitor. We also evaluated the expression of programmed death-ligand 1 (PD-L1) as an exploratory analysis in 32 eligible patients. Safety was the primary end point. Overall survival (OS), disease-free survival (DFS), event-free survival, and major pathologic response were the key secondary end points. RESULTS: With the mean follow-up of 37.8 months, 3-year OS rate was 88.5% and the 3-year DFS rate was 75.0% among patients who underwent R0 resection. In patients with positive PD-L1 expression, 3-year OS and DFS rates were 95.5% and 81.8%, respectively. Eight patients had recurrent tumors, including local recurrence, lung metastasis, brain metastasis, and bone metastasis. Patients with PD-L1 greater than or equal to 1% had more favorable clinical outcomes than the other subgroup (hazard ratio = 0.275, 95% confidence interval: 0.078-0.976). No more new adverse events have occurred in the 3-year follow-up because we first reported them in the former publication. CONCLUSIONS: This is the first study to report the long-term survival probability of patients with NSCLC receiving PD-1 inhibitors as the neoadjuvant treatment. The 3-year follow-up results revealed that patients with positive PD-L1 expression and high tumor mutation burden have favorable clinical outcomes.