Minimal residual disease profiling predicts pathological complete response in esophageal squamous cell carcinoma.

Accurate presurgical prediction of pathological complete response (pCR) can guide treatment decisions, potentially avoiding unnecessary surgeries and improving the quality of life for cancer patients. We developed a minimal residual disease (MRD) profiling approach with enhanced sensitivity and specificity for detecting minimal tumor DNA from cell-free DNA (cfDNA). The approach was validated in two independent esophageal squamous cell carcinoma (ESCC) cohorts. In a cohort undergoing neoadjuvant, surgical, and adjuvant therapy (NAT cohort), presurgical MRD status precisely predicted pCR. All MRD-negative cases (10/10) were confirmed as pCR by pathological evaluation on the resected tissues. In contrast, MRD-positive cases included all the 27 non-pCR cases and only one pCR case (10/10 vs 1/28, P < 0.0001, Fisher's exact test). In a definitive radiotherapy cohort (dRT cohort), post-dRT MRD status was closely correlated with patient prognosis. All MRD-negative patients (25/25) remained progression-free during the follow-up period, while 23 of the 26 MRD-positive patients experienced disease progression (25/25 vs 3/26, P < 0.0001, Fisher's exact test; progression-free survival, P < 0.0001, log-rank test). The MRD profiling approach effectively predicted the ESCC patients who would achieve pCR with surgery and those likely to remain progression-free without surgery. This suggests that the cancer cells in these MRD-negative patients have been effectively eliminated and they could be suitable candidates for a watch-and-wait strategy, potentially avoiding unnecessary surgery.

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.

Surgery challenges and postoperative complications of lung cancer after neoadjuvant immunotherapy.

BACKGROUND: In China, real-world data on surgical challenges and postoperative complications after neoadjuvant immunotherapy of lung cancer are limited. METHODS: Patients were retrospectively enrolled from January 2018 to January 2023, and their clinical and pathological characters were subsequently analyzed. Surgical difficulty was categorized into a binary classification according to surgical duration: challenging or routine. Postoperative complications were graded using Clavien-Dindo grades. Logistic regression was used to identify risk factors affecting the duration of surgery and postoperative complications greater than Clavien-Dindo grade 2. RESULTS: In total, 261 patients were included. Of these, stage III patients accounted for 62.5% (163/261) at initial diagnosis, with 25.3% (66/261) at stage IIIB. Central-type non-small-cell lung cancer accounted for 61.7% (161/261). One hundred and forty patients underwent video-assisted thoracoscopic surgery and lobectomy accounted for 53.3% (139/261) of patients. Surgical time over average duration was defined as challenging surgeries, accounting for 43.7%. The postoperative complications rate of 261 patients was only 22.2%. Smoking history (odds ratio [OR] = 9.96, 95% [CI] 1.15-86.01, p = 0.03), chemoimmunotherapy (OR = 2.89, 95% CI 1.22-6.86, p = 0.02), and conversion to open surgery (OR = 11.3, 95% CI 1.38-92.9, p = 0.02) were identified as independent risk factors for challenging surgeries, while pneumonectomy (OR = 0.36, 95% CI 0.15-0.86, p= 0.02) was a protective factor. Meanwhile, pneumonectomy (OR = 7.51, 95% CI 2.40-23.51, p < 0.01) and challenging surgeries (OR = 5.53, 95% CI 1.50-20.62, p = 0.01) were found to be risk factors for postoperative complications greater than Clavien-Dindo grade 2. CONCLUSIONS: Compared to immunotherapy alone or in combination with apatinib, neoadjuvant chemoimmunotherapy could increase the difficulty of surgery while the incidence of postoperative complications remained acceptable. The conversion to open surgery and pneumonectomy after neoadjuvant immunotherapy should be reduced.

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.

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.

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.

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.

Multiple primary lung cancer: Updates of clinical management and genomic features.

In recent decades, multiple primary lung cancer (MPLC) has been increasingly prevalent in clinical practice. However, many details about MPLC have not been completely settled, such as understanding the driving force, clinical management, pathological mechanisms, and genomic architectures of this disease. From the perspective of diagnosis and treatment, distinguishing MPLC from lung cancer intrapulmonary metastasis (IPM) has been a clinical hotpot for years. Besides, compared to patients with single lung lesion, the treatment for MPLC patients is more individualized, and non-operative therapies, such as ablation and stereotactic ablative radiotherapy (SABR), are prevailing. The emergence of next-generation sequencing has fueled a wave of research about the molecular features of MPLC and advanced the NCCN guidelines. In this review, we generalized the latest updates on MPLC from definition, etiology and epidemiology, clinical management, and genomic updates. We summarized the different perspectives and aimed to offer novel insights into the management of MPLC.

Treatments and whole exon sequencing of a case with multiple primary lung cancer.

INTRODUCTION: The number of patients with synchronous multiple primary lung cancer (sMPLC) has increased recently. However, diagnosing and selecting the appropriate therapeutic strategy for this type of disease is not simple. CASE PRESENTATION: This report presented a case of sMPLC with lymph node metastasis. With no smoking and cancer history, this patient had seven nodules in the right lung and underwent single-portal video-assisted thoracoscopic surgery (VATS). In addition, she received four cycles of chemotherapy after the operation. Whole exon sequencing (WES) was performed in five resected tissue samples (four tumors and one lymph node). We conducted genomic profiling and clone evolution analysis of the five samples. Gene detection helped to confirm that the metastasis lymph node was transferred from one nodule. There was apparent heterogeneity of gene mutations among the five samples of the patient, with only one shared "neurofilament heavy polypeptide" (NEFH) mutation. A dominant substitution of C > T/G > A was found in all the samples. Pyclone model was used to calculate all tissues' cellular prevalence (CP) values, and NEFH mutations were thought to be the ancestral clones. During the follow-up period, residual lesions showed no apparent changes and limited response to chemotherapy. CONCLUSIONS: This report showed an essential role in genomic detection and selecting the appropriate treatment of sMPLC. Surgery remains the primary treatment strategy for this type of disease, and the occurrence and development of sMPLC need more in-depth research.

Genetic trajectory and clonal evolution of multiple primary lung cancer with lymph node metastasis.

Multiple primary lung cancer (MPLC) with lymph node metastasis (LNM) is a rare phenomenon of multifocal lung cancer. The genomic landscapes of MPLC and the clonal evolution pattern between primary lung lesions and lymph node metastasis haven't been fully illustrated. We performed whole-exome sequencing (WES) on 52 FFPE (Formalin-fixed Paraffin-Embedded) samples from 11 patients diagnosed with MPLC with LNM. Genomic profiling and phylogenetic analysis were conducted to infer the evolutional trajectory within each patient. The top 5 most frequently mutated genes in our study were TTN (76.74%), MUC16 (62.79%), MUC19 (55.81%), FRG1 (46.51%), and NBPF20 (46.51%). For most patients in our study, a substantial of genetic alterations were mutually exclusive among the multiple pulmonary tumors of the same patient, suggesting their heterogenous origins. Individually, the genetic profile of lymph node metastatic lesions overlapped with that of multiple lung cancers in different degrees but are more genetically related to specific pulmonary lesions. SETD2 was a potential metastasis biomarker of MPLC. The mean putative neo-antigen number of the primary tumor (646.5) is higher than that of lymph node metastases (300, p = 0.2416). Primary lung tumors and lymph node metastases are highly heterogenous in immune repertoires. Our findings portrayed the comprehensive genomic landscape of MPLC with LNM. We characterized the genomic heterogeneity among different tumors. We offered novel clues to the clonal evolution between MPLC and their lymphatic metastases, thus advancing the treatment strategies and preventions of MPLC with LNM.

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.

Glycolysis in tumor microenvironment as a target to improve cancer immunotherapy.

Cancer cells and immune cells all undergo remarkably metabolic reprogramming during the oncogenesis and tumor immunogenic killing processes. The increased dependency on glycolysis is the most typical trait, profoundly involved in the tumor immune microenvironment and cancer immunity regulation. However, how to best utilize glycolytic targets to boost anti-tumor immunity and improve immunotherapies are not fully illustrated. In this review, we describe the glycolytic remodeling of various immune cells within the tumor microenvironment (TME) and the deleterious effects of limited nutrients and acidification derived from enhanced tumor glycolysis on immunological anti-tumor capacity. Moreover, we elucidate the underlying regulatory mechanisms of glycolytic reprogramming, including the crosstalk between metabolic pathways and immune checkpoint signaling. Importantly, we summarize the potential glycolysis-related targets that are expected to improve immunotherapy benefits. Our understanding of metabolic effects on anti-tumor immunity will be instrumental for future therapeutic regimen development.

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.

PVR/TIGIT and PD-L1/PD-1 expression predicts survival and enlightens combined immunotherapy in lung squamous cell carcinoma.

PVR/TIGIT and PD-L1/PD-1 axes play essential roles in tumor immune evasion and could be potential targets for combined immunotherapy. We aimed to evaluate the expression status of the above-mentioned immune markers in lung squamous cell carcinoma (LUSC), and investigate their survival impact and relevance with the immune microenvironment and clinicopathological features. We retrospectively collected specimens from 190 LUSC patients, who underwent pulmonary surgeries, and we performed immunohistochemistry assays of PVR, TIGIT, PD-L1, PD-1 and CD8. In our cohort, the positive rate of PVR was 85.8%, which was much higher than the positive rate of PD-L1 at 26.8%. A total of 32 (16.8%) patients demonstrated co-expression of PVR/PD-L1. High TIGIT density was correlated with positive PD-L1 expression, high PD-1 density, and high CD8 density (PD-L1, P=0.033; PD-1, P<0.001; CD8, P<0.001), and positive PVR expression was correlated with positive PD-L1 expression (P=0.046). High TIGIT density and high PVR/TIGIT expression were correlated with advanced TNM stage (TIGIT density, P=0.020; PVR/TIGIT expression, P=0.041). Patients with positive PVR expression, high TIGIT density, high PVR/TIGIT expression and PVR/PD-L1 co-expression exhibited a significantly worse prognosis (PVR, P=0.038; TIGIT, P=0.027; PVR/TIGIT, P=0.014; PVR/PD-L1, P=0.018). Multivariate analysis demonstrated that PVR/PD-L1 co-expression (Hazard ratio [HR], 1.756, 95% CI, 1.152-2.676, P=0.009) was an independent prognostic factor in LUSC patients. In conclusion, we demonstrated the expression status of PVR/TIGIT and PD-L1/PD-1 in LUSC. PVR/PD-L1 co-expression was an independent prognostic factor in LUSC patients and may serve as a potential predictive biomarker for dual-targeting immunotherapy.

TIGIT/CD47 dual high expression predicts prognosis and is associated with immunotherapy response in lung squamous cell carcinoma.

BACKGROUND: Recent studies indicated that T cell immunoreceptor with immunoglobulin and ITIM domains (TIGIT) and cluster of differentiation 47 (CD47) have emerged as new potential immunotherapy targets. However, the roles of TIGIT and CD47 in lung squamous cell carcinoma (LUSC) have not been fully illustrated. METHODS: The specimens and clinicopathological information from 190 LUSC patients who underwent surgeries in our center were retrospectively collected. Immunohistochemical staining for TIGIT and CD47 was conducted. Transcriptional and clinical data of 479 LUSC were downloaded from The Cancer Genome Atlas (TCGA). RESULTS: In the TCGA LUSC cohort, 142 (29.6%) cases were TIGIT/CD47 dual high expression at RNA level. The expression levels of TIGIT and CD47 were significantly correlated (p < 0.001). The proportions of patients with high TIGIT expression (p = 0.001) and high TIGIT/CD47 dual high expression (p = 0.049) were both higher in female cases. Advanced TNM stage (p = 0.006) and TIGIT/CD47 dual high expression (p = 0.047) were independent prognostic factors for LUSC. In the 190 LUSC cohort of our center, 75 (39.5%) cases were TIGIT/CD47 dual high expression at protein level. Cross-table analysis showed a correlation between TIGIT and CD47 expression. Older age (p = 0.001), advanced TNM stage (p < 0.001) and TIGIT/CD47 dual high expression (p = 0.046) were independent prognostic factors in our cohort. CONCLUSION: We found that TIGIT and CD47 dual high expression was associated with poor prognosis in LUSC. We speculated that patients with dual high expression of CD47/TIGIT might be suitable for new target immunotherapy in the future.

PSC subtyping based on TTF-1 and p40 expression reveals distinct molecular characteristics and therapeutic strategies.

Pulmonary sarcomatoid carcinoma (PSC) is a unique form of poorly differentiated nonsmall cell lung cancer (NSCLC) and is notorious for its highly malignant nature and dismal prognosis. To introduce effective treatment for PSC patients, precise subtyping of PSC is demanding. In our study, TTF-1 and P40 immunohistochemistry (IHC) staining were applied to 56 PSC patients with multiomics data. According to IHC results, we categorized these patients into three subgroups and profiled their molecular contexture using bioinformatic skills. IHC results classified these patients into three subgroups: TTF-1 positive subgroup (n = 27), P40 positive subgroup (n = 15) and double-negative subgroup (n = 14). Spindle cell samples accounted for 35.71% (5/14) of double-negative patients, higher than others (P = .034). The three subgroups were heterogeneous in the genomic alteration spectrum, showing significant differences in the RTK/RAS pathway (P = .004) and the cell cycle pathway (P = .030). The methylation profile of the double-negative subgroup was between the other two subgroups. In similarity analysis, the TTF-1 and p40 subgroups were closely related to LUAD and LUSC, respectively. The TTF-1 positive subgroup had the highest leukocyte fraction (LF) among several cancer types, and the tumor mutation burden (TMB) of the p40 positive subgroup ranked third in the TMB list, suggesting the applicability of immunotherapy for PSC. The study established a new subtyping method of PSC based on IHC results and reveals three subgroups with distinct molecular features, providing evidence for refined stratification in the treatment of PSC.

Identifying novel tumor-related antigens and immune phenotypes for developing mRNA vaccines in lung adenocarcinoma.

The mRNA vaccines have been a novel strategy of immunotherapies for multiple cancers. Although several types of mRNA vaccines have been investigated and validated in some studies, their efficacy among patients with lung adenocarcinoma (LUAD) remains largely unknown. The number of tumor-associated antigens is not enough and no study focuses on stratifying the subgroup of LUAD patients suitable for vaccination. Based on the expression profiles of immune-related genes, consensus clustering was performed to identify the most appropriate phenotype for vaccination. The immune landscape of LUAD was shown via the graph learning-based dimensionality reduction analysis. We screened for five mutated and upregulated LUAD-related antigens (CCNB1, KIAA0101, PBK, OIP5 and PLEK2) that were highly correlated with immune infiltrating cells and unfavorable clinical outcomes. And three distinct immune phenotypes were identified in the TCGA and GSE72094 cohorts. Group S1 was an immunological "hot" cluster and related to a better prognosis, whereas Group S2&S3 was an immunological "cold" cluster and associated with a poorer prognosis. At last, the results revealed heterogeneity of LUAD patients in the immune landscape. We identified five potential cancer-related antigens for mRNA vaccines, and Group S2&S3 were the most suitable phenotypes for vaccination.