Project description:Immunotherapy has improved the prognosis of patients with advanced non-small cell lung
cancer (NSCLC), but only a small subset of patients achieved clinical benefit. The purpose of our study was to integrate multidimensional data using a machine learning method to predict the therapeutic efficacy of immune checkpoint inhibitors (ICIs) monotherapy in patients with advanced NSCLC.The authors retrospectively enrolled 112 patients with stage IIIB-IV NSCLC receiving ICIs monotherapy. The random forest (RF) algorithm was used to establish efficacy prediction models based on five different input datasets, including precontrast computed tomography (CT) radiomic data, postcontrast CT radiomic data, combination of the two CT radiomic data, clinical data, and a combination of radiomic and clinical data. The 5-fold cross-validation was used to train and test the random forest classifier. The performance of the models was assessed according to the area under the curve (AUC) in the receiver operating characteristic (ROC) curve. Among these models(RF MLP LR XGBoost), our reproduced onnx models have better performance, especially for random forest. The response variable with a value (1/0) indicates the (efficacy/inefficacy) of PD-1/PD-L1 monotherapy in patients with advanced NSCLC
Project description:Anti-PD(L)1 with chemotherapy is a standard of care for non-small cell lung cancer (NSCLC), but a varying degree of response to the same regimen is observed in patients. The tumor immune microenvironment (TIME) plays a key role in response to immunotherapy, and the TIME heterogeneity in association with therapeutic outcome is incompletely understood. Here we prospectively applied single-cell RNA and TCR sequencing to characterize post-neoadjuvant chemo-immunotherapy treatment tumor samples of 234 NSCLC patients. Our study provides a fine-grained dissection of the TIME heterogeneity underlying response to chemo-immunotherapy in NSCLC, thus representing a valuable resource for improved management of NSCLC.
Project description:Ongoing immunomodulatory strategies in tumors characterized by an overall hot immune phenotype may improve prognosis of patients with non-small cell lung cancer (NSCLC). Our objective was to develop a reliable and stable scoring system for the identification of immunologically hot NSCLC and to evaluate its association with response to immunotherapies. A Hot Oral Tumor (HOT) score was developed using data from The Cancer Genome Atlas. HOT score was computed in 82 patients with NSCLC treated with second-line immunotherapy targeting PD-1/PD-L1. High HOT score was associated with a statistically significant improved clinical outcome.
Project description:Improper use of antibiotics in swine could reduce commensal bacteria and possibly increase pathogen infections via the gut resistome. This study aimed to compare the metaproteomic profiles of gut resistome and related metabolism in the cecal microbiota of fattening pigs raised under antibiotic-free (ABF) conditions with those of ordinary industrial pigs (CTRL).
Project description:Immunotherapy in immunologically active tumors may improve prognosis of non-small cell lung carcinomas (NSCLC). Our objective was to develop a reliable and stable scoring system for the identification of immunologically active HNSCC and to evaluate its association with response to immunotherapies. A Hot Oral Tumor (HOT) score was developed using data from The Cancer Genome Atlas in human-papillomavirus negative head and neck squamous cell carcinomas; it was computed in 92 patients with early stage NSCLC treated with surgery to evaluate the prognostic impact of the HOT score.
Project description:Lung cancer is the second most commonly diagnosed cancer and the leading cause of cancer death worldwide, of which approximately 85% are non-small cell lung cancer (NSCLC). The overall survival (OS) of patients with advanced NSCLC was significantly prolonged with immune checkpoint inhibitors (ICIs) targeting the programmed cell death-1 (PD-1) and programmed death-ligand 1 (PD-L1) axis. For early-stage lung cancer, the 5-year survival rate for patients ranges from 80% in stage IA to 41% in stage IIIA, and many cases relapse after surgical resection. Currently, multiple clinical trials have manifested the encouraging efficacy of neoadjuvant immunotherapy in stage I-IIIA resectable NSCLC. However, the effect of immunotherapy in ultra early-stage NSCLC patients with micro-invasive or even pre-invasive lesions remains unclear. In this study, we aimed to evaluate the activity and safety of sintilimab on high-risk ground glass opacity lesions in multiple primary lung cancer patients.
Project description:Immunotherapy resistance in non-small cell lung cancer (NSCLC) may be mediated by an immunosuppressive microenvironment, which can be shaped by the mutational landscape of the tumor. Here, we observed genetic alterations in the PTEN/PI3K/AKT/mTOR pathway and/or loss of PTEN expression in >25% NSCLC patients, with higher frequency in lung squamous carcinomas (LUSCs). Patients with PTEN-low tumors had higher levels of PD-L1 and PD-L2 and showed worse progression-free survival when treated with immunotherapy. Development of a Ptennull LUSC mouse model revealed that tumors with PTEN loss were refractory to antiPD-1, highly metastatic and fibrotic, and secreted TGF-β/CXCL10 to promote conversion of CD4+ lymphocytes into regulatory T cells (Tregs). Human and mouse PTEN-low tumors were enriched in Tregs and expressed higher levels of immunosuppressive genes. Importantly, treatment of mice bearing Pten-null tumors with TLR agonists and anti-TGF-β antibody aimed to alter this immunosuppressive microenvironment led to tumor rejection and immunological memory in 100% of mice. These results demonstrate that lack of PTEN causes immunotherapy resistance in LUSC by establishing an immunosuppressive tumor microenvironment that can be reversed therapeutically.
