Project description:NGS data of 12 patients enrolled in the Chinese Patient Assistance Program from multiple centers who received pemetrexed alone or combined with platinum as initial chemotherapy and continued pemetrexed maintenance therapy for advanced lung adenocarcinoma from November 2014 to June 2017.
Project description:Pemetrexed is a multitargeted antifolate, which primarily inhibits thymidylate synthase, dihydrofolate reductase, and glycinamide ribonucleotide formyltransferase in the folate-dependent metabolic process. Nowadays, pemetrexed is used to treat malignant pleural mesothelioma and non-squamous non-small cell lung cancer. Preclinical and clinical studies showed that pemetrexed had cytotoxic activity in many kinds of cancers including colorectal cancer. Erlotinib is a tyrosine-kinase inhibitor of EGFR, which was approved for the treatment of non-small cell lung cancer. Erlotinib also showed activity to colorectal cancer cells. Recently, Zhang et al. demonstrated synergistic cytotoxicity of pemetrexed and gefitinib in preclinical study.
In this multicenter, non randomized, open label phase II study, investigators aimed to evaluate the efficacy and safety of Pemetrexed and Erlotinib combination.
Project description:The tumor microenvironment strongly influences cancer development, progression and metastasis. The role of carcinoma-associated fibroblasts (CAFs) in these processes and their clinical impact has not been studied systematically in non-small cell lung carcinoma (NSCLC). We established primary cultures of CAFs and matched normal fibroblasts (NFs) from 15 resected NSCLC. We demonstrate that CAFs have greater ability than NFs to enhance the tumorigenicity of lung cancer cell lines. Microarray gene expression analysis of the 15 matched CAF and NF cell lines identified 46 differentially expressed genes, encoding for proteins that are significantly enriched for extracellular proteins regulated by the TGF-beta signaling pathway. We have identified a subset of 11 genes that formed a prognostic gene expression signature, which was validated in multiple independent NSCLC microarray datasets. Functional annotation using protein-protein interaction analyses of these and published cancer stroma-associated gene expression changes revealed prominent involvement of the focal adhesion and MAPK signalling pathways. Fourteen (30%) of the 46 genes also were differentially expressed in laser-capture micro-dissected corresponding primary tumor stroma compared to the matched normal lung. Six of these 14 genes could be induced by TGF-beta1 in NF. The results establish the prognostic impact of CAF-associated gene expression changes in NSCLC patients. This SuperSeries is composed of the following subset Series: GSE22862: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [expression profiling_CAFs] GSE22863: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [expression profiling_NSCLC stroma] GSE27284: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [methylation profiling] GSE27289: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [genome variation profiling]
Project description:Immune checkpoint inhibitors are increasingly used in combination with chemotherapy for treatment of non-small cell lung cancer, yet the success of combination therapies is relatively limited. Thus, more detailed insight regarding the tumour molecular markers that may affect the responsiveness of patients to therapy is required. Here, we set out to explore the proteome of two lung adenocarcinoma cell lines (HCC-44 and A549) treated with cisplatin, pemetrexed, durvalumab, and the corresponding mixtures to establish the differences in post-treatment protein expression that can serve as markers of chemosensitivity or resistance.
Project description:Primary tumor recurrence occurs commonly after surgical resection of lung squamous cell carcinoma (SCC). The aim of this study was to identify genes involved in recurrence in lung squamous cell carcinoma patients. Array comparative genomic hybridization (aCGH) was performed on DNA extracted from tumour tissue from 62 patients with primary lung squamous cell carcinomas. aCGH data was analysed to identify genes affected by copy number alterations that may be involved in SCC recurrence. Candidate genes were then selected for technical validation based on differential copy number between recurrence and non-recurrence SCC tumour samples. Genes technically validated advanced to tests of biological replication by qPCR using an independent test set of 72 primary lung SCC tumour samples. 18q22.3 loss was identified by aCGH as significantly associated with recurrence (p=0.038). Although aCGH copy number loss associated with recurrence was found for seven genes within 18q22.3, only SOCS6 copy number loss was both technically replicated by qPCR and biologically validated in the test set. DNA copy number profiling using 44K element array comparative genomic hybridization microarrays of 62 primary lung squamous cell carcinomas.
Project description:MicroRNA expression in eight human non-small cell lung carcinoma xenografts grown in SCID mice was compared with the primary human tumors. Formalin-fixed and paraffin-embedded tissue specimens were used.
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