Project description:EGFR-mutated non-small cell lung cancers bear hallmarks including sensitivity to EGFR inhibitors, and low proliferation, and increased MET. However, the biology of EGFR dependence is still poorly understood. Using a training cohort of chemo-naive lung adenocarcinomas, we have developed a 72-gene signature that predicts (i) EGFR mutation status in four independent datasets; (ii) sensitivity to erlotinib in vitro; and (iii) improved survival, even in the wild-type EGFR subgroup. The signature includes differences associated with enhanced receptor tyrosine kinase (RTK) signaling, such as increased expression of endocytosis-related genes, decreased phosphatase levels, decreased expression of proliferation-related genes, increased folate receptor-1 (FOLR1) (a determinant of pemetrexed response), and higher levels of MACC1 (which we identify as a regulator of MET in EGFR-mutant NSCLC). Those observations provide evidence that the EGFR-mutant phenotype is associated with alterations in the cellular machinery that links the EGFR and MET pathways and create a permissive environment for RTK signaling. We have developed a gene expression signature that predicts (i) EGFR mutation in chemo-naive and, to a lesser extent, in chemo-refractory NSCLC patients; (ii) EGFR TKI response in vitro; and (iii) survival in wild-type EGFR patients. The signature also identifies novel features of EGFR mutant NSCLC including increased levels of endocytosis-related genes and MACC1, which appears be an EGFR mutant associated regulator of MET. Gene expression profiles were measured in 124 core biopsies from patients with refractory non-small cell lung cancer in the Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial. We used the BATTLE dataset to test an EGFR-mutation gene expression signature trained in chemo-naive lung adenocarcinoma. The signature was computed as an index, called EGFR index.
Project description:We applied a meta-analysis of datasets from seven different microarray studies on lung cancer for differentially expressed genes related to survival time (under 2 y and over 5 y). Systematic bias adjustment in the datasets was performed by distance-weighted discrimination (DWD). We identified a gene expression signature consisting of 64 genes that is highly predictive of which stage I lung cancer patients may benefit from more aggressive therapy. Experiment Overall Design: RNA was extracted from frozen tissue of primary stage I Non-Small Cell lung tumors for gene array analysis
Project description:Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Proteogenomic characterization analysis holds promise to improve clinical stratification, thus paving the way for individualized therapy. We investigated proteogenomic characterization and performed comprehensive integrative genomic analysis of human large cell lung cancer. Here we analyzed proteomes of 29 paired normal lung tissues and large cell lung cancer, identified significantly deregulated proteins associated with large cell lung cancer.
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:Lung adenocarcinoma is the most common histological subtype of lung cancer. Although early-stage LUAD (esLUAD) patients have much better prognosis than patients with advanced disease, among early stage patients treated primarily with surgery, some of early stage patients will develop metastasis with overall 5 year survival for stage 1 and 2 non-small cell lung cancer of 70% and 35%, respectively. Within lung adenocarcinoma, histology is heterogenous and associated with tumor invasion and clinical outcomes. Invasiveness is one of cancer hallmarks and is directly related with metastatic potential and clinical outcomes of the tumor. In this study, we characterize invasiveness mechanisms in esLUAD by analyzing gene expression of a novel cohort of 53 histologically heterogenous esLUAD samples.
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:Validation of a histology-independent prognostic gene signature for early stage, non-small cell lung cancer including stage IA patients
Project description:We developed a 33-gene signature that is strongly correlated to the time to recurrence in non-small cell lung cancer (NSCLC). The signature was validated retrospectively in 5 cohorts of 972 NSCLC patients and in one prospective study of 111 NSCLC Stage IA patients. In all cohorts, and all stages of the disease, the signature identified a rare, aggressive tumor type that had a high proportion of recurrence after surgery and a median survival of 35 months (95% C.I.: 19-58). This tumor type forms a separate cluster in an analysis of the expression of the 33 genes in patient tumors. The signature is associated with cellular processes required by rapidly growing and spreading tumors: cell migration and invasion, vascularization, and response to hypoxia. The signature also identifies patients with good prognosis (median survival 114 months, (95% C.I.: 85-160), and intermediate prognosis (median survival 61 months (95% C. I.: 50-73). The signature is quite robust and works on tumor samples archived in RNAlater, Tissue-Tek, or formalin-fixed and paraffin embedded. 156 samples -------------------------------- *** Submitter has not provided information such as time to recurrence. Thus, the data is incomplete.
Project description:Anti-programmed death-1 (PD-1) treatment for advanced non-small-cell lung cancer (NSCLC) has improved the survival of patients. However, a significant percentage of patients do not respond. We examined the use of DNA methylation profiles to determine the efficacy of anti-PD-1 treatment in stage IV NSCLC patients.