Project description:Whole exome sequencing of tumors and paired adjacent uninvolved tissues from 222 early stage NSCLC patients, in order to identify genomic drivers present in early-stage non-small cell lung cancer and determine the overall tumor mutational burden in early-stage non-small cell lung cancer.
Project description:Plasma samples from 100 early stage (I to IIIA) non–small-cell lung cancer (NSCLC) patients and 100 non-cancer controls were screened for 754 circulating microRNAs via qRT-PCR, using TaqMan MicroRNA Arrays. Our objective was to identify a panel of circulating microRNAs in plasma that will contribute to early detection of 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:RRBS data from TRACERx non-small cell lung cancer (NSCLC) tumours and matched normal adjacent tissue.
TRACERx (TRAcking Cancer Evolution through therapy (Rx)) is a prospective cohort study designed to investigate intratumor heterogeneity (ITH) in relation to clinical outcome, and to determine the clonal nature of driver events and evolutionary processes in early stage non-small cell lung cancer (NSCLC).
Project description:The overall objective of this study was to characterize the diversity and ontogeny of CD8 T cells in untreated lung tumors. This was accomplished by performing bulk RNAseq and flow cytometry analysis on CD8+ T cell subsets isolated from tumor tissue, normal adjacent to tumor (juxta0 tissue samples from patients undergoing surgical resection for early-stage, untreated non-small-cell lung cancer (NSCLC).
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:We systematically profiled the genome-wide alternative splicing events in Lung Adenocarcinoma (LUAD) and Lung Squomous Cell Carcinoma (LUSC) against Lung Control through Human Transcriptome Array2.0. Non-invasive Stage IIIA non-small cell lung cancer (NSCLC) is heterogeneous in nature which makes it difficult to predict, diagnose and prognose owing to lower 5-year survival rate and 75-85% brain or bone metastasis. Hence, we hypothesized to develop transcript-based signature to categorize Stage IIIA-NSCLC-LUAD and LUSC, as well as identify markers which could indicate towards prognosis of disease. We were able to molecularly-categorize LUAD- and LUSC-tissue more precisely through HTA array2.0.
Project description:Lung cancer remains the most common cause of cancer deaths worldwide, yet there is currently a lack of diagnostic noninvasive biomarkers that could guide treatment decisions. Small molecules (<1500 Da) were measured in urine collected from 469 lung cancer patients and 536 population controls using unbiased liquid chromatography-mass spectrometry. Clinical putative diagnostic and prognostic biomarkers were validated by quantitation and normalized to creatinine levels at two different time points and further validated in an independent sample set, which comprises 80 cases and 78 population controls, with similar demographic and clinical characteristics when compared to the training set. Creatine riboside (IUPAC name: 2-{2-[(2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)-oxolan-2-yl]-1-methylcarbamimidamido}acetic acid), a novel molecule identified in this study, and N-acetylneuraminic acid (NANA), were each significantly (P <0.00001) elevated in non–small cell lung cancer (NSCLC) and associated with worse prognosis (hazard ratio (HR) =1.81 [P =0.0002], and 1.54 [P =0.025], respectively). Creatine riboside was the strongest classifier of lung cancer status in all and stage I–II cases, important for early detection, and also associated with worse prognosis in stage I–II lung cancer (HR =1.71, P =0.048). All measurements were highly reproducible with intraclass correlation coefficients ranging from 0.82 – 0.99. Both metabolites were significantly (P <0.03) enriched in tumor tissue compared to adjacent non-tumor tissue (N =48), thus revealing their direct association with tumor metabolism. Creatine riboside and NANA may be robust urinary clinical metabolomic markers that are elevated in tumor tissue and associated with early lung cancer diagnosis and worse prognosis.
Project description:Through multidimensional genomic/protein multiomics analysis and clinical information integration of cancer tissue samples, a prognostic method for lung cancer, including non-small cell lung cancer (NSCLC), is developed and applied to precision medical care after discovering new drug targets.