Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA253120 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The Lung3 data set consists of 89 NSCLC patients that were treated at MAASTRO Clinic, The Netherlands. For these patients pretreatment CT scans, tumour delineations and gene expression profiles were available. We used this data set to associate imaging features with gene-expression profiles.
2014-06-19 | GSE58661 | GEO
Project description:The Lung3 data set consists of 89 NSCLC patients that were treated at MAASTRO Clinic, The Netherlands. For these patients pretreatment CT scans, tumour delineations and gene expression profiles were available. We used this data set to associate imaging features with gene-expression profiles. 89 samples from NSCLC patients. Samples were obtained by biopsies from cancerous tissue. Tumors were classified as Adenocarcinoma, Papillary, NOS; Squamous Cell Carcinoma, NOS; Non-Small Cell; Papillary Type; or Solid Type.
2014-06-19 | E-GEOD-58661 | biostudies-arrayexpress
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
2023-07-11 | BIOMD0000001074 | BioModels
Project description:To rapidly identify new prognostic imaging biomarkers, we propose a bioinformatics approach that integrates gene expression and image data and leverages public gene expression data. We demonstrate our approach in non-small cell lung carcinoma patients for whom CT, PET/CT and gene expression data are available but without clinical follow-up. We extracted 180 image features and 56 high quality gene expression clusters, represented by metagenes. 115 image features were expressed in terms of metagenes, using sparse linear regression and cross-validation, with an accuracy of 65-86%. After mapping the signatures to a public gene expression dataset, 26 image features were significantly associated with recurrence-free survival and 22 with overall survival. A multivariate analysis identified multiple image features that were prognostic, independent of clinical covariates. Identifying prognostic imaging biomarkers by linking images and gene expression with outcomes in public gene expression datasets promises to accelerate the role of imaging in personalized medicine. We studied 26 cases of NSCLC with both PET/CT and microarray data under IRB approval from Stanford University and the Veterans Administration Palo Alto Health Care System. The collection of tissue samples consisted of a distribution of poorly- to well-differentiated adenocarcinomas and squamous cell cancers. The surgeon had removed necrotic debris during excision and sampled cavitary lesions to include as much solid component as practical. Then, from the excised tumor, he cut a 3 to 5 mm thick slice along its longest axis, and froze it within 30 minutes of excision. We retrieved the frozen tissue and extracted the RNA that was then processed by the Stanford Functional Genomics Facility using Illumina Whole Genome Bead Chips (Human HT-12 v3.0)
2012-05-31 | E-GEOD-28827 | biostudies-arrayexpress
Project description:Purpose: To create a radiogenomic map linking computed tomographic (CT) image features and gene expression profiles generated by RNA sequencing for patients with non-small cell lung cancer (NSCLC). Methods: A cohort of 113 patients with NSCLC diagnosed between April 2008 and September 2014 who had preoperative CT data and tumor tissue available was studied. For each tumor, a thoracic radiologist recorded 87 semantic image features, selected to reflect radiologic characteristics of nodule shape, margin, texture, tumor environment, and overall lung characteristics. Next, total RNA was extracted from the tissue and analyzed with RNA sequencing technology. Ten highly coexpressed gene clusters, termed metagenes, were identified, validated in publicly available gene-expression cohorts, and correlated with prognosis. Next, a radiogenomics map was built that linked semantic image features to metagenes by using the t statistic and the Spearman correlation metric with multiple testing correction. Results: RNA sequencing analysis resulted in 10 metagenes that capture a variety of molecular pathways, including the epidermal growth factor (EGF) pathway. A radiogenomic map was created with 32 statistically significant correlations between semantic image features and metagenes. Conclusions: Radiogenomic analysis of NSCLC showed multiple associations between semantic image features and metagenes that represented canonical molecular pathways
2018-08-03 | GSE103584 | GEO
Project description:To rapidly identify new prognostic imaging biomarkers, we propose a bioinformatics approach that integrates gene expression and image data and leverages public gene expression data. We demonstrate our approach in non-small cell lung carcinoma patients for whom CT, PET/CT and gene expression data are available but without clinical follow-up. We extracted 180 image features and 56 high quality gene expression clusters, represented by metagenes. 115 image features were expressed in terms of metagenes, using sparse linear regression and cross-validation, with an accuracy of 65-86%. After mapping the signatures to a public gene expression dataset, 26 image features were significantly associated with recurrence-free survival and 22 with overall survival. A multivariate analysis identified multiple image features that were prognostic, independent of clinical covariates. Identifying prognostic imaging biomarkers by linking images and gene expression with outcomes in public gene expression datasets promises to accelerate the role of imaging in personalized medicine.
2012-06-01 | GSE28827 | GEO
Project description:BACKGROUND. Improving and predicting tumor response to immunotherapy remains challenging. Combination therapy with a transforming growth factor β (TGF-β) inhibitor that targets cancer associated fibroblasts (CAFs) is promising to enhance efficacy of cancer immunotherapies. However, the effect of this approach in clinical trials is limited, requiring in vivo methods to better assess tumor responses to combination therapy. METHODS. We measure CAFs in vivo using gallium 68-labeled fibroblast activation protein inhibitor (68Ga-FAPI) for PET/CT imaging to guide TGF-β inhibition and sensitize metastatic colorectal cancer (CRC) to immunotherapy. A total of 131 patients with metastatic CRC underwent 68Ga-FAPI and 18F-fludeoxyglucose (18F-FDG) PET/CT imaging. Fourteen patients underwent surgery after the imaging. Relationship between uptake of 68Ga-FAPI and tumor immunity was analyzed. Mouse cohorts of metastatic CRC were treated with TGF-β receptor (TGF-βR) inhibitor combined with KN046 which blocks PD-L1 and CTLA4, followed with 68Ga-FAPI and 18F-FDG micro-PET/CT imaging to assess tumor responses. RESULTS. Patients with metastatic CRC demonstrated high uptakes of 68Ga-FAPI, along with suppressive tumor immunity and poor prognosis. TGF-βR inhibitor enhanced tumor infiltrating T cells and significantly sensitized metastatic CRC to KN046. 68Ga-FAPI PET/CT imaging accurately monitored the dynamical changes of CAFs and tumor response to combined TGF-βR inhibitor with immunotherapy. CONCLUSION. 68Ga-FAPI PET/CT imaging is powerful in assessing tumor immunity and response to immunotherapy in metastatic CRC. This study supports future clinical application of 68Ga-FAPI PET/CT to stratify and guide patients with CRC for precise TGF-β inhibition plus immunotherapy, recommending 68Ga-FAPI and 18F-FDG dual PET/CT for CRC management.
2023-12-19 | GSE247303 | GEO
Project description:This study focuses on platform comparison to assess performance variability in circulating microRNA (ct-miR) detection, agreement in assignment of a miR signature classifier (MSC) and concordance for the identification of cancer-associated miRs in plasma samples from non‐small cell lung cancer (NSCLC) patients. A plasma cohort of 10 NSCLC patients and 10 healthy donors matched for clinical features and MSC risk level was profiled for miRs expression using two sequencing- and three quantitative PCR (qPCR)-based platforms. Intra- and inter-platform variations were examined by correlation and concordance analysis. MSC risk levels were compared to those estimated using a reference method. Differentially expressed ct-miRs were identified among NSCLC patients and donors and the diagnostic value of those dysregulated in patients was assessed by receiver operating characteristic curve analysis. Downregulation of miR-150-5p was verified by qPCR. The Cancer Genome Atlas (TCGA) lung carcinoma dataset was used for validation at tissue level. Intra-platform reproducibility was consistent whereas the highest values of inter-platform correlations were among qPCR-based platforms. MSC classification concordance was >80% for four platforms. Dysregulation and discriminatory power of miR-150-5p and -210-3p were documented. Both were significantly dysregulated also on TCGA tissue-originated profiles from lung cell carcinoma in comparison to normal samples. Overall, our studies provide a large performance analysis between five different platforms for miRs quantification, indicate the solidity of MSC classifier and identify two noninvasive biomarkers for NSCLC
2022-08-10 | GSE204942 | GEO
Project description:This study focuses on platform comparison to assess performance variability in circulating microRNA (ct-miR) detection, agreement in assignment of a miR signature classifier (MSC) and concordance for the identification of cancer-associated miRs in plasma samples from non‐small cell lung cancer (NSCLC) patients. A plasma cohort of 10 NSCLC patients and 10 healthy donors matched for clinical features and MSC risk level was profiled for miRs expression using two sequencing- and three quantitative PCR (qPCR)-based platforms. Intra- and inter-platform variations were examined by correlation and concordance analysis. MSC risk levels were compared to those estimated using a reference method. Differentially expressed ct-miRs were identified among NSCLC patients and donors and the diagnostic value of those dysregulated in patients was assessed by receiver operating characteristic curve analysis. Downregulation of miR-150-5p was verified by qPCR. The Cancer Genome Atlas (TCGA) lung carcinoma dataset was used for validation at tissue level. Intra-platform reproducibility was consistent whereas the highest values of inter-platform correlations were among qPCR-based platforms. MSC classification concordance was >80% for four platforms. Dysregulation and discriminatory power of miR-150-5p and -210-3p were documented. Both were significantly dysregulated also on TCGA tissue-originated profiles from lung cell carcinoma in comparison to normal samples. Overall, our studies provide a large performance analysis between five different platforms for miRs quantification, indicate the solidity of MSC classifier and identify two noninvasive biomarkers for NSCLC
2022-08-10 | GSE203514 | GEO
Project description:This study focuses on platform comparison to assess performance variability in circulating microRNA (ct-miR) detection, agreement in assignment of a miR signature classifier (MSC) and concordance for the identification of cancer-associated miRs in plasma samples from non‐small cell lung cancer (NSCLC) patients. A plasma cohort of 10 NSCLC patients and 10 healthy donors matched for clinical features and MSC risk level was profiled for miRs expression using two sequencing- and three quantitative PCR (qPCR)-based platforms. Intra- and inter-platform variations were examined by correlation and concordance analysis. MSC risk levels were compared to those estimated using a reference method. Differentially expressed ct-miRs were identified among NSCLC patients and donors and the diagnostic value of those dysregulated in patients was assessed by receiver operating characteristic curve analysis. Downregulation of miR-150-5p was verified by qPCR. The Cancer Genome Atlas (TCGA) lung carcinoma dataset was used for validation at tissue level. Intra-platform reproducibility was consistent whereas the highest values of inter-platform correlations were among qPCR-based platforms. MSC classification concordance was >80% for four platforms. Dysregulation and discriminatory power of miR-150-5p and -210-3p were documented. Both were significantly dysregulated also on TCGA tissue-originated profiles from lung cell carcinoma in comparison to normal samples. Overall, our studies provide a large performance analysis between five different platforms for miRs quantification, indicate the solidity of MSC classifier and identify two noninvasive biomarkers for NSCLC
2022-08-10 | GSE204950 | GEO