Copy number variations distinguish lung adenocarcinomas from squamous cell carcinomas
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE74948 | GEO | 2015/11/13
SECONDARY ACCESSION(S): PRJNA301991
REPOSITORIES: GEO
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Project description:Background: Non-small cell lung cancers (NSCLCs) consist of adenocarcinoma (ADC), squamous cell carcinoma (SCC) and other types. Since most NSCLCs are now diagnosed from small biopsies or cytology materials, classification is not always accurate. This is a problem as many therapy regimens and clinical trials are histology-dependent. Specific Aim: To develop an RNA expression signature as an adjunct test for routine histo-pathological classification of NSCLCs. Methods: A microarray dataset of resected ADC and SCC cases was used as the learning set for an ADC-SCC signature. The Cancer Genome Atlas (TCGA) lung RNAseq dataset was used as a validation set. Another microarray dataset of ADCs and non-malignant lung was used as the learning set for a Tumor-Nonmalignant signature. The classifiers were selected as the most differentially expressed genes and sample classification was determined by a nearest distance approach. Results: We developed a 42-gene expression signature that contained many genes used in immunostains for NSCLC typing. Testing of the TCGA and other public datasets resulted in high accuracies (93-95%). We also observed that most non-malignant lung samples were classified as â??adenocarcinomasâ??, so we added 20 genes to differentiate tumor from non-malignant lung. Together, the 62-gene signature can discriminate ADC, SCC, and non-malignant lung. Additionally, a prediction score was derived that correlated both with histologic grading and survival. Summary and significance: Our histologic classifier provides a non-subjective method to aid in the pathological diagnosis of lung cancer and assist enrollment onto histology-based clinical trials 83 lung adenocarcinomas and 83 matched adjacent non-malignant lung were profiled on Illumina WG6-V3 expression arrays
2016-06-01 | E-GEOD-75037 | biostudies-arrayexpress
Project description:Background: Non-small cell lung cancers (NSCLCs) consist of adenocarcinoma (ADC), squamous cell carcinoma (SCC) and other types. Since most NSCLCs are now diagnosed from small biopsies or cytology materials, classification is not always accurate. This is a problem as many therapy regimens and clinical trials are histology-dependent. Specific Aim: To develop an RNA expression signature as an adjunct test for routine histo-pathological classification of NSCLCs. Methods: A microarray dataset of resected ADC and SCC cases was used as the learning set for an ADC-SCC signature. The Cancer Genome Atlas (TCGA) lung RNAseq dataset was used as a validation set. Another microarray dataset of ADCs and non-malignant lung was used as the learning set for a Tumor-Nonmalignant signature. The classifiers were selected as the most differentially expressed genes and sample classification was determined by a nearest distance approach. Results: We developed a 42-gene expression signature that contained many genes used in immunostains for NSCLC typing. Testing of the TCGA and other public datasets resulted in high accuracies (93-95%). We also observed that most non-malignant lung samples were classified as “adenocarcinomas”, so we added 20 genes to differentiate tumor from non-malignant lung. Together, the 62-gene signature can discriminate ADC, SCC, and non-malignant lung. Additionally, a prediction score was derived that correlated both with histologic grading and survival. Summary and significance: Our histologic classifier provides a non-subjective method to aid in the pathological diagnosis of lung cancer and assist enrollment onto histology-based clinical trials
2016-06-01 | GSE75037 | GEO
Project description:Non-small cell lung cancer (NSCLC) accounts for 85% of lung cancers, and is subdived into 2 major histological subtypes: adenocacinoma (ADC) and squamous cell carcinoma (SCC). Super-SILAC methodology was used to facilitate the quantitative comparisons of human primary tumor-derived xenograft proteomes by high resolution MS analysis. Xeno092 and Xeno441 were ADCs. Xeno 561 and Xeno691 were SCCs. Principal component analysis and supervised t-tests revealed differentially expressed proteins responsible for the classification of the samples according to their distinctive proteomes. These results confirm known differentiators between ADC and SCC subtypes of NSCLC (e.g. a keratin signature, cell adhesion molecules, and metabolism proteins), and reveal novel candidates that may be useful to classify and treat NSCLC tumors. The raw files were analyzed by MaxQuant (version 1.3.0.5). Peaks were searched against the UniProt human database (released Jul, 2012; http://www.uniprot.org) using the Andromeda search engine included in MaxQuant. Two miscleavages were allowed and at least six amino acids per identified peptide were required. The false discovery rate for peptides and proteins was set at 0.01. 'Match between runs' option was selected within a time window of 2 min. A minimum ratio count of 2 was used for quantification of SILAC pairs. Further data analysis was performed in Perseus.
2014-01-28 | PXD000438 | Pride
Project description:Identifying biomarkers predictive for early esophageal cancer detection is critical considering the dismal survival rates. We investigated the involvement of microRNAs (miRNAs), their utility as biomarkers, and their association with survival in esophageal cancer, including Barrett’s associated and sporadic adenocarcinoma (ADC), and squamous cell carcinoma (SCC). MiRNA expression was measured in cancerous and adjacent non-cancerous tissue pairs collected from 76 US and Japanese patients enrolled in 3 distinct cohorts. In ADC patients, miR-21, miR-194, miR-293, and miR-223 expression was elevated, while miR-375 and miR-203 expression was reduced in cancerous tissue compared to non-cancerous tissue. Increased levels of miR-192 and miR-194 were observed in Barrett’s associated compared to sporadic ADC cancerous tissue. In SCC patients, miR-21, miR-181b, miR-155, and miR-146b expression was elevated while miR-375 and miR-203 levels were reduced in cancerous tissue compared to non-cancerous tissue. Significantly, elevated mir-21 expression in non-cancerous tissue was strongly associated with worse prognosis, independent of nodal status and age. Sample classification using miRNA expression yielded accuracies as high as 86% for diagnosis and 78% for Barrett’s esophagus status. Our results highlight that miRNAs are deregulated in esophageal carcinogenesis and Barrett’s esophagus, and that their expression is associated with survival in cancer patients. Sample classification using miRNA expression demonstrates their potential utility as biomarkers for esophageal carcinoma diagnosis. MiRNA microarray expression was measured using miRNA microarray chips version 3 (Ohio State University) in 44 SCC cases and 32 ADC cases, of which 18 were also diagnosed with Barrett’s esophagus.
2010-07-09 | E-GEOD-13937 | biostudies-arrayexpress
Project description:Non-small cell lung cancer (NSCLC) accounts for 85% of lung cancers, and is subdived into 2 major histological subtypes: adenocacinoma (ADC) and squamous cell carcinoma (SCC). Super-SILAC methodology was used to facilitate the quantitative comparisons of human primary tumor-derived xenograft proteomes by high resolution MS analysis. Xeno092 and Xeno441 were ADCs. Xeno 561 and Xeno691 were SCCs. Principal component analysis and supervised t-tests revealed differentially expressed proteins responsible for the classification of the samples according to their distinctive proteomes. These results confirm known differentiators between ADC and SCC subtypes of NSCLC (e.g. a keratin signature, cell adhesion molecules, and metabolism proteins), and reveal novel candidates that may be useful to classify and treat NSCLC tumors. The raw files were analyzed by MaxQuant (version 1.3.0.5). Peaks were searched against the UniProt human database (released Jul, 2012; http://www.uniprot.org) using the Andromeda search engine included in MaxQuant. Two miscleavages were allowed and at least six amino acids per identified peptide were required. The false discovery rate for peptides and proteins was set at 0.01. 'Match between runs' option was selected within a time window of 2 min. A minimum ratio count of 2 was used for quantification of SILAC pairs. Further data analysis was performed in Perseus.
2016-07-02 | MSV000079881 | MassIVE
Project description:Histological classification by routine pathology assessment with hematoxylin-eosin staining and immunostaining for poorly differentiated tumors, particularly those from small biopsies, is still challenging. In this study, using gene expression profiles of pathologically-determined lung squamous cell carcinomas and adenocarcinomas, denoted as pSCC and pADC respectively, we developed a qualitative transcriptional signature, based on the within-sample relative gene expression orderings (REOs) of gene pairs, to individually distinguish ADC from SCC. The signature was validated in the frozen tissues, FFPE materials, mixed tumors, small biopsy specimens and poorly differentiated samples.In summary, the qualitative signature, independent of the subjective diagnosis of pathologists, would be an effective auxiliary tool in distinguishing ADC from SCC.
2018-10-11 | GSE121090 | GEO
Project description:Background: The development of a more refined prognostic methodology for early non-small cell lung cancer (NSCLC) is an unmet clinical need. An accurate prognostic tool might help to select patients at early stages for adjuvant therapies. Results: A new integrated bioinformatics searching strategy, that combines gene copy number alterations and expression, together with clinical parameters was applied to derive two prognostic genomic signatures. The proposed methodology combines data from patients with and without clinical data with a priori information on the ability of a gene to be a prognostic marker. Two initial candidate sets of 513 and 150 genes for lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), respectively, were generated by identifying genes which have both: a) significant correlation between copy number and gene expression, and b) significant prognostic value at the gene expression level in external databases. From these candidates, two panels of 7 (ADC) and 5 (SCC) genes were further identified via semi-supervised learning. These panels, together with clinical data (stage, age and sex), were used to construct the ADC and SCC hazard scores combining clinical and genomic data. The signatures were validated in two independent datasets (n=73 for ADC, n=97 for SCC), confirming that the prognostic value of both clinical-genomic models is robust, statistically significant (P=0.008 for ADC and P=0.019 for SCC) and outperforms both the clinical models (P=0.060 for ADC and P=0.121 for SCC) and the genomic models applied separately (P=0.350 for ADC and P=0.269 for SCC). Conclusion: The present work provides a methodology to generate a robust signature using copy number data that can be potentially used to any cancer. Using it, we found new prognostic scores based on DNA s that, jointly with clinical information, are able to predict overall survival (OS) in patients with early-stage ADC and SCC.
2015-08-20 | GSE72194 | GEO
Project description:Background: The development of a more refined prognostic methodology for early non-small cell lung cancer (NSCLC) is an unmet clinical need. An accurate prognostic tool might help to select patients at early stages for adjuvant therapies. Results: A new integrated bioinformatics searching strategy, that combines gene copy number alterations and expression, together with clinical parameters was applied to derive two prognostic genomic signatures. The proposed methodology combines data from patients with and without clinical data with a priori information on the ability of a gene to be a prognostic marker. Two initial candidate sets of 513 and 150 genes for lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), respectively, were generated by identifying genes which have both: a) significant correlation between copy number and gene expression, and b) significant prognostic value at the gene expression level in external databases. From these candidates, two panels of 7 (ADC) and 5 (SCC) genes were further identified via semi-supervised learning. These panels, together with clinical data (stage, age and sex), were used to construct the ADC and SCC hazard scores combining clinical and genomic data. The signatures were validated in two independent datasets (n=73 for ADC, n=97 for SCC), confirming that the prognostic value of both clinical-genomic models is robust, statistically significant (P=0.008 for ADC and P=0.019 for SCC) and outperforms both the clinical models (P=0.060 for ADC and P=0.121 for SCC) and the genomic models applied separately (P=0.350 for ADC and P=0.269 for SCC). Conclusion: The present work provides a methodology to generate a robust signature using copy number data that can be potentially used to any cancer. Using it, we found new prognostic scores based on DNA s that, jointly with clinical information, are able to predict overall survival (OS) in patients with early-stage ADC and SCC.
2015-08-20 | GSE72192 | GEO
Project description:Identifying biomarkers predictive for early esophageal cancer detection is critical considering the dismal survival rates. We investigated the involvement of microRNAs (miRNAs), their utility as biomarkers, and their association with survival in esophageal cancer, including Barrett’s associated and sporadic adenocarcinoma (ADC), and squamous cell carcinoma (SCC). MiRNA expression was measured in cancerous and adjacent non-cancerous tissue pairs collected from 76 US and Japanese patients enrolled in 3 distinct cohorts. In ADC patients, miR-21, miR-194, miR-293, and miR-223 expression was elevated, while miR-375 and miR-203 expression was reduced in cancerous tissue compared to non-cancerous tissue. Increased levels of miR-192 and miR-194 were observed in Barrett’s associated compared to sporadic ADC cancerous tissue. In SCC patients, miR-21, miR-181b, miR-155, and miR-146b expression was elevated while miR-375 and miR-203 levels were reduced in cancerous tissue compared to non-cancerous tissue. Significantly, elevated mir-21 expression in non-cancerous tissue was strongly associated with worse prognosis, independent of nodal status and age. Sample classification using miRNA expression yielded accuracies as high as 86% for diagnosis and 78% for Barrett’s esophagus status. Our results highlight that miRNAs are deregulated in esophageal carcinogenesis and Barrett’s esophagus, and that their expression is associated with survival in cancer patients. Sample classification using miRNA expression demonstrates their potential utility as biomarkers for esophageal carcinoma diagnosis.
2010-07-09 | GSE13937 | GEO
Project description:Adenosquamous lung tumors may result from cellular plasticity. We demonstrate lineage switching of KRAS+ lung adenocarcinomas (ADC) to squamous cell carcinoma (SCC) through deletion of Lkb1 (Stk11) in autochthonous and transplant models. Chromatin analysis reveals loss of H3K27me3 and gain of H3K27ac and H3K4me3 at squamous lineage genes, included Sox2, ΔNp63 and Ngfr. SCC lesions have higher levels of the H3K27 methyltransferase EZH2 than the ADC lesions, but there is a clear lack of the essential Polycomb Repressive Complex 2 (PRC2) subunit EED in the SCC lesions.
2017-02-02 | GSE94365 | GEO