Mixed Histology Lung Cancers Driven by Transcriptomic Features rather than Genomic Characteristics
Ontology highlight
ABSTRACT:
PROVIDER: EGAS00001005140 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Histology plays an essential role in therapeutic decision-making for lung cancer patients. However, the molecular determinants of lung cancer histology are largely unknown. We conducted whole-exome sequencing (WES) and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histology. A median of 68.9% of point mutations and 83% of copy number aberrations were shared between different histologic components within the same tumors. Furthermore, different histologic components within the tumors demonstrated similar subclonal architecture. On the other hand, transcriptomic profiling revealed shared pathways between the same histologic subtypes from different patients, which was supported by the analyses of the transcriptomic data from 141 cell lines and 343 lung cancers of different histologic subtypes. These data derived from mixed histologic subtypes in the setting of identical genetic background and exposure history support that the histologic fate of lung cancer cells is associated with transcriptomic features rather than the genomic profiles. The in-house clariom.s.human microarray data were analyzed using Bioconductor packages Oligo, pd.clariom.s.human, and limma following standard workflow. Gene set enrichment analysis using Hallmark dataset was carried out using fgsea Bioconductor package and the genes are pre-ranked by (signed log2FoldChange) * -log10(p-value) for all the public datasets. For the in-house microarray data, we computed the fold change between distinct histologies within the same patient and rank the genes by the fold change.
2021-11-13 | GSE188665 | GEO
Project description:We conducted whole-exome sequencing (WES) and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histologic patterns including 6 LUAD, 6 LCNEC, 3 SCLC, 3 LUSC, and one poorly differentiated NSCLC-NOS.
| EGAD00001007076 | EGA
Project description:Proteomics characterization of the ontogeny of protein changes during normal lung development in laser capture micro-dissected alveolar tissue containing ~4,000 cells per sample
2016-06-23 | MSV000079850 | MassIVE
Project description:The histologic phenotype of lung cancers is associated with transcriptomic features rather than genomic characteristics
| PRJNA779915 | ENA
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:Improved understanding of lung transplant disease states is essential because failure rates are high, often due to chronic lung allograft dysfunction. However, histologic assessment of lung transplant transbronchial biopsies (TBBs) is difficult and often uninterpretable even with 10 pieces. All 242 single-piece TBBs produced reliable transcript measurements. Paired TBB pieces available from 12 patients showed significant similarity but also showed some sampling variance. Alveolar content, as estimated by surfactant transcript expression, was a source of sampling variance. To offset sampling variation, for analysis we selected 152 single-piece TBBs with high surfactant transcripts. Unsupervised archetypal analysis identified four idealized phenotypes (archetypes) and scored biopsies for their similarity to each: normal, T cell-mediated rejection (TCMR; T cell transcripts), antibody-mediated rejection (ABMR)-like (endothelial transcripts), and injury (macrophage transcripts). Molecular TCMR correlated with histologic TCMR. The relationship of molecular scores to histologic ABMR could not be assessed because of the paucity of ABMR in this population. Molecular assessment of single-piece TBBs can be used to classify lung transplant biopsies and correlated with rejection histology. Two or three pieces for each TBB will probably be needed to offset sampling variance.
2019-08-16 | GSE125478 | GEO
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:The results provide information on the gene expression in dissected KrasG12D-driven lung tumor in mice. Mice were fed on high-calorie diet starting 3 months before tumor induction. Dissected tumors were obtained 11 weeks after tumor induction.
2014-03-27 | GSE56260 | GEO
Project description:The results provide information on the gene expression in dissected KrasG12D-driven lung tumor in mice. Mice were fed on high-calorie diet starting 3 months before tumor induction. Dissected tumors were obtained 11 weeks after tumor induction. Total RNA extracted from Kras-driven lung tumors of mice fed with standard diet or high calorie diet
2014-03-27 | E-GEOD-56260 | biostudies-arrayexpress
Project description:<p>Three genetic loci for lung cancer risk have been identified by genome-wide association studies (GWAS), but inherited susceptibility to specific histologic types of lung cancer is not well established. We conducted a GWAS of lung cancer and its major histologic types genotyping 515,922 single nucleotide polymorphisms (SNPs) in 5,739 incident lung cancer cases and 5,848 controls from one population-based case-control study and three cohort studies. Results were combined with summary data from 10 additional studies for a total of 13,300 cases and 19,666 controls of European descent. Four studies also provided histology data for replication resulting in 3,333 adenocarcinomas (AD), 2,589 squamous cell carcinomas (SQ), and 1,418 small cell carcinomas (SC).</p> <p>In analyses by histology, rs2736100 (<i>TERT</i>) on chromosome 5p15.33, was associated with risk of adenocarcinoma (OR=1.23, 95%CI=1.13-1.33, P=3.02x10<sup>-7</sup>), but not other histologic types (OR=1.01, P=0.84, and OR=1.00, P=0.93, for SQ and SC, respectively). This finding was confirmed in each replication study and overall meta-analysis (OR=1.24, 95%CI=1.17-1.31, P=3.74x10<sup>-14</sup> for AD and OR=0.99, P=0.69 and OR=0.97, P=0.48, for SQ and SC, respectively). Other previously reported association signals on 15q25 and 6p21 were also refined, but no additional loci reached genome-wide significance. In conclusion, a lung cancer GWAS identified a distinct hereditary contribution to adenocarcinoma.</p> <p><i>Note: The lung study dataset to be released to dbGaP and caBIG excludes 47 individuals from the PLCO cohort who consented to participate only in cancer research projects and 22 individuals because of updated QC. Thus, the released dataset is derived from 11517 subjects, 5699 cases and 5818 controls. After the updated QC, the dataset to be released to dbGaP and caBIG includes 506062 SNPs.</i></p>
| phs000336 | dbGaP