Molecular classification of histological origin of NSCLC
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE168466 | GEO | 2022/04/01
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:Molecular classification of histological origin of NSCLC
| PRJNA707488 | ENA
Project description:We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of NSCLC. A prognostic signature of 17 genes showed the best association with post-surgery survival time. The performance of the signatures was validated using a patient cohort of similar size A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples
2010-05-28 | E-GEOD-19188 | biostudies-arrayexpress
Project description:BACKGROUND: Global gene expression analysis provides a comprehensive molecular characterization of non-small cell lung cancer. The aim of this study was to evaluate the feasibility of integrating expression profiling into routine clinical work-up by including minute bronchoscopic biopsies and develop a robust prognostic gene expression signature METHODS: Tissue samples from a series of 41 chemotherapy-naïve non-small cell lung cancer patients and 15 control patients with inflammatory lung diseases were obtained during routine clinical work-up and gene expression profiles were gained using a highly sensitive oligonucleotide array platform (Novachip ; 34'207 transcripts). Gene expression signatures were analyzed by correlation with histological and clinical parameters and validated on independent published datasets and immunohistochemistry. RESULTS: Tumor tissue classification based on the gene expression results was strongly dependent on the proportion of tumor cells present in the biopsies and showed an overall sensitivity of 80% and specificity of 89%. For prognostication we developed a metagene consisting of 13 genes, which was validated on 4 independent published datasets. The robustness of this metagene has been demonstrated by a virtual independence from tumor cells present in the biopsies. Furthermore, vascular endothelial growth factor-beta, one of the key prognostic genes was validated by immunohistochemistry on 508 independent tumor samples. CONCLUSIONS: The proposed strategy of integrating functional genomics into routine clinical work-up allows molecular tumor classification and prediction of survival in patients with non-small cell lung cancer of all stages and is suitable for an integration in the daily clinical practice. Keywords: Gene expression profiling for disease state analysis in lung cancer patients 56 lung biopsies, 4 different Phenotypes: NSCLC-squa., NSCLC-NOS, NSCLC-Adeno, Ctr.-Infl.
2008-04-10 | E-GEOD-11117 | biostudies-arrayexpress
Project description:Non-small cell lung cancer (NSCLC) death rates exceed the next 3 prevalent cancers combined; however, most NSCLC tumors lack actionable mutations. Recent studies of NSCLC and other cancers revealed profound proteome remodelling with prognostic impact that is not fully predicted by DNA or RNA analyses. These revelations portend proteome-based cancer classification and treatment. This will require model systems that recapitulate tumor proteomes and phenotypes. A subset (~35%) of the most aggressive NSCLC can form a patient-derived xenograft (PDX). We generated 137 PDX models of aggressive NSCLC, which represent the histological, genome, transcriptome, and DNA methylation features and proteome remodelling of primary NSCLC. The models indicate 3 lung adenocarcinoma and 2 squamous cell carcinoma proteotypes that are associated with different patient outcomes, protein-phosphotyrosine profiles, candidate targets, and in adenocarcinoma, distinct stromal immune features. The PDX resource will foster proteome-directed stratification and development of new treatments for aggressive NSCLC.
2021-11-23 | GSE166998 | GEO
Project description:Non-small cell lung cancer (NSCLC) death rates exceed the next 3 prevalent cancers combined; however, most NSCLC tumors lack actionable mutations. Recent studies of NSCLC and other cancers revealed profound proteome remodelling with prognostic impact that is not fully predicted by DNA or RNA analyses. These revelations portend proteome-based cancer classification and treatment. This will require model systems that recapitulate tumor proteomes and phenotypes. A subset (~35%) of the most aggressive NSCLC can form a patient-derived xenograft (PDX). We generated 137 PDX models of aggressive NSCLC, which represent the histological, genome, transcriptome, and DNA methylation features and proteome remodelling of primary NSCLC. The models indicate 3 lung adenocarcinoma and 2 squamous cell carcinoma proteotypes that are associated with different patient outcomes, protein-phosphotyrosine profiles, candidate targets, and in adenocarcinoma, distinct stromal immune features. The PDX resource will foster proteome-directed stratification and development of new treatments for aggressive NSCLC.
2021-11-23 | GSE166997 | GEO
Project description:One hundred and seven lung Squamous Cell Carcinomas collected from early stage (stage I+II; AJCC 7th edition) patients at the National Cancer Center Hospital (Tokyo, Japan) between 1997 and 2008 were hybridized to the Human Transcriptome (HT) Array 2.0
2016-09-14 | GSE74777 | GEO
Project description:Methyltransferase-like 3 (METTL3), a core catalytic subunit for RNA N6-methyladenosine (m6A) modification, plays a crucial role in regulating gene expression to govern various physiological and pathological processes. However, whether and how METTL3 regulates TME and anti-tumor immunity in non-small-cell lung cancer (NSCLC) remains poorly understood. To understand the function and mechanism of METTL3 in NSCLC TME, we compare the differential expression genes (DEGs) in STM2457 (METTL3 specific inhibitor) versus DMSO treated lewis lung carcinoma cells (LLC).
2023-01-31 | GSE212177 | GEO
Project description:Hypothesis: Non-small cell lung cancer (NSCLC) is characterized by a multitude of genetic aberrations with unknown clinical impact. In this study, we aimed to identify gene copy number changes that correlate with clinical outcome in NSCLC. To maximize the chance to identify clinically relevant events, we applied a strategy involving two prognostically extreme patient groups. Results: Genetic aberrations were strongly associated with tumor histology. In adenocarcinoma (n=50), gene copy number gains on chromosome 8q21-q24.3 (177 genes) were more frequent in long-term survivors. In squamous cell carcinoma (n=28), gains on chromosome 14q23.1-24.3 (133 genes) were associated with shorter survival, whereas losses in a neighboring region, 14q31.1-32.33 (110 genes), correlated with favorable outcome. In accordance with copy number gains and losses, mRNA expression levels of corresponding genes were increased or decreased, respectively. Conclusion: Comprehensive tumor profiling permits the integration of genomic, histologic and clinical data. We identified gene copy number gains and losses, with corresponding changes in mRNA levels, that were associated with prognosis in adenocarcinoma and squamous cell carcinoma of the lung. Short-term (<20 months; n=53) and long-term survivors (>58 months; n=47) were selected from a clinically well-characterized NSCLC patient cohort with available fresh-frozen tumor specimens. The samples were analyzed using array-based gene expression profiling. The molecular data was combined with information on clinical parameters.
2011-10-27 | E-GEOD-28571 | biostudies-arrayexpress