Molecular classification of histological origin of NSCLC
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ABSTRACT:
PROVIDER: PRJNA707488 | ENA |
REPOSITORIES: ENA
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Project description:Lung cancer is one of the most common malignant tumors in the world. The latest edition of WHO classifies lung cancer on the basis of histological morphology and molecular typing, which is a relatively complete pathological classification of lung cancer at present. However, in clinical practice, it is difficult to make accurate subtype classification of NSCLC only by using morphological structure and immunohistochemical characteristics, and the degree of coincidence with gene mutation and clinicopathological parameters is poor, which has limited guiding effect on clinical diagnosis and treatment. In this study, we proposed Molecular Pathological Classification Model of NSCLC Tissue Origin.
2022-04-01 | GSE168466 | GEO
Project description:Carcinomas of unknown primary origin constitute 3-5% of all newly diagnosed metastatic cancers, of which the primary source is difficult to classify with current histological methods. Effective cancer treatment depends on early and accurate identification of the tumor, which is why patients with metastases of unknown origin have poor prognosis and short survival. Because microRNA expression is highly tissue specific, the microRNA profile of a metastasis may be used to identify its origin. As a first step to realize this goal, we evaluated the potential of microRNA profiling for identification of the primary tumor of known metastases. 208 formalin-fixed paraffin-embedded samples representing 15 different histologies were profiled on an LNA-enhanced microarray platform, which allows for highly sensitive and specific detection of microRNA. Based on these data, we developed and cross-validated a novel classification algorithm, LASSO (Least Absolute Shrinkage and Selection Operator), which had an overall accuracy of 85%. When the classifier was applied on an independent test set of 48 metastases, the primary site was correctly identified in 42 cases (88% accuracy). Our findings suggest that microRNA expression profiling on paraffin tissue can efficiently predict the primary origin of a tumor, and may provide pathologists with a molecular diagnostic tool that can improve their capability to correctly identify the origin of hitherto unidentifiable metastatic tumors, and eventually, enable tailored therapy. 94 samples
2013-12-01 | E-GEOD-50890 | 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:Carcinomas of unknown primary origin constitute 3-5% of all newly diagnosed metastatic cancers, of which the primary source is difficult to classify with current histological methods. Effective cancer treatment depends on early and accurate identification of the tumor, which is why patients with metastases of unknown origin have poor prognosis and short survival. Because microRNA expression is highly tissue specific, the microRNA profile of a metastasis may be used to identify its origin. As a first step to realize this goal, we evaluated the potential of microRNA profiling for identification of the primary tumor of known metastases. 208 formalin-fixed paraffin-embedded samples representing 15 different histologies were profiled on an LNA-enhanced microarray platform, which allows for highly sensitive and specific detection of microRNA. Based on these data, we developed and cross-validated a novel classification algorithm, LASSO (Least Absolute Shrinkage and Selection Operator), which had an overall accuracy of 85%. When the classifier was applied on an independent test set of 48 metastases, the primary site was correctly identified in 42 cases (88% accuracy). Our findings suggest that microRNA expression profiling on paraffin tissue can efficiently predict the primary origin of a tumor, and may provide pathologists with a molecular diagnostic tool that can improve their capability to correctly identify the origin of hitherto unidentifiable metastatic tumors, and eventually, enable tailored therapy.
2013-12-01 | GSE50890 | GEO
Project description:Lupus nephritis (LN) is characterized by immune-complex deposition in kidney glomeruli and has been classified according to histological features, but has not been characterized on a molecular level. This study aimed to characterize the relationship between histological and molecular phenotypes in LN. Renal compartmental mRNA expression was measured in 54 kidney biopsy specimens from patients with LN and correlated to histological phenotypes. The top identified transcripts were compared to a separate longitudinal cohort of 36 patients with paired kidney biopsies obtained at the time of flare and at follow up. Unsupervised clustering based on mRNA abundance resulted in clear separation by renal compartment, but did not demonstrate a relationship with LN class based on the International Society of Nephrology/Renal Pathology Society (ISN/RPS) classification, or the NIH activity and chronicity indices. A strong interferon gene signature was observed in both cohorts. FN1, SPP1, and LGALS-3 correlated with disease activity in both cohorts. The relationship between mRNA expression and ISN/RPS classification is modest. However, correlation of mRNA expression with individual histologic lesions identifies transcripts that change with resolution of disease flare, and provide insights into the molecular pathways potentially responsible for pathologic kidney lesions. Combining molecular and pathologic kidney biopsy phenotype may hold promise to better classify disease and identify actionable treatment targets.
2019-10-29 | GSE127797 | GEO
Project description:The Molecular Phenotype of Lupus Nephritis Histological Lesions
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Project description:Carcinomas of unknown primary origin constitute 3-5% of all newly diagnosed metastatic cancers, of which the primary source is difficult to classify with current histological methods. Effective cancer treatment depends on early and accurate identification
2013-12-01 | GSE50891 | GEO
Project description:Cervical cancers is the second most malignancy in women. It has been clinically important histological variants such as squamous cell carcinoma (SCC) and adenocarcinoma (AC) and adenosquamous carcinomas (ASC). It has been postulated that AC and ASC has a worse prognosis than pure SCC. However, many of the mixed or other types confuses its diagnosis and aggressive/resistant behavior of some tumors has resulted in debate for prognostic role of empirical pathological classification. In addition, the prognosis of adenosquamous carcinoma is still under debate. To establish a novel molecular classification of cervical cancer, we investigated intrinsic characteristics using expression profile.
2010-09-28 | GSE20167 | GEO
Project description:Carcinomas of unknown primary origin constitute 3-5% of all newly diagnosed metastatic cancers, of which the primary source is difficult to classify with current histological methods. Effective cancer treatment depends on early and accurate identification 220 samples
2013-12-01 | E-GEOD-50891 | biostudies-arrayexpress
Project description:DNA methylation profiling has become a powerful tool for neuro-oncology diagnostics. We investigated the value of using DNA methylation profiling to achieve molecular diagnosis in adult primary diffuse lower-grade gliomas according to WHO 2016 classification system of central nervous system tumors. We further evaluated the use of methylation profiling for improved molecular characterization of the tumors and identify prognostic differences beyond histological grade and molecular markers (IDH mutation and 1p/19q codeletion status).
2023-02-27 | GSE175877 | GEO