scholarly journals Comprehensive RNA analysis of CSF reveals a role for CEACAM6 in lung cancer leptomeningeal metastases

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Yingmei Li ◽  
Dina Polyak ◽  
Layton Lamsam ◽  
Ian David Connolly ◽  
Eli Johnson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Single Cell ◽  
Cell Lines ◽  
Expression Profiles ◽  
Leptomeningeal Metastases ◽  
Detection Sensitivity ◽  
Nsclc Cell ◽  
Rna Seq ◽  
The Brain

AbstractNon-small cell lung cancer (NSCLC) metastatic to the brain leptomeninges is rapidly fatal, cannot be biopsied, and cancer cells in the cerebrospinal fluid (CSF) are few; therefore, available tissue samples to develop effective treatments are severely limited. This study aimed to converge single-cell RNA-seq and cell-free RNA (cfRNA) analyses to both diagnose NSCLC leptomeningeal metastases (LM), and to use gene expression profiles to understand progression mechanisms of NSCLC in the brain leptomeninges. NSCLC patients with suspected LM underwent withdrawal of CSF via lumbar puncture. Four cytology-positive CSF samples underwent single-cell capture (n = 197 cells) by microfluidic chip. Using robust principal component analyses, NSCLC LM cell gene expression was compared to immune cells. Massively parallel qPCR (9216 simultaneous reactions) on human CSF cfRNA samples compared the relative gene expression of patients with NSCLC LM (n = 14) to non-tumor controls (n = 7). The NSCLC-associated gene, CEACAM6, underwent in vitro validation in NSCLC cell lines for involvement in pathologic behaviors characteristic of LM. NSCLC LM gene expression revealed by single-cell RNA-seq was also reflected in CSF cfRNA of cytology-positive patients. Tumor-associated cfRNA (e.g., CEACAM6, MUC1) was present in NSCLC LM patients’ CSF, but not in controls (CEACAM6 detection sensitivity 88.24% and specificity 100%). Cell migration in NSCLC cell lines was directly proportional to CEACAM6 expression, suggesting a role in disease progression. NSCLC-associated cfRNA is detectable in the CSF of patients with LM, and corresponds to the gene expression profile of NSCLC LM cells. CEACAM6 contributes significantly to NSCLC migration, a hallmark of LM pathophysiology.

Characteristics of NSCLCs harboring NRAS mutations.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 7532-7532
Author(s):  
Kadoaki Ohashi ◽  
Lecia V. Sequist ◽  
Martin Sos ◽  
Xi Chen ◽  
Charles M. Rudin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cell Lines ◽  
Expression Profiles ◽  
Nsclc Cell ◽  
Disease Stage ◽  
Smoking History ◽  
Egfr Mutations ◽  
Targeted Agents ◽  
Mek Inhibitors

7532 Background: We sought to determine the frequency and clinical characteristics of patients with non-small cell lung cancers (NSCLCs) harboring NRAS mutations. We used preclinical models to identify targeted therapies likely to be of benefit against NRAS mutant lung cancer cells. Methods: We reviewed data in the Catalogue of Somatic Mutations in Cancer (COSMIC) and clinical history from patients with NSCLC whose tumors underwent systematic screening for driver mutations including NRAS. Patient characteristics examined included age, gender, race, smoking history, disease stage, treatment history, and overall survival (OS). 6 NSCLC cell lines with NRAS mutations were screened for sensitivity against multiple targeted agents. Gene expression was profiled using Affymetrix U133A arrays in 5 NRAS mutant NSCLC cell lines, 8 with EGFR mutations and 17 with KRAS mutations. Results: Among 4524 patients with NSCLC tested, NRAS mutations were present in 29 (0.64%). The types of substitutions found were Q61H/K/L/R and G12A/C/D/R/S, with NRAS Q61L the most common (n=14; 48%). One tumor had a concurrent KRAS mutation. 83% had adenocarcinoma histology, with no significant differences in gender. While 90% of patients were former or current smokers, smoking-related G:C>T:A transversions were significantly less frequent in NRAS than in KRAS-mutant NSCLC (KRAS: 66%, NRAS: 13%, p<0.05). Systemic chemotherapy showed limited efficacy in 7 patients with metastatic disease (median OS 7 mos). 5 of 6 NRAS mutant lung cancer cell lines were sensitive to the MEK inhibitors, AZD6244 and GSK1120212, while other targeted agents (against EGFR, ALK, MET, IGF-1R, PIK3CA, BRAF) were minimally effective. Gene expression profiles of NRAS mutant cell lines were distinct from those with KRAS or EGFR mutations. Conclusions: NRAS mutations define a distinct subset of NSCLCs (~1%) with potential sensitivity to MEK inhibitors. While NRAS gene mutations are more common in current/former smokers, the types of mutations are not those classically associated with smoking.

scholarly journals Expression Profiles of microRNAs in Drug-Resistant Non-Small Cell Lung Cancer Cell Lines Using microRNA Sequencing

2018 ◽  
Vol 51 (6) ◽  
pp. 2509-2522 ◽  
Author(s):  
Shousen Hu ◽  
Yongliang Yuan ◽  
Zhizhen Song ◽  
Dan Yan ◽  
Xiangzhen Kong
Keyword(s):  
Lung Cancer ◽  
Drug Resistance ◽  
Small Cell Lung Cancer ◽  
Cell Lines ◽  
Cell Lung Cancer ◽  
Expression Profiles ◽  
Small Cell ◽  
Nsclc Cell ◽  
Drug Resistant ◽  
Small Cell Lung

Background/Aims: Drug resistance remains a main obstacle to the treatment of non- small cell lung cancer (NSCLC). The aim of this study was to identify the expression profiles of microRNAs (miRNAs) in drug-resistant NSCLC cell lines. Methods: The expression profiles of miRNAs in drug-resistant NSCLC cell lines were examined using miRNA sequencing, and the common dysregulated miRNAs in these cell lines were identified and analyzed by bioinformatics methods. Results: A total of 29 upregulated miRNAs and 36 downregulated miRNAs were found in the drug-resistant NSCLC cell lines, of which 26 upregulated and 36 downregulated miRNAs were found to be involved in the Ras signaling pathway. The expression levels, survival analysis, and receiver operating characteristic curve of the dysregulated miRNAs based on The Cancer Genome Atlas database for lung adenocarcinoma showed that hsa-mir-192, hsa-mir-1293, hsa-mir-194, hsa-mir-561, hsa-mir-205, hsa-mir-30a, and hsa-mir-30c were related to lung cancer, whereas only hsa-mir-1293 and hsa-mir-561 were not involved in drug resistance. Conclusion: The results of this study may provide novel biomarkers for drug resistance in NSCLC and potential therapies for overcoming drug resistance, and may also reveal the potential mechanisms underlying drug resistance in this disease.

scholarly journals SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

2020 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

Abstract Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

scholarly journals Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

2020 ◽  
Vol 21 (S9) ◽  
Author(s):  
Mona Maharjan ◽  
Raihanul Bari Tanvir ◽  
Kamal Chowdhury ◽  
Wenrui Duan ◽  
Ananda Mohan Mondal
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Cell Lines ◽  
High Expression ◽  
Different Types ◽  
Low Expression

Abstract Background Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment. Results The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes – one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups. Conclusion A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies – non-treatment and treatment – are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

scholarly journals Comparison of the Gene Expression Profiles Between Smokers With and Without Lung Cancer Using RNA-Seq

2012 ◽  
Vol 13 (8) ◽  
pp. 3605-3609 ◽  
Author(s):  
Peng Cheng ◽  
You Cheng ◽  
Yan Li ◽  
Zhenguo Zhao ◽  
Hui Gao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Rna Seq

scholarly journals SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References

2019 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M. Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

AbstractRecent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

O-76 Oligonucleotide microarray gene expression profiles of lung cancer cell lines sensitive and resistant to ZD-1839

Lung Cancer ◽  
2003 ◽  
Vol 41 ◽  
pp. S25
Author(s):  
Wilbur A. Franklin ◽  
Barbara A. Helfrich ◽  
Michio Sugita ◽  
Razvan Lapadat ◽  
Fred R. Hirsch ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Cell Lines ◽  
Lung Cancer Cell ◽  
Microarray Gene Expression ◽  
Microarray Gene

scholarly journals Sources of Variation in Cell-Type RNA-Seq Profiles

2020 ◽  
Author(s):  
Johan Gustafsson ◽  
Felix Held ◽  
Jonathan Robinson ◽  
Elias Björnson ◽  
Rebecka Jörnsten ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Specific ◽  
Technical Factors

Abstract Background Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. Results We evaluated different normalization methods, quantified the magnitude of variation introduced by different sources, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We applied methods such as random forest regression to a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is of the same magnitude as the biological variation across cell types. Tissue of origin and cell subtype are less important but still substantial factors, while the difference between individuals is relatively small. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample.Conclusions Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.

scholarly journals Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

2020 ◽  
Author(s):  
Jeffrey J. Quinn ◽  
Matthew G. Jones ◽  
Ross A. Okimoto ◽  
Shigeki Nanjo ◽  
Michelle M. Chan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Single Cell ◽  
Cancer Progression ◽  
Cell Lineage ◽  
Lineage Tracing ◽  
Rna Seq ◽  
Disease Etiology ◽  
Cell Lineages ◽  
Transient Events

AbstractCancer progression is characterized by rare, transient events which are nonetheless highly consequential to disease etiology and mortality. Detailed cell phylogenies can recount the history and chronology of these critical events – including metastatic seeding. Here, we applied our Cas9-based lineage tracer to study the subclonal dynamics of metastasis in a lung cancer xenograft mouse model, revealing the underlying rates, routes, and drivers of metastasis. We report deeply resolved phylogenies for tens of thousands of metastatically disseminated cancer cells. We observe surprisingly diverse metastatic phenotypes, ranging from metastasis-incompetent to aggressive populations. These phenotypic distinctions result from pre-existing, heritable, and characteristic differences in gene expression, and we demonstrate that these differentially expressed genes can drive invasiveness. Furthermore, metastases transit via diverse, multidirectional tissue routes and seeding topologies. Our work demonstrates the power of tracing cancer progression at unprecedented resolution and scale.One Sentence SummarySingle-cell lineage tracing and RNA-seq capture diverse metastatic behaviors and drivers in lung cancer xenografts in mice.

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