Dataset Information

MigExpress discovers essential cell migration control genes in non-small cell lung cancer

ABSTRACT: Cell migration is an essential process in health and disease - especially in cancer metastasis. The difficulty to assess migration in high throughput presents a methodological hurdle - hence, only very few screens revealed factors controlling this important process. Here, we introduce MigExpress as a platform for the "identification of Migration control genes by differential Expression". MigExpress exploits the combination of in-depth molecular profiling and the robust quantitative analysis of migration capacity in a broad panel of samples and identifies migration-associated genes by their differential expression in slowly versus fast migrating cells. We applied MigExpress to non-small cell lung cancer (NSCLC), the most frequent cause of cancer mortality mainly due to metastasis. In 54 NSCLC cell lines, we comprehensively determined mRNA and protein expression. Correlating the transcriptome and proteome profiles with the quantified migration properties led to the discovery and validation of FLNC, DSE, CPA4, TUBB6 and BICC1 as migration control factors in NSCLC cells, which also negatively correlated with patient survival. Notably, FLNC was the least expressed filamin in NSCLC, but the only one controlling cell migration and correlating with patient survival and metastatic disease stage.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Lung, Non-small Cell Lung Cancer Cell Line, Lung Cancer Cell Line

DISEASE(S): Lung Cancer,Lung Carcinoma,Non-small Cell Lung Carcinoma

SUBMITTER: Chen Meng

LAB HEAD: Christina Ludwig

PROVIDER: PXD022146 | Pride | 2021-04-20

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	009606_Human_uniprot-sp_canonical_160322_PFAM_v03.12.15.fasta	Fasta
	20181119_TMT_column_and_row_wise_normalized.xlsx	Xlsx
	BBM_059_P059_01_HEK_001.raw	Raw
	BBM_059_P059_01_HEK_002.raw	Raw
	BBM_059_P059_01_HEK_003.raw	Raw

Items per page:

1 - 5 of 229

Similar Datasets

Project description:Epithelial/mesenchymal transition (EMT) is associated with loss of cell adhesion molecules, such as E-cadherin, and increased invasion, migration, and proliferation in epithelial cancers. In non-small cell lung cancer (NSCLC), EMT is associated with greater resistance to EGFR inhibitors. However, its potential to predict response to other targeted drugs or chemotherapy has not been well characterized. The goal of this study was to develop a robust, platform-independent EMT gene expression signature and to investigate the association of EMT and drug response in NSCLC. A 76-gene EMT signature was derived in 54 DNA-fingerprinted NSCLC cell lines and tested in an independent set of cell lines and in NSCLC patients from the BATTLE clinical trial. The signature classified cell lines as epithelial or mesenchymal independent of the microarray platform and correlated strongly with E-cadherin protein levels, as measured by reverse phase protein array. Higher protein expression of Rab25 (in epithelial lines) and Axl (in mesenchymal lines), two signature genes associated with in EMT in other cancer types, was also confirmed. Mesenchymal cell lines demonstrated significantly greater resistance to EGFR inhibition, independent of EGFR mutation status and were more resistant to drugs targeting the PI3K/Akt pathway. We observed no association between EMT and response to cytotoxic chemotherapies, including cisplatin, pemetrexed, and docetaxel monotherapy and/or doublets (p-values ≥0.2). In NSCLC patients, the EMT signature predicted 8-week disease control in the erlotinib arm, but not in other treatment arms. In conclusion, we have developed a robust EMT signature that predicts resistance to EGFR inhibitors and PI3K/Akt pathway inhibitors. Gene expression profiles were measured in 131 core biopsies from patients with refractory non-small cell lung cancer in the Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial. We used the BATTLE dataset to test an EMT gene expression signature trained in cell lines and independant of the microarray platform.

Dataset Information

MigExpress discovers essential cell migration control genes in non-small cell lung cancer

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets