Proteomics

Dataset Information

0

Quantitative Proteomic Analysis Identifies IGFBP5 as a Secreted Marker for ASCL1High Pulmonary Neuroendocrine Tumors


ABSTRACT: Lung cancer is the leading cause of cancer death in the United States. Among the various subtypes of lung cancers, pulmonary neuroendocrine (NE) cancer, including small cell lung cancer (SCLC) and NE-non-small cell lung cancer (NE-NSCLC), is a particularly aggressive malignancy that is distinct from classic non–small cell lung cancer (NSCLC) in its metastatic potential and treatment response. Recently, the lineage-specific transcription factors Achaete-scute homolog 1 (ASCL1), NEUROD1, and POU2F3 have been reported to identify heterogeneity in pulmonary NE cancers. These transcription factors bind different genomic loci to regulate distinct gene programs in pulmonary NE cancers. However, the signaling pathways downstream of these transcription factors that distinguish these pulmonary NE cancer subtypes are not well characterized. Regulated protein secretion is critically involved in cell signaling and cell-cell communication events, and is known to be a hallmark associated with pulmonary NE tumors. Using a large-scale mass spectrometric approach, we performed quantitative secretomic analysis 13 cell lines including a pair of isogenic cell lines, i.e., an immortalized human bronchial epithelial cell and an ASCL1high NE NSCLC line. This panel also contained 6 additional ASCL1High and 5 NEUROD1High NE-lung cancer cell lines. From the conditioned media of the 13 cell lines, we identified and quantified 1,626 proteins. The NE-specific secretome is associated with a number of biological processes related to neurodevelopment. Further analysis of the upregulated proteins in ASCL1High subtype NE-lung cancer cells leads to the identification of IGFBP5 being a specific secreted marker for ASCL1High pulmonary NE cancer cells. Furthermore, IGFBP5 is also upregulated in the serum of a genetically modified mouse model of ASCL1High SCLC, as well as in human ASCL1High SCLC tumors. Mechanistically, ASCL1 binds to E-box elements in the IGFBP5 gene and directly regulates its transcription. Knockdown of ASCL1 in SCLC decreases IGFBP5 expression, which, in turn, leads to hyperactivation of the IGF-1R pathway. Pharmacological co-targeting of ASCL1 and IGF-1R signaling results in markedly synergistic, growth inhibitory effects in ASCL1High SCLC both in vitro and in vivo. Together, our quantitative proteomic analysis identifies a novel secreted marker and a new combination therapy for ASCL1High pulmonary NE cancer cells. In addition, we expect that the data sets will serve as an invaluable resource, providing the foundation for future mechanistic studies and biomarker discovery that helps delineate the molecular underpinnings of pulmonary NE tumors.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Epithelial Cell, Cell Culture

DISEASE(S): Pulmonary Neuroendocrine Tumor

SUBMITTER: Xu-Dong Wang  

LAB HEAD: Yonghao Yu

PROVIDER: PXD013267 | Pride | 2019-07-22

REPOSITORIES: Pride

Dataset's files

Source:
Action DRS
F05864.raw Raw
F05865.raw Raw
F05866.raw Raw
F05867.raw Raw
F05868.raw Raw
Items per page:
1 - 5 of 17
altmetric image

Publications

Subtype-specific secretomic characterization of pulmonary neuroendocrine tumor cells.

Wang Xu-Dong XD   Hu Rongkuan R   Ding Qing Q   Savage Trisha K TK   Huffman Kenneth E KE   Williams Noelle N   Cobb Melanie H MH   Minna John D JD   Johnson Jane E JE   Yu Yonghao Y  

Nature communications 20190719 1


Pulmonary neuroendocrine (NE) cancer, including small cell lung cancer (SCLC), is a particularly aggressive malignancy. The lineage-specific transcription factors Achaete-scute homolog 1 (ASCL1), NEUROD1 and POU2F3 have been reported to identify the different subtypes of pulmonary NE cancers. Using a large-scale mass spectrometric approach, here we perform quantitative secretome analysis in 13 cell lines that signify the different NE lung cancer subtypes. We quantify 1,626 proteins and identify  ...[more]

Similar Datasets

2019-07-03 | PXD013298 | Pride
2023-01-12 | GSE192475 | GEO
2023-01-12 | GSE159801 | GEO
2016-07-21 | E-GEOD-69393 | biostudies-arrayexpress
2016-07-21 | E-GEOD-69394 | biostudies-arrayexpress
2024-08-28 | GSE256347 | GEO
2024-08-28 | GSE256346 | GEO
2024-08-28 | GSE256345 | GEO
2024-05-15 | PXD052033 | Pride
2022-07-05 | GSE193960 | GEO