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Use of gene expression profiling to direct in vivo molecular imaging of lung cancer.

ABSTRACT: Using gene expression profiling, we identified cathepsin cysteine proteases as highly up-regulated genes in a mouse model of human lung adenocarcinoma. Overexpression of cathepsin proteases in these lung tumors was confirmed by immunohistochemistry and Western blotting. Therefore, an optical probe activated by cathepsin proteases was selected to detect murine lung tumors in vivo as small as 1 mm in diameter and spatially separated. We generated 3D maps of the fluorescence signal and fused them with anatomical computed tomography images to show a close correlation between fluorescence signal and tumor burden. By serially imaging the same mouse, optical imaging was used to follow tumor progression. This study demonstrates the capability for molecular imaging of a primary lung tumor by using endogenous proteases expressed by a tumor. It also highlights the feasibility of using gene expression profiling to identify molecular targets for imaging lung cancer.

SUBMITTER: Grimm J

PROVIDER: S-EPMC1242291 | biostudies-literature | 2005 Oct

REPOSITORIES: biostudies-literature

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Use of gene expression profiling to direct in vivo molecular imaging of lung cancer.

Grimm Jan J Kirsch David G DG Windsor Stephen D SD Kim Carla F Bender CF Santiago Philip M PM Ntziachristos Vasilis V Jacks Tyler T Weissleder Ralph R

Proceedings of the National Academy of Sciences of the United States of America 20050923 40

Using gene expression profiling, we identified cathepsin cysteine proteases as highly up-regulated genes in a mouse model of human lung adenocarcinoma. Overexpression of cathepsin proteases in these lung tumors was confirmed by immunohistochemistry and Western blotting. Therefore, an optical probe activated by cathepsin proteases was selected to detect murine lung tumors in vivo as small as 1 mm in diameter and spatially separated. We generated 3D maps of the fluorescence signal and fused them w ...[more]

PMID: 16183744

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Project description:BackgroundNon-small cell lung cancer (NSCLC) is the most common type of lung cancer and has a poor prognosis. Identifying biomarkers based on molecular mechanisms is critical for early diagnosis, timely treatment, and improved prognosis of lung cancer. MALAT1 has been reported to have overexpressed and tumor-promoting functions in NSCLC. It has been proposed as a potential biomarker for the diagnosis and prognosis of cancer. Therefore, this study was conducted to profile the changes in gene expression according to the regulation of expression of MALAT1 in NSCLC cell lines and to investigate the correlation through bioinformatic analysis of differentially expressed genes (DEGs).MethodsMALAT1 expression levels were measured using RT-qPCR. The biological functions of MALAT1 in NSCLC were analyzed by cell counting, colony forming, wound-healing, and Transwell invasion assays. In addition, gene expression profiling in response to the knockdown of MALAT1 was analyzed by transcriptome sequencing, and differentially expressed genes regulated by MALAT1 were performed by GO and KEGG pathway enrichment analyses. Bioinformatic databases were used for gene expression analysis and overall survival analysis.ResultsComparative analysis versus MALAT1 expression in MRC5 cells (a normal lung cell line) and the three NSCLC cell lines showed that MALAT1 expression was significantly higher in the NSCLC cells. MALAT1 knockdown decreased cell survival, proliferation, migration, and invasion in all three NSCLC cell lines. RNA-seq analysis of DEGs in NSCLC cells showed 198 DEGs were upregulated and 266 DEGs downregulated by MALAT1 knockdown in all three NSCLC cell lines. Survival analysis on these common DEGs performed using the OncoLnc database resulted in the selection of five DEGs, phosphoglycerate mutase 1 (PGAM1), phosphoglycerate mutase 4 (PGAM4), nucleolar protein 6 (NOL6), nucleosome assembly protein 1 like 5 (NAP1L5), and sestrin1 (SESN1). The gene expression levels of these selected DEGs were proved to gene expression analysis using the TNMplot database.ConclusionMALAT1 might function as an oncogene that enhances NSCLC cell survival, proliferation, colony formation, and invasion. RNA-seq and bioinformatic analyses resulted in the selection of five DEGs, PGAM1, PGAM4, NOL6, NAP1L5, and SESN1, which were found to be closely related to patient survival and tumorigenesis. We believe that further investigation of these five DEGs will provide valuable information on the oncogenic role of MALAT1 in NSCLC.

Project description:Purpose Gene expression profiling assays are frequently used to guide adjuvant chemotherapy decisions in hormone receptor-positive, lymph node-negative breast cancer. We hypothesized that the clinical value of these new tools would be more fully realized when appropriately integrated with high-quality clinicopathologic data. Hence, we developed a model that uses routine pathologic parameters to estimate Oncotype DX recurrence score (ODX RS) and independently tested its ability to predict ODX RS in clinical samples. Patients and Methods We retrospectively reviewed ordered ODX RS and pathology reports from five institutions (n = 1,113) between 2006 and 2013. We used locally performed histopathologic markers (estrogen receptor, progesterone receptor, Ki-67, human epidermal growth factor receptor 2, and Elston grade) to develop models that predict RS-based risk categories. Ordering patterns at one site were evaluated under an integrated decision-making model incorporating clinical treatment guidelines, immunohistochemistry markers, and ODX. Final locked models were independently tested (n = 472). Results Distribution of RS was similar across sites and to reported clinical practice experience and stable over time. Histopathologic markers alone determined risk category with > 95% confidence in > 55% (616 of 1,113) of cases. Application of the integrated decision model to one site indicated that the frequency of testing would not have changed overall, although ordering patterns would have changed substantially with less testing of estimated clinical risk-high or clinical risk-low cases and more testing of clinical risk-intermediate cases. In the validation set, the model correctly predicted risk category in 52.5% (248 of 472). Conclusion The proposed model accurately predicts high- and low-risk RS categories (> 25 or ≤ 25) in a majority of cases. Integrating histopathologic and molecular information into the decision-making process allows refocusing the use of new molecular tools to cases with uncertain risk.

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Use of gene expression profiling to direct in vivo molecular imaging of lung cancer.

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Use of gene expression profiling to direct in vivo molecular imaging of lung cancer.

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