Gene expression analysis and clinical diagnosis

Sandvik, Arne K.; Alsberg, Bjørn K.; Nørsett, Kristin G.; Yadetie, Fekadu; Waldum, Helge L.; Lægreid, Astrid

doi:10.1016/j.cccn.2005.05.046

Cited by 34 publications

(22 citation statements)

References 58 publications

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“…Because quantitative PCR is sensitive, reproducible and reliable for determining the level of c-Kit mRNA, this gene expression analysis may have a larger potential to identify the patients more likely to benefit from c-Kit-targeted therapies in ACC [24], [25]. These therapies may include targeting c-Kit protein or upstream molecules that regulate it.…”

Section: Discussionmentioning

confidence: 99%

c-Kit Expression is Rate-Limiting for Stem Cell Factor-Mediated Disease Progression in Adenoid Cystic Carcinoma of the Salivary Glands

Phuchareon

Zante

Overdevest

et al. 2014

Translational Oncology

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Adenoid cystic carcinoma (ACC) is an aggressive malignant neoplasm of the salivary glands in which c-Kit is overexpressed and activated, although the mechanism for this is as yet unclear. We analyzed 27 sporadic ACC tumor specimens to examine the biologic and clinical significance of c-Kit activation. Mutational analysis revealed expression of wild-type c-Kit in all, eliminating gene mutation as a cause of activation. Because stem cell factor (SCF) is c-Kit's sole ligand, we analyzed its expression in the tumor cells and their environment. Immunohistochemistry revealed its presence in c-Kit–positive tumor cells, suggesting an activation of autocrine signaling. We observed a significant induction of ERK1/2 in the cells. SCF staining was also found in other types of non-cancerous cells adjacent to tumors within salivary glands, including stromal fibroblasts, neutrophils, peripheral nerve, skeletal muscle, vascular endothelial cells, mucous acinar cells, and intercalated ducts. Quantitative PCR showed that the top quartile of c-Kit mRNA expression distinguished ACCs from normal salivary tissues and was cross-correlated with short-term poor prognosis. Expression levels of SCF and c-Kit were highly correlated in the cases with perineural invasion. These observations suggest that c-Kit is potentially activated by receptor dimerization upon stimulation by SCF in ACC, and that the highest quartile of c-Kit mRNA expression could be a predictor of poor prognosis. Our findings may support an avenue for c-Kit-targeted therapy to improve disease control in ACC patients harboring the top quartile of c-Kit mRNA expression.

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Section: Discussionmentioning

confidence: 99%

c-Kit Expression is Rate-Limiting for Stem Cell Factor-Mediated Disease Progression in Adenoid Cystic Carcinoma of the Salivary Glands

Phuchareon

Zante

Overdevest

et al. 2014

Translational Oncology

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“…MARS has been previously shown to be potentially useful for gene association studies and the detection of variable interactions [2,19]. As a new basis of diagnostic tests is being provided by the vast amount of data on gene expression that are now becoming available through large-scale measurement of mRNA abundance [15], we expect that MARS should become a useful tool for disease diagnosis using gene expression data. To facilitate the employment of the proposed method, the sample code for the analysis of DeLong et al's data is provided in the appendix.…”

Section: Discussionmentioning

confidence: 99%

Approximating the risk score for disease diagnosis using MARS

2009

Journal of Applied Statistics

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In disease screening and diagnosis, often multiple markers are measured and they are combined in order to improve the accuracy of diagnosis. McIntosh and Pepe (2002, Biometrics 58, 657-644) showed that the risk score, defined as the probability of disease conditional on multiple markers, is the optimal function for classification based on the Neyman-Pearson Lemma. They proposed a twostep procedure to approximate the risk score. However, the resulted ROC curve is only defined in a subrange (L, h) of the false-positive rates in (0,1) and determination of the lower limit L needs extra prior information. In practice, most diagnostic tests are not perfect and it is usually rare that a single marker is uniformly better than the other tests. Using simulation, I show that multivariate adaptive regression spline (MARS) is a useful tool to approximate the risk score when combining multiple markers, especially when the ROC curves from multiple tests cross. The resulted ROC is defined in the whole range of (0,1) and it is easy to implement and has intuitive interpretation. The sample code of the application is shown in the appendix.

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“…When these challenges are overcome, proteomics researchers should look to the microarray community which have experience with similar kinds of data and now possesses knowledge about the importance of unifying the requirements for a valid analysis and design pipeline [68].…”

Section: Table 2 Illustrative Expression Matrix Of Peptides and Patimentioning

confidence: 99%

Proteomic-Based Biomarker Discovery with Emphasis on Cerebrospinal Fluid and Multiple Sclerosis

Berven¹,

Flikka

Berle³

et al. 2006

CPB

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Discovery of disease specific biomarkers in human body fluids has become an important challenge in clinical proteomics. Facing the increasing threat of degenerative and disabling diseases like cancer, cardiovascular, neurological and inflammatory diseases in large parts of the world's population, there is an urgent need to improve early diagnostics. In this review we discuss possibilities and limitations connected to using mass spectrometry based proteomics in the search for novel biomarkers, with focus on multiple sclerosis as a typical representative for the large group of non-curable degenerative and disabling disease with the lack of specific tests for early diagnosis. Careful control of the pre-analytical phase including sampling, storage and fractionation of samples, in addition to a thoroughly considered patient selection, is important in order to avoid false biomarkers to appear in the resulting mass spectra. Furthermore, advanced computational tools are needed in order to discover potential biomarkers from the enormous data amounts generated by the mass spectrometers. The development of such computer tools is a research field currently in the start phase and could prove to be a bottle neck in the biomarker discovery the next years. Therefore, a rather detailed review of the most used computational and pre-analytical methods is given in this review. Mass spectrometry based biomarker discovery is undoubtedly still in its early infancy. However, in light of the potential of this technology to provide deep coverage of the body fluid proteomes, it will certainly consolidate its role in developing molecular medicine into clinical practice.

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Gene expression analysis and clinical diagnosis

Cited by 34 publications

References 58 publications

c-Kit Expression is Rate-Limiting for Stem Cell Factor-Mediated Disease Progression in Adenoid Cystic Carcinoma of the Salivary Glands

c-Kit Expression is Rate-Limiting for Stem Cell Factor-Mediated Disease Progression in Adenoid Cystic Carcinoma of the Salivary Glands

Approximating the risk score for disease diagnosis using MARS

Proteomic-Based Biomarker Discovery with Emphasis on Cerebrospinal Fluid and Multiple Sclerosis

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