CiteULike: nedwards's He

Gene expression profiling predicts clinical outcome of breast cancer.

LJ van 't Veer — 2006-02-14T07:49:54-00:00

Nature, Vol. 415, No. 6871. (31 January 2002), pp. 530-536.

Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.

Analysis of human liver proteome using replicate shotgun strategy

Ming Chen — 2007-08-27T18:04:04-00:00

PROTEOMICS, Vol. 7, No. 14. (2007), pp. 2479-2488.

In this study, a liquid-based shotgun strategy was used to comprehensively identify the expression of human liver proteome. Proteins were extracted from human liver tissue and digested in-solution. The tryptic digest mixture was desalted and separated by off-line strong cation exchange (SCX) chromatography with a 60-min elution. The MS/MS spectra were acquired in data-dependent mode after an RP chromatographic separation combined with linear IT MS analysis. To obtain the most comprehensive human liver proteome, each SCX fraction was run six times in RPLC MS/MS manner. Finally, more than 6 000 000 MS/MS spectra were collected. Using a relatively strict filter criteria, 24 311 proteins (48.42% of the predicted human proteome from human International Protein Index (IPI) protein database 3.07) corresponding to 13 150 nonredundant proteins were successfully identified, in which 7001 proteins (53.24%) were identified by two or more peptides, which could be considered as a high-confident dataset. Among the 6149 proteins (46.76%) identified by single peptide, 3812 proteins (61.99%) were detected more than twice in six repeated runs. Comparative analysis between different runs shows that the overlap of identified proteins between any two runs ranged from 25 to 44%. Of the nonredundant proteins identified, 8919 proteins (67.83%) were detected more than twice and 4231 proteins (32.17%) were detected only once in six RPLC MS/MS runs. The Gene Ontology annotation shows that the identified proteins come from various subcellular components. In addition, a large number of low abundant proteins were identified. The dynamic range of the approach reached at least nine orders of magnitude by estimating the concentration of proteins.

Reproducibility Assessment of Relative Quantitation Strategies for LC-MS Based Proteomics

YJ Kim — 2007-06-25T02:37:04-00:00

Anal. Chem. (20 June 2007)

Abstract: The reproducibility of a given method for relative quantitation governs the reliability of liquid chromatography-mass spectrometry (LC-MS) based differential analysis in proteomic studies. Understanding the noise level introduced from biological, chemical, and instrumental sources not only helps to determine the experimental design but also aids in assessing the reliability of expression ratios used for quantitation. Here we present a reproducibility assessment method for relative quantitation based on the intensity ratio distribution of common features in LC-MS replicates. This method applies to both decoupled (label-free quantitation) and coupled (label-dependent quantitation) methods. Aligning the features of LC-MS maps directly for the decoupled method or by matching an LC-MS map and its virtual map for the coupled method results in a list of common features for replicate samples. We find that the ratio distribution of the common features successfully indicates the reproducibility of each experiment prior to MS/MS peptide sequencing in three different quantitation strategies: decoupled, coupled isotope-coded affinity tag, and coupled stable isotope labeling of amino acids in cell culture experiments.