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Diagnostic Imaging

Disease Markers: Cancer Genomics by Robert L. Strausberg

By Robert L. Strausberg

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Weinstein et al. / Searching for pharmacogenomic markers: The synergy between omic and hypothesis-driven research Fig. 2. Proteomic profiling of the NCI-60 cell lines [28]. A: 2-D gels were run for duplicate harvests of the 60 cell lines, the aim being to index spots across the 60 and develop quantitative patterns of protein expression analogous to those for compound activities. B: Computer-processed pseudocolor image of the central section of a master gel based on breast cancer line MCF-7. The spots are modeled as bivariate Gaussians of Coommassie blue intensity, and spot "volumes" are calculated as the integral of intensity over the area of the spot.

Each point and each patch of color in a CIM (such as that in Fig. 5) represents a possible story. N. Weinstein et al. / Searching for pharmacogenomic markers: The synergy between otnic and hypothesis-driven research Fig. 3. Clustering of cells in two ways: Based on patterns of cDNA microarray gene expression patterns (A) and on drug sensitivity patterns ( B ) . "Indicates parental and transfectant cell lines from the pleural effusion of a breast cancer patient but expressing the proteins and transcripts characteristic of melanoma (as discussed in [32.

The serum levels of different soluble CD44 molecules (CD44 standard form and CD 44 splice variant V6) were measured with an enzyme immunoassay method in venous blood samples preoperatively collected from 100 patients with invasive breast carcinoma. Preoperative serum soluble CD44 V6 was found to be closely related to distant metastases and TNM staging, indicating that CD44 V6 may have prognostic value in breast carcinoma. It is becoming clear that CD44 variants may be useful as diagnostic or prognostic markers in some human malignant diseases.

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