1. Genomic analysis: Toward a new approach in breast cancer management
Sebastiano Cavallaro, Sabrina Paratore, Femke de Snoo, Edvige Salomone, Loredana Villari, Calogero Buscarino, Francesco Ferraù, Giuseppe Banna, Marco Furci, Angela Strazzanti, Rosario Cunsolo, Salvatore Pezzino, Santi Gangi, Francesco Basile 
Critical Reviews in Oncology / Hematology 
Volume 81, Issue 3, Pages (March 2012)
DOI: /j.critrevonc
Copyright © 2011 Elsevier Ireland Ltd Terms and Conditions

2. Fig. 1
Schematic representation of comparative genomic hybridization array methodology. Briefly, genomic DNA is extracted from breast tumor and normal tissue (from a core needle or at surgery) and labeled with different fluorochromes (e.g. Cy3 and Cy5). Following dye-swap labeling (tumoral Cy3-DNA vs. normal Cy5-DNA and tumoral Cy5-DNA vs. normal Cy3-DNA), labeled mixtures are co-hybridized into a microarray spotted with specific DNA probe sets. At the end of the hybridization, a laser scanner collects the image produced by fluorochromes. Tumoral and normal samples competitively bind to the spots and the resulting fluorescence intensity ratios are reflected by their relative quantities. Specialized software captures the images and converts the fluorescence intensity data to a linear red-to-green ratio profile that correlates with the hybridization intensity, which mainly depends on the extent and size of DNA tumor changes. The processed data are used to compare the clinical data available with the gene copy number changes observed. In the upper part of the figure (on the right) a diagram of chromosome 1 is reported. In the diagram, each dot represents a single clone spotted on the array and vertical lines adjacent to the chromosome indicate the thresholds for losses (red) and gain (green).
Critical Reviews in Oncology / Hematology  , DOI: ( /j.critrevonc )
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3. Fig. 2
Schematic description of gene expression array methodology. RNA samples extracted from breast tumor and normal tissue (from a core needle or at surgery) can be reverse transcribed, differentially labeled and simultaneously co-hybridized to microarray. Intensity values from each spot are calculated and then analyzed by specific software (lower part of the figure). Data can be represented graphically by a scatter plot, with the values of sample one plotted on the x-axis and the values of sample two plotted on the y-axis. Data obtained under different conditions (e.g. different time points) can be analyzed with different cluster algorithms. Most cluster analysis techniques are hierarchical, the resultant classification has an increasing number of nested classes and the result resembles a phylogenetic classification. Non-hierarchical clustering techniques also exist, such as k-means clustering, which simply partition objects into different clusters without trying to specify the relationship between individual elements.
Critical Reviews in Oncology / Hematology  , DOI: ( /j.critrevonc )
Copyright © 2011 Elsevier Ireland Ltd Terms and Conditions

4. Fig. 3
Illustration of microarray-based 70 gene profile analysis. To assess global gene expression, messenger RNA (mRNA) is extracted from the fresh tumor sample (a biopsy punch of 3mm, as shown in the left lower part of the figure) and labeled with a fluorescent dye. The labeled mRNA, together with labeled mRNA from a reference sample, is hybridized on a microarray. A specific algorithm is used to compare gene activity to that of a specific expression signature [99] that is strongly prognostic for the development of distant metastasis in lymph node negative patients, thereby producing a score that determines whether the patient is deemed at Low Risk or High Risk for metastasis (upper part of the figure). The Kaplan-Meier curves reported in the right lower part of the figure show how the 70 genes expression profile analyzed in the test predict 10-year disease-free survival more accurately than the St. Gallen criteria [119].
Critical Reviews in Oncology / Hematology  , DOI: ( /j.critrevonc )
Copyright © 2011 Elsevier Ireland Ltd Terms and Conditions