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Figure 4: RMS classification of the testing tumors: sporadic and post-radiotherapy breast cancer.Testing tumor (sporadic breast cancers (control22 and control10)) and post-radiotherapy breast cancer (BfHL53 and BfHL31) (dot) classification considering all eigenvectors (validation space) in the RMS computation. Scatter plot of RMSsporadicmatrix as a function of the RMSpost-radiotherapymatrix for the learning tumors and the RMSsporadicclass as a function of the RMSpost-radiotherapyclass of the considered testing tumor. The RMS values of the training tumors fall into two distinct groups (control sporadic breast tumors, square and post-radiotherapy breast tumors, triangle). A, B: examples of correctly classified tumors, BfHL53 and control22, respectively. C, D: outlier classification of the BfHL31 and the control10, respectively. BfHl65 and Control26 are the two learning tumors that delineate their respective validation space.

Image Text (High Precision): Post Sporadic radiotherapy tumors

Other Images from "Strategy to Find Molecular Signatures in a Small Series of Rare Cancers: Validation for Radiation-Induced Breast and Thyroid Tumors":


Figure 4 RMS classification of the testing tumors...

Figure 1 Two-step PCA positioning of the testing ...

Figure 2 RMS classification of the testing tumors...

Figure 3 RMS classification of the testing tumors...

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Abstract

Methods of classification using transcriptome analysis for case-by-case tumor diagnosis could be limited by tumor heterogeneity and masked information in the gene expression profiles, especially as the number of tumors is small. We propose a new strategy, EMts_2PCA, based on: 1) The identification of a gene expression signature with a great potential for discriminating subgroups of tumors (EMts stage), which includes: a) a learning step, based on an expectation-maximization (EM) algorithm, to select sets of candidate genes whose expressions discriminate two subgroups, b) a training step to select from the sets of candidate genes those with the highest potential to classify training tumors, c) the compilation of genes selected during the training step, and standardization of their levels of expression to finalize the signature. 2) The predictive classification of independent prospective tumors, according to the two subgroups of interest, by the definition of a validation space based on a two-step principal component analysis (2PCA). The present method was evaluated by classifying three series of tumors and its robustness, in terms of tumor clustering and prediction, was further compared with that of three classification methods (Gene expression bar code, Top-scoring pair(s) and a PCA-based method). Results showed that EMts_2PCA was very efficient in tumor classification and prediction, with scores always better that those obtained by the most common methods of tumor clustering. Specifically, EMts_2PCA permitted identification of highly discriminating molecular signatures to differentiate post-Chernobyl thyroid or post-radiotherapy breast tumors from their sporadic counterparts that were previously unsuccessfully classified or classified with errors.


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