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Motivation: In recent years increasing evidence appeared that breastcancer may not constitute a single disease at the molecular level,but comprises a heterogeneous set of subtypes. This suggests that instead of building a single predictor, better predictors might be constructed that solely target samples of a designated subtype. An unavoidable drawback of developing subtype-specific predictors, however,is that a stratification by subtype drastically reduces the numberof samples available for their construction. It is therefore questionable whether the potential benefit of subtyping can outweigh the drawback of a severe loss in sample size. Factors like unequal class distributions and differences in the number of samples per subtype, further complicate comparisons. Results: We present several evaluation strategies that facilitate a comprehensive comparison between subtype-specific predictors and predictors that do not take subtype information into account. Emphasis lies on careful control of sample size as well as class and subtype distributions. The methodology is applied to a large breast cancer compendium involving over 1500 arrays,using a state-of-the-art subtyping scheme. We show that the resulting subtype-specific predictors outperform those that do not take subtype information into account, especially when taking sample size considerations into account.

The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to identify genes criticalto the development and progression of cancer. We seek to identify those genetic and epigenetic aberrations that have the most impact ongene function within the tumor. First, we perform a bioinformatic analysis of copy number variation (CNV) and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We separately examined CNV and DNA methylation for 42 primary serous ovarian cancersamples using MOMA-ROMA assays and 379 tumor samples analyzed by TheCancer Genome Atlas. We have identified 346 genes with significantdeletions or amplifications among the tumor samples. Utilizing associated gene expression data we predict 156 genes with altered copy number and correlated changes in expression. Among these genes CCNE1,POP4, UQCRB, PHF20L1 and C19orf2 were identified within both data sets. We were specifically interested in copy number variation as ourbase genomic property in the prediction of tumor suppressors and oncogenes in the altered ovarian tumor. We therefore identify changes in DNA methylation and expression for all amplified and deleted genes. We statistically define tumor suppressor and oncogenic features for these modalities and perform a correlation analysis with expression. We predicted 611 potential oncogenes and tumor suppressors candidates by integrating these data types. Genes with a strong correlation for methylation dependent expression changes exhibited at varyingcopy number aberrations include CDCA8, ATAD2, CDKN2A, RAB25, AURKA,BOP1 and EIF2C3. We provide copy number variation and DNA methylation analysis for over 11,500 individual genes covering the genetic landscape of ovarian cancer tumors. We show the extent of genomic and epigenetic alterations for known tumor suppressors and oncogenes andalso use these defined features to identify potential ovarian cancergene candidates.

Backgroud: This study was to validate the feasibility of using clinical 3.0T MRI to monitor the migration of autotransplanted bone marrow (BM)-derived stem-progenitor cells (SPC) to the injured arteries of near-human sized swine for potential cell-based arterial repair.Methodology: The study was divided into two phases. For in vitro evaluation, BM cells were extracted from the iliac crests of 13 domestic pigs and then labeled with a T2 contrast agent, Feridex, and/or afluorescent tissue marker, PKH26. The viability, the proliferation efficiency and the efficacies of Feridex and/or PKH26 labeling were determined. For in vivo validation, the 13 pigs underwent endovascular balloon-mediated intimal damages of the iliofemoral arteries. Thelabeled or un-labeled BM cells were autotransplanted back to the same pig from which the BM cells were extracted. Approximately three weeks post-cell transplantation, 3.0T T2-weighted MRI was performed todetect Feridex-created signal voids of the transplanted BM cells inthe injured iliofemoral arteries, which was confirmed by subsequenthistologic correlation. Principal Findings: Of the in vitro study,the viability of dual-labeled BM cells was 95-98%. The proliferation efficiencies of dual-labeled BM cells were not significantly different compared to those of non-labeled cells. The efficacies of Feridex- and PKH26 labeling were 90% and 100%, respectively. Of the in vivo study, 3.0T MRI detected the auto-transplanted BM cells migratedto the injured arteries, which was confirmed by histologic examinations. Conclusion: This study demonstrates the capability of using clinical 3.0T MRI to monitor the auto-transplantation of BM cells thatmigrate to the injured arteries of large animals, which may providea useful MRI technique to monitor cell-based arterial repair.