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1.  Differing clinical impact of BRCA1 and BRCA2 mutations in serous ovarian cancer 
Pharmacogenomics  2012;13(13):1523-1535.
A key function of BRCA1 and BRCA2 is the participation in dsDNAbreak repair via homologous recombination. BRCA1 and BRCA2 mutations, which occur in most hereditary ovarian cancers (OCs) and approximately 10% of all OC cases, are associated with defects in homologous recombination and genomic instability, a phenotype termed ‘BRCAness’. The clinical effects of BRCA1 and BRCA2 mutations have commonly been analyzed together; however, it is becoming increasingly apparent that these mutations do not have the same effects in OC. Recently, three major reports highlighted the unequal clinical characteristics of OCs with BRCA1 and BRCA2 mutations. All studies demonstrated that BRCA2-mutated patients are associated with better survival and therapeutic response than BRCA1-mutated and wild-type patients with serous OC. The differing prognostic effects of the BRCA2 and BRCA1 mutations is likely due to differing roles of BRCA1 and BRCA2 in homologous recombination repair and a stronger association between the BRCA2 mutation and a hypermutator phenotype. These new findings have potentially important implications for clinical management of patients with serous OC.
doi:10.2217/pgs.12.137
PMCID: PMC3603383  PMID: 23057551
BRCA mutation; drug response; homologous recombination; ovarian cancer; PARP inhibitor; survival
2.  On the Limitations of Biological Knowledge 
Current Genomics  2012;13(7):574-587.
Scientific knowledge is grounded in a particular epistemology and, owing to the requirements of that epistemology, possesses limitations. Some limitations are intrinsic, in the sense that they depend inherently on the nature of scientific knowledge; others are contingent, depending on the present state of knowledge, including technology. Understanding limitations facilitates scientific research because one can then recognize when one is confronted by a limitation, as opposed to simply being unable to solve a problem within the existing bounds of possibility. In the hope that the role of limiting factors can be brought more clearly into focus and discussed, we consider several sources of limitation as they apply to biological knowledge: mathematical complexity, experimental constraints, validation, knowledge discovery, and human intellectual capacity.
doi:10.2174/138920212803251445
PMCID: PMC3468890  PMID: 23633917
Complexity; Gene regulatory networks; Epistemology; Experimental design; Genomics; Knowledge discovery; Modeling; Validation.
3.  Fastbreak: a tool for analysis and visualization of structural variations in genomic data 
Genomic studies are now being undertaken on thousands of samples requiring new computational tools that can rapidly analyze data to identify clinically important features. Inferring structural variations in cancer genomes from mate-paired reads is a combinatorially difficult problem. We introduce Fastbreak, a fast and scalable toolkit that enables the analysis and visualization of large amounts of data from projects such as The Cancer Genome Atlas.
doi:10.1186/1687-4153-2012-15
PMCID: PMC3605143  PMID: 23046488
Cancer genomics; Structural variation; Translocation
4.  Increasing Coverage of Transcription Factor Position Weight Matrices through Domain-level Homology 
PLoS ONE  2012;7(8):e42779.
Transcription factor-DNA interactions, central to cellular regulation and control, are commonly described by position weight matrices (PWMs). These matrices are frequently used to predict transcription factor binding sites in regulatory regions of DNA to complement and guide further experimental investigation. The DNA sequence preferences of transcription factors, encoded in PWMs, are dictated primarily by select residues within the DNA binding domain(s) that interact directly with DNA. Therefore, the DNA binding properties of homologous transcription factors with identical DNA binding domains may be characterized by PWMs derived from different species. Accordingly, we have implemented a fully automated domain-level homology searching method for identical DNA binding sequences.
By applying the domain-level homology search to transcription factors with existing PWMs in the JASPAR and TRANSFAC databases, we were able to significantly increase coverage in terms of the total number of PWMs associated with a given species, assign PWMs to transcription factors that did not previously have any associations, and increase the number of represented species with PWMs over an order of magnitude. Additionally, using protein binding microarray (PBM) data, we have validated the domain-level method by demonstrating that transcription factor pairs with matching DNA binding domains exhibit comparable DNA binding specificity predictions to transcription factor pairs with completely identical sequences.
The increased coverage achieved herein demonstrates the potential for more thorough species-associated investigation of protein-DNA interactions using existing resources. The PWM scanning results highlight the challenging nature of transcription factors that contain multiple DNA binding domains, as well as the impact of motif discovery on the ability to predict DNA binding properties. The method is additionally suitable for identifying domain-level homology mappings to enable utilization of additional information sources in the study of transcription factors. The domain-level homology search method, resulting PWM mappings, web-based user interface, and web API are publicly available at http://dodoma.systemsbiology.netdodoma.systemsbiology.net.
doi:10.1371/journal.pone.0042779
PMCID: PMC3428306  PMID: 22952610
5.  Integrated Analysis of Gene Expression and Tumor Nuclear Image Profiles Associated with Chemotherapy Response in Serous Ovarian Carcinoma 
PLoS ONE  2012;7(5):e36383.
Background
Small sample sizes used in previous studies result in a lack of overlap between the reported gene signatures for prediction of chemotherapy response. Although morphologic features, especially tumor nuclear morphology, are important for cancer grading, little research has been reported on quantitatively correlating cellular morphology with chemotherapy response, especially in a large data set. In this study, we have used a large population of patients to identify molecular and morphologic signatures associated with chemotherapy response in serous ovarian carcinoma.
Methodology/Principal Findings
A gene expression model that predicts response to chemotherapy is developed and validated using a large-scale data set consisting of 493 samples from The Cancer Genome Atlas (TCGA) and 244 samples from an Australian report. An identified 227-gene signature achieves an overall predictive accuracy of greater than 85% with a sensitivity of approximately 95% and specificity of approximately 70%. The gene signature significantly distinguishes between patients with unfavorable versus favorable prognosis, when applied to either an independent data set (P = 0.04) or an external validation set (P<0.0001). In parallel, we present the production of a tumor nuclear image profile generated from 253 sample slides by characterizing patients with nuclear features (such as size, elongation, and roundness) in incremental bins, and we identify a morphologic signature that demonstrates a strong association with chemotherapy response in serous ovarian carcinoma.
Conclusions
A gene signature discovered on a large data set provides robustness in accurately predicting chemotherapy response in serous ovarian carcinoma. The combination of the molecular and morphologic signatures yields a new understanding of potential mechanisms involved in drug resistance.
doi:10.1371/journal.pone.0036383
PMCID: PMC3348145  PMID: 22590536

Results 1-5 (5)