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The high mortality rate from ovarian cancers can be attributed to late-stage diagnosis and lack of effective treatment. Despite enormous effort to develop better targeted therapies, platinum-based chemotherapy still remains the standard of care for ovarian cancer patients, and resistance occurs at a high rate. One of the rate limiting factors for translation of new drug discoveries into clinical treatments has been the lack of suitable preclinical cancer models with high predictive value. We previously generated genetically engineered mouse (GEM) models based on perturbation of Tp53 and Rb with or without Brca1 or Brca2 that develop serous epithelial ovarian cancer (SEOC) closely resembling the human disease on histologic and molecular levels. Here, we describe an adaptation of these GEM models to orthotopic allografts that uniformly develop tumors with short latency and are ideally suited for routine preclinical studies. Ovarian tumors deficient in Brca1 respond to treatment with cisplatin and olaparib, a PARP inhibitor, whereas Brca1-wild type tumors are non-responsive to treatment, recapitulating the relative sensitivities observed in patients. These mouse models provide the opportunity for evaluation of effective therapeutics, including prediction of differential responses in Brca1-wild type and Brca1-deficient tumors and development of relevant biomarkers.

As the discipline of biomedical science continues to apply new technologies capable of producing unprecedented volumes of noisy and complex biological data, it has become evident that available methods for deriving meaningful information from such data are simply not keeping pace. In order to achieve useful results, researchers require methods that consolidate, store and query combinations of structured and unstructured data sets efficiently and effectively. As we move towards personalized medicine, the need to combine unstructured data, such as medical literature, with large amounts of highly structured and high-throughput data such as human variation or expression data from very large cohorts, is especially urgent. For our study, we investigated a likely biomedical query using the Hadoop framework. We ran queries using native MapReduce tools we developed as well as other open source and proprietary tools. Our results suggest that the available technologies within the Big Data domain can reduce the time and effort needed to utilize and apply distributed queries over large datasets in practical clinical applications in the life sciences domain. The methodologies and technologies discussed in this paper set the stage for a more detailed evaluation that investigates how various data structures and data models are best mapped to the proper computational framework.

Conservation strategies for African elephants would be advanced by resolution of conflicting claims that they comprise one, two, three or four taxonomic groups, and by development of genetic markers that establish more incisively the provenance of confiscated ivory. We addressed these related issues by genotyping 555 elephants from across Africa with microsatellite markers, developing a method to identify those loci most effective at geographic assignment of elephants (or their ivory), and conducting novel analyses of continent-wide datasets of mitochondrial DNA. Results showed that nuclear genetic diversity was partitioned into two clusters, corresponding to African forest elephants (99.5% Cluster-1) and African savanna elephants (99.4% Cluster-2). Hybrid individuals were rare. In a comparison of basal forest "F" and savanna "S" mtDNA clade distributions to nuclear DNA partitions, forest elephant nuclear genotypes occurred only in populations in which S clade mtDNA was absent, suggesting that nuclear partitioning corresponds to the presence or absence of S clade mtDNA. We reanalyzed African elephant mtDNA sequences from 81 locales spanning the continent and discovered that S clade mtDNA was completely absent among elephants at all 30 sampled tropical forest locales. The distribution of savanna nuclear DNA and S clade mtDNA corresponded closely to range boundaries traditionally ascribed to the savanna elephant species based on habitat and morphology. Further, a reanalysis of nuclear genetic assignment results suggested that West African elephants do not comprise a distinct third species. Finally, we show that some DNA markers will be more useful than others for determining the geographic origins of illegal ivory. These findings resolve the apparent incongruence between mtDNA and nuclear genetic patterns that has confounded the taxonomy of African elephants, affirm the limitations of using mtDNA patterns to infer elephant systematics or population structure, and strongly support the existence of two elephant species in Africa.

African-American breast cancer patients experience higher mortality rates than European-American patients despite having a lower incidence of the disease. We tested the hypothesis that intrinsic differences in the tumor biology may contribute to this cancer health disparity.Using laser capture microdissection, we examined genome-wide mRNA expression specific to tumor epithelium and tumor stroma in 18 African-American and 17 European-American patients. Numerous genes were differentially expressed between these two patient groups and a two-gene signature in the tumor epithelium distinguished between them. To identify the biological processes in tumors that are different by race/ethnicity, Gene Ontology and disease association analyses were performed. Several biological processes were identified which may contribute to enhanced disease aggressiveness in African-American patients, including angiogenesis and chemotaxis. African-American tumors also contained a prominent interferon signature. The role of angiogenesis in the tumor biology of African-Americans was further investigated by examining the extent of vascularization and macrophage infiltration in an expanded set of 248 breast tumors. Immunohistochemistry revealed that microvessel density and macrophage infiltration is higher in tumors of African-Americans than in tumors of European-Americans. Lastly, using an in silico approach, we explored the potential of tailored treatment options for African-American patients based on their gene expression profile. This exploratory approach generated lists of therapeutics that may have specific antagonistic activity against tumors of African-American patients, e.g., sirolimus, resveratrol, and chlorpromazine in estrogen receptor-negative tumors.The gene expression profiles of breast tumors indicate that differences in tumor biology may exist between African-American and European-American patients beyond the knowledge of current markers. Notably, pathways related to tumor angiogenesis and chemotaxis could be functionally different in these two patient groups.

Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. This gene-level paradigm can be limited as a result of valid sample fluctuations and biological complexities. In this report, we describe a novel method, SLEPR, which eliminates this limitation by relying on pathway-level consistencies. Our method first selects the sample-level differentiated genes from each individual sample, capturing genes missed by other analysis methods, ascertains the enrichment levels of associated pathways from each of those lists, and then ranks annotated pathways based on the consistency of enrichment levels of individual samples from both sample classes. As a proof of concept, we have used this method to analyze three public microarray datasets with a direct comparison with the GSEA method, one of the most popular pathway-level analysis methods in the field. We found that our method was able to reproduce the earlier observations with significant improvements in depth of coverage for validated or expected biological themes, but also produced additional insights that make biological sense. This new method extends existing analyses approaches and facilitates integration of different types of HTP data.

Accurate assessment of the association between continuous variables such as gene expression and survival is a critical aspect of precision medicine. In this report, we provide a review of some of the available survival analysis and validation tools by referencing published studies that have utilized these tools. We have identified pitfalls associated with the assumptions inherent in those applications that have the potential to impact scientific research through their potential bias. In order to overcome these pitfalls, we have developed a novel method that enables the logrank test method to handle continuous variables that comprehensively evaluates survival association with derived aggregate statistics. This is accomplished by exhaustively considering all the cutpoints across the full expression gradient. Direct side-by-side comparisons, global ROC analysis, and evaluation of the ability to capture relevant biological themes based on current understanding of RAS biology all demonstrated that the new method shows better consistency between multiple datasets of the same disease, better reproducibility and robustness, and better detection power to uncover biological relevance within the selected datasets over the available survival analysis methods on univariate gene expression and penalized linear model-based methods.