Knowledge and Theme Discovery across Very Large Biological Data Sets Using Distributed Queries: A Prototype Combining Unstructured and Structured Data

<div><p>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.</p></div

Growth of articles in MEDLINE.

<p>A bar chart displaying the number of baseline records in NLM MEDLINE’s 2001 baseline release to 2012 baseline release. (<a href="http://www.nlm.nih.gov/bsd/licensee/2012_stats/baseline_doc.html" target="_blank">http://www.nlm.nih.gov/bsd/licensee/2012_stats/baseline_doc.html</a>).</p

Network of Cancer-Gene associations from literature.

<p>Network of Cancer/Gene associations displaying shared genes between cancers and genes specific to certain cancer types based on literature evidence. Cancer terms are represented as labeled nodes, genes are unlabeled pink nodes and the edges represent at least one publication with a co-occurrence of the cancer term and gene.</p

Load and query times using simulated gene expression data.

*<p> Query to get the DEG list was not run on the 8TB data due to time constraints.</p

Bubble chart of Cancer-Gene associations from literature.

<p>A bubble chart representation with cancer terms on the x-axis and genes on the y-axis. The size of the bubble is directly proportional to the number of literature articles where the cancer and gene terms co-occur.</p

Cancer term occurrences in the literature.

<p>A bar chart representation with cancer terms on the y-axis and publication counts on the x-axis. Only the cancer terms with high literature occurrences are shown.</p

Architecture for integrating structured and unstructured data in Hadoop.

<p>Architectural diagram detailing the steps in creating the categorical lexicons and using them to get the PMID counts from literature. DEG stands for Differentially Expressed Gene while DE miRNA stands for Differentially Expressed miRNA.</p

Pathway-Specific Engineered Mouse Allograft Models Functionally Recapitulate Human Serous Epithelial Ovarian Cancer

<div><p>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 <i>Tp53</i> and <i>Rb</i> with or without <i>Brca1</i> or <i>Brca2</i> 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 <i>Brca1</i> respond to treatment with cisplatin and olaparib, a PARP inhibitor, whereas <i>Brca1</i>-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 <i>Brca1</i>-wild type and <i>Brca1</i>–deficient tumors and development of relevant biomarkers.</p></div

<i>In vitro</i> sensitivity of human and mouse ovarian epithelial carcinoma cells to anti-cancer treatment.

<p>Human (A, B) and murine (C–H) ovarian cancer cells were exposed to cisplatin, olaparib or vehicle for 7 days after which cell viability was measured using XTT reagent. Proportional viability was calculated by comparing the drugs with vehicle controls whose viability was assumed to be 100%. C. Panel of murine cell lines was selected based on <i>Brca1</i> status containing <i>Brca1</i>-deficient (striped bar) and –wild type lines (filled bars) maintaining similar <i>Brca1</i> expression as their tumors of origin (D). IC50 values for individual cancer cell lines are shown in the legends (A, B, E, G, parenthesis behind the cell line designation). Comparison of the IC50 for cisplatin (F) and olaparib (H) in wild type (<i>TgK18G_T121^tg/+/p53^Δ/Δ</i>; R5810T, R5838T) and mutant (<i>TgK18G_T121^tg/+/Brca1^Δ/Δ/p53^Δ/Δ</i>; 39647T, 60577T, 60580T, 82394T) murine cell lines shows significant difference between the genotypes (T-test, 333.7±54.1 vs 146.0±8.2, p<0.01 and 18.2±1.4 vs 10.4±1.2, p<0.05, respectively for drugs). The average and standard error is shown.</p

Quantification of tumor progression in orthotopic SEOC models and treatment with cisplatin and/or olaparib.

<p>A, Schematics of dosing regimen and imaging in efficacy studies. B, Inhibition of PAR formation in tumor lysates treated with olaparib for 2(C). The average and standard error is shown. D, Representative MR images before and after 2 weeks of treatment with vehicle, olaparib, cisplatin or combination of olaparib and cisplatin are shown. Scale bar represents 1 cm. White arrows point to the tumors, green arrows point to contralateral ovaries. MRI based quantification of tumor volume changes expressed as RTV following 2 week (E) and 3 week (F) treatment regimen. Statistical differences between groups were analyzed by one-way ANOVA and Tukey's multiple comparisons test. Each point represents one animal. V; vehicle, O; olaparib, C; cisplatin, O+C; olaparib and cisplatin.</p