We constructed a bacterial artificial chromosome (BAC)-based physical map of chromosomes 2 and 3 of Drosophila melanogaster, which constitute 81% of the genome. Sequence tagged site (STS) content, restriction fingerprinting, and polytene chromosome in situ hybridization approaches were integrated to produce a map spanning the euchromatin. Three of five remaining gaps are in repeat-rich regions near the centromeres. A tiling path of clones spanning this map and STS maps of chromosomes X and 4 was sequenced to low coverage; the maps and tiling path sequence were used to support and verify the whole-genome sequence assembly, and tiling path BACs were used as templates in sequence finishing.
Cytological profiling (CP) is an unbiased image-based screening technique that uses automated microscopy and image analysis to profile compounds based on numerous quantifiable phenotypic features. We used CP to evaluate a library of nearly 500 compounds with documented mechanisms of action (MOAs) spanning a wide range of biological pathways. We developed informatics techniques for generating dosage-independent phenotypic "fingerprints" for each compound, and for quantifying the likelihood that a compound's CP fingerprint corresponds to its annotated MOA. We identified groups of features that distinguish classes with closely related phenotypes, such as microtubule poisons vs. HSP90 inhibitors, and DNA synthesis vs. proteasome inhibitors. We tested several cases in which cytological profiles indicated novel mechanisms, including a tyrphostin kinase inhibitor involved in mitochondrial uncoupling, novel microtubule poisons, and a nominal PPAR-gamma ligand that acts as a proteasome inhibitor, using independent biochemical assays to confirm the MOAs predicted by the CP signatures. We also applied maximal-information statistics to identify correlations between cytological features and kinase inhibitory activities by combining the CP fingerprints of 24 kinase inhibitors with published data on their specificities against a diverse panel of kinases. The resulting analysis suggests a strategy for probing the biological functions of specific kinases by compiling cytological data from inhibitors of varying specificities.
Current efforts to understand the mechanism of cancer involve using various whole-genome -omics measurements over large patient cohorts. Since a patient response to treatments is highly variable, the challenge then is to integrate the data in order to infer patient-specific disease mechanisms. Recent advances in the analysis of cancer (TCGA ovarian serous carcinoma and glioblastoma multiforme) has shown that a pathway interpretation of DNA copy number, DNA methylation, mRNA expression, and mutations offers a powerful framework for interpreting complex data. The hope is that a pathway-level interpretation of -omics data can identify pathway signatures to predict differences in clinical outcome, whereas traditional machine learning algorithms do not take advantage of the pathway structure of biological data. We are developing a pathway prediction method based on PARADIGM to discriminate patient outcome based on pathway signatures. Utilizing conditional random fields (CRFs) allow for formal search for a graphical model that optimizes the prediction of a particular variable of interest (VOI) defined by the given classification task, as opposed to a generative model that optimizes the model to explain the data. The method first merges pathways to build a core network around the VOI. The model then seeks to extend the pathway to include new genes and interactions which improve the model's predictive ability on the training data. Application of our method to 50 breast cancer cell-lines treated with 80 different compounds revealed general and subtype-specific signatures of response in breast cancer. We compared our CRF-based method against a compendium of standard machine-learning algorithms and found that our CRF outperformed all methods on a majority of drugs tested. We also tested the method on a cancer benchmark consisting of a dozen prediction challenges all involving the prediction of clinical outcomes on large patient cohorts using gene expression and copy number data. Again, the CRF model outperformed a majority of classifiers and performed comparably to the best classifiers on most challenges. We expect our method to generalize to a wide variety of biological systems for which high-throughput genomics and functional genomics are available. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 49. doi:10.1158/1538-7445.AM2011-49
With increasing ubiquity of genome-wide assays it is now common to molecularly subtype cancers to predict patient therapy response. However identifying high-performing, robust molecular signatures for predictions remains difficult. Presented here is work towards a novel machine-learning algorithm that discovers intuitively understood and clinically relevant stratifying molecular signatures. This classification method, as well as many competing methods (SVM, random forests, Bayes nets, etc.), were applied by predicting drug-sensitivity to hundreds of compounds tested on the NCI60 cell lines. This new drug-sensitivity prediction method competes with and in many cases outperforms leading classifiers. The prediction results, as well as the molecular signatures that they are derived from, are publicly available for web-browsing through a new extension to the UCSC Cancer Genomics Browser, hgClassifications (http://genome-cancer.soe.ucsc.edu/hgClassifications). Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 43. doi:10.1158/1538-7445.AM2011-43
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