Response projected clustering for direct association with physiological and clinical response data

Prediction of drug efficacy for cancer treatment based on comparative analysis of chemosensitivity and gene expression data

The NCI60 database is the largest available collection of compounds with measured anti-cancer activity. The strengths and limitations for using the NCI60 database as a source of new anti-cancer agents are explored and discussed in relation to previous studies. We selected a sub-set of 2333 compounds with reliable experimental half maximum growth inhibitions (GI(50)) values for 30 cell lines from the NCI60 data set and evaluated their growth inhibitory effect (chemosensitivity) with respect to tissue of origin. This was done by identifying natural clusters in the chemosensitivity data set and in a data set of expression profiles of 1901 genes for the corresponding tumor cell lines. Five clusters were identified based on the gene expression data using self-organizing maps (SOM), comprising leukemia, melanoma, ovarian and prostate, basal breast, and luminal breast cancer cells, respectively. The strong difference in gene expression between basal and luminal breast cancer cells was reflected clearly in the chemosensitivity data. Although most compounds in the data set were of low potency, high efficacy compounds that showed specificity with respect to tissue of origin could be found. Furthermore, eight potential topoisomerase II inhibitors were identified using a structural similarity search. Finally, a set of genes with expression profiles that were significantly correlated with anti-cancer drug activity was identified. Our study demonstrates that the combined data sets, which provide comprehensive information on drug activity and gene expression profiles of tumor cell lines studied, are useful for identifying potential new active compounds.

Sparse multitask regression for identifying common mechanism of response to therapeutic targets

Motivation: Molecular association of phenotypic responses is an important step in hypothesis generation and for initiating design of new experiments. Current practices for associating gene expression data with multidimensional phenotypic data are typically (i) performed one-to-one, i.e. each gene is examined independently with a phenotypic index and (ii) tested with one stress condition at a time, i.e. different perturbations are analyzed separately. As a result, the complex coordination among the genes responsible for a phenotypic profile is potentially lost. More importantly, univariate analysis can potentially hide new insights into common mechanism of response.

Results: In this article, we propose a sparse, multitask regression model together with co-clustering analysis to explore the intrinsic grouping in associating the gene expression with phenotypic signatures. The global structure of association is captured by learning an intrinsic template that is shared among experimental conditions, with local perturbations introduced to integrate effects of therapeutic agents. We demonstrate the performance of our approach on both synthetic and experimental data. Synthetic data reveal that the multi-task regression has a superior reduction in the regression error when compared with traditional L₁-and L₂-regularized regression. On the other hand, experiments with cell cycle inhibitors over a panel of 14 breast cancer cell lines demonstrate the relevance of the computed molecular predictors with the cell cycle machinery, as well as the identification of hidden variables that are not captured by the baseline regression analysis. Accordingly, the system has identified CLCA2 as a hidden transcript and as a common mechanism of response for two therapeutic agents of CI-1040 and Iressa, which are currently in clinical use.

Contact:b_parvin@lbl.gov

Molecular predictors of 3D morphogenesis by breast cancer cell lines in 3D culture

Correlative analysis of molecular markers with phenotypic signatures is the simplest model for hypothesis generation. In this paper, a panel of 24 breast cell lines was grown in 3D culture, their morphology was imaged through phase contrast microscopy, and computational methods were developed to segment and represent each colony at multiple dimensions. Subsequently, subpopulations from these morphological responses were identified through consensus clustering to reveal three clusters of round, grape-like, and stellate phenotypes. In some cases, cell lines with particular pathobiological phenotypes clustered together (e.g., ERBB2 amplified cell lines sharing the same morphometric properties as the grape-like phenotype). Next, associations with molecular features were realized through (i) differential analysis within each morphological cluster, and (ii) regression analysis across the entire panel of cell lines. In both cases, the dominant genes that are predictive of the morphological signatures were identified. Specifically, PPARγ has been associated with the invasive stellate morphological phenotype, which corresponds to triple-negative pathobiology. PPARγ has been validated through two supporting biological assays.

Cell culture models are an important vehicle for understanding biological processes and evaluation of therapeutic reagents. More importantly, the literature suggests that tumor cells grown in 3D exhibit pronounced drug and radiation resistances that are remarkably similar to that of tumors in vivo. Therefore, the needs for quantifying 3D assays continue to grow. In this paper, we develop robust computational methods to integrate morphometric and molecular information for a panel of breast cancer cell lines that are grown in 3D. Specifically, morphometric traits are imaged through microscopy, and then quantified computationally. We then show that these morphometric traits can identify subtypes within this panel of breast cancer cell lines, and that the subtypes are clinically relevant in terms of being ERBB2 positive or triple negative. These subtypes and their representations are then associated with their molecular data to reveal PPARG as an important marker for triple-negative breast cancer. Finally, we design two independent experiments to show the validity of this marker in both 3D cell culture models and human breast cancer tissue.

Hierarchical cluster analysis of polychlorinated dioxins and furans in Michigan, USA, soils: evaluation of industrial and background congener profiles

As part of the University of Michigan Dioxin Exposure Study, soil samples were collected from 766 residential properties near the Tittabawassee River between Midland and Saginaw; near the Dow Chemical Facility in Midland; and, for comparison, in the other areas of Midland and Saginaw Counties and in Jackson and Calhoun Counties, all located in the state of Michigan, USA. A total of 2,081 soil samples were analyzed for 17 polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs). In order to better understand the distribution and sources of the PCDD/F congeners in the study area, hierarchical cluster analysis (HCA) was used to statistically group samples with similar congener patterns. The analysis yielded a total of 13 clusters, including: 3 clusters among the soils impacted by contamination present in the Tittabawassee River sediments, a cluster comprised mainly of samples collected within the depositional area of the Dow incinerator complex, a small cluster of samples with elevated 2,3,7,8-tetrachlorinated dibenzo-p-dioxin (TCDD), and several clusters exhibiting background patterns. The clusters related to the Tittabawassee River floodplain contamination all contained elevated PCDF levels and were differentiated from one another primarily by their relative concentrations of higher-chlorinated PCDDs, a difference likely related to both extent and timing of impacts from Tittabawassee sediments. The background clusters appear to be related to combustion processes and are differentiated, in part, by their relative fractions of TCDD. Thus, HCA was useful for identifying congener profile characteristics in both contaminated and background soil samples.