Modeling Mutual Exclusivity of Cancer Mutations

<div><p>In large collections of tumor samples, it has been observed that sets of genes that are commonly involved in the same cancer pathways tend not to occur mutated together in the same patient. Such gene sets form mutually exclusive patterns of gene alterations in cancer genomic data. Computational approaches that detect mutually exclusive gene sets, rank and test candidate alteration patterns by rewarding the number of samples the pattern covers and by punishing its impurity, i.e., additional alterations that violate strict mutual exclusivity. However, the extant approaches do not account for possible observation errors. In practice, false negatives and especially false positives can severely bias evaluation and ranking of alteration patterns. To address these limitations, we develop a fully probabilistic, generative model of mutual exclusivity, explicitly taking coverage, impurity, as well as error rates into account, and devise efficient algorithms for parameter estimation and pattern ranking. Based on this model, we derive a statistical test of mutual exclusivity by comparing its likelihood to the null model that assumes independent gene alterations. Using extensive simulations, the new test is shown to be more powerful than a permutation test applied previously. When applied to detect mutual exclusivity patterns in glioblastoma and in pan-cancer data from twelve tumor types, we identify several significant patterns that are biologically relevant, most of which would not be detected by previous approaches. Our statistical modeling framework of mutual exclusivity provides increased flexibility and power to detect cancer pathways from genomic alteration data in the presence of noise. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5.</p></div

Enhanced expression of vacuolar H+-ATPase subunit E in the roots is associated with the adaptation of Broussonetia papyrifera to salt stress.

Vacuolar H(+)-ATPase (V-H(+)-ATPase) may play a pivotal role in maintenance of ion homeostasis inside plant cells. In the present study, the expression of V-H(+)-ATPase genes was analyzed in the roots and leaves of a woody plant, Broussonetia papyrifera, which was stressed with 50, 100 and 150 mM NaCl. Moreover, the expression and distribution of the subunit E protein were investigated by Western blot and immunocytochemistry. These showed that treatment of B. papyrifera with NaCl distinctly changed the hydrolytic activity of V-H(+)-ATPase in the roots and leaves. Salinity induced a dramatic increase in V-H(+)-ATPase hydrolytic activity in the roots. However, only slight changes in V-H(+)-ATPase hydrolytic activity were observed in the leaves. In contrast, increased H(+) pumping activity of V-H(+)-ATPase was observed in both the roots and leaves. In addition, NaCl treatment led to an increase in H(+)-pyrophosphatase (V-H(+)-PPase) activity in the roots. Moreover, NaCl treatment triggered the enhancement of mRNA levels for subunits A, E and c of V-H(+)-ATPase in the roots, whereas only subunit c mRNA was observed to increase in the leaves. By Western blot and immunocytological analysis, subunit E was shown to be augmented in response to salinity stress in the roots. These findings provide evidence that under salt stress, increased V-H(+)-ATPase activity in the roots was positively correlated with higher transcript and protein levels of V-H(+)-ATPase subunit E. Altogether, our results suggest an essential role for V-H(+)-ATPase subunit E in the response of plants to salinity stress

Simultaneous Reconstruction of Multiple Signaling Pathways via the Prize-Collecting Steiner Forest Problem

Signaling and regulatory networks are essential for cells to control processes such as growth, differentiation, and response to stimuli. Although many "omic'' data sources are available to probe signaling pathways, these data are typically sparse and noisy. Thus, it has been difficult to use these data to discover the cause of the diseases and to propose new therapeutic strategies. We overcome these problems and use "omic'' data to reconstruct simultaneously multiple pathways that are altered in a particular condition by solving the prize-collecting Steiner forest problem. To evaluate this approach, we use the well-characterized yeast pheromone response. We then apply the method to human glioblastoma data, searching for a forest of trees, each of which is rooted in a different cell-surface receptor. This approach discovers both overlapping and independent signaling pathways that are enriched in functionally and clinically relevant proteins, which could provide the basis for new therapeutic strategies. Although the algorithm was not provided with any information about the phosphorylation status of receptors, it identifies a small set of clinically relevant receptors among hundreds present in the interactome

Simultaneous Reconstruction of Multiple Signaling Pathways via the Prize-Collecting Steiner Forest Problem

Signaling networks are essential for cells to control processes such as growth and response to stimuli. Although many “omic” data sources are available to probe signaling pathways, these data are typically sparse and noisy. Thus, it has been difficult to use these data to discover the cause of the diseases. We overcome these problems and use “omic” data to simultaneously reconstruct multiple pathways that are altered in a particular condition by solving the prize-collecting Steiner forest problem. To evaluate this approach, we use the well-characterized yeast pheromone response. We then apply the method to human glioblastoma data, searching for a forest of trees each of which is rooted in a different cell surface receptor. This approach discovers both overlapping and independent signaling pathways that are enriched in functionally and clinically relevant proteins, which could provide the basis for new therapeutic strategies.National Institutes of Health (U.S.) (NIH grant U54CA112967)National Institutes of Health (U.S.) (NIH grant R01GM089903)Massachusetts Institute of Technology (Eugene Bell Career Development Chair)National Science Foundation (U.S.) (Award No. DB1- 082139)European Research Council (ERC grant OPTINF 267915)European Commission (EC grant STAMINA 265496