6,054 research outputs found
Charge structure in volcanic plumes: a comparison of plume properties predicted by an integral plume model to observations of volcanic lightning during the 2010 eruption of Eyjafjallajökull, Iceland
Cancer is a heterogeneous disease with different combinations of genetic alterations driving its development in different individuals. We introduce CoMEt, an algorithm to identify combinations of alterations that exhibit a pattern of mutual exclusivity across individuals, often observed for alterations in the same pathway. CoMEt includes an exact statistical test for mutual exclusivity and techniques to perform simultaneous analysis of multiple sets of mutually exclusive and subtype-specific alterations. We demonstrate that CoMEt outperforms existing approaches on simulated and real data. We apply CoMEt to five different cancer types, identifying both known cancer genes and pathways, and novel putative cancer genes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-015-0700-7) contains supplementary material, which is available to authorized users
Wanted: cancer boss
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62915/1/440978a.pd
Homeostatic competition drives tumor growth and metastasis nucleation
We propose a mechanism for tumor growth emphasizing the role of homeostatic
regulation and tissue stability. We show that competition between surface and
bulk effects leads to the existence of a critical size that must be overcome by
metastases to reach macroscopic sizes. This property can qualitatively explain
the observed size distributions of metastases, while size-independent growth
rates cannot account for clinical and experimental data. In addition, it
potentially explains the observed preferential growth of metastases on tissue
surfaces and membranes such as the pleural and peritoneal layers, suggests a
mechanism underlying the seed and soil hypothesis introduced by Stephen Paget
in 1889 and yields realistic values for metastatic inefficiency. We propose a
number of key experiments to test these concepts. The homeostatic pressure as
introduced in this work could constitute a quantitative, experimentally
accessible measure for the metastatic potential of early malignant growths.Comment: 13 pages, 11 figures, to be published in the HFSP Journa
One-carbon metabolism in cancer
Cells require one-carbon units for nucleotide synthesis, methylation and reductive metabolism, and these pathways support the high proliferative rate of cancer cells. As such, anti-folates, drugs that target one-carbon metabolism, have long been used in the treatment of cancer. Amino acids, such as serine are a major one-carbon source, and cancer cells are particularly susceptible to deprivation of one-carbon units by serine restriction or inhibition of de novo serine synthesis. Recent work has also begun to decipher the specific pathways and sub-cellular compartments that are important for one-carbon metabolism in cancer cells. In this review we summarise the historical understanding of one-carbon metabolism in cancer, describe the recent findings regarding the generation and usage of one-carbon units and explore possible future therapeutics that could exploit the dependency of cancer cells on one-carbon metabolism
A reaction-diffusion model for the growth of avascular tumor
A nutrient-limited model for avascular cancer growth including cell
proliferation, motility and death is presented. The model qualitatively
reproduces commonly observed morphologies for primary tumors, and the simulated
patterns are characterized by its gyration radius, total number of cancer
cells, and number of cells on tumor periphery. These very distinct
morphological patterns follow Gompertz growth curves, but exhibit different
scaling laws for their surfaces. Also, the simulated tumors incorporate a
spatial structure composed of a central necrotic core, an inner rim of
quiescent cells and a narrow outer shell of proliferating cells in agreement
with biological data. Finally, our results indicate that the competition for
nutrients among normal and cancer cells may be a determinant factor in
generating papillary tumor morphology.Comment: 9 pages, 6 figures, to appear in PR
An approach for the identification of targets specific to bone metastasis using cancer genes interactome and gene ontology analysis
Metastasis is one of the most enigmatic aspects of cancer pathogenesis and is
a major cause of cancer-associated mortality. Secondary bone cancer (SBC) is a
complex disease caused by metastasis of tumor cells from their primary site and
is characterized by intricate interplay of molecular interactions.
Identification of targets for multifactorial diseases such as SBC, the most
frequent complication of breast and prostate cancers, is a challenge. Towards
achieving our aim of identification of targets specific to SBC, we constructed
a 'Cancer Genes Network', a representative protein interactome of cancer genes.
Using graph theoretical methods, we obtained a set of key genes that are
relevant for generic mechanisms of cancers and have a role in biological
essentiality. We also compiled a curated dataset of 391 SBC genes from
published literature which serves as a basis of ontological correlates of
secondary bone cancer. Building on these results, we implement a strategy based
on generic cancer genes, SBC genes and gene ontology enrichment method, to
obtain a set of targets that are specific to bone metastasis. Through this
study, we present an approach for probing one of the major complications in
cancers, namely, metastasis. The results on genes that play generic roles in
cancer phenotype, obtained by network analysis of 'Cancer Genes Network', have
broader implications in understanding the role of molecular regulators in
mechanisms of cancers. Specifically, our study provides a set of potential
targets that are of ontological and regulatory relevance to secondary bone
cancer.Comment: 54 pages (19 pages main text; 11 Figures; 26 pages of supplementary
information). Revised after critical reviews. Accepted for Publication in
PLoS ON
A Study of the PDGF Signaling Pathway with PRISM
In this paper, we apply the probabilistic model checker PRISM to the analysis
of a biological system -- the Platelet-Derived Growth Factor (PDGF) signaling
pathway, demonstrating in detail how this pathway can be analyzed in PRISM. We
show that quantitative verification can yield a better understanding of the
PDGF signaling pathway.Comment: In Proceedings CompMod 2011, arXiv:1109.104
A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer
Recently, several classifiers that combine primary tumor data, like gene
expression data, and secondary data sources, such as protein-protein
interaction networks, have been proposed for predicting outcome in breast
cancer. In these approaches, new composite features are typically constructed
by aggregating the expression levels of several genes. The secondary data
sources are employed to guide this aggregation. Although many studies claim
that these approaches improve classification performance over single gene
classifiers, the gain in performance is difficult to assess. This stems mainly
from the fact that different breast cancer data sets and validation procedures
are employed to assess the performance. Here we address these issues by
employing a large cohort of six breast cancer data sets as benchmark set and by
performing an unbiased evaluation of the classification accuracies of the
different approaches. Contrary to previous claims, we find that composite
feature classifiers do not outperform simple single gene classifiers. We
investigate the effect of (1) the number of selected features; (2) the specific
gene set from which features are selected; (3) the size of the training set and
(4) the heterogeneity of the data set on the performance of composite feature
and single gene classifiers. Strikingly, we find that randomization of
secondary data sources, which destroys all biological information in these
sources, does not result in a deterioration in performance of composite feature
classifiers. Finally, we show that when a proper correction for gene set size
is performed, the stability of single gene sets is similar to the stability of
composite feature sets. Based on these results there is currently no reason to
prefer prognostic classifiers based on composite features over single gene
classifiers for predicting outcome in breast cancer
The predictive and prognostic potential of plasma telomerase reverse transcriptase (TERT) RNA in rectal cancer patients
Background: Preoperative chemoradiotherapy (CRT) followed by surgery is the standard care for locally advanced rectal cancer,
but tumour response to CRT and disease outcome are variable. The current study aimed to investigate the effectiveness of plasma
telomerase reverse transcriptase (TERT) levels in predicting tumour response and clinical outcome.
Methods: 176 rectal cancer patients were included. Plasma samples were collected at baseline (before CRT\ubcT0), 2 weeks after
CRT was initiated (T1), post-CRT and before surgery (T2), and 4\u20138 months after surgery (T3) time points. Plasma TERT mRNA levels
and total cell-free RNA were determined using real-time PCR.
Results: Plasma levels of TERT were significantly lower at T2 (Po0.0001) in responders than in non-responders. Post-CRT TERT
levels and the differences between pre- and post-CRT TERT levels independently predicted tumour response, and the prediction
model had an area under curve of 0.80 (95% confidence interval (CI) 0.73\u20130.87). Multiple analysis demonstrated that patients with
detectable TERT levels at T2 and T3 time points had a risk of disease progression 2.13 (95% CI 1.10\u20134.11)-fold and 4.55 (95% CI
1.48\u201313.95)-fold higher, respectively, than those with undetectable plasma TERT levels.
Conclusions: Plasma TERT levels are independent markers of tumour response and are prognostic of disease progression in rectal
cancer patients who undergo neoadjuvant therapy
- …