8,611 research outputs found
BeWith: A Between-Within Method to Discover Relationships between Cancer Modules via Integrated Analysis of Mutual Exclusivity, Co-occurrence and Functional Interactions
The analysis of the mutational landscape of cancer, including mutual
exclusivity and co-occurrence of mutations, has been instrumental in studying
the disease. We hypothesized that exploring the interplay between
co-occurrence, mutual exclusivity, and functional interactions between genes
will further improve our understanding of the disease and help to uncover new
relations between cancer driving genes and pathways. To this end, we designed a
general framework, BeWith, for identifying modules with different combinations
of mutation and interaction patterns. We focused on three different settings of
the BeWith schema: (i) BeME-WithFun in which the relations between modules are
enriched with mutual exclusivity while genes within each module are
functionally related; (ii) BeME-WithCo which combines mutual exclusivity
between modules with co-occurrence within modules; and (iii) BeCo-WithMEFun
which ensures co-occurrence between modules while the within module relations
combine mutual exclusivity and functional interactions. We formulated the
BeWith framework using Integer Linear Programming (ILP), enabling us to find
optimally scoring sets of modules. Our results demonstrate the utility of
BeWith in providing novel information about mutational patterns, driver genes,
and pathways. In particular, BeME-WithFun helped identify functionally coherent
modules that might be relevant for cancer progression. In addition to finding
previously well-known drivers, the identified modules pointed to the importance
of the interaction between NCOR and NCOA3 in breast cancer. Additionally, an
application of the BeME-WithCo setting revealed that gene groups differ with
respect to their vulnerability to different mutagenic processes, and helped us
to uncover pairs of genes with potentially synergetic effects, including a
potential synergy between mutations in TP53 and metastasis related DCC gene
Efficient algorithms to discover alterations with complementary functional association in cancer
Recent large cancer studies have measured somatic alterations in an
unprecedented number of tumours. These large datasets allow the identification
of cancer-related sets of genetic alterations by identifying relevant
combinatorial patterns. Among such patterns, mutual exclusivity has been
employed by several recent methods that have shown its effectivenes in
characterizing gene sets associated to cancer. Mutual exclusivity arises
because of the complementarity, at the functional level, of alterations in
genes which are part of a group (e.g., a pathway) performing a given function.
The availability of quantitative target profiles, from genetic perturbations or
from clinical phenotypes, provides additional information that can be leveraged
to improve the identification of cancer related gene sets by discovering groups
with complementary functional associations with such targets.
In this work we study the problem of finding groups of mutually exclusive
alterations associated with a quantitative (functional) target. We propose a
combinatorial formulation for the problem, and prove that the associated
computation problem is computationally hard. We design two algorithms to solve
the problem and implement them in our tool UNCOVER. We provide analytic
evidence of the effectiveness of UNCOVER in finding high-quality solutions and
show experimentally that UNCOVER finds sets of alterations significantly
associated with functional targets in a variety of scenarios. In addition, our
algorithms are much faster than the state-of-the-art, allowing the analysis of
large datasets of thousands of target profiles from cancer cell lines. We show
that on one such dataset from project Achilles our methods identify several
significant gene sets with complementary functional associations with targets.Comment: Accepted at RECOMB 201
Finding Mutated Subnetworks Associated with Survival in Cancer
Next-generation sequencing technologies allow the measurement of somatic
mutations in a large number of patients from the same cancer type. One of the
main goals in analyzing these mutations is the identification of mutations
associated with clinical parameters, such as survival time. This goal is
hindered by the genetic heterogeneity of mutations in cancer, due to the fact
that genes and mutations act in the context of pathways. To identify mutations
associated with survival time it is therefore crucial to study mutations in the
context of interaction networks.
In this work we study the problem of identifying subnetworks of a large
gene-gene interaction network that have mutations associated with survival. We
formally define the associated computational problem by using a score for
subnetworks based on the test statistic of the log-rank test, a widely used
statistical test for comparing the survival of two populations. We show that
the computational problem is NP-hard and we propose a novel algorithm, called
Network of Mutations Associated with Survival (NoMAS), to solve it. NoMAS is
based on the color-coding technique, that has been previously used in other
applications to find the highest scoring subnetwork with high probability when
the subnetwork score is additive. In our case the score is not additive;
nonetheless, we prove that under a reasonable model for mutations in cancer
NoMAS does identify the optimal solution with high probability. We test NoMAS
on simulated and cancer data, comparing it to approaches based on single gene
tests and to various greedy approaches. We show that our method does indeed
find the optimal solution and performs better than the other approaches.
Moreover, on two cancer datasets our method identifies subnetworks with
significant association to survival when none of the genes has significant
association with survival when considered in isolation.Comment: This paper was selected for oral presentation at RECOMB 2016 and an
abstract is published in the conference proceeding
QuaDMutEx: quadratic driver mutation explorer
Background
Somatic mutations accumulate in human cells throughout life. Some may have no adverse consequences, but some of them may lead to cancer. A cancer genome is typically unstable, and thus more mutations can accumulate in the DNA of cancer cells. An ongoing problem is to figure out which mutations are drivers - play a role in oncogenesis, and which are passengers - do not play a role. One way of addressing this question is through inspection of somatic mutations in DNA of cancer samples from a cohort of patients and detection of patterns that differentiate driver from passenger mutations. Results
We propose QuaDMutEx, a method that incorporates three novel elements: a new gene set penalty that includes non-linear penalization of multiple mutations in putative sets of driver genes, an ability to adjust the method to handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets that minimize the penalty, through a combination of heuristic Monte Carlo optimization and exact binary quadratic programming. Compared to existing methods, the proposed algorithm finds sets of putative driver genes that show higher coverage and lower excess coverage in eight sets of cancer samples coming from brain, ovarian, lung, and breast tumors. Conclusions
Superior ability to improve on both coverage and excess coverage on different types of cancer shows that QuaDMutEx is a tool that should be part of a state-of-the-art toolbox in the driver gene discovery pipeline. It can detect genes harboring rare driver mutations that may be missed by existing methods. QuaDMutEx is available for download from https://github.com/bokhariy/QuaDMutEx under the GNU GPLv3 license
Computational Cancer Biology: An Evolutionary Perspective
ISSN:1553-734XISSN:1553-735
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