Exploring tumor-normal cross-talk with TranNet: Role of the environment in tumor progression.

There is a growing awareness that tumor-adjacent normal tissues used as control samples in cancer studies do not represent fully healthy tissues. Instead, they are intermediates between healthy tissues and tumors. The factors that contribute to the deviation of such control samples from healthy state include exposure to the tumor-promoting factors, tumor-related immune response, and other aspects of tumor microenvironment.

Exploring tumor-normal cross-talk with TranNet: role of the environment in tumor progression.

There is a growing awareness that tumor-adjacent normal tissues used as control samples in cancer studies do not represent fully healthy tissues. Instead, they are intermediates between healthy tissues and tumors. The factors that contribute to the deviation of such control samples from healthy state include exposure to the tumor-promoting factors, tumor-related immune response, and other aspects of tumor microenvironment.

Influence network model uncovers relations between biological processes and mutational signatures.

Background: There has been a growing appreciation recently that mutagenic processes can be studied through the lenses of mutational signatures, which represent characteristic mutation patterns attributed to individual mutagens. However, the causal links between mutagens and observed mutation patterns as well as other types of interactions between mutagenic processes and molecular pathways are not fully understood, limiting the utility of mutational signatures.

Methods: To gain insights into these relationships, we developed a network-based method, named GENESIGNET that constructs an influence network among genes and mutational signatures.

Mutational Signatures as Sensors of Environmental Exposures: Analysis of Smoking-Induced Lung Tissue Remodeling.

Smoking is a widely recognized risk factor in the emergence of cancers and other lung diseases. Studies of non-cancer lung diseases typically investigate the role that smoking has in chronic changes in lungs that might predispose patients to the diseases, whereas most cancer studies focus on the mutagenic properties of smoking. Large-scale cancer analysis efforts have collected expression data from both tumor and control lung tissues, and studies have used control samples to estimate the impact of smoking on gene expression.

RepairSig: Deconvolution of DNA damage and repair contributions to the mutational landscape of cancer.

Cancer genomes accumulate a large number of somatic mutations resulting from a combination of stochastic errors in DNA processing, cancer-related aberrations of the DNA repair machinery, or carcinogenic exposures; each mutagenic process leaves a characteristic mutational signature. A key challenge is understanding the interactions between signatures, particularly as DNA repair deficiencies often modify the effects of other mutagens. Here, we introduce RepairSig, a computational method that explicitly models additive primary mutagenic processes; non-additive secondary processes, which interact with the primary processes; and a mutation opportunity, that is, the distribution of sites across the genome that are vulnerable to damage or preferentially repaired.

DEOD: uncovering dominant effects of cancer-driver genes based on a partial covariance selection method.

Motivation: The generation of a large volume of cancer genomes has allowed us to identify disease-related alterations more accurately, which is expected to enhance our understanding regarding the mechanism of cancer development. With genomic alterations detected, one challenge is to pinpoint cancer-driver genes that cause functional abnormalities.

Results: Here, we propose a method for uncovering the dominant effects of cancer-driver genes (DEOD) based on a partial covariance selection approach.

WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.

Sub-networks can expose complex patterns in an entire bio-molecular network by extracting interactions that depend on temporal or condition-specific contexts. When genes interact with each other during cellular processes, they may form differential co-expression patterns with other genes across different cell states. The identification of condition-specific sub-networks is of great importance in investigating how a living cell adapts to environmental changes.