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Assessment of subnetwork detection methods for breast cancer.

ABSTRACT: Subnetwork detection is often used with differential expression analysis to identify modules or pathways associated with a disease or condition. Many computational methods are available for subnetwork analysis. Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker. These methods represent distinctly different computational strategies and are among the most widely used. Each of these methods was used to analyze gene expression data consisting of paired tumor and normal samples from 50 breast cancer patients. While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods. Within the core set, 12 genes were found to be known breast cancer genes.

SUBMITTER: Jiang B

PROVIDER: S-EPMC4256043 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Assessment of subnetwork detection methods for breast cancer.

Jiang Biaobin B Gribskov Michael M

Cancer informatics 20141202 Suppl 6

Subnetwork detection is often used with differential expression analysis to identify modules or pathways associated with a disease or condition. Many computational methods are available for subnetwork analysis. Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker. These methods represent distinctly different computational strategies and are among the most widely used. Eac ...[more]

PMID: 25520555

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Project description:ImportanceCurrent treatment cures most cases of early-stage, primary breast cancer. However, better techniques are required to identify which patients are at risk of relapse.ObjectiveTo assess the clinical validity of molecular relapse detection with circulating tumor DNA (ctDNA) analysis in early-stage breast cancer.Design, setting, and participantsThis prospective, multicenter, sample collection, validation study conducted at 5 United Kingdom medical centers from November 24, 2011, to October 18, 2016, assessed patients with early-stage breast cancer irrespective of hormone receptor and ERBB2 (formerly HER2 or HER2/neu) status who were receiving neoadjuvant chemotherapy followed by surgery or surgery before adjuvant chemotherapy. The study recruited 170 women, with mutations identified in 101 patients forming the main cohort. Secondary analyses were conducted on a combined cohort of 144 patients, including 43 patients previously analyzed in a proof of principle study.InterventionsPrimary tumor was sequenced to identify somatic mutations, and personalized tumor-specific digital polymerase chain reaction assays were used to monitor these mutations in serial plasma samples taken every 3 months for the first year of follow-up and subsequently every 6 months.Main outcomes and measuresThe primary end point was relapse-free survival analyzed with Cox proportional hazards regression models.ResultsIn the main cohort of 101 female patients (mean [SD] age, 54 [11] years) with a median follow-up of 35.5 months (interquartile range, 27.9-43.0 months), detection of ctDNA during follow-up was associated with relapse (hazard ratio, 25.2; 95% CI, 6.7-95.6; P < .001). Detection of ctDNA at diagnosis, before any treatment, was also associated with relapse-free survival (hazard ratio, 5.8; 95% CI, 1.2-27.1; P = .01). In the combined cohort, ctDNA detection had a median lead time of 10.7 months (95% CI, 8.1-19.1 months) compared with clinical relapse and was associated with relapse in all breast cancer subtypes. Distant extracranial metastatic relapse was detected by ctDNA in 22 of 23 patients (96%). Brain-only metastasis was less commonly detected by ctDNA (1 of 6 patients [17%]), suggesting relapse sites less readily detectable by ctDNA analysis.Conclusions and relevanceThe findings suggest that detection of ctDNA during follow-up is associated with a high risk of future relapse of early-stage breast cancer. Prospective studies are needed to assess the potential of molecular relapse detection to guide adjuvant therapy.

Project description:Network biology has become a key tool in unravelling the mechanisms of complex diseases. Detecting dys-regulated subnetworks from molecular networks is a task that needs efficient computational methods. In this work, we constructed an integrated network using gene interaction data as well as protein-protein interaction data of differentially expressed genes derived from the microarray gene expression data. We considered the level of differential expression as well as the topological weight of proteins in interaction network to quantify dys-regulation. Then, a nature-inspired Smell Detection Agent (SDA) optimisation algorithm is designed with multiple agents traversing through various paths in the network. Finally, the algorithm provides a maximum weighted module as the optimum dys-regulated subnetwork. The analysis is performed for samples of triple-negative breast cancer as well as colorectal cancer. Biological significance analysis of module genes is also done to validate the results. The breast cancer subnetwork is found to contain (i) valid biomarkers including PIK3CA, PTEN, BRCA1, AR and EGFR; (ii) validated drug targets TOP2A, CDK4, HDAC1, IL6, BRCA1, HSP90AA1 and AR; (iii) synergistic drug targets EGFR and BIRC5. Moreover, based on the weight values assigned to nodes in the subnetwork, PLK1, CTNNB1, IGF1, AURKA, PCNA, HSPA4 and GAPDH are proposed as drug targets for further studies. For colorectal cancer module, the analysis revealed the occurrence of approved drug targets TYMS, TOP1, BRAF and EGFR. Considering the higher weight values, HSP90AA1, CCNB1, AKT1 and CXCL8 are proposed as drug targets for experimentation. The derived subnetworks possess cancer-related pathways as well. The SDA-derived breast cancer subnetwork is compared with that of tools such as MCODE and Minimum Spanning Tree, and observed a higher enrichment (75%) of significant elements. Thus, the proposed nature-inspired algorithm is a novel approach to derive the optimum dys-regulated subnetwork from huge molecular network.

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Assessment of subnetwork detection methods for breast cancer.

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Assessment of subnetwork detection methods for breast cancer.

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