Interactions between the tumor and the blood systemic response of breast cancer patients

Dumeaux, Vanessa; Fjukstad, Bjørn; Fjøsne, Hans E.; Frantzen, Jan Ole; Holmen, Marit Muri; Rodegerdts, Enno; Schlichting, Ellen; Børresen-Dale, Anne Lise; Bongo, Lars Ailo; Lund, Eiliv; Hallett, Michael

doi:10.1371/journal.pcbi.1005680

Cited by 26 publications

(29 citation statements)

References 69 publications

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“…In a recent analysis of interactions between gene expression in BC tissues and blood samples nested in the NOWAC postgenome study, 18 we found that genes expressed in tumors and used as markers are not necessarily expressed in a similar manner in blood. Not surprisingly, tumors were enriched for genes related to cancer hallmarks, while in blood, the genes represented either general cellular or immune-related processes.…”

Section: Discussionmentioning

confidence: 85%

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Each pregnancy linearly changes immune gene expression in the blood of healthy women compared with breast cancer patients

Lund¹,

Nakamura²,

Snapkov³

et al. 2018

CLEP

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BackgroundThere is a large body of evidence demonstrating long-lasting protective effect of each full-term pregnancy (FTP) on the development of breast cancer (BC) later in life, a phenomenon that could be related to both hormonal and immunological changes during pregnancies. In this work, we studied the pregnancy-associated differences in peripheral blood gene expression profiles between healthy women and women diagnosed with BC in a prospective design.MethodsUsing an integrated system epidemiology approach, we modeled BC incidence as a function of parity in the Norwegian Women and Cancer (NOWAC) cohort (165,000 women) and then tested the resulting mathematical model using gene expression profiles in blood in a nested case–control study (460 invasive case–control pairs) of women from the NOWAC postgenome cohort. Lastly, we undertook a gene set enrichment analysis for immunological gene sets.ResultsA linear trend fitted the dataset precisely showing an 8% decrease in risk of BC for each FTP, independent of stratification on other risk factors and lasting for decades after a woman’s last FTP. Women with six children demonstrated 48% reduction in the incidence of BC compared to nulliparous. When we looked at gene expression, we found that 756 genes showed linear trends in cancer-free controls (false discovery rate [FDR] 5%), but this was not the case for any of the genes in BC cases. Gene set enrichment analysis of immunologic gene sets (C7 collection in Molecular Signatures Database) revealed 215 significantly enriched human gene sets (FDR 5%).ConclusionWe found marked differences in gene expression and enrichment profiles of immunologic gene sets between BC cases and healthy controls, suggesting an important protective effect of the immune system on BC risk.

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Section: Discussionmentioning

confidence: 85%

“… 17 Additionally, in a recent study, we compared gene expression in blood and breast tumor tissue. 18 We revealed some similarities in expression profiles mainly for immunogenic tumors; however, we demonstrated that there is no obligate common gene expression pattern.…”

Section: Introductionmentioning

confidence: 76%

Each pregnancy linearly changes immune gene expression in the blood of healthy women compared with breast cancer patients

Lund¹,

Nakamura²,

Snapkov³

et al. 2018

CLEP

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“…[16], [17]). We have also used the gene expression datasets to explore gene expression signals in blood and interactions between the tumor and the blood systemic response of breast cancer patients [18], [19]. Some analyses have resulted in patents [20] and commercialization efforts.…”

Section: Data Analysis Lessons Learned In the Norwegian Women And Canmentioning

confidence: 99%

Reproducible data management and analysis using R

Fjukstad

Shvetsov

Nøst

et al. 2019

Preprint

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Background. Standardizing and documenting computational analyses are necessary to ensure reproducible results. It is especially important for large and complex projects where data collection, analysis, and interpretation may span decades. Our objective is therefore to provide methods, tools, and best practice guidelines adapted for analyses in epidemiological studies that use -omics data. Results.We describe an R-based implementation of data management and preprocessing. The method is well-integrated with the analysis tools typically used for statistical analysis of -omics data. We document all datasets thoroughly and use version control to track changes to both datasets and code over time. We provide a web application to perform the standardized preprocessing steps for gene expression datasets. We provide best practices for reporting data analysis results and sharing analyses. Conclusion.We have used these tools to organize data storage and documentation, and to standardize the analysis of gene expression data, in the Norwegian Women and Cancer (NOWAC) system epidemiology study. We believe our approach and lessons learned are applicable to analyses in other large and complex epidemiology projects.

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“…[5] We generated and analyzed expression profiles from blood and matched tumor cells in 173 breast cancer patients included in the Norwegian Women and Cancer (NOWAC) study. The MIxT analysis starts by identifying sets of genes tightly co-expressed across all patients in each tissue.…”

Section: Motivating Examplementioning

confidence: 99%

“…In other languages, libraries for biological bomputation are often availalbe like BioPython [2] and biogo [3] for Python or Go, respectively. Projects such as Galaxy 5 and Common Workflow Language (CWL) 6 enable resarchers to build and run biological data analysis pipelines consisting of a wide range of tools. Although these framework are tremendously helpful, we need novel approaches to build applications that integrate high-performance bioinformatics tools with specialized user-interfaces and interactive visualizations.…”

Section: Introductionmentioning

confidence: 99%

Building applications for interactive data exploration in systems biology

Fjukstad

Dumeaux

Olsen

et al. 2017

Preprint

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As the systems biology community generates and collects data at an unprecedented rate, there is a growing need for interactive data exploration tools to explore the datasets. These tools need to combine advanced statistical analyses, relevant knowledge from biological databases, and interactive visualizations in an application with clear user interfaces. To answer specific research questions tools must provide specialized user interfaces and visualizations. While these are application-specific, the underlying components of a data analysis tool can be shared and reused later. Application developers can therefore compose applications of reusable services rather than implementing a single monolithic application from the ground up for each project.Our approach for developing data exploration applications in systems biology builds on the microservice architecture. Microservice architectures separates an application into smaller components that communicate using language-agnostic protocols. We show that this design is suitable in bioinformatics applications where applications often use different tools, written in different languages, by different research groups. Packaging each service in a software container enables re-use and sharing of key components between applications, reducing development, deployment, and maintenance time.We demonstrate the viability of our approach through a web application, MIxT blood-tumor, for exploring and comparing transcriptional profiles from blood and tumor samples in breast cancer patients. The application integrates advanced statistical software, up-to-date information from biological databases, and modern data visualization libraries. The web application for exploring transcriptional profiles, MIxT, is online at mixt-blood-tumor. bci.mcgill.ca and open-sourced at github.com/ fjukstad/mixt. Packages to build the supporting microservices are open-sourced as a part of Kvik at github.com/fjukstad/kvik.

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Interactions between the tumor and the blood systemic response of breast cancer patients

Cited by 26 publications

References 69 publications

Each pregnancy linearly changes immune gene expression in the blood of healthy women compared with breast cancer patients

Each pregnancy linearly changes immune gene expression in the blood of healthy women compared with breast cancer patients

Reproducible data management and analysis using R

Building applications for interactive data exploration in systems biology

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