A census of pathway maps in cancer systems biology

Abstract

A key goal of cancer systems biology is to use big data to elucidate the molecular networks by which cancer develops. However, to date there has been no systematic evaluation of how far these efforts have progressed. In this Analysis, we survey six major systems biology approaches for mapping and modelling cancer pathways with attention to how well their resulting network maps cover and enhance current knowledge. Our sample of 2,070 systems biology maps captures all literature-curated cancer pathways with significant enrichment, although the strong tendency is for these maps to recover isolated mechanisms rather than entire integrated processes. Systems biology maps also identify previously underappreciated functions, such as a potential role for human papillomavirus-induced chromosomal alterations in ovarian tumorigenesis, and they add new genes to known cancer pathways, such as those related to metabolism, Hippo signalling and immunity. Notably, we find that many cancer networks have been provided only in journal figures and not for programmatic access, underscoring the need to deposit network maps in community databases to ensure they can be readily accessed. Finally, few of these findings have yet been clinically translated, leaving ample opportunity for future translational studies. Periodic surveys of cancer pathway maps, such as the one reported here, are critical to assess progress in the field and identify underserved areas of methodology and cancer biology.

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Fig. 1: Structure of the analysis.
Fig. 2: Cancer systems biology approaches covered in this analysis.
Fig. 3: Coverage of LCpathways by SBmaps.
Fig. 4: Assessment of relative research coverage of cancer pathways by systems biology.
Fig. 5: Representative SBmaps not previously reported in the literature.
Fig. 6: Potential new mechanisms emerging from cancer systems biology studies.

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Acknowledgements

The authors gratefully acknowledge the support for this work provided by grants from the US National Institutes of Health to T.I. (CA209891, CA184427, ES014811) and B.M.K. (CA212456).

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B.M.K. researched data for the article. B.M.K. and T.I. discussed the content and wrote, reviewed and edited the manuscript.

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Correspondence to Trey Ideker.

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Nature Reviews Cancer thanks A. Mardinoglu, M. Vidal, D. Hill and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

American Association for Cancer Research project Genomics Evidence Neoplasia Information Exchange: http://www.aacr.org/Research/Research/Pages/aacr-project-genie.aspx

BioModels: https://www.ebi.ac.uk/biomodels/

Cancer Systems Biology Consortium: http://csbconsortium.org/

CellML: https://www.cellml.org/

ClinicalTrials.gov: https://clinicaltrials.gov/

Gene Ontology: http://geneontology.org/

GitHub: https://github.com/

Human Protein Atlas: http://proteinatlas.org/

ISRCTN: https://www.isrctn.com/

Network Data Exchange: http://www.ndexbio.org

Oncology Research Information Exchange Network: http://oriencancer.org

Quantum Immuno-oncology Lifelong Trial programme: https://clinicaltrials.gov/ct2/results?term=QUILT

Supplementary information

Glossary

Fuzzy logic

A predictive model that attempts to use vague or imprecise information to obtain accurate predictions and solve complex problems.

Adjacency matrix

A square matrix used to represent the structure of a finite network in which rows and columns represent nodes in the network and the binary elements of the matrix represent the edges.

Interaction list

A simple, tabular network representation containing two columns (source and target) detailing the edges of a network.

Cytoscape

An open-source software platform for visualizing complex networks and integrating these with any type of attribute data for further analyses.

Functional enrichment analysis

A method to identify collections of genes or proteins (often disease-associated pathways) that are over represented or under represented in a large set of genes or proteins.

Hypergeometric test

A statistical test used to calculate the statistical significance of having drawn specific successes from a given population, often used to identify subpopulations that are over represented or under represented in that population.

F score

A measure of a test’s accuracy that takes into account both the precision and the recall of the test to compute the score. Similarly to precision and recall, the F score has a highest value of 1 and a lowest value of 0.

STRING

A database of known and predicted protein–protein interactions that includes both direct (physical) and indirect (functional) interactions.

Epistasis

The phenomenon whereby genetic alterations at two or more genetic loci (for example, mutations or deletions in different genes) produce a phenotype that is unexpected on the basis of the phenotypes of each of the single genetic alterations.

CRISPR interference

A genetic perturbation technique that allows sequence specific repression of gene expression in prokaryotic and eukaryotic cells.

Network diffusion

A method to analyse how the topology of a network impacts how information spreads across a given network.

Striatin-interacting phosphatase and kinase (STRIPAK) and integrator complex

An evolutionarily conserved supramolecular protein complex which regulates the phosphorylation status and therefore activation status of various pathways.

k-nearest neighbours model

A non-parametric machine learning method used for classification and regression tasks that learns to classify new cases on the basis of a similarity measure (for example, distance functions).

Basket trials

Trials designed to test the effects of a single drug, or a combination of drugs, in a variety of cancer types on the basis of the presence of a specific biomarker.

Umbrella trials

Trials designed to test the effect of different drugs on the basis of the presence of different biomarkers within a single cancer type.

k-fold cross validation

A resampling procedure used to evaluate machine learning models on a limited data sample by repeatedly splitting the data into training and test sets.

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Kuenzi, B.M., Ideker, T. A census of pathway maps in cancer systems biology. Nat Rev Cancer 20, 233–246 (2020). https://doi.org/10.1038/s41568-020-0240-7

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