Identifying patient-specific flow of signal transduction perturbed by multiple single-nucleotide alterations

Abstract

Background: Identifying patient-specific flow of signal transduction perturbed by multiple single-nucleotide alterations is critical for improving patient outcomes in cancer cases. However, accurate estimation of mutational effects at the pathway level for such patients remains an open problem. While probabilistic pathway topology methods are gaining interest among the scientific community, the overwhelming majority do not account for network perturbation effects from multiple single-nucleotide alterations.

Methods: Here we present an improvement of the mutational forks formalism to infer the patient-specific flow of signal transduction based on multiple single-nucleotide alterations, including non-synonymous and synonymous mutations. The lung adenocarcinoma and skin cutaneous melanoma datasets from TCGA Pan-Cancer Atlas have been employed to show the utility of the proposed method.

Results: We have comprehensively characterized six mutational forks. The number of mutated nodes ranged from one to four depending on the topological characteristics of a fork. Transitional confidences (TCs) have been computed for every possible combination of single-nucleotide alterations in the fork. The performed analysis demonstrated the capacity of the mutational forks formalism to follow a biologically explainable logic in the identification of high-likelihood signaling routes in lung adenocarcinoma and skin cutaneous melanoma patients. The findings have been largely supported by the evidence from the biomedical literature.

Conclusion: We conclude that the formalism has a great chance to enable an assessment of patient-specific flow by leveraging information from multiple single-nucleotide alterations to adjust the transitional likelihoods that are solely based on the canonical view of a disease.

Graphical abstract

Keywords

mutational forks / signaling pathways / cancer / single-nucleotide alterations

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SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.1007/s40484-020-0227-0.

ACKNOWLEDGEMENTS

We appreciate the critical input of members from the Translational and Cancer Bioinformatics (TCBI) Laboratory at the University of Missouri-Columbia. This work has been supported by the MU Institute for Data Science and Informatics (IDSI) to O.K., by the Department of Pathology & Anatomical Sciences, School of Medicine, University of Missouri-Columbia to D.S and O.K. and in part by Career Development Award #IK2-BX004346-01A1 (JBM) from the United States (U.S.) Department of Veterans Affairs Biomedical Laboratory Research and Development Service. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Department of Veterans’ Affairs.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Olha Kholod, Chi-Ren Shyu, Jonathan Mitchem, Jussuf Kaifi and Dmitriy Shin declare that they have no conflict of interests.ƒThis article does not contain any studies with human or animal subjects performed by any of the authors.

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