Uncovering the subtype-specific temporal order of cancer pathway dysregulation

Sahand Khakabimamaghani, Dujian Ding, Oliver Snow and Martin Ester

PLOS Computational Biology, 2019, vol. 15, issue 11, 1-19

Abstract: Cancer is driven by genetic mutations that dysregulate pathways important for proper cell function. Therefore, discovering these cancer pathways and their dysregulation order is key to understanding and treating cancer. However, the heterogeneity of mutations between different individuals makes this challenging and requires that cancer progression is studied in a subtype-specific way. To address this challenge, we provide a mathematical model, called Subtype-specific Pathway Linear Progression Model (SPM), that simultaneously captures cancer subtypes and pathways and order of dysregulation of the pathways within each subtype. Experiments with synthetic data indicate the robustness of SPM to problem specifics including noise compared to an existing method. Moreover, experimental results on glioblastoma multiforme and colorectal adenocarcinoma show the consistency of SPM’s results with the existing knowledge and its superiority to an existing method in certain cases. The implementation of our method is available at https://github.com/Dalton386/SPM.Author summary: Different biological processes within a cell are performed through biological pathways. A biological pathway consists of a group of proteins and other molecules and complex interactions between them. It is known that cancer arises due to malfunction, also known as dysregulation, of one or more pathways. Interestingly, a dysregulation in a patient is often caused by mutations in only one (and not more) molecule in the pathway. This phenomenon is known as mutual exclusivity of mutations and can be used for identification of groups of genes forming (cancer) pathways. The same type of cancer in different patients can result due to different trajectories of dysregulations in possibly different pathways resulting in cancer heterogeneity. Cancer heterogeneity implies that cancer treatment should be personalized according to each patient’s specific characteristics and mutations. Therefore, grouping patients based on their pathway dysregulation trajectories into cancer subtypes can help identify different cancer mechanisms, inform subtype-specific treatment strategies and improve efficacy. In this paper, we provide a method that uses patients’ mutation information captured by DNA sequencing and identifies dysregulated pathways (i.e. molecules involved in each cancer pathway), cancer subtypes (i.e. groups of patients sharing a common pathway dysregulation trajectory) and subtype-specific pathway dysregulation orders (i.e. trajectories defining the different subtypes). The results on synthetic and real-world data indicate that the method can recover meaningful information about the progression of cancer in different groups of patients.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1007451

DOI: 10.1371/journal.pcbi.1007451

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