ArticlePLoS computational biology2019
Uncovering the subtype-specific temporal order of cancer pathway dysregulation.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Order-of-Mutation Effects on Cancer Progression: Models for Myeloproliferative Neoplasm.Bulletin of mathematical biology · 2024Article
- Parsimonious Clone Tree Integration in cancer.Algorithms for molecular biology : AMB · 2022Article
- Identifying cancer pathway dysregulations using differential causal effects.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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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.
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