Single-cell systems analysis: decision geometry in outliers
Open accessPublished version of a preprint
Abstract
Motivation: Anti-cancer therapeutics of the highest calibre currently focus on combinatorial targeting of specific oncoproteins and tumour suppressors. Clinical relapse depends upon intratumoral heterogeneity which serves as substrate variation during evolution of resistance to therapeutic regimens. Results: The present review advocates single-cell systems biology as the optimal level of analysis for remediation of clinical relapse. Graph theory approaches to understanding decision-making in single cells may be abstracted one level further, to the geometry of decision-making in outlier cells, in order to define evolution-resistant cancer biomarkers. Systems biologists currently working with omics data are invited to consider phase portrait analysis as a mediator between graph theory and deep learning approaches. Perhaps counter-intuitively, the tangible clinical needs of cancer patients may depend upon the adoption of higher level mathematical abstractions of cancer biology. Supplementary information: Supplementary data are available at Bioinformatics online.
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- Authors
- Abrahams, L.
- Affiliations
- Ronin Institute (US)
- Corresponding author
- Abrahams, Lianne
- ORCID
- Abrahams, Lianne
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-12-26
- Volume
- 37
- Issue
- 12
- Pages
- 1747-1755
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W3114362851
- Cited by
- 3
- References
- 6
- Citations by year
- 2026: 2 · 2024: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- intratumoral heterogeneity, tumor recurrence, drug resistance, tumor markers, phase plane analysis, graph theory, deep learning, combination cancer therapy
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