Mathematical Models in Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 1245

Special Issue Editor


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Guest Editor
Frederick National Laboratory for Cancer Research, Cancer Research, Frederick, MD, USA
Interests: virtual human models; predictive oncology; cancer systems biology; machine learning; artificial intelligence; mechanistic models; digital twins; precision oncology; precision medicine; computational oncology; cancer

Special Issue Information

Dear Colleagues,

The range of mathematical models developed for cancer applications continues to rise, driven by multiple trends including the development of artificial intelligence models, the evolution of mechanistic and systematic biology models with cutting-edge data insights, and interest in predictive models aiming to improve decisions and provide explanations. With mathematics serving as a common foundation for expressing and sharing key relationships in cancer, the ability to relate, integrate and couple mathematical models in cancer to improve discovery insights and health outcomes provides a unifying vision for this Special Issue.

This Special Issue gives prominence to predictive mathematical models in cancer, highlighting the accelerating translational use of their applications to benefit patients. The range of predictive mathematical models in cancer serves as the foundation for integrated virtual representations of cancer across various scales, with potential applications at the molecular, cellular and tumor level, and clinical predictions at the patient and population level. With an emphasis on human translation, including personalized approaches of mathematical models for cancer, this Special Issue also embraces mathematical models in areas such as side effects, toxicity and recurrence predictions.

Dr. Eric A. Stahlberg
Guest Editor

Manuscript Submission Information

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Keywords

  • virtual human models
  • predictive oncology
  • cancer systems biology
  • machine learning
  • artificial intelligence
  • mechanistic models
  • digital twins
  • precision oncology
  • precision medicine
  • computational oncology
  • cancer

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Published Papers (1 paper)

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Research

17 pages, 9728 KiB  
Article
Pan-Cancer, Genome-Scale Metabolic Network Analysis of over 10,000 Patients Elucidates Relationship between Metabolism and Survival
by Jesse Bucksot, Katherine Ritchie, Matthew Biancalana, John A. Cole and Daniel Cook
Cancers 2024, 16(13), 2302; https://doi.org/10.3390/cancers16132302 - 22 Jun 2024
Viewed by 1125
Abstract
Despite the high variability in cancer biology, cancers nevertheless exhibit cohesive hallmarks across multiple cancer types, notably dysregulated metabolism. Metabolism plays a central role in cancer biology, and shifts in metabolic pathways have been linked to tumor aggressiveness and likelihood of response to [...] Read more.
Despite the high variability in cancer biology, cancers nevertheless exhibit cohesive hallmarks across multiple cancer types, notably dysregulated metabolism. Metabolism plays a central role in cancer biology, and shifts in metabolic pathways have been linked to tumor aggressiveness and likelihood of response to therapy. We therefore sought to interrogate metabolism across cancer types and understand how intrinsic modes of metabolism vary within and across indications and how they relate to patient prognosis. We used context specific genome-scale metabolic modeling to simulate metabolism across 10,915 patients from 34 cancer types from The Cancer Genome Atlas and the MMRF-COMMPASS study. We found that cancer metabolism clustered into modes characterized by differential glycolysis, oxidative phosphorylation, and growth rate. We also found that the simulated activities of metabolic pathways are intrinsically prognostic across cancer types, especially tumor growth rate, fatty acid biosynthesis, folate metabolism, oxidative phosphorylation, steroid metabolism, and glutathione metabolism. This work shows the prognostic power of individual patient metabolic modeling across multiple cancer types. Additionally, it shows that analyzing large-scale models of cancer metabolism with survival information provides unique insights into underlying relationships across cancer types and suggests how therapies designed for one cancer type may be repurposed for use in others. Full article
(This article belongs to the Special Issue Mathematical Models in Cancer)
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