Abstract
Theoretical and empirical work over the past several decades suggests that oncogenesis and disease progression represents an evolutionary story. Despite this knowledge, current anti-resistance strategies to drugs are often managed through treating cancers as independent biological agents divorced from human activity. Yet once drug resistance to cancer treatment is understood as a product of artificial or anthropogenic rather than unconscious selection, oncologists could improve outcomes for their patients by consulting evolutionary studies of oncology prior to clinical trial and treatment plan design. In the setting of multiple cancer types, for example, a machine learning algorithm can predict the genetic changes known to be related to drug resistance. In this way, a unity between technology and theory might have practical clinical implications—and may pave the way for a new paradigm shift in medicine.
Highlights
Reviewed by: Stefano Fais, Istituto Superiore di Sanità (ISS), Italy Ali M
Once drug resistance to cancer treatment is understood as a product of artificial or anthropogenic rather than unconscious selection, oncologists could improve outcomes for their patients by consulting evolutionary studies of oncology prior to clinical trial and treatment plan design
Researchers across disciplines are improving the understanding of cancer from both the theoretical and treatment-oriented perspectives, with, as has been shown in the first studies of their kind, better outcomes in the clinical trials developed from an evolutionary point of view
Summary
As a uniquely predictive theoretical science, evolutionary biology is, starting to affect how clinically devastating phenomena, such as drug resistance, are understood. Immunotherapies including vaccines or checkpoint inhibitors are less effective in settings where tumors are large or metastatic: immunosuppressive signals expressed by cells within the tumor microenvironment preclude long-term survival improvements for patients, if an immunotherapy is used as a monotherapy [15]. Targeted therapies, such as BRAF inhibitors for melanoma, are rendered ineffective if cancer cells proliferate via other mechanisms, for example secondary BRAFV600 mutations, N-RAS upregulation, and activation of survival pathways via tyrosine kinase receptor–mediation [16, 17]. We highlight several models and trials that have and are changing oncology into a more evolutionarily focused discipline, and suggest that a unity of data science with evolutionary theory will be necessary for true clinical improvements across oncology
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