The promising connection between data science and evolutionary theory in oncology
File(s)
Published version
Author(s)
Ashrafian, Hutan
Goodman, Jonathan
Type
Journal Article
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.
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.
Date Issued
2020-01-20
Date Acceptance
2019-12-18
Citation
Frontiers in Oncology, 2020, 9
ISSN
2234-943X
Publisher
Frontiers Media
Journal / Book Title
Frontiers in Oncology
Volume
9
Copyright Statement
© 2020 Goodman and Ashrafian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Subjects
1112 Oncology and Carcinogenesis
Publication Status
Published
Article Number
ARTN 1527