Toward a science of tumor forecasting for clinical oncology
Abstract
We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. Furthermore, with a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.
- Authors:
-
- Vanderbilt Univ., Nashville, TN (United States). Inst. of Imaging Science; Dept. of Radiology and Radiological Sciences; Dept. of Biomedical Engineering; Dept. of Physics and Astronomy; Dept. of Cancer Biology; Vanderbilt-Ingram Cancer Center
- Vanderbilt Univ., Nashville, TN (United States). Dept. of Cancer Biology; Vanderbilt-Ingram Cancer Center
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division
- Vanderbilt Univ., Nashville, TN (United States). Dept. of Physics and Astronomy
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1185639
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Cancer Research
- Additional Journal Information:
- Journal Volume: 75; Journal Issue: 6; Journal ID: ISSN 0008-5472
- Publisher:
- American Association for Cancer Research
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; nonlinear models of tumor growth; analogies to weather and climate forecasting
Citation Formats
Yankeelov, Thomas E., Quaranta, Vito, Evans, Katherine J., and Rericha, Erin C. Toward a science of tumor forecasting for clinical oncology. United States: N. p., 2015.
Web. doi:10.1158/0008-5472.CAN-14-2233.
Yankeelov, Thomas E., Quaranta, Vito, Evans, Katherine J., & Rericha, Erin C. Toward a science of tumor forecasting for clinical oncology. United States. https://doi.org/10.1158/0008-5472.CAN-14-2233
Yankeelov, Thomas E., Quaranta, Vito, Evans, Katherine J., and Rericha, Erin C. Sun .
"Toward a science of tumor forecasting for clinical oncology". United States. https://doi.org/10.1158/0008-5472.CAN-14-2233. https://www.osti.gov/servlets/purl/1185639.
@article{osti_1185639,
title = {Toward a science of tumor forecasting for clinical oncology},
author = {Yankeelov, Thomas E. and Quaranta, Vito and Evans, Katherine J. and Rericha, Erin C.},
abstractNote = {We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. Furthermore, with a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.},
doi = {10.1158/0008-5472.CAN-14-2233},
journal = {Cancer Research},
number = 6,
volume = 75,
place = {United States},
year = {Sun Mar 15 00:00:00 EDT 2015},
month = {Sun Mar 15 00:00:00 EDT 2015}
}
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