Exploring the role of host-tumor interactions in tumor growth and regression

Authors:
William C. Troy and Stewart J. Anderson

Journal:
Quart. Appl. Math. **73** (2015), 131-161

MSC (2010):
Primary 82B10

DOI:
https://doi.org/10.1090/S0033-569X-2015-01385-X

Published electronically:
January 21, 2015

MathSciNet review:
3322728

Full-text PDF

Abstract | References | Similar Articles | Additional Information

Abstract: In the last 35 years the characterization of tumor growth using Gompertzian models has led to a new understanding of the spread of tumor cells, and has enabled researchers to develop novel therapeutic strategies to eradicate disease in some cases (see Ciron et al. (2003), Norton et al. (1976), Norton and Simon (1986), and Simon and Norton (2006)). A long standing assertion is that the Gompertzian framework does not allow characterizations of complex biological and clinical phenomena such as tumor regression and dormancy (see Retsky et al. (1998)). We propose a generalized Gompertzian system of delay-differential equations to study host-tumor interaction effects in the absence of external therapy. Our model is a parsimonious extension of the Norton et al. (1976) model: denotes tumor volume, represents host-tumor interactions, and and represents time of response of the host to the presence of tumor cells. The first step is to set and study Setting we derive ODEs satisfied by and prove existence and qualitative properties of infinitely many branches of solutions. Therefore, where are eigenfunctions. Substituting into the ODE, we: **(I)** identify an ``optimal immunological response'' range where host-tumor interactions can cause a tumor to remain dormant, or regress from growth state into dormancy, and **(II)** replicate observed tumor growth in mammograms of 32 breast cancer patients.

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Additional Information

**William C. Troy**

Affiliation:
Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania 15260

Email:
troy@math.pitt.edu

**Stewart J. Anderson**

Affiliation:
Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, 312 Parran Hall, 130 DeSoto Street, Pittsburgh, Pennsylvania 15261

Email:
sja@pitt.edu

DOI:
https://doi.org/10.1090/S0033-569X-2015-01385-X

Received by editor(s):
March 2, 2013

Published electronically:
January 21, 2015

Article copyright:
© Copyright 2015
Brown University