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A mathematical model of tumor regression and recurrence after therapeutic oncogene inactivation

Sharon S Hori1,2,3, Ling Tong4,5, Srividya Swaminathan5,6

  • 1Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA. shori@stanford.edu.

Scientific Reports
|January 15, 2021
PubMed

Insights

Targeting oncogenes can shrink tumors, but resistance causes recurrence. Mathematical modeling and natural killer (NK) cell therapy can predict and delay cancer recurrence, aiding personalized treatment.

Area of Science:

  • Oncology
  • Immunology
  • Mathematical Biology

Background:

  • Oncogene addiction drives cancer regression but is often overcome by therapeutic resistance, leading to recurrence.
  • Immune surveillance plays a role in counteracting cancer recurrence after oncogene inactivation.
  • Predicting resistance timing is crucial for developing effective cancer therapies.

Purpose of the Study:

  • To develop a quantitative mathematical model for understanding cancer response to oncogene inactivation.
  • To predict tumor growth, regression, and recurrence dynamics.
  • To evaluate the potential of immune-based therapies, like natural killer (NK) cell therapy, in combination treatments.

Main Methods:

  • Development of a novel 3-compartment mathematical model for oncogene-driven tumor dynamics.
  • Validation of the model using a MYC-driven transgenic mouse model of T-cell acute lymphoblastic leukemia.
  • Utilizing imaging-based measurements of tumor burden to quantify drug-sensitive and drug-resistant cancer cells.

Main Results:

  • The mathematical model accurately predicts cancer cell dynamics in response to oncogene inactivation.
  • Natural killer (NK) cell adoptive therapy was shown to delay cancer recurrence.
  • NK cell therapy reduces the net-growth rate of drug-resistant cancer cells, impacting recurrence timing.

Conclusions:

  • Mathematical modeling provides a quantitative framework for analyzing cancer response to oncogene inactivation.
  • Immune surveillance, specifically via NK cells, can be leveraged to delay cancer recurrence.
  • This approach offers a novel strategy for evaluating combination therapies in personalized cancer treatment.

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