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Mathematical Model of Intrinsic Drug Resistance in Lung Cancer

Emilia Kozłowska¹, Andrzej Swierniak¹

¹Department of Systems Biology and Engineering, Silesian University of Technology, 44100 Gliwie, Poland.

International Journal of Molecular Sciences

|November 14, 2023

Abstract:

Drug resistance is a bottleneck in cancer treatment. Commonly, a molecular treatment for cancer leads to the emergence of drug resistance in the long term. Thus, some drugs, despite their initial excellent response, are withdrawn from the market. Lung cancer is one of the most mutated cancers, leading to dozens of targeted therapeutics available against it. Here, we have developed a mechanistic mathematical model describing sensitization to nine groups of targeted therapeutics and the emergence of intrinsic drug resistance. As we focus only on intrinsic drug resistance, we perform the computer simulations of the model only until clinical diagnosis. We have utilized, for model calibration, the whole-exome sequencing data combined with clinical information from over 1000 non-small-cell lung cancer patients. Next, the model has been applied to find an answer to the following questions: When does intrinsic drug resistance emerge? And how long does it take for early-stage lung cancer to grow to an advanced stage? The results show that drug resistance is inevitable at diagnosis but not always detectable and that the time interval between early and advanced-stage tumors depends on the selection advantage of cancer cells.

Insights

Drug resistance in cancer treatment is inevitable, even at diagnosis. Mathematical modeling shows that the progression from early to advanced lung cancer depends on cancer cell selection advantage.

Area of Science:

Oncology
Mathematical Biology
Genomics

Background:

Drug resistance is a major challenge in cancer therapy, often leading to treatment failure and drug withdrawal.
Lung cancer, characterized by high mutation rates, has numerous targeted therapies, yet resistance remains a significant hurdle.

Purpose of the Study:

To develop a mechanistic mathematical model for targeted drug resistance in non-small cell lung cancer (NSCLC).
To investigate the emergence timing of intrinsic drug resistance and the progression time from early to advanced lung cancer stages.

Main Methods:

Developed a mechanistic mathematical model to simulate sensitization and intrinsic drug resistance to nine targeted therapeutic groups.
Calibrated the model using whole-exome sequencing and clinical data from over 1000 NSCLC patients.
Performed computer simulations focusing on intrinsic resistance up to clinical diagnosis.

Main Results:

Intrinsic drug resistance is found to be inevitable at the time of diagnosis, though not always clinically detectable.
The time for early-stage lung cancer to progress to an advanced stage is contingent upon the selection advantage of cancer cells.

Conclusions:

Mathematical modeling provides insights into the dynamics of drug resistance and tumor progression in lung cancer.
Understanding the emergence of intrinsic resistance is crucial for developing effective long-term cancer treatment strategies.

Read full analysis

Keywords:

branching process model lung cancer mathematical modeling targeted treatment

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