Mathematical Model Identifies PARP Inhibitor Resistance Biomarkers in Triple-Negative Breast Cancer
Key Insights
A novel mathematical model was developed to analyze tumor growth dynamics in triple-negative breast cancer (search) (TNBC (search)) patient-derived xenografts treated with olaparib.
The model identifies the pre-treatment resistance fraction as a key predictor of response to olaparib, distinguishing complete responders, initial responders, and non-responders.
Correlation analysis reveals potential biomarkers within the CIViC gene list, offering insights into mechanisms driving resistance to PARP (search) inhibitors.
A new study published in npj Breast Cancer (search) utilizes a mathematical model to dissect the variability in tumor growth observed in triple-negative breast cancer (TNBC (search)) xenograft models treated with olaparib, a PARP (search) inhibitor. The research identifies potential biomarkers associated with resistance to the drug, offering insights into personalized treatment strategies.
Modeling Tumor Growth Dynamics
The researchers developed a mathematical model incorporating both sensitive and resistant tumor cell populations to capture the diverse responses observed in TNBC (search) patient-derived xenografts (PDXs) treated with olaparib. The model effectively characterized complete responders, initial responders, and non-responders based on the pre-treatment resistance fraction.
The model is based on the Mayneord model, an empirical model of tumour growth where the rate of change of the volume is proportional to the surface of the tumour. By introducing two compartments, one for the sensitive and one for the resistant part of the tumour, the model can capture the diverse dynamics of these data sets.
Biomarker Discovery
By correlating the model-derived resistance fraction with pre-treatment mRNA expression data, the study identified several potential biomarkers within the CIViC (clinical interpretation of variants in cancer) gene list. These genes showed significant correlations with olaparib resistance, suggesting their involvement in resistance mechanisms.
A linear correlation (Pearson’s correlation) was performed between the resistance fraction, in the PDX level and the pre-treatment processed mRNA expression values of certain genes. A p-value less than or equal to 0.05 was used for that purpose.
Clinical Implications
The identification of predictive biomarkers for olaparib resistance could significantly impact treatment decisions for TNBC (search) patients. By understanding the underlying mechanisms driving resistance, researchers can develop strategies to overcome these barriers and improve patient outcomes. The study also highlights the power of mathematical modeling in deciphering complex biological systems and accelerating drug development.
Study Details
The study involved 33 patient-derived xenografts (PDX) treated with olaparib as a single agent. Olaparib was administered orally six times per week at 50 or 100 mg/kg. Tumor growth was measured bi-weekly, and transcriptomic data were obtained for each PDX before treatment. The mathematical model was developed using non-linear mixed effects (NLME) and validated using independent datasets. Modified Response Evaluation Criteria in Solid Tumours (RECIST) criteria was used which was based on the % tumour volume change: complete response (CR), best response ← 95%; partial response (PR), best response ← 30%; stable disease (SD), −30% < best response < +20%; progressive disease (PD), best response > +20%.