Hierarchical cluster analysis and nonlinear mixed-effects modelling for candidate biomarker detection in preclinical models of cancer.

BACKGROUND

Preclinical models of cancer can be of translational benefit when assessing how different biomarkers are regulated in response to particular treatments. Detection of molecular biomarkers in preclinical models of cancer is difficult due inter-animal variability in responses, combined with limited accessibility of longitudinal data.

METHODS

Nonlinear mixed-effects modelling (NLME) was used to analyse tumour growth data based on expected tumour growth rates observed 7 days after initial doses (DD7) of Radiotherapy (RT) and Combination of RT with DNA Damage Response Inhibitors (DDRi). Cox regression was performed to confirm an association between DD7 and survival. Hierarchical Cluster Analysis (HCA) was then used to identify candidate biomarkers impacting responses to RT and RT/DDRi and these were validated using NLME.

RESULTS

Cox regression confirmed significant associations between DD7 and survival. HCA of RT treated samples, combined with NLME confirmed significant associations between DD7 and Cluster specific CD8

CONCLUSION

Application of NLME, as well as HCA of candidate biomarkers may provide additional avenues to assess the effect of RT in MC38 syngeneic tumour models. Additional studies would need to be conducted to confirm association between DD7 and biomarkers in RT/DDRi treated mice.

Informations de copyright

Copyright © 2024. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest SG and MD are currently employed by AstraZeneca and hold stock options. JY is a former associate of AstraZeneca. All other authors declare no conflict of interest.