Understanding tumour growth variability in breast cancer xenograft models identifies PARP inhibition resistance biomarkers

• Published in: NPJ precision oncology Vol. 8; no. 1; pp. 266 - 12
• Main Authors: Voulgarelis, D., Forment, J. V., Herencia Ropero, A., Polychronopoulos, D., Cohen-Setton, J., Bender, A., Serra, V., O’Connor, M. J., Yates, J. W. T., Bulusu, K. C.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18-11-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1347; 631/67/1857; 631/67/2329; Biomarkers; Breast cancer; Cancer Research; Gene Therapy; Human Genetics; Internal Medicine; Medicine; Medicine & Public Health; Oncology
• ISSN: 2397-768X; 2397-768X
• DOI: 10.1038/s41698-024-00702-x

Understanding the mechanisms of resistance to PARP inhibitors (PARPi) is a clinical priority, especially in breast cancer. We developed a novel mathematical framework accounting for intrinsic resistance to olaparib, identified by fitting the model to tumour growth metrics from breast cancer patient-derived xenograft (PDX) data. Pre-treatment transcriptomic profiles were used with the calculated resistance to identify baseline biomarkers of resistance, including potential combination targets. The model provided both a classification of responses, as well as a continuous description of resistance, allowing for more robust biomarker associations and capturing the observed variability. Thirty-six resistance gene markers were identified, including multiple homologous recombination repair (HRR) pathway genes. High WEE1 expression was also linked to resistance, highlighting an opportunity for combining PARP and WEE1 inhibitors. This framework facilitates a fully automated way of capturing intrinsic resistance, and accounts for the pharmacological response variability captured within PDX studies and hence provides a precision medicine approach.