Abstract P042: A genomic model to predict cellular radiosensitivity

Purpose: This group has previously worked on Radiosensitivity Index (RSI),^1,2 which has been clinically validated in 1919 patients across multiple cohorts.³ The Linear Quadratic (LQ) model is a widely accepted model used to prescribe radiation dose and fractionation. We hypothesize that clonogenic data can be fitted to the linear quadratic equation to predict α and β based on gene expression. Here we introduce a novel genomic model (gLQ) developed in cell lines to estimate the α and β parameters in cell lines. Methods: We analyzed public data on 494 irradiated clonogenic cell lines,⁴ considering Survival Fraction (SF) at known radiation dosages (2-10 Gy, i.e., SF2 through SF10) and fractions to calculate “ground truth” α and β using RAD-ADAPT software.⁵ Ground truth α and β were aligned to publically available Affymetrix U133 2.0 plus microarray and RNA seq data. Normalization was performed by Robust Multi-array average and RSEM respectively. 90% of the 494 cell line data (n=444) was used to train the model, while 10% (n=50) was left untouched. To narrow the data on 18,468 genes for each cell line to a smaller set of representative genes, we used a combination of clustering analysis, feature selection using Max-min Markov Blanket, and evaluating the functionality of selected features as they relate to radiosensitivity and cancer.⁶ Limiting the genes used as inputs in the machine-trained regression improved model training time. Machine learning, specifically Bayesian Ridge Regression, was utilized to identify relationships between calculated α and β and the representative gene expression profile for each cell line. 598 genes were selected for the α model, and 1198 genes were selected for the β model. These models were then locked down and tested on the 50 untouched validation cell lines. Results: Calculated ground truth α and β had a strong fit (R²=0.94). The resulting trained, locked-down model predicted α and β values for the remaining unseen 10% of the cell lines as a separate validation cohort with R² values of 0.8809 and 0.8175, respectively. Conclusions: This suggests that genomic data can be used to effectively predict cellular radiosensitivity. References: 1.Torres-Roca JF. A molecular assay of tumor radiosensitivity: a roadmap towards biology-based personalized radiation therapy. Per Med. 2012;9(5):547-557. doi:10.2217/pme.12.55 2.Eschrich SA, Pramana J, Zhang H, et al. A gene expression model of intrinsic tumor radiosensitivity: prediction of response and prognosis after chemoradiation. Int J Radiat Oncol Biol Phys. 2009;75(2):489-496. doi:10.1016/j.ijrobp.2009.06.014 3.Scott JG, Sedor G, Ellsworth P, et al. Pan-cancer prediction of radiotherapy benefit using genomic-adjusted radiation dose (GARD): a cohort-based pooled analysis. Lancet Oncol. 2021;22(9):1221-1229. doi:10.1016/S1470-2045(21)00347-8 4.Yard, B., Adams, D., Chie, E. et al. "A genetic basis for the variation in the vulnerability of cancer to DNA damage," Nat Commun, 7, 11428 (2016) 5.https://bmsr.usc.edu/software/rad-adapt 6.https://academic.oup.com/bioinformatics

Citation Format: Benjamin M Honan, Jeffrey S. Peacock. A genomic model to predict cellular radiosensitivity. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P042.