WE-D-BRE-02: BEST IN PHYSICS (THERAPY) - Radiogenomic Modeling of Normal Tissue Toxicities in Prostate Cancer Patients Receiving Hypofractionated Radiotherapy

Purpose: It has been realized that inter-patient radiation sensitivity variability is a multifactorial process involving dosimetric, clinical, and genetic factors. Therefore, we explore a new framework to integrate physical, clinical, and biological data denoted as radiogenomic modeling. In demonstrating the feasibility of this work, we investigate the association of genetic variants (copy number variations [CNVs] and single nucleotide polymorphisms [SNPs]) with radiation induced rectal bleeding (RB) and erectile dysfunction (ED) while taking into account dosimetric and clinical variables in prostate cancer patients treated with curative irradiation. Methods: A cohort of 62 prostate cancer patients who underwent hypofractionated radiotherapy (66 Gy in 22 fractions) was retrospectively genotyped for CNV and SNP rs25489 in the xrcc1 DNA repair gene. Dosevolume metrics were extracted from treatment plans of 54 patients who had complete dosimetric profiles. Treatment outcomes were considered to be a Result of functional mapping of radiogenomic input variables according to a logit transformation. Model orders were estimated using resampling by leave-one out cross-validation (LOO-CV). Radiogenomic model performance was evaluated using area under the ROC curve (AUC) and LOO-CV. For continuous univariate dosimetric and clinical variables, Spearmans rank coefficients were calculated and p-values reported accordingly. In the case of binary variables, Chi-squared statistics and contingency table calculations were used. Results: Ten patients were found to have three copies of xrcc1 CNV (RB: χ2=14.6 [p<0.001] and ED: χ2=4.88[p=0.0272]) and twelve had heterozygous rs25489 SNP (RB: χ2=0.278[p=0.599] and ED: χ2=0.112[p=0.732]). LOO-CV identified penile bulb D60 as the only significant QUANTEC predictor (rs=0.312 [p=0.0145]) for ED. Radiogenomic modeling yielded statistically significant, cross-validated NTCP models for RB (rs=0.243[p=0.0443], AUC=0.665) and ED (rs=0.276[p=0.0217], AUC=0.754). Conclusion: The radiogenomic modeling approach presented herein has been shown to identify NTCP models which have increased predictive power. Furthermore, CNVs appears to be useful genetic variants when added to dosimetric NTCP models. This work was partially supported by CIHR grant MOP-114910.

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