RESUMO
The multitude of DNA lesion types, and the nuclear dynamic context in which they occur, present a challenge for genome integrity maintenance as this requires the engagement of different DNA repair pathways. Specific 'repair controllers' that facilitate DNA repair pathway crosstalk between double strand break (DSB) repair and base excision repair (BER), and regulate BER protein trafficking at lesion sites, have yet to be identified. We find that DNA polymerase ß (Polß), crucial for BER, is ubiquitylated in a BER complex-dependent manner by TRIP12, an E3 ligase that partners with UBR5 and restrains DSB repair signaling. Here we find that, TRIP12, but not UBR5, controls cellular levels and chromatin loading of Polß. Required for Polß foci formation, TRIP12 regulates Polß involvement after DNA damage. Notably, excessive TRIP12-mediated shuttling of Polß affects DSB formation and radiation sensitivity, underscoring its precedence for BER. We conclude that the herein discovered trafficking function at the nexus of DNA repair signaling pathways, towards Polß-directed BER, optimizes DNA repair pathway choice at complex lesion sites.
RESUMO
PURPOSE: To evaluate the potential of synthetic radiomic data generation in addressing data scarcity in radiomics/radiogenomics models. METHODS: This study was conducted on a retrospectively collected cohort of 386 colorectal cancer patients (n = 2570 lesions) for whom matched contrast-enhanced CT images and gene TP53 mutational status were available. The full cohort data was divided into a training cohort (n = 2055 lesions) and an independent and fixed test set (n = 515 lesions). Differently sized training sets were subsampled from the training cohort to measure the impact of sample size on model performance and assess the added value of synthetic radiomic augmentation at different sizes. Five different tabular synthetic data generation models were used to generate synthetic radiomic data based on "real-world" radiomics data extracted from this cohort. The quality and reproducibility of the generated synthetic radiomic data were assessed. Synthetic radiomics were then combined with "real-world" radiomic training data to evaluate their impact on the predictive model's performance. RESULTS: A prediction model was generated using only "real-world" radiomic data, revealing the impact of data scarcity in this particular data set through a lack of predictive performance at low training sample numbers (n = 200, 400, 1000 lesions with average AUC = 0.52, 0.53, and 0.56 respectively, compared to 0.64 when using 2055 training lesions). Synthetic tabular data generation models created reproducible synthetic radiomic data with properties highly similar to "real-world" data (for n = 1000 lesions, average Chi-square = 0.932, average basic statistical correlation = 0.844). The integration of synthetic radiomic data consistently enhanced the performance of predictive models trained with small sample size sets (AUC enhanced by 9.6%, 11.3%, and 16.7% for models trained on n_samples = 200, 400, and 1000 lesions, respectively). In contrast, synthetic data generated from randomised/noisy radiomic data failed to enhance predictive performance underlining the requirement of true signal data to do so. CONCLUSION: Synthetic radiomic data, when combined with real radiomics, could enhance the performance of predictive models. Tabular synthetic data generation might help to overcome limitations in medical AI stemming from data scarcity.