Prediction of radiosensitivity and radiocurability using a novel supervised artificial neural network

Abstract

Background Radiotherapy has been widely used to treat various cancers, but its efficacy depends on the individual involved. Traditional gene-based machine-learning models have been widely used to predict radiosensitivity. However, there is still a lack of emerging powerful models, artificial neural networks (ANN), in the practice of gene-based radiosensitivity prediction. In addition, ANN may overfit and learn biologically irrelevant features. Methods We developed a novel ANN with Selective Connection based on Gene Patterns (namely ANN-SCGP) to predict radiosensitivity and radiocurability. We creatively used gene patterns (gene similarity or gene interaction information) to control the "on–off" of the first layer of weights, enabling the low-dimensional features to learn the gene pattern information. ANN-SCGP was trained and tested in 82 cell lines and 1,101 patients from the 11 pan-cancer cohorts. Results For survival fraction at 2 Gy, the root mean squared errors (RMSE) of prediction in ANN-SCGP was the smallest among all algorithms (mean RMSE: 0.1587–0.1654). For radiocurability, ANN-SCGP achieved the first and second largest C-index in the 12/20 and 4/20 tests, respectively. The low dimensional output of ANN-SCGP reproduced the patterns of gene similarity. Moreover, the pan-cancer analysis indicated that immune signals and DNA damage responses were associated with radiocurability. Conclusions As a model including gene pattern information, ANN-SCGP had superior prediction abilities than traditional models. Our work provided novel insights into radiosensitivity and radiocurability.

Molecular Biomarkers for Predicting Cancer Patient Radiosensitivity and Radiotoxicity in Clinical Practice

“…Big data from clinical patient cohorts are becoming publicly available. At the same time, machine learning algorithms are employed for radiosensitivity predictions [191,192], while artificial neural networks are increasingly being utilized to uncover new patterns and increase radiosensitivity predictions [193]. Starting in 2020, the Department of Energy (DOE) Office of Science directed National Laboratories in the United States to explore the potential of machine learning and AI capabilities based on models generated through a DOE-National Cancer Institute (NCI) collaboration, termed CANDLE (CANcer Distributed Learning Environment) that focused on precision medicine [194,195].…”