Radiogenomics and radiotherapy response modeling

Abstract

Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.

Figure 1

The human body is a valuable resource for varying solid and fluid types of specimens, which can yield different –omic (genomics, transcriptomics, proteomics, metabolomics, radiomics) predictive biomarkers, in addition to dosimetric and clinical factors used in radiotherapy that would undergo major processes of annotation, curation, and preparation before being applied into radiogenomics modeling of treatment outcomes (e.g., tumor response, toxicity).

Figure 2

The informatics understanding of heterogeneous variable interactions as a feedback into the treatment planning system to improve patient’s outcomes. The noise reflects the uncertainties in the measured (clinical, physical, and biological) factors). The computer represents the modeling process, which could be embedded in a treatment planning system. The gear crankshaft is part of the continuous feedback during the course of treatment or a clinical trial to refine or take new updated measurements (El Naqa, 2013).

Figure 3

Comparison between the predicted incidence of grade 2+ rectal bleeding and the actual incidence of grade 2+ rectal bleeding. The predicted outcomes were produced after applying the logistic regression to outputs of the LASSO model using 2 principal components on the validation data set with 484 SNPs that entered the LASSO. Based on the sorted predicted outcomes, the patients were binned into 6 groups, with the first being the lowest toxicity group and the sixth being the highest. The ratio above each group represents the observed number of patients who experienced grade 2+ rectal bleeding and the total number of patients in the group (Kerns et al., 2015).

Figure 4

Ten-year receiver operating characteristic (ROC) curves (A) and Kaplan–Meier survival estimate (B) analysis in validation dataset of radiosensitivity in breast cancer using random forest machine learning (Speers et al., 2015).

Figure 5

A radiogenomic model using systems biology techniques based on Bayesian networks is used for modeling radiation pneumonitis (RP) in lung cancer. First row shows pre-treatment BN modeling of RP. (a) Markov blanket, (b) BN structure, and (c) ROC analysis on cross-validation. The second row shows during-treatment BN modeling of RP. (d) Markov blanket, (e) BN structure, and (f) ROC analysis on cross-validation (Luo et al., 2017).