Phosphatidylinositol-3-Kinase Mutations Are Associated With Increased Local Failure in Brain Metastases Treated With Radiation

Factors associated with the local control of brain metastases: a systematic search and machine learning application

BACKGROUND

Enhancing Local Control (LC) of brain metastases is pivotal for improving overall survival, which makes the prediction of local treatment failure a crucial aspect of treatment planning. Understanding the factors that influence LC of brain metastases is imperative for optimizing treatment strategies and subsequently extending overall survival. Machine learning algorithms may help to identify factors that predict outcomes.

METHODS

This paper systematically reviews these factors associated with LC to select candidate predictor features for a practical application of predictive modeling. A systematic literature search was conducted to identify studies in which the LC of brain metastases is assessed for adult patients. EMBASE, PubMed, Web-of-Science, and the Cochrane Database were searched up to December 24, 2020. All studies investigating the LC of brain metastases as one of the endpoints were included, regardless of primary tumor type or treatment type. We first grouped studies based on primary tumor types resulting in lung, breast, and melanoma groups. Studies that did not focus on a specific primary cancer type were grouped based on treatment types resulting in surgery, SRT, and whole-brain radiotherapy groups. For each group, significant factors associated with LC were identified and discussed. As a second project, we assessed the practical importance of selected features in predicting LC after Stereotactic Radiotherapy (SRT) with a Random Forest machine learning model. Accuracy and Area Under the Curve (AUC) of the Random Forest model, trained with the list of factors that were found to be associated with LC for the SRT treatment group, were reported.

RESULTS

The systematic literature search identified 6270 unique records. After screening titles and abstracts, 410 full texts were considered, and ultimately 159 studies were included for review. Most of the studies focused on the LC of the brain metastases for a specific primary tumor type or after a specific treatment type. Higher SRT radiation dose was found to be associated with better LC in lung cancer, breast cancer, and melanoma groups. Also, a higher dose was associated with better LC in the SRT group, while higher tumor volume was associated with worse LC in this group. The Random Forest model predicted the LC of brain metastases with an accuracy of 80% and an AUC of 0.84.

CONCLUSION

This paper thoroughly examines factors associated with LC in brain metastases and highlights the translational value of our findings for selecting variables to predict LC in a sample of patients who underwent SRT. The prediction model holds great promise for clinicians, offering a valuable tool to predict personalized treatment outcomes and foresee the impact of changes in treatment characteristics such as radiation dose.

Radiotherapy as a tool to elicit clinically actionable signalling pathways in cancer

A variety of targeted anticancer agents have been successfully introduced into clinical practice, largely reflecting their ability to inhibit specific molecular alterations that are required for disease progression. However, not all malignant cells rely on such alterations to survive, proliferate, disseminate and/or evade anticancer immunity, implying that many tumours are intrinsically resistant to targeted therapies. Radiotherapy is well known for its ability to activate cytotoxic signalling pathways that ultimately promote the death of cancer cells, as well as numerous cytoprotective mechanisms that are elicited by cellular damage. Importantly, many cytoprotective mechanisms elicited by radiotherapy can be abrogated by targeted anticancer agents, suggesting that radiotherapy could be harnessed to enhance the clinical efficacy of these drugs. In this Review, we discuss preclinical and clinical data that introduce radiotherapy as a tool to elicit or amplify clinically actionable signalling pathways in patients with cancer.