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.

Parallel genomic alterations of antigen and payload targets mediate polyclonal acquired clinical resistance to sacituzumab govitecan in triple-negative breast cancer.

Abnormal pH is a common feature of malignant tumours and has been associated clinically with sub-optimal outcomes. Amide proton transfer magnetic resonance imaging (APT MRI) holds promise as a means to non-invasively measure tumour pH, yet multiple factors collectively make quantification of tumour pH from APT MRI data challenging. The purpose of this study was to improve our understanding of the biophysical sources of altered APT MRI signals in tumours. Combining in vivo APT MRI measurements with ex vivo histological measurements of protein concentration in a rat model of brain metastasis, we determined that the proportion of APT MRI signal originating from changes in protein concentration was approximately 66%, with the remaining 34% originating from changes in tumour pH. In a mouse model of hypopharyngeal squamous cell carcinoma (FaDu), APT MRI showed that a reduction in tumour hypoxia was associated with a shift in tumour pH. The results of this study extend our understanding of APT MRI data and may enable the use of APT MRI to infer the pH of individual patients' tumours as either a biomarker for therapy stratification or as a measure of therapeutic response in clinical settings.

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Radiation therapy is a first-line treatment option for localized prostate cancer and radiation-induced normal tissue damage are often the main limiting factor for modern radiotherapy regimens. Conversely, under-dosing of target volumes in an attempt to spare adjacent healthy tissues limits the likelihood of achieving local, long-term control. Thus, the ability to generate personalized data-driven risk profiles for radiotherapy outcomes would provide valuable prognostic information to help guide both clinicians and patients alike. Big data applied to radiation oncology promises to deliver better understanding of outcomes by harvesting and integrating heterogeneous data types, including patient-specific clinical parameters, treatment-related dose–volume metrics, and biological risk factors. When taken together, such variables make up the basis for a multi-dimensional space (the “RadoncSpace”) in which the presented modeling techniques search in order to identify significant predictors. Herein, we review outcome modeling and big data-mining techniques for both tumor control and radiotherapy-induced normal tissue effects. We apply many of the presented modeling approaches onto a cohort of hypofractionated prostate cancer patients taking into account different data types and a large heterogeneous mix of physical and biological parameters. Cross-validation techniques are also reviewed for the refinement of the proposed framework architecture and checking individual model performance. We conclude by considering advanced modeling techniques that borrow concepts from big data analytics, such as machine learning and artificial intelligence, before discussing the potential future impact of systems radiobiology approaches.

Tumour hypoxia is a well-recognised barrier to anti-cancer therapy and represents one of the best validated targets in oncology. Previous attempts to tackle hypoxia have focussed primarily on increasing tumour oxygen supply; however, clinical studies using this approach have yielded only modest clinical benefit, with often significant toxicity and practical limitations. Therefore, there are currently no anti-hypoxia treatments in widespread clinical use. As an emerging alternative strategy, we discuss the relevance of inhibiting tumour oxygen metabolism to alleviate hypoxia and highlight recently initiated clinical trials using this approach.

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Machine learning (ML) provides a broad framework for addressing high‐dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.