The Role of Genomic Techniques in Predicting Response to Radiation Therapy
The Role of Genomic Techniques in Predicting Response to Radiation Therapy
The understanding of the relationship between genetic variation and an individual patient’s response to radiation therapy (RT) has gained significant ground over the past several years. Genetic markers have been identified that could ultimately serve as the foundation for predictive models in clinical practice, and that hold the potential to revolutionize the delivery of precision medicine in oncology. Single nucleotide polymorphisms, single genes, and/or gene signatures could ultimately serve as the basis for patient stratification in prospective clinical trials. Currently, molecular markers relevant to breast, lung, and head and neck cancers have been integrated into clinical practice and serve as predictive tools to guide systemic therapy. In the future, the use of predictive models based on genomic determinants may become standard practice in radiation oncology, offering the potential to further personalize the delivery of RT and optimize the therapeutic ratio.
Radiation therapy (RT) remains a mainstay of modern oncologic treatment, with more than half of all patients receiving RT during their treatment course. However, individual responses to RT vary widely among disease types and patient populations. Recent years have been marked by the development and expanded use of precision medicine in cancer therapeutics. Precision medicine refers to the tailoring of treatment to the individual characteristics of each patient, based on inherent susceptibilities. Although enormous strides have been made in tailoring a variety of approaches to systemic therapy, the role of radiation oncology in precision medicine is just beginning to emerge.
Precision in RT has been advancing along multiple parallel paths. There have been improvements in the precision of anatomic target delineation with the use of intensity-modulated RT, volumetric arc therapy, and stereotactic RT, all of which allow for improved target dose conformality. Concurrent with technical advances in treatment delivery, the field of radiogenomics, or the interplay between genomic elements and radiation response at the cellular level, continues to evolve. Indexing the determinants of radiation response at the cellular level has the potential to allow for more personalized delivery of RT and to further increase the therapeutic ratio of our treatment.
As the rates of cancer survival continue to improve, the effect of treatment toxicity on normal tissue will play an increasingly important role in treatment selection. Capturing patient-reported outcomes from the growing and evolving survivor population sheds light on the potential far-reaching impact of radiogenomics beyond traditional survival measures. Specifically, by recognizing the connection between genotypic variation and normal tissue response, our ability to predict severe toxicities following RT may spare selected individuals from significant morbidity and mortality following treatment. Moreover, studies investigating genetic assays predictive of tumor radiosensitivity may be complementary to studies evaluating the radiosensitivity of noncancerous tissue. The purpose of the current article is multifold: Herein, we will review the background and history of genomic predictors of RT response; evaluate candidate genes and polymorphisms dictating responses to radiation; discuss emerging data on the use of genetic signatures; and review current guidelines on the use of genomic predictors to tailor therapy. The article is structured to discuss outcomes and toxicities based on precision medicine in RT within each of these sections.
Background and History
Prognostic vs predictive markers in oncology
Biomarkers have long been used in the field of oncology as an adjunct to traditional staging information to estimate treatment outcomes. In this field of study, it is important to distinguish between prognostic and predictive biomarkers. Prognostic markers are associated with a clinical outcome, such as overall survival (OS), regardless of the treatment delivered. For example, the prostate-specific antigen (PSA) has been proven to be an important biomarker in prostate cancer, correlating with the risk of recurrence and OS. Although elevated PSA levels are associated with worse outcomes, measurement of PSA alone does not yet predict the patient response to specific treatments.
Predictive markers, on the other hand, are indicators of the likely benefit following specific treatment. These markers are therefore useful in tailoring treatment decisions. An example of a predictive marker is ERBB2 gene amplification (resulting in overexpression of human epidermal growth factor receptor 2 [HER2]) in breast cancer, since clinical outcomes are improved by the addition of trastuzumab to the chemotherapy regimen in patients with this genetic aberration. The National Comprehensive Cancer Network (NCCN), in updating the NCCN Biomarkers Compendium, recently released a task force report addressing the use of molecular biomarkers in six major disease sites. While prognostic biomarkers provide important information regarding clinical outcome, implicit to the goal of precision medicine is the identification of predictive biomarkers to help direct individual treatment. Despite the significant progress made by radiogenomics in this regard over the past 20 years—from focused gene studies to genome-wide association studies (GWAS)—in the field of radiation oncology, clinical translation of these principles remains a goal on the horizon.
Initial discovery of discrepant radiation responses
Studies investigating variable responses of tissues to RT date back more than 60 years ago to the investigations carried out by Gray and colleagues.[6-8] Specifically studied was the effect of oxygenation on RT response. The tumor microenvironment has been demonstrated to have topographic variability; certain regions possess particularly low extracellular pH, low nutrient content, and hypoxia. Given the often tortuous and malformed vasculature of tumors, blood flow to the microenvironment contributes to an imbalance in the supply of and demand for oxygen. The resulting hypoxia correlates with tumor cell radioresistance, since the maximal effect of RT is achieved by the generation of free radicals. Preceding the early discovery of the effect of hypoxia on radioresistance was the demonstration of individual variation in the response of normal tissue following treatment with a given dose of radiation. This was first formally described in 1936 with the publication of the now well-described sigmoid dose–response curve. Alongside the discovery of differing individual responses to similar radiation doses was the detection of RT hypersensitivity in patients with certain rare genetic syndromes. The first such documented adverse reaction occurred in a 10-year-old patient with mutation of the ATM gene, who died from complications related to radiation toxicity in normal tissues. Since this initial case was reported, the ATM mutation has been intricately linked with the DNA damage response and studied extensively. While the demonstration of radiosensitivity in patients with rare genetic disorders has been instrumental in our understanding of differential radiation responses, it does not yet explain the wide range of radiation responses seen in patients without known genetic syndromes.