A study published in Lancet Oncology finds that the genomic-adjusted radiation dose (GARD) model may be used to personalize radiotherapy (RT) to maximize the therapeutic effect of a given physical RT dose.
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“As opposed to physical RT dose, which is the measure of what comes out of the machine and is delivered to the patient, GARD quantifies the biological effect on an individual patient of that delivered dose,” says Cleveland Clinic radiation oncologist Jacob G. Scott, MD, DPhil, first author of the study. “We’ve found that the physical dose of radiation does not associate with outcomes, but GARD does.”
A group of investigators from Cleveland Clinic, Case Western Reserve University School of Medicine and Moffitt Cancer Center authored the study, which represents the validation of a quantifiable parameter of the clinical effect of radiation, a parameter that serves as a predictor of the therapeutic benefit of RT for each individual patient.
According to Dr. Scott, despite recent advances in cancer genomics, the field of radiation oncology has, unlike medical oncology, not entered the precision medicine era, where patient-specific genomic data drives therapeutic decision-making. He notes that RT is still largely prescribed using an empiric approach that only considers the specific cancer diagnosis/tumor location to decide on standard dosing. In an attempt to move the field forward, his team of collaborators successfully devised and introduced the concept of GARD in a previous study.
“In our previous paper we showed the possibility of individualizing radiation dosing using patient tumor genomics and canonical models of radiation response. Using these models, with genomics, GARD revealed significant heterogeneity within datasets that wasn’t visible before, and while it seemed to associate with outcomes in each disease site we analyzed, we lacked the statistical power to be certain,” he says. “The thrust of that work was really to explain the derivation of GARD itself because it was a new concept.”
GARD is derived from the gene-expression based radiation-sensitivity index (RSI) and the canonical linear quadratic model used to describe the biological response to radiation. RSI is, in turn, based on the expression of ten specific genes in the biopsied tumor tissue and serves as a molecular estimate for cellular survival fraction at 2 Gy (SF2). Put simply, RSI reflects the tumor’s sensitivity to radiation. The changes in the 10-gene signature that occur inside the tumor, the authors argue, are reflections of the changes that take place in the entire gene network, so they function like canaries in the coal mine, providing signals of radiation sensitivity secondary to many different possible changes at the genomic or epigenetic level. Many earlier studies have validated the gene-expression RSI/GARD as a biomarker of tumor radiosensitivity in patients across many cancer subtypes.
In explaining the utility of GARD, Dr. Scott notes that it quantifies the biologic effect of radiation into a numerical parameter that involves physical RT dose, giving oncologists an objective way to understand the relative therapeutic benefit of their prescribed RT, and allowing them to modify the physical dose of RT to optimize radiation therapy benefit for each individual patient. And because GARD integrates RSI and the patient’s individual tumor gene expression signature into its algorithm, it is a truly personalized tool, which, unlike standard biomarkers, allows the clinician to modulate the dose given based on each patient’s predicted response. GARD provides the first validated decision support tool where radiation oncologists can utilize genomic information to modulate the potential benefit of radiation therapy for each patient.
The current study used previously published data on cancers of the breast, head and neck, endometrium, melanoma, glioma, pancreas and lung to test the association between GARD, RT dose and patient outcomes using two endpoints: time to first recurrence and overall survival. The analysis included 1,615 patients from 11 separate cohorts including seven disease sites. To test whether the GARD-based RT dosing paradigm is associated with the outcomes, a pooled pan-cancer analysis was performed.
“GARD is not a standalone biomarker; rather, it is a dynamic parameter that changes based on the prescribed RT dose, which allows the clinician to directly modulate it,” says Dr. Scott. The higher the GARD value, the higher the predicted therapeutic benefit of radiotherapy at that specific dose, but, he adds, these increases may not always be worth the risk of dose escalation. “Some patients have large increases in GARD with more dose, and some patients do not respond as well near the range of standard of care, so the discussion with the clinician and the treating oncologist’s understanding of the risk-benefit balance remain paramount.”
Leaving behind a decades-old way of prescribing RT to cancer patients will take time, Dr. Scott says, but he is optimistic that the field will soon embrace GARD as a valuable tool for personalizing RT.
“This test is now available from a laboratory at Moffitt Cancer Center and will be available commercially very soon,” he says. “In this paper we show level 1 evidence for GARD’s utility, which should give clinicians the confidence to begin using this genomic decision support tool and adjusting dose within the standard of care. While we are eager to see adoption in the near term, we’re also interested in prospective trials, disease by disease, to begin defining disease-specific GARD-based dosing targets.”
Learn more by visiting Dr. Scott’s Theory Division Lab webpage.