Genome-based model developed for personalized radiation therapy treatment

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Javier F. Torres-Roca, MD A genome-based clinical model to individualize radiation doses based on the radiosensitivity of a patient’s tumor has been developed and validated by researchers at the Moffitt Cancer Center in Tampa, Florida. A study used the genomic-adjusted radiation dose (GARD) model to accurately predict the clinical outcomes of more than 250 breast cancer patients. GARD, described in a December article published online in The Lancet Oncology, can facilitate the use of precision medicine for radiation therapy, and offers the opportunity to design efficient, genomically guided clinical trials.

Precision medicine is being used in chemotherapy to tailor treatment to individual tumor biology by customizing the selection of chemotherapy drugs that provide the greatest benefit to a patient. The research underway at Moffitt Cancer Center represents the use of a patient-specific molecular signature of radiation sensitivity to identify the optimum radiation dose for delivery to a specific type of tumor.

Principal investigator Javier F. Torres-Roca, MD, (pictured left) director of research in the Department of Radiation Oncology, and colleagues, conducted their retrospective study to determine whether a patient-specific molecular signature of radiation sensitivity could identify an optimum radiation therapy dose. They previously had developed and validated a gene-expression-based radiosensitivity index (RSI) that predicted tumor sensitivity to radiation therapy based on the expression of 10 specific genes. The RSI accurately predicts clinical outcomes of patients treated with radiation therapy for glioblastoma; metastatic colorectal cancer; and cancers of the breast, head and neck, and pancreas. Their research supports the concept that clinical benefit from radiation therapy is not uniform and is highest in a radiosensitive subpopulation.

Dr. Torres-Roca and colleagues combined the RSI with a validated linear quadratic model used to estimate different radiation fractionation schemes to create the GARD model. They created GARD values for 8,271 tissue samples in the Total Cancer Care (TCC) prospective tissue collection protocol to assess the range of GARD values within and between tumor types. Data from five clinical cohorts were used to determine whether GARD was associated with clinical outcomes. GARD scores were derived using the linear quadratic model, the individual gene-expression-based RSI, and the standard of care radiation dose and the fractionation schedule received by each patient in the clinical cohorts. The higher a GARD score was, the higher the radiation therapeutic effect would be. The article includes a detailed explanation of the process.

GARD scores showed that a higher radiation therapy dose does not always result in a higher radiotherapeutic effect across a population. As an example, patients with highly radiosensitive breast cancer received the same radiotherapeutic effect with less radiation than those with low radiosensitivity who received higher doses. Similarly, GARD scores differed by cancer type, conforming to research showing that radiation has a better outcome rate for some types of cancer — such as oropharyngeal and cervical cancer — compared to other types.

Because GARD was developed to enable the adjustment of radiation dose to match an individual’s tumor’s radiosensitivity, it is possible to proactively test the clinical validity of GARD by testing whether patients with higher GARD values have better clinical outcomes. The genomic-based clinical trial design is expected to greatly reduce the cost and improve the efficiency of radiation oncology clinical trials by significantly reducing the number of patients needed to participate in and the time to complete the trial.

The future of genomic radiation therapy

One prospective clinical trial to be implemented at Moffitt Cancer Center this year involves patients with HPV-positive head and neck cancer. For these patients, dose de-escalation while maintaining excellent clinical outcomes is a critical goal. Jimmy J. Caudell, MD, PhD, will serve as the principal investigator.

“In the current design, we will use RSI/GARD to identify patient subpopulations that are best candidates for dose de-escalation, and then we will guide the dose range based on what the model predicts,” Dr. Torres-Roca said. “Imaging and early response at day 20 of treatment will be utilized as well to finalize the dosing decision.”

He added that all treatment plans will be optimized using pGRT – the precision genomic radiation therapy being commercialized by Cvergenx Inc.,Tampa, Florida – as a decision support tool for integration into treatment planning system software.

The authors hope radiation oncologists might be convinced to use pGRT in clinical practice if evidence shows outcomes are better in patients who achieve a specific GARD threshold. They believe current standard and uniform radiotherapy doses can be further optimized with tumor-specific genomic data using pGRT.

When asked how a genomic-based clinical trial could improve radiation therapy, Dr. Torres-Roca explained that over the last 30 years, the field of radiation oncology has focused on developing technologies that allow for more precise delivery of radiation dose to an anatomical area. The idea was that dose escalation would improve clinical outcomes across disease sites. But while technical developments have allowed for safe dose escalation of 10%-20%, the clinical benefits and outcomes have fallen short of predictions. “It is possible that our models that assume uniform dose escalation would result in uniform benefit were wrong because there is a biological heterogeneity in tumor radiosensitivity, or we have already maximized the clinical benefit from radiation therapy, and the only way forward is to combine radiation therapy with radiosensitizers and/or other agents, such as immunotherapy,” he said.

“Our model provides an approach to quantify the potential radiotherapy therapeutic benefit,” Dr. Torres-Roca continued. “We hope that our approach will provide a framework to design genomic-based clinical trials that will use RSI to identify subpopulations most likely to benefit from the intervention being tested.”

He added: “I think there is significant untapped potential for radiation therapy that remains hidden from clinical view,” he added. “The integration of a biological framework to understand the differences between patients who benefit more from radiation therapy treatment and those who benefit less will bring strategies to improve the clinical benefit of radiation therapy.”

REFERENCES

  1. Poortmans P, Kaidar-Person O, Span P. Radiation oncology enters the era of individualised medicine. Lancet Oncol. Published online December 16, 2016. DOI: http://www.thelancet.com/journals/lanonc/article/PIIS1470-2045(16)30660-X/abstract
  1. Scott JG, Berglund A, Schell MJ, et al. A genome-base model for adjusting radiotherapy dose (GARD): a retrospective, cohort-based study. Lancet Oncol. Published online December 16, 2016. DOI: http://www.thelancet.com/journals/lanonc/article/PIIS1470-2045(16)30648-9/abstract

 Cynthia E. Keen is a medical writer based in Sanibel Island, Florida, and is a regular contributor to Applied Radiation Oncology.

 

 

 

 

 

 

 

 

 

 

 

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Genome-based model developed for personalized radiation therapy treatment.  Appl Rad Oncol. 

By Cynthia E. Keen| January 09, 2017

About the Author

Cynthia E. Keen

Cynthia E. Keen

Cynthia E. Keen is a medical writer and regular contributor to Applied Radiation Oncology. She is based in Sanibel Island, Florida.

 



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 2017