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Abstract

BACKGROUND:

Despite its common use in cancer treatment, radiotherapy has not yet entered the era of precision medicine, and there have been no approaches to adjust dose based on biological differences between or within tumours. We aimed to assess whether a patient-specific molecular signature of radiation sensitivity could be used to identify the optimum radiotherapy dose.

METHODS:

We used the gene-expression-based radiation-sensitivity index and the linear quadratic model to derive the genomic-adjusted radiation dose (GARD). A high GARD value predicts for high therapeutic effect for radiotherapy; which we postulate would relate to clinical outcome. Using data from the prospective, observational Total Cancer Care (TCC) protocol, we calculated GARD for primary tumours from 20 disease sites treated using standard radiotherapy doses for each disease type. We also used multivariable Cox modelling to assess whether GARD was independently associated with clinical outcome in five clinical cohorts: Erasmus Breast Cancer Cohort (n=263); Karolinska Breast Cancer Cohort (n=77); Moffitt Lung Cancer Cohort (n=60); Moffitt Pancreas Cancer Cohort (n=40); and The Cancer Genome Atlas Glioblastoma Patient Cohort (n=98).

FINDINGS:

We calculated GARD for 8271 tissue samples from the TCC cohort. There was a wide range of GARD values (range 1·66-172·4) across the TCC cohort despite assignment of uniform radiotherapy doses within disease types. Median GARD values were lowest for gliomas and sarcomas and highest for cervical cancer and oropharyngeal head and neck cancer. There was a wide range of GARD values within tumour type groups. GARD independently predicted clinical outcome in breast cancer, lung cancer, glioblastoma, and pancreatic cancer. In the Erasmus Breast Cancer Cohort, 5-year distant-metastasis-free survival was longer in patients with high GARD values than in those with low GARD values (hazard ratio 2·11, 95% 1·13-3·94, p=0·018).

INTERPRETATION:

A GARD-based clinical model could allow the individualisation of radiotherapy dose to tumour radiosensitivity and could provide a framework to design genomically-guided clinical trials in radiation oncology.

FUNDING: None.​

Author information

Scott JG1, Berglund A2, Schell MJ2, Mihaylov I3, Fulp WJ2, Yue B2, Welsh E2, Caudell JJ4, Ahmed K4, Strom TS4, Mellon E4, Venkat P4, Johnstone P4, Foekens J5, Lee J2, Moros E4, Dalton WS6, Eschrich SA2, McLeod H6, Harrison LB4, Torres-Roca JF7.

1 Department of Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Department of Integrated Mathematical Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.

2 Department of Integrated Bioinformatics and Biostatistics, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.

3 Department of Radiation Oncology, University of Miami, Miami, FL, USA.

4 Department of Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.

5 Department of Medical Oncology and Cancer Genomics, Erasmus Medical Center, Rotterdam, Netherlands.

6 DeBartolo Family Personalized Medicine Institute, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.

7 Department of Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Department of Chemical Biology and Molecular Medicine, Moffitt Cancer Center and Research Institute, Tampa, FL, USA. Electronic address: javier.torresroca@moffitt.org.