Your browser doesn't support javascript.
loading
A genome-based model for adjusting radiotherapy dose (GARD): a retrospective, cohort-based study.
Scott, Jacob G; Berglund, Anders; Schell, Michael J; Mihaylov, Ivaylo; Fulp, William J; Yue, Binglin; Welsh, Eric; Caudell, Jimmy J; Ahmed, Kamran; Strom, Tobin S; Mellon, Eric; Venkat, Puja; Johnstone, Peter; Foekens, John; Lee, Jae; Moros, Eduardo; Dalton, William S; Eschrich, Steven A; McLeod, Howard; Harrison, Louis B; Torres-Roca, Javier F.
Afiliação
  • Scott JG; 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.
  • Berglund A; Department of Integrated Bioinformatics and Biostatistics, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Schell MJ; Department of Integrated Bioinformatics and Biostatistics, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Mihaylov I; Department of Radiation Oncology, University of Miami, Miami, FL, USA.
  • Fulp WJ; Department of Integrated Bioinformatics and Biostatistics, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Yue B; Department of Integrated Bioinformatics and Biostatistics, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Welsh E; Department of Integrated Bioinformatics and Biostatistics, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Caudell JJ; Department of Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Ahmed K; Department of Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Strom TS; Department of Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Mellon E; Department of Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Venkat P; Department of Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Johnstone P; Department of Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Foekens J; Department of Medical Oncology and Cancer Genomics, Erasmus Medical Center, Rotterdam, Netherlands.
  • Lee J; Department of Integrated Bioinformatics and Biostatistics, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Moros E; Department of Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Dalton WS; DeBartolo Family Personalized Medicine Institute, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Eschrich SA; Department of Integrated Bioinformatics and Biostatistics, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • McLeod H; DeBartolo Family Personalized Medicine Institute, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Harrison LB; Department of Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Torres-Roca JF; 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.
Lancet Oncol ; 18(2): 202-211, 2017 02.
Article em En | MEDLINE | ID: mdl-27993569
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Pancreáticas / Tolerância a Radiação / Biomarcadores Tumorais / Genoma Humano / Glioblastoma / Neoplasias Pulmonares / Modelos Genéticos Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Pancreáticas / Tolerância a Radiação / Biomarcadores Tumorais / Genoma Humano / Glioblastoma / Neoplasias Pulmonares / Modelos Genéticos Idioma: En Ano de publicação: 2017 Tipo de documento: Article