OBJECTIVE: Although various single genetic factors have been shown to affect radiosensitivity, high-throughput DNA sequencing analyses have revealed complex genomic landscapes in many cancer types. The aim of this study is to elucidate the association between accumulated alterations in driver and passenger genes and radiation therapy response. METHODS: We used 59 human solid cancer cell lines derived from 11 organ sites. Radiation-induced cell death was measured using a standard colony-forming assay delivered as a single dose ranging from 0 to 12 Gy. Comprehensive genomic data for the cell lines were acquired from the Catalogue Of Somatic Mutations In Cancer v. 80. Random forest classifiers were constructed to predict radioresistant phenotypes using genomic features. The Cancer Genome Atlas data sets were used to evaluate the clinical impact of the genomic feature following radiotherapy. RESULTS: The 59 cancer cell lines harbored either nucleotide variations or copy number variations in a median of 157 genes per cell. Radiosensitivity of the cancer cells was correlated with neither the number of driver gene mutations nor the number of passenger gene mutations. However, the proportion of driver gene alterations to total gene alterations in gene sets selected from the Kyoto Encyclopedia Genes and Genomes predicted radioresistant cells with sensitivity of 85% and specificity of 73%. High probability of radioresistance predicted by the model was associated with worse overall survival following definitive radiotherapy in patients of The Cancer Genome Atlas data sets. CONCLUSION: Cellular radiosensitivity was associated with the proportion of driver to total gene alterations in the selected oncogenic pathways, which may be a biomarker candidate for response to radiation therapy. ADVANCES IN KNOWLEDGE: These findings suggest that accumulated alterations in not only driver genes but also passenger genes affect radiosensitivity.
OBJECTIVE: Although various single genetic factors have been shown to affect radiosensitivity, high-throughput DNA sequencing analyses have revealed complex genomic landscapes in many cancer types. The aim of this study is to elucidate the association between accumulated alterations in driver and passenger genes and radiation therapy response. METHODS: We used 59 human solid cancer cell lines derived from 11 organ sites. Radiation-induced cell death was measured using a standard colony-forming assay delivered as a single dose ranging from 0 to 12 Gy. Comprehensive genomic data for the cell lines were acquired from the Catalogue Of Somatic Mutations In Cancer v. 80. Random forest classifiers were constructed to predict radioresistant phenotypes using genomic features. The Cancer Genome Atlas data sets were used to evaluate the clinical impact of the genomic feature following radiotherapy. RESULTS: The 59 cancer cell lines harbored either nucleotide variations or copy number variations in a median of 157 genes per cell. Radiosensitivity of the cancer cells was correlated with neither the number of driver gene mutations nor the number of passenger gene mutations. However, the proportion of driver gene alterations to total gene alterations in gene sets selected from the Kyoto Encyclopedia Genes and Genomes predicted radioresistant cells with sensitivity of 85% and specificity of 73%. High probability of radioresistance predicted by the model was associated with worse overall survival following definitive radiotherapy in patients of The Cancer Genome Atlas data sets. CONCLUSION: Cellular radiosensitivity was associated with the proportion of driver to total gene alterations in the selected oncogenic pathways, which may be a biomarker candidate for response to radiation therapy. ADVANCES IN KNOWLEDGE: These findings suggest that accumulated alterations in not only driver genes but also passenger genes affect radiosensitivity.
Authors: Mark G Kris; Bruce E Johnson; Lynne D Berry; David J Kwiatkowski; A John Iafrate; Ignacio I Wistuba; Marileila Varella-Garcia; Wilbur A Franklin; Samuel L Aronson; Pei-Fang Su; Yu Shyr; D Ross Camidge; Lecia V Sequist; Bonnie S Glisson; Fadlo R Khuri; Edward B Garon; William Pao; Charles Rudin; Joan Schiller; Eric B Haura; Mark Socinski; Keisuke Shirai; Heidi Chen; Giuseppe Giaccone; Marc Ladanyi; Kelly Kugler; John D Minna; Paul A Bunn Journal: JAMA Date: 2014-05-21 Impact factor: 56.272
Authors: W G McKenna; M C Weiss; V J Bakanauskas; H Sandler; M L Kelsten; J Biaglow; S W Tuttle; B Endlich; C C Ling; R J Muschel Journal: Int J Radiat Oncol Biol Phys Date: 1990-04 Impact factor: 7.038
Authors: Sally A Amundson; Khanh T Do; Lisa C Vinikoor; R Anthony Lee; Christine A Koch-Paiz; Jaeyong Ahn; Mark Reimers; Yidong Chen; Dominic A Scudiero; John N Weinstein; Jeffrey M Trent; Michael L Bittner; Paul S Meltzer; Albert J Fornace Journal: Cancer Res Date: 2008-01-15 Impact factor: 12.701
Authors: Helen E Bryant; Niklas Schultz; Huw D Thomas; Kayan M Parker; Dan Flower; Elena Lopez; Suzanne Kyle; Mark Meuth; Nicola J Curtin; Thomas Helleday Journal: Nature Date: 2005-04-14 Impact factor: 69.504
Authors: Simon A Forbes; David Beare; Prasad Gunasekaran; Kenric Leung; Nidhi Bindal; Harry Boutselakis; Minjie Ding; Sally Bamford; Charlotte Cole; Sari Ward; Chai Yin Kok; Mingming Jia; Tisham De; Jon W Teague; Michael R Stratton; Ultan McDermott; Peter J Campbell Journal: Nucleic Acids Res Date: 2014-10-29 Impact factor: 16.971
Authors: Hannah Farmer; Nuala McCabe; Christopher J Lord; Andrew N J Tutt; Damian A Johnson; Tobias B Richardson; Manuela Santarosa; Krystyna J Dillon; Ian Hickson; Charlotte Knights; Niall M B Martin; Stephen P Jackson; Graeme C M Smith; Alan Ashworth Journal: Nature Date: 2005-04-14 Impact factor: 69.504