Unknown,Transcriptomics,Genomics,Proteomics

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Investigation of radiosensitivity gene signatures in cancer cell lines

ABSTRACT: Commercially available cell lines derived from Head and Neck Squamous cell carcinoma (HNSCC) were cultured and baseline gene expression values were assayed. We used microarrays to detail the global programme of gene expression underlying differences in radiosensitivity as measured by surviving fraction at 2Gy (SF2) HNSCC Cell lines (n=11) were cultured in standard conditions. RNA was extracted at subconfluence.

ORGANISM(S): Homo sapiens

SUBMITTER: John Hall

PROVIDER: E-GEOD-51370 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Purpose To develop a radiosensitivity gene expression assay to predict the response to adjuvant radiotherapy (RT) after breast conserving surgery (BCS) in breast cancer. Patients and methods Fresh frozen primary tumors from 336 patients operated with BCS with or without RT were collected. Patients were split in a discovery cohort (N=172) and a validation cohort (N=164). Genes predicting ipsilateral breast tumor recurrence (IBTR) in an Illumina HT12 v4 whole transcriptome analysis were combined with genes from the literature (248 genes in total) to develop a targeted radiosensitivity assay on the Nanostring nCounter platform. Single sample predictors (SSPs) for IBTR based on a k-top scoring pairs algorithm were trained stratified for estrogen receptor (ER) status and RT. Two previously published profiles (radiosensitivity index, RSI, and radiosensitivity score, RSS) were also tested in our data Results The SSPs were prognostic for IBTR in ER+RT- patients (AUC 0.67, p=0.005), ER+RT- patients (AUC=0.89, p=0.015) and ER-RT+ patients (AUC=0.78, p<0.001). Among ER+ patients, radiosensitive tumors had an excellent effect of RT (p<0.001), while radioresistant tumors had no effect of RT (p=0.4) and a high risk of IBTR (55% at 10 years). Our SSPs developed in ER+ tumors and the RSS correlated with proliferation, while SSPs developed in ER- tumors correlated with immune response. RSI negatively correlated with both proliferation and immune response. Conclusions Our targeted SSPs were prognostic for IBTR and has the potential to stratify patients for RT. The biology behind models may explain the different performance in subgroups of breast cancer. Purpose To develop a radiosensitivity gene expression assay to predict the response to adjuvant radiotherapy (RT) after breast conserving surgery (BCS) in breast cancer. Patients and methods Fresh frozen primary tumors from 336 patients operated with BCS with or without RT were collected. Patients were split in a discovery cohort (N=172) and a validation cohort (N=164). Genes predicting ipsilateral breast tumor recurrence (IBTR) in an Illumina HT12 v4 whole transcriptome analysis were combined with genes from the literature (248 genes in total) to develop a targeted radiosensitivity assay on the Nanostring nCounter platform. Single sample predictors (SSPs) for IBTR based on a k-top scoring pairs algorithm were trained stratified for estrogen receptor (ER) status and RT. Two previously published profiles (radiosensitivity index, RSI, and radiosensitivity score, RSS) were also tested in our data Results The SSPs were prognostic for IBTR in ER+RT- patients (AUC 0.67, p=0.005), ER+RT- patients (AUC=0.89, p=0.015) and ER-RT+ patients (AUC=0.78, p<0.001). Among ER+ patients, radiosensitive tumors had an excellent effect of RT (p<0.001), while radioresistant tumors had no effect of RT (p=0.4) and a high risk of IBTR (55% at 10 years). Our SSPs developed in ER+ tumors and the RSS correlated with proliferation, while SSPs developed in ER- tumors correlated with immune response. RSI negatively correlated with both proliferation and immune response. Conclusions Our targeted SSPs were prognostic for IBTR and has the potential to stratify patients for RT. The biology behind models may explain the different performance in subgroups of breast cancer.

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Investigation of radiosensitivity gene signatures in cancer cell lines

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