Homo sapiens
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PROVIDER: PRJNA406920 | ENA |
REPOSITORIES: ENA
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Project description:Identification and validation of single sample breast cancer radiosensitivity gene expression predictors
| PRJNA406914 | ENA
Project description:Identification and validation of single sample breast cancer radiosensitivity gene expression predictors [Nanostring custom assay data]
| PRJNA406918 | ENA
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.
2018-07-01 | GSE103745 | GEO
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.
2018-07-01 | GSE103744 | GEO
Project description:CD3+ T cells were isolated by FACS from primary tumour tissues of two triple-negative breast cancer patients. Sorted cells were submitted to a 10X Genomics Chromium System for single cell capture. cDNA synthesis and library preparation were done according to the protocol supplied by the manufacturer. Libraries were sequenced on an Illumina HiSeq 2500 High Output Mode using V4 clustering and sequencing chemistry to achieve 100 bp paired-end reads.
2018-06-24 | GSE110686 | GEO
Project description:Genome wide expression profiling of breast normal cell line MCF-10A. The Illumina HumanHT-12 v4 Expression BeadChip was used to obtain expression profiles. This profiling indicates that MicroRNA-7 mediates the activity of HGF to suppress oncogenic proteins, which inhibits cancer progress.
2017-12-06 | GSE102758 | GEO
Project description:RNAseq was done on Breast cancer PDX samples uisng Library protocol =llumina TruSeq Stranded Total RNA Kit with Ribo-Zero Gold , HiSeq 50 Cycle Single-Read Sequencing v4
2019-03-01 | GSE113986 | GEO
Project description:Gene expression profiles in normal fibroblasts from patients with breast cancer - comparison of clinical radiosensitivity
2007-02-01 | E-MEXP-821 | biostudies-arrayexpress
Project description:Growing interest in the cellular origins of different breast cancer subtypes has prompted investigations into the subpopulations of the normal breast epithelia and their differentiation hierarchy. Several groups have demonstrated a likely luminal-progenitor cell origin for basal-like breast cancer. However, the molecular and cellular mechanisms underlying why one breast cell type might be more susceptible to transformation are yet to be elucidated. To observe the molecular differences in the different cell subpopulation response to ionizing radiation (IR), we performed gene expression profiling of MUC1+-sorted and CD10+-sorted primary human mammary epithelial cell cultures. Transcriptional response was measured at 2 and 24 hr after treatment with 2 and 5 Gy IR using Illumina HumanHT-12 v4 Expression Beadchips. The complete sample cohort included time-point matched untreated (0 Gy) controls in a total of 5 individual patients. Our analyses indicated several cell-type specific differences in response to IR.
2016-02-07 | GSE67595 | GEO
Project description:Results: Normal tissue contamination caused misclassification of tumors in all predictors, but different breast cancer predictors showed different susceptibility to normal tissue bias. Sensitivity and negative predictive value (NPV) of the PAM50 assay was improved by accounting for normal tissue. Conclusions: Normal tissue sampled concurrently with tumor tissue is an important source of bias in genomic predictors. Adjustments for normal tissue contamination could improve the application of breast cancer genomic predictors in both research and in clinical settings. Reference x breast tumor samples.
2011-07-18 | E-GEOD-22384 | biostudies-arrayexpress