ArticleBreast cancer research : BCR2018
Identification and validation of single-sample breast cancer radiosensitivity gene expression predictors.
Article in Breast cancer research : BCR, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Validation of a breast cancer assay for radiotherapy omission: an individual participant data meta-analysis.Journal of the National Cancer Institute · 2025Pooled it
- Integrating Tumor-Intrinsic and Immunologic Factors to Identify Immunogenic Breast Cancers from a Low-Risk Cohort: Results from the Randomized SweBCG91RT Trial.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Trial
- Analysis of the Radiosensitivity Index in Paired Preneoadjuvant and Postneoadjuvant Therapy Triple-Negative Breast Cancer.International journal of radiation oncology, biology, physics · 2026Article
- Association between transcriptomic metrics of exogenous antigen presentation and adaptive immunity with locoregional recurrence in localized estrogen receptor negative breast cancer: retrospective review of multi-institutional datasets.Breast cancer research : BCR · 2025Article
- Evaluating the Radiation Sensitivity Index and 12-Chemokine Gene Expression Signature for Clinical Use in a CLIA Laboratory.Cancer research communications · 2025Article
- Evaluating the Radiation Sensitivity Index and 12-chemokine gene expression signature for clinical use in a CLIA laboratory.bioRxiv : the preprint server for biology · 2024Article
- Integrating radiosensitivity index and triple-negative breast cancer subtypes reveals SERPINB5 as a radioresistance biomarker in triple-negative breast cancer.Clinical and translational medicine · 2024Article
- Clinical Biomarkers of Tumour Radiosensitivity and Predicting Benefit from Radiotherapy: A Systematic Review.Cancers · 2024Review
- Article
- Review
- A genomic score to predict local control among patients with brain metastases managed with radiation.Neuro-oncology · 2023Article
- Machine Learning & Molecular Radiation Tumor Biomarkers.Seminars in radiation oncology · 2023Review
- Evolution-Informed Strategies for Combating Drug Resistance in Cancer.International journal of molecular sciences · 2023Review
- Prediction of radiosensitivity and radiocurability using a novel supervised artificial neural network.BMC cancer · 2022Article
- The Radiosensitivity Index Gene Signature Identifies Distinct Tumor Immune Microenvironment Characteristics Associated With Susceptibility to Radiation Therapy.International journal of radiation oncology, biology, physics · 2022Article
- Translation of Precision Medicine Research Into Biomarker-Informed Care in Radiation Oncology.Seminars in radiation oncology · 2022Review
- Prognostic value of metabolic signature on 18F-FDGuptake in breast cancer patients after radiotherapy.Molecular therapy oncolytics · 2021Article
- A CTL/M2 macrophage-related four-gene signature predicting metastasis-free survival in triple-negative breast cancer treated with adjuvant radiotherapy.Breast cancer research and treatment · 2021Article
- Pan-cancer prediction of radiotherapy benefit using genomic-adjusted radiation dose (GARD): a cohort-based pooled analysis.The Lancet. Oncology · 2021Article
- Proteogenomic Workflow Reveals Molecular Phenotypes Related to Breast Cancer Mammographic Appearance.Journal of proteome research · 2021Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdjuvant radiotherapy is the standard of care after breast-conserving surgery for primary breast cancer, despite a majority of patients being over- or under-treated. In contrast to adjuvant endocrine therapy and chemotherapy, no diagnostic tests are in clinical use that can stratify patients for adjuvant radiotherapy. This study presents the development and validation of a targeted gene expression assay to predict the risk of ipsilateral breast tumor recurrence and response to adjuvant radiotherapy after breast-conserving surgery in primary breast cancer.
methodsFresh-frozen primary tumors from 336 patients radically (clear margins) operated on with breast-conserving surgery with or without radiotherapy were collected. Patients were split into a discovery cohort (N = 172) and a validation cohort (N = 164). Genes predicting ipsilateral breast tumor recurrence in an Illumina HT12 v4 whole transcriptome analysis were combined with genes identified in the literature (248 genes in total) to develop a targeted radiosensitivity assay on the Nanostring nCounter platform. Single-sample predictors for ipsilateral breast tumor recurrence based on a k-top scoring pairs algorithm were trained, stratified for estrogen receptor (ER) status and radiotherapy. Two previously published profiles, the radiosensitivity signature of Speers et al., and the 10-gene signature of Eschrich et al., were also included in the targeted panel.
resultsDerived single-sample predictors were prognostic for ipsilateral breast tumor recurrence in radiotherapy-treated ER+ patients (AUC 0.67, p = 0.01), ER+ patients without radiotherapy (AUC = 0.89, p = 0.02), and radiotherapy-treated ER- patients (AUC = 0.78, p < 0.001). Among ER+ patients, radiotherapy had an excellent effect on tumors classified as radiosensitive (p < 0.001), while radiotherapy had no effect on tumors classified as radioresistant (p = 0.36) and there was a high risk of ipsilateral breast tumor recurrence (55% at 10 years). Our single-sample predictors developed in ER+ tumors and the radiosensitivity signature correlated with proliferation, while single-sample predictors developed in ER- tumors correlated with immune response. The 10-gene signature negatively correlated with both proliferation and immune response.
conclusionsOur targeted single-sample predictors were prognostic for ipsilateral breast tumor recurrence and have the potential to stratify patients for adjuvant radiotherapy. The correlation of models with biology may explain the different performance in subgroups of breast cancer.
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