A six-gene-based signature for breast cancer radiotherapy sensitivity estimation

Abstract

Breast cancer (BRCA) represents the most common malignancy among women worldwide with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Here, we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity estimation. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA samples compared with their paracancerous samples in the training set were identified by using the edgeR Bioconductor package. Univariate Cox regression analysis and LASSO Cox regression method were applied to screen optimal genes for constructing a radiotherapy sensitivity estimation signature. Nomogram combining independent prognostic factors was used to predict 1-, 3-, and 5-year OS of radiation-treated BRCA patients. Relative proportions of tumor infiltrating immune cells (TIICs) calculated by CIBERSORT and mRNA levels of key immune checkpoint receptors was adopted to explore the relation between the signature and tumor immune response. As a result, 603 DEGs were obtained in BRCA tumor samples, six of which were retained and used to construct the radiotherapy sensitivity prediction model. The signature was proved to be robust in both training and testing sets. In addition, the signature was closely related to the immune microenvironment of BRCA in the context of TIICs and immune checkpoint receptors’ mRNA levels. In conclusion, the present study obtained a radiotherapy sensitivity estimation signature for BRCA, which should shed new light in clinical and experimental research.

Radiation Response in the Tumour Microenvironment: Predictive Biomarkers and Future Perspectives

“…While a clear step forward, RRS remains a prognostic signature for loco-regional control, and not predictive of radiation response. Similar predictive gene signatures have been developed, including a six-gene signature (including genes such as HOXB13 and NKX2-2) that was also shown to predict radiotherapy sensitivity in breast cancer [ 77 ]. Applying a 24-gene signature to prostate cancer patients who had undergone radical prostatectomy to identify those most likely to benefit from postoperative radiotherapy similarly found that those with a high PROTOS (postoperative radiation therapy outcomes score), indicative of radiation-sensitive tumours, significantly benefited from radiotherapy, with a 10-year metastasis rate of 4% (95% CI 0–10) versus 35% (CI 7–54) for those not receiving radiotherapy.…”

More evidence for prediction model of radiosensitivity

“…Some patients are more sensitive to radiotherapy while others may not benefit from the radiotherapy. Recently, Chen et al [ 1 ] developed a signature for breast cancer radiosensitivity and this signature shed new light in the treatment of breast cancer.…”

“…Recently, a six-gene-based signature established a prediction model for breast cancer radiotherapy sensitivity estimation [ 1 ]. The six genes in this signature include HOXB13, NKX2-2, ADAMTS20 , LOC284930, ACTL8 and LOC101928978 [ 1 ].…”

“…Recently, a six-gene-based signature established a prediction model for breast cancer radiotherapy sensitivity estimation [ 1 ]. The six genes in this signature include HOXB13, NKX2-2, ADAMTS20 , LOC284930, ACTL8 and LOC101928978 [ 1 ]. And the researchers constructed a radiotherapy sensitivity prediction model by using those six genes, selected by univariate Cox regression and Lasso Cox regression.…”

“…In summary, to define the radiosensitivity for a group of patients, at least three kinds of test could be given. (1). Survival comparison under non-radiotherapy; (2) Survival comparison under radiotherapy and non-radiotherapy; (3) Survival comparison under radiotherapy.…”