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Integrating Radiosensitivity and Immune Gene Signatures for Predicting Benefit of Radiotherapy in Breast Cancer.

ABSTRACT: Purpose: Breast cancer is a heterogeneous disease and not all patients respond equally to adjuvant radiotherapy. Predictive biomarkers are needed to select patients who will benefit from the treatment and spare others the toxicity and burden of radiation.Experimental Design: We first trained and tested an intrinsic radiosensitivity gene signature to predict local recurrence after radiotherapy in three cohorts of 948 patients. Next, we developed an antigen processing and presentation-based immune signature by maximizing the treatment interaction effect in 129 patients. To test their predictive value, we matched patients treated with or without radiotherapy in an independent validation cohort for clinicopathologic factors including age, ER status, HER2 status, stage, hormone-therapy, chemotherapy, and surgery. Disease-specific survival (DSS) was the primary endpoint.Results: Our validation cohort consisted of 1,439 patients. After matching and stratification by the radiosensitivity signature, patients who received radiotherapy had better DSS than patients who did not in the radiation-sensitive group [hazard ratio (HR), 0.68; P = 0.059; n = 322], whereas a reverse trend was observed in the radiation-resistant group (HR, 1.53; P = 0.059; n = 202). Similarly, patients treated with radiotherapy had significantly better DSS in the immune-effective group (HR, 0.46; P = 0.0076; n = 180), with no difference in DSS in the immune-defective group (HR, 1.27; P = 0.16; n = 348). Both signatures were predictive of radiotherapy benefit (P interaction = 0.007 and 0.005). Integration of radiosensitivity and immune signatures further stratified patients into three groups with differential outcomes for those treated with or without radiotherapy (P interaction = 0.003).Conclusions: The proposed signatures have the potential to select patients who are most likely to benefit from radiotherapy. Clin Cancer Res; 24(19); 4754-62. ©2018 AACR.

SUBMITTER: Cui Y

PROVIDER: S-EPMC6168425 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Integrating Radiosensitivity and Immune Gene Signatures for Predicting Benefit of Radiotherapy in Breast Cancer.

Cui Yi Y Li Bailiang B Pollom Erqi L EL Horst Kathleen C KC Li Ruijiang R

Clinical cancer research : an official journal of the American Association for Cancer Research 20180619 19

<b>Purpose:</b> Breast cancer is a heterogeneous disease and not all patients respond equally to adjuvant radiotherapy. Predictive biomarkers are needed to select patients who will benefit from the treatment and spare others the toxicity and burden of radiation.<b>Experimental Design:</b> We first trained and tested an intrinsic radiosensitivity gene signature to predict local recurrence after radiotherapy in three cohorts of 948 patients. Next, we developed an antigen processing and presentatio ...[more]

PMID: 29921729

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Project description:Background: Biomarkers predicting response to bevacizumab in breast cancer are still missing. Since epigenetic modifications can contribute to an aberrant regulation of angiogenesis and treatment resistance, we investigated the influence of DNA methylation patterns on bevacizumab efficacy. Methods: Genome-wide methylation profiling using the Illumina Infinium HumanMethylation450 BeadChip was performed in archival FFPE specimens of 36 patients with HER2-negative metastatic breast cancer treated with chemotherapy in combination with bevacizumab as first-line therapy (learning set). Based on objective response and progression-free survival (PFS) and considering ER expression, patients were divided in responders (R) and non-responders (NR). Significantly differentially methylated gene loci (CpGs) with a strong change in methylation levels (??>0.15 or ??<-0.15) between R and NR were identified and further investigated in 80 bevacizumab-treated breast cancer patients (optimization set) and in 15 patients treated with chemotherapy alone (control set) using targeted deep amplicon bisulfite sequencing. Methylated gene loci were considered predictive if there was a significant association with outcome (PFS) in the optimization set but not in the control set using Spearman rank correlation, Cox regression, and logrank test. Results: Differentially methylated loci in 48 genes were identified, allowing a good separation between R and NR (odds ratio (OR) 101, p<0.0001). Methylation of at least one cytosine in 26 gene-regions was significantly associated with progression-free survival (PFS) in the optimization set, but not in the control set. Using information from the optimization set, the panel was reduced to a 9-gene signature, which could divide patients from the learning set into 2 clusters, thereby predicting response with an OR of 40 (p<0.001) and an AUC of 0.91 (LOOCV). A further restricted 3-gene methylation model showed a significant association of predicted responders with longer PFS in the learning and optimization set even in multivariate analysis with an excellent and good separation of R and NR with AUC=0.94 and AUC=0.86, respectively. Conclusion: Both a 9-gene and 3-gene methylation signature can discriminate between R and NR to a bevacizumab-based therapy in MBC and could help identify patients deriving greater benefit from bevacizumab.

Project description:BackgroundFemale breast cancer (BC) has become the most common cancer in the world, and its mortality was considerably higher in transitioning vs. transitioned countries. Pyroptosis, an inflammation-dependent programmed cell death mediated by inflammasomes, has been observed in human colorectal tumors and gliomas. However, the characteristics of pyrolysis-related genes and their influence and mechanism on the tumorigenesis and progress of BC were unknown.MethodsBased on the global public database, we used comprehensive bioinformatics analysis to systematically analyze the expression of pyroptosis-related genes in BC and their relationship in tumor progression. In addition, BC patients were divided into two groups, and the clinical features and outcomes could be better predicted by the consistent clustering of pyroptosis-related genes. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to establish a risk score. Then, we further explored the prognostic value and clinical features of pyroptosis genes. Finally, we used the Human Protein Atlas (HPA) platform to identify the expression at protein levels of the key genes.ResultsWe confirmed that the expression of pyroptosis-related genes was different in BC and normal breast tissues. A high frequency of somatic mutations occurred in BC. In addition, 33 pyroptosis-related proteins interacted frequently. Based on univariate analysis and the LASSO Cox model, five pyroptosis-related genes [including GADMA, interleukin-6 (IL-6), NLR pyrin domain-containing protein 6 (NLRP6), caspase-1 (CASP1), and caspase-9 (CASP9)], were obtained to calculate a risk score. The risk score was identified as an independent risk factor for the prognosis of BC and might play an auxiliary role in clinical classification. The HPA platform confirmed that the expression trends of the key genes were consistent with our previous studies.ConclusionPyroptosis had an important effect on the progression of BC. And the pyroptosis-related genes could be used as new prognostic biomarkers and therapeutic targets for BC.

Dataset Information

Integrating Radiosensitivity and Immune Gene Signatures for Predicting Benefit of Radiotherapy in Breast Cancer.

Publications

Integrating Radiosensitivity and Immune Gene Signatures for Predicting Benefit of Radiotherapy in Breast Cancer.

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