scholarly journals DNA Damage Response Genes in Osteosarcoma

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ying Tang ◽  
Yan-xia Liu ◽  
Xiuning Huang ◽  
Peng Li
Keyword(s):  
Dna Damage ◽  
High Risk ◽  
Dna Damage Response ◽  
Risk Score ◽  
Cox Regression ◽  
Excision Repair ◽  
Gene Signature ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Damage Response

Background. Improving the osteosarcoma (OS) patients’ survival has long been a challenge, even though the disease’s treatment is on the verge of progress. DNA damage response (DDR) has traditionally been associated with carcinogenesis, tumor growth, and genomic instability. No study has used DDR genes as a signature to identify the prognosis of OS. The goal of this work was to find an effective possible DDR gene biomarker for predicting OS prognosis, which may be useful in clinical diagnosis and therapy. Methods. To assess gene methylation, univariate and multivariate cox regression analyses were performed on data from OS patients. The data were retrieved from public databases, including the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and the Gene Expression Omnibus (GEO). Results. The DDR gene signature was chosen, which included seven genes (NHEJ1, RMI2, SWI5, ERCC2, CLK2, POLG, and MLH1). In the TARGET dataset, patients were categorized into two groups: high-risk and low-risk. Patients with a high-risk score revealed a shorter OS rate (hazard ratio (HR): 3.15, 95% confidence interval (CI): 1.38–4.34, P < 0.001 ) in comparison with the patients with a low-risk score in the TARGET as a training group. The validation of the prognostic signature accuracy was carried out in relapse and validation cohorts (TARGET, n = 75; GSE21257, n = 53). The signature was found to be an independent predictive factor for OS in multivariate cox regression analysis, and a nomogram model was developed to predict an individual’s risk of OS. DDR gene signature involved in Fanconi anemia pathway, nonhomologous end−joining pathway, mismatch repair, and nucleotide excision repair pathway. Conclusions. Our study suggests that the identified novel DDR genes could be a powerful prognostic tool for prognosis evaluation and a valuable tool in predicting the risk factors in OS patients.

Analysis of DNA damage response gene signature for prognosis prediction of esophageal squamous cell carcinoma.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16073-e16073
Author(s):  
Weitao Zhuang ◽  
Xiao-song Ben ◽  
Dan Tian ◽  
Zihao Zhou ◽  
Gang Chen ◽  
...  
Keyword(s):  
High Risk ◽  
Dna Damage Response ◽  
Squamous Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Prognostic Signature ◽  
Training Set ◽  
Prognosis Prediction ◽  
Damage Response

e16073 Background: Esophageal squamous cell cancer (ESCC) is a malignant tumor with a poor 5-year relative survival. A prognosis prediction signature associated with DNA Damage Response (DDR) genes in ESCC was explored in this study. Methods: The clinical and gene expression profiles of ESCC patients were downloaded from the GEO and TCGA database. Univariate Cox regression and 1000 iterations of 10-fold cross-validation of LASSO Cox regression with binomial deviance minimization criteria were used to identify DDR genes as potential object and a prognostic signature for ESCC survival prediction, followed by validation of the signature via TCGA cohort and identification of independent prognostic predictors. A nomogram for prognosis prediction was built and Gene Set Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Results: A signature of 8 DDR genes were constructed as being significantly associated with overall survival (OS) among patients with esophageal squamous cell carcinoma. The pronostic signature stratified ESCC patients into low- vs high-risk groups in terms of OS in the training set, testing set and the validation cohorts, and remained as an independent prognostic factor in multivariate analyses (hazard ratio (HR) in training set, 0.17 [95% CI, 0.09-0.35; P < 0 .001], HR in testing set, 0.38 [95% CI, 0.16-0.93; P = 0.029], HR in discovery cohort, 0.171 [95% CI, 0.03-0.48; P < 0 .001]) after adjusting for clinicopathological factors. The 8-DDR gene signature achieved a higher accuracy (C-index, 0.69; AUCs for 1-, 3- and 5-year OS, 0.74, 0.77 and 0.76, respectively) than 7 previously reported multigene signatures (C-index range, 0.53 to 0.60; AUCs range, 0.55to 0.66, 0.54 to 0.64 and 0.62 to 0.66, respectively) for estimation of survival in comparable cohorts. A nomogram incorporating tumor location, grade, adjuvant therapy and signature-based risk group showed better predictive performance for 1- and 3- year survival than for 5 year survival. Moreover, GSEA revealed that the DNA repair was more prominently enriched in the high-risk group while the low-risk group had not enrichment of any process (P > 0.05 for all). Conclusions: Taken together, our study identified 8 DDR genes related to the prognosis of ESCC patients, and constructed a robust prognostic signature to effectively stratify ESCC patients with different survival rates, which may help recognize high-risk patients potentially benefiting from more aggressive treatment.

scholarly journals Identification of a Novel Glycolysis-Related Signature to Predict the Prognosis of Patients with Breast Cancer

2021 ◽  
Author(s):  
Menglin He ◽  
Cheng Hu ◽  
Jian Deng ◽  
Hui Ji ◽  
Weiqian Tian
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Expression Levels ◽  
Incidence And Mortality

Abstract Background: Breast cancer (BC) is a kind of cancer with high incidence and mortality in female. Conventional clinical characteristics are far from accurate to predict individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC. Methods: We analyzed the data of a training cohort from the TCGA database and a validation cohort from GEO database. After the applications of GSEA and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed. The signature contains AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Then, we constructed a risk score formula to classify the patients into high and low-risk groups based on the expression levels of six-gene in patients. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Next, a nomogram was developed to predict the outcomes of patients with risk score and clinical features in 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues.Results: We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in high-risk group showed poor prognosis than that in low-risk group. The AUC values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues except AK3. Conclusion: We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate and obvious prediction and might be used to expand the prediction methods in clinical.

scholarly journals A Ferroptosis-Related Signature Robustly Predicts Clinical Outcomes and Associates With Immune Microenvironment for Thyroid Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Mingqin Ge ◽  
Jie Niu ◽  
Ping Hu ◽  
Aihua Tong ◽  
Yan Dai ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Low Risk ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Objective: This study aimed to construct a prognostic ferroptosis-related signature for thyroid cancer and probe into the association with tumor immune microenvironment.Methods: Based on the expression profiles of ferroptosis-related genes, a LASSO cox regression model was established for thyroid cancer. Kaplan-Meier survival analysis was presented between high and low risk groups. The predictive performance was assessed by ROC. The predictive independency was validated via multivariate cox regression analysis and stratified analysis. A nomogram was established and verified by calibration curves. The enriched signaling pathways were predicted via GSEA. The association between the signature and immune cell infiltration was analyzed by CIBERSORT. The ferroptosis-related genes were validated in thyroid cancer tissues by immunohistochemistry and RT-qPCR.Results: A ferroptosis-related eight gene model was established for predicting the prognosis of thyroid cancer. Patients with high risk score indicated a poorer prognosis than those with low risk score (p = 1.186e-03). The AUCs for 1-, 2-, and 3-year survival were 0.887, 0.890, and 0.840, respectively. Following adjusting other prognostic factors, the model could independently predict the prognosis (p = 0.015, HR: 1.870, 95%CI: 1.132–3.090). A nomogram combining the signature and age was constructed. The nomogram-predicted probability of 1-, 3-, and 5-year survival approached the actual survival time. Several ferroptosis-related pathways were enriched in the high-risk group. The signature was distinctly associated with the immune cell infiltration. After validation, the eight genes were abnormally expressed between thyroid cancer and control tissues.Conclusion: Our findings established a prognostic ferroptosis-related signature that was associated with the immune microenvironment for thyroid cancer.

scholarly journals A Novel Ferroptosis-Related Genes Model for Prognosis Prediction of Lung Adenocarcinoma

2021 ◽  
Author(s):  
Fei Li ◽  
Dongcen Ge ◽  
Shu-lan Sun
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Prediction Model ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background. Ferroptosis is a newly discovered form of cell death characterized by iron-dependent lipid peroxidation. The aim of this study is to investigate the relationship between ferroptosis and the prognosis of lung adenocarcinoma (LUAD).Methods. RNA-seq data was collected from the LUAD dataset of The Cancer Genome Altas (TCGA) database. We used ferroptosis-related genes as the basis, and identify the differential expression genes (DEGs) between cancer and paracancer. The univariate Cox regression analysis were used to screen the prognostic-related genes. We divided the patients into training and validation sets. Then, we screened out key genes and built a 5 genes prognostic prediction model by the applications of the least absolute shrinkage and selection operator (LASSO) 10-fold cross-validation and the multi-variate Cox regression analysis. We divided the cases by the median value of risk score and validated this model in the validation set. Meanwhile, we analyzed the somatic mutations, and estimated the score of immune infiltration in the high- and low-risk groups, as well as performed functional enrichment analysis of DEGs.Results. The result revealed that the high-risk score triggered the worse prognosis. The maximum area under curve (AUC) of the training set and the validation set of in this study was 0.7 and 0.69. Moreover, we integrated the age, gender, and tumor stage to construct the composite nomogram. The charts indicated that the AUC of cases with survival time of 1, 3 and 5 years are 0.698, 0.71 and 0.73. In addition, the mutation frequency of patients in the high-risk group was higher than that in the low-risk group. Simultaneously, DEGs were mainly enriched in ferroptosis-related pathways by analyzing the functional results.Conclusion. This study constructed a novel LUAD prognosis prediction model base on 5 ferroptosis-related genes, which can provide a prognostic evaluation tool for the clinical therapeutic decision.

scholarly journals A Novel Glucose Metabolism-Related Gene Signature for Overall Survival Prediction in Patients with Glioblastoma

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chaocai Zhang ◽  
Minjie Wang ◽  
Fenghu Ji ◽  
Yizhong Peng ◽  
Bo Wang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Glucose Metabolism ◽  
Cox Regression ◽  
Gene Signature ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Survival Prediction ◽  
Related Gene ◽  
Cox Regression Analysis

Introduction. Glioblastoma (GBM) is one of the most frequent primary intracranial malignancies, with limited treatment options and poor overall survival rates. Alternated glucose metabolism is a key metabolic feature of tumour cells, including GBM cells. However, due to high cellular heterogeneity, accurately predicting the prognosis of GBM patients using a single biomarker is difficult. Therefore, identifying a novel glucose metabolism-related biomarker signature is important and may contribute to accurate prognosis prediction for GBM patients. Methods. In this research, we performed gene set enrichment analysis and profiled four glucose metabolism-related gene sets containing 327 genes related to biological processes. Univariate and multivariate Cox regression analyses were specifically completed to identify genes to build a specific risk signature, and we identified ten mRNAs (B4GALT7, CHST12, G6PC2, GALE, IL13RA1, LDHB, SPAG4, STC1, TGFBI, and TPBG) within the Cox proportional hazards regression model for GBM. Results. Depending on this glucose metabolism-related gene signature, we divided patients into high-risk (with poor outcomes) and low-risk (with satisfactory outcomes) subgroups. The results of the multivariate Cox regression analysis demonstrated that the prognostic potential of this ten-gene signature is independent of clinical variables. Furthermore, we used two other GBM databases (Chinese Glioma Genome Atlas (CGGA) and REMBRANDT) to validate this model. In the functional analysis results, the risk signature was associated with almost every step of cancer progression, such as adhesion, proliferation, angiogenesis, drug resistance, and even an immune-suppressed microenvironment. Moreover, we found that IL31RA expression was significantly different between the high-risk and low-risk subgroups. Conclusion. The 10 glucose metabolism-related gene risk signatures could serve as an independent prognostic factor for GBM patients and might be valuable for the clinical management of GBM patients. The differential gene IL31RA may be a potential treatment target in GBM.

scholarly journals Identification of an Immune-Related Gene Signature Based on Immunogenomic Landscape Analysis to Predict the Prognosis of Adult Acute Myeloid Leukemia Patients

2020 ◽  
Vol 10 ◽  
Author(s):  
Ruiqi Zhu ◽  
Huishan Tao ◽  
Wenyi Lin ◽  
Liang Tang ◽  
Yu Hu
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Myeloid Leukemia ◽  
Cox Regression ◽  
Gene Signature ◽  
Related Gene ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Immune Related Gene

Acute myeloid leukemia (AML) is a hematopoietic malignancy characterized by highly heterogeneous molecular lesions and cytogenetic abnormalities. Immune disorders in AML and impaired immune cell function have been found to be associated with abnormal karyotypes in AML patients. Immunotherapy has become an alternative therapeutic method that can improve the outcomes of AML patients. For solid tumors, the expression patterns of genes associated with the immune microenvironment provide valuable prognostic information. However, the prognostic roles of immune genes in AML have not been studied as yet. In this study, we identified 136 immune-related genes associated with overall survival in AML patients through a univariate Cox regression analysis using data from TCGA-AML and GTEx datasets. Next, we selected 24 hub genes from among the 136 genes based on the PPI network analysis. The 24 immune-related hub genes further underwent multivariate Cox regression analysis and LASSO regression analysis. Finally, a 6 immune-related gene signature was constructed to predict the prognosis of AML patients. The function of the hub IRGs and the relationships between hub IRGs and transcriptional factors were investigated. We found that higher levels of expression of CSK, MMP7, PSMA7, PDCD1, IKBKG, and ISG15 were associated with an unfavorable prognosis of AML patients. Meanwhile, patients in the TCGA-AML datasets were divided into a high risk score group and a low risk score group, based on the median risk score value. Patients in the high risk group tended to show poorer prognosis [P = 0.00019, HR = 1.89 (1.26–2.83)]. The area under the curve (AUC) was 0.6643. Multivariate Cox Regression assay confirmed that the 6 IRG signature was an independent prognostic factor for AML. The prognostic role of the immune related-gene signature was further validated using an independent AML dataset, GSE37642. In addition, patients in the high risk score group in the TCGA dataset were found to be of an advanced age, IDH mutation, and M5 FAB category. These results suggested that the proposed immune related-gene signature may serve as a potential prognostic tool for AML patients.

scholarly journals Identification of a Ten-Gene Signature of DNA Damage Response Pathways with Prognostic Value in Esophageal Squamous Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weitao Zhuang ◽  
Xiaosong Ben ◽  
Zihao Zhou ◽  
Yu Ding ◽  
Yong Tang ◽  
...  
Keyword(s):  
Dna Damage ◽  
Dna Damage Response ◽  
Squamous Cell ◽  
Cox Regression ◽  
Cell Cancer ◽  
Gene Expression Signature ◽  
Treatment Decision ◽  
Prognostic Indicator ◽  
Gene Signature ◽  
Damage Response

Molecular prognostic signatures are critical for treatment decision-making in esophageal squamous cell cancer (ESCC), but the robustness of these signatures is limited. The aberrant DNA damage response (DDR) pathway may lead to the accumulation of mutations and thus accelerate tumor progression in ESCC. Given this, we applied the LASSO Cox regression to the transcriptomic data of DDR genes, and a prognostic DDR-related gene expression signature (DRGS) consisting of ten genes was constructed, including PARP3, POLB, XRCC5, MLH1, DMC1, GTF2H3, PER1, SMC5, TCEA1, and HERC2. The DRGS was independently associated with overall survival in both training and validation cohorts. The DRGS achieved higher accuracy than six previously reported multigene signatures for the prediction of prognosis in comparable cohorts. Furtherly, a nomogram incorporating DRGS and clinicopathological features showed improved predicting performance. Taken together, the DRGS was identified as a novel, robust, and effective prognostic indicator, which may refine the scheme of risk stratification and management in ESCC patients.

scholarly journals Identification of Metastasis-Related Signature For Predicting Survival In Osteosarcoma

2021 ◽  
Author(s):  
Song Shi ◽  
Shuaijie Yang ◽  
Zhenyu Zhou ◽  
Kai Sun ◽  
Ran Tao ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Signature ◽  
Low Risk ◽  
Risk Scores ◽  
Activation Markers

Abstract BackgroundRNA sequencing has become a powerful tool for exploring tumor recurrence or metastasis mechanisms. In this study, we aimed to develop a signature to improve the prognostic predictions of osteosarcoma.Materials and methodsBy comparing the expression profiles between metastatic and non-metastatic samples, we obtained 57 metastatic-related gene signatures. Then we constructed a 3‐gene signature to predict the prognostic risk of osteosarcoma patients by the Cox proportional hazards regression model. The risk score derived from this signature could successfully stratify osteosarcoma patients into subgroups with different survival outcomes.ResultsPatients in the low-risk group showed more prolonged overall survival than those in the high-risk group. And the performance was validated with another independent dataset. Multivariate cox regression revealed that the risk score served as an independent risk factor. Besides, we found that patients with low-risk scores had higher expression levels of immune-related signatures, suggesting an active immune status in those patients. Using the CIBERSORT database, we further systematically analyzed the relationships between the risk score and immune cell infiltration levels, as well as the immune activation markers. Higher infiltration of immune cells (CD8 T cells, monocytes, M2 macrophages, and memory B cells) and higher levels of immune cytotoxic markers (GZMA, GMZB, IFNG, and TNF) were observed in patients in the low-risk group.ConclusionsIn summary, this 3-gene signature could be a reliable marker for prognostic evaluation and help clinicians identify high‐risk patients.

scholarly journals Identification of a novel glycolysis-related signature to predict the prognosis of patients with breast cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Menglin He ◽  
Cheng Hu ◽  
Jian Deng ◽  
Hui Ji ◽  
Weiqian Tian
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Expression Levels ◽  
Incidence And Mortality

Abstract Background Breast cancer (BC) has a high incidence and mortality rate in females. Its conventional clinical characteristics are far from accurate for the prediction of individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC. Methods We analyzed the data of a training cohort from the Cancer Genome Atlas (TCGA) database and a validation cohort from the Gene Expression Omnibus (GEO) database. After the applications of Gene Set Enrichment Analysis (GSEA) and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed; the signature contained AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Furthermore, on the basis of expression levels of the six-gene signature, we constructed a risk score formula to classify the patients into high- and low-risk groups. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Later, a nomogram was developed to predict the outcomes of patients with risk score and clinical features over a period of 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues, which were taken as control. Results We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in the high-risk group showed poor prognosis than those in the low-risk group. The area under the curve (AUC) values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients with BC without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues; however, AK3 was an exception. Conclusion We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate prediction and might be useful in expanding the hypothesis in clinical research.

Impact of DNA repair deficiency signature on outcomes in triple negative breast cancer (TNBC) patients treated with AC chemotherapy (SWOG S9313).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 529-529 ◽  
Author(s):  
Priyanka Sharma ◽  
William E. Barlow ◽  
Andrew K. Godwin ◽  
Laura A Knight ◽  
Steven M. Walker ◽  
...  
Keyword(s):  
Dna Damage ◽  
Dna Damage Response ◽  
Cox Regression ◽  
Early Stage ◽  
Selection Criterion ◽  
Tumor Infiltrating Lymphocytes ◽  
Gene Signature ◽  
Molecular Signature ◽  
Response To Chemotherapy ◽  
Damage Response

529 Background: Biomarkers of response and resistance to adjuvant chemotherapy for TNBC are needed. Deficiency in DNA damage response (DDR) and repair pathways have been reported in TNBC and may impact response to chemotherapy. Aims: To investigate DNA damage response deficiency (DDRD) molecular signature, BRCA1mRNA expression and Tumor Infiltrating Lymphocytes (TILs) as prognostic markers in TNBC patients treated with adjuvant AC on S9313. Methods: S9313 accrued 3125 early stage BC patients to two alternative schedules of AC with no difference in outcomes between the two arms. We identified 425 (14%) patients with centrally determined TNBC with tissue availability. DDRD signature (44 gene signature, Almac Inc.) and BRCA1expression (NanoString nCounter) were performed on RNA isolated from pre-treatment FFPE tumor tissue. DDRD score was classified in quartiles. TILs evaluation was performed using previously described criteria. Markers were tested for prognostic effect on DFS and OS using Cox regression model with adjustment for randomized treatment assignment. Results: For 425 TNBC patients median age: 45 yrs, and 5 year DFS and OS = 74% and 82%, respectively. DDRD signature was successfully evaluated in 89.6% (381/425) but only 267 (62.8%) met 60% tumor content criterion for inclusion. DDRD score quartiles were associated with DFS (5 year DFS 59% & 82% in the lowest & highest quartiles respectively, p = 0.0005) and OS (5 year OS 74% and 86% in lowest and highest quartiles respectively, p = 0.008). BRCA1 expression and TILs were successfully determined in 78% and 99% samples, respectively. BRCA1expression was not associated with DFS. TILs were associated with DFS (10% increase HR = 0.88; 95% CI 0.79-0.97; p = 0.016) and OS (HR = 0.84; 95% CI 0.74-0.94; p = 0.0005). DDRD score and TILs were highly correlated (Pearson = 0.62). In multivariate model of DFS including TILs and DDRD quartiles, only DDRD remains significant (p = 0.018). Conclusions: DDRD signature was prognostic in TNBC patients treated with AC chemotherapy and has the potential to be used as a selection criterion to identify TNBC patients whose prognosis is sufficiently poor to justify evaluation of alternative treatment.

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